From contact at sscc.fr Fri Oct 1 03:13:29 2021 From: contact at sscc.fr (SSCC) Date: Fri, 1 Oct 2021 09:13:29 +0200 Subject: Connectionists: [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <00d301d7b693$e0c91040$a25b30c0$@sscc.fr> Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience . Smart networks for smart cities . Security management in smart cities . Security in distributed systems . Modeling, analysis and detection of IoT attacks . Data mining for cybersecurity in smart cities . Decentralized architecture for smart cities . Consensus protocols and applications IoT & AI . IoT Indoor deployment . IoT communication protocols . Building information modeling (BIM) IoT-based HVAC control in smart buildings . Artificial Intelligence in Cyber Physical Energy Systems . Optimization for IoT and smart cities . Dynamic scheduling for IoT deployment . Autonomous and Smart decisions Edge and Cloud . Cloud-Edge for IoT and smart cities . Fog and Edge computing for smart cities . Applications/services for Edge AI . Software Platforms for Edge Social aspects and applications . Behavioral and Energy Consumption Analytics . Indoor comfort . Human factors and organizational resilience for distributed systems Important Dates . Paper Submission Date: 31 October, 2021 . Notification to Authors: 15 November, 2021 . Camera Ready Submission: 21 December 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From george at cs.ucy.ac.cy Fri Oct 1 05:31:25 2021 From: george at cs.ucy.ac.cy (George A. Papadopoulos) Date: Fri, 1 Oct 2021 12:31:25 +0300 Subject: Connectionists: 9th European Conference on Service-Oriented and Cloud Computing (ESOCC 2022): Second Call for Contributions Message-ID: <72OQ8LSA-118-PZER-J48B-E64CD5JAWE@cs.ucy.ac.cy> *** Second Call for Contributions *** 9th European Conference on Service-Oriented and Cloud Computing (ESOCC 2022) March 22-24, 2022, Lutherstadt Wittenberg, Germany https://www.esocc-conf.eu Scope Service-oriented and cloud computing have made a huge impact both on the software industry and on the research community. Today, service and cloud technologies are applied to build large-scale software landscapes as well as to provide single software services to end users. Services today are independently developed and deployed as well as freely composed while they can be implemented in a variety of technologies, a quite important fact from a business perspective. Similarly, cloud computing aims at enabling flexibility by offering a centralised sharing of resources. The industry's need for agile and flexible software and IT systems has made cloud computing the dominating paradigm for provisioning computational resources in a scalable, on- demand fashion. Nevertheless, service developers, providers, and integrators still need to create methods, tools and techniques to support cost-effective and secure development as well as use of dependable devices, platforms, services and service- oriented applications in the cloud. The European Conference on Service-Oriented and Cloud Computing (ESOCC) is the premier conference on advances in the state of the art and practice of service- oriented computing and cloud computing in Europe. The main objectives of this conference are to facilitate the exchange between researchers and practitioners in the areas of service-oriented computing and cloud computing, as well as to explore the new trends in those areas and foster future collaborations in Europe and beyond. Tracks - Main conference: three days of invited talks, panels, and presentations of selected research papers, including a dedicated day to satellite workshops. - PhD Symposium: an opportunity for PhD students to present their research activities and perspectives, to critically discuss them with other PhD students and with established researchers in the area, hence getting fruitful feedback and advices on their research activities. - Projects Track: a useful opportunity for researchers to disseminate the latest research developments in their projects and meet representatives of other consortia. Details about all the tracks are available at the conference web site: https://www.esocc-conf.eu . Topics of interest ESOCC 2022 seeks original, high quality papers related to all aspects of service- oriented and cloud computing. Specific topics of interest include but are not limited to: - Service and Cloud Computing Models  ? Design patterns, guidelines and methodologies  ? Governance models  ? Architectural models  ? Requirements engineering  ? Formal Methods  ? Model-Driven Engineering  ? Quality models  ? Security, Privacy & Trust models  ? Self-Organising Service-Oriented and Cloud Architectures Models  ? Testing models - Service and Cloud Computing Engineering  ? Service Discovery, Matchmaking, Negotiation and Selection  ? Monitoring and Analytics  ? Governance and management  ? Cloud Interoperability, Multi-Cloud, Cross-Cloud, Federated Cloud solutions  ? Frameworks & Methods for Building Service and Cloud based Applications  ? Cross-layer adaptation  ? Edge/Fog computing  ? Cloud, Service Orchestration & Management  ? Service Level Agreement Management  ? Service Evolution/Optimisation  ? Service & Cloud Testing and Simulation  ? QoS for Services and Clouds  ? Semantic Web Services  ? Service mining  ? Service & Cloud Standards  ? FaaS / Serverless computing - Technologies  ? DevOps in the Cloud  ? Containerized services  ? Emerging Trends in Storage, Computation and Network Clouds  ? Microservices: Design, Analysis, Deployment and Management  ? Next Generation Services Middleware and Service Repositories  ? RESTful Services  ? Service and Cloud Middleware & Platforms  ? Blockchain for Services & Clouds  ? Services and Clouds with IoT  ? Fog Computing with Service and Cloud - Business and Social aspects  ? Enterprise Architectures for Service and Cloud  ? Service-based Workflow Deployment & Life-cycle Management  ? Core Applications, e.g., Big Data, Commerce, Energy, Finance, Health, Scientific Computing, Smart Cities  ? Business Process as a Service - BPaaS  ? Service and Cloud Business Models  ? Service and Cloud Brokerage  ? Service and Cloud Marketplaces  ? Service and Cloud Cost & Pricing  ? Crowdsourcing Business Services  ? Social and Crowd-based Cloud  ? Energy issues in Cloud Computing  ? Sustainability issues Submissions from industry are welcome (for example, use cases). Submissions ESOCC 2022 invites submissions in all the tracks: - Regular research papers (15 pages including references) - PhD Symposium (8 pages including references, authored by the PhD student with indication of his/her supervisors' names) - Projects Track (1 to 5 pages including references, describing an ongoing project) We only accept original papers, not submitted for publication elsewhere. The papers must be formatted according to the LNCS proceedings guidelines. They must be submitted to the EasyChair site at https://easychair.org/conferences/?conf=esocc2022 by selecting the right track. All accepted regular research papers are expected to be published in the main conference proceedings by Springer in the Lecture Notes in Computer Science (LNCS) series (http://www.springer.com/lncs). Accepted papers of the other tracks and the satellite workshops are expected to be published by Springer in the Communications in Computer and Information Science (CCIS) series (https://www.springer.com/series/7899). At least one author of each accepted paper is expected to register and present the work at the conference. A journal special issue is planned, and authors of selected accepted papers will be invited to submit extended versions of their articles. Workshop Proposals ESOCC 2022 also invites proposals for satellite workshops. More details about the proposal format and submission can be found at https://esocc-conf.eu/index.php/workshops/ . Important Dates Regular research & industrial papers: - Paper submission: 31 October 2021 - Notifications: 7 January 2022 - Camera Ready versions due: 20 January 2022 Projects track: - Paper submission: 14 January 2022 - Paper notification: 25 February 2022 - Camera Ready Version: 11 March 2022 PhD Symposium Track: - Paper submission: 14 January 2022 - Paper notification: 25 February 2022 - Camera Ready Version: 11 March 2022 Industrial Track: - Paper submission: 14 January 2022 - Paper notification: 25 February 2022 - Camera Ready Version: 11 March 2022 Satellite Workshops: - Workshop Proposal submission: 8 October 2021 - Workshop Proposal notification: 15 October 2021 - Workshop Paper submission: 14 January 2022 - Workshop Paper notification: 25 February 2022 - Workshop Camera Ready Version: 11 March 2022 Organization General Chair ? Wolf Zimmermann (Martin Luther University Halle-Wittenberg, Germany) Programme Co-Chairs ? Fabrizio Montesi (University of Southern Denmark, Denmark) ? George A. Papadopoulos (University of Cyprus, Cyprus) Industrial Track Chair ? Andreas Both (Anhalt University of Applied Science) Projects Track Chair ? Damian Tamburri (Technical University Eindhoven) Workshops Co-Chairs ? Guadalupe Ortiz (University of C?diz, Spain) ? Christian Zirpins (Karlsruhe University of Applied Science) PhD Symposium Co-Chair ? Jacopo Soldani (University of Pisa) ? Massimo Villari (University of Messina) -------------- next part -------------- An HTML attachment was scrubbed... URL: From tomas.hromadka at gmail.com Fri Oct 1 18:33:29 2021 From: tomas.hromadka at gmail.com (Tomas Hromadka) Date: Sat, 2 Oct 2021 00:33:29 +0200 Subject: Connectionists: COSYNE 2022: Call for workshop proposals Message-ID: <988d66f1-9a8a-1dcf-18d9-54b2b30cdc40@gmail.com> ==================================================== Computational and Systems Neuroscience 2022 (Cosyne) MAIN MEETING 17 - 20 March 2022 Lisbon, Portugal WORKSHOPS 21 - 22 March 2022 Cascais, Portugal www.cosyne.org ==================================================== ---------------------------------------------------- MEETING ANNOUNCEMENT ---------------------------------------------------- The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. The MAIN MEETING is single-track. A set of invited talks is selected by the Executive Committee, and additional talks and posters are selected by the Program Committee, based on submitted abstracts. The WORKSHOPS feature in-depth discussion of current topics of interest, in a small group setting. All abstract submissions will be reviewed double blind. The deadline for Abstract submission will be 20 November 2021. Cosyne topics include but are not limited to: neural basis of behavior, sensory and motor systems, circuitry, learning, neural coding, natural scene statistics, dendritic computation, neural basis of persistent activity, nonlinear receptive field mapping, representations of time and sequence, reward systems, decision-making, synaptic plasticity, map formation and plasticity, population coding, attention, neuromodulation, and computation with spiking networks. We would like to foster increased participation from experimental groups as well as computational ones. Please circulate widely and encourage your students and postdocs to apply. IMPORTANT DATES Abstract submission opens: 15 October 2021 Workshop pre-proposal deadline: 15 October 2021 Travel grant submission opens: 01 November 2021 Workshop proposal deadline: 12 November 2021 Abstract submission deadline: 20 November 2021 When preparing an abstract, authors should be aware that not all abstracts can be accepted for the meeting. Abstracts will be selected based on the clarity with which they convey the substance, significance, and originality of the work to be presented. ORGANIZING COMMITTEE General Chairs: Anne-Marie Oswald (U Pittsburgh) and Srdjan Ostojic (Ecole Normale Superieure Paris) Program Chairs: Laura Busse (LMU Munich) and Tim Vogels (IST Austria) Workshop Chairs: Anna Schapiro (U Penn) and Blake Richards (McGill) Tutorial Chair: Kanaka Rajan (Mount Sinai) DEIA Committee: Bianca Jones Martin (Columbia), Gabrielle Gutierrez (Columbia), and Stefano Recanatesi (U Washington) Undergraduate Travel Chairs: Angela Langdon (Princeton) and Bob Wilson (U Arizona) Fundraising Chair: Michael Long (NYU) Social Media Chair: Grace Lindsay (Columbia) Poster Design: Maja Bialon PROGRAM COMMITTEE Laura Busse (U Munich) Tim Vogels (IST Austria) Athena Akrami (UCL) Omri Barak (Technion) Brice Bathellier (Paris) Bing Brunton (U Washington) Yoram Burak (Hebrew University) SueYeon Chung (Columbia) Christine Constantinople (NYU) Victor de Lafuente (UNAM Mexico) Jan Drugowitsch (Harvard) Alexander Ecker (G?ttingen) Tatiana Engel (Cold Spring Harbor) Annegret Falkner (Princeton) Kevin Franks (Duke) Jens Kremkow (Berlin) Andrew Leifer (Princeton) Sukbin Lim (Shanghai) Scott Linderman (Stanford) Emilie Mace (MPI Neurobiology) Mackenzie Mathis (EPFL Lausanne) Ida Momennejad (Microsoft) Jill O'Reilly (Oxford) Il Memming Park (Stony Brook) Adrien Peyrache (McGill Montr?al) Yiota Porazi (FORCE) Nathalie Rochefort (Edinburgh) Christina Savin (NYU) Daniela Vallentin (MPI Ornithology) Brad Wyble (Penn State) EXECUTIVE COMMITTEE Stephanie Palmer (U Chicago) Zachary Mainen (Champalimaud) Alexandre Pouget (U Geneva) Anthony Zador (CSHL) CONTACT meeting [at] cosyne.org ----------------------------------------------------- CALL FOR WORKSHOP PROPOSALS ----------------------------------------------------- A series of workshops will be held after the main Cosyne meeting. The goal is to provide an informal forum for the discussion of important research questions and challenges. Controversial issues, open problems, comparisons of competing approaches, and alternative viewpoints are encouraged. The overarching goal of all workshops should be the integration of empirical and theoretical approaches, in an environment that fosters collegial discussion and debate. - There will be 6-10 workshops/day, running in parallel. - Each workshop is expected to draw between 20 and 100 people. - The workshops will be split into morning (9.30am-12.30pm) and afternoon (3.30pm-6.30pm) sessions. - Workshops will be held at the Hotel Cascais Miragem. - Workshop speakers do *not* receive free registration, travel expenses, or accommodation for either the main meeting or the workshop sessions. Organizers should let invited speakers know that they are expected to pay for workshop registration fees Workshop organizer responsibilities include coordinating workshop participation and content, scheduling all speakers, submitting a final schedule for the workshop program, and moderating the discussion. Organizers can, but need not, be speakers. One complimentary (free) organizer registration is provided per workshop. For workshops with two organizers, the free registration can be given to one of the organizers or split evenly between them. Any required multimedia resources beyond a projector, screen, and microphones will be the responsibility of the workshop organizers to coordinate with Cosyne and Hotel staff. EVALUATION CRITERIA As stated above, the goal of Cosyne workshops is to provide an interactive, informal forum for discussions. Relevant topics include, but are not limited to: sensory processing; motor planning and control; functional neural circuits; motivation, reward and decision making; learning and memory; adaptation and plasticity; neural coding; neural circuitry and network models; and methods in computational or systems neuroscience. Workshop proposals will be evaluated by the Cosyne Workshop Chairs, and a subset of the proposals will be selected. The proposals will be evaluated according to the following six metrics (which will be weighted equally): 1. Relevance to the Cosyne community 2. Integration of experimental and theoretical issues 3. Current interest in the topic in the scientific community 4. Potential for new research directions and interactions to emerge 5. Distinctive scope/approach compared to other workshops (including previous years?) 6. Diversity and equity in the proposed speakers PRE-PROPOSALS - Deadline 15 October 2021 In an effort to coordinate submissions, workshop organizers are *strongly encouraged* to submit a pre-proposal by *15 October 2021* Submission instructions for workshop (pre)-proposals are available at Cosyne.org -> Workshops. Pre-proposals may be shared among submitters to reduce topical overlap or facilitate merging of workshops. Pre-proposals are not mandatory, but workshops with a pre-proposal will have priority. In order to foster discussion within Workshops and reduce overlap between workshops, organizers should inform invited speakers that a speaker can take part in no more than two Workshops. The pre-proposals should include: - Name(s) and email address(es) of the organizers (no more than 2 organizers per session, please). A primary contact should be designated. - A title. - A brief description of 1) what the workshop will address and accomplish, 2) why the topic is of interest, 3) who is the targeted group of participants. - Names of potential invitees, with indication of confirmed speakers. Preference will be given to workshops with confirmed speakers. - Proposed workshop length (1 or 2 days). Most workshops will be limited to a single day. If you think your workshop needs two days, please explain why. - A brief resume of the workshop organizer(s) along with a short list of workshop-relevant publications (about half a page total). Experience has shown that the best discussions during a workshop are those that arise spontaneously. A good way to foster these is to have short talks and long question periods (e.g. 30+15 minutes), and have plenty of breaks. We recommend keeping the number of talks small (i.e., fewer than 10 talks per day). FULL PROPOSALS - Deadline 12 November 2021 The full proposals should include the list of confirmed speakers in addition to all of the components required for a pre-proposal. WORKSHOP COSTS Detailed registration costs, etc, will be available at www.cosyne.org. Please note: Cosyne does *not* provide travel funding for workshop speakers. Organizers should let invited speakers know that they are expected to pay for workshop registration fees. Participants are encouraged to register early, in order to qualify for discounted registration rates. One complimentary (free) organizer registration is provided per workshop. For workshops with two organizers, the free registration can be given to one of the organizers or split evenly between them. COSYNE 2022 WORKSHOP CHAIRS Anna Schapiro (U Penn) and Blake Richards (McGill) COSYNE WORKSHOPS CONTACT workshops [at] cosyne.org COSYNE MAILING LISTS Please consider adding yourself to Cosyne mailing lists (groups) to receive email updates with various Cosyne-related information and join in helpful discussions. See Cosyne.org -> Mailing lists for details. From confaiforpeople at gmail.com Sat Oct 2 05:01:04 2021 From: confaiforpeople at gmail.com (Conf. AI for People) Date: Sat, 2 Oct 2021 10:01:04 +0100 Subject: Connectionists: =?utf-8?q?CFP_Extended_Deadline=3A_Call_for_Paper?= =?utf-8?q?s_for_the_Int=2E_Conference_=E2=80=9CAI_for_People=3A_To?= =?utf-8?b?d2FyZHMgU3VzdGFpbmFibGUgQUnigJ0gKENBSVDigJkyMSk=?= Message-ID: Dear Colleague, we are writing to you to invite you to submit to the International Conference ?AI for People: Towards Sustainable AI? (CAIP?21). The deadline for paper submission has been extended to *17th October 2021* Below you will find the official Call for Full papers. Please feel free to distribute it to mailing lists you manage and to everybody who may be interested. Thank you and we hope to see you in CAIP?21! ------------------------------------------------ International Conference ?AI for People: Towards Sustainable AI? (CAIP?21) November 20-24, 2021 https://aiforpeople.org/conference Call for Full Papers https://aiforpeople.org/conference/cfp.php Please distribute (Apologies for cross-posting) If you wish to receive more information about CAIP?21 https://mailchi.mp/281085ba7b4b/caip21 Technically sponsored by: European Alliance for Innovation (EAI). Supported by: Technology Innovation Institute (TII), European Association for Artificial Intelligence (EurAI). ------------------------------------------------ The International Conference ?AI for People: Towards Sustainable AI? was born out of the idea of shaping Artificial Intelligence technology around human and societal needs. While Artificial Intelligence (AI) can be a beneficial tool, its development and its deployment impact society and the environment in ways that need to be thoroughly addressed and confronted. This year?s edition will focus on Sustainable AI, covering different aspects of social development, environmental protection, and economic growth applied in the design and deployment of AI systems. The conference will provide its participants with opportunities to gain a better understanding of the major challenges of utilizing AI for the societal good. Additionally, it should serve as an incubator for interdisciplinary communities that share a research agenda to exchange and discuss ideas related to the design and application of Sustainable AI. Here, Sustainable AI is a movement to foster change towards greater ecological integrity and social justice in the entire life cycle of AI systems. *** Themes and Topics *** The conference will be interdisciplinary and it welcomes contributions from different disciplines, spanning from computer science, the social sciences, and the humanities. Possible topics include but are not limited to: - AI applications for the social good and towards sustainable development goals - Ethics of Artificial Intelligence - Sustainable AI for Smart Cities - Policy recommendations for Sustainable AI - Green AI for environmental protection - Accuracy and Robustness of AI systems - Bias and Fairness in AI Systems - Privacy and Accountability in AI Systems - Safety and Security in AI Systems - Explainability and Transparency in AI Systems *** Important Dates (extended) *** Submission deadline: October 17th, 2021 Notification: November 11th, 2021 Camera-ready: November 16th, 2021 Conference Days: November 20-24, 2021 *** EAI Proceedings *** Conference proceedings will be published in the EAI CORE Proceedings and included in the European Digital Library (EUDL) and will be submitted for inclusion in leading indexing services, including Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP. *** Keynote Speakers *** CAIP?21 will host international keynote speakers (to be completed): - Priya Donti, Chair at Climate Change AI - Sneha Revanur, Founder of Encode Justice - Nicolas Miailhe, President of The Future Society - Maria De-Arteaga, Assistant Prof. at University Texas *** Organising Committee *** The International Conference on ?AI for People: Towards Sustainable AI? is organized by the nonprofit international organization ?AI for People? ( aiforpeople.org). General Chairs - Marta Ziosi (Oxford Internet Institute, University Oxford) - Philipp Wicke (University College, Dublin) - Jo?o Miguel Cunha (University of Coimbra) - Angelo Trotta (University of Bologna) -Program Chairs - Lea Buchhorn (Leiden University) - Vincenzo Lomonaco (University of Pisa) Finance Chair - Aina Turillazzi (Tilburg University) Publication Chair - Angelo Trotta (University of Bologna) ? TPC Chair - Kevin Trebing (Plan D GmbH) - Gabriele Graffieti (University of Bologna) *Technical Program Committee* - Adam Poulsen ? Charles Sturt University - Aiste Gerybaite ? University of Bologna - Andrea Cossu ? Scuola Normale Superiore - An?bal Monasterio Astobiza ? IFS-CSIC - Christoph L?tge ? Technical University of Munich - Cl?udia Figueras ? Stockholm University - Claudio Gallicchio ? University of Pisa - Daniel Schiff ? Georgia Institute of Technology - Federico Montori ? University of Bologna - Giovanni Sartor ? EUI/CIRSFID - Jake Goldenfein ? University of Melbourne - Jakob M?kander ? Oxford Internet Institute, University of Oxford. - Jason Borenstein ? Georgia Institute of Technology - Keng Siau ? City University of Hong Kong - Laurynas Adomaitis ? NordSec - Lorenzo Pellegrini ? University of Bologna - Luca Bedogni ? University of Modena and Reggio Emilia - Maria Celia Fern?ndez Aller ? Technical University of Madrid - Maria Milossi ? University of Macedonia - Marianna Ganapini ? Union College - Marija Slavkovik ? University of Bergen - Michele Loi ? University of Zurich - Pradeep Murukannaiah ? TU Delft, NL - Rajitha Ramanayake ? University College Dublin - Seth D. Baum ? Global Catastrophic Risk Institute - Stefan Sarkadi ? INRIA, France Follow us at: http://aiforpeople.org/conference https://www.linkedin.com/company/19176054/ https://www.facebook.com/aiforpeople https://twitter.com/AIforPeople https://www.instagram.com/ai_for_people/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 2 04:41:38 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 2 Oct 2021 10:41:38 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Winter: early registration October 9 Message-ID: <1522467248.1697135.1633164098664@webmail.strato.com> ****************************************************************** 5th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Winter Bournemouth, UK January 17-21, 2022 https://irdta.eu/deeplearn/2022wi/ *********** Co-organized by: Department of Computing and Informatics Bournemouth University Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: October 9, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw and Las Palmas de Gran Canaria. Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main components of the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be: TBA STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills PROFESSORS AND COURSES: Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint Joachim M. Buhmann (Swiss Federal Institute of Technology, Z?rich), [introductory/advanced] Model and Algorithm Validation for Data Science Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare R?diger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail Rob Fergus (New York University), [intermediate/advanced] Self-supervised Learning of Visual Representations for Recognition and Interaction Jo?o Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How? Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by January 9, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. ORGANIZING COMMITTEE: Rashid Bakirov (Bournemouth, co-chair) Nan Jiang (Bournemouth, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022wi/registration/ The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn/2022wi/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Bournemouth University Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 2 04:43:12 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 2 Oct 2021 10:43:12 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Spring: early registration October 15 Message-ID: <148768194.1697284.1633164192895@webmail.strato.com> ****************************************************************** 6th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Spring Guimar?es, Portugal April 18-22, 2022 https://irdta.eu/deeplearn2022sp/ ***************** Co-organized by: Algoritmi Center University of Minho, Guimar?es Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: October 15, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Spring will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, and Bournemouth. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Spring is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Spring will take place in Guimar?es, in the north of Portugal, listed as UNESCO World Heritage Site and often referred to as the birthplace of the country. The venue will be: TBA STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Christopher Manning (Stanford University), Self-supervised and Naturally Supervised Learning Using Language Kate Smith-Miles (University of Melbourne), Stress-testing Optimisation Algorithms via Instance Space Analysis Zhongming Zhao (University of Texas, Houston), Deep Learning Approaches for Predicting Virus-Host Interactions and Drug Response PROFESSORS AND COURSES: Eneko Agirre (University of the Basque Country), [intermediate] Deep Learning for Natural Language Processing Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision Altan ?ak?r (Istanbul Technical University), [introductory] Introduction to Deep Learning with Apache Spark Rylan Conway (Amazon), [introductory/intermediate] Deep Learning for Digital Assistants Jifeng Dai (SenseTime Research), [intermediate] AutoML for Generic Computer Vision Tasks Jianfeng Gao (Microsoft Research), [introductory/intermediate] An Introduction to Conversational Information Retrieval Daniel George (JPMorgan Chase), [introductory] An Introductory Course on Machine Learning and Deep Learning with Mathematica/Wolfram Language Bohyung Han (Seoul National University), [introductory/intermediate] Robust Deep Learning Lina J. Karam (Lebanese American University), [introductory/intermediate] Deep Learning for Quality Robust Visual Recognition Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for Trustworthy Biometrics Jennifer Ngadiuba (Fermi National Accelerator Laboratory), [intermediate] Ultra Low-latency and Low-area Machine Learning Inference at the Edge Lucila Ohno-Machado (University of California, San Diego), [introductory] Use of Predictive Models in Medicine and Biomedical Research Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to Quantum Neural Networks Bart ter Haar Romenij (Eindhoven University of Technology), [intermediate] Deep Learning and Perceptual Grouping Kaushik Roy (Purdue University), [intermediate] Re-engineering Computing with Neuro-inspired Learning: Algorithms, Architecture, and Devices Walid Saad (Virginia Polytechnic Institute and State University), [intermediate/advanced] Machine Learning for Wireless Communications: Challenges and Opportunities Yvan Saeys (Ghent University), [introductory/intermediate] Interpreting Machine Learning Models Martin Schultz (J?lich Research Centre), [intermediate] Deep Learning for Air Quality, Weather and Climate Richa Singh (Indian Institute of Technology, Jodhpur), [introductory/intermediate] Trusted AI Sofia Vallecorsa (European Organization for Nuclear Research), [introductory/intermediate] Deep Generative Models for Science: Example Applications in Experimental Physics Michalis Vazirgiannis (?cole Polytechnique), [intermediate/advanced] Graph Neural Networks with Applications Guowei Wei (Michigan State University), [introductory/advanced] Integrating AI and Advanced Mathematics with Experimental Data for Forecasting Emerging SARS-CoV-2 Variants Xiaowei Xu (University of Arkansas, Little Rock), [intermediate/advanced] Deep Learning for NLP and Causal Inference Guoying Zhao (University of Oulu), [introductory/intermediate] Vision-based Emotion AI OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by April 10, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. ORGANIZING COMMITTEE: Dalila Dur?es (Braga, co-chair) Jos? Machado (Braga, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) Paulo Novais (Braga, co-chair) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn2022sp/registration/ The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn2022sp/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Centro Algoritmi, Universidade do Minho, Guimar?es Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From terry at snl.salk.edu Fri Oct 1 08:54:35 2021 From: terry at snl.salk.edu (Terry Sejnowski) Date: Fri, 1 Oct 2021 05:54:35 -0700 Subject: Connectionists: DEADLINE: 2021 Misha Mahowald Prize, and 2021 Mahowald Early Career Award In-Reply-To: <3fb0009c-d961-e9dc-48dc-eaa7b82a2c82@iniforum.ch> References: <3fb0009c-d961-e9dc-48dc-eaa7b82a2c82@iniforum.ch> Message-ID: <9b83a5f3-0032-c868-f9cf-1a4d5f05f5cd@snl.salk.edu> CALL FOR SUBMISSIONS The 2021 Misha Mahowald Prize for Neuromorphic Engineering and The 2021 Mahowald Early Career Award *The Misha Mahowald Prize* recognizes outstanding research in neuromorphic engineering in a broad sense: neurally-inspired hardware, software, and algorithms; as well as other novel architectures. The award is for an individual or a group. The Prize is awarded by a jury of international experts Chaired by Prof. Terrence Sejnowski, and carries a prize of USD 10?000. *The Mahowald Early Career Award* recognizes outstanding Master's students, PhD doctoral students, or postdocs within one year of PhD degree conferral date. The Award is for an individual (not a group) who has created innovative neuromorphic hardware, software, or an algorithm. The Award is decided by a jury of established mid-career scientists Chaired by Prof. Andre van Schaik, and carries a prize of USD 2?000 together with a guaranteed seat at either the Capo Caccia or the Telluride Workshops for Cognitive Neuromorphic Engineering. Misha Mahowald, for whom the Prize and Award are named, was a charismatic, talented and influential pioneer of neuromorphic engineering whose creative life unfortunately ended prematurely. She worked at the California Institute of Technology, Oxford University, and was a founding member of the Institute of Neuroinformatics at the University of Zurich and ETH Zurich. Her novel designs of brain-inspired CMOS VLSI circuits for vision and computation have continued to influence a generation of engineers. *The submission deadline for both the Prize and the Award is at 23:59 UTC, 31 October 2021.* For more information about the Prize and Award, see the home page: https://mahowaldprize.org/ -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- _______________________________________________ Announce mailing list Announce at mail.capocaccia.cc https://urldefense.com/v3/__http://lists.mail.capocaccia.cc/mailman/listinfo/announce__;!!GX6Nv3_Pjr8b-17qtCok029Ok438DqXQ!iqYRuhB1xUFD19GYg5wMpnt9BKi4XBjQ2Otz9evtmaRfIXKin8KbaiBOjUqY-g$ From benabbessarra at gmail.com Sat Oct 2 04:52:28 2021 From: benabbessarra at gmail.com (=?UTF-8?Q?Sarra_Ben_Abb=C3=A8s?=) Date: Sat, 2 Oct 2021 10:52:28 +0200 Subject: Connectionists: [CFP][Deadline Extended] 1st International Workshop on Deep Learning for Question Answering @ KGSWC 2021 Message-ID: Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Deep Learning for Question Answering" which will be held online - November 19-24, 2021. *DLQA: **Deep Learning for Question Answering* 1st International Workshop, in conjunction with KGSWC 2021 November 19- 24, 2021 - Online *https://kgswc.org/iwdlq2021/ * *Important dates:* ? Workshop paper submission due: October 18, 2021 ? Workshop paper notifications: October 29, 2021 ? Workshop paper camera-ready versions due: November 08, 2021 ? Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* Question Answering System (QAS) is an important area in Artificial Intelligence. Generating automatic response is a fastidious and time-consuming task for there exists only some very general approaches to understand the intent of users. Question Answering (QA) is applied in many domain applications such as medical, finance, e-commerce, etc. Given a list of documents, a QAS can provide the right answer to the query pose in natural language. It combines natural language processing (NLP), information retrieval (IR) and knowledge representation and reasoning (KRR) as a relevant component for this process. The general process of QA is composed of different steps: (i) user query, (ii) question analysis (simple or complex query, open or closed domain, linguistic layer, semantic layer, etc), (iii) answer retrieval, and (iv) answer extraction from a set of candidate ones. All these steps are important to answer correctly, precisely and briefly to the user native language question. The answer can refer to a term, a sentence, an image, an audio, a video or to the full textual document. Recent Deep Learning approaches and information retrieval are implemented in order to reason over the questions and its links with the corresponding response. Modern NLP techniques make it possible for computers to read and interpret text, hear and understand speech, measure sentiment, and determine which parts in a document are important. Another important aspect of QAS is the integration of knowledge graphs (KGs) as a new dimension to provide a concise answer issued from the KG. KG is graph-based data model that structure and store real-world entities (abstract concepts) and their relationships (hierarchical and associative) in a graph. The KG is the most suitable and beneficial way to solve many challenging problems related to information domain. *Objective:* The first edition of this workshop aims at highlighting recent and future advances on question answering systems over structured semantic and unstructured textual data and to demonstrate the role of deep learning algorithms to enrich this process. In addition to that, the goal of this workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in Question Answering approaches. *Topics of interests*: ? Question answering over Linked Data ? Knowledge Graphs for Question Answering ? Complex Question Answering over texts and linked data ? Reasoning for Complex Question Answering ? Natural Language Processing based question answering ? Hybrid text and knowledge graph reasoning ? SPARQL query pattern generation ? Natural language querying of RDF exposed as Linked Data ? Ontology-based query answering ? Visual Question Answering ? Image question answering ? Audio and Speech Question Answering ? Video Question Answering ? Datasets combining structured and unstructured knowledge ? Applications of question answering ? and so on. *Submission:* The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions are not anonymous and must be PDF documents written in English and formatted using the following style files: KGSWC2021_authors_kit Papers are to be submitted through the workshop's *EasyChair * submission page. We welcome the following types of contributions: - *Full papers* (9-12 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending). At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the *KGSWC 2021 * page. *Workshop chairs:*Sarra Ben Abb?s, Engie, France Rim Hantach, Engie, France Philippe Calvez, Engie, France -------------- next part -------------- An HTML attachment was scrubbed... URL: From bhammer at techfak.uni-bielefeld.de Fri Oct 1 12:28:29 2021 From: bhammer at techfak.uni-bielefeld.de (Barbara Hammer) Date: Fri, 1 Oct 2021 18:28:29 +0200 Subject: Connectionists: Several positions in the ERC Synergy Grant Water Futures Message-ID: <3af307ae-3dbb-f641-18fb-a9edcf0fd038@techfak.uni-bielefeld.de> Apologies for cross-posting. How to use explainable machine learning to design the next generation of urban drinking water systems? The machine learning lab at Bielefeld University offers several fully funded research positions for enthusiastic researchers willing to pursue a PhD in machine learning or machine learning experts who are eager to work on explainability in graph neural networks, in combining deep learning and physical modeling, in learning in non-stationary environments, or ML for human-centered decision support in the prestigious ERC Synergy grant Water Futures (https://cordis.europa.eu/project/id/951424). More information on the application process can be found here: https://uni-bielefeld.hr4you.org/job/view/867/research-positions-eu-project-erc-synergy-grant-water-futures?page_lang=en -- Prof. Dr. Barbara Hammer Machine Learning Group, CITEC Bielefeld University D-33594 Bielefeld Phone: +49 521 / 106 12115 From george at cs.ucy.ac.cy Sun Oct 3 08:05:44 2021 From: george at cs.ucy.ac.cy (George A. Papadopoulos) Date: Sun, 3 Oct 2021 15:05:44 +0300 Subject: Connectionists: 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS 2022): Third Call for Papers and Final Call for Special Sessions Proposals Message-ID: *** Third Call for Papers and Final Call for Special Sessions Proposals *** 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS 2022) May 25-27, 2022, Golden Bay Hotel 5*, Larnaca, Cyprus http://cyprusconferences.org/eais2022/ (Proceedings to be published by the IEEE Xplore Digital Library; Special Journal Issue with Evolving Systems, Springer) IEEE EAIS 2022 will provide a working and friendly atmosphere and will be a leading international forum focusing on the discussion of recent advances, the exchange of recent innovations and the outline of open important future challenges in the area of Evolving and Adaptive Intelligent Systems. Over the past decade, this area has emerged to play an important role on a broad international level in today's real-world applications, especially those ones with high complexity and dynamic changes. Its embedded modelling and learning methodologies are able to cope with real-time demands, changing operation conditions, varying environmental influences, human behaviours, knowledge expansion scenarios and drifts in online data streams. Conference Topics Basic Methodologies Evolving Soft Computing Techniques. Evolving Fuzzy Systems. Evolving Rule-Based Classifiers. Evolving Neuro-Fuzzy Systems. Adaptive Evolving Neural Networks. Online Genetic and Evolutionary Algorithms. Data Stream Mining. Incremental and Evolving Clustering. Adaptive Pattern Recognition. Incremental and Evolving ML Classifiers. Adaptive Statistical Techniques. Evolving Decision Systems. Big Data. Problems and Methodologies in Data Streams Stability, Robustness, Convergence in Evolving Systems. Online Feature Selection and Dimension Reduction. Online Active and Semi-supervised Learning. Online Complexity Reduction. Computational Aspects. Interpretability Issues. Incremental Adaptive Ensemble Methods. Online Bagging and Boosting. Self-monitoring Evolving Systems. Human-Machine Interaction Issues. Hybrid Modelling, Transfer Learning. Reservoir Computing. Applications of EAIS Time Series Prediction. Data Stream Mining and Adaptive Knowledge Discovery. Robotics. Intelligent Transport and Advanced Manufacturing. Advanced Communications and Multimedia Applications. Bioinformatics and Medicine. Online Quality Control and Fault Diagnosis. Condition Monitoring Systems. Adaptive Evolving Controller Design. User Activities Recognition. Huge Database and Web Mining. Visual Inspection and Image Classification. Image Processing. Cloud Computing. Multiple Sensor Networks. Query Systems and Social Networks. Alternative Statistical and Machine Learning Approaches. Special Sessions Proposals Distinguished researchers working in theory, analysis and applications of evolving adaptive and intelligent systems and related areas are encouraged to submit proposals within the technical scope of IEEE EAIS 2022. Researchers interested in organising special sessions are invited to submit a formal proposal to the Special Sessions Chair Gabriella Casalino (gabriella.casalino at uniba.it) and to the General Chair George Angelos Papadopoulos (george at cs.ucy.ac.cy), by specifying: - Special Session Lead Organiser email address, details and mini-bio - Co-Organiser(s) details and mini-bio (optional) - Special Session title - Aim and scope of the Special Session (~half a page) - A list of main topics of the Special Session - A list of potential/expected contributors Submissions Submitted papers should not exceed 8 pages plus at most 2 pages overlength. Submissions of full papers are accepted online through Easy Chair (https://easychair.org/conferences/?conf=eais2022). The EAIS 2022 proceedings will be published on IEEE Xplore Digital Library. Authors of selected papers will be invited to submit extended versions for possible inclusion in a special issue of Evolving Systems - An Interdisciplinary Journal for Advanced Science and Technology (Springer). Important Dates ? Special Session proposal submission: October 15, 2021 ? Special Session proposal acceptance: October 22, 2021 ? Paper submission: January 10, 2022 ? Notification of acceptance/rejection: February 19, 2022 ? Camera ready submission: March 20, 2022 ? Authors registration: March 20, 2022 ? Conference Dates: May 25-27, 2022 Social Media FB: https://www.facebook.com/IEEEEAIS Twitter: https://twitter.com/IEEE_EAIS Linkedin: https://www.linkedin.com/events/2022ieeeconferenceonevolvingand6815560078674972672/ Organization Honorary Chairs ? Dimitar Filev, Ford Motor Co., USA ? Nikola Kasabov, Auckland University of Technology, New Zealand General Chairs ? George A. Papadopoulos, University of Cyprus, Nicosia, Cyprus ? Plamen Angelov, Lancaster University, UK Program Committee Chairs ? Giovanna Castellano, University of Bari, Italy ? Jos? A. Iglesias, Carlos III University of Madrid, Spain -------------- next part -------------- An HTML attachment was scrubbed... URL: From k.wong-lin at ulster.ac.uk Sun Oct 3 19:32:07 2021 From: k.wong-lin at ulster.ac.uk (Wong-Lin, Kongfatt) Date: Sun, 3 Oct 2021 23:32:07 +0000 Subject: Connectionists: Last call - Register for free - ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3) Message-ID: LAST CALL: Register for FREE and attend our ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3) at the following link: https://store.ulster.ac.uk/product-catalogue/faculty-of-computing-engineering/school-of-computing-and-intelligent-systems/isrccn3-autumn-school Student bursaries are available - please state this when registering. This Autumn School (25-29 October) is organised by our Intelligent Systems Research Centre (ISRC) at Ulster University, Magee campus. More details can be found here: https://sites.google.com/view/isrc-cn3/home Please share this with your contacts to help promote the event. If you have any further queries about this event, please contact KongFatt Wong-Lin (k.wong-lin at ulster.ac.uk). This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: From timofte.radu at gmail.com Sun Oct 3 13:08:00 2021 From: timofte.radu at gmail.com (Radu Timofte) Date: Sun, 3 Oct 2021 19:08:00 +0200 Subject: Connectionists: Open Positions for PhD Students and PostDocs in Computer Vision and Machine Learning Message-ID: PostDoc and PhD Student Open Positions in Computer Vision, Image Processing, and Machine Learning (Apologies for cross-postings.) The Computer Vision Laboratory led by Prof.Dr. Radu Timofte, from the newly established Center for Artificial Intelligence and Data Science , University of Wurzburg, is looking for outstanding candidates to fill several computer vision and machine learning fully-funded postdoc and PhD student positions . Julius Maximilians University of W?rzburg (JMU), founded in 1402, is one of the leading institutions of higher education in Germany and well-known on the international stage for delivering research excellence with a global impact. The University of W?rzburg is proud to be the home of outstanding researchers and fourteen Nobel Prize Laureates. W?rzburg is a vibrant city in Bavaria, Germany?s economically strongest state and home base to many international companies. We look forward to welcoming you to the University of W?rzburg! Computer Vision Laboratory and University of W?rzburg in general are an exciting environment for research, for independent thinking. Prof. Radu Timofte?s team is highly international, with people from about 12 countries, and the members have already won awards at top conferences (ICCV, CVPR, ICRA, NIPS, ...), founded successful spinoffs, and/or collaborated with industry. Prof. Radu Timofte is a 2022 winner of the prestigious Humboldt Professorship for Artificial Intelligence Award. Prof. Radu Timofte also leads the Augmented Perception Group at ETH Zurich. Depending on the position, the successful candidate will focus on a subset of the following Research Topics: ? deep learning ? computational photography ? domain translation ? learned image/video compression ? image/video super-resolution ? learning paradigms ? image/video understanding ? augmented and mixed reality ? edge inference and mobile AI The tasks will involve designing, developing, and testing novel ideas in cutting-edge research, as well as coordinating and conducting data collection for their evaluation. The successful candidate will conduct research on deep learning machines and a new cluster with hundreds of GPUs. The successful candidate will also collaborate with industry. Profile ? Master's degree in computer science, electrical engineering, physics or applied mathematics/ statistics. ? Good programming knowledge and experience with Python / C++ / MATLAB. ? Interest, prior knowledge and experience in one or more of the following is a plus: computer vision, deep learning, machine learning, image processing. ? Enthusiasm for leading-edge research, team spirit, and capability of independent problem-solving. ? Fluent written and spoken English is a must. ? Postdoc applicants are expected to have a strong track of published research, including top, high impact, journal (such as PAMI, IJCV, TIP, NEUCOM, JMLR, CVIU) or conference (such as ICCV, CVPR, ECCV, ICRA, NeurIPS, ICLR) papers. Timeline The positions are open immediately, fully funded, the salaries of the doctoral students and postdocs are competitive on the German scales TV-L E13 and E14, up to 60k euros per year, before tax. Typically a PhD takes ~4 years to complete and a PostDoc position is for at least 1 year. Applications will be reviewed on a rolling basis until all positions are filled. Application Interested applicants should email asap their PDF documents (including CV, diplomas, transcripts of records, referees / recommendation letters, etc.) to Prof.Dr. Radu Timofte, radu.timofte at uni-wuerzburg.de or radu.timofte at vision.ee.ethz.ch -------------- next part -------------- An HTML attachment was scrubbed... URL: From chengchenghuang11 at gmail.com Sun Oct 3 17:39:39 2021 From: chengchenghuang11 at gmail.com (Chengcheng Huang) Date: Sun, 3 Oct 2021 17:39:39 -0400 Subject: Connectionists: Faculty Position in Mathematical Biology at the University of Pittsburgh Message-ID: *The Department of Mathematics at the University of Pittsburgh* invites applications for *a tenure-track faculty position at the Assistant Professor level in the area of Mathematical Biology (including computational neuroscience)* to begin in the fall term of 2022, pending budgetary approval. We seek outstanding candidates with expertise in developing and applying mathematical methods to address biological questions, who will complement and enhance our existing strength in this area. We are dedicated to the mutual success of our faculty and students in our research and education enterprise. The University of Pittsburgh is an active center for interdisciplinary research in the life sciences, offering exciting opportunities for collaborations across departments as well as with our highly ranked medical school and nearby Carnegie Mellon University. The University is currently ranked third in the nation in total NIH funding. Successful candidates will have a Ph.D. in Mathematics or another quantitative field and should demonstrate substantial research accomplishment and dedication to teaching. Review of applications will begin on *November 15, 2021*, and will continue until the position is filled. Applicants can apply online at: *https://www.join.pitt.edu/* (under Faculty Positions, Keyword search for Mathematical Biology or Mathematics). Candidates should submit (a) a cover letter, (b) a CV, (c) a statement of research accomplishments and future plans, (d) a brief description of teaching experience and interests, and (e) a brief description of how your research, teaching or service demonstrates a commitment to diversity and inclusion. Applicants should also have (f) at least three letters of reference emailed to math at pitt.edu by November 15, 2021. Any questions about the application process can be directed to Dana Freund at daf125 at pitt.edu. The Dietrich School of Arts and Sciences is committed to building and fostering a culturally diverse environment, so the ability to work effectively with a wide range of individuals and constituencies in support of a diverse community is essential. The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equality of opportunity, human dignity, and diversity. EOE, including disability/vets. -------------- next part -------------- An HTML attachment was scrubbed... URL: From erikf at kth.se Mon Oct 4 03:26:25 2021 From: erikf at kth.se (=?Windows-1252?Q?Erik_Frans=E9n?=) Date: Mon, 4 Oct 2021 07:26:25 +0000 Subject: Connectionists: Postdoc in Machine learning applied to brain activity data Message-ID: <302dcc0df1f34817a0dd065b0629c353@kth.se> Postdoc in Machine learning applied to brain activity data We are seeking a candidate with a PhD in one of the following areas Machine Learning, Computational Neuroscience, Computer Science or Physics. The project will entail analysis of neural data. We are currently analyzing data from Parkinson?s patients (eye-tracking, MEG) and extracting features to be used for disease diagnostics and prediction. The candidate will work in close collaboration with other postdocs and PIs in the consortium. dBRAIN is an interdisciplinary initiative to better understand neurodegenerative diseases such as Parkinson?s disease and Alzheimer?s disease. We combine brain imaging, machine learning, topological data analysis and computational modelling of biological neural networks at multiple scales to identify causal links among disease biomarkers and disease symptoms. This understanding should improve diagnosis, prediction of the disease progression and suggest better therapies. For more information about the position and to apply, visit the web site: https://www.kth.se/en/api/2.61673/what:job/jobID:432976/where:4/ Deadline for application: 01.Nov.2021 11:59 PM CET Prof. Erik Frans?n Dept. of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm, Sweden https://www.kth.se/profile/erikf https://scholar.google.se/citations?user=VFtGuvcAAAAJ&hl=sv -------------- next part -------------- An HTML attachment was scrubbed... URL: From benabbessarra at gmail.com Mon Oct 4 03:18:27 2021 From: benabbessarra at gmail.com (=?UTF-8?Q?Sarra_Ben_Abb=C3=A8s?=) Date: Mon, 4 Oct 2021 09:18:27 +0200 Subject: Connectionists: [CFP][Deadline Extended] 1st International Workshop on Joint Use of Probabilistic Graphical Models and Ontology @KGSWC 202 Message-ID: Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Joint Use of Probabilistic Graphical Models and Ontology" which will be held online - November 19-24, 2021. *PGMOnto: * *Joint Use of Probabilistic Graphical Models and Ontology* 1st International Workshop, in conjunction with KGSWC 2021 November 19- 24, 2021 - Online https://kgswc.org/pgmonto2021/ *Important dates:* ? Workshop paper submission due: *October 18, 2021* ? Workshop paper notifications: October 29, 2021 ? Workshop paper camera-ready versions due: November 08, 2021 ? Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* An ontology is well known to be the best way to represent knowledge in a domain of discourse. It is defined by Gruber as ?an explicit specification of a conceptualization?. It allows to represent explicitly and formally existing entities, their relationships, and their constraints in an application domain. This representation is the most suitable and beneficial way to resolve many challenging problems related to the information domain (e.g., semantic interoperability among systems, knowledge sharing, and knowledge capitalization). Ontology formalization can be based on First order logic (FOL) to describe concepts, relationships, and constraints, enabling it to make inferences and giving it a graphical representation. Using ontology has many advantages, among them we can cite ontology reusing, reasoning and explanation, commitment, and agreement on a domain of discourse, ontology evolution and mapping, etc. Over the last three decades, graphical probabilistic models (PGMs) have enjoyed a surge of interest as a practically feasible framework of expert knowledge encoding and as a new comprehensive data analysis framework. Probabilistic graphical models (PGMs) such as Bayesian network, influence diagram or probabilistic relational model are considered as one of the most successful tools that enable a compact representation of complex systems and the increased ability to effectively learn and perform inference in large networks. Besides the compact representation of probability, PGMs are also intuitively easier for a human to understand than joint probabilities because they highlight the direct dependencies between random variables and their overall semantics is easily captured visually through their graphical representation. In practice, the combination of PGMs and ontologies might be beneficial to have high expressiveness and reasoning possibilities under uncertainty. Despite the difference between these two domain representation models, they have the potential to complement each other: part of the value of ontology baseline knowledge may be used to enhance PGM by resolving challenging tasks: (i) the identification of relevant variables (variable selection), (ii) the determination of structural relationships between the considered variables (arcs), and (iii) the estimation of parameters associated to the model. Once the PGM is learned, its results can be used together with an ontology reasoning engine to perform probabilistic inference. This first regular workshop aims at demonstrating recent and future advances in Semantic Probabilistic Graphical Models and Probabilistic Ontologies. Moreover, this workshop offers an invaluable opportunity to boost collaboration and conversation between Industrial Experts and academic researchers, allowing therefore exchanging ideas and presenting results of on-going research in structured knowledge and causality approaches. *Objective:* We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged. *Topics of interests*: - Construction of probabilistic ontologies - Construction of semantic probabilistic graphical model - Semantic causality and probability - Causality and ontology - PGM for ontology mapping - PGM learning - Ontology for PGM construction - Probabilistic inference engine - Tools, systems and applications - and so on. *Submission:* The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions are not anonymous and must be PDF documents written in English and formatted using the following style files: KGSWC2021_authors_kit Papers are to be submitted through the workshop's *EasyChair* submission page. We welcome the following types of contributions: - *Full papers* (8-10 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending) At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the *KGSWC 2021 * page. *Workshop chairs:* Sarra Ben Abb?s, Engie, France Ahmed Mabrouk, Engie, France Lynda Temal, Engie, France Philippe Calvez, Engie, France -------------- next part -------------- An HTML attachment was scrubbed... URL: From contact at sscc.fr Mon Oct 4 05:04:00 2021 From: contact at sscc.fr (SSCC) Date: Mon, 4 Oct 2021 11:04:00 +0200 Subject: Connectionists: [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <006901d7b8fe$cf7c6900$6e753b00$@sscc.fr> De : SSCC Envoy? : vendredi 1 octobre 2021 09:16 Objet : [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience ? Smart networks for smart cities ? Security management in smart cities ? Security in distributed systems ? Modeling, analysis and detection of IoT attacks ? Data mining for cybersecurity in smart cities ? Decentralized architecture for smart cities ? Consensus protocols and applications IoT & AI ? IoT Indoor deployment ? IoT communication protocols ? Building information modeling (BIM) IoT-based HVAC control in smart buildings ? Artificial Intelligence in Cyber Physical Energy Systems ? Optimization for IoT and smart cities ? Dynamic scheduling for IoT deployment ? Autonomous and Smart decisions Edge and Cloud ? Cloud-Edge for IoT and smart cities ? Fog and Edge computing for smart cities ? Applications/services for Edge AI ? Software Platforms for Edge Social aspects and applications ? Behavioral and Energy Consumption Analytics ? Indoor comfort ? Human factors and organizational resilience for distributed systems Important Dates ? Paper Submission Date: 31 October, 2021 ? Notification to Authors: 15 November, 2021 ? Camera Ready Submission: 21 December 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From vito.trianni at istc.cnr.it Mon Oct 4 05:53:02 2021 From: vito.trianni at istc.cnr.it (Vito Trianni) Date: Mon, 4 Oct 2021 11:53:02 +0200 Subject: Connectionists: [jobs] call for expressions of interest: 2 PhD fellowships in robotics and AI for precision agriculture Message-ID: <08B16172-53BD-4EA9-B383-9D0AE6FA1ECA@istc.cnr.it> Call for Expression of Interest 2 PhD Positions in Robotics and Artificial Intelligence Advanced perception and control for precision agriculture Institute of Cognitive Sciences and Technologies, Italian National Research Council Sapienza University Rome, Italy _________________________________________________ Research topic: One of the world's greatest challenges is to ensure sufficient food production for an ever-growing population. This is in contrast to the reduction of arable land, and the need for ecologically compatible production. Only through advanced precision farming techniques it will be possible to ensure increased production with reduced inputs of water, fertilisers and chemical plant protection products. The projects aim to study robotic and AI systems capable of minimising or completely eliminating the use of chemical agents in agriculture. Specifically, the aim is to automate precision spraying techniques, which have been shown to reduce the use of pesticides or herbicides by up to 90%. In addition, the project will assess the impact of completely replacing chemical agents with mechanical interventions using robotic actuators. The fundamental component of a precision spraying system is the ability to accurately identify the areas to be sprayed, assessing surfaces and volumes so as to be able to minimise the amount of chemicals applied while preserving the effectiveness of the intervention. It is therefore a computer vision problem for the recognition of the parameters of interest and for 3D reconstruction, which presupposes the ability to observe from several perspectives and to integrate the different observations for a more accurate estimate. In order to optimise the observation process, an active monitoring approach is proposed (possibly exploiting a multi-robot system), whereby observation points are determined based on the need to acquire information relevant to both recognition and estimation of the parameters of interest. Techniques based on information theory will be studied to minimise the uncertainty related to the observed objects, and to determine future actions and observations based on the expected information gain. These fellowships are available in the context of the projects AGR-o-RAMA (http://www.agrorama.it), E-CROPS (https://www.e-crops.it/il-progetto/) and with support from the Italian Ministry of Research on the FSE REACT-EU fundings. _________________________________________________ Skills and background knowledge: - Advanced methods for object detection and semantic segmentation - Recurrent convolutional neutral networks, LSTM - Active vision - Information theory - Bayesian inference - Information-based motion planning _________________________________________________ Application / Enquiry: Expressions of interest should be submitted by October the 15th, 2021 The formal call for the studentship will be released on October 11th, 2021, however those who are interested in the position could informally contact Dr. Vito Trianni (vito.trianni at istc.cnr.it) for discussing details about the project or the admission process. The admission process will take place at the beginning of November 2021. ======================================================================== Vito Trianni, Ph.D. vito.trianni@(no_spam)istc.cnr.it ISTC-CNR http://www.istc.cnr.it/people/vito-trianni Via San Martino della Battaglia 44 Tel: +39 06 44595277 00185 Roma Fax: +39 06 44595243 Italy ======================================================================== From stmanion at gmail.com Mon Oct 4 09:43:27 2021 From: stmanion at gmail.com (Sean Manion) Date: Mon, 4 Oct 2021 09:43:27 -0400 Subject: Connectionists: Neuro/Perspective input requested for SfN Poster - 5 min survey Message-ID: Hi all, Along with Dr. Amicia Elliott of NIMH, we are preparing a poster presentation for the upcoming Society for Neuroscience meeting for the historical track focused on neuroscience frameworks and perspectives. We would love to have your anonymous input on 9 simple questions (and one background Y/N question) to help in our efforts. This should not take more than 5min, unless of course you want to dive in deeper. Neuroscience Questionnaire - Google Forms Thank you! Sean -------------- next part -------------- An HTML attachment was scrubbed... URL: From leslie.perez at pucv.cl Mon Oct 4 21:11:50 2021 From: leslie.perez at pucv.cl (Leslie Angelica Perez Caceres) Date: Mon, 4 Oct 2021 22:11:50 -0300 Subject: Connectionists: Fourth Call for Papers: EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation Message-ID: (Apologies for cross-posting) ************************************************************************************* Fourth Call for Papers: EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation http://www.evostar.org/2022/evocop/ April 20 - 22, 2022 held as part of EvoStar (http://www.evostar.org) Venue: *Somewhere On Earth!* ** EvoCOP is now CORE Rank B ** Submission deadline: November 1, 2021 ************************************************************************************* The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation is a multidisciplinary conference that brings together researchers working on applications and theory of evolutionary computation methods and other metaheuristics for solving difficult combinatorial optimisation problems appearing in various industrial, economic, and scientific domains. Successfully solved problems include, but are not limited to, multi-objective, uncertain, dynamic and stochastic problems in the context of scheduling, timetabling, network design, transportation and distribution, vehicle routing, stringology, graphs, satisfiability, energy optimisation, cutting, packing, planning and search-based software engineering. The EvoCOP 2021 conference will be held somewhere on Earth, together with EuroGP (the 24th European Conference on Genetic Programming), EvoMUSART (the 10th European conference on evolutionary and biologically inspired music, sound, art and design) and EvoApplications (the 24th European Conference on the Applications of Evolutionary Computation), and a new special track on Evolutionary Machine Learning in a joint event collectively known as EvoStar (Evo*). Accepted papers will be published by Springer Nature in the Lecture Notes in Computer Science series. (See https://link.springer.com/conference/evocop for previous proceedings.) The best regular paper presented at EvoCOP 2022 will be distinguished with a Best Paper Award. EvoCOP conference is now ranked B in the CORE 2021 ranking: http://portal.core.edu.au/conf-ranks/2195/ **** Areas of Interest and Contributions **** EvoCOP welcomes submissions in all experimental and theoretical aspects of evolutionary computation and other metaheuristics to combinatorial optimisation problems, including (but not limited to) the following areas: * Applications of metaheuristics to combinatorial optimisation problems * Theoretical developments * Neighbourhoods and efficient algorithms for searching them * Variation operators for stochastic search methods * Constraint-handling techniques * Parallelisation and grid computing * Search space and landscape analyses * Comparisons between different (also exact) methods * Automatic algorithm configuration and design Prominent examples of metaheuristics include (but are not limited to): * Evolutionary algorithms * Estimation of distribution algorithms * Swarm intelligence methods such as ant colony and particle swarm optimisation * Artificial immune systems * Local search methods such as simulated annealing, tabu search, variable neighbourhood search, iterated local search, scatter search and path relinking * Hybrid methods such as memetic algorithms * Matheuristics (hybrids of exact and heuristic methods) * Hyper-heuristics and autonomous search * Surrogate-model-based methods Notice that, by tradition, continuous/numerical optimisation is *not* part of the topics of interest of EvoCOP. Interested authors might consider submitting to other EvoStar conferences such as EvoApplications. **** Submission Details **** Paper submissions must be original and not published elsewhere. The submissions will be peer reviewed by members of the program committee. The reviewing process will be double-blind, please omit information about the authors in the submitted paper. Submit your manuscript in Springer LNCS format: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 Page limit: 16 pages Submission link: coming soon The authors of accepted papers will have to improve their paper on the basis of the reviewers? comments and will be asked to send a camera-ready version of their manuscripts. At least one author of each accepted work has to register for the conference, attend the conference and present the work. **** Important Dates **** Submission deadline: November 1, 2021 EvoStar: April 20-22, 2022 **** EvoCOP Programme Chairs **** Leslie P?rez C?ceres Pontificia Universidad Cat?lica de Valpara?so, Chile leslie.perez at pucv.cl S?bastien Verel Universit? du Littoral C?te d'Opale (ULCO), France verel at univ-littoral.fr -- Leslie P?rez C?ceres Escuela de Ingenier?a Inform?tica Pontificia Universidad Cat?lica de Valpara?so Directora Diplomado en Inteligencia Artificial http://diplomadoia.inf.ucv.cl Co-chair , EvoCOP 2022, April 20-22 http://www.evostar.org/2022/evocop/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From the.braining.club at gmail.com Mon Oct 4 13:03:09 2021 From: the.braining.club at gmail.com (Reading Club) Date: Mon, 4 Oct 2021 19:03:09 +0200 Subject: Connectionists: Fwd: Brains Through Time Reading Club In-Reply-To: References: Message-ID: Dear all, We would like to bring to your attention the Brains Through Time Reading Club we are organizing. As the name implies, the idea is to review the book Brains Through Time , by Georg Striedter and Glenn Northcutt . It is a masterful synthesis of much of what is known about brain evolution, and it offers great insights to anyone interested in a broad understanding of how the brain produces behavior. We will dedicate a ~90 minute session to each of the 7 chapters. The first session will be on the 6th of October (6pm CEST / 12 EST) and will include the participation of Georg Striedter (one of the authors), Luis Puelles and Paul Cisek . If you are interested, please visit our website for more information and to register! The Braining Club -------------- next part -------------- An HTML attachment was scrubbed... URL: From marcin at amu.edu.pl Tue Oct 5 03:34:09 2021 From: marcin at amu.edu.pl (Marcin Paprzycki) Date: Tue, 5 Oct 2021 09:34:09 +0200 Subject: Connectionists: Call for Technical Sessions -- FedCSIS 2022 -- CORE Ranked B conference; IEEE# 54150 In-Reply-To: References: Message-ID: Call for Technical Sessions 17th Conference on Computer Science and Intelligence Systems (FedCSIS 2022) Sofia, Bulgaria, 4-7 September 2022 CORE Ranked B conference; IEEE# 54150 www.fedcsis.org The 17th Conference on Computer Science and Information Systems (FedCSIS 2022) cordially invites you to contribute a Technical Session (conference, symposium, workshop, consortium meeting, project dissemination meeting, special session, etc.). The FedCSIS conferences consist of five Tracks that contain Technical Sessions. Many of them are recurring yearly events, but we solicit and welcome proposals for new Technical Sessions until November 12, 2021. Moreover, existing events that would like to join the FedCSIS conference series, are also welcome. The Technical Sessions can run over any span of time within the conference dates; from half-day to three days. FedCSIS is a large rigorous scientific conference staged across Europe. Annually, it attracts submissions from ~40 countries from around the world. Papers are accepted in three categories: regular (full and short), position, and communication. All regular, presented, papers are submitted to IEEE Xplore Digital Library, and many are offered post-publication opportunities (journals, Springer volumes, etc.) after appropriate extensions and changes. Position and communication papers are published in separate volumes of the Annals of Computer Science and Information systems series. All papers are published with ISBN, ISSN and DOI numbers, and submitted to, and indexed in, most influential indexing services. Since 2012, FedCSIS Proceedings are indexed in SCOPUS. Any newly proposed Technical Session will be evaluated on the basis of scientific/technical interest and/or its relevance to practitioners in its topics, the clarity of the proposal in addressing the requested information, the innovativeness of the topics, and a ?value-added? to the denoted FedCSIS track, i.e.: * Artificial Intelligence and Applications * Computer Science and Systems * Network Systems and Applications * Information Technology for Management, Business & Society * Software and Systems Development Call for Papers for Technical Sessions will be announced by three means: (1) by FedCSIS Chairs in generic Calls for Conference Papers, to reach out to the computer science and intelligence systems researchers at large; (2) by Track Chairs in Calls for Track Papers, to reach out to the researchers specifically interested in track topics; (3) by Technical Session Chairs in Calls for Technical Session Papers, to reach out to the professional communities involved in research addressed by the Technical Session. All papers will have to be submitted to the FedCSIS Conference System (EasyChair). However, in all cases, paper submitters will have to select a Track/Technical Session (event) within which their paper will be reviewed for acceptance. Technical Session proposals should include the following information (3 pages maximum): 1. The nature of the Technical Session (conference, symposium, workshop, consortium meeting, project dissemination meeting, special session, etc.). 2. The title and acronym of the Technical Session, and a list of topics. 3. A brief explanation of why the proposed event will fit into the FedCSIS aims and program. 3. The contact information of the Technical Session chairs, including a link to their personal websites, and an overview of previous experiences with organization of scientific events. 4. Preliminary list of PC members who have agreed (or are likely to agree) to join. 5. Indication of the expected number of papers/attendees. 6. Information of expected post-publications of extended and revised papers in high-quality journals, edited volumes, etc. Technical Session organizers should email their proposals (in a single pdf file) by November 12, 2021 to the FedCSIS Secretariat at: secretariat at fedcsis.org More information concerning FedCSIS and its scientific, organization and financial rules can be found at: https://www.fedcsis.org -- Ta wiadomo?? zosta?a sprawdzona na obecno?? wirus?w przez oprogramowanie antywirusowe Avast. https://www.avast.com/antivirus From benoit.frenay at unamur.be Tue Oct 5 13:41:28 2021 From: benoit.frenay at unamur.be (=?UTF-8?B?QmVub8OudCBGcsOpbmF5?=) Date: Tue, 5 Oct 2021 19:41:28 +0200 Subject: Connectionists: 3-year Postdoc in ML to integrate constraints for I4.0 sensors Message-ID: <40c4a7de-d97f-9bbf-d530-f056dbee9b32@unamur.be> Dear coll_eagues,_ I have an open position in my human-centered ML team to integrate domain-knowledge constraints in I4.0 sensors with strict consumption limitations. _Context:_ SMARTSENS is a project funded by the Service public de wallonie Win2Wal program that will design micro-sensors to monitor gas concentrations in various environments, such as industrial facilities and storage locations. The project will be conducted in partnership with the University of Namur, the Haute Ecole Louvain en Hainaut (CeREF research center) and the Belgian VOCSENS company. The project benefits from the University of Namur expertise in machine learning and the CeREF expertise in edge computing for the processing of data from the sensors. Indeed, models will be used locally with strict consumption limitations. _Job description:_ The goal of this postdoctoral research is to define, design and evaluate models of sensors to predict gas concentrations in the environment. Due to data scarcity, we will integrate domain constraints given by experts (i.e., physical and technological constraints) to help learning algorithms. One of the scientific challenges will therefore be to design relevant types of constraints and to prioritise them. The candidate will work with Prof. Benoit Fr?nay and collaborate with SMARTSENS partners, including the CeREF research center for edge computing integration and the VOCSENS company for its expertise in remote gas sensing solutions. _Job Requirements:_ * PhD in Machine Learning; * Autonomy and ability to work in a multi-disciplinary team; * Excellent publication record; * Proficiency in English (both speaking and writing) is mandatory, knowledge of French and/or Dutch is considered as a plus. _About the employer _ The Faculty of Computer Science provides cutting-edge teaching and research, with a view to putting computers at the service of society, by taking into account their impact on the environment and by respecting the values of solidarity and sustainable development. The Faculty is a founding member of the Namur Digital Institute (NADI) which gathers over 150 researchers in the field of digital technology. It has a multi-disciplinary approach and addresses in particular the issues and challenges of computer science in organisations and in society. The Faculty of Computer Science has over 400 students, 80 members of staff including 18 professors and around 50 researchers. Founded in 1968, the Faculty of Computer Science has trained over 1,800 high-level computer science graduates since then. _How to apply_ Applications should be sent by e-mail to: benoit.frenay at unamur.be and contain the following: * Motivation letter * Curriculum vitae, including publication list * Copy of Diplomas (Bachelor, Master and PhD, if available) * PhD thesis (if available) * 2-3 recent publications * Names and e-mail addresses of 3 reference persons to be contacted upon request Note: Soon to be graduating PhD students are welcome to apply provided that they will have defended their PhD prior to the start of the position. _Important dates_ Submission deadline: October, 31^st , 2021 (11h59 pm AoE). Expected starting date: December 1^st or January 1st, 2022 (negotiable, depending on COVID or candidate constraints). -------------- next part -------------- An HTML attachment was scrubbed... URL: From elmead at ualr.edu Tue Oct 5 16:09:54 2021 From: elmead at ualr.edu (Esther Mead) Date: Tue, 5 Oct 2021 15:09:54 -0500 Subject: Connectionists: Due October 8, 2021 - Call For Papers: Workshop on Deviant Activities on Social Media (DEVIANCE 2021) Message-ID: DEVIANCE 2021 Workshop on Deviant Activities on Social Media Co-located with 2021 IEEE Conference on Big Data (IEEE BigData 2021) December 15-18, 2021 (due to COVID-19 the workshop will be held virtually) Website: http://cosmos.ualr.edu/workshops/deviance/2021/ Introduction: With the proliferation of smart devices, mobile applications, and social network platforms, the social side effects of these technologies have become more profound, especially in social and political disintegration. Several journalistic and academic investigations have reported that modern communication platforms such as social media (e.g., Twitter, Facebook, blogs, YouTube, and the ?deep web? channels) are strategically used to coordinate various deviant activities such as cyber propaganda campaigns. Several researchers have studied these deviant activities and identified various tactics, techniques, and procedures used by various online deviant groups, e.g., online propagandist groups,terrorist groups sympathizers, black-hat hacker groups, and internet trolls. Various social media platforms utilized the research findings to detect and curb some of these deviant activities. However, the techniques that are used by the aforementioned deviant groups evolve and adapt to go on undetected. This is a growing problem on social media that needs to be explored and solved. In this workshop, our aim is to have a scientific discussion among the experts who study deviant activities on social media, including but not limited to, detection of deviant/disruptive behaviors on social media; misinformation detection, identification, and dissemination; case studies of misinformation; etc. This includes, but is not limited to, the following topics: Research Topics: - Misinformation detection - Misinformation dissemination tactics such as misdirection, hashtag-latching, smoke screening - Multiple platform orchestration (cross-platform dissemination strategies) - Algorithmic manipulation such as exploiting recommendation algorithm bias manipulation - Deviant behaviors on social media platforms such as cyber bullying, organized hacking - Spamming, spear phishing through social media channels - Coordination strategies and detection - Mobs such as cyber flash mobs, smart mobs, deviant cyber flash mobs, automated deviant mobs - Coordinated inauthentic behaviors - Machine driven communications (bots, botnets, social bots, etc.) - Troll detection and strategies - Hate speech (toxic, polarizing, or disruptive content) - Narratives analysis during misinformation online campaigns - Stance detection and intent classification - Policy implications **In the light of recent elections worldwide, the COVID-19 pandemic, and covid-related deviant behaviors such as anti-lockdown campaigns, vaccine hesitancy, etc., we also solicit papers focused on disinformation and misinformation being disseminated related to these events.** Important Dates: Submission Deadline: October 8, 2021 Notification to Authors: October 29, 2021 Camera Ready Due: November 12, 2021 Venue Information: Due to the current COVID-19 pandemic, it is decided that the IEEE BigData conference and the DEVIANCE 2021 Workshop will be held virtually. Further information will be provided closer to the workshop. Submission: Full paper manuscripts must be in English with a maximum length of 10 pages (using the IEEE two-column template). Submissions should be in PDF and include the title, author(s), affiliation(s), e-mail address(es) and abstract on the first page. Workshop papers can be submitted through the submission portal . Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines . All papers accepted for the workshop will be included in the Conference Proceedings published by the IEEE Computer Society Press. Special Issue: Selected presentations will be invited to submit extended studies to Springer?s Social Network Analysis and Mining (SNAM) special issue on Deviant Behaviors on Social Media . Workshop Chairs: Esther Ledelle Mead Postdoctoral Research Fellow, COSMOS Center, UA Little Rock elmead at ualr.edu Muhammad Nihal Hussain Lead Data Scientist, Equifax Inc. mnhussain at ualr.edu Kiran Kumar Bandeli Senior Data Scientist, Walmart Inc. KiranKumar.Bandeli at walmart.com Samer Al-khateeb Assistant Professor, Creighton University SamerAl-Khateeb1 at creighton.edu Nitin Agarwal Jerry L. Maulden-Entergy Chair & Distinguished Professor Director, COSMOS Center, UA Little Rock nxagarwal at ualr.edu Web Chair: Thomas Marcoux Computer & Information Science PhD candidate - COSMOS Center, UA Little Rock txmarcoux at ualr.edu -- Esther Ledelle Mead, PhD Postdoctoral Research Fellow, Collaboratorium for Social Media and Online Behavioral Studies (COSMOS) Information Science, College of Engineering and Information Technology University of Arkansas at Little Rock -------------- next part -------------- An HTML attachment was scrubbed... URL: From dxkeec at gmail.com Tue Oct 5 22:37:33 2021 From: dxkeec at gmail.com (Dhireesha Kudithipudi) Date: Tue, 5 Oct 2021 21:37:33 -0500 Subject: Connectionists: [Endowed Associate/Full Professor] MATRIX AI Consortium (University of Texas at San Antonio) in Inclusive & Human-Centered AI In-Reply-To: References: Message-ID: Over the past two years, The University of Texas at San Antonio (UTSA) has made significant progress in interdisciplinary AI research and scholarship efforts, notably through the newly launched MATRIX AI Consortium and establishing the School of Data Science. UTSA has a focused hiring plan in the area of Artificial Intelligence, specifically in Inclusive & Human-Centered AI Systems and AI Accelerators, both of which advance the research thrusts in the MATRIX . The initiative will serve as the nexus for leading AI innovators to perform transdisciplinary research, engage in high-impact partnerships, and provide thought leadership and domain expertise to solve intractable problems in AI. Highlighted position: Endowed Full Professor with a joint appointment in the Department of Computer Science and the Department of Electrical and Computer Engineering. Qualifications 1. *Inclusive and Human-Centered AI Systems: *Outstanding senior candidates are invited for an Endowed Full/Associate professor position in ?Inclusive and Human-centered AI Systems?. Human-centered AI, is a multidisciplinary effort to advance AI research and deployment in ways that dramatically amplify human performance in several settings. This area bridges the fields of AI models (*eg*: explainable AI, fairness in AI models, decentralized decision making, dynamic & adaptive models) with human engagement in high-consequence contexts. Researchers in this area will have the opportunity to collaborate with MATRIX partners in developing convergent AI solutions that are inclusive. Successful candidates will demonstrate (1) a record of high-quality research, publication record, and track-record of external funding in human-centered AI & related areas, (2) excellence in undergraduate and graduate education, and (3) a commitment to inclusion and diversity. Responsibilities Responsibilities include research (individual and collaborative), teaching at the graduate and undergraduate levels, and program development. Candidates for the endowed professor/associate professor should be creating research products, expected but are not limited to communicating the research project results in diverse academic outlets; contributing to open-source scientific software, curated datasets, and/or blogs; and developing thought leadership pieces with academic, government, and industry partners. Posting End Date All applications received by *December 1st, 2021* will be given full consideration. Applications received after that date will be accepted and reviewed until the position is filled. Required Application Materials Applicants should submit their application packages via the UTSA HR website: https://www.utsa.edu/hr/employment/ - Curriculum Vitae - Research and teaching statements, which include discussion on the role diversity and inclusion plays in an academic environment (3-page limit) - Complete contact information for at least three professional references *Please submit all documents together in a single PDF in order to be considered.* Questions and nominations for any position should be sent to the Director of MATRIX AI Consortium, Dhireesha Kudithipudi, Search Committee Chair at dk at utsa.edu. Required Qualifications - Doctorate degree in Computer Engineering, Computer Science, Electrical Engineering, and/or related fields. - Appropriate research and teaching record for appointment at the rank for each position (for those seeking appointments with tenure, this is contingent upon Board of Regents? approval) - Demonstrated commitment to inclusion and diversity. - Moreover, the successful candidate(s) must demonstrate their ability to work with and be sensitive to the educational needs of diverse urban populations and support the University?s commitment to thrive as a Hispanic Serving Institution and a model for student success. - The most competitive candidates will also have experience in interdisciplinary research and/or curriculum development, and scientific open-source projects. Preferred Qualifications - Ideal candidates include those who demonstrate evidence of a commitment to collaboration, diversity, equity, and inclusion through research, teaching, service endeavors, and those who can show a commitment to data-intensive research and software reproducibility. -------------- next part -------------- An HTML attachment was scrubbed... URL: From dxkeec at gmail.com Tue Oct 5 22:38:15 2021 From: dxkeec at gmail.com (Dhireesha Kudithipudi) Date: Tue, 5 Oct 2021 21:38:15 -0500 Subject: Connectionists: [Assistant Professor] MATRIX AI Consortium (University of Texas at San Antonio) in AI Accelerators Area In-Reply-To: References: Message-ID: Over the past two years, The University of Texas at San Antonio (UTSA) has made significant progress in interdisciplinary AI research and scholarship efforts, notably through the newly launched MATRIX AI Consortium and establishing the School of Data Science. UTSA has a focused hiring plan in the area of Artificial Intelligence, specifically in Inclusive & Human-Centered AI Systems and AI Accelerators, both of which advance the research thrusts in the MATRIX . The initiative will serve as the nexus for leading AI innovators to perform transdisciplinary research, engage in high-impact partnerships, and provide thought leadership and domain expertise to solve intractable problems in AI. *Highlighted position: *Assistant Professor in the field of AI Accelerators will have an appointment in the Department of Electrical and Computer Engineering. The required qualifications of the successful candidates are a doctorate degree in Computer Engineering, Computer Science, Electrical Engineering, and/or related fields, with appropriate research and teaching record for appointment at the rank for each position (for those seeking appointments with tenure, this is contingent upon Board of Regents? approval), and demonstrated commitment to inclusion and diversity. Successful candidates will demonstrate (1) a record of high-quality research and scholarship, or for assistant professor candidates, demonstration of a solid research agenda and publication and external funding capability, (2) excellence in undergraduate and graduate education or demonstration of ability to teach, and (3) a demonstrated commitment to inclusion and diversity. *AI Accelerators: *Outstanding candidates at the Assistant Professor level are invited for a position in ?AI Accelerators?. AI?s tremendous capability depends on development and deployment of the lightweight, efficient AI models that can be executed on resource constrained hardware. The move towards bigger models requires ever-growing datasets and compute budgets, which incur massive energy bills over the model deployment lifecycle. It is crucial to design new AI accelerators/AI hardware (eg: digital accelerators, custom ASIC design for AI chips, mixed-signal AI chip design, emerging materials and device technologies for AI systems) that can support the rapid growth of the models and reduce the overall carbon footprint. Researchers in this area will have the opportunity to collaborate with MATRIX researchers with expertise in AI devices, circuit design, and embedded system design. Moreover, the successful candidate(s) must demonstrate their ability to work with and be sensitive to the educational needs of diverse urban populations and support the University?s commitment to thrive as a?Hispanic Serving Institution and a model for student success. Posting End Date Review of applications begins December 1st, 2021 and will continue until the position is filled. Apply Here Required Application Materials 1. Curriculum Vitae 2. Research and teaching statements, which include discussion on the role diversity and inclusion plays in an academic environment (3-page limit) 3. Complete contact information for at least three professional references *Please submit all documents together in a single PDF in order to be considered.* Questions and nominations for any position should be sent to the Director of MATRIX AI Consortium, Dhireesha Kudithipudi, Search Committee Chair at dk at utsa.edu Required Qualifications - Doctorate degree in Computer Engineering, Computer Science, Electrical Engineering, and/or related fields. - Appropriate research and teaching record for appointment at the rank for each position (for those seeking appointments with tenure, this is contingent upon Board of Regents? approval). - Demonstrated commitment to inclusion and diversity. - Must demonstrate their ability to work with and be sensitive to the educational needs of diverse urban populations and support the University?s commitment to thrive as a Hispanic Serving Institution and a model for student success. Preferred Qualifications - Ideal candidates include those who demonstrate evidence of a commitment to collaboration, diversity, equity, and inclusion through research, teaching, and service endeavors. - Those who can show a commitment to data-intensive research and software reproducibility. -------------- next part -------------- An HTML attachment was scrubbed... URL: From suashdeb at gmail.com Tue Oct 5 23:02:35 2021 From: suashdeb at gmail.com (Suash Deb) Date: Wed, 6 Oct 2021 08:32:35 +0530 Subject: Connectionists: ISMSI22 (Seoul, Korea) - One & half months away (from submission deadline) Message-ID: Dear friends & esteemed colleagues, This is just to refresh your memory that the paper submission deadline for ISMSI22 is exactly one & half months away. I warmly invite you to consider submission of your manuscript(s) and endeavor to be a part of this exciting event which will provide you with dual opportunities of possible publication of your conference papers in ACM(ICPS) proceedings coupled with possibilities of publications of the extended versions of a subset of ISMSI22 papers in high ranking SCIE indexed journals. For further info, pls. visit http://ismsi.org Stay safe & kind regards, Suash -------------- next part -------------- An HTML attachment was scrubbed... URL: From antona at alleninstitute.org Wed Oct 6 01:32:02 2021 From: antona at alleninstitute.org (Anton Arkhipov) Date: Wed, 6 Oct 2021 05:32:02 +0000 Subject: Connectionists: Open position in Modeling at the Allen Institute Message-ID: <20D38B7F-6D5A-45A2-9E9A-90A2BA42465D@alleninstitute.org> Are you excited about building bio-realistic, large-scale models of brain circuits? Do you enjoy collaborative, team science environment? Do you want to contribute to understanding fundamental mechanisms of brain function? If yes, this job opening might be for you! Check out this Scientist position in the lab of Anton Arkhipov at the Allen Institute in Seattle: https://alleninstitute.hrmdirect.com/employment/job-opening.php?req=1770679 The Scientist will build bio-realistic models and simulate the entrainment of neuronal populations by periodic sensory stimulation at different frequencies. This NIH BRAIN Initiative-sponsored project will leverage our expertise in modeling and unique data on cortex composition and connectivity at the Allen Institute, as well as experimental data from our collaborators at the group of Dr. Li-Huei Tsai at MIT. Relevant resources and publications: * https://portal.brain-map.org/explore/models/mv1-all-layers * https://portal.brain-map.org/explore/connectivity * Iaccarino et al., Nature (2016); Adaikkan et al., Neuron (2019); Martorell et al., Cell (2019) The Allen Institute believes that team science significantly benefits from the participation of diverse voices, experiences and backgrounds. High-quality science can only be produced when it includes different perspectives. We are committed to increasing diversity across every team and encourage people from all backgrounds to apply for this role. Anton Arkhipov Associate Investigator T: 206.548.8414 E: antona at alleninstitute.org [Text Description automatically generated] alleninstitute.org brain-map.org -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 26603 bytes Desc: image001.png URL: From ioannakoroni at csd.auth.gr Wed Oct 6 07:35:08 2021 From: ioannakoroni at csd.auth.gr (Ioanna Koroni) Date: Wed, 6 Oct 2021 14:35:08 +0300 Subject: Connectionists: AIDA Excellence Lecture Series: 'Challenges and Opportunities for Human-Centred AI', Lectures and dialog between Yoshua Bengio (Turing Award) and Ben Shneiderman 14 October 16.00-18.00 CEST References: <014201d7b938$3d61dd90$b82598b0$@csd.auth.gr> Message-ID: <07d901d7baa6$37ed1220$a7c73660$@csd.auth.gr> Dear AI scientist/engineer/student/enthusiast, you are welcomed to attend the lectures of two prominent AI researchers followed by dialog session: Challenges and Opportunities for Human-Centred AI: A dialog between Yoshua Bengio (Turing Award) and Ben Shneiderman, moderated by Virginia Dignum. This online event on 14 October 16.00-18.00 CEST is part of the European Advanced Course on AI (acai2021.org) and is open to all, with the support of HumaneAI-net, EURAI, the Hertie School, and the AIDA doctoral academy. You can attend this event through the Youtube stream: https://youtu.be/uc0bYp_-JLA The International AI Doctoral Academy (AIDA), a joint initiative of the European R&D projects AI4Media, ELISE, Humane AI Net, TAILOR and VISION is very pleased to offer you top quality scientific lectures on several current hot AI topics. Lectures will be offered alternatingly by: a) Top highly-cited senior AI scientists internationally or b) Young AI scientists with promise of excellence (AI sprint lectures) The lectures are disseminated through multiple channels and email lists (we apologize if you received it through various channels). If you want to stay informed on future lectures, you can register in the AIDA and CVML email lists: https://lists.auth.gr/sympa/info/aida https://lists.auth.gr/sympa/info/cvml Best regards Profs. M. Chetouani, P. Flach, B. O?Sullivan, I. Pitas, N. Sebe -- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus From contact at sscc.fr Thu Oct 7 04:15:10 2021 From: contact at sscc.fr (SSCC) Date: Thu, 7 Oct 2021 10:15:10 +0200 Subject: Connectionists: [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <005201d7bb53$7aa56ba0$6ff042e0$@sscc.fr> Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience . Smart networks for smart cities . Security management in smart cities . Security in distributed systems . Modeling, analysis and detection of IoT attacks . Data mining for cybersecurity in smart cities . Decentralized architecture for smart cities . Consensus protocols and applications IoT & AI . IoT Indoor deployment . IoT communication protocols . Building information modeling (BIM) IoT-based HVAC control in smart buildings . Artificial Intelligence in Cyber Physical Energy Systems . Optimization for IoT and smart cities . Dynamic scheduling for IoT deployment . Autonomous and Smart decisions Edge and Cloud . Cloud-Edge for IoT and smart cities . Fog and Edge computing for smart cities . Applications/services for Edge AI . Software Platforms for Edge Social aspects and applications . Behavioral and Energy Consumption Analytics . Indoor comfort . Human factors and organizational resilience for distributed systems Important Dates . Paper Submission Date: 31 October, 2021 . Notification to Authors: 15 November, 2021 . Camera Ready Submission: 21 December 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From tiako at ieee.org Wed Oct 6 16:50:23 2021 From: tiako at ieee.org (Pierre F. Tiako) Date: Wed, 6 Oct 2021 15:50:23 -0500 Subject: Connectionists: (CFP-Due Oct 16) 2021 OkIP Conf on Automated & Intelligent Systems|| OkCity, USA|| Nov 15-18 Message-ID: >> Important Dates (Extended): - Submission: Oct 16, 2021 - Conference: Nov 15-18, 2021 --- Call for Abstracts and Papers ------------- 2021 OkIP International Conference on Automated and Intelligent Systems (CAIS) MNTC Conference Center, Oklahoma City, OK, USA & Online November 15-18, 2021 https://eventutor.com/e/CAIS001 >> Co-located Conferences and Events https://eventutor.com/event/4/page/4-conferences >> Keynotes/Invited Talks ?Machine Learning for Critical Systems Security? - Nancy R. Mead, PhD, Carnegie Mellon University, USA ?Sustainable Energy Harvesting and Wireless Power Transfer Systems? - Manos M. Tentzeris, PhD, Georgia Institute of Technology, USA "Blockchain Technology and its implications in Business Applications and Healthcare IT" - Akhil Kumar, PhD, Penn State University, USA >> Technical Research & Industry Tracks - Agent-based, Automated, and Distributed Supports - Intelligent Systems and Applications - Machine Learning - Knowledge-based and Control Supports - Robotics and Vehicles >> Contribution Types - Full Paper: Accomplished research results (6 pages) - Short Paper: Work in progress/fresh developments (3 pages) - Poster/Journal First: Displayed/Oral presented (1 page) >> Technical Program Committee https://eventutor.com/event/6/page/12-committee >> Venue https://eventutor.com/event/4/page/9-venue >> For more information, submission details, and important dates, visit: https://eventutor.com/e/CAIS001 Please feel free to contact us for any inquiry at: info at okipublishing.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From liufengchaos at gmail.com Thu Oct 7 00:34:54 2021 From: liufengchaos at gmail.com (Feng Liu) Date: Thu, 7 Oct 2021 00:34:54 -0400 Subject: Connectionists: CfP: Graph Learning for Brain Imaging in Frontiers of Neuroscience (deadline extended) Message-ID: Dear Colleagues, We are writing to let you know that we are organizing a special issue "Graph Learning for Brain Imaging" in Frontiers in Neuroscience (impact factor 4.7). We believe this is a timely special issue to showcase the new developments using graph representation, deep learning on graph-structured data to address important brain imaging and computational neuroscience problems. *Link*: https://www.frontiersin.org/research-topics/23683/graph-learning-for-brain-imaging *Keywords*: Brain Networks, Graph Neural Networks, Brain Imaging, Graph Embedding, Multi-Modal Imaging. *Topics*: We are looking for original, high-quality submissions on innovative research and developments in the analysis of brain imaging using graph learning techniques. Topics of interest include (but are not limited to): ? Graph neural networks (GNN) for network neuroscience applications ? Graph neural network for brain mapping and data integration ? Graph convolution network (GCN) for brain disorder classification ? (Dynamic) Functional brain networks ? Brain networks development trajectories ? Graphical model for brain imaging data analysis ? Spatial-temporal brain network modeling ? Graph embedding and graph representation learning ? Information fusion for brain networks from multiple modalities or scales (fMRI, M/EEG, DTI, PET, genetics) ? Generative graph models in brain imaging ? Brain network inference: scalable, online, and from non-linear relationships ? Machine learning over graphs: kernel-based techniques, clustering methods, scalable algorithms for brain imaging ? A few-shot learning for learning from limited brain data ? Graph federated learning for brain imaging *Important Dates*: *Abstract: 30-Oct-2021 * Full paper: 30-Dec-2021 *Background:* Unprecedented collections of large-scale brain imaging data, such as MRI, PET, fMRI, M/EEG, DTI, etc. provide a unique opportunity to deepen our understanding of the brain working mechanisms, improve prognostic predictions for mental disorders, and tailor personalized treatment plans for brain diseases. Recent advances in machine learning and large-scale brain imaging data collection, storage, and sharing lead to a series of novel interdisciplinary approaches among the fields of computational neuroscience, signal processing, deep learning, brain imaging, cognitive science, and computational psychiatry, among which graph learning provides a valuable means to address important questions in brain imaging. Graph learning refers to designing effective machine learning and deep learning methods extracting important information from graphs or exploiting the graph structure in the data to guide the knowledge discovery. Given the complex data structure in different imaging modalities as well as the networked organizational structure of the human brain, novel learning methods based on graphs inferred from imaging data, graph regularizations for the data, and graph embedding of the recorded data, have shown great promise in modeling the interactions of multiple brain regions, information fusion among networks derived from different brain imaging modalities, latent space modeling of the high dimensional brain networks, and quantifying topological neurobiomarkers. The goal of this Research Topic is to synergize the start-of-the-art discoveries in terms of new computational brain imaging models and insights of brain mechanisms through the lens of brain networks and graph learning. --On Behalf of all the Guest Editors Feng Liu, Stevens Institute of Technology, Hoboken, NJ, USA Yu Zhang, Lehigh University, Bethlehem, PA, USA Jordi Sol?-Casals, Universitat de Vic - Universitat Central de Catalunya, Barcelona, SpainIslem Rekik, Istanbul Technical University, Istanbul, TurkeyYehia Massoud, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Thank you! Best regards, Feng Liu -------------- next part -------------- An HTML attachment was scrubbed... URL: From cognitivium at sciencebeam.com Wed Oct 6 09:31:57 2021 From: cognitivium at sciencebeam.com (Mary) Date: Wed, 6 Oct 2021 17:01:57 +0330 Subject: Connectionists: FINAL CALL-Neurofeedback and QEEG workshop in Istanbul Message-ID: <202110061332.196DW1WD008023@scs-mx-01.andrew.cmu.edu> FINAL CALL 3-Day Neurofeedback and QEEG workshop in Istanbul, Turkey On the occasion of the Opening Ceremony of ScienceBeam's office in Istanbul, attend this comprehensive workshop with a %50 discount! This is the FINAL CALL to join us for the 3-day hands-on Neurofeedback and QEEG workshop, in Istanbul, Turkey.? This fantastic workshop is being held on the occasion of the?Grand Opening Ceremony of ScienceBeam?s office in?Istanbul, Turkey, on October 15-17, with a?50% discount! This workshop, which will be led by ScienceBeam experts in both Neurofeedback and QEEG areas, provides not only a broad and up-to-date exposure to the current state of QEEG and Neurofeedback but also provides an opportunity to do Neurofeedback treatment as well as QEEG recording and analysis during hands-on sessions. All the attendees who have little or broad experience in QEEG and neurofeedback will find this meeting a comprehensive and engaging workshop that will include essential material to ensure a solid understanding of current QEEG and Neurofeedback concepts. We are delighted to invite you to join us for this fantastic meeting.? You are more than welcome to invite your friends and colleagues to attend to this fantastic workshop with you (Lower admission fee for group registration). We are planning for a fantastic meeting and gathering with researchers and clinicians from over the world, as well as a fancy stay at one of the most beautiful and live cities in the whole world. Notably, this is a great opportunity for clinicians and researchers who?d want to purchase the Neurofeedback or QEEG equipment, as well as learning all the related concepts. For them, the workshop cost would be free. So, even if you are not in Istanbul, do not miss this extraordinary opportunity and book your flight promptly. We are excited to see you! ? For more information regarding the workshop schedule and registration please visit the link below: https://sciencebeam.com/neurofeedback-and-qeeg-workshop/? ? Please bear in mind that the registration deadline is October 12th.? If you have any questions regarding the workshop, do not hesitate to contact us (workshop at sciencebeam.com , or WhatsApp: 008613380781282). We hope to see you soon in Istanbul. ? Mary Reae Human Neuroscience Dept. Manager @ScienceBeam mary at sciencebeam.com www.sciencebeam.com? -------------- next part -------------- An HTML attachment was scrubbed... URL: From mpavone at dmi.unict.it Thu Oct 7 04:38:08 2021 From: mpavone at dmi.unict.it (Mario Pavone) Date: Thu, 07 Oct 2021 10:38:08 +0200 Subject: Connectionists: 14th Metaheuristics International Conference, 11-14 July 2022, Ortigia-Syracuse, Italy Message-ID: <20211007103808.Horde.ocaIFeph4B9hXrHw31jxFCA@mbox.dmi.unict.it> Apologies for cross-posting. Appreciate if you can distribute this CFP to your network. ********************************************************* MIC 2022 - 14th Metaheuristics International Conference 11-14 July 2021, Ortigia-Syracuse, Italy https://www.ANTs-lab.it/mic2022/ ********************************************************* ** Submission deadline: 30th March 2022 ** *Scope of the Conference ======================== The Metaheuristics International Conference (MIC) conference series was established in 1995 and this is its 14th edition! MIC is nowadays the main event focusing on the progress of the area of Metaheuristics and their applications. As in all previous editions, provides an opportunity to the international research community in Metaheuristics to discuss recent research results, to develop new ideas and collaborations, and to meet old and make new friends in a friendly and relaxed atmosphere. Considering the particular moment, the conference will be held in presence and online mode. Of course, in case the conference will be held in presence, the organizing committee will ensure compliance of all safety conditions. MIC 2022 is focus on presentations that cover different aspects of metaheuristic research such as new algorithmic developments, high-impact and original applications, new research challenges, theoretical developments, implementation issues, and in-depth experimental studies. MIC 2022 strives a high-quality program that will be completed by a number of invited talks, tutorials, workshops and special sessions. *Plenary Speakers ======================== + Holger H. Hoos, Leiden University, The Netherlands + Christian Blum, Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC) More Plenary Speakers will be announced soon. *Relevant Research Areas ======================== MIC 2022 solicits contributions dealing with any aspect of metaheuristics. Typical, but not exclusive, topics of interest are: + Metaheuristic techniques such as tabu search, simulated annealing, iterated local search, variable neighborhood search, memory-based optimization, dynamic local search, evolutionary algorithms, memetic algorithms, ant colony optimization, variable neighborhood search, particle swarm optimization, scatter search, path relinking, etc. + Techniques that enhance the usability and increase the potential of metaheuristic algorithms such as reactive search mechanisms for self-tuning, offline metaheuristic algorithm configuration techniques, algorithm portfolios, parallelization of metaheuristic algorithms, etc. + Empirical and theoretical research in metaheuristics including large-scale experimental analyses, algorithm comparisons, new experimental methodologies, engineering methodologies for metaheuristic algorithms, search space analysis, theoretical insights into properties of metaheuristic algorithms, etc. + High-impact applications of metaheuristics in fields such as bioinformatics, electrical and mechanical engineering, telecommunications, sustainability, business, scheduling and timetabling. Particularly welcome are innovative applications of metaheuristic algorithms that have a potential of pushing research frontiers. + Contributions on the combination of metaheuristic techniques with those from other areas, such as integer programming, constraint programming, machine learning, etc. + Contributions on the use of metaheuristic techniques in machine learning and deep learning for finetuning and neural architecture search, etc. + Challenging applications areas such as continuous, mixed discrete-continuous, multi-objective, stochastic, or dynamic problems. Important Dates ================ Submission deadline March 30th, 2022 Notification of acceptance May 10th, 2022 Camera ready copy May 25th, 2022 Early registration May 25th , 2022 Submission Details =================== We will accept submissions in three different formats. S1) Original research contributions for publication in the conference proceedings of a maximum of 10 pages S2) Extended abstracts of work-in-progress and position papers on an important research aspect of a maximum of 3 pages S3) High-quality manuscripts that have recently, within the last year, been submitted or accepted for journal publication Accepted contributions of categories S1 and S2 will be published in the MIC 2022 conference proceedings. Accepted contributions of category S3 will be orally presented at the conference, but not be included into the conference proceedings. From boubchir at ai.univ-paris8.fr Thu Oct 7 02:10:18 2021 From: boubchir at ai.univ-paris8.fr (Larbi Boubchir) Date: Thu, 7 Oct 2021 08:10:18 +0200 Subject: Connectionists: [CfP] The 2nd international workshop on Machine Learning for EEG Signal Processing (MLESP) In-Reply-To: References: Message-ID: [Apologies for multiple postings]* * *CALL FOR PAPERS* The 2^nd international workshop on Machine Learning for EEG Signal Processing (MLESP 2021, https://mlesp2021.sciencesconf.org/) will be held online, from 9 to 12 december 2021, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021, https://ieeebibm.org/BIBM2021/) *Overview* EEG signal processing involves the analysis and treatment of the electrical activity of the brain measured with Electroencephalography, or EEG, in order to provide useful information on which decisions can be made. The recent advances in signal processing and machine learning for EEG data processing have brought an impressive progress to solve several practical and challenging problems in many areas such as healthcare, biomedicine, biomedical engineering, BCI and biometrics. The aim of this workshop is to present and discuss the recent advances in machine learning for EEG signal analysis and processing. We are inviting original research work, as well as significant work-in-progress, covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in EEG data analytics. This workshop is an opportunity to bring together academic and industrial scientists to discuss the recent advances. The topics of interest include but not limited to: - EEG signal processing and analysis - Time-frequency EEG signal analysis - Signal processing for EEG Data - EEG feature extraction and selection - Machine learning for EEG signal processing - EEG classification and Hierarchical clustering - EEG abnormalities detection (e.g. Epileptic seizure, Alzheimer's disease, etc.) - Machine learning in EEG Big Data - Deep Learning for EEG Big Data - Neural Rehabilitation Engineering - Brain-Computer Interface - Neurofeedback - EEG-based Biometrics - Related applications Important Dates Oct. 25, 2021 (11:59 pm CST): Due date for full workshop papers submission Nov. 10, 2021: Notification of paper acceptance to authors Nov. 15, 2021: Camera-ready of accepted papers Dec 3-6, 2021: Workshops Paper Submission - Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. You can download the format instruction here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html - Electronic submissions in PDF format are required. Online Submission https://wi-lab.com/cyberchair/2021/bibm21/index.php Publication All accepted papers will be published in the BIBM proceedings and IEEE Xplore Digital Library. Journal Special Issue Selected high-quality papers will be invited for publication in a special issue in highly respected journal. *Contact* Prof. Larbi Boubchir /(//Workshop Chair/),University of Paris 8, France E-mail: larbi.boubchir at univ-paris8.fr -------------- next part -------------- An HTML attachment was scrubbed... URL: From benoit.frenay at unamur.be Thu Oct 7 02:44:17 2021 From: benoit.frenay at unamur.be (=?UTF-8?B?QmVub8OudCBGcsOpbmF5?=) Date: Thu, 7 Oct 2021 08:44:17 +0200 Subject: Connectionists: Postdoc in ML testing with an emphasis on constraints Message-ID: Dear colleagues, We have a two-year postdoc position to fill in the EOS VeriLearn (Verifying Learning Artificial Intelligence Systems) project at the Faculty of Computer Science of UNamur (Namur, Belgium).? VeriLearn is a large project involving 10 academics from three Belgian unversities.? Within VeriLearn, the candidate will be integrated in an interdisciplinary team working at the intersection of AI, ML and SE. The goal of this postdoctoral research is to define, design and evaluate ?active testing? (a mix of active learning and software testing) techniques for ML models. In particular, the successful candidate will analyse automatically the datasets to infer properties of interest in the form of constraints, rank them depending on the desired validation goal and integrate them in testing techniques (metamorphic testing, constraint-guided fuzzing, etc.). This candidate will work with Prof. Benoit Fr?nay and Dr. Gilles Perrouin in the Faculty of Computer Science at the University of Namur in collaboration with the KULeuven DTAI group. More infos on * https://jobs.unamur.be/emploi.2021-09-09.2912866089/view * https://euraxess.ec.europa.eu/jobs/681147 Deadline: October, 15th, 2021 (11h59 pm AoE).? Expected starting date: January 1st, 2022 (negotiable, depending on COVID or candidate constraints). Please send your application or questions to benoit.frenay at unamur.be and gilles.perrouin at unamur.be. Best regards, Beno?t Fr?nay Gilles Perrouin -------------- next part -------------- An HTML attachment was scrubbed... URL: From bwyble at gmail.com Thu Oct 7 00:06:02 2021 From: bwyble at gmail.com (Brad Wyble) Date: Thu, 7 Oct 2021 00:06:02 -0400 Subject: Connectionists: Neuromatch Conference 4.0, Submission deadline Oct 25, conference Dec 1-2. Message-ID: After three conferences in 2020, Neuromatch is pleased to be holding our fourth on December 1-2, 2021. Neuromatch 4.0 will be focused on Computational Neuroscience and will be similar to the scope of the first two NMC events in spring 2020. Agendas from previous NMC editions (1.0, 2.0 and 3.0) can be found on the Agenda from the conference website under the gear icon. The scope of NMC4 includes computational neuroscience and machine learning work that has a biological link. Registration costs are set at 15USD to cover technical costs, and if this is a difficulty, the fee can be waived without question. Each of our conferences has led the way in providing new innovations to the virtual conference space, and this year we are excited to offer you: - A combination of the virtual with the in-person conference experiences. You are encouraged to host or attend a local meetup, bringing people together to enjoy the conference, without the incredible economic and environmental costs of flying around the world. - The maintalks will be hosted on Crowdcast. Instead of posters, we offer flash talks (brief pre-recorded videos), and dedicated meet up times for discussion within Discord. - NMC for kids: a special session of talks for a younger audience and the young at heart interested in neuroscience. - We are developing new mechanisms to connect talks in the conference to corresponding preprints on bioRxiv, and we will provide links to preprints on other platforms as well. Read more at http://conference.neuromatch.io/ Important dates: - October 7, 2021: Registration and submission opens - October 25, 2021: Abstract submission deadline - Early November: local meetups portal opens - November 1 - 2, 2021: Abstract acceptance and live talk selection news - November 12, 2021: Video submission deadline for flashtalks - December 1 - 2, 2021: NMC 4.0 Each of the two conference days will have three sessions, each of which is 3 hours long. Here are the hours for each of the sessions, as they would occur across five time zones from around the world: We look forward to seeing you at NMC 4.0! All the best, Neuromatch Conference Organizing Committee -- Brad Wyble Associate Professor Psychology Department Penn State University http://wyblelab.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From maanakg at gmail.com Wed Oct 6 20:12:14 2021 From: maanakg at gmail.com (Maanak Gupta) Date: Wed, 6 Oct 2021 19:12:14 -0500 Subject: Connectionists: 27th ACM Symposium on Access Control Models and Technologies Message-ID: ?ACM SACMAT 2022 New York City, New York ----------------------------------------------- | Hybrid Conference (Online + In-person) | ----------------------------------------------- Call for Research Papers ============================================================== Papers offering novel research contributions are solicited for submission. Accepted papers will be presented at the symposium and published by the ACM in the symposium proceedings. In addition to the regular research track, this year SACMAT will again host the special track -- "Blue Sky/Vision Track". Researchers are invited to submit papers describing promising new ideas and challenges of interest to the community as well as access control needs emerging from other fields. We are particularly looking for potentially disruptive and new ideas which can shape the research agenda for the next 10 years. We also encourage submissions to the "Work-in-progress Track" to present ideas that may have not been completely developed and experimentally evaluated. Topics of Interest ============================================================== Submissions to the regular track covering any relevant area of access control are welcomed. Areas include, but are not limited to, the following: * Systems: * Operating systems * Cloud systems and their security * Distributed systems * Fog and Edge-computing systems * Cyber-physical and Embedded systems * Mobile systems * Autonomous systems (e.g., UAV security, autonomous vehicles, etc) * IoT systems (e.g., home-automation systems) * WWW * Design for resiliency * Designing systems with zero-trust architecture * Network: * Network systems (e.g., Software-defined network, Network function virtualization) * Corporate and Military-grade Networks * Wireless and Cellular Networks * Opportunistic Network (e.g., delay-tolerant network, P2P) * Overlay Network * Satellite Network * Privacy and Privacy-enhancing Technologies: * Mixers and Mixnets * Anonymous protocols (e.g., Tor) * Online social networks (OSN) * Anonymous communication and censorship resistance * Access control and identity management with privacy * Cryptographic tools for privacy * Data protection technologies * Attacks on Privacy and their defenses * Authentication: * Password-based Authentication * Biometric-based Authentication * Location-based Authentication * Identity management * Usable authentication * Mechanisms: * Blockchain Technologies * AI/ML Technologies * Cryptographic Technologies * Programming-language based Technologies * Hardware-security Technologies (e.g., Intel SGX, ARM TrustZone) * Economic models and game theory * Trust Management * Usable mechanisms * Data Security: * Big data * Databases and data management * Data leakage prevention * Data protection on untrusted infrastructure * Policies and Models: * Novel policy language design * New Access Control Models * Extension of policy languages * Extension of Models * Analysis of policy languages * Analysis of Models * Policy engineering and policy mining * Verification of policy languages * Efficient enforcement of policies * Usable access control policy New in ACM SACMAT 2022 ============================================================== We are moving ACM SACMAT 2022 to have two submission cycles. Authors submitting papers in the first submission cycle will have the opportunity to receive a major revision verdict in addition to the usual accept and reject verdicts. Authors can decide to prepare a revised version of the paper and submit it to the second submission cycle for consideration. Major revision papers will be reviewed by the program committee members based on the criteria set forward by them in the first submission cycle. Regular Track Paper Submission and Format ============================================================== Papers must be written in?English. Authors are required to use the ACM format for papers, using the two-column SIG Proceedings Template (the sigconf template for LaTex) available in the following link: https://www.acm.org/publications/authors/submissions The length of the paper in the proceedings format must not exceed?twelve?US letter pages formatted for 8.5" x 11" paper and be no more than 5MB in size. It is the responsibility of the authors to ensure that their submissions will print easily on simple default configurations. The submission must be anonymous, so information that might identify the authors - including author names, affiliations, acknowledgments, or obvious self-citations - must be excluded. It is the authors' responsibility to ensure that their anonymity is preserved when citing their work. Submissions should be made to the EasyChair conference management system by the paper submission deadline of: November 15th, 2021 (Submission Cycle 1) February 18th, 2022 (Submission Cycle 2) All submissions must contain a?significant original contribution. That is, submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal, conference, or workshop. In particular, simultaneous submission of the same work is not allowed. Wherever appropriate, relevant related work, including that of the authors, must be cited. Submissions that are not accepted as full papers may be invited to appear as short papers. At least one author from each accepted paper must register for the conference before the camera-ready deadline. Blue Sky Track Paper Submission and Format ============================================================== All submissions to this track should be in the same format as for the regular track, but the length must not exceed ten US letter pages, and the submissions are not required to be anonymized (optional). Submissions to this track should be submitted to the EasyChair conference management system by the same deadline as for the regular track. Work-in-progress Track Paper Submission and Format ============================================================== Authors are invited to submit papers in the newly introduced work-in-progress track. This track is introduced for (junior) authors, ideally, Ph.D. and Master's students, to obtain early, constructive feedback on their work. Submissions in this track should follow the same format as for the regular track papers while limiting the total number of pages to six US letter pages. Paper submitted in this track should be anonymized and can be submitted to the EasyChair conference management system by the same deadline as for the regular track. Call for Lightning Talk ============================================================== Participants are invited to submit proposals for 5-minute lightning talks describing recently published results, work in progress, wild ideas, etc. Lightning talks are a new feature of SACMAT, introduced this year to partially replace the informal sharing of ideas at in-person meetings. Submissions are expected??by May 27, 2022. Notification of acceptance will be on June 3, 2022. Call for Posters ============================================================== SACMAT 2022 will include a poster session to promote discussion of ongoing projects among researchers in the field of access control and computer security. Posters can cover preliminary or exploratory work with interesting ideas, or research projects in the early stages with promising results in all aspects of access control and computer security. Authors interested in displaying a poster must submit a poster abstract in the same format as for the regular track, but the length must not exceed three US letter pages, and the submission should not be anonymized. The title should start with "Poster:". Accepted poster abstracts will be included in the conference proceedings. Submissions should be emailed to the poster chair by Apr 15th, 2022. The subject line should include "SACMAT 2022 Poster:" followed by the poster title. Call for Demos ============================================================== A demonstration proposal should clearly describe (1) the overall architecture of the system or technology to be demonstrated, and (2) one or more demonstration scenarios that describe how the audience, interacting with the demonstration system or the demonstrator, will gain an understanding of the underlying technology. Submissions will be evaluated based on the motivation of the work behind the use of the system or technology to be demonstrated and its novelty. The subject line should include "SACMAT 2022 Demo:" followed by the demo title. Demonstration proposals should be in the same format as for the regular track, but the length must not exceed four US letter pages, and the submission should not be anonymized. A two-page description of the demonstration will be included in the conference proceedings. Submissions should be emailed to the Demonstrations Chair by Apr 15th, 2022. Financial Conflict of Interest (COI) Disclosure: ============================================================== In the interests of transparency and to help readers form their own judgments of potential bias, ACM SACMAT requires authors and PC members to declare any competing financial and/or non-financial interests in relation to the work described. Definition ------------------------- For the purposes of this policy, competing interests are defined as financial and non-financial interests that could directly undermine, or be perceived to undermine the objectivity, integrity, and value of a publication, through a potential influence on the judgments and actions of authors with regard to objective data presentation, analysis, and interpretation. Financial competing interests include any of the following: Funding: Research support (including salaries, equipment, supplies, and other expenses) by organizations that may gain or lose financially through this publication. A specific role for the funding provider in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript, should be disclosed. Employment: Recent (while engaged in the research project), present or anticipated employment by any organization that may gain or lose financially through this publication. Personal financial interests: Ownership or contractual interest in stocks or shares of companies that may gain or lose financially through publication; consultation fees or other forms of remuneration (including reimbursements for attending symposia) from organizations that may gain or lose financially; patents or patent applications (awarded or pending) filed by the authors or their institutions whose value may be affected by publication. For patents and patent applications, disclosure of the following information is requested: patent applicant (whether author or institution), name of the inventor(s), application number, the status of the application, specific aspect of manuscript covered in the patent application. It is difficult to specify a threshold at which a financial interest becomes significant, but note that many US universities require faculty members to disclose interests exceeding $10,000 or 5% equity in a company. Any such figure is necessarily arbitrary, so we offer as one possible practical alternative guideline: "Any undeclared competing financial interests that could embarrass you were they to become publicly known after your work was published." We do not consider diversified mutual funds or investment trusts to constitute a competing financial interest. Also, for employees in non-executive or leadership positions, we do not consider financial interest related to stocks or shares in their company to constitute a competing financial interest, as long as they are publishing under their company affiliation. Non-financial competing interests: Non-financial competing interests can take different forms, including personal or professional relations with organizations and individuals. We would encourage authors and PC members to declare any unpaid roles or relationships that might have a bearing on the publication process. Examples of non-financial competing interests include (but are not limited to): * Unpaid membership in a government or non-governmental organization * Unpaid membership in an advocacy or lobbying organization * Unpaid advisory position in a commercial organization * Writing or consulting for an educational company * Acting as an expert witness Conference Code of Conduct and Etiquette ============================================================== ACM SACMAT will follow the ACM Policy Against Harassment at ACM Activities. Please familiarize yourself with the ACM Policy Against Harassment (available at https://www.acm.org/special-interest-groups/volunteer-resources/officers-manual/ policy-against-discrimination-and-harassment) and guide to Reporting Unacceptable Behavior (available at https://www.acm.org/about-acm/reporting-unacceptable-behavior). AUTHORS TAKE NOTE ============================================================== The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks before the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.) Important dates ============================================================== **Note that, these dates are currently only tentative and subject to change.** * Paper submission: November 15th, 2021 (Submission Cycle 1) February 18th, 2022 (Submission Cycle 2) * Rebuttal: December 16th - December 20th, 2021 (Submission Cycle 1) March 24th - March 28th, 2022 (Submission Cycle 2) * Notifications: January 14th, 2022 (Submission Cycle 1) April 8th, 2022 (Submission Cycle 2) * Systems demo and Poster submissions: April 15th, 2022 * Systems demo and Poster notifications: April 22nd, 2022 * Panel Proposal: March 18th, 2022 * Camera-ready paper submission: April 29th, 2022 * Conference date: June 8 - June 10, 2022 -------------- next part -------------- An HTML attachment was scrubbed... URL: From marius.pedersen at ntnu.no Thu Oct 7 02:07:38 2021 From: marius.pedersen at ntnu.no (Marius Pedersen) Date: Thu, 7 Oct 2021 06:07:38 +0000 Subject: Connectionists: Postdoctoral Fellow in Image Quality Assessment using Deep Learning at NTNU, Norway Message-ID: <20d81314822b4e8c9c502762e03dbaac@ntnu.no> We have an open 41 months Postdoctoral position in image quality assessment at Norwegian Colour and Visual Computing Laboratory (Colourlab) at the Norwegian university of Science and Technology (NTNU). The post doc position is part of the research project "Quality and Content: understanding the influence of content on subjective and objective image quality assessment". The project aims to advance the understanding of image quality and develop more precise and better performing image quality metrics based on improved understanding on how content influences image quality. The Postdoctoral position will focus on developing more precise and better performing image quality metrics using deep learning and understanding on how content influences image quality. Deadline: 24th of October 2021 More information and the process for applying is found at https://www.jobbnorge.no/en/available-jobs/job/212748/postdoctoral-fellow-in-image-quality-assessment Best regards Marius Pedersen Professor of Colour Imaging Director of the Norwegian Colour and Visual Computing Laboratory www.colourlab.no Department of Computer Science NTNU marius.pedersen at ntnu.no - (+47) 93 63 43 85 -------------- next part -------------- An HTML attachment was scrubbed... URL: From boubchir at ai.univ-paris8.fr Thu Oct 7 08:00:09 2021 From: boubchir at ai.univ-paris8.fr (Larbi Boubchir) Date: Thu, 7 Oct 2021 14:00:09 +0200 Subject: Connectionists: [CfP] International Workshop on Artificial Intelligence & Edge Computing (AIEC 2021) in conjunction with FMEC In-Reply-To: References: Message-ID: <30292b50-4021-6c33-59d0-1736598fe630@ai.univ-paris8.fr> [Apologies if you got multiple copies of this invitation] ** *International Workshop on Artificial Intelligence & Edge Computing (AIEC 2021)* https://sites.google.com/view/waiec2021/ in conjunction with *The Sixth International Conference on Fog and Mobile Edge Computing (FMEC 2021)* Gandia, Spain. December 6-9, 2021 (virtual) *AIEC 2020 CFP* Artificial Intelligence (AI) became a popular wide area for the latest- generation of software-oriented solutions. Lately, AI has been considered as a hot topic due to its huge applications and it attracted attention from academia as well as industrial and end users while receiving positive media coverage. AI is advancing at considerable speed and lead to many widely beneficial applications, variant from Machine Translation to Medical Image Computing. From R&D perspectives, AI acquires incredible amounts of progress in terms funding investors devoted on AI applications. According to AI efforts, Edge Computing (EC) has become an essential solution to overcome the strangulation of emerging technology development according to its benefits of minimizing data transmission, reducing service latency and easing cloud computing pressure. Also, the scope of EC is very diverse on large application area, such as smart grid and smart city, logistics and transportation, manufacturing and healthcare. Furthermore, the EC provide significant gains which include low-latency thus allowing close cooperation in sophisticated mobile applications and on the core network to reduce traffic volume as data streaming along the way to the distant data center is no longer necessary. The evolution of new technologies of communication such as 5G made communication so much easier where the latency is lower and memory bandwidth is higher, the border between edge infrastructure and mobile devices will be even imprecise and EC will become more attractive. The Workshop on Artificial Intelligence & Edge Computing (AIEC) aims to bring together researchers and practitioners from both academia and industry who are working on Artificial Intelligence and Edge Computing as well as their integration, to exchange research ideas and identify new research challenges in this emerging field. *Topics* The Workshop on Artificial Intelligence & Edge Computing (AIEC) calls for contributions that address fundamental research and solutions issues in Artificial Intelligence and Edge Computing including but not limited to the following : ?Big Data mining at the Edge ?Machine Learning at the edge ?Architectures of Edge AI for IoT ?Security on the Edge ?Resource-friendly Edge AI Model Design ?Resource Management for Edge AI ?Applications/services for Edge AI ?Communication and Networking Protocols for Edge AI ?Software Platforms for Edge *Important Dates * ?*Submission Date: 17^th October, 2021* ?Notification to Authors: *1^st November, 2021* ?Camera Ready Submission: *10^th November, 2021* *Submissions Guidelines and Proceedings * Papers selected for presentation will appear in the FMEC Proceedings, which will be published by the IEEE Computer Society and be submitted to IEEE Xplore for inclusion. Papers must be 6 pages in IEEE format, 10pt font using the IEEE 8.5" x 11" two-column format, single space, A4 format. All papers should be in PDF format, and submitted electronically at Paper Submission Link. A full paper must not exceed the stated length (including all figures, tables and references). Submitted papers must present original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines may be rejected without review. Also submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. Authors may contact the Program Chair for further information or clarification. *Submission System * *https://easychair.org/conferences/?conf=waiec2021 * *Journal Special Issues * Selected papers from the will be invited to submit an extended version to the following journal. Papers will be selected based on their reviewers? scores and appropriateness to the Journal?s theme. All extended versions will undergo reviews and must represent original unpublished research work. Further details will be made available at a later stage. Please send any inquiry on AIEC 2020 to the Emerging Tech. Network Team at: _emergingtechnetwork at gmail.com _ -- _____________________________________________________ Prof. Larbi Boubchir, /SMIEEE/ LIASD - University of Paris 8 2 rue de la Libert?, 93526 Saint-Denis, France Tel. (+33) 1 49 40 67 95 Email. larbi.boubchir at univ-paris8.fr http://www.ai.univ-paris8.fr/~boubchir/ _____________________________________________________ -------------- next part -------------- An HTML attachment was scrubbed... URL: From terry at salk.edu Thu Oct 7 10:35:24 2021 From: terry at salk.edu (Terry Sejnowski) Date: Thu, 07 Oct 2021 07:35:24 -0700 Subject: Connectionists: NEURAL COMPUTATION - October 1, 2021 In-Reply-To: <199703060454.UAA08685@helmholtz.salk.edu> Message-ID: Neural Computation - Volume 33, Number 10 - October 1, 2021 available online for download now: http://www.mitpressjournals.org/toc/neco/33/10 http://cognet.mit.edu/content/neural-computation ----- Articles Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation Alfred Rajakumar, John Rinzel, and Zhe Chen Restricted Boltzmann Machines as Models of Interacting Variables Nicola Bulso, Yasser Roudi Letters Tracking Fast and Slow Changes in Synaptic Weights From Simultaneously Observed Pre- and Postsynaptic Spiking Ganchao Wei, Ian Stevenson Chance-Constrained Active Inference Thijs van de Laar, Ismail Senoz, Ay?a ?z?elikkale, and Henk Wymeersch Multibranch Formal Neuron: An Internally Nonlinear Learning Unit Marifi Guler Realising Active Inference in Variational Message Passing: The Outcome-blind Certainty Seeker Theophile Champion, Marek Grzes, and Howard Bowman Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach Richard Carl Gerum, Achim Schilling Multilinear Common Component Analysis via Kronecker Product Representation Kohei Yoshikawa, Shuichi Kawano ----- ON-LINE -- http://www.mitpressjournals.org/neco MIT Press Journals, One Rogers Street, Cambridge, MA 02142-1209 Tel: (617) 253-2889 FAX: (617) 577-1545 journals-cs at mit.edu ----- From michael.felsberg at liu.se Fri Oct 8 04:11:30 2021 From: michael.felsberg at liu.se (Michael Felsberg) Date: Fri, 8 Oct 2021 08:11:30 +0000 Subject: Connectionists: Deadline extension: Assistant Professor (tenure track) in Machine Learning Message-ID: <4881f9b68728e98a614699e626e992a862bd14cd.camel@liu.se> Link?ping University (Sweden) is looking for good candidates for positions as Assistant Professor (tenure track) in Machine Learning with welcome package (2 PhD students, postdoc). The new deadline is October 22, for further details, see https://liu.se/en/article/open-positions-at-isy Note also that Swedish Universities grant their employees keeping their IPR, which is perfect for own entrepreneurship! Best regards Michael Felsberg -- Professor Michael Felsberg Tel: +46 13 282460 Computer Vision Laboratory Mobile:+46 702 202460 Link?ping University email: michael.felsberg at liu.se SE-581 83 Link?ping, Sweden https://liu.se/en/employee/micfe03 From coralie.gregoire at insa-lyon.fr Fri Oct 8 06:17:03 2021 From: coralie.gregoire at insa-lyon.fr (Coralie Gregoire) Date: Fri, 8 Oct 2021 12:17:03 +0200 (CEST) Subject: Connectionists: [CFP] The ACM Web Conference 2022 - Tutorials Proposal Message-ID: <146147603.542284.1633688223385.JavaMail.zimbra@insa-lyon.fr> [Apologies for the cross-posting, this call is sent to numerous lists you may have subscribed to] [CFP] The ACM Web Conference 2022 - Tutorials Proposal We invite tutorial proposals to be held at The Web Conference 2022 (formerly known as WWW). The conference will take place online, hosted by Lyon, France, on April 25-29, 2022. ------------------------------------------------------------ Call for Tutorial Proposals *Important Dates* -Submission deadline: November 4, 2021 -Notification: December 16, 2021 -Online material due: April 7, 2022 Tutorials chairs: (www2022-tutorials at easychair.org) -Senjuti Basu Roy (New Jersey Institute of Technology, USA) -Riccardo Tommasini (INSA Lyon, France) We invite tutorial proposals on current and emerging topics related to the World Wide Web, broadly construed to include mobile and other Internet and online-enabled modes of interaction and communication. Tutorials are intended to provide a high-quality learning experience to conference attendees. It is expected that tutorials will address an audience with a varied range of interests and backgrounds: beginners, developers, designers, researchers, practitioners, users, lecturers, and representatives of governments and funding agencies who wish to learn new technologies. *Organization Details for Tutorials* The Web Conf 2022 welcomes two types of tutorials. Lecture-style tutorials will be typically 1.5 hours in duration, while hands-on tutorials can be either 1.5 hours or 3 hours long. 1. A lecture-style tutorial (L) will cover the state-of-the-art research, development, and applications in a specific web computing and related area, and stimulate and facilitate future work. Tutorials on interdisciplinary directions, bridging scientific research and applied communities, novel and fast growing directions, and significant applications are highly encouraged. 2. A hands-on tutorial (H) will feature in-depth hands-on training on cutting-edge systems and tools of relevance to the web conference community. These sessions are targeted at novice as well as moderately skilled users. The focus should be on providing hands-on experience to the attendees. Tutorials should introduce the motivation behind the tool, associated fundamental concepts, and work through examples, and demonstrate its application to relatable real-life use cases. The pace of the tutorial should be adequate for beginners, e.g., early-stage Ph.D. and master students. We welcome tutorials on the following topics while this not being an exhaustive list: -Recommender systems -Scaling NLP systems -Advertisement on the Web -Responsible web computing -Web search and mining -Decentralized web data management, web-scale computing, and data integration -Knowledge graph (Common sense Knowledge Graph, Extraction, construction, and maintenance of Knowledge Graphs) -Stream Reasoning, Web velocity, and dynamic knowledge graph -Learning, reasoning, and inference on the Web -Fact-checking in the context of misinformation and disinformation propagation -Human-in-the-loop on the Web -Web-engineering -Conversational AI -Neurocomputing on Web -FAIR web data management The Web Conference 2022 is also featuring special tracks on the Web for Good, on e-sports and online gaming as well on the History of the Web. Tutorials on topics related to these themes are highly encouraged. All tutorials will be part of the main conference technical program. *Submission Guidelines* Tutorial proposals should be in English and should contain no more than five (5) pages in length (according to the ACM format acmart.cls, using the ?sigconf? option). Submissions must be in PDF and must be made through the EasyChair system at https://easychair.org/conferences/?conf=thewebconf2022 (select the Tutorial track). In addition, a video teaser of up to 3 minutes must be prepared as additional material of the submission (see also below). Proposals should follow the following outline: - General information Title of the tutorial Organizers & presenters names, affiliation, contact information, and brief bio. - Abstract 1-2 paragraphs suitable for inclusion in the conference registration material. - Topic and relevance A description of the tutorial topic, providing a sense of both the scope of the tutorial and depth within the scope, and a statement on why the tutorial is important and timely, how it is relevant to The Web Conference, and why the presenters are qualified for a high-quality introduction of the topic. - Style & duration Please indicate whether this will be a lecture-style or hands-on tutorial. In the case of the latter, please indicate the equipment needs for participants (eg. pre-installed Jupyter notebook with specific packages). Please also indicate the proposed duration of the hands-on tutorial (could be 1.5 hours or 3 hours, whereas, lecture-style tutorials are 1.5 hours), together with the justification that a high-quality learning experience will be achieved within the chosen time period. - Audience A description of the intended audience, prerequisite knowledge, and the expected learning outcomes. - Previous editions If the tutorial was given before, where and when was it presented? Please give details on the number of attendees, and how the proposed tutorial differs or builds on the previous ones. If possible, provide a link to the slides of the previous tutorial presentation. - Tutorial materials What tutorial materials will be provided to attendees? Are there any copyright issues? - Video teaser A Video teaser, up to 3 min, is required at the time of submission. The video can be hosted on any video sharing platform (e.g. YouTube) or any file sharing service (e.g. WeTransfer, Dropbox) and the link to the video MUST be included in the proposal. - Organization details Tutorial organizers are required to provide a backup plan that overcomes the potential occurrence of technical problems, e.g., pre-recorded lectures, self-paced exercises. The tutorial presenter(s) will be responsible for making sure that the slides and any material needed for the tutorial are made available online in advance for attendees. Review Process The decision about acceptance or rejection of tutorial proposals will be made by the Tutorial Co-chairs, that may be supported by a small program committee and in consultation with the General and Program Committee Co-chairs, taking into account several factors including the timeliness of the topic, the topic fit with respect to The Web Conference 2022, the coverage of the topic in other tracks of the conference, the capacity of the venue, and the expertise of the presenters. You can reach the Tutorials Chairs at www2022-tutorials at easychair.org ============================================================ Contact us: contact at thewebconf.org - Facebook: https://www.facebook.com/TheWebConf - Twitter: https://twitter.com/TheWebConf - LinkedIn: https://www.linkedin.com/showcase/18819430/admin/ - Website: https://www2022.thewebconf.org/ ============================================== From coralie.gregoire at insa-lyon.fr Fri Oct 8 08:40:55 2021 From: coralie.gregoire at insa-lyon.fr (Coralie Gregoire) Date: Fri, 8 Oct 2021 14:40:55 +0200 (CEST) Subject: Connectionists: [CFP] The ACM Web Conference 2022 - PhD Symposium Message-ID: <1386895691.680982.1633696855596.JavaMail.zimbra@insa-lyon.fr> [Apologies for the cross-posting, this call is sent to numerous lists you may have subscribed to] [CFP] The ACM Web Conference 2022 - PhD Symposium We invite contributions to the PhD Symposium to be held at The Web Conference 2022 (formerly known as WWW). The conference will take place online, hosted by Lyon, France, on April 25-29, 2022. ------------------------------------------------------------ Call for PhD Symposium Papers *Important dates* - Submission deadline: February 3, 2022 - Notification of acceptance: February 24, 2022 - Camera-ready version: March 10, 2022 - PhD Symposium: April 26, 2022 (to be confirmed) PhD Symposium chairs: (www2022-phd-symposium at easychair.org) - Hala Skaf-Molli (University of Nantes, France) - Elena Demidova (University of Bonn, Germany) The PhD Symposium of The Web Conference 2022 welcomes submissions from PhD students on their ongoing research related to the main conference topics. These topics include: Semantics and Knowledge, Web Search, Web Systems and Infrastructure, Web Mining and Content Analysis, Economics, Monetization, and Online Markets, User Modeling and Personalization, Web and Society, Web of Things, Ubiquitous and Mobile Computing, Social Network Analysis and Graph Algorithms, Security, Privacy, Trust and Social Web (see also the Research Tracks). We are particularly interested in submissions aimed to enhance our understanding of the Web, provide intuitive access to Web information and knowledge, strengthen the positive impact of the Web on society, take advantage of the Web of Things and enhance security, privacy protection, and trust. The goal of the PhD Symposium is to provide a platform for PhD students to present and receive feedback on their ongoing research. Students at different stages of their research will have the opportunity to present and discuss their research questions, goals, methods, and results. The symposium aims to provide students guidance on various aspects of their research from established researchers and other PhD students working in research areas related to the World Wide Web. Finally, the symposium aims to enable PhD students to interact with other participants of The Web Conference and potential collaborators by stimulating the exchange of ideas and experiences. *Eligibility* The PhD Symposium is open to all PhD students. PhD students at the beginning stages of their doctoral work are particularly welcome when they have a well-defined problem statement and ideas about the solutions they would like to discuss. PhD students in a more advanced stage of their work are also welcome to share and discuss their research results and experiences. *Submission Guidelines* Submissions should be written based on the following structure, which focuses on the key methodological components required for a sound research synthesis: - Abstract: A self-sustained short description of the paper. - Introduction/Motivation: Provide a general introduction to the topic and indicate its importance/impact on Web research and real-world applications. - Problem: Describe the core problem of the PhD thesis. - State of the art: Briefly describe the most relevant related work. - Proposed approach: Briefly present the approach taken and motivate how this is novel regarding existing works. - Methodology: Sketch the methodology that is (or will be) adopted and, in particular, the approach to be taken for evaluating the results of the work. - Results: Describe the current status of the work and the most significant results that have been obtained so far. - Conclusions and future work: Conclude and specify the major items of future work. Submissions should be written in English and must be no longer than five (5) pages in length (according to the ACM format acmart.cls, using the ?sigconf? option). Submissions must be in PDF and must be made through the EasyChair system at https://easychair.org/conferences/?conf=thewebconf2022 (select the PhD Symposium track). Submissions must be single-author and be on the topic of the doctoral work. The supervisor?s name must be clearly marked (? supervised by ? ?) on the paper, under the author?s name. Submissions that do not comply with the formatting guidelines will be rejected without review. Selected papers will be published in the Companion Proceedings of The Web Conference 2022 and made available through the ACM Digital Library. *Review Process* All submissions will be reviewed by the members of the Program Committee of the PhD Symposium, who are experienced researchers in the relevant areas. Students of accepted submissions will have the opportunity to discuss their submissions in more detail and receive additional feedback from mentors. You can reach the PhD symposium chairs at www2022-phd-symposium at easychair.org ============================================================ Contact us: contact at thewebconf.org - Facebook: https://www.facebook.com/TheWebConf - Twitter: https://twitter.com/TheWebConf - LinkedIn: https://www.linkedin.com/showcase/18819430/admin/ - Website: https://www2022.thewebconf.org/ ============================================== From george at cs.ucy.ac.cy Fri Oct 8 09:23:44 2021 From: george at cs.ucy.ac.cy (George A. Papadopoulos) Date: Fri, 8 Oct 2021 16:23:44 +0300 Subject: Connectionists: ACM International Conference on Information Technology for Social Good (GoodIT 2022): First Call for Contributions Message-ID: *** First Call for Contributions *** ACM International Conference on Information Technology for Social Good (GoodIT 2022) 7?9 September, 2022, 5* St. Raphael Resort & Marina, Limassol, Cyprus https://cyprusconferences.org/goodit2022/ Scope The ACM GoodIT conference seeks papers describing significant research contributions related to the application of information technologies (IT) to social good. Social good is typically defined as something that provides a benefit to the general public. In this context, clean air and water, Internet connection, education, and healthcare are all good examples of social goods. However, new media innovations and the explosion of online communities have added new meaning to the term. Social good is now about global citizens uniting to unlock the potential of individuals, technology, and collaboration to create a positive societal impact. GoodIT solicits papers that address important research challenges related to, but not limited to: ? Citizen science ? Civic intelligence ? Decentralized approaches to IT ? Digital solutions for Cultural Heritage ? Environmental monitoring ? Ethical computing ? Frugal solutions for IT ? Game, entertainment, and multimedia applications ? Health and social care ? IT for automotive ? IT for development ? IT for education ? T for smart living ? Privacy, trust and ethical issues in ICT solutions ? Smart governance and e-administration ? Social informatics ? Socially responsible IT solutions ? Sustainable cities and transportation ? Sustainable IT ? Technology addressing the digital divide Main Track Paper Submission The papers should not exceed six (6) pages (US letter size) double-column, including figures, tables, and references in standard ACM format (https://cyprusconferences.org/goodit2022/index.php/authors/). They must be original works and must not have been previously published. At least one of the authors of all accepted papers must register and present the work at the conference; otherwise, the paper will not be published in the proceedings. All accepted and presented papers will be included in the conference proceedings published in the ACM Digital Library. Special journal issues will be organised where the best accepted papers will be invited to submit extended versions. Separate call-for-papers will be announced for the special tracks (more information will be available on the conference web site). Work-in-Progress and PhD Track Inside ACM GoodIT, the Work-in-Progress and PhD Track provides an opportunity to showcase interesting new work that is still at an early stage. We encourage practitioners and researchers to submit to the Work-in-Progress venue as it provides a unique opportunity for sharing valuable ideas, eliciting feedback on early-stage work, and fostering discussions and collaborations among colleagues. Moreover, this track provides a platform for PhD students to present and receive feedback on their ongoing research. Students at different stages of their research will have the opportunity to present and discuss their research questions, goals, methods and results. This is an opportunity to obtain guidance on various aspects of their research from established researchers and other PhD students working in research areas related to technologies for social good. Important: For this specific track, papers must not exceed four (4) pages (US letter size) double column, including figures, tables, and references in standard ACM format (https://cyprusconferences.org/goodit2022/index.php/authors/). Important Dates ? Submission deadline for all types of contributions: 23 May 2022 ? Notification of acceptance: 20 June 2022 ? Camera-ready submission and author registration: 11 July 2022 Program Chairs ? Costas Mourlas, University of Athens, Greece ? Diogo Pacheco, University of Exeter, UK ? Catia Prandi, University of Bologna, Italy WiP & PhD Track Chairs ? Marco Furini, University of Modena e Reggio Emilia, Italy ? Dimitris Gouscos, University of Athens, Greece ? Barbara Guidi, University of Pisa, Italy -------------- next part -------------- An HTML attachment was scrubbed... URL: From Paul.Linton.2 at city.ac.uk Fri Oct 8 13:38:24 2021 From: Paul.Linton.2 at city.ac.uk (Linton, Paul) Date: Fri, 8 Oct 2021 17:38:24 +0000 Subject: Connectionists: New Approaches to 3D Vision, 1st-4th Nov 2021, Royal Society online meeting Message-ID: <7BE6A564-41AE-4BEE-8638-CF9B7D878B12@city.ac.uk> New Approaches to 3D Vision, 1st-4th November 2021, hosted by the Royal Society This Royal Society online meeting brings together researchers from computer vision, animal vision, and human vision to explore recent developments in 3D vision. Speakers reflect both academia and industry, with representatives from DeepMind, Facebook Reality Labs, Google, and Microsoft Research. Date/Time: 1st-4th November 2021, 3-6pm (3-6:30pm on the 4th), UK Time Please note that the USA/UK time difference is 1hr less than usual Website: https://royalsociety.org/science-events-and-lectures/2021/11/3d-vision/ Registration (Free): https://www.eventbrite.co.uk/e/new-approaches-to-3d-vision-registration-165729267701 DAY ONE (1st Nov) - Seeing Beyond SLAM Chair: Andrew Fitzgibbon (Microsoft) Session One: Neural Scene Representation SM Ali Eslami (DeepMind): ?Neural priors, neural encoders and neural renderers? Ida Momennejad (Microsoft Research): ?Multi-scale predictive representations and human-like RL? Session Two: Perception-Action Loop Sergey Levine (UC Berkeley and Google): TBC Andrew Glennerster (University of Reading): ?Understanding 3D vision as a policy network? DAY TWO (2nd Nov) - Animals in Action Chair: Matteo Carandini (University College London) Session One: Locating Prey and Rewards Jenny Read (Newcastle University): ?Stupid stereoscopic algorithms that still work? Aman Saleem (University College London): ?Visual processing in the brain during navigation? Session Two: Navigation in 3D Space Kate Jeffery (University College London): ?The cognitive map of 3D space: not as metric as we thought?? Gily Ginosar (Weizmann Institute of Science): ?Locally ordered representation of 3D space in the entorhinal cortex? DAY THREE (3rd Nov) - Experiencing Space Chair: Mar Gonzalez-Franco (Microsoft Research) Session One: Theories of Visual Space Dhanraj Vishwanath (University of St Andrews): ?Tripartite encoding of visual 3D space? Paul Linton (City, University of London): ?New approaches to visual scale and visual shape? Session Two: Challenges for Virtual Reality Sarah Creem-Regehr (University of Utah): ?Perception and Action in Virtual and Augmented Reality? Douglas Lanman (Facebook Reality Labs): TBC DAY FOUR (4th Nov) - Grasping the World Chair: Jody Culham (Western University) Session One: One Visual Stream or Two? Fulvio Domini (Brown University): ?A novel non-probabilistic model of 3D cue integration explains both perception and action? Irene Sperandio (University of Trento): ?Dissociations between perception and action in size-distance scaling? Session Two: 3D Space and Visual Impairment Ione Fine (University of Washington): ?Do you hear what I see? How do early blind individuals experience object motion?? Ewa Niechwiej-Szwedo (University of Waterloo): ?The role of binocular vision in the development of visuomotor control and performance of fine motor skills? Session Three: Panel Discussion Andrew Fitzgibbon (Microsoft), Matteo Carandini (University College London), Mar Gonzalez-Franco (Microsoft Research), and Jody Culham (Western University), share their thoughts on future directions for 3D vision. Organisers: Michael Morgan FRS (City, University of London), Paul Linton (City, University of London), Jenny Read (Newcastle University), Dhanraj Vishwanath (University of St Andrews), Sarah Creem-Regehr (University of Utah), Fulvio Domini (Brown University) -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 9 11:56:05 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 9 Oct 2021 17:56:05 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Summer: early registration November 4 Message-ID: <1906714238.3297155.1633794965395@webmail.strato.com> ****************************************************************** 7th INTERNATIONAL GRAN CANARIA SCHOOL ON DEEP LEARNING DeepLearn 2022 Summer Las Palmas de Gran Canaria, Spain July 25-29, 2022 Co-organized by: University of Las Palmas de Gran Canaria Institute for Research Development, Training and Advice ? IRDTA Brussels/London https://irdta.eu/deeplearn/2022su/ ****************************************************************** Early registration: November 4, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Summer will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Bournemouth, and Guimar?es. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Summer is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Summer will take place in Las Palmas de Gran Canaria, on the Atlantic Ocean, with a mild climate throughout the year, sandy beaches and a renowned carnival. The venue will be: Instituci?n Ferial de Canarias Avenida de la Feria, 1 35012 Las Palmas de Gran Canaria https://www.infecar.es/index.php?option=com_k2&view=item&layout=item&id=360&Itemid=896 STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Wahid Bhimji (Lawrence Berkeley National Laboratory), Deep Learning on Supercomputers for Fundamental Science Rich Caruana (Microsoft Research), Friends Don?t Let Friends Deploy Black-box Models: The Importance of Interpretable Neural Nets in Machine Learning Kate Saenko (Boston University), Learning from Biased Data PROFESSORS AND COURSES: (to be completed) T?lay Adal? (University of Maryland Baltimore County), [intermediate] Data Fusion Using Matrix and Tensor Factorizations Pierre Baldi (University of California Irvine), [intermediate/advanced] Deep Learning: From Theory to Applications in the Natural Sciences Arindam Banerjee (University of Illinois Urbana-Champaign), [intermediate/advanced] Deep Generative and Dynamical Models Mikhail Belkin (University of California San Diego), [intermediate/advanced] Modern Machine Learning and Deep Learning through the Prism of Interpolation Dumitru Erhan (Google), [intermediate/advanced] Visual Self-supervised Learning and World Models Arthur Gretton (University College London), [intermediate/advanced] Probability Divergences and Generative Models Phillip Isola (Massachusetts Institute of Technology), [intermediate] Deep Generative Models Irwin King (Chinese University of Hong Kong), [introductory/intermediate] Introduction to Graph Neural Networks Vincent Lepetit (Paris Institute of Technology), [intermediate] AI and 3D Geometry for [Self-supervised] 3D Scene Understanding Yan Liu (University of Southern California), [introductory/intermediate] Deep Learning for Time Series Dimitris N. Metaxas (Rutgers, The State University of New Jersey), [intermediate/advanced] Model-based, Explainable, Semisupervised and Unsupervised Machine Learning for Dynamic Analytics in Computer Vision and Medical Image Analysis Sean Meyn (University of Florida), [introductory/intermediate] Reinforcement Learning: Fundamentals, and Roadmaps for Successful Design Louis-Philippe Morency (Carnegie Mellon University), [intermediate/advanced] Multimodal Machine Learning Clara I. S?nchez (University of Amsterdam), [introductory/intermediate] Mechanisms for Trustworthy AI in Medical Image Analysis and Healthcare Bj?rn W. Schuller (Imperial College London), [introductory/intermediate] Deep Multimedia Processing Jonathon Shlens (Google), [introductory/intermediate] Introduction to Deep Learning in Computer Vision Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning, Neural Networks and Kernel Machines 1. Murat Tekalp (Ko? University), [intermediate/advanced] Deep Learning for Image/Video Restoration and Compression Alexandre Tkatchenko (University of Luxembourg), [introductory/intermediate] Machine Learning for Physics and Chemistry Li Xiong (Emory University), [introductory/intermediate] Differential Privacy and Certified Robustness for Deep Learning Ming Yuan (Columbia University), [intermediate/advanced] Low Rank Tensor Methods in High Dimensional Data Analysis OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by July 17, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 17, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 17, 2022. ORGANIZING COMMITTEE: Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) David Silva (London, organization chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022su/registration/ The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn/2022su/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Universidad de Las Palmas de Gran Canaria Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.fleuret at unige.ch Sat Oct 9 11:48:06 2021 From: francois.fleuret at unige.ch (=?UTF-8?Q?Fran=c3=a7ois_Fleuret?=) Date: Sat, 9 Oct 2021 17:48:06 +0200 Subject: Connectionists: One post-doctoral and one PhD position in the field of deep machine learning at the University of Geneva Message-ID: <19a5dec3-be53-b6bf-b295-4857e804ff3e@unige.ch> *One post-doctoral and one PhD position in the field of deep machine learning at the University of Geneva* The Machine Learning Group at the University of Geneva , headed by Prof. Francois Fleuret , has one open post-doctoral position in the field of deep generative methods for flagrance synthesis, and one open PhD position in the field of representation learning and interpretability for reinforcement learning. Ideal starting time is as soon as possible for the post-doctoral position, and early 2022 for the PhD candidate. Salaries are highly competitive. Applicants must have a strong background in mathematics, in particular in probabilities, signal processing, optimization, and algorithmic and be seasoned programmers used to development with collaborative tools and machine learning frameworks (e.g. git, numpy, PyTorch, TensorFlow, JAX). Application have to be submitted on-line at https://fleuret.org/francois/apply_form.html Founded in 1559, the University of Geneva is the third largest university in Switzerland by number of students, and ranked third according to the Shanghai Ranking of World Universities 2020. The city of Geneva is centrally located in Europe, host of numerous international organizations (e.g. UN, WHO, WIPO, WTO, ICRC, CERN), and provides an ideal environment for foreign students and researchers. Switzerland is regularly ranked among the countries with the best quality of life. -- Francois Fleuret https://fleuret.org/francois/ From david at irdta.eu Sat Oct 9 11:52:16 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 9 Oct 2021 17:52:16 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Winter: early registration October 9 Message-ID: <1961028287.3296939.1633794736478@webmail.strato.com> ****************************************************************** 5th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Winter Bournemouth, UK January 17-21, 2022 https://irdta.eu/deeplearn/2022wi/ *********** Co-organized by: Department of Computing and Informatics Bournemouth University Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: October 9, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw and Las Palmas de Gran Canaria. Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 23 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main components of the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be: TBA STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills PROFESSORS AND COURSES: Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint Joachim M. Buhmann (Swiss Federal Institute of Technology, Z?rich), [introductory/advanced] Model and Algorithm Validation for Data Science Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare R?diger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail Rob Fergus (New York University), [intermediate/advanced] Self-supervised Learning of Visual Representations for Recognition and Interaction Jo?o Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How? Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by January 9, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. ORGANIZING COMMITTEE: Rashid Bakirov (Bournemouth, co-chair) Nan Jiang (Bournemouth, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022wi/registration/ The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn/2022wi/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Bournemouth University Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From alamer_2005 at yahoo.com Sat Oct 9 19:58:05 2021 From: alamer_2005 at yahoo.com (Ali Ismail Awad) Date: Sat, 9 Oct 2021 23:58:05 +0000 (UTC) Subject: Connectionists: [Call for book Chapters]: Internet of Things Security and Privacy: Practical and Management Perspectives - CRC Press In-Reply-To: <693799095.1429374.1633823829380@mail.yahoo.com> References: <1828995994.481545.1614841815922.ref@mail.yahoo.com> <1828995994.481545.1614841815922@mail.yahoo.com> <1276095279.1245307.1627852143727@mail.yahoo.com> <693799095.1429374.1633823829380@mail.yahoo.com> Message-ID: <1481110987.1429787.1633823885998@mail.yahoo.com> The deadline has been extended! Your contributions are highly welcome!? http://staff.www.ltu.se/~ismawa/iotsp/? == Call for book Chapters== Internet of Things Security and Privacy: Practical and Management Perspectives Aims and Scope: The Internet of Things (IoT) is an emerging paradigm due to extensive developments in information and communication technology (ICT). The purpose of IoT is to expand the functions of the first version of the Internet by increasing the ability to connect numerous objects. The wide facilities offered by IoT and other sensing facilities have led to a huge amount of data generated from versatile domains, thus, cybersecurity has become an inevitable requirement not only for personal safety but also for assuring the sustainability of the IoT paradigm itself. From the perspective of the organizational technology layer, attack surfaces have dramatically expanded (e.g., new entry points from endpoints and legacy devices, more vulnerable industrial control systems without suitable cybersecurity solutions, more proprietary software that is hard to update and patch, and poor security design), whereas security countermeasures have developed comparatively slowly. This edited book will encompass both management and technical cybersecurity and privacy (including compliance) research in the IoT domain. Of particular interest is research that integrates management and technical approaches to IoT security and privacy; research that studies the real-world problem of IoT security in organizations, and research that contributes sound practical advice to industry. The topics covered by the book will present a collection of high-quality research works concerning IoT architecture, security techniques, and application areas. These will make the book unique in its contents and theme. The book outlines key emerging trends in IoT security and privacy that span the entire IoT architecture (perception, network, and applications) layers, with a focus on different critical IoT applications such as smart homes and cities, e-health, critical infrastructure, and industrial IoT (IIoT) applications. The state-of-the-art body of knowledge presented in this book is a vital need for researchers, practitioners, and postgraduate students in the IoT development and deployment domains. Topics of interest include, but not limited to: == Practical Security and Privacy Perspectives: ? IoT devices and protocols security ? Attack detection and prevention in IoT ? Privacy-preserving techniques in IoT ? Secure integration of IoT and cloud /Edge computing ? Machine learning techniques for IoT security ? Secure network architecture for IoT ? Secure data management approaches ? Security in cyber-physical systems ? Authentication and access control ? Blockchain technologies for reliable and trustworthy IoTs == Management Security and Privacy Perspectives: ? Cybersecurity prevention and response strategies ? Emergent cybersecurity risks arising from IoT-enabled 5G ? Situation awareness of IoT environment ? Risk identification, assessment, and mitigation ? Governance, policy, and compliance ? Awareness and training approaches ? Security architecture and frameworks ? Threat and vulnerability analysis Publication Schedule: The tentative schedule of the book publication is as follows: Deadline for chapter submission: December 01, 2021 Author notification: January 01, 2022Final version due: February 01, 2022 Acceptance notification: February 15, 2022 Publication date: Second quarter of 2022 Submission Procedure: Authors are invited to submit original, high-quality, unpublished results of both practical and management security and privacy domains. Prospective authors need to electronically submit their contributions using the EasyChair submission system. Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. The accepted contributions will be published as a book volume by CRC Press. == Please consider the following points when preparing your manuscript: ? The optimum manuscript length is 20-30 A4 pages. ? The publication of the selected chapters will be free of charge. ? Submitted manuscripts should conform to the author?s guidelines of the CRC Press mentioned in the following two points (Please review the book website). ? Latex is the preferable word processing tool for preparing the chapters. ? MS Word is an acceptable word processing tool for preparing the chapters. Book Editors: Ali Ismail Awad Department of Computer Science, Electrical and Space Engineering Lule? University of Technology Lule?, Sweden E-mail: ali.awad[at]ltu.se Atif Ahmad School of Computing and Information Systems University of Melbourne Melbourne, Australia E-mail: atif[at]unimelb.edu.au Kim-Kwang Raymond Choo Cloud Technology Endowed Professor The University of Texas at San Antonio Texas, USA E-mail: raymond.choo[at]fulbrightmail.org Saqib Hakak Canadian Institute for Cybersecurity (CIC) Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada E-mail: saqib.hakak[at]unb.ca Mohsen Guizani College of Engineering Qatar University Doha, Qatar E-mail: mguizani[at]qu.edu.qa -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 9 11:54:02 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 9 Oct 2021 17:54:02 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Spring: early registration October 15 Message-ID: <950475399.3297043.1633794842522@webmail.strato.com> ****************************************************************** 6th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Spring Guimar?es, Portugal April 18-22, 2022 https://irdta.eu/deeplearn/2022sp/ ***************** Co-organized by: Algoritmi Center University of Minho, Guimar?es Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: October 15, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Spring will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, and Bournemouth. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Spring is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Spring will take place in Guimar?es, in the north of Portugal, listed as UNESCO World Heritage Site and often referred to as the birthplace of the country. The venue will be: TBA STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Christopher Manning (Stanford University), Self-supervised and Naturally Supervised Learning Using Language Kate Smith-Miles (University of Melbourne), Stress-testing Algorithms via Instance Space Analysis Zhongming Zhao (University of Texas, Houston), Deep Learning Approaches for Predicting Virus-Host Interactions and Drug Response PROFESSORS AND COURSES: Eneko Agirre (University of the Basque Country), [intermediate] Deep Learning for Natural Language Processing Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision Altan ?ak?r (Istanbul Technical University), [introductory] Introduction to Deep Learning with Apache Spark Rylan Conway (Amazon), [introductory/intermediate] Deep Learning for Digital Assistants Jifeng Dai (SenseTime Research), [intermediate] AutoML for Generic Computer Vision Tasks Jianfeng Gao (Microsoft Research), [introductory/intermediate] An Introduction to Conversational Information Retrieval Daniel George (JPMorgan Chase), [introductory] An Introductory Course on Machine Learning and Deep Learning with Mathematica/Wolfram Language Bohyung Han (Seoul National University), [introductory/intermediate] Robust Deep Learning Lina J. Karam (Lebanese American University), [introductory/intermediate] Deep Learning for Quality Robust Visual Recognition Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for Trustworthy Biometrics Jennifer Ngadiuba (Fermi National Accelerator Laboratory), [intermediate] Ultra Low-latency and Low-area Machine Learning Inference at the Edge Lucila Ohno-Machado (University of California, San Diego), [introductory] Use of Predictive Models in Medicine and Biomedical Research Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to Quantum Neural Networks Bart ter Haar Romenij (Eindhoven University of Technology), [intermediate] Deep Learning and Perceptual Grouping Kaushik Roy (Purdue University), [intermediate] Re-engineering Computing with Neuro-inspired Learning: Algorithms, Architecture, and Devices Walid Saad (Virginia Polytechnic Institute and State University), [intermediate/advanced] Machine Learning for Wireless Communications: Challenges and Opportunities Yvan Saeys (Ghent University), [introductory/intermediate] Interpreting Machine Learning Models Martin Schultz (J?lich Research Centre), [intermediate] Deep Learning for Air Quality, Weather and Climate Richa Singh (Indian Institute of Technology, Jodhpur), [introductory/intermediate] Trusted AI Sofia Vallecorsa (European Organization for Nuclear Research), [introductory/intermediate] Deep Generative Models for Science: Example Applications in Experimental Physics Michalis Vazirgiannis (?cole Polytechnique), [intermediate/advanced] Graph Neural Networks with Applications Guowei Wei (Michigan State University), [introductory/advanced] Integrating AI and Advanced Mathematics with Experimental Data for Forecasting Emerging SARS-CoV-2 Variants Xiaowei Xu (University of Arkansas, Little Rock), [intermediate/advanced] Deep Learning for NLP and Causal Inference Guoying Zhao (University of Oulu), [introductory/intermediate] Vision-based Emotion AI OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by April 10, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. ORGANIZING COMMITTEE: Dalila Dur?es (Braga, co-chair) Jos? Machado (Braga, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) Paulo Novais (Braga, co-chair) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022sp/registration/ The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn/2022sp/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Centro Algoritmi, Universidade do Minho, Guimar?es Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessio.ferone at uniparthenope.it Sun Oct 10 12:57:27 2021 From: alessio.ferone at uniparthenope.it (ALESSIO FERONE) Date: Sun, 10 Oct 2021 16:57:27 +0000 Subject: Connectionists: WILF2021 - News Message-ID: Dears, we would like to inform that two calls for papers for Special Issues on Information Sciences and Soft Computing journals, including the main topics of the conference, have been published. See the conference website for more details (https://sites.google.com/view/wilf-2021/special-issues). We invite you to submit your valuable contributions. We remark that WILF2021 aims to bring our community together to share and discuss our ideas and passions. Thematic panels and project ideas will be scheduled. Further updates will be posted on the website. We look forward to seeing you soon in Vietri. Best Regards, WILF2021 chairs ... Apologies for multiple posting ... Back together again! ------------------------------------------------------------------- Workshop WILF 2021 (13th International Workshop on Fuzzy Logic and Applications) from Dec. 20th to Dec. 22th at Vietri sul Mare (SA), Italy (IIASS) and online For full information: https://sites.google.com/view/wilf-2021/home ------------------------------------------------------------------- NEWS * Special Issues Special Issues on Information Sciences and Soft Computing journals have been published on main aspects of the conference (https://sites.google.com/view/wilf-2021/special-issues). Keynote speakers ? Humberto Bustince (Public University of Navarra, Spain) o Extensions of fuzzy integrals and applications to the computational brain ? Scott Dick (University of Alberta, Canada) o Fuzzy Logic and XAI: Past, Present, and Some Thoughts on the Future ? Didier Dubois (IRIT, France) o Fuzzy sets: the legacy and its future Important Dates * Notification of acceptance + early registration opening: October 29, 2021 * Special Issue submission opening: November 1, 2021 (Information Sciences Journal) ? December 1, 2021 (Soft Computing Journal) * Camera-ready + early registration deadline: November 26, 2021 * EUSFLAT student grant applications: November 26, 2021 * Late registration deadline: t.b.d. * Special Issue publication: Fall 2022 Aim and Scope The 13th International Workshop on Fuzzy Logic and Applications (WILF 2021) covers all topics in theoretical, experimental and applied Fuzzy and Computational Intelligence techniques and systems. It is aimed to bring together researchers and developers from both academia and industry to report on the latest scientific and theoretical advances in this field, and to demonstrate the state-of-the-art systems. Three types of talks The main aim of WILF 2021 is to allow scientific communication and fruitful exchange of opinions. A lightweight approach is adopted to foster participation and promote the exchange of ideas. To provide room for discussing present, past and future research topics, the following types of talks are called for: * Regular communications - Current, unpublished work that is being presented for the first time. * Ideas - New research directions, opinions, position talks: promising or interesting work that does not fit the standards for conventional publication. * Research highlights - Talks summarising work that was recently (3 years) published in prominent journals, intended to increase awareness about interesting work. Publication * Short-form papers for all presentations will be published in a volume of CEUR Workshop Proceedings in the AI*IA proceedings series (http://ceur-ws.org/aixia.html). * Special Issues of high-rank journals will be organised as a follow-up for possible submission of full papers on the topics of the workshop. EUSFLAT Student grants EUSFLAT will provide four students with a 150? grant. Organizing Committee * Angelo Ciaramella - Universit? degli Studi di Napoli Parthenope, Italy * Corrado Mencar - Universit? degli Studi di Bari Aldo Moro, Italy * Susana Montes - Universidad de Oviedo, Spain * Stefano Rovetta - Universit? degli Studi di Genova, Italy Steering Committee * Angelo Ciaramella - University of Naples Parthenope, Italy * Antonio Di Nola - University of Salerno, Italy * Luis Magdalena - Universidad Politecnica de Madrid, Spain * Francesco Masulli - University of Genova, Italy * Gabriella Pasi - University of Milan Bicocca, Italy * Witold Pedrycz - University of Alberta, Canada Contact wilf.2021.vsm at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From andrea.simonelli at unitn.it Sun Oct 10 15:40:15 2021 From: andrea.simonelli at unitn.it (Andrea Simonelli) Date: Sun, 10 Oct 2021 21:40:15 +0200 Subject: Connectionists: [TODAY @ ICCV21] Workshop on 3D Object Detection from Images (3DODI) Message-ID: Workshop on 3D Object Detection from Images (3DODI) *** TODAY (Oct 11) @ ICCV 2021 *** https://sites.google.com/unitn.it/3dodi *When?* Today (Oct 11) from 8AM to 12PM EDT (2PM to 6PM CEST) *How to participate?* Login into the ICCV21 website ( https://www.eventscribe.net/2021/ICCV/login.asp), go to our workshop's page and click on the "Zoom link" button. Description The 1st Workshop on 3D Object Detection from Images aims to gather researchers and engineers from academia and industry to discuss the latest advances in Image-based 3D object detection. Invited Speakers - Andreas Geiger, University of Tubingen - Adrien Gaidon, Toyota Research Institute - Aljosa Osep, Technical University of Munich - Fisher Yu, ETH Zurich - Xiaoming Liu, Michigan State University *Program* Please find the program at https://sites.google.com/unitn.it/3dodi/program Contact For any information contact 3dodi.workshop at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From dorien.herremans at gmail.com Sun Oct 10 21:45:50 2021 From: dorien.herremans at gmail.com (Dorien Herremans) Date: Mon, 11 Oct 2021 09:45:50 +0800 Subject: Connectionists: RA in music AI Message-ID: Good morning everyone, Our team at Singapore University of Technology and Design (SUTD) is looking for an RA or postdoc in music and AI . You will be joining our AMAAI Lab in music/audio/vision AI supervised by Prof. Dorien Herremans. At our lab, we aim to advance the state-of-the-art in AI for music and audio. More information on the music/audio team here. We have multiple research lines going that need your expertise, either in symbolic music (midi) as well as audio. Ideal skill set: - strong knowledge of deep neural networks in PyTorch - interest in machine learning for midi/audio - experience in Music Information Retrieval, ideally AI music generation and or audio transcription - interest or experience in music generation systems or audio classification systems - savvy programmer in PyTorch - creative problem solver- good academic writing skills - BA, MA, or PhD in computer science, MIR, neural networks, or related - possible knowledge of computer visionOngoing projects: - chord generation to match video - audio transcription - maintaining and building our nnAudio library - building deep multitask audio representations The ideal candidate has experience working with either music generation, audio transcription and representation, or a strong background in machine learning, with familiarity in sound and music computing, and is willing to work with other PhD students and postdoc in the lab to leverage individual efforts into a combined system. This is a project sponsored by the Ministry of Education. We offer a competitive salary, with first contract until end of June, extendible for 6 months. For any info, feel free to contact me. Please email your application, including your CV, any first author publications, and the name/email of a reference, to dorien_herremans [ad] sutd.edu.sg with subject [application MOE RA]. *Due to covid visa restrictions we can unfortunately only hire Singaporean citizens or PRs at the moment. * Best, -- Dorien Herremans, PhD Assistant Professor, ISTD & DAI Director, SUTD Game Lab Lead, AMAAI (Audio, Music and AI Lab) http://dorienherremans.com Singapore University of Technology and Design Information Technology and Design Pillar Office 1.502-18 -------------- next part -------------- An HTML attachment was scrubbed... URL: From contact at sscc.fr Tue Oct 12 05:28:11 2021 From: contact at sscc.fr (SSCC) Date: Tue, 12 Oct 2021 11:28:11 +0200 Subject: Connectionists: [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <023b01d7bf4b$83b48fe0$8b1dafa0$@sscc.fr> Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience . Smart networks for smart cities . Security management in smart cities . Security in distributed systems . Modeling, analysis and detection of IoT attacks . Data mining for cybersecurity in smart cities . Decentralized architecture for smart cities . Consensus protocols and applications IoT & AI . IoT Indoor deployment . IoT communication protocols . Building information modeling (BIM) IoT-based HVAC control in smart buildings . Artificial Intelligence in Cyber Physical Energy Systems . Optimization for IoT and smart cities . Dynamic scheduling for IoT deployment . Autonomous and Smart decisions Edge and Cloud . Cloud-Edge for IoT and smart cities . Fog and Edge computing for smart cities . Applications/services for Edge AI . Software Platforms for Edge Social aspects and applications . Behavioral and Energy Consumption Analytics . Indoor comfort . Human factors and organizational resilience for distributed systems Important Dates . Paper Submission Date: 31 October, 2021 . Notification to Authors: 15 November, 2021 . Camera Ready Submission: 21 November 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From ioannakoroni at csd.auth.gr Tue Oct 12 06:04:11 2021 From: ioannakoroni at csd.auth.gr (Ioanna Koroni) Date: Tue, 12 Oct 2021 13:04:11 +0300 Subject: Connectionists: AIDA Lecture series/semester course and short course offers Fall 2021. Digital image Processing, Advanced Computer Vision, CVML Web Lecture series offers from AUTH References: <00e401d7bc30$a8098fd0$f81caf70$@csd.auth.gr> Message-ID: <055801d7bf50$818c3f60$84a4be20$@csd.auth.gr> Dear AIDA Students, the Fall 2021 semester just started. AIDA has very interesting course offers for you: 14 short courses, 7 lecture series/semester courses as you can see in: https://www.i-aida.org/phd-studies/short-courses/ https://www.i-aida.org/phd-studies/lecture-series-2/ As the AIDA www system is brand new, due to some system glitches (to be fixed very soon), you cannot use it presently for online registration in courses and descriptions are still missing in some courses. You will receive another message, when the system glitches are fixed to do your on-line registration. In order to avoid delays in Fall 2021 AIDA course registration, I suggest you contact directly the AIDA Lecturers by email for the courses of your choice. As for Aristotle University of Thessaloniki, Fall courses start on 11/10/2021 (hence this message). It offers the following: 1. Computer Vision and Machine Learning Web Lecture series contains 208 asynchronous Lectures organized in 21 Lecture modules (each up to 16 lectures) covering diverse topics on:Deep Learning and Neural Networks * Deep Learning and Computer Vision Foundations and Tools * Computer Vision/Image Processing and 3D Imaging * Autonomous Systems * Human centered computing. Social Networks. Graph Theory * Digital Signal Processing and Applications. 2. Advanced Computer Vision (full semester course, MSc level), 3. Digital image Processing (full semester course, senior undergraduate level). Details can be found in https://www.i-aida.org/phd-studies/lecture-series-2/ and in the respective course pages. Interested AIDA students can contact me (pitas at csd.auth.gr ) or Ms Ioanna Koroni koroniioanna at csd.auth.gr You may receive other notification messages for other AIDA Fall 2021 courses in the near future. Sincerely yours Ioannis Pitas _____ This email has been checked for viruses by Avast antivirus software. www.avast.com -- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus -------------- next part -------------- An HTML attachment was scrubbed... URL: From k.wong-lin at ulster.ac.uk Tue Oct 12 10:45:12 2021 From: k.wong-lin at ulster.ac.uk (Wong-Lin, Kongfatt) Date: Tue, 12 Oct 2021 14:45:12 +0000 Subject: Connectionists: Post-doctoral Research Associate in Computational Neuroscience (Computational Modelling of Decision Making) In-Reply-To: References: Message-ID: Applications are invited for a US/NIH-Ireland funded Post-doctoral Research Associate post at the Intelligent Systems Research Centre in Ulster University, UK. The successful candidate will develop and apply advanced computational modelling techniques to understand brain and behavioural data across primate species, and to apply biologically based neural network modelling to elucidate the mechanisms underlying various forms of decision making. The duration of the post is 27 months, with possible extension. The personnel will work with Dr. KongFatt Wong-Lin at Ulster University, while collaborating closely with international collaborators in the USA and the Republic of Ireland, namely, Prof. Michael Shadlen at Columbia University (USA), Prof. Stephan Bickel at Northwell-Hofstra School of Medicine (USA), Prof. Redmond O'Connell at Trinity College Dublin (Ireland), and Prof. Simon Kelly at University College Dublin (Ireland). All applicants should hold a degree in in Computational Neuroscience, Computational Biology, Neuroscience, Computing, Engineering, Mathematics, Data Science, Physical Sciences, Biology, or a cognate area. Apply online: https://www.jobs.ac.uk/job/CJU115/research-associate-in-computational-neuroscience?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic Closing date for receipt of completed applications: 24th October 2021. Job Ref: 008566. If you have questions regarding this post, please contact: Dr. KongFatt Wong-Lin, email: k.wong-lin at ulster.ac.uk . ------------------- Dr. KongFatt Wong-Lin Reader Intelligent Systems Research Centre School of Computing, Engineering and Intelligent Systems Ulster University, UK https://www.ulster.ac.uk/staff/k-wong-lin This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: From davy.weissenbacher at gmail.com Tue Oct 12 11:53:42 2021 From: davy.weissenbacher at gmail.com (Davy Weissenbacher) Date: Tue, 12 Oct 2021 11:53:42 -0400 Subject: Connectionists: BioCreative VII: CFP extended Message-ID: Dear colleagues, BioCreative VII workshop registration is now open ( https://biocreative.bioinformatics.udel.edu/events/biocreative-vii/biocreative-vii-workshop/) - November 8-10, it is virtual and free. *We are accepting abstracts for posters (and flash talks) until October 18* . Abstracts could be on topics related to BioCreative VII tracks (including chemical name entity recognition, relation extraction involving chemicals, COVID-19 related text mining, datasets and standards related to these) Specification: Length up to 500 words (excluding title and authors info), font Times New Roman, size 11. In addition, you can include up to two figures and 5 references (using numbers in parentheses in text). To submit your work go to Easychair: https://easychair.org/conferences/?conf=bc7 , and select the applicable option (a track or poster). Format for all submissions is *PDF*. Looking forward to your submissions. -------------- next part -------------- An HTML attachment was scrubbed... URL: From iswc.conf at gmail.com Tue Oct 12 13:23:18 2021 From: iswc.conf at gmail.com (International Semantic Web Conference) Date: Tue, 12 Oct 2021 13:23:18 -0400 Subject: Connectionists: =?utf-8?q?Int=2E_Semantic_Web_Conference_ISWC_202?= =?utf-8?q?1=E2=80=94Final_Program_Online=E2=80=94Early_Rate_Regist?= =?utf-8?q?ration_Ending?= In-Reply-To: References: Message-ID: *20th International Semantic Web Conference (ISWC 2021)* Virtual, October 24-28, 2021 https://iswc2021.semanticweb.org *Final Program* The final program of ISWC 2021 is now set and available online at: https://iswc2021.semanticweb.org/final-programme *Registration* The Registration for ISWC 2021 is open. Early-rate tickets are available until *October 15*. Register at: https://iswc2021.semanticweb.org/attending *Invited Speakers* - Abraham Bernstein, University of Z?rich - Yoelle Maarek, Amazon - Julia Stoyanovich, New York University Details about the ISWC 2021 speakers can be found at: https://iswc2021.semanticweb.org/keynote-speakers Follow ISWC on social media: - Twitter: @iswc_conf #iswc_conf (https://twitter.com/iswc_conf) - LinkedIn: https://www.linkedin.com/groups/13612370 - Facebook: https://www.facebook.com/ISWConf The ISWC 2021 Organising Team https://iswc2021.semanticweb.org/organizing-committee -------------- next part -------------- An HTML attachment was scrubbed... URL: From cgf at isep.ipp.pt Tue Oct 12 19:27:48 2021 From: cgf at isep.ipp.pt (Carlos) Date: Wed, 13 Oct 2021 00:27:48 +0100 Subject: Connectionists: CFP: DATA STREAMS TRACK - ACM SAC 2022 (Extended deadline: October 24, 2021) Message-ID: <04191c1f-99bc-aa84-04a6-3eb1be88b507@isep.ipp.pt> *ACM Symposium on Applied Computing * The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic April 25 ? April 29, 2022 https://www.sigapp.org/sac/sac2022/ *Data Streams Track * https://abifet.github.io/SAC2022/ *Call for Papers * The rapid development in Big Data information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. Many sources produce data continuously. Examples include the Internet of Things (IoT), Smart Cities, Urban Computing, sensor networks, wireless networks, radio frequency identification, health-care devices and information systems, customer click streams, telephone records, multimedia data, scientific data, sets of retail chain transactions, etc. These sources are called data streams. A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, flowing at high-speed, and generated by non stationary distributions. *TOPICS OF INTEREST * We are looking for original, unpublished work related to algorithms, methods and applications on big data streams and large scale machine learning. Topics include (but are not restricted) to: * Real-Time Analytics * Big Data Mining * Data Stream Models * Large Scale Machine Learning * Languages for Stream Query * Continuous Queries * Clustering from Data Streams * Decision Trees from Data Streams * Association Rules from Data Streams * Decision Rules from Data Streams * Bayesian Networks from Data Streams * Neural Networks for Data Streams * Feature Selection from Data Streams * Visualization Techniques for Data Streams * Incremental on-line Learning Algorithms * Single-Pass Algorithms * Temporal, spatial, and spatio-temporal data mining * Scalable Algorithms * Real-Time and Real-World Applications using Stream data * Distributed and Social Stream Mining * Urban Computing, Smart Cities * Internet of Things (IoT) * IMPORTANT DATES * 1. Submission deadline (Extended): October 24, 2021 2. Notification deadline: December 10, 2021 3. Camera-ready deadline: December 21, 2021 *PAPER SUBMISSION GUIDELINES * Papers should be submitted in PDF. Authors are invited to submit original papers in all topics related to data streams. All papers should be submitted in ACM 2-column camera ready format for publication in the symposium proceedings. ACM SAC follows a double blind review process. Consequently, the author(s) name(s) and address(s) must NOT appear in the body of the submitted paper, and self-references should be in the third person. This is to facilitate double blind review required by ACM. All submitted papers must include the paper identification number provided by the eCMS system when the paper is first registered. The number must appear on the front page, above the title of the paper. Each submitted paper will be fully refereed and undergo a blind review process by at least three referees. The conference proceedings will be published by ACM. The maximum number of pages allowed for the final papers is 8 pages. There is a set of templates to support the required paper format for a number of document preparation systems at https://www.sigapp.org/sac/sac2022/authorkit.html Important notice: 1. Please submit your contribution via SAC 2022 Webpage: https://www.softconf.com/m/sac2022/ 2. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library. If you encounter any problems with your submission, please contact the Program Coordinator. Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. Ant?nio Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From Bing.Xue at ecs.vuw.ac.nz Tue Oct 12 22:56:06 2021 From: Bing.Xue at ecs.vuw.ac.nz (Bing XUE) Date: Wed, 13 Oct 2021 15:56:06 +1300 Subject: Connectionists: CFPs EuroGP 2022 - 25th European Conference on Genetic Programming - 20-22 April 2022 Message-ID: Dear Colleague(s), ?*** Apologies for cross-posting *** We would like to invite you to submit papers to EuroGP 2022: THE 25th EUROPEAN CONFERENCE ON GENETIC PROGRAMMING which will be held on April 20-22, 2022 Please visit http://www.evostar.org/2022/eurogp / for more details. *** Important dates *** Submission deadline: 1 November 2021 EvoStar Conference: 20-22 April, 2022 Submission link: https://easychair.org/conferences/?conf=evo2022 *** EuroGP *** EuroGP is the premier annual conference on Genetic Programming (GP), the oldest and the only meeting worldwide devoted specifically to this branch of evolutionary computation. It is always a high-quality, enjoyable, friendly event, attracting participants from all continents, and offering excellent opportunities for networking, informal contact, and exchange of ideas with fellow researchers. It will feature a mixture of oral presentations and poster sessions and invited keynote speakers. EuroGP is featured in the conference ranking database CORE (http://portal.core.edu.au/conf-ranks/481/) *** EvoStar *** EvoStar is a leading international event devoted to evolutionary computing, comprising four conferences, EuroGP, EvoApplications, EvoCOP, and EvoMUSART. The low-cost registration includes access to all of them, as well as daily lunch and the conference reception and banquet. *** Topics *** Topics to be covered include, but are not limited to: Innovative applications of GP, Theoretical developments, GP performance and behaviour, Fitness landscape analysis of GP, Algorithms, representations and operators for GP, Search-based software engineering, Genetic improvement programming, Evolutionary design, Evolutionary robotics, Tree-based GP and Linear GP, Graph-based GP and Grammar-based GP, Evolvable hardware, Self-reproducing programs, Multi-population GP,? Multi-objective GP, Parallel GP, Probabilistic GP, Object-orientated GP, Hybrid architectures including GP, Coevolution and modularity in GP, Semantics in GP, Unconventional GP, Automatic software maintenance, Evolutionary inductive programming, Evolution of automata or machines. ***the EvoML joint track *** Please visit: http://www.evostar.org/2022/eml/ This joint track on Evolutionary Machine Learning (EML) will provide a specialized forum of discussion and exchange of information for researchers interested in exploring approaches that combine nature and nurture, with the long-term goal of evolving Artificial Intelligence (AI). In response to the growing interest in the area, and consequent advances of the state-of-the-art, the special session covers theoretical and practical advances on the combination of Evolutionary Computation (EC) and Machine Learning (ML) techniques. As a joint EuroGP+EvoAPPS track, authors should decide whether their paper will be treated within EvoApplications or EuroGP at the submission time. *** Paper submission *** High-quality submissions not exceeding 16 pages (including references) in Springer LNCS format are now solicited. Accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science series. The highest quality papers may also be invited to submit extensions for publication in a special issue of the journal Genetic Programming and Evolvable Machines (GPEM). *** Organization *** Program Chairs: Eric Medvet, University of Trieste, Italy Gisele Pappa, Universidade Federal de Minas Gerais, Brazil Publication Chair: Bing Xue, Victoria University of Wellington *** Program Committee *** (To be announced) For further information please visit http://www.evostar.org/2022/eurogp/ Eric Medvet, Gisele Pappa, and Bing Xue EuroGP Chairs -- ---------------------------------------------- Dr Bing Xue (she/her), MIEEE, MACM Professor | Ahorangi School of Engineering and Computer Science | Te Kura M?tai P?kaha, P?rorohiko Victoria University of Wellington | Te Herenga Waka New Zealand | Aotearoa Phone: +64 4 463 5542 Homepage: https://homepages.ecs.vuw.ac.nz/~xuebing/index.html ---------------------------------------------- From i.bojak at reading.ac.uk Tue Oct 12 14:59:02 2021 From: i.bojak at reading.ac.uk (Ingo Bojak) Date: Tue, 12 Oct 2021 18:59:02 +0000 Subject: Connectionists: Postdoctoral Research Assistant at the University of Reading, UK Message-ID: The following position is aimed at Systems / Computational Biologists, or Experimental Biologists with computational aptitude. However, a (Computational) Neuroscientist willing to work in different biological systems will be considered on equal terms. Unusually, interest in the Philosophy of Biology is required. Postdoctoral Research Assistant University of Reading - School of Psychology & Clinical Language Sciences and School of Biological Sciences Job References: SRF37226 Closing date: 7th November 2021 Interview date: week commencing 15th November 2021 We are seeking a Postdoctoral Research Assistant for an exciting multi-disciplinary project combining computational modelling and experimental research in platelet biology, a dynamic field within cardiovascular biology. This project is part of the John Templeton Foundation Cohort Program ?Agency, Directionality, and Function: Foundations for a Science of Purpose?. The program brings together 24 international research teams, ours being one of four based in the UK. The project is particularly suited for creative minds combining strong, proven technical skills with a genuine interest in the philosophy of biology. We aim to establish a novel framework that operationalizes concepts of goal-directedness in terms of biological mistake-mistaking. While initially, and for the successful applicant primarily, this will be developed and validated within a well-established cell system concerned with blood clotting, we aim to translate philosophical classifications into practical methods, models, and prediction directly relevant to working scientists across all of the life sciences. The successful applicant will be expected to contribute to these innovative theoretical advances. The project offers the opportunity to work in a unique setting that brings together theoretical and experimental natural scientists as well as philosophers ? locally, and under the umbrella of a massive international collaboration working towards a common goal. As this position has two core technical aspects, computational modelling and experimental haematology, we welcome applications from candidates who are strong in one aspect but are keen and capable of learning the other. The position is for a fixed period of up to 2 years and 8 months from 1st December 2021. A delay in start date may limit the duration of the position. The successful applicant will: * have a PhD in a subject area appropriate to this field of research * have research experience with the modelling of complex / biological systems * be able to program / script in a suitable language, e.g., Python, C(++), Matlab * know, or be willing to learn, techniques to contribute to experimental studies * be interested in (the development of) the analysis of large datasets * be able to work effectively as a team member within a large interdisciplinary group * enjoy discussing ideas with people from a wide variety of academic backgrounds * have an interest in philosophy, in particular the philosophy of biology * have a proven track record through the publication of research Informal contact details Prof Ingo Bojak, i.bojak at reading.ac.uk & Prof Jon Gibbins, j.m.gibbins at reading.ac.uk Apply here: https://jobs.reading.ac.uk/displayjob.aspx?jobid=8546&source=JOBTRAINjobsite More detail (PDF download): SRF37226- Job Description and Person Specification Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. By reference to the applicable SOC code for this role, sponsorship may be possible under the Skilled Worker Route. Applicants should ensure that they are able to meet the points requirement under the PBS. There is further information about this on the UK Visas and Immigration Website. The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and is a Diversity Champion for Stonewall, the leading LGBT+ rights organisation. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs. -------------- next part -------------- An HTML attachment was scrubbed... URL: From vito.trianni at istc.cnr.it Wed Oct 13 03:38:44 2021 From: vito.trianni at istc.cnr.it (Vito Trianni) Date: Wed, 13 Oct 2021 09:38:44 +0200 Subject: Connectionists: [jobs] PhD position at Sapienza University / ISTC-CNR, Rome, Italy: Robotics and AI for precision spraying in agriculture Message-ID: <4CF6A1EE-EA1C-456B-B83F-6814C04A5C67@istc.cnr.it> A PhD position is now open in the context of PON research and innovation projects at Sapienza University, PhD in Engineering in Computer Science. https://phd.uniroma1.it/web/concorso37pon.aspx?i=3514 **Deadline for applications: October 27, 2021 at 2.00 pm CEST** General instructions for applicants are available here: https://www.uniroma1.it/en/pagina/phd-programmes https://www.uniroma1.it/sites/default/files/field_file_allegati/bando_pon_ricerca_e_innovazione_37_english_rev_1_gp.pdf The research will be conducted in cooperation with the ISTC-CNR (https://istc.cnr.it) with an internship at AIgriTech (https://www.aigritec.com), and will focus on precision spraying for precision agriculture. Title: Robotics and AI for Precision Spraying Abstract: One of the world's greatest challenges is to ensure sufficient food production for an ever-growing population. This is in contrast to the reduction of arable land, and the need for ecologically compatible production. Only through advanced precision farming techniques it will be possible to ensure increased production with reduced inputs of water, fertilisers and chemical plant protection products. The project aims to study robotic and AI systems capable of minimising or completely eliminating the use of chemical agents in agriculture. Specifically, the aim is to automate precision spraying techniques, which have been shown to reduce the use of pesticides or herbicides by up to 90%. In addition, the project will assess the impact of completely replacing chemical agents with mechanical interventions using robotic actuators. Details (Italian/English): https://phd.uniroma1.it/dottorati/stampeModuloRichiestaBorsaWeb.aspx?id=29 _________________________________________________ Skills and background knowledge: - Advanced methods for object detection and semantic segmentation - Recurrent convolutional neutral networks, LSTM - Active vision - Information theory - Bayesian inference - Information-based motion planning ======================================================================== Vito Trianni, Ph.D. vito.trianni@(no_spam)istc.cnr.it ISTC-CNR http://www.istc.cnr.it/people/vito-trianni Via San Martino della Battaglia 44 Tel: +39 06 44595277 00185 Roma Fax: +39 06 44595243 Italy ======================================================================== From thanh.dinhvan at gmail.com Wed Oct 13 04:45:08 2021 From: thanh.dinhvan at gmail.com (Thanh Dinh) Date: Wed, 13 Oct 2021 15:45:08 +0700 Subject: Connectionists: KR 2021: Final Call for Participation (Online event, free registration until Friday, October 15) Message-ID: <3c9317e3-2ca9-6655-6a10-37988224ad63@gmail.com> FINAL CALL FOR PARTICIPATION 18th International Conference on Principles of Knowledge Representation and Reasoning (KR 2021) November 3-12, 2021 - Virtual https://kr2021.kbsg.rwth-aachen.de/ *** Registration is free but mandatory (deadline: October 15, 2021) *** Knowledge Representation and Reasoning (KR) is a well-established and lively field of research.? In KR, a fundamental assumption is that an agent's knowledge is explicitly represented in a declarative form, suitable for processing by dedicated reasoning engines. This assumption, that much of what an agent deals with is knowledge-based, is common in many modern intelligent systems.? Consequently, KR has contributed to the theory and practice of various areas in AI, including automated planning and natural language understanding, and to fields beyond AI, including databases, verification, software engineering, and robotics. In recent years, KR has contributed also to new and emerging fields, including the semantic web, computational biology, cyber security, and the development of software agents. The KR conference series is the leading forum for timely in-depth presentation of progress in the theory and principles underlying the representation and computational management of knowledge. CONFERENCE FORMAT AND REGISTRATION KR 2021 will be organized as a virtual conference and welcomes all researchers interested in KR to participate! We are happy to announce that participation is *free*. The deadline for registering is October 15, 2021. Information about how to register for the main conference and associated events can be found at: https://kr2021.kbsg.rwth-aachen.de/page/registration There will be also be a limited-capacity *live gathering* in Rome to watch the conference and exchange with other participants. For details, consult: https://kr2021.kbsg.rwth-aachen.de/page/live_gathering_in_rome The list of papers accepted at the main conference and the Doctoral Consortium can also be found on the conference webpage. INVITED TALKS Martin Grohe (RWTH Aachen University, Germany) > The Logic of Graph Neural Networks Jochen Renz? (Australian National University, Australia) > Spatial and Physical Reasoning: From Angry Birds to Open World AI Uli Sattler? (University of Manchester, UK) > Description Logic and OWL: A Tale of Discoveries, Design Choices, ? Challenges, and Lessons Learnt (Great Moments in KR) Joshua Tenenbaum? (MIT, USA) > Reverse Engineering Human Cognitive Development: What Do We Start With, ? and How Do We Learn The Rest? Francesca Toni? (Imperial College, UK) > The Interactionist View of Reasoning for Explainable AI TRACKS & SPECIAL SESSIONS * Applications and Systems Track * Recent Published Research Track * Special Session: KR and Machine Learning * Special Session: KR and Robotics * Special Session: Diversity and Inclusion WORKSHOPS * Explainable Logic-Based Knowledge Representation (XLoKR) * Computational Machine Ethics (CME) * Knowledge Representation for Hybrid and Compositional AI (KRHCAI) * Ontology Uses and Contribution to Artificial Intelligence (OnUCAI) * Second-Order Quantifier Elimination and Related Topics (SOQE) * Semantics-Powered Health Data Analytics (SEPDA) TUTORIALS * Answer Set Programming: From Theory to Practice ? by Roland Kaminski, Javier Romero, Torsten Schaub & Philipp Wanko * Belief Revision and Judgment Aggregation in Ontologies ? by Jake Chandler and Richard Booth * Completeness, Recall, and Negation in Open-World Knowledge Bases ? by Simon Razniewski, Hiba Arnaout, Shrestha Ghosh & Fabian M. Suchanek * Complex Event Recognition and Forecasting ?by Elias Alevizos and Alexander Artikis * KR&R Meets Cyber-Physical Systems: Formalization, Behavior, Trustworthiness ?by Marcello Balduccini, Edward Griffor & Tran Cao Son * Planning with multi-agent, flexible, temporal, epistemic & contingent ?(MAFTEC) aspects ?by Aur?lie Beynier, Fr?d?ric Maris & Francois Schwarzentruber * Proof-Theoretic Approaches to Logical Argumentation ?by Ofer Arieli & Christian Strasser * Solving equations in modal and description logics ?by Philippe Balbiani CO-LOCATED EVENTS * NMR 2021 (20th International Workshop on Non-Monotonic Reasoning) CONFERENCE CHAIRS General: * Esra Erdem (Sabanci University, Turkey) Program: * Meghyn Bienvenu (CNRS & University of Bordeaux, France) * Gerhard Lakemeyer (RWTH Aachen University, Germany) Applications and Systems Track: * Martin Gebser (University of Klagenfurt, Austria) * Ulrike Sattler (University of Manchester, UK) Recently Published Research Track: * Vladimir Lifschitz (University of Texas at Austin, USA) * Pierre Marquis (Artois University & Institut Universitaire de France, France) Special Session on KR & Machine Learning: * Vaishak Belle (University of Edinburgh, UK) * Luc de Raedt (KU Leuven, Belgium) Special Session on KR & Robotics: * Alessandro Saffioti (University of ?rebro, Sweden) * Mary-Anne Williams (University of Technology Sydney, Australia) Workshop and Tutorials: * Markus Kroetzsch (TU Dresden, Germany) * Yongmei Liu (Sun Yat-sen University, China) Doctoral Consortium: * Jens Classen (Simon Fraser University) * Magdalena Ortiz (TU Vienna, Austria) Local Organization: * Giuseppe De Giacomo (Sapienza University, Italy) * Son Tran (New Mexico State University, USA) * Long Tran-Thanh (University of Warwick, UK) * Thanh Van Dinh (East Asia University of Technology, Vietnam) Virtual Conference Arrangements: * Stefan Borgwardt (TU Dresden, Germany) * Marco Console (Sapienza University Italy) * Long Tran-Thanh (University of Warwick, UK) Sponsorship: * Kuldeep S. Meel (NUS, Singapore) * Zeynep G. Saribatur (TU Wien, Austria) Publicity: * Thanh Van Dinh (East Asia University of Technology, Vietnam) * Paolo Felli (Free University of Bozen-Bolzano, Italy) Diversity and Inclusion: * Magdalena Ortiz (TU Vienna, Austria) * Maria Vanina Martinez (Universidad de Buenos Aires, Argentina) * Marco Maratea (University of Genova, Italy) From donatello.conte at univ-tours.fr Wed Oct 13 07:42:17 2021 From: donatello.conte at univ-tours.fr (Donatello Conte) Date: Wed, 13 Oct 2021 13:42:17 +0200 Subject: Connectionists: [Deadline extended] CfP Graph Models for Learning and Recognition (GMLR) Track at 37th ACM-SAC 2022 in Brno, Czech Republic Message-ID: <00ed01d7c027$5fc061e0$1f4125a0$@univ-tours.fr> --------------------------------------------------------- Apologies for multiples copies --------------------------------------------------------- Deadline Extended Call for Papers Graph Models for Learning and Recognition (GMLR) Track The 37th ACM Symposium on Applied Computing (SAC 2022) April 25-29, 2022, Brno, Czech Republic http://phuselab.di.unimi.it/GMLR2022 Important Dates Submission of regular papers: October 15, 2021 October 24, 2021 Notification of acceptance/rejection: December 10, 2021 Camera-ready copies of accepted papers: December 21, 2021 SAC Conference: April 25 - 29, 2022 Motivations and topics Authors of selected top papers of this track will be asked to publish an extended version in a Special Issue of a good quoted International Journal. The ACM Symposium on Applied Computing (SAC 2022) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic. The technical track on Graph Models for Learning and Recognition (GMLR) is the first edition and is organized within SAC 2022. Graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. Encouraged by the success of CNNs, a wide variety of methods have redefined the notion of convolution for graphs. These new approaches have in general enabled effective training and achieved in many cases better performances than competitors, though at the detriment of computational costs. Typical examples of applications dealing with graph-based representation are: scene graph generation, point clouds classification, and action recognition in computer vision; text classification, inter-relations of documents or words to infer document labels in natural language processing; forecasting traffic speed, volume or the density of roads in traffic networks, whereas in chemistry researchers apply graph-based algorithms to study the graph structure of molecules/compounds. This track intends to focus on all aspects of graph-based representations and models for learning and recognition tasks. GMLR spans, but is not limited to, the following topics: ? Graph Neural Networks: theory and applications ? Deep learning on graphs ? Graph or knowledge representationa learning ? Graphs in pattern recognition ? Graph databases and linked data in AI ? Benchmarks for GNN ? Dynamic, spatial and temporal graphs ? Graph methods in computer vision ? Human behavior and scene understanding ? Social networks analysis ? Data fusion methods in GNN ? Efficient and parallel computation for graph learning algorithms ? Reasoning over knowledge-graphs ? Interactivity, explainability and trust in graph-based learning ? Probabilistic graphical models ? Biomedical data analytics on graphs Scientific Program Committee Davide Boscaini Federico Castelletti Vittorio Cuculo Alessandro D?Amelio Gabriele Gianini Alessio Micheli Ryan A. Rossi Carlo Vercellis Naoufel Werghi Submission Guidelines Authors are invited to submit original and unpublished papers of research and applications for this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and self-reference should be in the third person. This is to facilitate double-blind review. Please, visit the website for more information about submission SAC No-Show Policy Paper registration is required, allowing the inclusion of the paper/poster in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for the paper/poster to be included in the ACM digital library. No-show of registered papers and posters will result in excluding them from the ACM digital library. Track Chairs Donatello Conte (University of Tours) Giuliano Grossi (University of Milan) Raffaella Lanzarotti (University of Milan) Jianyi Lin (Universit? Cattolica del Sacro Cuore) Jean-Yves Ramel (University of Tours) -------------- next part -------------- An HTML attachment was scrubbed... URL: From calendarsites at insticc.org Wed Oct 13 07:04:19 2021 From: calendarsites at insticc.org (calendarsites at insticc.org) Date: Wed, 13 Oct 2021 12:04:19 +0100 Subject: Connectionists: SENSORNETS 2022 - New Submission Opportunity Message-ID: <025701d7c022$149a0c20$3dce2460$@insticc.org> CALL FOR PAPERS 11th International Conference on Sensor Networks **Submission Deadline: November 8, 2021** https://sensornets.scitevents.org/ February 07 - 08, 2022 Online Streaming Dear Colleagues, We would be very pleased to receive a regular or position paper submission from you, with recent results, to be presented at SENSORNETS 2022 until 8 November 2021. This would be a nice opportunity for you to join a large and growing community of researchers who have already submitted their work to this event. SENSORNETS intends to be the meeting point of researchers and practitioners to share experiences and ideas on innovative developments in any aspect of sensor networks, including Hardware of Sensor Networks, Wireless Communication Protocols, Sensor Networks Software and Architectures, Wireless Information Networks, Data Manipulation, Signal Processing, Localization and Object Tracking through Sensor Networks, Machine Learning in sensor networks leading to resource optimizations and many Applications. In the last 5 years, the SENSORNETS proceedings have been fully indexed by SCOPUS and beside this index all the proceedings have also been submitted to Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Microsoft Academic, Engineering Index (EI) and Web of Science / Conference Proceedings Citation Index. The conference will include in its technical program remarkable distinguished speakers, such as: Luca Mottola, Politecnico di Milano, Italy Biplab Sikdar, University of Singapore, Singapore Niki Trigoni, University of Oxford, United Kingdom We hope this interests you, as it would be a great pleasure to count on your participation at our conference. Kind regards, Monica Saramago SENSORNETS Secretariat Web: https://sensornets.scitevents.org/ e-mail: sensornets.secretariat at insticc.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From habesm at gmail.com Wed Oct 13 09:25:13 2021 From: habesm at gmail.com (Mohamad Habes) Date: Wed, 13 Oct 2021 08:25:13 -0500 Subject: Connectionists: Postdoc position in deep learning and neuroimaging -University of Texas Health San Antonio- Message-ID: The Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) are recruiting a postdoctoral fellow in deep learning and neuroimaging -University of Texas Health San Antonio- Are you excited about deep learning and transfer learning? Do you want to apply and develop deep learning methods to high dimensional neuroimaging data and make new discoveries which could advance our understanding of Alzheimer?s disease? We are looking for a postdoctoral fellow who is willing to research more deep and transfer learning methods in large cohort based studies. Alzheimer?s disease and other dementias are heterogeneous conditions, which makes differentiating between them and their subtypes very challenging. Our goal is to use neuroimaging data and deep learning to help uncover and detect specific pathologies and patterns emerging in early Alzheimer?s disease. In this position your challenge will be to develop new deep learning architectures and algorithms that allow current pathology detection and prediction of possible future disease trajectories. Your work environment will be the Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC). We build advanced neuroimage analytical techniques to derive discovery. Data-driven approaches are of special interest in our lab, as machine learning and machine intelligence will guide the scientist towards the finding. On broader goal, our tools help delivering precise diagnostics on an individual?s level and ultimately could guide treatment progress. We are part of the Biggs Institute (https://biggsinstitute.org), which is being established as a flagship, free-standing institute within University of Texas Health San Antonio (UTHSA), with the mission of establishing an interdisciplinary, integrated program to provide comprehensive clinical care and undertake innovative and important research into the prevention and treatment of Alzheimer?s Disease and other neurodegenerative conditions, including vascular contributions to dementia, Parkinson?s disease and frontotemporal dementia. It has strong institutional and community support and will benefit from existing resources within UTHSA such as the Barshop Institute for Longevity and Aging Studies, the Center for Biomedical Neuroscience, the School of Nursing, the Cancer Center, and the Research Imaging Institute along with the San Antonio campus of the UT Health Houston School of Public Health. *Responsibilities* ? Develop, test and validate novel architectures and algorithms for deep (transfer) learning with neuroimaging data ? Apply your validated methods to large scale research and real life everyday clinical routine neuroimaging data ? Willingness to work in teams, within NAL, BINC and Biggs and with national and international collaborators ? Communicate your research results to the larger communities through publications in international conferences and journals ? Work with great deal of independence in achieving research goals *Requirements* ? A PhD degree in Artificial Intelligence, Machine Learning, Computer Vision or Medical Image Analytics with solid experience in deep learning; Experience in Neuroimaging and Dementia Research is a plus. ? Great eagerness to solve scientific problems ? Strong programming skills, e.g. in Python, R, C++ and Java. Experience with Python deep learning toolboxes and high-performance computational facilities could be a plus; ? Excellent record of publishing in relevant high-quality journals in the above fields ? Excellent communication abilities in English; spoken and written. If interested, please send a copy of your CV to Dr. Habes (habes at uthscsa.edu ) -------------- next part -------------- An HTML attachment was scrubbed... URL: From Marie.PELLEAU at univ-cotedazur.fr Wed Oct 13 12:06:49 2021 From: Marie.PELLEAU at univ-cotedazur.fr (Marie Pelleau) Date: Wed, 13 Oct 2021 16:06:49 +0000 Subject: Connectionists: CPAIOR 2022 - Call for Papers Message-ID: ****************** Our apologies for multiple reception of this announcement ***************** ****** This call can be seen online: https://sites.google.com/usc.edu/cpaior-2022/cfp ****** Call for Papers The 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research will be held in Los Angeles, US, June 20th-June 23th, 2022. The aim of the conference is to bring together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications and to provide an opportunity for researchers in one area to learn about techniques in the others. A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems. Therefore, papers that actively combine, integrate, or contrast approaches from more than one of the areas are especially solicited. High quality papers from a single area are also welcome, if they are of interest to other communities involved. Application papers showcasing CP/AI/OR techniques on novel and challenging applications or experience reports on such applications are strongly encouraged. The program committee invites submissions that include but are not limited to the following topics: - Inference and relaxation methods: constraint propagation, cutting planes, global constraints, graph algorithms, dynamic programming, Lagrangian and convex relaxations, heuristic functions based on constraint relaxation. - Search methods: branch and bound, intelligent backtracking, incomplete search, randomized search, portfolios, column generation, Benders decompositions or any other decomposition methods, local search, meta-heuristics. - AI and Machine Learning techniques applied to solve optimization and Operations Research problems or CP/OR techniques to solve AI and machine learning problems. - Integration methods: solver communication, model transformations and solver selection, parallel and distributed solving, combining machine learning with combinatorial optimization. - Modeling methods: comparison of models, symmetry breaking, uncertainty, dominance relationships. - Innovative applications of CP/AI/OR techniques. - Implementation of CP/AI/OR techniques and optimization systems. Submissions are of two types: regular papers (submitted for publication and presentation) and extended abstracts (submitted for presentation only). This conference will give a distinguished paper award and a student paper award. Important dates for REGULAR PAPERS: Submission schedule for regular papers (both long and short): - Abstracts: November 29, 2021 (anywhere on Earth: AoE) - Full papers: December 3, 2021 (AoE) - Rebuttal Phase: January 12-19, 2022 - Notification: February 3, 2022 - Camera-ready version: April 15, 2022 (AoE) Important dates for EXTENDED ABSTRACTS: - Abstract: March 3, 2022 (AoE) - Notification: April 4, 2022 Instructions for Regular Papers Regular papers should present original unpublished work and can be of two types: - long papers (at most 15 pages plus references) - short papers (at most 8 pages plus references) Both long and short papers will undergo rigorous review and are subject to the same criteria of quality. Both types are also eligible for distinguished paper or student paper award. Short papers are particularly encouraged for interesting and novel work in progress, for which the practical or theoretical relevance is not yet fully identified. The conference proceedings will be published in the Springer Lecture Notes in Computer Science series. Authors should consult Springer?s authors? guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made. Instructions for Extended Abstracts Extended abstracts should be 1 or 2 pages in length and may present preliminary work or work already published in other outlets. The extended abstracts are submitted for presentation only (if accepted), and will not be formally published in the LNCS conference volume. A collection of the accepted extended abstracts will be published on the conference website. A submission representing work submitted or published in another outlet should state that outlet. Extended abstracts will be reviewed to ensure appropriateness for the conference. Submission Process All papers are to be submitted electronically in PDF format by following the instructions at the URL https://easychair.org/conferences/?conf=cpaior2022 Questions For any queries on the submission process, please contact the Program Chair Pierre Schaus at: cpaior2022 at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From hans.opdebeeck at kuleuven.be Wed Oct 13 17:21:57 2021 From: hans.opdebeeck at kuleuven.be (Hans Op de Beeck) Date: Wed, 13 Oct 2021 21:21:57 +0000 Subject: Connectionists: Postdoc position in Computational & cognitive neuroscience (Belgium) In-Reply-To: <85BBE40A-55CA-4BD4-9749-F4AFDBFFEA06@kuleuven.be> References: <85BBE40A-55CA-4BD4-9749-F4AFDBFFEA06@kuleuven.be> Message-ID: Postdoc position ?Computational cognitive neuroscience of vision and learning? The research group of Hans Op de Beeck at the University of Leuven (Belgium) has a position for a postdoc to investigate the neural basis of visual cognition and learning from a computational & cognitive neuroscience perspective. We have a particular interest in the neural basis of human visual and social cognition, and hope to better understand the factors that constrain the functioning of the underlying systems and their further adaptation due to learning experiences and visual expertise. Current research lines involve multivariate fMRI & EEG and computational modelling, often using learning paradigms and/or populations of real-world experts, and applications in the context of neurodevelopment disorders (e.g., autism). The postdoc would be involved in multiple projects in which we compare the performance and information processing in deep convolutional neural networks with human behavior and neuroimaging data, and would be expected to have a major responsibility for the computational aspects. More information on our research group can be found at hoplab.be. There is a possibility to also work on your own interests if they fit sufficiently. Eligible candidates have research experience and a PhD in a relevant area, preferably demonstrated with first-author peer-reviewed publications in the domain of computational & cognitive neuroscience. A computational and quantitative background is important, including good programming skills (e.g. Python, Matlab), experience applying and developing artificial neural networks, and advanced statistical knowledge. Further assets are experience with neuroimaging methods and the cognitive neuroscience literature, a creative mind, good problem solving skills, international and interdisciplinary mobility, and a collaborative, open-minded, and collegial attitude. We currently have funding for up to three years, starting with a one year contract. We have a good track record helping postdocs obtain their own personal prestigious fellowships afterwards, as such allowing them to extend their stay and broaden the range of research questions. The net amount of the salary will be competitive and in accordance with the University fellowship scales for postdoctoral researchers. The position is research-only, without teaching obligations, but teaching opportunities can be arranged if wanted. KU Leuven is one of the best European universities according to international rankings (#42 world-wide in the THE ranking). KU Leuven houses more than 100 labs in neuroscience, brought together in the Leuven Brain Institute. Our research group belongs to the department of Brain & Cognition that contains almost 100 scientists, many of them with interest in vision, learning, and neuroscience. The department is located in the centre of Leuven, a vibrant university town which is only a 25-minute commute from the centre of Brussels and its international airport. For further information contact Hans Op de Beeck. Applications can be submitted through the electronic application system of KU Leuven (https://www.kuleuven.be/personeel/jobsite/jobs/60065973 ), and should include a motivation letter and CV, mentioning the names and contact information of at least two referents. The provisional submission deadline is October 25. Late submissions might still be considered, but no guarantees. The starting date would ideally be early in 2022, but obviously we will be flexible given the current international travel restrictions and further personal constraints. --- Prof. Dr. Hans P. Op de Beeck, PhD Research Unit Brain & Cognition, Leuven Brain Institute University of Leuven (KU Leuven), Belgium Tiensestraat 102, 3000 Leuven, Belgium, PO Box 3714 Email: hans.opdebeeck at kuleuven.be Personal profile: https://ppw.kuleuven.be/bc/team/00029058 Lab Website: https://www.hoplab.be -------------- next part -------------- An HTML attachment was scrubbed... URL: From dayan at tue.mpg.de Thu Oct 14 06:50:46 2021 From: dayan at tue.mpg.de (Peter Dayan) Date: Thu, 14 Oct 2021 12:50:46 +0200 Subject: Connectionists: 5-Year MSc/PhD program in Computational Neuroscience in Tuebingen Message-ID: <20211014105046.wtbpasjz3dbrdz6v@tuebingen.mpg.de> International Max Planck Research School: The Mechanisms of Mental Function and Dysfunction 5-Year combined MSc/PhD program The Max Planck Institute for Biological Cybernetics, the Hertie Institute for Clinical Brain Research and the University of T?bingen invite students from all over the world to apply for their interdisciplinary 5-year combined MSc/PhD program leading to a PhD in Neuroscience. Full funding is available for top-ranked applicants. We are seeking talented, curious and open-minded scientists with strong backgrounds in neuroscience, biomedical sciences, computational science, applied mathematics, statistics, artificial intelligence, or engineering. Successful candidates will possess a burning aspiration to shape the future of neuroscience and the ability to thrive in a fast-paced, interdisciplinary, environment. The application deadline is 30th November 2021. Please visit: https://www.kyb.tuebingen.mpg.de/imprs-mmfd https://www.neuroschool-tuebingen.de/about-imprs/ for more details and information about applying. The MSc/PhD program is a collaboration between the Max Planck Institute for Biological Cybernetics, the Hertie Institute for Clinical Brain Research and the University of T?bingen. It is closely affiliated with the renowned Graduate Training Centre of Neuroscience, the centerpiece of neuroscience training in T?bingen. Students (who should have been awarded a Bachelor's degree by September 2022) will receive a broad interdisciplinary training in neuroscience, including expert teaching by international renowned scientists and individual and intensive mentoring. Potential research topics cover a variety of fields in systems neuroscience, cognitive and behavioral neuroscience, computational neuroscience, translational and clinical neuroscience as well as cellular and molecular neuroscience. Teaching and research are conducted in English. -------------- next part -------------- A non-text attachment was scrubbed... Name: smime.p7s Type: application/x-pkcs7-signature Size: 4936 bytes Desc: not available URL: From ioannakoroni at csd.auth.gr Thu Oct 14 08:40:08 2021 From: ioannakoroni at csd.auth.gr (Ioanna Koroni) Date: Thu, 14 Oct 2021 15:40:08 +0300 Subject: Connectionists: 21 Asynchronous Web e-Courses offered on Deep Learning, Computer Vision, Autonomous Systems, Signal/Image/Video Processing, Human-centered Computing, Social Media, Mathematical Foundations, CVML SW tools References: <014f01d7c0f3$ad3e34c0$07ba9e40$@csd.auth.gr> Message-ID: <01db01d7c0f8$9ffd50c0$dff7f240$@csd.auth.gr> Dear Computer Vision, Machine Learning, Autonomous Systems, DSP/DIP, Social Media Engineers, Scientists and Enthusiasts, you are welcomed to register and attend one or more of the 21 CVML Web e-Course Modules on offer, each consisting of Lecture Series (208 lectures in total). These asynchronous Web e-Course Modules (Lecture Series) provide an overview and in-depth presentation of 21 different domains: * Deep Learning and Neural Networks: Machine Learning (12 Lectures), Neural Networks/Deep Learning (14 Lectures), Advanced Deep Learning (7 Lectures) * Deep Learning and Computer Vision Foundations and Tools: Mathematical Foundations (9 Lectures), SW Development and Programming Tools (3 Lectures) * Computer Vision/Image Processing and 3D Imaging: Computer Vision (12 Lectures), 2D Computer Vision/Image Analysis (8 Lectures), Image Processing (21 Lectures), Video Processing and Analysis (17 Lectures), 3D Imaging (9 Lectures), 3D Computer Graphics and Virtual Reality (5 Lectures) * Autonomous Systems: Autonomous Systems principles (8 Lectures), Robotics and Automatic Control (3 Lectures), Autonomous Cars (8 Lectures), Autonomous Drones (15 Lectures), Autonomous Marine Systems (4 Lectures). * Human centered computing. Social Networks. Graph Theory: Human Centered Computing (15 Lectures), Network Theory. Social Media Analysis (10 Lectures). * Digital Signal Processing and Applications: Signal and Systems (11 Lectures), Digital Signal Processing and Analysis (7 Lectures), Medical Image and Signal Analysis (4 Lectures), Acoustics, Speech, Natural Language Processing and Analysis (4 Lectures), Communications (2 Lectures). You can combine CVML Web e-Course Modules to create CVML Web e-Courses (typically consisting of 16 lectures) of your own choice that cater your needs. Each CVML Web e-Course you will create (16 lectures) provides you material that can cover a semester course, but you can master it in approximately 1 month. Asynchronous tutor support will be provided in case of questions. CVML Web e-Course Module materials typically consist of: a) a lecture pdf/ppt, b) lecture self-assessment understanding questionnaire and lecture video, programming exercises, tutorial exercises (for several modules/lectures) and overall course module satisfaction questionnaire. Course materials have been very successfully used in many top conference keynote speeches/tutorials worldwide and in short courses, summer schools, semester courses delivered by AIIA Lab physically or on-line from 2018 onwards, attracting many hundreds of registrants. Course materials are at senior undergraduate/MSc level in a CS, CSE, EE or ECE or related Engineering or Science Department. Their structure, level and offer are completely different from what you can find in either Coursera or Udemy. You can find sample Web e-Course Module material to make up your mind and/or can perform CVML Web e-Course registration in: http://icarus.csd.auth.gr/cvml-web-lecture-series/ For questions, please contact: Ioanna Koroni > Academic/Research/Industry offer and arrangements Special arrangements can be made to offer the material of these CVML Web e-Course Modules at University/Department/Company level: * by granting access to the material to University/research/industry lecturers to be used as an aid in their teaching, * by enabling class registration in CVML Web e-Courses * by delivering such live short courses physically or on-line by Prof. Ioannis Pitas * by combinations of the above. The CVML Web e-Course is organized by Prof. I. Pitas, IEEE and EURASIP fellow, Coordinator of International AI Doctoral Academy (AIDA), past Chair of the IEEE SPS Autonomous Systems Initiative, Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle University of Thessaloniki, Greece, Coordinator of the European Horizon2020 R&D project Multidrone. He is ranked 249-top Computer Science and Electronics scientist internationally by Guide2research (2018). He has 34100+ citations to his work and h-index 87+. The informatics Department at AUTH ranked 106th internationally in the field of Computer Science for 2019 in the Leiden Ranking list Relevant links: 1. Prof. I. Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ &hl=el 2. International AI Doctoral Academy (AIDA): https://www.i-aida.org/ 3. Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/ 4. Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/ 5. Horizon2020 EU funded R&D project AI4Media: https://ai4media.eu/ 6. AIIA Lab: https://aiia.csd.auth.gr/ Sincerely yours Prof. I. Pitas Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab) Aristotle University of Thessaloniki, Greece Post scriptum: To stay current on CVML matters, you may want to register to the CVML email list, following instructions in https://lists.auth.gr/sympa/info/cvml -- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus -------------- next part -------------- An HTML attachment was scrubbed... URL: From tiako at ieee.org Thu Oct 14 14:03:19 2021 From: tiako at ieee.org (Pierre F. Tiako) Date: Thu, 14 Oct 2021 13:03:19 -0500 Subject: Connectionists: (CFP-Due Oct 16) 2021 OkIP Conf on Data Technology and Engineering|| OkCity, USA|| Nov 15-18 Message-ID: >> Important Dates (Extended): - Submission: Oct 16, 2021 - Conference: Nov 15-18, 2021 --- Call for Abstracts and Papers ------------- 2021 OkIP International Conference on Data Technology and Engineering (CDTE) MNTC Conference Center, Oklahoma City, OK, USA & Online November 15-18, 2021 https://eventutor.com/e/CDTE001 >> Co-located Conferences and Events https://eventutor.com/event/4/page/4-conferences >> Keynotes/Invited Talks "Blockchain Technology and its implications in Business Applications and Healthcare IT" - Akhil Kumar, PhD, Penn State University, USA ?Machine Learning for Critical Systems Security? - Nancy R. Mead, PhD, Carnegie Mellon University, USA ?Sustainable Energy Harvesting and Wireless Power Transfer Systems? - Manos M. Tentzeris, PhD, Georgia Institute of Technology, USA >> Technical Research & Industry Tracks - Data Concepts - Data Analytics and Processing - Databases - AI in Data and Big Data - Data and Databases Applications - Data and Legal Issues >> Contribution Types, OkIP Published and SCOPUS/WoS Indexed - Full Paper: Accomplished research results (6 pages) - Short Paper: Work in progress/fresh developments (3 pages) - Poster/Journal First: Displayed/Oral presented (1 page) >> Technical Program Committee https://eventutor.com/event/8/page/16-committee >> Venue https://eventutor.com/event/4/page/9-venue >> For more information, submission details, and important dates visit: https://eventutor.com/e/CDTE001 Please feel free to contact us for any inquiry at: info at okipublishing.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.dorrn at fz-juelich.de Thu Oct 14 11:23:46 2021 From: a.dorrn at fz-juelich.de (Anja Dorrn) Date: Thu, 14 Oct 2021 17:23:46 +0200 Subject: Connectionists: Hands-on workshop on High Performance Computing in Jan 2022 - registration is now open Message-ID: <355f8020-a0ab-ba76-678c-b4e07860bd97@fz-juelich.de> The Bernstein Network Computational Neuroscience & Bernstein Facility for High Performance Simulation and Data Analysis invite for a 2,5 day hands-on workshop in a hybrid format: "Advancing your neuroscientific research to the next level - How to use high performance computing and cloud services to answer your scientific questions" Dates: January 18-20, 2022 Registration deadline: November 25, 2021 On-site: J?lich Supercomputing Centre, Forschungszentrum J?lich GmbH, (limited to 25 participants) & Online: Zoom (Link will be sent prior to the event) Neuroscientific research comes along with a multitude of highly complex data. Using high performance computing (HPC) and cloud services can help to better process and understand your data and to get the maximum benefit for your personal research. This is why the Bernstein Facility for High Performance Simulation and Data Analysis is the central port of call for Bernstein members to access the facilities of the high-performance computers at Forschungszentrum J?lich. This hands-on workshop will help you understand how to access the resources of the J?lich Supercomputing Center (JSC) and make the most of this opportunity for your specific research. Get inspired by professional use cases and benefit from the one-on-one exchange with the experts in high performance simulation and data analysis from J?lich. Participation in the 2,5 day event is open to early career researchers (PhD and early Post-docs), regardless of their affiliation with the Bernstein Network. Currently, the event is planned as a hybrid workshop enabling both online and on-site participation. Yet, the global Covid-19 situation might call for a change of plan. In this case, the organizers will inform registered participants straight away. More information and registration: https://bit.ly/3mVW6bS -- Dr. Anja Dorrn Scientific Coordinator Bernstein Network Computational Neuroscience | Bernstein Coordination Site (BCOS) Branch Office of the Forschungszentrum J?lich at the University of Freiburg Hansastr. 9A | 79104 Freiburg, Germany phone: (+49) 0761 203 9589 web: www.bernstein-network.de ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ -------------- next part -------------- An HTML attachment was scrubbed... URL: From Donald.Adjeroh at mail.wvu.edu Fri Oct 15 00:49:37 2021 From: Donald.Adjeroh at mail.wvu.edu (Donald Adjeroh) Date: Fri, 15 Oct 2021 04:49:37 +0000 Subject: Connectionists: CFP: IEEE BIBM-LncRNA'21: 2 weeks to submission deadline -- See our array of speakers ! In-Reply-To: References: , , , , Message-ID: Apologies if you receive multiple copies ... We have an exciting array of speakers for the workshop -- both In-Person presenters in Dubai, UAE, and Online/remote presenters !! See our website: BIBM- LncRNA'2021: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ Paper submission deadline is Nov. 1, just 2-weeks away -- see below Selected papers will be invited for review and possible publication in a special issue the journal -- Non Coding RNA. Call for Papers The IEEE BIBM 2021 Workshop on Long Non-Coding RNAs: Mechanism, Function, and Computational Analysis (BIBM-LncRNA) will be held in conjunction with the 2021 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021), Dec. 9 - 12, 2021. Though the BIBM conference will be virtual/online, the LncRNA workshop will be held in a mixed mode -- both virtual/remote and face-to-face in Dubai, UAE. BIBM- LncRNA'2021: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ IEEE BIBM 2021: https://ieeebibm.org/BIBM2021/ The recent application of high throughput technologies to transcriptomics has changed our view of gene regulation and function. The discovery of extensive transcription of large RNA transcripts, termed long noncoding RNAs (lncRNAs), provide an important and new perspective on the centrality of RNA in gene regulation. LncRNAs are involved in various biological and cellular processes, such as genetic imprinting, chromatin remodeling, gene regulation and embryonic development. LncRNAs have been implicated in several chronic diseases, such as cancers, and heart disease, etc. Various types of genomic data on lncRNAs are currently available, including sequences, secondary/tertiary structures, transcriptome data, and their interactions with related proteins or genes. The key challenge is how to integrate data from myriad sources to determine the functions and the regulatory mechanism of these ubiquitous lncRNAs. Research topics: The potential topics include, but not limited to, the following: lncRNA detection and biomarker discovery CLIP-Seq and RIP-Seq data analysis Prediction of physical binding between lncRNA and DNA, RNA and protein. Competition and interaction between lncRNA, miRNA and mRNA Studying methylation regulating lncRNA functions Function Prediction for lncRNAs Deep learning approaches to lncRNA/RNA binding protein prediction Computational approaches to analyzing lncRNA lncRNA 3D secondary structures lncRNA-protein interactions lncRNA in epigenetic regulation lncRNA associated diseases network lncRNAs in plant genomics lncRNAs in phenotype-genotype problems lncRNAs and single cell transcriptomics lncRNAs and spatial transcriptomics CRISPR/Cas9 and Genome editing in lncRNAs We invite you to submit papers with unpublished, original research describing recent advances on the areas related to this workshop. All papers will undergo peer review by the conference program committee. All papers accepted will be included in the Workshop Proceedings published by the IEEE Computer Society Press and will be available at the workshop. Authors of selected papers will be invited to extend their papers for submission to special issues in prestigious Journals. Fellowships: Funds are available for limited fellowships to support the participation of students, and of researchers from underrepresented minority groups in the workshop. Journal Special Issue: Authors of selected submissions will be invited to extend their papers for submission for review and possible publication in a special issue of the journal -- Non Coding RNA. https://www.mdpi.com/journal/ncrna Paper Submission: Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. Electronic submissions in pdf format are required. For paper submission click on the following link: https://wi-lab.com/cyberchair/2021/bibm21/scripts/submit.php?subarea=S08&undisplay_detail=1&wh=/cyberchair/2021/bibm21/scripts/ws_submit.php Important Dates: Nov 1, 2021 11:59:59 PM WST: Due date for full workshop paper submission. Nov 14, 2021: Notification of paper decision to authors Nov 21, 2021: Camera-ready of accepted papers Dec 9-12, 2021: Workshops BIBM-LncRNA'21 Workshop home page: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From jiangdm at nwpu.edu.cn Thu Oct 14 23:47:47 2021 From: jiangdm at nwpu.edu.cn (=?UTF-8?B?6JKL5Yas5qKF?=) Date: Fri, 15 Oct 2021 11:47:47 +0800 (GMT+08:00) Subject: Connectionists: ACM ICMI 2021: Announcing Detailed Program and Tutorials Message-ID: <237c2f9f.4045f.17c82101fcf.Coremail.jiangdm@nwpu.edu.cn> Announcing Detailed Program and Tutorials *************************************** ACM ICMI 2021: Announcing Detailed Program and Tutorials https://icmi.acm.org/2021/index.php?id=program#detailedprogram 18-22 Oct 2021, Montreal, Canada *************************************** ACM ICMI 2021 is around the corner! Please visit https://icmi.acm.org/2021/index.php?id=program#detailedprogram to see the detailed program. Additionally the tutorials will be accessible to all registered participants on the 18th. Please visit the tutorials page at https://icmi.acm.org/2021/index.php?id=tutorial. The 23rd ACM International Conference on Multimodal Interaction (ICMI 2021) will be held in Montreal, Canada October 18-22, 2021. ICMI is the premier international forum for multidisciplinary research on multimodal human-human and human-computer interaction, interfaces, and system development. The main conference themes in 2021 will be behavioral health and virtual connectivity, but other major topics of central interest include human communication and multimodal language/dialogue processing, human-robot/agent interaction, affective computing and social interaction, cognitive modeling, multimodal representations and fusion-based architectures, machine learning for multimodal interaction and system applications, speech, gesture, haptics, olfaction, gaze and vision, multimodal datasets and platforms, mobile and ubiquitous interfaces, interfaces for virtual/augmented reality, smart environments, and assistive technologies. *********************************** Dongmei Jiang Professor, School of Computer Science, Northwestern Polytechnical University Xi'an China. Tel: +86-29-88431532 Email: jiangdm at nwpu.edu.cn *********************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From angelo.cangelosi at manchester.ac.uk Fri Oct 15 07:59:41 2021 From: angelo.cangelosi at manchester.ac.uk (Angelo Cangelosi) Date: Fri, 15 Oct 2021 11:59:41 +0000 Subject: Connectionists: PhD in Machine Learning and Human-Robot Interaction, University of Manchester Message-ID: <37300FA9-0D8B-44D3-91E8-CE57C5A145C1@manchester.ac.uk> The University of Manchester Department of Computer Science PhD in Machine Learning and Human-Robot Interaction This candidate will be enrolled in a PhD investigating the role of multimodal interaction and theory of mind in human robot cooperation and the acceptability and trust of robot companions. This will involve use of the latest machine learning methods for robot???s cognitive architectures. The PhD research will develop novel cognitive robotics architecture for trust and will carry out human-robot experiments. The student will work collaboratively as part of the cognitive robotics lab at the Department of Computer Science at the University of Manchester under the supervision of Professor Angelo Cangelosi. Close collaboration with the other THRIVE++ project staff and partners will also be required. See thrive-project.org The project funding will cover UK home fees and a PhD bursary. For informal enquiries, email angelo.cangelosi at manchester.ac.uk Start date: January 2022, or as soon as the student is available -------------- next part -------------- An HTML attachment was scrubbed... URL: From aldo.lipani at acm.org Fri Oct 15 12:59:27 2021 From: aldo.lipani at acm.org (Aldo Lipani) Date: Fri, 15 Oct 2021 18:59:27 +0200 Subject: Connectionists: CIKM 2021 - Fee Waiver for Attendees from Historically Underrepresented Communities or Facing Financial Difficulties In-Reply-To: References: Message-ID: Hello everyone! CIKM 2021 will provide free conference registrations to support attendees from historically underrepresented communities and those facing financial difficulties in registering. To apply for this registration fee waiver, please fill in the form below before October 22, 2021. https://forms.office.com/r/PNyUYXzcMV After the deadline, all the applications will be reviewed and a short list of participants will be selected whose registration fees will be waived. Attendees from historically underrepresented groups and countries will be given a priority in the approval process. Best regards, Bhaskar Mitra CIKM 2021 Diversity and Inclusion Scholarship Chair -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephan.chalup at newcastle.edu.au Fri Oct 15 17:34:17 2021 From: stephan.chalup at newcastle.edu.au (Stephan Chalup) Date: Fri, 15 Oct 2021 21:34:17 +0000 Subject: Connectionists: [jobs] PhD topics within the Mathematical Thinking Project 2022 Message-ID: <5DED4466-0117-447D-9293-D45A77A8D402@newcastle.edu.au> The Mathematical Thinking Initiative within the Priority Research Centre on Computer Assisted Research Mathematics and Applications (CARMA) at the University of Newcastle, Australia, offers PhD supervision on a range of topics related to computer assisted proofs using tools such as Lean, machine learning, mathematics, and cognition. More specific topics include: 1. Translation of arguments concerning topological groups into Lean and their implementation as minimisation algorithms. 2. Machine learning and automated mathematical verification using Lean 3. Number theory and Lean 4. Mathematical psychology and mathematical thinking 5. Teaching of critical thinking in mathematics 6. Teaching of mathematics for social justice 7. Mathematical modelling and optimisation Participating academics are: Prof George Willis, A/Prof Stephan Chalup, Prof Florian Breuer, A/Prof Ami Eidels, A/Prof Elena Prieto-Rodriguez, Dr. Hamish Waterer and others. Students with outstanding track record who are aiming to start a PhD are encouraged to contact the PRC CARMA by 4 November 2021 with a brief EOI (preferred topic area, resume, list of publications) to be shortlisted for scholarship nomination by one of the participating academics. Nominated candidates will then be asked to apply formally for an Australian Government Research Training Program Scholarship ? Academic Pathway Scheme (Vice Chancellor Scholarship). * Domestic and international students can apply * All communications in our program are in English * You will join a high-profile research team as a doctoral student and conduct focused research * The VC scholarship includes opportunities for some academic training experience beyond the research experience. Candidates with background in mathematics, machine learning, physics, neuroscience, control theory, computer science, material science, artificial intelligence, deep learning, or related fields are encouraged to apply. CARMA is committed to widening participation, promoting diversity and fairness, and overcoming injustice. Please submit your EOI for nomination to PRC CARMA by 4 November 2021 using the following email Stephan.Chalup at newcastle.edu.au or contact one of the other above listed academics directly. -------------- next part -------------- An HTML attachment was scrubbed... URL: From whitney.tabor at uconn.edu Fri Oct 15 22:25:34 2021 From: whitney.tabor at uconn.edu (Tabor, Whitney) Date: Sat, 16 Oct 2021 02:25:34 +0000 Subject: Connectionists: 5 PhD positions in Language and Cognition at the University of Connecticut Message-ID: The Language and Cognition program in the Department of Psychological Sciences is recruiting diverse and creative scholars to join our PhD program in Fall 2022. We have a strong track-record of interdisciplinary research spanning from theory and computational modeling to empirical cognitive and neuroscience approaches. Research in our program examines a number of major themes, including neurobiological mechanisms in speech perception, reading, sentence processing, semantic memory and concept formation, event cognition, individual differences, and dynamical systems approaches to language and cognition, in typical and atypical populations. We have strong collaborative links to researchers outside of UConn as well as our colleagues at UConn including those in Linguistics, Speech, Language, and Hearing Sciences, Educational Psychology, Philosophy, Engineering, and Medicine. We are affiliated with UConn?s Cognitive Science program, the CT Institute for the Brain and Cognitive Sciences (IBACS), and Haskins Laboratories. Facilities include state-of-the-art MRI, high-density EEG, eye-tracking, TMS, and other neuromodulation techniques at UConn?s Brain Imaging Research Center and IBACS, as well as access to computing clusters, lab space, and a dynamic program of colloquia, internal talk series, and interest groups. Graduate students are unionized and funding includes health insurance. DIVERSITY, EQUITY, AND INCLUSION Our faculty and program are actively committed to supporting diversity, equity, and inclusion in our local and global communities. We welcome all people to apply, and we particularly seek applicants who have been underserved by current societal norms including members of the BIPOC community, LGBTQIA+ community, people with disabilities, first-generation college students, and individuals from low-income backgrounds. TRAINING GRANTS Our faculty are core members of current training grants from the NIH, the NSF, and the Department of Education. These grants provide many opportunities for trainee support, including stipend support and funding for conference travel, professional development, and research. Moreover, these grants provide innovative, interdisciplinary training opportunities that support advanced training including training in science communication and teaching excellence. CURRICULUM Our program provides broad training in foundations of language and cognition while simultaneously supporting individualized training goals including expertise in cognitive neuroscience, computational modeling, science communication, and teaching excellence. COMMUNITY Our trainees are part of a vibrant, interdisciplinary community, exploring ideas and practices to creatively expand science and also to creatively change the status quo with regard to diversity, equity, and inclusion. There is a strong emphasis on students taking the lead and a strong spirit of community involvement. CAREER PATHS Our graduates pursue careers in academia and industry, and are successful in securing employment upon graduation including postdoctoral fellowships, faculty positions, and positions at data analysis and cognitive science oriented companies. Further info and applications instructions are available at: langcog.psychology.uconn.edu The Language & Cognition faculty, and their interests, include: Gerry Altmann Sentence processing and prediction; the mapping between language and vision; event cognition. Christian Brodbeck Cognitive neuroscience of language, speech perception, EEG/MEG. Roeland Hancock (Associate Director, Brain Imaging Research Center). Neurochemistry and neuromodulation; Neurobiology of sentence processing; Auditory Processing. Fumiko Hoeft (Director, Brain Imaging Research Center). Brain development; various neuroimaging methods; machine learning; individual differences; literacy acquisition; dyslexia. Jim Magnuson (Director, NSF NRT training program in Science of Learning & Art of Communication). Neurobiology and psychology of language; spoken language understanding; computational modeling; language and learning over the lifespan; science communication. Emily Myers (Co-Director, NIH training program in the Cognitive Neuroscience of Communication). Speech perception; cognitive neuroscience of speech and language; aphasia; second language acquisition. Ken Pugh (President, Haskins Laboratories). Reading; reading disorders; neurobiology of language. Jay Rueckl Neurobiology and psychology of reading; implicit and explicit memory; statistical learning; computational modeling and dynamical systems. Whit Tabor Sentence processing; theory of grammar; dynamical systems; neural networks; language change; group coordination. Rachel Theodore (Director, Neurobiology of Language training program). Speech perception; perceptual learning; phonetic variability; individual differences; cognitive neuroscience; language acquisition. Eiling Yee Semantic memory and the neural representation of concepts; spoken word recognition and situated/embodied language processing. To apply: Contact a potential faculty advisor from the list above, explore UConn and, by Dec 1, 2021, complete the application procedure. Whitney Tabor (860) 486-4910 (office) Department of Psychology (860) 486-2760 (fax) University of Connecticut (860) 486-6080 (lab) Storrs, CT 06269-1020 whitney.tabor at uconn.edu USA BOUS Room 124 (office) https://wp.solab.uconn.edu/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 16 11:42:33 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 16 Oct 2021 17:42:33 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Winter - DeepLearn 2022 Spring - DeepLearn 2022 Summer Message-ID: <338809468.4675580.1634398953603@webmail.strato.com> Dear all, DeepLearn, the International School on Deep Learning, is running since 2017 successfully. Please note the next three editions of the program in 2022: https://irdta.eu/deeplearn/2022wi/ https://irdta.eu/deeplearn/2022sp/ https://irdta.eu/deeplearn/2022su/ Best regards, DeepLearn organizing team -------------- next part -------------- An HTML attachment was scrubbed... URL: From uwe.aickelin at unimelb.edu.au Fri Oct 15 20:03:05 2021 From: uwe.aickelin at unimelb.edu.au (Uwe Aickelin) Date: Sat, 16 Oct 2021 00:03:05 +0000 Subject: Connectionists: Multiple vacancies at the University of Melbourne Message-ID: We have advertised five opportunities to join the top ranked School of Computing and Information Systems in Australia (THES and QS). Please share with your networks and encourage applications.. Full details below. Algorithmic Fairness: https://jobs.unimelb.edu.au/en/job/906606/senior-lecturerassociate-professor-in-algorithms-and-fairness Machine Learning and Systems (two positions): https://jobs.unimelb.edu.au/caw/en/job/906506/lecturer-or-senior-lecturer-in-machine-learningcomputer-systems Digital Ethics: https://jobs.unimelb.edu.au/caw/en/job/906636/senior-lecturerassociate-professor-digital-ethics Information Systems: https://jobs.unimelb.edu.au/caw/en/job/904639/lecturersenior-lecturer-information-systems-female-only Professor Uwe Aickelin | Head of School of Computing and Information Systems Faculty of Engineering and Information Technology Level 3, Melbourne Connect ? 700 Swanston Street The University of Melbourne, Victoria 3010 Australia T: +61 3 8344 3635 E: uwe.aickelin at unimelb.edu.au http://aickelin.com/ | http://linkedin.com/in/aickelin -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephan.chalup at newcastle.edu.au Fri Oct 15 17:32:33 2021 From: stephan.chalup at newcastle.edu.au (Stephan Chalup) Date: Fri, 15 Oct 2021 21:32:33 +0000 Subject: Connectionists: [jobs] PhD scholarship (International, Australia): Machine Learning for Grinding Mill Design Optimisation Message-ID: PhD Scholarship: Machine Learning for Grinding Mill Design Optimisation This project aims to apply machine learning in combination with programmable mechanical design modelling software to automatically optimise grinding mill geometries for desired operational outcomes. The machine learning component will address deep reinforcement learning as an innovative technology in this application domain. The project will be conducted in close collaboration with industry partner Bradken in Newcastle, Australia. The PhD candidate will be part of an interdisciplinary team of researchers from engineering, computer science and industrial researchers. The candidate should have good programming skills, knowledge in machine learning and engineering judgement. The project will require design and training of deep neural networks where existing libraries can be combined with new implementations. One of the core techniques is reinforcement learning. The PhD project is part of ARC LP190100378 https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP190100378 Scholarship details: https://www.newcastle.edu.au/study/research/phd-scholarships/phd-scholarships/machine-learning-for-grinding-mill-design-optimisation Contact: Associate Professor Stephan Chalup Deputy Director, Priority Research Centre for Computer-Assisted Research Mathematics and Its Applications (CARMA) School of Information and Physical Sciences The University of Newcastle Callaghan NSW 2308, Australia https://www.newcastle.edu.au/profile/stephan-chalup -------------- next part -------------- An HTML attachment was scrubbed... URL: From contact at sscc.fr Mon Oct 18 04:23:20 2021 From: contact at sscc.fr (SSCC) Date: Mon, 18 Oct 2021 10:23:20 +0200 Subject: Connectionists: [SSCC] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <06ed01d7c3f9$743e4530$5cbacf90$@sscc.fr> Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience . Smart networks for smart cities . Security management in smart cities . Security in distributed systems . Modeling, analysis and detection of IoT attacks . Data mining for cybersecurity in smart cities . Decentralized architecture for smart cities . Consensus protocols and applications IoT & AI . IoT Indoor deployment . IoT communication protocols . Building information modeling (BIM) IoT-based HVAC control in smart buildings . Artificial Intelligence in Cyber Physical Energy Systems . Optimization for IoT and smart cities . Dynamic scheduling for IoT deployment . Autonomous and Smart decisions Edge and Cloud . Cloud-Edge for IoT and smart cities . Fog and Edge computing for smart cities . Applications/services for Edge AI . Software Platforms for Edge Social aspects and applications . Behavioral and Energy Consumption Analytics . Indoor comfort . Human factors and organizational resilience for distributed systems Important Dates . Paper Submission Date: 31 October, 2021 . Notification to Authors: 15 November, 2021 . Camera Ready Submission: 21 November 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From kostal at biomed.cas.cz Mon Oct 18 04:57:00 2021 From: kostal at biomed.cas.cz (Lubomir Kostal) Date: Mon, 18 Oct 2021 10:57:00 +0200 (CEST) Subject: Connectionists: Postdoc position in computational neuroscience, Prague, Czech Republic Message-ID: The Laboratory of Computational Neuroscience, Institute of Physiology, Academy of Sciences of the Czech Republic, Prague, invites applications for a postdoctoral position to a motivated person to work in the field of theoretical neuroscience. Research focus Theoretical methods are employed to describe and understand particular processes in neural systems on the level of single cells or populations. Especially: - Metabolic cost of neuronal information and coding efficiency - Applications of information theory and estimation theory to computational neuroscience - Signal processing in insect olfactory system - Measures of neuronal spiking variability See: http://www.biomed.cas.cz/~kostal/ for more details. Conditions - Full-time fixed term contract for 12 months, extensions negotiable - Gross salary: 40.000 CZK per month - Position starting January 2022 (or earlier) - No teaching duties will be required - Subsidized accommodation within the institutional campus (serviced apartments, approx. 6.000 CZK/month rental) might be available (depends on the current demand) - Cost of living comparison: https://www.numbeo.com/cost-of-living/in/Prague Requirements - The applicant should hold a PhD, be fluent in English and be highly motivated to work on new topics - Have strong mathematical and programming skills (background in either physics, mathematics, statistics or electrical engineering) - Prior experience in computational neuroscience is advantageous but not required - The applications should be sent to Lubomir Kostal (kostal at biomed.cas.cz) and contain: - CV with research experience, list of publications, scientific and technical skills - Two letters of recommendation - Certificate of the PhD degree Upozorneni: Neni-li v teto zprave vyslovne uvedeno jinak, ma tato E-mailova zprava nebo jeji prilohy pouze informativni charakter. Tato zprava ani jeji prilohy v zadnem ohledu ustavy AV CR, v.v.i. k nicemu nezavazuji. Text teto zpravy nebo jejich priloh neni navrhem na uzavreni smlouvy, ani prijetim pripadneho navrhu na uzavreni smlouvy, ani jinym pravnim jednanim smerujicim k uzavreni jakekoliv smlouvy a nezaklada predsmluvni odpovednost ustavu AV CR, v.v.i. Disclaimer: If not expressly stated otherwise, this e-mail message (including any attached files) is intended purely for informational purposes and does not represent a binding agreement on the part of Institutes of the Czech Academy of Sciences. The text of this message and its attachments cannot be considered as a proposal to conclude a contract, neither the acceptance of a proposal to conclude a contract, nor any other legal act leading to concluding any contract; nor does it create any pre-contractual liability on the part of Institutes of the Czech Academy of Sciences. From leslie.perez at pucv.cl Mon Oct 18 19:22:33 2021 From: leslie.perez at pucv.cl (Leslie Angelica Perez Caceres) Date: Mon, 18 Oct 2021 20:22:33 -0300 Subject: Connectionists: Last Call for Papers: EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation Message-ID: (Apologies for cross-posting) ************************************************************************************* Last Call for Papers: EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation http://www.evostar.org/2022/evocop/ April 20 - 22, 2022 held as part of EvoStar (http://www.evostar.org) Venue: Seville, Spain ** EvoCOP is now CORE Rank B ** Submission deadline: November 1, 2021 ************************************************************************************* The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation is a multidisciplinary conference that brings together researchers working on applications and theory of evolutionary computation methods and other metaheuristics for solving difficult combinatorial optimisation problems appearing in various industrial, economic, and scientific domains. Successfully solved problems include, but are not limited to, multi-objective, uncertain, dynamic and stochastic problems in the context of scheduling, timetabling, network design, transportation and distribution, vehicle routing, stringology, graphs, satisfiability, energy optimisation, cutting, packing, planning and search-based software engineering. The EvoCOP 2021 conference will be held somewhere on Earth, together with EuroGP (the 24th European Conference on Genetic Programming), EvoMUSART (the 10th European conference on evolutionary and biologically inspired music, sound, art and design) and EvoApplications (the 24th European Conference on the Applications of Evolutionary Computation), and a new special track on Evolutionary Machine Learning in a joint event collectively known as EvoStar (Evo*). Accepted papers will be published by Springer Nature in the Lecture Notes in Computer Science series. (See https://link.springer.com/conference/evocop for previous proceedings.) The best regular paper presented at EvoCOP 2022 will be distinguished with a Best Paper Award. EvoCOP conference is now ranked B in the CORE 2021 ranking: http://portal.core.edu.au/conf-ranks/2195/ **** Areas of Interest and Contributions **** EvoCOP welcomes submissions in all experimental and theoretical aspects of evolutionary computation and other metaheuristics to combinatorial optimisation problems, including (but not limited to) the following areas: * Applications of metaheuristics to combinatorial optimisation problems * Theoretical developments * Neighbourhoods and efficient algorithms for searching them * Variation operators for stochastic search methods * Constraint-handling techniques * Parallelisation and grid computing * Search space and landscape analyses * Comparisons between different (also exact) methods * Automatic algorithm configuration and design Prominent examples of metaheuristics include (but are not limited to): * Evolutionary algorithms * Estimation of distribution algorithms * Swarm intelligence methods such as ant colony and particle swarm optimisation * Artificial immune systems * Local search methods such as simulated annealing, tabu search, variable neighbourhood search, iterated local search, scatter search and path relinking * Hybrid methods such as memetic algorithms * Matheuristics (hybrids of exact and heuristic methods) * Hyper-heuristics and autonomous search * Surrogate-model-based methods Notice that, by tradition, continuous/numerical optimisation is *not* part of the topics of interest of EvoCOP. Interested authors might consider submitting to other EvoStar conferences such as EvoApplications. **** Submission Details **** Paper submissions must be original and not published elsewhere. The submissions will be peer reviewed by members of the program committee. The reviewing process will be double-blind, please omit information about the authors in the submitted paper. Submit your manuscript in Springer LNCS format: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 Page limit: 16 pages Submission link: coming soon The authors of accepted papers will have to improve their paper on the basis of the reviewers? comments and will be asked to send a camera-ready version of their manuscripts. At least one author of each accepted work has to register for the conference, attend the conference and present the work. **** Important Dates **** Submission deadline: November 1, 2021 EvoStar: April 20-22, 2022 **** EvoCOP Programme Chairs **** Leslie P?rez C?ceres Pontificia Universidad Cat?lica de Valpara?so, Chile leslie.perez at pucv.cl S?bastien Verel Universit? du Littoral C?te d'Opale (ULCO), France verel at univ-littoral.fr -- Leslie P?rez C?ceres Escuela de Ingenier?a Inform?tica Pontificia Universidad Cat?lica de Valpara?so Directora Diplomado en Inteligencia Artificial http://diplomadoia.inf.ucv.cl Co-chair , EvoCOP 2022, April 20-22 http://www.evostar.org/2022/evocop/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From massimo.srt at gmail.com Mon Oct 18 14:16:34 2021 From: massimo.srt at gmail.com (Massimo Sartori) Date: Mon, 18 Oct 2021 20:16:34 +0200 Subject: Connectionists: [jobs] Postdoc Research Fellow: Measuring Skeletal Muscle Adaptation to Exercise for Rehab. Robotics; University of Twente Message-ID: The Neuro-Mechanical Modeling and Engineering Lab (Department of Biomechanical Engineering, University of Twente) is seeking for one outstanding postdoctoral fellow to work as part of the INTERACT Project, funded by the European Research Council: https://cordis.europa.eu/project/id/803035 This post-doc opening focuses on the experimental study of skeletal muscle adaptation and remodeling in response to load and strain across large time scales, e.g., multiple weeks. The successful candidate will employ multi-scale measuring techniques to investigate changes in muscle activation, fascicle length, pennation angle, volume, and force-generating capacity (e.g., torque and stiffness). This will involve the combined use of imaging (i.e., ultrasound, MRI), dynamometry and electromyography techniques. In you're interested and passionate about investigating how human skeletal muscles change their biological structure in response to injury and robotic training, then please read more about the opening from the link below and apply by November 8th, 2021: https://www.utwente.nl/en/organisation/careers/!/207/ We offer high-reaching positions with a generous allowance as well as extraordinary research facilities and working environment. Your work will be facilitated by in-house expertise and mentorship. You will collaborate with top-scientists on aspects including motor unit electrophysiology, muscle modelling and wearable robotics, giving large opportunities for growth and to perform impactful research! The project and the opening have large potential to make high scientific and industrial impact. Please, contact me if you have any questions. Regards, ---Massimo Sartori, Ph.D.Professor and Chair, Neuromechanical EngineeringDirector, Neuromechanical Modeling & Engineering Lab University of Twente & TechMed CentreFaculty of Engineering TechnologyDepartment of Biomechanical Engineering7500 AE, The Netherlands Personal Website: https://people.utwente.nl/m.sartoriLab Website: https://bit.ly/NMLabLab YouTube Channel: https://bit.ly/NMLTube -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.antonucci at idsia.ch Tue Oct 19 03:58:29 2021 From: alessandro.antonucci at idsia.ch (Antonucci Alessandro) Date: Tue, 19 Oct 2021 07:58:29 +0000 Subject: Connectionists: Ph.D. Position @ Idsia in the area of Multi-Agent Deep Reinforcement Learning Message-ID: Ph.D. Position @Idsia in the area of Multi-Agent Deep Reinforcement Learning Deadline: 15th November 2021 The Dalle Molle Institute for Artificial Intelligence (IDSIA) has an open position for a Ph.D. student. This is a full time (100%) position for a student working in the area of multi-agent deep reinforcement learning. The position, granted by the center of technology of the Swiss Department of Defense (armasuisse S+T), is part of a program for the automatic control of flight simulation environments in multi-agent setups. The successful candidate holds a Master degree in Engineering or another STEM field (obtained or close to be obtained). Experience with Pytorch, proficiency in Python and C++, and interests in aviation and simulators (e.g., JSBSim and FlightGear) are optional but desirable skills. The starting date is February 2022 (or as can be arranged by mutual agreement). For further information, please contact Alessandro Antonucci (alessandro.antonucci at idsia.ch). Useful links: https://www.supsi.ch/home_en/supsi/lavora-con-noi/2021-11-15-bando909.html (Call) https://form-ru.app.supsi.ch/view.php?id=579784 (Application Form) https://www.idsia.ch (IDSIA) https://www.ar.admin.ch/en/armasuisse-wissenschaft-und-technologie-w-t/cyber-defence_campus.html (armasuisse S+T) ------------------- University of Applied Sciences and Arts of Southern Switzerland Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI Alessandro Antonucci Senior Lecturer-Researcher Polo universitario Lugano Via la Santa 1 CH ? 6962 Lugano-Viganello alessandro at idsia.ch idsia.ch/~alessandro +41586666579 alessandro.antonucci (skype) alessandro.antonucci at supsi.ch (teams) From andre.gruening at hochschule-stralsund.de Tue Oct 19 04:55:30 2021 From: andre.gruening at hochschule-stralsund.de (=?UTF-8?B?QW5kcsOpIEdyw7xuaW5n?=) Date: Tue, 19 Oct 2021 10:55:30 +0200 Subject: Connectionists: Professorship in AI at the Stralsund University of Applied Sciences Message-ID: <20211019105530.4abb7da8.andre.gruening@hochschule-stralsund.de> Professorship in AI at the Stralsund University of Applied Sciences In the Faculty of Electrical Engineering and Computer Science we have an open position for a permanent W2 professorship in Artificial Intelligence. Application deadline is 7.11.2021 with planned start date for the new appointee on 1.9.2022. The post involves teaching and (applied) research. Willingness to participate in the self-administration of the institution is also expected. The formal and binding announcement of the opening (in German only) can be found at: or We are in particular looking for a candidate with comprehensive knowledge of and practical experience in at least one of the following areas: - neurocognition - neurorobotics, - deep learning - natural language processing - computer brain interfaces. Note that normally one of the requirements for an appointment is that the candidate has work experience of at least 5 years prior to the appointment, of which at least three shall be outside the (immediate) University sector. For further information, contact the chair person of the appointment committee Prof Andre Gruening, -- Prof. Dr. Andr? Gr?ning -- Mathematik und K?nstliche Intelligenz Fakult?t Elektrotechnik und Informatik Hochschule Stralsund -- Mathematics and Computational Intelligence Faculty of Electrical Engineering and Computer Science University of Applied Sciences Stralsund -- 18435 Stralsund Deutschland/Germany From bei.xiao at gmail.com Tue Oct 19 22:42:51 2021 From: bei.xiao at gmail.com (bei.xiao at gmail.com) Date: Tue, 19 Oct 2021 22:42:51 -0400 Subject: Connectionists: Faculty Position (Open Rank) in Computer Science, American University Message-ID: *Position Announcement: Open Rank* *Department of Computer Science* *American University, Washington, DC* The Department of Computer Science in the College of Arts and Sciences at American University invites applications for one *full-time, open-rank, tenure-line position beginning August 1, 2022.* Members of typically marginalized groups including, but not restricted to Women, African American/Black, Hispanic/Latino, Native American/Alaska Native are welcome and strongly encouraged to apply. Applicants should have a PhD or an anticipated PhD completion by August 2022 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate or full professor level and we welcome applications from both academic and non-academic organizations. The Department of Computer Science is a small but exciting department with a growing student population and strong research achievements. At last count, the student population in the department was comprised of 37.2% female, 6.6% black or African American, 7.2% Hispanic or Latino and 4.7% multiracial students. American University has identified Computer Science as one of its targets for growth. Computer Science also falls within several areas of strategic focus identified by the university president in her strategic plan, including Data Science. Along with the Department of Mathematics and Statistics, the Department of Physics, the Game Lab, and the Entrepreneurship and Innovation Incubator, the Department of Computer Science is located in the new Don Myers Technology & Innovation Building. Computer Science currently offers an undergraduate and a Master?s program with four different tracks (Applied, Data Science, Game, and Cybersecurity). A combined Ph.D. program with the Mathematics and Statistics Department is being developed. Learn more about the College of Arts and Sciences at https://www.american.edu/cas/ and about the Department of Computer Science at https://www.american.edu/cas/cs/. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark and join a friendly, collegial and highly accomplished team. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. *The committee will consider candidates engaged in research in any area of Data Analysis with an emphasis on Natural Language Processing, Graph Analysis, and general research in Deep Learning. *This includes researchers working on Fairness and Bias in Machine Learning and Machine Learning for Social Good. Excellent candidates in other research areas, especially with domains of applications compatible with those outlined in the strategic plan (e.g., Environmental Science and Health Sciences) will also be considered as we welcome researchers who cross traditional disciplinary boundaries. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected. Salary and benefits are competitive. An overview of the benefits offered by American University can be found at https://www.american.edu/hr/benefits/ . Review of applications will begin on November 15. Please submit applications via Interfolio : http://apply.interfolio.com/97293. Please include a letter of application, curriculum vitae, list of three references, recent teaching evaluations (when possible), a diversity statement, and copies of recent published papers or working papers. Please contact Department Chair Nathalie Japkowicz at japkowic at american.edu if you have any questions. American University is a private institution located in the nation?s capital and within easy reach of the many centers of government, business, research, and the arts. For more information about American University, visit www.american.edu. American University is an equal opportunity, affirmative action institution that operates in compliance with applicable laws and regulations. The university does not discriminate on the basis of race, color, national origin, religion, sex (including pregnancy), age, sexual orientation, disability, marital status, personal appearance, gender identity and expression, family responsibilities, political affiliation, source of income, veteran status, an individual?s genetic information or any other bases under federal or local laws (collectively ?Protected Bases?) in its programs and activities. American University is a tobacco and smoke-free campus. -- Bei Xiao, PhD Associate Professor Computer Science & Center for Behavioral Neuroscience American University, Washington DC Homepage: https://sites.google.com/site/beixiao/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From s.heijnen at fsw.leidenuniv.nl Wed Oct 20 04:45:11 2021 From: s.heijnen at fsw.leidenuniv.nl (Heijnen, S. (Saskia)) Date: Wed, 20 Oct 2021 08:45:11 +0000 Subject: Connectionists: Survey invite: considerations in computational modeling Message-ID: <5d4f7f5b11b945a29b7463e62cccf9a8@fsw.leidenuniv.nl> Dear colleague, Are you a cognitive (neuro)scientist using computational models? Check out this survey! For my PhD, I am studying the choices you make in modeling, and how these relate to your scientific goals. Follow https://leidenuniv.eu.qualtrics.com/jfe/form/SV_aY2PmSnku3ZmgDk to contribute. The survey takes about 25 minutes to complete. The survey will be open until the 19th of November. If you have any questions or comments, feel free to get in touch! Kind regards, Saskia Heijnen, MSc, MA | PhD Candidate | Leiden University | Cognitive Psychology Unit & Leiden Institute for Brain and Cognition -------------- next part -------------- An HTML attachment was scrubbed... URL: From eswc2022 at gmail.com Wed Oct 20 05:51:52 2021 From: eswc2022 at gmail.com (ESWC 2022) Date: Wed, 20 Oct 2021 11:51:52 +0200 Subject: Connectionists: Call for Workshop & Tutorial Proposals ESWC 2022 Message-ID: Call for Workshop & Tutorial Proposals ESWC 2022 ESWC is a key academic conference for research results and new developments in the area of the Semantic Web and Knowledge Graphs. ESWC2022 marks the 19th edition, which will take place from 29 May to 2 June 2022, in Heraklion, Greece. ESWC2022 organizes Tutorials and Workshops - Call for Tutorials https://2022.eswc-conferences.org/call-for-tutorials/ - Call for Workshops https://2022.eswc-conferences.org/call-for-workshops/ Deadline: November 24th, 2021 Notification of acceptance: December 8th, 2021 All deadlines are 23:59 anywhere on earth (UTC-12). Workshop & Tutorials Chairs Mehwish Alam, FIZ Karlsruhe ? Leibniz Institute for Information Infrastructure, Karlsruhe, Germany Anastasia Dimou, KU Leuven, Belgium -------------- next part -------------- An HTML attachment was scrubbed... URL: From imprsis at tuebingen.mpg.de Tue Oct 19 09:38:36 2021 From: imprsis at tuebingen.mpg.de (IMPRS IS) Date: Tue, 19 Oct 2021 15:38:36 +0200 Subject: Connectionists: Funded Ph.D. Positions at the International Max Planck Research School for Intelligent Systems Message-ID: The Max Planck Institute for Intelligent Systems and the Universities of Stuttgart and T?bingen collaborate to offer an interdisciplinary Ph.D. program, the International Max Planck Research School for Intelligent Systems (IMPRS-IS). This doctoral program will accept its sixth generation of Ph.D. students in spring of 2022. This school is a key element of Baden-W?rttemberg?s Cyber Valley initiative to accelerate basic research and commercial development in artificial intelligence and robotics. We seek students who want to earn a doctorate while contributing to world-leading research in areas such as: ? Biomedical Technology ? Computational Cognitive Science ? Computer Vision and Graphics ? Control Systems and Optimization ? Data Science ? Haptics and Human-Computer Interaction ? Machine Learning ? Micro- and Nano-Robotics ? Neuroscience ? Perceptual Inference ? Robotics and Human-Robot Interaction ? Soft Robotics and Materials The participating faculty are Aamir Ahmad, Zeynep Akata, Frank Allg?wer, Alexander Badri-Spr?witz, Robert Bamler, Christian F. Baumgartner, Philipp Berens, Matthias Bethge, Michael J. Black, Wieland Brendel, Andr?s Bruhn, Andreas Bulling, Paul-Christian B?rkner, Martin Butz, Caterina De Bacco, Christian Ebenbauer, Benedikt V. Ehinger, Peer Fischer, Andreas Geiger, Martin A. Giese, Moritz Hardt, Daniel H?ufle, Matthias Hein, Philipp Hennig, Ardian Jusufi, Christoph Keplinger, Katherine J. Kuchenbecker, Hendrik Lensch, Falk Lieder, Nicole Ludwig, Jakob Macke, Setareh Maghsudi, Georg Martius, Michael M?hlebach, Gerard Pons-Moll, Michael Pradel, Tian Qiu, C. David Remy, Samira Samadi, Syn Schmitt, Bernhard Sch?lkopf, Gabriele Schweikert, Michael Sedlmair, Fabian Sinz, Metin Sitti, Steffen Staab, Ingo Steinwart, J?rg St?ckler, Justus Thies, Benjamin Unger, Ulrike von Luxburg, Felix Wichmann, Bob Williamson, Thomas Wortmann, and Charley M. Wu. Associated faculty include R. Harald Baayen, Peter Dayan, Alexander Ecker, Jonathan Fiene, Bedartha Goswami, Ksenia Keplinger, Miriam Klopotek, Anna Levina, Jim Mainprice, Kay Nieselt, Peter Ochs, Mijung Park, Nico Pfeifer, Peter Pott, Gunther Richter, Ludovic Righetti, Marc Toussaint, Sebastian Trimpe, Isabel Valera, Maria Wirzberger, and Li Zhaoping. Intelligent systems that can successfully perceive, act, and learn in complex environments hold great potential for aiding society. We seek doctoral students who are curious, creative, and passionate about research to join our school and help advance human knowledge about intelligent systems. ? Admitted Ph.D. students can join our program starting in spring of 2022. ? You will be mentored by our internationally renowned faculty. ? You will register as a university doctoral student and conduct research. ? IMPRS-IS offers a wide variety of scientific seminars, workshops, and social activities. ? All aspects of our program are in English. ? Your doctoral degree will be conferred when you successfully complete your Ph.D. project. ? Our dedicated staff members will assist you throughout your time as a doctoral student. People with a strong academic background and a master?s degree (conferred or expected soon) in Engineering, Computer Science, Cognitive Science, Mathematics, Control Theory, Neuroscience, Materials Science, Physics, or related fields should apply. We seek to increase the number of women in areas where they are underrepresented, so we explicitly encourage women to apply. We are committed to employing more handicapped individuals and especially encourage them to apply. We are an equal opportunity employer and value diversity at our institutions. Admission will be competitive. If selected, you will receive funding via an employment contract, subject to the rules of the Max Planck Society and the two participating universities. You can apply at https://imprs.is.mpg.de/application before 11:59 p.m. (23:59) CET on November 1, 2021. Finalists will be invited to selection interviews that will take place online from January 18 to January 21, 2022. For further information, please visit https://imprs.is.mpg.de From ioannakoroni at csd.auth.gr Wed Oct 20 04:53:24 2021 From: ioannakoroni at csd.auth.gr (Ioanna Koroni) Date: Wed, 20 Oct 2021 11:53:24 +0300 Subject: Connectionists: =?utf-8?q?Live_e-Lecture_by_Petros_Maragos=3A_?= =?utf-8?q?=E2=80=9CIntroduction_to_Tropical_Geometry_and_its_Appli?= =?utf-8?q?cations_to_Machine_Learning=E2=80=9D=2C_26th_October_202?= =?utf-8?q?1_17=3A00-18=3A00_CET=2E_Upcoming_AIDA_AI_excellence_lec?= =?utf-8?q?tures?= References: <0afc01d7c581$6ac97680$405c6380$@csd.auth.gr> <002d01d7c582$5b670ee0$12352ca0$@csd.auth.gr> Message-ID: <0c1101d7c58f$f18995c0$d49cc140$@csd.auth.gr> Dear AI scientist/engineer/student/enthusiast, Lecture by Prof. Petros Maragos (NTUA Laboratory on Intelligent Robotics and Automation, Greece), a prominent AI researcher internationally, will deliver the e-lecture: ?Introduction to Tropical Geometry and its Applications to Machine Learning?, on Tuesday 26th October 2021 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST), see details in: http://www.i-aida.org/event_cat/ai-lectures/ You can join for free using the zoom link: https://authgr.zoom.us/s/99103637681 & Passcode: 148148 The International AI Doctoral Academy (AIDA), a joint initiative of the European R&D projects AI4Media, ELISE, Humane AI Net, TAILOR, VISION, currently in the process of formation, is very pleased to offer you top quality scientific lectures on several current hot AI topics. Lectures are typically held once per week, Tuesdays 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST). Attendance is free. Other upcoming lectures: 1. Prof. Nicol? Cesa-Bianchi (UNIVERSITA` DEGLI STUDI DI MILANO (UNIMI), Italy), 9th November 2021 17:00 ? 18:00 CET. 2. Prof. Cees Snoek (UNIVERSITEIT VAN AMSTERDAM, Netherlands), 23rd November 2021 17:00 ? 18:00 CET. More lecture infos in: https://www.i-aida.org/event_cat/ai-lectures/?type=future The lectures are disseminated through multiple channels and email lists (we apologize if you received it through various channels). If you want to stay informed on future lectures, you can register in the email lists AIDA email list and CVML email list. Best regards Profs. M. Chetouani, P. Flach, B. O?Sullivan, I. Pitas, N. Sebe -- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus -------------- next part -------------- An HTML attachment was scrubbed... URL: From boubchir at ai.univ-paris8.fr Mon Oct 18 11:43:43 2021 From: boubchir at ai.univ-paris8.fr (Larbi Boubchir) Date: Mon, 18 Oct 2021 17:43:43 +0200 Subject: Connectionists: [CfP] [Deadline approaching] International Workshop on Artificial Intelligence & Edge Computing (AIEC 2021) in conjunction with FMEC In-Reply-To: <30292b50-4021-6c33-59d0-1736598fe630@ai.univ-paris8.fr> References: <30292b50-4021-6c33-59d0-1736598fe630@ai.univ-paris8.fr> Message-ID: [Apologies if you got multiple copies of this invitation] ** *International Workshop on Artificial Intelligence & Edge Computing (AIEC 2021)* https://sites.google.com/view/waiec2021/ in conjunction with *The Sixth International Conference on Fog and Mobile Edge Computing (FMEC 2021)* Gandia, Spain. December 6-9, 2021 (virtual) *AIEC 2020 CFP* Artificial Intelligence (AI) became a popular wide area for the latest- generation of software-oriented solutions. Lately, AI has been considered as a hot topic due to its huge applications and it attracted attention from academia as well as industrial and end users while receiving positive media coverage. AI is advancing at considerable speed and lead to many widely beneficial applications, variant from Machine Translation to Medical Image Computing. From R&D perspectives, AI acquires incredible amounts of progress in terms funding investors devoted on AI applications. According to AI efforts, Edge Computing (EC) has become an essential solution to overcome the strangulation of emerging technology development according to its benefits of minimizing data transmission, reducing service latency and easing cloud computing pressure. Also, the scope of EC is very diverse on large application area, such as smart grid and smart city, logistics and transportation, manufacturing and healthcare. Furthermore, the EC provide significant gains which include low-latency thus allowing close cooperation in sophisticated mobile applications and on the core network to reduce traffic volume as data streaming along the way to the distant data center is no longer necessary. The evolution of new technologies of communication such as 5G made communication so much easier where the latency is lower and memory bandwidth is higher, the border between edge infrastructure and mobile devices will be even imprecise and EC will become more attractive. The Workshop on Artificial Intelligence & Edge Computing (AIEC) aims to bring together researchers and practitioners from both academia and industry who are working on Artificial Intelligence and Edge Computing as well as their integration, to exchange research ideas and identify new research challenges in this emerging field. *Topics* The Workshop on Artificial Intelligence & Edge Computing (AIEC) calls for contributions that address fundamental research and solutions issues in Artificial Intelligence and Edge Computing including but not limited to the following : ?Big Data mining at the Edge ?Machine Learning at the edge ?Architectures of Edge AI for IoT ?Security on the Edge ?Resource-friendly Edge AI Model Design ?Resource Management for Edge AI ?Applications/services for Edge AI ?Communication and Networking Protocols for Edge AI ?Software Platforms for Edge *Important Dates * ?*Submission Date: 24^th October, 2021* ?Notification to Authors: *1^st November, 2021* ?Camera Ready Submission: *10^th November, 2021* *Submissions Guidelines and Proceedings * Papers selected for presentation will appear in the FMEC Proceedings, which will be published by the IEEE Computer Society and be submitted to IEEE Xplore for inclusion. Papers must be 6 pages in IEEE format, 10pt font using the IEEE 8.5" x 11" two-column format, single space, A4 format. All papers should be in PDF format, and submitted electronically at Paper Submission Link. A full paper must not exceed the stated length (including all figures, tables and references). Submitted papers must present original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines may be rejected without review. Also submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. Authors may contact the Program Chair for further information or clarification. *Submission System * *https://easychair.org/conferences/?conf=waiec2021 * *Journal Special Issues * Selected papers from the will be invited to submit an extended version to the following journal. Papers will be selected based on their reviewers? scores and appropriateness to the Journal?s theme. All extended versions will undergo reviews and must represent original unpublished research work. Further details will be made available at a later stage. Please send any inquiry on AIEC 2020 to the Emerging Tech. Network Team at: _emergingtechnetwork at gmail.com _ -- _____________________________________________________ Prof. Larbi Boubchir, /SMIEEE/ LIASD - University of Paris 8 2 rue de la Libert?, 93526 Saint-Denis, France Tel. (+33) 1 49 40 67 95 Email. larbi.boubchir at univ-paris8.fr http://www.ai.univ-paris8.fr/~boubchir/ _____________________________________________________ -------------- next part -------------- An HTML attachment was scrubbed... URL: From xiaochun.cheng at gmail.com Wed Oct 20 04:49:21 2021 From: xiaochun.cheng at gmail.com (Xiaochun Cheng) Date: Wed, 20 Oct 2021 09:49:21 +0100 Subject: Connectionists: Special Issue: Secure Smart Solutions for High Performance and Cluster Computing Message-ID: Apologize for cross-posting Special Issue: Secure Smart Solutions for High Performance and Cluster Computing https://www.springer.com/journal/10586/updates/19247360 Submission site: https://www.editorialmanager.com/clus/default.aspx Initial paper submission deadline: October 26, 2021 From evomusart at gmail.com Tue Oct 19 12:10:43 2021 From: evomusart at gmail.com (EvoMUSART) Date: Tue, 19 Oct 2021 18:10:43 +0200 Subject: Connectionists: Last Call for Papers - EvoMUSART 2022 (20-22 April 2022) Message-ID: ------------------------------------------------ Last Call for papers for the 11th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) ? Please distribute ? Apologies for cross-posting ------------------------------------------------ The 11th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) will take place on 20-22 April 2022, as part of the evo* event. EvoMUSART webpage: www.evostar.org/2022/evomusart/ Submission deadline: 1 November 2021 Conference: 20-22 April 2022 EvoMUSART is a multidisciplinary conference that brings together researchers who are working on the application of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Cellular Automata, Alife, and other Artificial Intelligence techniques in creative and artist fields such as Visual Art, Music, Architecture, Video, Digital Games, Poetry, or Design. This conference gives researchers in the field the opportunity to promote, present and discuss ongoing work in the area. Submissions must be at most 16 pages long, in Springer LNCS format. Each submission must be anonymised for a double-blind review process. Accepted papers will be presented orally or as posters at the event and included in the EvoMUSART proceedings published by Springer Nature in a dedicated volume of the Lecture Notes in Computer Science series. In addition, an agreement has been reached with Entropy journal (IF 2.524; JCR Q2; ISSN 1099-4300) whereby it will publish a special issue of EvoMUSART every year. Entropy journal has already published the special issues entitled ?Artificial Intelligence and Complexity in Art, Music, Games and Design? for EvoMUSART 2020 (Volume 1) and 2021 (Volume 2). All papers accepted in EvoMUSART 2022 will be encouraged to submit to a new special issue of Entropy. Indicative topics include but are not limited to: * Systems that create drawings, images, animations, sculptures, poetry, text, designs, webpages, buildings, etc.; * Systems that create musical pieces, sounds, instruments, voices, sound effects, sound analysis, etc.; * Systems that create artefacts such as game content, architecture, furniture, based on aesthetic and/or functional criteria; * Systems that resort to artificial intelligence to perform the analysis of image, music, sound, sculpture, or some other types of artistic objects; * Systems in which artificial intelligence is used to promote the creativity of a human user; * Theories or models of computational aesthetics; * Computational models of emotional response, surprise, novelty; * Representation techniques for images, videos, music, etc.; * Surveys of the current state-of-the-art in the area; * New ways of integrating the user in the process (e.g. improvisation, co-creation, participation). More information on the submission process and the topics of EvoMUSART: www.evostar.org/2022/evomusart/ Flyer of EvoMUSART 2022: http://www.evostar.org/2022/flyers/evomusart Papers published in EvoMUSART: https://evomusart-index.dei.uc.pt We look forward to seeing you in EvoMUSART 2022! The EvoMUSART 2022 organisers Tiago Martins Nereida Rodr?guez-Fernandez S?rgio Rebelo (publication chair) -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.dorrn at fz-juelich.de Wed Oct 20 09:26:36 2021 From: a.dorrn at fz-juelich.de (Anja Dorrn) Date: Wed, 20 Oct 2021 15:26:36 +0200 Subject: Connectionists: Announcing "Bernstein SmartSteps - Featuring Early Career Scientists" Message-ID: The Bernstein SmartSteps series provides a unique platform for selected Early Career Scientists of the Bernstein Network Computational Neuroscience to present their research projects to the scientific community. Each session one PhD candidate and one Postdoc who are ready to take their next (smart) step for their scientific career will discuss their research in an online seminar. Join the Bernstein SmartSteps Series as an audience if you are interested in cutting-edge computational neuroscience and want to watch out for new talents in the field. The live talks will be held in Zoom. A recording will be published in a separate collection on the Bernstein Vimeo Channel. Please register here, if you would like to join for the live talks. A link to the event in Zoom will be sent shortly before via email. Fall series 2021 Thursday, November 4, 4 pm CET * Miriam Henning (Silies lab) | University of Mainz, Germany An optimal population code for global motion estimation in local direction-selective cells * Barna Zajzon (Morrison lab) | Forschungszentrum J?lich, Germany Representation transfer and signal denoising through topographic modularity Thursday, November 25, 4 pm CET * Han Lu (Vlachos lab) | University of Freiburg, Germany Homeostatic structural plasticity of neuronal connectivity triggered by optogenetic stimulation * Golan Karvat (Diester lab) | University of Freiburg, Germany Spontaneous activity competes with externally evoked responses in sensory cortex Thursday, December 9, 4 pm CET * Bin Wang (Aljadeff lab) | University of California San Diego, USA A nonlinear shot noise model for calcium-based synaptic plasticity * Helene Schreyer (Gollisch lab) | University Medical Center G?ttingen, Germany Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli -- Dr. Anja Dorrn Scientific Coordinator Bernstein Network Computational Neuroscience | Bernstein Coordination Site (BCOS) Branch Office of the Forschungszentrum J?lich at the University of Freiburg Hansastr. 9A | 79104 Freiburg, Germany phone: (+49) 0761 203 9589 web: www.bernstein-network.de ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ -------------- next part -------------- An HTML attachment was scrubbed... URL: From bisant at umbc.edu Wed Oct 20 14:51:11 2021 From: bisant at umbc.edu (David B) Date: Wed, 20 Oct 2021 14:51:11 -0400 Subject: Connectionists: FLAIRS-35 May 15-18, 2022, Jensen Beach, Florida Call for Papers In-Reply-To: References: Message-ID: FLAIRS-35 Special Track on Neural Networks and Data Mining The Florida Artificial Intelligence Research Symposium (FLAIRS) is an interdisciplinary conference which features double-blind reviewing, free tutorials, and a warm and sunny venue. Abstract Due Date: January 17, 2022 Submission Due Date: January 24, 2022 Conference: May 15-18, 2022 Jensen Beach, Florida Website: https://sites.google.com/view/flairs-35-nn-dm-track/home URL: https://www.flairs-35.info/call-for-papers Papers are being solicited for a special track on Neural Networks and Data Mining at the 35th International FLAIRS Conference (https://www.flairs-35.info/home). This special track will be devoted to neural networks and data mining with the aim of presenting new and important contributions in these areas. Papers and contributions are encouraged for any work related to neural networks, data mining, or the intersection thereof. Topics of interest may include (but are in no way limited to): applications such as Pattern Recognition, Control and Process Monitoring, Biomedical Applications, Robotics, Text Mining, Diagnostic Problems, Telecommunications, Power Systems, Signal Processing; Intelligence analysis, medical and health applications, text, video, and multi-media mining, E-commerce and web data, financial data analysis, cyber security, remote sensing, earth sciences, bioinformatics, and astronomy; algorithms such as new developments in Back Propagation, RBF, SVM, Deep Learning, Ensemble Methods, Kernel Approaches; hybrid approaches such as Neural Networks/Genetic Algorithms, Neural Network/Expert Systems, Causal Nets trained with Backpropagation, and Neural Network/Fuzzy Logic applications such as Intelligence analysis, medical and health applications, text, video, and multi-media mining, E-commerce and web data, financial data analysis, cyber security, remote sensing, earth sciences, bioinformatics, and astronomy; modeling algorithms such as hidden Markov models, decision trees, neural networks, statistical methods, or probabilistic methods; case studies in areas of application, or over different algorithms and approaches; graph modeling, pattern discovery, and anomaly detection; feature extraction and selection; post-processing techniques such as visualization, summarization, or trending; preprocessing and data reduction; and knowledge engineering or warehousing. Questions regarding the track should be addressed to: David Bisant at bisant at umbc.edu, Steven Gutstein at s.m.gutstein at gmail.com, or Bill Eberle at weberle at tntech.edu. From ieee.besc2021 at gmail.com Wed Oct 20 20:38:32 2021 From: ieee.besc2021 at gmail.com (Md Rafiqul Islam) Date: Thu, 21 Oct 2021 11:38:32 +1100 Subject: Connectionists: Call for Participation - BESC2021 Message-ID: Dear Colleague, You are all invited to attend the 8th International Conference on Behavioral and Social Computing (BESC 2021) that will take place on October 29-31, 2021 (fully virtual). We will have a great program: two tutorials (1- Sentiment Analysis and 2- Assessing digital skills and competencies), long and short papers, and two keynote talks by Dr. Ingmar Weber on Big data usage to monitor sustainable development and Prof. Christian Montag on the strategies to combat Internet Use Disorders. The full program is available below. Full Program: http://besc-conf.org/2021/agenda.html The BESC organizers are happy to announce that we secured funding to waive the registration fees. Registration link: https://www.hbku.edu.qa/en/besc/registration The Conference page: http://besc-conf.org/2021/index.html Best Regards, Wajdi Zaghouani, HBKU, Qatar Xiaohui Tao, University of Southern Queensland, Australia Raian Ali, HBKU, Qatar Guandong Xu, University of Technology Sydney, Australia -------------- next part -------------- An HTML attachment was scrubbed... URL: From rpaudel142 at gmail.com Wed Oct 20 23:04:44 2021 From: rpaudel142 at gmail.com (Ramesh Paudel) Date: Wed, 20 Oct 2021 23:04:44 -0400 Subject: Connectionists: Call for Papers - ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS 2022) Message-ID: [Apologies if you got multiple copies of this invitation] Dear Colleagues, Please consider submitting and/or forwarding to the appropriate groups/personnel the opportunity to submit to the *ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS 2022)*, which will be held in Baltimore-Washington DC area (or virtually) on April 26, 2022 in conjunction with the 12th ACM Conference on Data and Application Security and Privacy (CODASPY 2022). *** Paper submission deadline:* December 30, 2021* *** *** Website: https://sites.google.com/view/sat-cps-2022/ *** SaT-CPS aims to represent a forum for researchers and practitioners from industry and academia interested in various areas of CPS security. SaT-CPS seeks novel submissions describing practical and theoretical solutions for cyber security challenges in CPS. Submissions can be from different application domains in CPS. Example topics of interest are given below, but are not limited to: - Secure CPS architectures - Authentication mechanisms for CPS - Access control for CPS - Key management in CPS - Attack detection for CPS - Threat modeling for CPS - Forensics for CPS - Intrusion and anomaly detection for CPS - Trusted-computing in CPS - Energy-efficient and secure CPS - Availability, recovery, and auditing for CPS - Distributed secure solutions for CPS - Metrics and risk assessment approaches - Privacy and trust - Blockchain for CPS security - Data security and privacy for CPS - Digital twins for CPS - Wireless sensor network security - CPS/IoT malware analysis - CPS/IoT firmware analysis - Economics of security and privacy - Securing CPS in medical devices/systems - Securing CPS in civil engineering systems/devices - Physical layer security for CPS - Security on heterogeneous CPS - Securing CPS in automotive systems - Securing CPS in aerospace systems - Usability security and privacy of CPS - Secure protocol design in CPS - Vulnerability analysis of CPS - Anonymization in CPS - Embedded systems security - Formal security methods in CPS - Industrial control system security - Securing Internet-of-Things - Securing smart agriculture and related domains The workshop is planned for one day,* April 26, 2022*, on the last day of the conference. *Instructions for Paper Authors* All submissions must describe original research, not published nor currently under review for another workshop, conference, or journal. All papers must be submitted electronically via the Easychair system: https://easychair.org/conferences/?conf=acmsatcps2022 *Full-length papers* Papers must be at most 10 pages in length in double-column ACM format (as specified at https://www.acm.org/publications/proceedings-template). Submission implies the willingness of at least one author to attend the workshop and present the paper. Accepted papers will be included in the ACM Digital Library. The presenter must register for the workshop before the deadline for author registration. *Position papers and Work-in-progress papers* We also invite short position papers and work-in-progress papers. Such papers can be of length up to 6 pages in double-column ACM format (as specified at https://www.acm.org/publications/proceedings-template), and must clearly state "Position Paper" or "Work in progress," as the case may be in the title section of the paper. These papers will be reviewed and accepted papers will be published in the conference proceedings. *Important Dates* Due date for full workshop submissions: December 30, 2021 Notification of acceptance to authors: February 10, 2022 Camera-ready of accepted papers: February 20, 2022 Workshop day: April 26, 2022 *------------------------------------------------------* *Ramesh Paudel, Ph.D.* Publicity and Web Co-Chair Research Scientist George Washington University Washington, DC. rpaudel42 at gwu.edu, https://rpaudel42.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From mail at mkaiser.de Wed Oct 20 15:51:50 2021 From: mail at mkaiser.de (Marcus Kaiser) Date: Wed, 20 Oct 2021 20:51:50 +0100 Subject: Connectionists: Two PostDoc positions, computational and experimental, on focused ultrasound neuromodulation Message-ID: Dear all, Two PostDoc positions for the development and experimental validation of computational models of the effects of human focused ultrasound neuromodulation are available in my lab. *About the project* This programme of work, using the NeuroFUS PRO equipment for focused ultrasound neuromodulation, will focus on developing a software framework for modelling the effects of brain stimulation. Our objectives are to establish models of the biological effect of focused ultrasound and to predict effects of stimulation in humans, based on experiments performed as part of this study at Nottingham, and non-human primates, based on data from collaborators. Validated models of stimulation effects will then be a starting point for personalized stimulation approaches. *Available RA positions* As part of this project, the lab of Prof. Marcus Kaiser ( http://www.dynamic-connectome.org/ ) is seeking talented and enthusiastic research assistants with a PhD awarded, or a PhD thesis about to be submitted, in computational biology or related subjects; prior experience in the neurosciences is desirable. Computational RA position: The research fellow will implement simulations of biological effects on non-invasive interventions on targeted brain regions and on indirect effects in the rest of the brain network. In addition, tools for comparing effects of stimulations within a computer model and effects observed in experimental studies will be developed. Experimental RA position: The research fellow will work on focused ultrasound experiments, will run simulations of the physical effects (pressure and thermal changes) of stimulation, and will perform the processing of neuroimaging data. *Research Environment* Nottingham ?the home of MRI? offers an excellent environment for imaging ( https://www.nottingham.ac.uk/research/beacons-of-excellence/precision-imaging/ and https://www.nottingham.ac.uk/research/groups/spmic/index.aspx ), is pioneering the use of neurotechnology for brain disorders ( http://mindtech.org.uk/ ), hosts the Institute of Mental Health ( https://www.institutemh.org.uk ), and has a large group of faculty members including four full professors (Stephen Coombes, Mark van Rossum, Mark Humphries, Marcus Kaiser) in the area of computational/mathematical neuroscience. *How to Apply*Apply before 2 November 2021 at https://jobs.nottingham.ac.uk/Vacancy.aspx?id=35657&forced=2 (experimental RA) https://jobs.nottingham.ac.uk/Vacancy.aspx?id=35654&forced=2 (computational RA) For informal queries, please contact Dr Marcus Kaiser, marcus.kaiser at nottingham.ac.uk Best, Marcus -- *Marcus Kaiser, Ph.D. FRSB* @ConnectomeLab *Professor of Neuroinformatics* *Precision Imaging Beacon, School of Medicine, University of Nottingham* Guanci Visiting Professor Rui Jin Hospital, Shanghai Jiao Tong University, China Book: Changing Connectomes https://mitpress.mit.edu/books/changing-connectomes Lab website: http://www.dynamic-connectome.org/ Neuroinformatics UK: http://www.neuroinformatics.org.uk/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From d.bach at ucl.ac.uk Thu Oct 21 07:04:28 2021 From: d.bach at ucl.ac.uk (Dominik R. Bach) Date: Thu, 21 Oct 2021 13:04:28 +0200 Subject: Connectionists: Post doc in Cognitive-Computational/Theoretical Neuroscience at University College London UK (PI Dominik Bach, ERC-funded) In-Reply-To: <6c7bf4cd-f78e-9e77-e8de-b6d50a636f47@ucl.ac.uk> References: <6c7bf4cd-f78e-9e77-e8de-b6d50a636f47@ucl.ac.uk> Message-ID: *Post doc in Cognitive-Computational Modelling/Theoretical Neuroscience at University College London: discrete and continuous human action control under threat* We are looking for a research fellow in an ERC-funded research project "Action selection under threat - the complex control of human defence" led by Dominik Bach (http://bachlab.org ) at University College London in collaboration with Peter Dayan, Max-Planck-Institute for Biological Cybernetics in T?bingen (https://www.kyb.tuebingen.mpg.de/de/computational-neuroscience ). The position will be based at Max-Planck UCL Centre for Computational Psychiatry (https://www.mps-ucl-centre.mpg.de/en ) and Wellcome Centre for Human Neuroimaging (http://www.fil.ion.ucl.ac.uk/ ). The overarching goal of the project is to understand the cognitive-computational control of human action selection under acute, immediate threat. We investigate this in an immersive virtual reality (VR) environment, in which people can move to avoid a large number of different threats. As part of this project, the candidate will build explicit theoretical and computational models of discrete and continuous action control and action updating under constraints of time pressure and unaffordable costs. These will be tested by the experimentalists in our interdisciplinary team, composed of VR experts, psychologists, and movement scientists. If desired there is a possibility to get involved in the experimental work, including behavioural experiments, motion capture and OPM-MEG. Within the topical focus, the project offers unique freedom to explore and develop novel directions and formalisms at the interface of classical discrete-space decision models and continous action control. We seek applicants with a track record of cognitive-computational modelling and a PhD in computational neuroscience, robotics with focus on action planning, computer science/mathematics/physics with a focus on decision science, or in a related area. We are looking for an individual who is strongly motivated to pursue an academic career and is excited by the opportunities for personal and career development this position can provide. The post is available from now with negotiable starting date. Funding is available for up to 3 years. Starting salary is on UCL grade 7, ranging from ?36,770 to ?44,388 per annum, inclusive of London Allowance, superannuable. More information, a full job description, and access to the UCL online application portal: https://bit.ly/3G6uQAg Please contact d.bach at ucl.ac.uk for any queries about the project or role. Closing date: 16 November 2021 Interviews will be held remotely in December. Apologies for cross-posting. -- ----------------------- Dominik R Bach MBBS PhD Professor for Cognitive-Computational and Clinical Neuroscience Max Planck UCL Centre for Computational Psychiatry and Ageing Research Wellcome Centre for Human Neuroimaging, University College London bachlab.org | @bachlab_cog -------------- next part -------------- An HTML attachment was scrubbed... URL: From lmuller2 at uwo.ca Thu Oct 21 13:07:37 2021 From: lmuller2 at uwo.ca (Lyle Muller) Date: Thu, 21 Oct 2021 17:07:37 +0000 Subject: Connectionists: Western-Fields Seminar Series | Frances Skinner Message-ID: The eighth talk in the 2021 Western-Fields Seminar Series in Networks, Random Graphs, and Neuroscience is next Thursday (28 October) at noon ET. Frances Skinner (Krembil Research Institute) will give a talk titled ?Tackling the cellular diversity in our brains? (abstract below). Dr. Skinner is a leader in computational neuroscience and has made fundamental contributions to the understanding of neural dynamics from single cells to whole networks. This seminar series features monthly virtual talks from a diverse group of researchers across computational neuroscience, physics, and graph theory. We are looking forward to talks from Alexander Lubotzky (Hebrew University of Jerusalem) in November and Jeannette Janssen (Dalhousie University) in December. Registration link: https://zoom.us/meeting/register/tJYuf-GppzkjHt0W5HMDpME2UpUiE7ntO5JS ? How do we address the challenge of bringing about a cellular-based understanding of our brain workings? From a neurological disease perspective, this needs to be addressed as pathologies involve cellular specifics. The ?brain hub? hippocampus, an essential brain structure for learning and memory, generates robust rhythmic activities. However, figuring out how the multitude of cell types in the hippocampus contribute to these states is an immense challenge. In this talk, I will describe the modeling approaches we are taking to obtain a cellular-based understanding of theta (3-12 Hz) rhythms in the hippocampus, an essential element of the ?phase-coding functional unit? in the hippocampus. -- Lyle Muller http://mullerlab.ca -------------- next part -------------- An HTML attachment was scrubbed... URL: From maanakg at gmail.com Thu Oct 21 13:18:40 2021 From: maanakg at gmail.com (Maanak Gupta) Date: Thu, 21 Oct 2021 12:18:40 -0500 Subject: Connectionists: Second Call: 27th ACM Symposium on Access Control Models and Technologies Message-ID: ?ACM SACMAT 2022 New York City, New York ----------------------------------------------- | Hybrid Conference (Online + In-person) | ----------------------------------------------- Call for Research Papers ============================================================== Papers offering novel research contributions are solicited for submission. Accepted papers will be presented at the symposium and published by the ACM in the symposium proceedings. In addition to the regular research track, this year SACMAT will again host the special track -- "Blue Sky/Vision Track". Researchers are invited to submit papers describing promising new ideas and challenges of interest to the community as well as access control needs emerging from other fields. We are particularly looking for potentially disruptive and new ideas which can shape the research agenda for the next 10 years. We also encourage submissions to the "Work-in-progress Track" to present ideas that may have not been completely developed and experimentally evaluated. Topics of Interest ============================================================== Submissions to the regular track covering any relevant area of access control are welcomed. Areas include, but are not limited to, the following: * Systems: * Operating systems * Cloud systems and their security * Distributed systems * Fog and Edge-computing systems * Cyber-physical and Embedded systems * Mobile systems * Autonomous systems (e.g., UAV security, autonomous vehicles, etc) * IoT systems (e.g., home-automation systems) * WWW * Design for resiliency * Designing systems with zero-trust architecture * Network: * Network systems (e.g., Software-defined network, Network function virtualization) * Corporate and Military-grade Networks * Wireless and Cellular Networks * Opportunistic Network (e.g., delay-tolerant network, P2P) * Overlay Network * Satellite Network * Privacy and Privacy-enhancing Technologies: * Mixers and Mixnets * Anonymous protocols (e.g., Tor) * Online social networks (OSN) * Anonymous communication and censorship resistance * Access control and identity management with privacy * Cryptographic tools for privacy * Data protection technologies * Attacks on Privacy and their defenses * Authentication: * Password-based Authentication * Biometric-based Authentication * Location-based Authentication * Identity management * Usable authentication * Mechanisms: * Blockchain Technologies * AI/ML Technologies * Cryptographic Technologies * Programming-language based Technologies * Hardware-security Technologies (e.g., Intel SGX, ARM TrustZone) * Economic models and game theory * Trust Management * Usable mechanisms * Data Security: * Big data * Databases and data management * Data leakage prevention * Data protection on untrusted infrastructure * Policies and Models: * Novel policy language design * New Access Control Models * Extension of policy languages * Extension of Models * Analysis of policy languages * Analysis of Models * Policy engineering and policy mining * Verification of policy languages * Efficient enforcement of policies * Usable access control policy New in ACM SACMAT 2022 ============================================================== We are moving ACM SACMAT 2022 to have two submission cycles. Authors submitting papers in the first submission cycle will have the opportunity to receive a major revision verdict in addition to the usual accept and reject verdicts. Authors can decide to prepare a revised version of the paper and submit it to the second submission cycle for consideration. Major revision papers will be reviewed by the program committee members based on the criteria set forward by them in the first submission cycle. Regular Track Paper Submission and Format ============================================================== Papers must be written in?English. Authors are required to use the ACM format for papers, using the two-column SIG Proceedings Template (the sigconf template for LaTex) available in the following link: https://www.acm.org/publications/authors/submissions The length of the paper in the proceedings format must not exceed?twelve?US letter pages formatted for 8.5" x 11" paper and be no more than 5MB in size. It is the responsibility of the authors to ensure that their submissions will print easily on simple default configurations. The submission must be anonymous, so information that might identify the authors - including author names, affiliations, acknowledgments, or obvious self-citations - must be excluded. It is the authors' responsibility to ensure that their anonymity is preserved when citing their work. Submissions should be made to the EasyChair conference management system by the paper submission deadline of: November 15th, 2021 (Submission Cycle 1) February 18th, 2022 (Submission Cycle 2) All submissions must contain a?significant original contribution. That is, submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal, conference, or workshop. In particular, simultaneous submission of the same work is not allowed. Wherever appropriate, relevant related work, including that of the authors, must be cited. Submissions that are not accepted as full papers may be invited to appear as short papers. At least one author from each accepted paper must register for the conference before the camera-ready deadline. Blue Sky Track Paper Submission and Format ============================================================== All submissions to this track should be in the same format as for the regular track, but the length must not exceed ten US letter pages, and the submissions are not required to be anonymized (optional). Submissions to this track should be submitted to the EasyChair conference management system by the same deadline as for the regular track. Work-in-progress Track Paper Submission and Format ============================================================== Authors are invited to submit papers in the newly introduced work-in-progress track. This track is introduced for (junior) authors, ideally, Ph.D. and Master's students, to obtain early, constructive feedback on their work. Submissions in this track should follow the same format as for the regular track papers while limiting the total number of pages to six US letter pages. Paper submitted in this track should be anonymized and can be submitted to the EasyChair conference management system by the same deadline as for the regular track. Call for Lightning Talk ============================================================== Participants are invited to submit proposals for 5-minute lightning talks describing recently published results, work in progress, wild ideas, etc. Lightning talks are a new feature of SACMAT, introduced this year to partially replace the informal sharing of ideas at in-person meetings. Submissions are expected??by May 27, 2022. Notification of acceptance will be on June 3, 2022. Call for Posters ============================================================== SACMAT 2022 will include a poster session to promote discussion of ongoing projects among researchers in the field of access control and computer security. Posters can cover preliminary or exploratory work with interesting ideas, or research projects in the early stages with promising results in all aspects of access control and computer security. Authors interested in displaying a poster must submit a poster abstract in the same format as for the regular track, but the length must not exceed three US letter pages, and the submission should not be anonymized. The title should start with "Poster:". Accepted poster abstracts will be included in the conference proceedings. Submissions should be emailed to the poster chair by Apr 15th, 2022. The subject line should include "SACMAT 2022 Poster:" followed by the poster title. Call for Demos ============================================================== A demonstration proposal should clearly describe (1) the overall architecture of the system or technology to be demonstrated, and (2) one or more demonstration scenarios that describe how the audience, interacting with the demonstration system or the demonstrator, will gain an understanding of the underlying technology. Submissions will be evaluated based on the motivation of the work behind the use of the system or technology to be demonstrated and its novelty. The subject line should include "SACMAT 2022 Demo:" followed by the demo title. Demonstration proposals should be in the same format as for the regular track, but the length must not exceed four US letter pages, and the submission should not be anonymized. A two-page description of the demonstration will be included in the conference proceedings. Submissions should be emailed to the Demonstrations Chair by Apr 15th, 2022. Financial Conflict of Interest (COI) Disclosure: ============================================================== In the interests of transparency and to help readers form their own judgments of potential bias, ACM SACMAT requires authors and PC members to declare any competing financial and/or non-financial interests in relation to the work described. Definition ------------------------- For the purposes of this policy, competing interests are defined as financial and non-financial interests that could directly undermine, or be perceived to undermine the objectivity, integrity, and value of a publication, through a potential influence on the judgments and actions of authors with regard to objective data presentation, analysis, and interpretation. Financial competing interests include any of the following: Funding: Research support (including salaries, equipment, supplies, and other expenses) by organizations that may gain or lose financially through this publication. A specific role for the funding provider in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript, should be disclosed. Employment: Recent (while engaged in the research project), present or anticipated employment by any organization that may gain or lose financially through this publication. Personal financial interests: Ownership or contractual interest in stocks or shares of companies that may gain or lose financially through publication; consultation fees or other forms of remuneration (including reimbursements for attending symposia) from organizations that may gain or lose financially; patents or patent applications (awarded or pending) filed by the authors or their institutions whose value may be affected by publication. For patents and patent applications, disclosure of the following information is requested: patent applicant (whether author or institution), name of the inventor(s), application number, the status of the application, specific aspect of manuscript covered in the patent application. It is difficult to specify a threshold at which a financial interest becomes significant, but note that many US universities require faculty members to disclose interests exceeding $10,000 or 5% equity in a company. Any such figure is necessarily arbitrary, so we offer as one possible practical alternative guideline: "Any undeclared competing financial interests that could embarrass you were they to become publicly known after your work was published." We do not consider diversified mutual funds or investment trusts to constitute a competing financial interest. Also, for employees in non-executive or leadership positions, we do not consider financial interest related to stocks or shares in their company to constitute a competing financial interest, as long as they are publishing under their company affiliation. Non-financial competing interests: Non-financial competing interests can take different forms, including personal or professional relations with organizations and individuals. We would encourage authors and PC members to declare any unpaid roles or relationships that might have a bearing on the publication process. Examples of non-financial competing interests include (but are not limited to): * Unpaid membership in a government or non-governmental organization * Unpaid membership in an advocacy or lobbying organization * Unpaid advisory position in a commercial organization * Writing or consulting for an educational company * Acting as an expert witness Conference Code of Conduct and Etiquette ============================================================== ACM SACMAT will follow the ACM Policy Against Harassment at ACM Activities. Please familiarize yourself with the ACM Policy Against Harassment (available at https://www.acm.org/special-interest-groups/volunteer-resources/officers-manual/ policy-against-discrimination-and-harassment) and guide to Reporting Unacceptable Behavior (available at https://www.acm.org/about-acm/reporting-unacceptable-behavior). AUTHORS TAKE NOTE ============================================================== The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks before the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.) Important dates ============================================================== **Note that, these dates are currently only tentative and subject to change.** * Paper submission: November 15th, 2021 (Submission Cycle 1) February 18th, 2022 (Submission Cycle 2) * Rebuttal: December 16th - December 20th, 2021 (Submission Cycle 1) March 24th - March 28th, 2022 (Submission Cycle 2) * Notifications: January 14th, 2022 (Submission Cycle 1) April 8th, 2022 (Submission Cycle 2) * Systems demo and Poster submissions: April 15th, 2022 * Systems demo and Poster notifications: April 22nd, 2022 * Panel Proposal: March 18th, 2022 * Camera-ready paper submission: April 29th, 2022 * Conference date: June 8 - June 10, 2022 -------------- next part -------------- An HTML attachment was scrubbed... URL: From maanakg at gmail.com Thu Oct 21 12:06:00 2021 From: maanakg at gmail.com (Maanak Gupta) Date: Thu, 21 Oct 2021 11:06:00 -0500 Subject: Connectionists: IEEE Workshop on Smart Farming, Precision Agriculture, and Supply Chain (SmartFarm 2021) Message-ID: Hi All, As a member of the Organization Committee, I invite you to submit your research work in the Workshop on Smart Farming, Precision Agriculture, and Supply Chain (SmartFarm 2021, https://sites.google.com/view/smartfarmworkshop/home). This workshop will be held in conjunction with the 2021 IEEE International Conference on Big Data (IEEE Big Data 2021, http://bigdataieee.org/BigData2021/), Atlanta, GA, USA, Dec 15-18, 2021. The important dates of the workshop are as follows: - Due date for the full workshop paper submission: Oct 25, 2021 - Notification on paper acceptance to authors: Nov 3, 2021 - Camera-ready of accepted papers: Nov 15, 2021 Research topics of the workshop include, but are not limited to - Big data and data-driven applications - AI supported smart solutions - ML enabled predictive and analytic solutions - Intelligence applications and crop monitoring - Cooperative farming solutions - Ontology and knowledge representation for cybersecurity - Information & knowledge mining from big datasets - Security and privacy in smart farming and related domains - Networking and infrastructure solutions in smart farms - Aerial drones, autonomous vehicles, and collaborative processing of remote and proximity sensor data in farms - Big data supported anomaly detection and security solutions - Cloud and edge assisted applications - Supply chain solutions, security and applications - Data sharing and privacy - Compliance and data ownership - Cost efficient wireless sensor nodes, network architecture and implementation - New environment sensor technologies - Energy harvesting techniques for battery-less IoT node Paper Formatting Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see the link to "formatting instructions"( http://bigdataieee.org/BigData2020/CallPapers.html)). Submission Link https://wi-lab.com/cyberchair/2021/bigdata21/scripts/submit.php?subarea=S21&undisplay_detail=1&wh=/cyberchair/2021/bigdata21/scripts/ws_submit.php Please help to circulate this announcement among your networks or any interested parties. We look forward to receiving your valuable research work. Thanks for your time and consideration. -------------- next part -------------- An HTML attachment was scrubbed... URL: From cgf at isep.ipp.pt Thu Oct 21 12:24:14 2021 From: cgf at isep.ipp.pt (Carlos) Date: Thu, 21 Oct 2021 17:24:14 +0100 Subject: Connectionists: CFP: International School and Conference on Network Science (NetSci-X 2022) Message-ID: <5cc8f0ce-94be-f157-2880-b20bc34fc114@isep.ipp.pt> ================================== International School and Conference on Network Science NetSci-X 2022 Porto, Portugal February 8-11, 2022 https://netscix.dcc.fc.up.pt/ ================================== Important Dates ----------------------------- Full Paper/Abstract Submission: November 26, 2021 (23:59:59 AoE) Author Notification: December 20, 2021 Keynote Speakers ----------------------------- Jure Leskovec, Stanford University, USA Jurgen Kurths, Humboldt University Berlin, Germany Manuela Veloso, JP Morgan AI Research & CMU, USA Stefano Boccaletti, Institute for Complex Systems, Florence, Italy Tijana Milenkovic, University of Notre Dame, USA Tiziana Di Matteo, King's College London, UK Tracks ----------------------------- We are now welcoming submissions to the Abstracts or Proceedings Track. All submissions will undergo a peer-review process. Abstracts Track: extended abstracts should not exceed 3 pages, including figures and references. Abstracts will be accepted for oral or poster presentation, and will appear in the book of abstracts only. Proceedings Track: full papers should have between 8 and 14 pages and follow the Springer Proceedings format. Accepted full papers will be presented at the conference and published by Springer. Only previously unpublished, original submissions will be accepted. Description ----------------------------- NetSci-X is the Network Science Society?s signature winter conference. It extends the popular NetSci conference series (https://netscisociety.net/events/netsci) to provide an additional forum for a growing community of academics and practitioners working on formal, computational, and application aspects of complex networks. The conference will be highly interdisciplinary, spanning the boundaries of traditional disciplines. Specific topics of interest include (but are not limited to): Models of Complex Networks Structural Network Properties Algorithms for Network Analysis Graph Mining Large-Scale Graph Analytics Epidemics Resilience and Robustness Community Structure Motifs and Subgraph Patterns Link Prediction Multilayer/Multiplex Networks Temporal and Spatial Networks Dynamics on and of Complex Networks Network Controllability Synchronization in Networks Percolation, Resilience, Phase Transitions Network Geometry Network Neuroscience Network Medicine Bioinformatics and Earth Sciences Applications Mobility and Urban Networks Computational Social Sciences Rumor and Viral Marketing Economics and Financial Networks Instructions for Submissions ----------------------- All papers and abstracts should be submitted electronically in PDF format. The website includes detailed information about the submission process. General Chairs Fernando Silva, University of Porto, Portugal Jos? Mendes, University of Aveiro, Portugal Ros?rio Laureano, Lisbon University Institute (ISCTE), Portugal Program Chair Pedro Ribeiro, Universidade do Porto Main Contact for NetSci-X 2022 netscix at dcc.fc.up.pt Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. Ant?nio Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From S.Qiu-1 at tudelft.nl Thu Oct 21 13:58:40 2021 From: S.Qiu-1 at tudelft.nl (Sihang Qiu - EWI) Date: Thu, 21 Oct 2021 17:58:40 +0000 Subject: Connectionists: Call for Participation: BHCC 2021 - 3rd symposium on Biases in Human Computation and Crowdsourcing Message-ID: <00AA3497-E6FF-493B-9F63-27148F344EE8@tudelft.nl> BHCC 2021 - Third symposium on Biases in Human Computation and Crowdsourcing, 10 - 12 November 2021, Delft, Netherlands (Online) *Registration* Registration is now live for BHCC 2021: https://www.bhcc-symposium.com/registration Registration is free and we welcome everyone to participate. Register before 25th October 2021 and be eligible to receive a cool BHCC 2021 welcome package. Only available for the first 50 registrations! *Program* An overview of BHCC 2021 Program is available here: https://www.bhcc-symposium.com/program On this page, you can find the details about BHCC Social Events, Interdisciplinary Panel on "Open Science & Good Research Practice", and the full schedule. *Invited Speakers* https://www.bhcc-symposium.com/invited-speakers - Ming Yin, Purdue University - Saiph Savage, Northeastern University - Alexandra Olteanu, Microsoft *Accepted Papers and Abstracts* View the list of accepted papers and abstracts: https://www.bhcc-symposium.com/program/accepted-papers-and-abstracts *Related Events* Please check out the workshop at ACM CSCW 2021: Investigating and Mitigating Biases in Crowdsourced Data on 23 October 2021! https://sites.google.com/view/biases-in-crowdsourced-data -------------- next part -------------- An HTML attachment was scrubbed... URL: From ajyu at ucsd.edu Fri Oct 22 17:06:00 2021 From: ajyu at ucsd.edu (Angela Yu) Date: Fri, 22 Oct 2021 14:06:00 -0700 Subject: Connectionists: Two postdoc positions with Dr. Angela Yu Message-ID: <4ED26AD0-F04E-4BD8-A115-711BFC329D53@ucsd.edu> Two postdoctoral positions are immediately available in Dr. Yu's Computational & Cognitive Neuroscience Lab at University of California San Diego. Initial appointment is for one year, renewable for up to 2-3 years. One position is primarily in computational modeling of human learning and decision making. A second position is primarily at the intersection of AI/ML and natural intelligence, with the goal of extracting representational and computational principles that support intelligent behavior. Other possible topics include computational modeling of perception, face processing, active learning/sensing, economic decision making, and social cognition. Dr. Yu?s lab applies modern machine learning and statistical tools to extract information processing principles that enable intelligent behavior, in particular how humans and other intelligent systems perform inference, learning, decision-making, and social interactions under conditions of uncertainty and non-stationarity. Applicants should be committed to applying rigorous mathematical tools to model cognitive functions and/or their neural underpinnings. Experience or interest in carrying out human behavioral experiments (either in person or on Amazon M-Turk) and/or collaborating with other neuroimaging/neurophysiology laboratories is desirable. Dr. Yu's lab is situated within the Cognitive Science department, and also affiliated with the Hal?c?o?lu Data Science Institute, the Computer Science Department, the Neurosciences Graduate Program, and the Institute of Neural Computation. There are ample opportunities for collaborations with related groups across the UCSD main campus, the medical school, and the Salk Institute. Interested candidates should send a research statement, along with a CV including publications, to Dr. Angela Yu (ajyu at ucsd.edu ) with the subject ?Postdoc Application?. The research statement should indicate which postdoc position the candidate is applying for, as well as how the candidate fits into Dr. Yu?s group. Two or more letters of references should be sent directly by the recommenders to ajyu at ucsd.edu . More information about Dr. Yu?s group can be found at https://www.cogsci.ucsd.edu/~ajyu . -------------------------------------------- Angela Yu Associate Professor Dept. of Cognitive Science & Hal?c?o?lu Data Science Institute UC San Diego 858-822-3317 http://www.cogsci.ucsd.edu/~ajyu --------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From kordjams at msu.edu Fri Oct 22 06:59:58 2021 From: kordjams at msu.edu (Kordjamshidi, Parisa) Date: Fri, 22 Oct 2021 10:59:58 +0000 Subject: Connectionists: Call for Participation EMNLP 2021 Message-ID: <79412BFD-EC75-49C2-B26D-2BAE3B03FB0C@msu.edu> ============================ Call for Participation at EMNLP 2021 Online and in-person 7th-11th November 2021 https://2021.emnlp.org/ ============================ Late Registration: Ends for in-person: Sunday October 27 2021 Late Registration: Will remain open through the end of the conference for virtual attendees Exciting Keynotes: Ido Dagan Where next? Towards multi-text consumption via three inspired research lines Evelina Fedorenko The language system in the human brain Steven Bird LT4All!? Rethinking the Agenda 6 great tutorials: https://2021.emnlp.org/tutorials And much more! See the full program at: https://2021.emnlp.org/program ============================ ------------------------------------------------ Kordjamshidi, Parisa Assistant Professor Computer Science and Engineering Michigan State University http://www.cse.msu.edu/~kordjams/ Heterogeneous Learning & Reasoning Lab: https://hlr.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From dwang at cse.ohio-state.edu Fri Oct 22 11:48:37 2021 From: dwang at cse.ohio-state.edu (Wang, Deliang) Date: Fri, 22 Oct 2021 15:48:37 +0000 Subject: Connectionists: NEURAL NETWORKS, Nov. 2021 Message-ID: Neural Networks - Volume 143, November 2021 https://www.journals.elsevier.com/neural-networks Convolutional fusion network for monaural speech enhancement Yang Xian, Yang Sun, Wenwu Wang, Syed Mohsen Naqvi The asymmetric learning rates of murine exploratory behavior in sparse reward environments Hiroyuki Ohta, Kuniaki Satori, Yu Takarada, Masashi Arake, ... Tatsuji Takahashi A distributed optimisation framework combining natural gradient with Hessian-free for discriminative sequence training Adnan Haider, Chao Zhang, Florian L. Kreyssig, Philip C. Woodland Bidirectional interaction between visual and motor generative models using Predictive Coding and Active Inference Louis Annabi, Alexandre Pitti, Mathias Quoy Visual-guided attentive attributes embedding for zero-shot learning Rui Zhang, Qi Zhu, Xiangyu Xu, Daoqiang Zhang, Sheng-Jun Huang Reliable impulsive synchronization for fuzzy neural networks with mixed controllers Fen Liu, Chang Liu, Hongxia Rao, Yong Xu, Tingwen Huang Finite time convergence of pinning synchronization with a single nonlinear controller Tianping Chen, Wenlian Lu, Xiwei Liu Internal manipulation of perceptual representations in human flexible cognition: A computational model Giovanni Granato, Gianluca Baldassarre A computational model of familiarity detection for natural pictures, abstract images, and random patterns: Combination of deep learning and anti-Hebbian training Yakov Kazanovich, Roman Borisyuk Periodic clustering of simple and complex cells in visual cortex Gwangsu Kim, Jaeson Jang, Se-Bum Paik A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation Chih-Hsu Huang, Chou-Ching K. Lin How to handle noisy labels for robust learning from uncertainty Daehyun Ji, Dokwan Oh, Yoonsuk Hyun, Oh-Min Kwon, Myeong-Jin Park Exponential passivity of discrete-time switched neural networks with transmission delays via an event-triggered sliding mode control Jinling Wang, Haijun Jiang, Cheng Hu, Tianlong Ma On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels B. Orkan Olcay, Murat Ozgoren, Bilge Karacali Augmented semantic feature based generative network for generalized zero-shot learning Zhiqun Li, Qiong Chen, Qingfa Liu Learning to recognize while learning to speak: Self-supervision and developing a speaking motor Xiang Wu, Juyang Weng Adversarial orthogonal regression: Two non-linear regressions for causal inference M. Reza Heydari, Saber Salehkaleybar, Kun Zhang A brain-inspired computational model for spatio-temporal information processing Xiaohan Lin, Xiaolong Zou, Zilong Ji, Tiejun Huang, ... Yuanyuan Mi A semi-supervised zero-shot image classification method based on soft-target Zhong Ji, Qiang Wang, Biying Cui, Yanwei Pang, ... Xuelong Li Locality preserving dense graph convolutional networks with graph context-aware node representations Wenfeng Liu, Maoguo Gong, Zedong Tang, A.K. Qin, ... Mingliang Xu Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application Ding Wang, Mingming Zhao, Mingming Ha, Jin Ren Correlating subword articulation with lip shapes for embedding aware audio-visual speech enhancement Hang Chen, Jun Du, Yu Hu, Li-Rong Dai, ... Chin-Hui Lee HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning Ting Ma, Shangwen Lv, Longtao Huang, Songlin Hu Variational policy search using sparse Gaussian process priors for learning multimodal optimal actions Hikaru Sasaki, Takamitsu Matsubara Label propagation via local geometry preserving for deep semi-supervised image recognition Yuanyuan Qing, Yijie Zeng, Guang-Bin Huang Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation Jingjing Tang, Jiahui Li, Weiqi Xu, Yingjie Tian, ... Jie Zhang Graph routing between capsules Yang Li, Wei Zhao, Erik Cambria, Suhang Wang, Steffen Eger IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction Jiyao An, Liang Guo, Wei Liu, Zhiqiang Fu, ... Tao Li A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme Huaqing Zhang, Yi-Fei Pu, Xuetao Xie, Bingran Zhang, ... Tingwen Huang Enhanced image prior for unsupervised remoting sensing super-resolution Jiaming Wang, Zhenfeng Shao, Xiao Huang, Tao Lu, ... Jiayi Ma When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning Harikrishnan N.B., Nithin Nagaraj Sensitivity - Local index to control chaoticity or gradient globally - Katsunari Shibata, Takuya Ejima, Yuki Tokumaru, Toshitaka Matsuki Sparse random feature maps for the item-multiset kernel Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models Hiroto Tamura, Gouhei Tanaka Levenberg-Marquardt multi-classification using hinge loss function Buse Melis Ozyildirim, Mariam Kiran Multi-view spectral clustering via common structure maximization of local and global representations Wenyu Hao, Shanmin Pang, Zhikai Chen Continual learning for recurrent neural networks: An empirical evaluation Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu Content-aware convolutional neural networks Yong Guo, Yaofo Chen, Mingkui Tan, Kui Jia, ... Jingdong Wang CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan Capsule networks with non-iterative cluster routing Zhihao Zhao, Samuel Cheng Quantum neuron with real weights Claudio A. Monteiro, Gustavo I.S. Filho, Matheus Hopper J. Costa, Fernando M. de Paula Neto, Wilson R. de Oliveira Hebbian semi-supervised learning in a sample efficiency setting Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato Active sensing with artificial neural networks Oleg Solopchuk, Alexandre Zenon Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks Oleg Nikitin, Olga Lukyanova, Alex Kunin A theory of capacity and sparse neural encoding Pierre Baldi, Roman Vershynin Experimental stability analysis of neural networks in classification problems with confidence sets for persistence diagrams Naoki Akai, Takatsugu Hirayama, Hiroshi Murase A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems Xingnan Wen, Linhua Luan, Sitian Qin A hybrid quantum-classical neural network with deep residual learning Yanying Liang, Wei Peng, Zhu-Jun Zheng, Olli Silven, Guoying Zhao Synchronization of recurrent neural networks with unbounded delays and time-varying coefficients via generalized differential inequalities Hao Zhang, Zhigang Zeng Fast mesh data augmentation via Chebyshev polynomial of spectral filtering Shih-Gu Huang, Moo K. Chung, Anqi Qiu Periodicity and multi-periodicity generated by impulses control in delayed Cohen-Grossberg-type neural networks with discontinuous activations Zuowei Cai, Lihong Huang, Zengyun Wang, Xianmin Pan, Shukun Liu Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems Yongchao Liu, Qidan Zhu On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model Francesca Elisa Leonelli, Elena Agliari, Linda Albanese, Adriano Barra Distributed learning for sketched kernel regression Heng Lian, Jiamin Liu, Zengyan Fan Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks P. Selvaraj, O.M. Kwon, S.H. Lee, R. Sakthivel Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control Xiaoxiao Lv, Jinde Cao, Leszek Rutkowski Smoothing neural network for regularized optimization problem with general convex constraints Wenjing Li, Wei Bian On the approximation of functions by tanh neural networks Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra Learning hierarchically-structured concepts Nancy Lynch, Frederik Mallmann-Trenn Effective and direct control of neural TTS prosody by removing interactions between different attributes Xiaochun An, Frank K. Soong, Shan Yang, Lei Xie Neural adaptive fault-tolerant finite-time control for nonstrict feedback systems: An event-triggered mechanism K. Sun, J. Qiu, H.R. Karimi Recurrent neural network pruning using dynamical systems and iterative fine-tuning Christos Chatzikonstantinou, Dimitrios Konstantinidis, Kosmas Dimitropoulos, Petros Daras Low-shot transfer with attention for highly imbalanced cursive character recognition Amin Jalali, Swathi Kavuri, Minho Lee Online sensorimotor learning and adaptation for inverse dynamics control Xiaofeng Xiong, Poramate Manoonpong Refined UNet v3: Efficient end-to-end patch-wise network for cloud and shadow segmentation with multi-channel spectral features Libin Jiao, Lianzhi Huo, Changmiao Hu, Ping Tang A full-parallel implementation of Self-Organizing Maps on hardware Leonardo A. Dias, Augusto M.P. Damasceno, Elena Gaura, Marcelo A.C. Fernandes -------------- next part -------------- An HTML attachment was scrubbed... URL: From cgf at isep.ipp.pt Fri Oct 22 12:40:27 2021 From: cgf at isep.ipp.pt (Carlos) Date: Fri, 22 Oct 2021 17:40:27 +0100 Subject: Connectionists: CFP: DATA STREAMS TRACK - ACM SAC 2022 (Extended deadline: October 24, 2021) Message-ID: <020a27c5-3d25-f019-6800-43f27fa01503@isep.ipp.pt> *ACM Symposium on Applied Computing * The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic April 25 ? April 29, 2022 https://www.sigapp.org/sac/sac2022/ *Data Streams Track * https://abifet.github.io/SAC2022/ *Call for Papers * The rapid development in Big Data information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. Many sources produce data continuously. Examples include the Internet of Things (IoT), Smart Cities, Urban Computing, sensor networks, wireless networks, radio frequency identification, health-care devices and information systems, customer click streams, telephone records, multimedia data, scientific data, sets of retail chain transactions, etc. These sources are called data streams. A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, flowing at high-speed, and generated by non stationary distributions. *TOPICS OF INTEREST * We are looking for original, unpublished work related to algorithms, methods and applications on big data streams and large scale machine learning. Topics include (but are not restricted) to: * Real-Time Analytics * Big Data Mining * Data Stream Models * Large Scale Machine Learning * Languages for Stream Query * Continuous Queries * Clustering from Data Streams * Decision Trees from Data Streams * Association Rules from Data Streams * Decision Rules from Data Streams * Bayesian Networks from Data Streams * Neural Networks for Data Streams * Feature Selection from Data Streams * Visualization Techniques for Data Streams * Incremental on-line Learning Algorithms * Single-Pass Algorithms * Temporal, spatial, and spatio-temporal data mining * Scalable Algorithms * Real-Time and Real-World Applications using Stream data * Distributed and Social Stream Mining * Urban Computing, Smart Cities * Internet of Things (IoT) * IMPORTANT DATES * 1. Submission deadline (Extended): October 24, 2021 2. Notification deadline: December 10, 2021 3. Camera-ready deadline: December 21, 2021 *PAPER SUBMISSION GUIDELINES * Papers should be submitted in PDF. Authors are invited to submit original papers in all topics related to data streams. All papers should be submitted in ACM 2-column camera ready format for publication in the symposium proceedings. ACM SAC follows a double blind review process. Consequently, the author(s) name(s) and address(s) must NOT appear in the body of the submitted paper, and self-references should be in the third person. This is to facilitate double blind review required by ACM. All submitted papers must include the paper identification number provided by the eCMS system when the paper is first registered. The number must appear on the front page, above the title of the paper. Each submitted paper will be fully refereed and undergo a blind review process by at least three referees. The conference proceedings will be published by ACM. The maximum number of pages allowed for the final papers is 8 pages. There is a set of templates to support the required paper format for a number of document preparation systems at https://www.sigapp.org/sac/sac2022/authorkit.html Important notice: 1. Please submit your contribution via SAC 2022 Webpage: https://www.softconf.com/m/sac2022/ 2. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library. If you encounter any problems with your submission, please contact the Program Coordinator. Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. Ant?nio Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From oliver at roesler.co.uk Fri Oct 22 15:12:28 2021 From: oliver at roesler.co.uk (Oliver Roesler) Date: Fri, 22 Oct 2021 19:12:28 +0000 Subject: Connectionists: Survey on Robot Behavior Adaptation to Human Social Norms Message-ID: <162c4761-40ed-fc8e-702f-b802c6282a63@roesler.co.uk> **Apologies for cross-posting** Dear All, many thanks to everyone who has already completed our survey about Robot Behavior Adaptation to Human Social Norms. The goal of the survey is to compile an overview of how socially aware robot behavior, which is conform to social norms, will influence the perception of robots by humans who are interacting with them. Additionally, the survey aims to determine which characteristics a good benchmark for the evaluation of social robot behavior regarding its compliance to social norms should have. If you haven't had the time yet to complete the survey and would like to participate, please access it here . The survey should take no more than 15 minutes. Thank you for your support! Best regards, Oliver -------------- next part -------------- An HTML attachment was scrubbed... URL: From malini.vinita.samarasinghe at ini.ruhr-uni-bochum.de Fri Oct 22 03:49:11 2021 From: malini.vinita.samarasinghe at ini.ruhr-uni-bochum.de (Vinita Samarasinghe) Date: Fri, 22 Oct 2021 09:49:11 +0200 Subject: Connectionists: Postdoc in comp. neuroscience - learning dynamics - Ruhr University Bochum, Germany Message-ID: <8b19d8e5-883f-1bf8-77fd-3cd80a932f3e@ini.ruhr-uni-bochum.de> Prof. Sen Cheng, Institute for Neural Computation at the Ruhr University Bochum, invites applications for a full time *Postdoctoral position* (TV-L E13) in Computational Neuroscience. The position starts on July 1, 2022 and is funded for three years. The position is part of the Collaborative Research Center ?Extinction Learning? (SFB 1280 ) and entails the following duties: * analyze learning dynamics in behavioral, neural, and psychophysiological data, which will be collected by other projects within the SFB 1280, * compare the learning dynamics between individuals, species, learning phases and learning paradigms, * develop algorithms to analyze the learning dynamics, * develop and study computational models of learning dynamics, * coordinate research with other participating projects. Candidates must have: * a doctorate degree in neuroscience, physics, mathematics, electrical/biomedical engineering or a closely related field, * relevant experience in mathematical modeling, * excellent programming skills (e.g., Python, C/C++, Matlab), * excellent communication skills in English, * the ability to work well in a team. Research experience in neuroscience would be a further asset. The position is third party funded and does not have any formal teaching duties attached. The research group is highly dynamic and uses diverse computational modeling approaches including biological neural networks, cognitive modeling, and machine learning to investigate learning and memory in humans and animals. For further information see www.rub.de/cns. The Ruhr University Bochum is home to a vibrant research community in neuroscience and cognitive science. The Institute for Neural Computation is an independent research unit and combines different areas of expertise ranging from experimental and theoretical neuroscience to machine learning and robotics. The Institute for Neural Computation focuses on the dynamics and learning of perception and behavior on a functional level but is otherwise very diverse, ranging from neurophysiology and psychophysics over computational neuroscience to machine learning and technical applications. Please send your application, including CV, transcripts and research statement electronically, as a *single PDF file*, to *samarasinghe at ini.rub.de*. In addition, at least two academic references must be sent independently to the above email address. The deadline for applications is *January 2, 2022*. Travel costs for interviews will not be reimbursed. The Ruhr University Bochum is committed to equal opportunity. We strongly encourage applications from qualified women and persons with disabilities. We are committed to providing a supportive work environment for female researchers, in particular those with young children. Our university provides mentoring and coaching opportunities specifically aimed at women in research. We have a strong research network with female role models and will provide opportunities to network with them. Wherever possible, events will be scheduled during regular childcare hours. Special childcare will be arranged if events have to be scheduled outside of regular hours, in case of sickness and during school or daycare closures. Where childcare is not an option parents will be offered a home office solution. If you have any questions please feel free to get in touch with Vinita Samarasinghe (contact below) -- Vinita Samarasinghe M.Sc., M.A. Science Manager Arbeitsgruppe Computational Neuroscience Institut f?r Neuroinformatik Ruhr-Universit?t Bochum, NB 3/73 Postfachnummer 110 Universit?tstr. 150 D-44801 Bochum Tel: +49 (0)234 32 27996 Email: samarasinghe at ini.rub.de -------------- next part -------------- An HTML attachment was scrubbed... URL: From boubchir at ai.univ-paris8.fr Sat Oct 23 03:35:17 2021 From: boubchir at ai.univ-paris8.fr (Larbi Boubchir) Date: Sat, 23 Oct 2021 09:35:17 +0200 Subject: Connectionists: [CfP] [Extended deadline] The 2nd international workshop on Machine Learning for EEG Signal Processing (MLESP) In-Reply-To: <0e006091-1510-fdc3-fde1-9add64d236cc@ai.univ-paris8.fr> References: <0e006091-1510-fdc3-fde1-9add64d236cc@ai.univ-paris8.fr> Message-ID: <6c986cd9-b8e5-84e0-c450-bffc8debb9be@ai.univ-paris8.fr> [Apologies for multiple postings] ** *CALL FOR PAPERS* The 2^nd international workshop on Machine Learning for EEG Signal Processing (MLESP 2021, https://mlesp2021.sciencesconf.org/) will be held online, from 9 to 12 december 2021, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021, https://ieeebibm.org/BIBM2021/) *Overview* EEG signal processing involves the analysis and treatment of the electrical activity of the brain measured with Electroencephalography, or EEG, in order to provide useful information on which decisions can be made. The recent advances in signal processing and machine learning for EEG data processing have brought an impressive progress to solve several practical and challenging problems in many areas such as healthcare, biomedicine, biomedical engineering, BCI and biometrics. The aim of this workshop is to present and discuss the recent advances in machine learning for EEG signal analysis and processing. We are inviting original research work, as well as significant work-in-progress, covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in EEG data analytics. This workshop is an opportunity to bring together academic and industrial scientists to discuss the recent advances. The topics of interest include but not limited to: - EEG signal processing and analysis - Time-frequency EEG signal analysis - Signal processing for EEG Data - EEG feature extraction and selection - Machine learning for EEG signal processing - EEG classification and Hierarchical clustering - EEG abnormalities detection (e.g. Epileptic seizure, Alzheimer's disease, etc.) - Machine learning in EEG Big Data - Deep Learning for EEG Big Data - Neural Rehabilitation Engineering - Brain-Computer Interface - Neurofeedback - EEG-based Biometrics - Related applications Important Dates *Nov. 1, 2021 (11:59 pm CST):* Due date for full workshop papers submission Nov. 14, 2021: Notification of paper acceptance to authors Nov. 21, 2021: Camera-ready of accepted papers Dec 3-6, 2021: Workshops Paper Submission - Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. You can download the format instruction here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html - Electronic submissions in PDF format are required. Online Submission https://wi-lab.com/cyberchair/2021/bibm21/scripts/submit.php?subarea=S25&undisplay_detail=1&wh=/cyberchair/2021/bibm21/scripts/ws_submit.php Publication All accepted papers will be published in the BIBM proceedings and IEEE Xplore Digital Library. Journal Special Issue Selected high-quality papers will be invited for publication in a special issue in highly respected journal. *Contact* Prof. Larbi Boubchir /(//Workshop Chair/),University of Paris 8, France E-mail: larbi.boubchir at univ-paris8.fr -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 23 13:20:50 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 23 Oct 2021 19:20:50 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Summer: early registration November 4 Message-ID: <1307882565.6065075.1635009650268@webmail.strato.com> ****************************************************************** 7th INTERNATIONAL GRAN CANARIA SCHOOL ON DEEP LEARNING DeepLearn 2022 Summer Las Palmas de Gran Canaria, Spain July 25-29, 2022 Co-organized by: University of Las Palmas de Gran Canaria Institute for Research Development, Training and Advice ? IRDTA Brussels/London https://irdta.eu/deeplearn/2022su/ ****************************************************************** Early registration: November 4, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Summer will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Bournemouth, and Guimar?es. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Summer is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Summer will take place in Las Palmas de Gran Canaria, on the Atlantic Ocean, with a mild climate throughout the year, sandy beaches and a renowned carnival. The venue will be: Instituci?n Ferial de Canarias Avenida de la Feria, 1 35012 Las Palmas de Gran Canaria https://www.infecar.es/index.php?option=com_k2&view=item&layout=item&id=360&Itemid=896 STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Wahid Bhimji (Lawrence Berkeley National Laboratory), Deep Learning on Supercomputers for Fundamental Science Rich Caruana (Microsoft Research), Friends Don?t Let Friends Deploy Black-box Models: The Importance of Interpretable Neural Nets in Machine Learning Kate Saenko (Boston University), Learning from Biased Data PROFESSORS AND COURSES: (to be completed) T?lay Adal? (University of Maryland Baltimore County), [intermediate] Data Fusion Using Matrix and Tensor Factorizations Pierre Baldi (University of California Irvine), [intermediate/advanced] Deep Learning: From Theory to Applications in the Natural Sciences Arindam Banerjee (University of Illinois Urbana-Champaign), [intermediate/advanced] Deep Generative and Dynamical Models Mikhail Belkin (University of California San Diego), [intermediate/advanced] Modern Machine Learning and Deep Learning through the Prism of Interpolation Dumitru Erhan (Google), [intermediate/advanced] Visual Self-supervised Learning and World Models Arthur Gretton (University College London), [intermediate/advanced] Probability Divergences and Generative Models Phillip Isola (Massachusetts Institute of Technology), [intermediate] Deep Generative Models Mohit Iyyer (University of Massachusetts Amherst), [intermediate/advanced] Natural Language Generation Irwin King (Chinese University of Hong Kong), [introductory/intermediate] Introduction to Graph Neural Networks Vincent Lepetit (Paris Institute of Technology), [intermediate] AI and 3D Geometry for [Self-supervised] 3D Scene Understanding Yan Liu (University of Southern California), [introductory/intermediate] Deep Learning for Time Series Dimitris N. Metaxas (Rutgers, The State University of New Jersey), [intermediate/advanced] Model-based, Explainable, Semisupervised and Unsupervised Machine Learning for Dynamic Analytics in Computer Vision and Medical Image Analysis Sean Meyn (University of Florida), [introductory/intermediate] Reinforcement Learning: Fundamentals, and Roadmaps for Successful Design Louis-Philippe Morency (Carnegie Mellon University), [intermediate/advanced] Multimodal Machine Learning Clara I. S?nchez (University of Amsterdam), [introductory/intermediate] Mechanisms for Trustworthy AI in Medical Image Analysis and Healthcare Bj?rn W. Schuller (Imperial College London), [introductory/intermediate] Deep Multimedia Processing Jonathon Shlens (Google), [introductory/intermediate] Introduction to Deep Learning in Computer Vision Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning, Neural Networks and Kernel Machines Csaba Szepesv?ri (University of Alberta), [intermediate/advanced] Tools and Techniques of Reinforcement Learning to Overcome Bellman's Curse of Dimensionality 1. Murat Tekalp (Ko? University), [intermediate/advanced] Deep Learning for Image/Video Restoration and Compression Alexandre Tkatchenko (University of Luxembourg), [introductory/intermediate] Machine Learning for Physics and Chemistry Li Xiong (Emory University), [introductory/intermediate] Differential Privacy and Certified Robustness for Deep Learning Ming Yuan (Columbia University), [intermediate/advanced] Low Rank Tensor Methods in High Dimensional Data Analysis OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by July 17, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 17, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 17, 2022. ORGANIZING COMMITTEE: Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) David Silva (London, organization chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022su/registration/ The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same. ACCOMMODATION: Accommodation suggestions will be available in due time at https://irdta.eu/deeplearn/2022su/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Universidad de Las Palmas de Gran Canaria Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 23 13:18:31 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 23 Oct 2021 19:18:31 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Winter: early registration November 2 Message-ID: <850245590.6064980.1635009512022@webmail.strato.com> ****************************************************************** 5th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Winter Bournemouth, UK January 17-21, 2022 https://irdta.eu/deeplearn/2022wi/ *********** Co-organized by: Department of Computing and Informatics Bournemouth University Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: November 2, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw and Las Palmas de Gran Canaria. Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 22 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main components of the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be: Talbot Campus Bournemouth University https://www.bournemouth.ac.uk/about/contact-us/directions-maps/directions-our-talbot-campus STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills PROFESSORS AND COURSES: Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint Joachim M. Buhmann (Swiss Federal Institute of Technology, Z?rich), [introductory/advanced] Model and Algorithm Validation for Data Science Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare R?diger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail Jo?o Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How? Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by January 9, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by January 9, 2022. ORGANIZING COMMITTEE: Rashid Bakirov (Bournemouth, co-chair) Nan Jiang (Bournemouth, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022wi/registration/ The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions are available at https://irdta.eu/deeplearn/2022wi/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Bournemouth University Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 23 13:19:36 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 23 Oct 2021 19:19:36 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Spring: early registration November 15 Message-ID: <851757304.6065027.1635009576848@webmail.strato.com> ****************************************************************** 6th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2022 Spring Guimar?es, Portugal April 18-22, 2022 https://irdta.eu/deeplearn/2022sp/ ***************** Co-organized by: Algoritmi Center University of Minho, Guimar?es Institute for Research Development, Training and Advice ? IRDTA Brussels/London ****************************************************************** Early registration: November 15, 2021 ****************************************************************** SCOPE: DeepLearn 2022 Spring will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, and Bournemouth. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience. Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ADDRESSED TO: Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Spring is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2022 Spring will take place in Guimar?es, in the north of Portugal, listed as UNESCO World Heritage Site and often referred to as the birthplace of the country. The venue will be: Hotel de Guimar?es Eduardo Manuel de Almeida 202 4810-440 Guimar?es http://www.hotel-guimaraes.com/ STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. Full in vivo online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Christopher Manning (Stanford University), Self-supervised and Naturally Supervised Learning Using Language Kate Smith-Miles (University of Melbourne), Stress-testing Algorithms via Instance Space Analysis Zhongming Zhao (University of Texas, Houston), Deep Learning Approaches for Predicting Virus-Host Interactions and Drug Response PROFESSORS AND COURSES: Eneko Agirre (University of the Basque Country), [intermediate] Deep Learning for Natural Language Processing Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision Altan ?ak?r (Istanbul Technical University), [introductory] Introduction to Deep Learning with Apache Spark Rylan Conway (Amazon), [introductory/intermediate] Deep Learning for Digital Assistants Jifeng Dai (SenseTime Research), [intermediate] AutoML for Generic Computer Vision Tasks Jianfeng Gao (Microsoft Research), [introductory/intermediate] An Introduction to Conversational Information Retrieval Daniel George (JPMorgan Chase), [introductory] An Introductory Course on Machine Learning and Deep Learning with Mathematica/Wolfram Language Bohyung Han (Seoul National University), [introductory/intermediate] Robust Deep Learning Lina J. Karam (Lebanese American University), [introductory/intermediate] Deep Learning for Quality Robust Visual Recognition Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for Trustworthy Biometrics Jennifer Ngadiuba (Fermi National Accelerator Laboratory), [intermediate] Ultra Low-latency and Low-area Machine Learning Inference at the Edge Lucila Ohno-Machado (University of California, San Diego), [introductory] Use of Predictive Models in Medicine and Biomedical Research Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to Quantum Neural Networks Bart ter Haar Romenij (Eindhoven University of Technology), [intermediate] Deep Learning and Perceptual Grouping Kaushik Roy (Purdue University), [intermediate] Re-engineering Computing with Neuro-inspired Learning: Algorithms, Architecture, and Devices Walid Saad (Virginia Polytechnic Institute and State University), [intermediate/advanced] Machine Learning for Wireless Communications: Challenges and Opportunities Yvan Saeys (Ghent University), [introductory/intermediate] Interpreting Machine Learning Models Martin Schultz (J?lich Research Centre), [intermediate] Deep Learning for Air Quality, Weather and Climate Richa Singh (Indian Institute of Technology, Jodhpur), [introductory/intermediate] Trusted AI Sofia Vallecorsa (European Organization for Nuclear Research), [introductory/intermediate] Deep Generative Models for Science: Example Applications in Experimental Physics Michalis Vazirgiannis (?cole Polytechnique), [intermediate/advanced] Machine Learning with Graphs and Applications Guowei Wei (Michigan State University), [introductory/advanced] Integrating AI and Advanced Mathematics with Experimental Data for Forecasting Emerging SARS-CoV-2 Variants Xiaowei Xu (University of Arkansas, Little Rock), [intermediate/advanced] Deep Learning for NLP and Causal Inference Guoying Zhao (University of Oulu), [introductory/intermediate] Vision-based Emotion AI OPEN SESSION: An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david at irdta.eu by April 10, 2022. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. EMPLOYER SESSION: Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by April 10, 2022. ORGANIZING COMMITTEE: Dalila Dur?es (Braga, co-chair) Jos? Machado (Braga, co-chair) Carlos Mart?n-Vide (Tarragona, program chair) Sara Morales (Brussels) Paulo Novais (Braga, co-chair) David Silva (London, co-chair) REGISTRATION: It has to be done at https://irdta.eu/deeplearn/2022sp/registration/ The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ACCOMMODATION: Accommodation suggestions are available at https://irdta.eu/deeplearn/2022sp/accommodation/ CERTIFICATE: A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. QUESTIONS AND FURTHER INFORMATION: david at irdta.eu ACKNOWLEDGMENTS: Centro Algoritmi, University of Minho, Guimar?es School of Engineering, University of Minho Intelligent Systems Associate Laboratory, University of Minho Municipality of Guimar?es Institute for Research Development, Training and Advice ? IRDTA, Brussels/London -------------- next part -------------- An HTML attachment was scrubbed... URL: From ssliu at coe.neu.edu Sat Oct 23 13:58:56 2021 From: ssliu at coe.neu.edu (Shanshan Liu) Date: Sat, 23 Oct 2021 17:58:56 +0000 Subject: Connectionists: Deadline Reminder - [IEEE TETC (IF:7.691) Special Section] Call for Papers: Emerging Techniques for Trusted and Reliable Machine Learning In-Reply-To: <20210706192019.Horde.MsBku_ozQHWg9zIAFQmDCA6@webmail.coe.neu.edu> Message-ID: <20211023175856.Horde.cvkZuCmdx6Xym8OGLzwMYg1@webmail.coe.neu.edu> Dear Colleague, The deadline of submission for our TETC special issue (please see details below) has been extended to October 30, 2021. We sincerely appreciate your support and look forward to your submission(s). Best regards, The GEs Quoting Shanshan Liu : > Dear Colleague, > > ? > IEEE Transactions on Emerging Topics in Computing (/TETC/) seeks > submissions for the upcoming special section on??TO BE SAFE AND > DEPENDABLE IN THE ERA OF ARTIFICIAL INTELLIGENCE: EMERGING TECHNIQUES FOR > TRUSTED AND RELIABLE MACHINE LEARNING?[1].? > ? > During the last decade, advances in areas such as convolutional neural > networks, deep learning, and hardware accelerators have enabled the > widespread and ubiquitous adoption of machine learning (ML) in real-world > systems. This trend is expected to continue and expand in the coming years, > leading to a world that depends heavily on ML-based systems. > ? > To be safe and dependable in this new era of artificial intelligence, these > innovative systems have to be reliable and secure. This poses many research > challenges. For example, fault tolerance is commonly achieved by redundant > design, but the implementation of deep neural networks is already > challenging, so there is little room to add additional elements for fault > tolerance. Similarly, understanding the vulnerabilities of advanced ML > systems is a complex issue, as shown by recent attacks on image > classification implementations. Therefore, it is essential to learn how to > build ML systems that cannot be manipulated or corrupted by malicious > attackers and that can operate reliably when its underlying hardware or > software suffers from errors. > ? > This special section is devoted to: 1) recent advances in techniques, > algorithms, and implementations for error-tolerant ML systems and 2) > trust/reliable aspects of ML systems and algorithms, including > vulnerabilities, management, protection, and mitigation schemes. Original > papers with substantial technical contribution are solicited on the > following topics: > > > * Design and analysis of trusted/reliable ML algorithms and systems > * Innovative computational paradigms for ML, such as > approximate/stochastic computing > * Fault/error-tolerant ML systems and techniques > * Trust, dependability, reliability, and security in ML implementations > * Adversarial and related techniques for ML systems and algorithms > * Techniques for trustworthy ML inclusive of detection, mitigation, and > defense > * Evaluation of ML for applications such as in safety-critical and > secure systems > ? > SCHEDULE > > > * DEADLINE FOR SUBMISSIONS: October 15, 2021 > * First decision (accept/reject/revise, tentative): January 15, 2022 > * Submission of revised papers: March 15, 2022 > * Notification of final decision (tentative): May 1, 2022 > * Journal publication (tentative): second half of 2022 > > SUBMISSION GUIDELINES > Submitted papers must include new significant research-based technical > contributions in the scope of the journal. Purely theoretical, > technological, or lacking methodological-and-generality papers are not > suitable for this special section. The submissions must include clear > evaluations of the proposed solutions (based on simulation and/or > implementation results) and comparison to state-of-the-art solutions. > Papers under review elsewhere are not acceptable for submission. Extended > versions of published conference papers (to be included as part of the > submission together with a summary of differences) are welcome but there > must have at least 40% of new impacting technical/scientific material in > the submitted journal version, and there should be less than 50% verbatim > similarity level as reported by a tool (such as CrossRef). Guidelines > concerning the submission process, LaTeX, and Word templates can be found > on?the Author Information page[2]. While submitting > through?ScholarOne[3], please select this special-section option. As > per?/TETC/?policies, only full-length papers (10-16 pages with technical > material, double column ? papers beyond 12 pages will be subject to MOPC, > as per CS policies -) can be submitted to special sections. The > bibliography should not exceed 45 items and each author?s bio should not > exceed 150 words. > > QUESTIONS? > Contact the guest editors at?ftsmltetcss at gmail.com[4]. > ? > GUEST EDITORS: > Shanshan Liu, Northeastern University, USA (IEEE Member) > Pedro Reviriego, Universidad Carlos III de Madrid, Spain (IEEE Senior > Member) > Fabrizio Lombardi, Northeastern University, USA (IEEE Fellow) > > CORRESPONDING?/TETC/?EDITOR: > Patrick Girard, LIRMM, France (IEEE Fellow) > > Further details > are?available?at?https://www.computer.org/digital-library/journals/ec/call-for-papers-special-section-on-to-be-safe-and-dependable-in-the-era-of-artificial-intelligence-emerging-techniques-for-trusted-and-reliable-machine-learning > > > > Links: > ------ > [1] > http://www.computer.org/digital-library/journals/ec/call-for-papers-special-section-on-to-be-safe-and-dependable-in-the-era-of-artificial-intelligence-emerging-techniques-for-trusted-and-reliable-machine-learning > [2] > https://www.computer.org/csdl/journals/ec/write-for-us/15071?title=Author%20Information&periodical=IEEE%20Transactions%20on%20Emerging%20Topics%20in%20Computing > [3] https://mc.manuscriptcentral.com/tetc-cs > [4] From O.Inel at tudelft.nl Mon Oct 25 02:47:28 2021 From: O.Inel at tudelft.nl (Oana Inel) Date: Mon, 25 Oct 2021 06:47:28 +0000 Subject: Connectionists: Call-for-Papers Special Issue "Explainable User Models" (Multimodal Technologies and Interaction Journal) Message-ID: <868FD021-7927-4063-B0F5-1497A3D7D96E@tudelft.nl> ? Apologies for cross-posting ? Special Issue "Explainable User Models" A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088). Important Dates & Facts: Abstract/title submission: ideally until November 5, 2021 Manuscripts due by: February 20, 2022 Notification to authors: March 15, 2022 Website: https://www.mdpi.com/journal/mti/special_issues/Explainable_User_Models Benefits of submission: - Experienced Guest Editor - Open Access with quick processing time - High Visibility: Indexed within Scopus, ESCI (Web of Science), Inspec, and many other databases. - Journal Rank: CiteScore - Q2 (Computer Science Applications) Special Issue Information This special issue addresses research on Explainable User Models. As AI systems? actions and decisions will significantly affect their users, it is important to be able to understand how an AI system represents its users. It is a well-known hurdle that many AI algorithms behave largely as black boxes. One key aim of explainability is, therefore, to make the inner workings of AI systems more accessible and transparent. Such explanations can be helpful in the case when the system uses information about the user to develop a working representation of the user, and then uses this representation to adjust or inform system behavior. E.g., an educational system could detect whether students have a more internal or external locus of control, a music recommender system could adapt the music it is playing to the current mood of a user, or an aviation system could detect the visual memory capacity of its pilots. However, when adapting to such user models it is crucial that these models are accurately detected. Furthermore, for such explanations to be useful, they need to be able to explain or justify their representations of users in a human-understandable way. This creates a necessity for techniques that will create models for the automatic generation of satisfactory explanations intelligible for human users interacting with the system. The scope of the special issue includes but is not limited to: Detection and Modelling ? Novel ways of Modeling User Preferences ? Types of information to model (Knowledge, Personality, Cognitive differences, etc.) ? Distinguishing between stationary versus transient user models (e.g., Personality vs Mood) ? Context modeling (e.g., at work versus at home, lean in versus lean out activities) ? User models from heterogeneous sources (e.g., behavior, ratings, and reviews) ? Enrichment and Crowdsourcing for Explainable User Models Ethics ? Detection of sensitive or rarely reported attributes (e.g., gender, race, sexial orientation) ? Implicit user modeling versus explicit user modeling (e.g., questionnaires versus inference from behavior) ? User modeling for self actualization (e.g., user modeling to improve dietary or news consumption habits) Human understandability ? Metrics and methodologies for evaluating fitness for the purpose of explanations ? Balancing completeness and understandability for complex user models ? Explanations to mitigate human biases (e.g., confirmation bias, anchoring) ? Effect of user model explanation on subsequent user interaction (e.g., simulations, and novel evaluation methodologies) Effectiveness ? Analysis or comparison of context of use of explanation (e.g., risk, time pressure, error tolerance) ? Analysis of context of use of system (e.g., decision support, prediction) ? Analysis or comparison of effect of explaining in specific domains (e.g., education, health, recruitment, security) Adaptive presentation of the explanations ? For different types of user ? Interactive explanations ? Investigation of which presentational aspects are beneficial to tailor in the explanation (e.g., level of detail, terminology, modality text or graphics, level of interaction) Prof. Dr. Nava Tintarev Ms. Oana Inel Guest Editors -------------- next part -------------- An HTML attachment was scrubbed... URL: From hocine.cherifi at gmail.com Mon Oct 25 06:42:02 2021 From: hocine.cherifi at gmail.com (Hocine Cherifi) Date: Mon, 25 Oct 2021 12:42:02 +0200 Subject: Connectionists: Call for Participation COMPLEX NETWORKS 2021 November 30 -December 02, 2021 Message-ID: *Tenth** International Conference on Complex Networks & Their Applications* http://www.complexnetworks.org *Registration*: https://complexnetworks.org/registration/ COMPLEX NETWORKS 2021 proceeds as a hybrid event. *Program: *https://easychair.org/smart-program/COMPLEXNETWORKS2021/ *SPEAKERS * ? Marc Barth?l?my CEA France ? Ginestra Bianconi Queen Mary University of London UK ? Jo?o Gama University of Porto Portugal ? Dirk Helbing ETH Z?rich Switzerland ? Yizhou Sun UCLA USA ? Alessandro Vespignani Northeastern University USA *TUTORIALS (November 29, 2021)* ? Elisabeth Lex Graz University of Technology Austria ? Giovanni Petri ISI Foundation Italy Best regards, and looking forward to seeing you at COMPLEX NETWORKS 2021. Rosa M. Benito, Hocine Cherifi, Esteban Moro COMPLEX NETWORKS General Chairs Join us at COMPLEX NETWORKS 2021 Madrid Spain *-------------------------* Hocine CHERIFI University of Burgundy Franche-Comt? Deputy Director LIB EA N? 7534 Editor in Chief Applied Network Science Editorial Board member PLOS One , IEEE ACCESS , Scientific Reports , Journal of Imaging , Quality and Quantity , Computational Social Networks , Complex Systems Complexity -------------- next part -------------- An HTML attachment was scrubbed... URL: From ccht at iit.it Mon Oct 25 11:06:52 2021 From: ccht at iit.it (ccht) Date: Mon, 25 Oct 2021 15:06:52 +0000 Subject: Connectionists: PostDoc on Machine Learning for Earth Observation data - Venice Message-ID: <634345de09af47e8bc52db83cc12fd72@iit.it> PostDoc on Machine Learning for Earth Observation data - (2000002S) The Centre for Cultural Heritage Technology (CCHT) of Istituto Italiano di Tecnologia (IIT) in Venice is currently seeking to appoint a PostDoc with a solid background in the field of Artificial Intelligence (AI) to develop new methods applicable to Earth Observation (EO) in order to detect yet unknown, subsoil Cultural Heritage (CH) sites. CCHT focuses on researching and promoting new technologies for recording, documenting, analysing and preserving CH in a broad sense. With its strongly interdisciplinary infrastructure, the Centre combines expertise from computational and conservation sciences domains, integrating these competencies to foster cutting-edge research. The candidate will support CCHT researchers in a project developed in partnership with the European Space Agency (ESA): 'Cultural Landscapes Scanner (CLS): Earth Observation and automated detection of subsoil undiscovered cultural heritage sites via AI approaches', contract number 4000132058/20/NL/MH/ic. Required qualifications: * Ph.D. in computer science or a related field with specialization in Machine Learning (ML) or in Remote Sensing (RS); * Experience in the development ML methods on EO data (for example, but not limited to, change and anomaly detection on EO time-series; semantic segmentation for land cover mapping, etc.); * Experience with numeric and geospatial Python packages Numpy, Scikit-learn, Gdal or similar; * Good understanding of Geomatics concepts; * Good communication skills and ability to cooperate; * Proficiency in English language (written and oral). Desirable skills: * Experience on unsupervised and semi-supervised Deep Neural Networks, such as autoencoders and Graph Neural Networks (GNNs); * Experience in handling and manipulating big remote sensing datasets. A relevant scientific track record on major ML and/or Remote Sensing conferences/journals (e.g. CVPR, NeurIPS, IGARSS, TPAMI, TGRS, MDPI RS, etc.) is a criterion for the selection. The position will require regular travel to partners' research facilities for periods from one to several weeks, depending on needs. The successful candidate will be offered a competitive salary commensurate to experience and skills. The call will remain open until the position is filled but a first deadline for evaluation of candidates will be November 30th, 2021. Please send your application using he online form. Your application must include (as separate documents): * CV with the publication list; * Brief description of research interests and main accomplishments; * Names and contacts of 2 referees. Istituto Italiano di Tecnologia, with its headquarters in Genoa, Italy, is a non-profit institution with the primary goal of creating and disseminating scientific knowledge and strengthening Italy's technological competitiveness. IIT's research endeavour focuses on high-tech and innovation, representing the forefront of technology with possible application from medicine to industry, computer science, robotics, life sciences and nanobiotechnologies. Istituto Italiano di Tecnologia is an Equal Opportunity Employer that actively seeks diversity in the workforce. We inform you that the data you provide will be processed for the sole purpose of evaluating professional profiles and selecting them according to the needs of the Fondazione Istituto Italiano di Tecnologia. Your data will be processed by the Fondazione Istituto Italiano di Tecnologia, based in Genova, Via Morego, 30, as Data Controller, in compliance with the rules on protection of personal data, including those related to data security. We also inform you that, pursuant to article 15 and following articles of EU Regulation 2016/679 ("General Data Protection Regulation"), you may exercise your rights at any time by contacting the Data Protection Officer (telephone 010 28961 - email: dpo[at]iit.it) Primary Location VENEZIA Job Postdoc Organization Cultural Heritage Technologies Application's deadline: the call will remain open until the position is filled but a first deadline for evaluation of candidates will be November 30ht, 2021. Apply online: https://iit.taleo.net/careersection/ex/jobdetail.ftl?lang=it&job=2000002S [cid:image001.png at 01D7C9C2.B40C6AB0] Center for Cultural Heritage Technology at Ca'Foscari VEGA - Parco Scientifico Tecnologico di Venezia Porta dell'Innovazione Via della Libert?, 12 30175 - Marghera (VE) Italy twitter.com/IITalk www.facebook.com/IITalk www.iit.it DONA IL TUO 5X1000 ALL'ISTITUTO ITALIANO DI TECNOLOGIA codice fiscale 97329350587 -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 10737 bytes Desc: image001.png URL: From kiigaya at gmail.com Mon Oct 25 16:14:11 2021 From: kiigaya at gmail.com (Kiyohito Iigaya) Date: Mon, 25 Oct 2021 16:14:11 -0400 Subject: Connectionists: Postdoc Position in Computational Psychiatry/Neuroscience @Columbia Message-ID: The Laboratory for Computational Psychiatry and Translational Neuroscience at Columbia University (PI: Kyo Iigaya. https://www.iigayalab.com) is looking for a motivated postdoc to work on research in computational neuroscience/psychiatry, with a particular focus on understanding neural computation underlying adaptive and maladaptive learning and decision-making under uncertainty. The starting date is flexible. We are a new laboratory working at the interface between computational neuroscience and psychiatry. Our goal is to understand neural computation underlying learning and decision-making across species in normal and abnormal states, and to translate our findings to clinical applications in psychiatry. We use both theoretical and experimental tools, including reinforcement-learning models, (deep, recurrent) neural network models, web-based behavioral experiments, and fMRI. We work closely with basic and clinical laboratories across Columbia, including labs at the Psychiatry Department, the Center for Theoretical Neuroscience, and the Zuckerman Mind Brain Behavior Institute. The ideal candidate has a strong background in the computational modeling of behavioral and neural dynamics, as well as in data analyses using machine learning tools. Experiences in programming online experiments (e.g., Mturk) or administrating fMRI experiments are a plus. The successful candidate should have a Ph.D. in computational neuroscience, physics, computer science, psychology, or related fields. Candidates should send a single PDF file to ki2151 at columbia.edu with the subject 'Postdoc Application'. The PDF should contain: 1) a CV 2) a brief summary of research interests, and 3) the names and contact details of three referees who will be willing to write reference letters. Informal inquiries about the position are encouraged. Application reviews will continue until the position is filled. Best wishes, Kyo Iigaya --------------------------------- Kiyohito Iigaya, Ph.D. Assistant Professor of Neurobiology (in Psychiatry) Columbia University Irving Medical Center -------------- next part -------------- An HTML attachment was scrubbed... URL: From calendarsites at insticc.org Tue Oct 26 05:42:06 2021 From: calendarsites at insticc.org (calendarsites at insticc.org) Date: Tue, 26 Oct 2021 10:42:06 +0100 Subject: Connectionists: [CFP] 2nd Int. Conf. on Image Processing and Vision Engineering :: Submission Deadline - 30th of November Message-ID: <011d01d7ca4d$bdfee490$39fcadb0$@insticc.org> CALL FOR PAPERS 2nd International Conference on Image Processing and Vision Engineering **Submission Deadline: November 30, 2021** https://improve.scitevents.org/ April 22 - 24, 2022 Online Streaming Dear Colleagues, Given the uncertainties of the current international situation, including constraints on traveling, logging and large gatherings we have decided to convert the conference completely into a web-based event. As a consequence of this change, we have strongly reduced the registration fees, so we would like to invite you to contribute and submit your original research paper to IMPROVE 2022. IMPROVE is a comprehensive conference of academic and technical nature, focused on image processing and computer vision practical applications. It brings together researchers, engineers and practitioners working either in fundamental areas of image processing, developing new methods and techniques, including innovative machine learning approaches, as well as multimedia communications technology and applications of image processing and artificial vision in diverse areas. Conference Chair Sebastiano Battiato, University of Catania, Italy Program Chair(s) Francisco Imai, Apple Inc., United States Cosimo Distante, CNR, Italy With the presence of internationally distinguished keynote speakers: Jiri Matas, Czech Technical University in Prague, Faculty of Electrical Engineering, Czech Republic Michael Bronstein, Imperial College London, United Kingdom Proceedings will be submitted for indexation by: SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Microsoft Academic, Engineering Index (EI), Web of Science / Conference Proceedings Citation Index. A short list of presented papers will be invited for a post-conference special issue of the Springer Nature Computer Science journal. All papers presented at the conference venue will also be available at the SCITEPRESS Digital Library. Kind regards, Monica Saramago IMPROVE Secretariat Web: http://improve.scitevents.org e-mail: improve.secretariat at insticc.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From christos.dimitrakakis at gmail.com Tue Oct 26 04:00:54 2021 From: christos.dimitrakakis at gmail.com (Christos Dimitrakakis) Date: Tue, 26 Oct 2021 10:00:54 +0200 Subject: Connectionists: PhD Position on Reinforcement Learning at the University of Neuchatel Message-ID: <3984be65-7a35-9600-805d-77315020f291@gmail.com> We are looking for a PhD student to join our group on reinforcement learning and decision making under uncertainty more generally, at the University of Neuchatel, Switzerland ( https://www.unine.ch/ ). We are particularly interested in candidates with a strong mathematical background. Prior research experience as documented by your Masters thesis is required. Within the area, we are looking for candidates with a strong research interest in the following fields - Reinforcement learning and decision making under uncertainty: 1. Exploration in reinforcement learning. 2. Decision making nuder partial information. 3. Representations of uncertainty in decision making. 4. Theory of reinforcement learning (e.g. PAC/regret bounds) 5. Bayesian inference and approximate Bayesian methods. Examples of our group's past and current research can be found on arxiv: https://arxiv.org/search/?searchtype=author&query=Dimitrakakis%2C+C. The student will have the opportunity to visit and work with other group members at the University of Oslo, Norway ( https://www.mn.uio.no/ifi/english/people/aca/chridim/index.html ) and Chalmers University of Technology, Sweden ( http://www.cse.chalmers.se/~chrdimi/ ). The PhD candidate must have a strong technical background, including: 1. Thorough knowledge of calculus and linear algebra. 2. A good theoretical background in probability and statistics/machine learning. 3. Practical experience with at least one programming language. The candidate's background will be mainly assessed through their MSc thesis and transcripts, and secondarily through an interview. * Starting date: 1 Februrary 2022. * * Application deadline: 30 November 2021* * Application procedure * For further information or to apply, please contact me directly at christos.dimitrakakis at gmail.com with the subject 'PhD Neuchatel Reinforcement Learning'. An application must include: 1. A statement of research interests and motivation relevant to the position. 2. A CV with a list of references. 3. Your MSc thesis or another research work demonstrating your academic writing. 4. A degree transcript. Feel free to include any other additional information. -- Christos Dimitrakakis https://sites.google.com/site/christosdimitrakakis/ From aldo.lipani at acm.org Tue Oct 26 09:54:46 2021 From: aldo.lipani at acm.org (Aldo Lipani) Date: Tue, 26 Oct 2021 15:54:46 +0200 Subject: Connectionists: CIKM 2021 - Final Call for Partecipation (It stats next week!) In-Reply-To: References: Message-ID: Apologies for cross-postings. ============================================================ ?? ? ?CIKM 2021: The 30th ACM International Conference ?? ? ? ? ?on Information and Knowledge Management ?? ? ? ? ? ? ? ?1 - 5 November 2021, Online ?? ? ? ? ? ? ? ? ?(Queensland, Australia) ?? ? ? ? ? ? ? ? ?http://www.cikm2021.org ============================================================ The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on artificial intelligence, search and discovery, data mining and database systems, all at a single conference. CIKM is uniquely situated to highlight technologies and insights that materialize the big data and artificial intelligence vision of the future. CIKM 2021 will take place online from 1-5 November 2021 in a lively and interactive manner. Now in its 30th year, CIKM is a leading international forum for research into information and knowledge management, as well as recent advances in data and knowledge bases. The list of keynote speakers for this year CIKM is available here: https://www.cikm2021.org/programme/keynote-speakers The program is available here: http://www.cikm2021.org/programme The registration page here: http://www.cikm2021.org/registration --------------------------------- Registration Fees --------------------------------- All registration fees are quoted in US Dollars ? USD Category - Author Author Registration (Until 15th September) Author ACM-SIG Member [Either Professional or Student] $279 Author Non-Member $329 Category ? Workshop Author Workshop Author Registration (Until 25 October) AUTHOR AnalytiCup or Workshop $49 (with full access to conference) Category - Attendee Attendee Registration (Until 25 October) Conference Attendee $49 --------------------------------- ACM-SIG Member --------------------------------- Accepted memberships include ACM, SIGIR and SIGweb. To join SIGIR as a member, please visit this link: https://sigir.org/general-information/membership/ To join SIGWEB as a member, please visit this link: https://www.sigweb.org/about-sigweb/join-acm-sigweb To join ACM, please visit this link: https://www.acm.org/membership/join --------------------------------- Registration includes --------------------------------- *? ?Admission to all sessions, including, plenary keynotes, main tracks sessions, posters, workshops, tutorials, and AnalytiCup *? ?Access to digital copy of the proceedings *? ?Gift: CIKM-21 polo shirt. Register by the early deadline of 15 September to get the shirt before the conference --------------------------------- Author Registration Policy --------------------------------- *? ?All author registrations should be received by 15 September in order to have the papers included in the proceedings. Only papers covered by a registration will be included in the conference proceedings. *? ?At least one author from each paper must register for the conference. AUTHORS TAKE NOTE: Each accepted paper must have an author registration fee paid. For example, if you are the solo author of two papers, you should pay the registration fee twice. On the other hand, if you are an author of two papers, each with other co-author, you may decide to register yourself for one of the papers, and one of your co-authors for the other paper. --------------------------------- Registration Methods --------------------------------- Please register through the online registration system. Methods of payment accepted include credit card and wire transfer. Any queries email: cikm21 at uq.edu.au Register: https://cvent.me/av1nnD --------------------------------- Topics of Interest --------------------------------- Conference sessions will include papers on all topics in the general areas of artificial intelligence, data science, databases, information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas: * Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling) * Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information ??credibility) * Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware) * Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data) * Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability) * Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction) * Information access and retrieval (e.g., ad hoc and web search, facets and entities, question answering and dialogue systems, retrieval models, query processing, personalization, recommender and filtering systems) * Users and interfaces for information and data systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces) * Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation,??best practices) * Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices) * Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations) * Data presentation (e.g., visualization, summarization, readability, VR, speech input/output) * Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media) --------------------------------- CIKM 2021 Terms & Conditions --------------------------------- *? ?Registrations are accepted only on full completion of the online registration form. *? ?No booking will be confirmed until the registration payment is received in full. *? ?CIKM 2021 Committee reserves the right to alter any of the program or other arrangements for this conference; including cancellation or postponement of the conference should unforeseen circumstances require it. The organisers accept no responsibility for resulting costs or inconvenience to participants in this case. *? ?It is the responsibility of the author or participant to ensure that adequate internet connectivity is available at their place of viewing for the duration of the online conference. --------------------------------- Cancellation Policy --------------------------------- Requests for cancellations or changes must be sent in writing to the registration desk: cikm21 at uq.edu.au If you cancel your registration: *??By 25 October Attendee registrations will receive a 50% refund *??No refunds issued from 26 October 2021 *??There is no refund for non-attendance *??No refunds for Author registrations --------------------------------- ACM Policy Against Discrimination --------------------------------- All authors and participants must adhere the ACM discrimination policy. For full details, please visit this site: https://www.acm.org/special-interest-groups/volunteer-resources/officers-manual/policy-against-discrimination-and-harassment ---------------------------- Contact Information ---------------------------- For any registration-related issues or queries please send an email to: cikm21 at uq.edu.au Dr. Aldo?Lipani?|?aldolipani.com Asst. Prof. in Machine Learning University College London (UCL) -------------- next part -------------- An HTML attachment was scrubbed... URL: From Paul.Linton.2 at city.ac.uk Tue Oct 26 09:56:57 2021 From: Paul.Linton.2 at city.ac.uk (Linton, Paul) Date: Tue, 26 Oct 2021 13:56:57 +0000 Subject: Connectionists: NEXT WEEK: New Approaches to 3D Vision, 1st-4th Nov 2021, Royal Society online meeting Message-ID: NEXT WEEK: NEW APPROACHES TO 3D VISION, 1st-4th November 2021, hosted by the Royal Society This Royal Society online meeting brings together researchers from computer vision, animal vision, and human vision to explore recent developments in 3D vision. Speakers reflect both academia and industry, with representatives from DeepMind, Facebook Reality Labs, Google, and Microsoft Research. Date/Time: 1st-4th November 2021, 3-6pm (3-6:30pm on the 4th), UK Time Please note that the USA/UK time difference is 1hr less than usual Website: https://royalsociety.org/science-events-and-lectures/2021/11/3d-vision/ Registration (Free): https://www.eventbrite.co.uk/e/new-approaches-to-3d-vision-registration-165729267701 DAY ONE (1st Nov) - Seeing Beyond SLAM Chair: Andrew Fitzgibbon (Microsoft) Session One: Neural Scene Representation SM Ali Eslami (DeepMind): ?Neural priors, neural encoders and neural renderers? Ida Momennejad (Microsoft Research): ?Multi-scale predictive representations and human-like RL? Session Two: Perception-Action Loop Sergey Levine (UC Berkeley and Google): ?Generalization in data-driven control? Andrew Glennerster (University of Reading): ?Understanding 3D vision as a policy network? DAY TWO (2nd Nov) - Animals in Action Chair: Matteo Carandini (University College London) Session One: Locating Prey and Rewards Jenny Read (Newcastle University): ?Stupid stereoscopic algorithms that still work? Aman Saleem (University College London): ?Visual processing in the brain during navigation? Session Two: Navigation in 3D Space Kate Jeffery (University College London): ?The cognitive map of 3D space: not as metric as we thought?? Gily Ginosar (Weizmann Institute of Science): ?Locally ordered representation of 3D space in the entorhinal cortex? DAY THREE (3rd Nov) - Experiencing Space Chair: Mar Gonzalez-Franco (Microsoft Research) Session One: Theories of Visual Space Dhanraj Vishwanath (University of St Andrews): ?Tripartite encoding of visual 3D space? Paul Linton (City, University of London): ?New approaches to visual scale and visual shape? Session Two: Challenges for Virtual Reality Sarah Creem-Regehr (University of Utah): ?Perception and Action in Virtual and Augmented Reality? Douglas Lanman (Facebook Reality Labs): ?Engineering challenges for realistic displays? DAY FOUR (4th Nov) - Grasping the World Chair: Jody Culham (Western University) Session One: One Visual Stream or Two? Fulvio Domini (Brown University): ?A novel non-probabilistic model of 3D cue integration explains both perception and action? Irene Sperandio (University of Trento): ?Dissociations between perception and action in size-distance scaling? Session Two: 3D Space and Visual Impairment Ione Fine (University of Washington): ?Do you hear what I see? How do early blind individuals experience object motion?? Ewa Niechwiej-Szwedo (University of Waterloo): ?The role of binocular vision in the development of visuomotor control and performance of fine motor skills? Session Three: Panel Discussion Andrew Fitzgibbon (Microsoft), Matteo Carandini (University College London), Mar Gonzalez-Franco (Microsoft Research), and Jody Culham (Western University), share their thoughts on the future of 3D vision with an interactive Question and Answer session. Organisers: Michael Morgan FRS (City, University of London), Paul Linton (City, University of London), Jenny Read (Newcastle University), Dhanraj Vishwanath (University of St Andrews), Sarah Creem-Regehr (University of Utah), Fulvio Domini (Brown University) -------------- next part -------------- An HTML attachment was scrubbed... URL: From aihuborg at gmail.com Tue Oct 26 10:01:16 2021 From: aihuborg at gmail.com (AIhub) Date: Tue, 26 Oct 2021 15:01:16 +0100 Subject: Connectionists: "What is AI? Stephen Hanson in conversation with..." a new video series Message-ID: "What is AI? Stephen Hanson in conversation with..." - a new video series Stephen Hanson (Rutgers University) is talking to AI researchers in a new video series for AIhub.org. So far, he has interviewed Michael Jordan and Richard Sutton. Stephen Hanson in conversation with Richard Sutton This discussion covers artificial general intelligence and whether this could be achieved through reward alone, connectionists, animal learning theory, and more: https://aihub.org/2021/10/14/what-is-ai-stephen-hanson-in-conversation-with-richard-sutton/ Stephen Hanson in conversation with Michael Jordan This discussion covers AI as an engineering discipline, what people call AI, so-called autonomous cars, and more. https://aihub.org/2021/09/07/what-is-ai-stephen-hanson-in-conversation-with-michael-jordan/ About AIhub: AIhub is a non-profit dedicated to connecting the AI community to the public by providing free, high-quality information through AIhub.org ( https://aihub.org/). We help researchers publish the latest AI news, summaries of their work, opinion pieces, tutorials and more. We are supported by many leading scientific organizations in AI, namely AAAI , NeurIPS , ICML , AIJ /IJCAI , ACM SIGAI , EurAI/AICOMM, CLAIRE and RoboCup . Twitter: @aihuborg -------------- next part -------------- An HTML attachment was scrubbed... URL: From hcalvert at sas.upenn.edu Tue Oct 26 15:37:23 2021 From: hcalvert at sas.upenn.edu (Calvert, Heather J) Date: Tue, 26 Oct 2021 19:37:23 +0000 Subject: Connectionists: MindCORE at UPenn Postdoctoral Research Fellowships Message-ID: MindCORE Postdoctoral Research Fellowships MindCORE seeks to recruit outstanding postdoctoral researchers for our Research Fellowship for Postdoctoral Scholars. Housed within the School of Arts and Sciences of the University of Pennsylvania, MindCORE (https://mindcore.sas.upenn.edu/) is an interdisciplinary effort to understand human intelligence and behavior. Designed for individuals who have recently obtained a PhD degree in psychology, linguistics, neuroscience, philosophy, computer science or other cognitive science discipline, the MindCORE Fellowship is a springboard for young researchers as they establish their own research program. Fellows are also encouraged to pursue collaborative research with faculty working across disciplines at Penn. Benefits Fellows receive a competitive salary, relocation allowance, health insurance, plus a modest research budget of $20,000. Fellows also benefit from access to the greater community of academics including visiting scholars plus leading research facilities equipped with cutting-edge instrumentation all on an urban campus in a vibrant city. Fellows are invited to join regular working group meetings within their field plus career development workshops aimed at young researchers, and will be provided with a mentoring committee. Funding is provided in one-year terms renewable for up to three years. Eligibility & Application We are accepting applications for 2022-2023 until January 10, 2022. Applicants must have formally completed all requirements of the PhD degree and provide a copy of their diploma at the time of appointment (typically July 1, 2022 - Jan 15, 2023). Candidates must submit a research statement that identifies at least three MindCORE faculty (https://mindcore.sas.upenn.edu/people/faculty-and-associates/) at Penn with whom the applicant could potentially collaborate; along with a CV, and contact information for two referees. Complete applications should be submitted along with some basic information using a form available on the website: https://mindcore.sas.upenn.edu/post-doctoral-research-fellowship/. Selection All eligible and complete applications will be evaluated by the Selection Committee after January 10. Applications are judged on the following criteria: Scientific excellence Scientific match and interdisciplinarity Career potential MindCORE seeks to award ~2 post-doctoral Fellowships per year. Positions may start as early as July 1, 2022. Questions? Contact pennmindcore at sas.upenn.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From Donald.Adjeroh at mail.wvu.edu Tue Oct 26 17:54:01 2021 From: Donald.Adjeroh at mail.wvu.edu (Donald Adjeroh) Date: Tue, 26 Oct 2021 21:54:01 +0000 Subject: Connectionists: CFP: IEEE BIBM-LncRNA'21: 6 days to deadline, journal special issue; see our array of speakers! In-Reply-To: References: , , , , Message-ID: Apologies if you receive multiple copies ... Authors of selected papers will be invited to submit extended versions for consideration for Journal Special Issue in MDPI Non-Coding RNA. We also have an exciting array of speakers for the workshop -- both In-Person presenters in Dubai, UAE, and online/remote presenters !! See our website: BIBM- LncRNA'2021: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ Our paper submission deadline Nov. 1, just 6-days away -- see below Call for Papers The IEEE BIBM 2021 Workshop on Long Non-Coding RNAs: Mechanism, Function, and Computational Analysis (BIBM-LncRNA) will be held in conjunction with the 2021 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021), Dec. 9 - 12, 2021. Though the BIBM conference will be virtual/online, the LncRNA workshop will be held in a mixed mode -- both virtual/remote and face-to-face in Dubai, UAE. BIBM- LncRNA'2021: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ IEEE BIBM 2021: https://ieeebibm.org/BIBM2021/ The recent application of high throughput technologies to transcriptomics has changed our view of gene regulation and function. The discovery of extensive transcription of large RNA transcripts, termed long noncoding RNAs (lncRNAs), provide an important and new perspective on the centrality of RNA in gene regulation. LncRNAs are involved in various biological and cellular processes, such as genetic imprinting, chromatin remodeling, gene regulation and embryonic development. LncRNAs have been implicated in several chronic diseases, such as cancers, and heart disease, etc. Various types of genomic data on lncRNAs are currently available, including sequences, secondary/tertiary structures, transcriptome data, and their interactions with related proteins or genes. The key challenge is how to integrate data from myriad sources to determine the functions and the regulatory mechanism of these ubiquitous lncRNAs. Research topics: The potential topics include, but not limited to, the following: lncRNA detection and biomarker discovery CLIP-Seq and RIP-Seq data analysis Prediction of physical binding between lncRNA and DNA, RNA and protein. Competition and interaction between lncRNA, miRNA and mRNA Studying methylation regulating lncRNA functions Function Prediction for lncRNAs Deep learning approaches to lncRNA/RNA binding protein prediction Computational approaches to analyzing lncRNA lncRNA 3D secondary structures lncRNA-protein interactions lncRNA in epigenetic regulation lncRNA associated diseases network lncRNAs in plant genomics lncRNAs in phenotype-genotype problems lncRNAs and single cell transcriptomics lncRNAs and spatial transcriptomics CRISPR/Cas9 and Genome editing in lncRNAs We invite you to submit papers with unpublished, original research describing recent advances on the areas related to this workshop. All papers will undergo peer review by the conference program committee. All papers accepted will be included in the Workshop Proceedings published by the IEEE Computer Society Press and will be available at the workshop. Authors of selected papers will be invited to extend their papers for submission to special issues in prestigious Journals. Fellowships: Funds are available for limited fellowships to support the participation of students, and of researchers from underrepresented minority groups in the workshop. We aim at supporting at least one author for each accepted paper, depending on number of papers, and on availability of funds. Journal Special Issue: Authors of selected submissions will be invited to extend their papers for submission for review and possible publication in a special issue of the journal -- Non-Coding RNA. https://www.mdpi.com/journal/ncrna Paper Submission: Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. Electronic submissions in pdf format are required. For paper submission click on the following link: https://wi-lab.com/cyberchair/2021/bibm21/scripts/submit.php?subarea=S08&undisplay_detail=1&wh=/cyberchair/2021/bibm21/scripts/ws_submit.php Important Dates: Nov 1, 2021 11:59:59 PM WST: Due date for full workshop paper submission. Nov 14, 2021: Notification of paper decision to authors Nov 21, 2021: Camera-ready of accepted papers Dec 9-12, 2021: Workshops BIBM-LncRNA'21 Workshop home page: https://community.wvu.edu/~daadjeroh/workshops/LNCRNA2021/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From juergen at idsia.ch Wed Oct 27 03:52:09 2021 From: juergen at idsia.ch (Schmidhuber Juergen) Date: Wed, 27 Oct 2021 07:52:09 +0000 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> Message-ID: Hi, fellow artificial neural network enthusiasts! The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. Thank you all in advance for your help! J?rgen Schmidhuber From barak at cs.nuim.ie Wed Oct 27 09:52:58 2021 From: barak at cs.nuim.ie (Barak A. Pearlmutter) Date: Wed, 27 Oct 2021 14:52:58 +0100 Subject: Connectionists: Postdoctoral and PhD Research Positions in Formal Verification [MAYNOOTH UNIVERSITY, IRELAND] In-Reply-To: References: Message-ID: We are currently seeking candidates for two positions on a project which investigates Modular AI Verification and Visualisation (MAIVV): Post Doctoral research (36 month contract, Eur 38,632 p.a.) Deadline for applications: November 5th 2021. Funded PhD position (2021 - 2025, Eur 102,000 over 4 years) Deadline for applications: November 19th 2021. Our overall goal is to provide scalable software development techniques that guarantee software dependability when deep learning techniques are employed. The output of this work will be a methodology which allows us to verify properties of hybrid systems with mixed discrete and continuous dynamics, and associated software tooling to integrate this with existing verification frameworks. A case study of a hybrid cyber-physical system will demonstrate the effectiveness of our approach. This project is led by Dr Rosemary Monahan and Professor Barak Pearlmutter in the Dept. of Computer Science and Hamilton Institute at Maynooth University, Ireland. From sepand.haghighi at yahoo.com Wed Oct 27 13:48:37 2021 From: sepand.haghighi at yahoo.com (Sepand Haghighi) Date: Wed, 27 Oct 2021 17:48:37 +0000 (UTC) Subject: Connectionists: PyCM 3.3 Released: Comparison of Classifiers Based on Confusion Matrix References: <1244267638.1058067.1635356917950.ref@mail.yahoo.com> Message-ID: <1244267638.1058067.1635356917950@mail.yahoo.com> https://github.com/sepandhaghighi/pycm https://www.pycm.irhttp://list.pycm.ir - __compare_weight_handler__?function added?#347 - is_imbalanced?parameter added to ConfusionMatrix?__init__?method?#276 - class_benchmark_weight?and?overall_benchmark_weight?parameters added to Compare?__init__?method?#347 - statistic_recommend?function modified?#276 - Compare?weight?parameter renamed to?class_weight?#347 - Document modified - License updated - AUTHORS.md?updated - README.md?modified - Block diagrams updated Best RegardsSepand Haghighi -------------- next part -------------- An HTML attachment was scrubbed... URL: From juyang.weng at gmail.com Wed Oct 27 14:52:34 2021 From: juyang.weng at gmail.com (Juyang Weng) Date: Wed, 27 Oct 2021 14:52:34 -0400 Subject: Connectionists: Scientific Integrity, the 2018 Turing Lecture In-Reply-To: References: Message-ID: Sorry, please kindly correct "2018" in my subject line to "2021" to be consistent with the original post. Thanks. On Wed, Oct 27, 2021 at 1:52 PM Juyang Weng wrote: > Dear Juergen, > > Thank you very much for bringing this important issue to the attention of > this email list. > > The Brain-Mind Institute (BMI) is planning on a series of grassroots > efforts to address this and other wider scope problems, so that the > scientific community, taxpayers and buyers of publicly listed stocks can > get informed. The phenomena you raised are not just a "Scientific > Integrity" issue, they are much wider and deeper. > > Here is a YouTube playlist around the Turing Award 2018: > https://www.youtube.com/playlist?list=PLntQ4jEfo0MvBeC2lWsRY2ErHs2dFmqY0 > > I suggest connectionists at cmu not reject such posts (which it blocked some > before, like a well known "archive" site), so that the scientific community > and public can communicate about such important matters. > > Yours humbly, > -John Weng > ---- > Message: 3 > Date: Wed, 27 Oct 2021 07:52:09 +0000 > From: Schmidhuber Juergen > To: "connectionists at cs.cmu.edu" > Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing > Lecture, etc. > Message-ID: > Content-Type: text/plain; charset="utf-8" > > Hi, fellow artificial neural network enthusiasts! > > The connectionists mailing list is perhaps the oldest mailing list on > ANNs, and many neural net pioneers are still subscribed to it. I am hoping > that some of them - as well as their contemporaries - might be able to > provide additional valuable insights into the history of the field. > > Following the great success of massive open online peer review (MOOR) for > my 2015 survey of deep learning (now the most cited article ever published > in the journal Neural Networks), I've decided to put forward another piece > for MOOR. I want to thank the many experts who have already provided me > with comments on it. Please send additional relevant references and > suggestions for improvements for the following draft directly to me at > juergen at idsia.ch: > > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > The above is a point-for-point critique of factual errors in ACM's > justification of the ACM A. M. Turing Award for deep learning and a > critique of the Turing Lecture published by ACM in July 2021. This work can > also be seen as a short history of deep learning, at least as far as ACM's > errors and the Turing Lecture are concerned. > > I know that some view this as a controversial topic. However, it is the > very nature of science to resolve controversies through facts. Credit > assignment is as core to scientific history as it is to machine learning. > My aim is to ensure that the true history of our field is preserved for > posterity. > > Thank you all in advance for your help! > > J?rgen Schmidhuber > > -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S. Shaw Lane, Room 3115 Michigan State University East Lansing, MI 48824 USA Office: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu http://www.cse.msu.edu/~weng/ ---- Technology transfer ---- GENISAMA LLC genesama.com --------- Outreach ---------- Brain-Mind Institute brain-mind-institute.org Brain-Mind Magazine brain-mind-magazine.org ----------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From Yuri.A.Dabaghian at uth.tmc.edu Wed Oct 27 18:41:18 2021 From: Yuri.A.Dabaghian at uth.tmc.edu (Dabaghian, Yuri A) Date: Wed, 27 Oct 2021 22:41:18 +0000 Subject: Connectionists: Postdoctoral Associate in Neural Circuits of Learning and Memory In-Reply-To: References: Message-ID: Dear Colleagues, I would appreciate sharing the announcement below. Apologies for cross postings... NIH-funded postdoctoral position is immediately available in the group of Dr. Yuri Dabaghian the Department of Neurology at McGovern Medical School of the University of Texas, Houston. We are looking for scientists with research interests at the intersection of mathematics, physics, and neuroscience, in the general area of emergent phenomena and representations, network dynamics, topological data analyses. Some projects aim to explain electrophysiological data patterns, some are theoretically motivated. Specifically, we are interested in circuit mechanisms of learning and memory and their involvement in neurological disorders, notably Alzheimer's Disease. Successful candidate will develop data-driven hippocampal neuronal network models explaining spatial learning dynamics, develop and creatively apply tools for the data analysis including novel methods of brain waves (EEG) analyses, Topological Data Analysis of spiking data, etc. The Position requires strong background in quantitative disciplines (PhD and ongoing interest in computational or theoretical neuroscience, physics, mathematics, or related), plus willingness to do programming and to analyze experimental data. Previous experiences in data analyses are preferred but not required. All appointments are initially for one year and are renewable for at least three years given satisfactory performance. The salary is competitive. The postdoc will be integrated into the large, vibrant neuroscience community of the Texas Medical Center and have the opportunity to participate in collaborations with groups at Baylor College of Medicine and Rice University. To apply, please send application materials including a detailed CV, a brief statement of research interests, and contact information of 3 references to Dr. Yuri Dabaghian at [Yuri.A.Dabaghian [at] uth.tmc.edu). The position is available immediately, until filled. For Additional information and informal inquiries, please contact Dr. Dabaghian. We Strive for a diverse and inclusive environment, and encourage applications from members of any identity. -------------- next part -------------- An HTML attachment was scrubbed... URL: From juyang.weng at gmail.com Wed Oct 27 13:52:33 2021 From: juyang.weng at gmail.com (Juyang Weng) Date: Wed, 27 Oct 2021 13:52:33 -0400 Subject: Connectionists: Scientific Integrity, the 2018 Turing Lecture Message-ID: Dear Juergen, Thank you very much for bringing this important issue to the attention of this email list. The Brain-Mind Institute (BMI) is planning on a series of grassroots efforts to address this and other wider scope problems, so that the scientific community, taxpayers and buyers of publicly listed stocks can get informed. The phenomena you raised are not just a "Scientific Integrity" issue, they are much wider and deeper. Here is a YouTube playlist around the Turing Award 2018: https://www.youtube.com/playlist?list=PLntQ4jEfo0MvBeC2lWsRY2ErHs2dFmqY0 I suggest connectionists at cmu not reject such posts (which it blocked some before, like a well known "archive" site), so that the scientific community and public can communicate about such important matters. Yours humbly, -John Weng ---- Message: 3 Date: Wed, 27 Oct 2021 07:52:09 +0000 From: Schmidhuber Juergen To: "connectionists at cs.cmu.edu" Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. Message-ID: Content-Type: text/plain; charset="utf-8" Hi, fellow artificial neural network enthusiasts! The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. Thank you all in advance for your help! J?rgen Schmidhuber -------------- next part -------------- An HTML attachment was scrubbed... URL: From pgarner at idiap.ch Wed Oct 27 11:39:11 2021 From: pgarner at idiap.ch (Phil Garner) Date: Wed, 27 Oct 2021 17:39:11 +0200 Subject: Connectionists: PhD / post-doc position in ASR & NLP Message-ID: Dear Colleagues, There is a fully funded PhD (or possibly post-doc) position open at Idiap Research Institute on "ASR & NLP for structured selection interviews". The project is a collaboration between psychology departments at the universities of Neuch?tel and Lausanne, and the speech and audio processing group at Idiap. The research will involve study of deep hierarchical architectures for semantic inference. Building on state of the art approaches to speech and language processing, the pipeline is likely to make use of pre-trained self supervised models (e.g., wav2vec2, BERT). The open questions include how to customise such approaches to structured selection interviews, in particular the role of storytelling. There will be ample opportunity for research in the component technologies, which are all currently pertinent in the general machine learning landscape. In particular, we expect to make advances in the technological interfaces between component technologies, and in the humanist interfaces between the machine learning practitioners and social scientists. For more information, and to apply, please follow this link: https://www.idiap.ch/education-and-jobs/job-10330 Idiap is located in Martigny in French speaking Switzerland, but functions in English and hosts many nationalities. PhD students are typically registered at EPFL. All positions offer quite generous salaries. Martigny is a local art, culture and viticulture hub, and is close to all manner of skiing, hiking and mountain life. There are other open positions on Idiap's main page https://www.idiap.ch/en/join-us/job-opportunities Sincerely, -- Phil Garner http://www.idiap.ch/~pgarner From pubconference at gmail.com Wed Oct 27 21:17:34 2021 From: pubconference at gmail.com (Pub Conference) Date: Wed, 27 Oct 2021 21:17:34 -0400 Subject: Connectionists: IEEE TNNLS Call for Special Issue on "Stream Learning", Submission Deadline: December 15, 2021 [EXTENDED] Message-ID: *CALL FOR PAPERS* *IEEE Transactions on Neural Networks and Learning Systems Special Issue on STREAM LEARNING* https://cis.ieee.org/images/files/Publications/TNNLS/special-issues/One-Page_IEEE_Transactions_on_NNLS-SI-CFP-Update.pdf *Introduction* In recent years, machine learning from streaming data (called *Stream Learning*) has enjoyed tremendous growth and exhibited a wealth of development at both the conceptual and application levels. Stream Learning is highly visible in both the machine learning and data science fields, and become a new hot direction in recent years. Research developments in Stream Learning include learning under concept drift detection (whether a drift occurs), understanding (where, when, and how a drift occurs), and adaptation (to actively or passively update models). Recently we have seen several new successful developments in Stream Learning such as massive stream learning algorithms; incremental and online learning for streaming data; and streaming data- based decision-making methods. These developments have demonstrated how Stream Learning technologies can contribute to the implementation of machine learning capability in dynamic systems. We have also witnessed compelling evidence of successful investigations on the use of Stream Learning to support business real-time prediction and decision making. In light of these observations, it is instructive, vital, and timely to offer a unified view of the current trends and form a broad forum for the fundamental and applied research as well as the practical development of Stream Learning for improving machine learning, data science and practical decision support systems of business. This special issue aims at reporting the progress in fundamental principles; practical methodologies; efficient implementations; and applications of Stream Learning methods and related applications. The special issue also welcomes contributions in relation to data streams, incremental learning and reinforcement learning in data streaming situations. Scope of the Special Issue We invite submissions on all topics of Stream Learning, including but not limited to: ? *Data stream prediction* ? *Concept drift detection, understanding and adaptation* ? *Recurrent concepts* ? *Experimental setup and Evaluation methods for stream learning* ? *Reinforcement learning on streaming data* ? *Streaming data-based real-time decision making* ? *Ensemble methods for stream learning* ? *Auto machine learning for stream algorithms* ? *Neural networks for big data streams* ? *Transfer learning for streaming data* ? *Real-world applications of stream learning* ? *Active learning for streaming data* ? *Online learning for streaming data* ? *Imbalance learning for streaming data* ? *Lifelong learning for streaming data* ? *Incremental learning for streaming data* ? *Continuous learning for streaming data* ? *Clustering for streaming data* ? *Audio/speech/music streams processing* ? *Stream learning benchmark datasets* ? *Multi-drift and multi-stream learning* ? *Stream processing platforms* Timeline ? Submission deadline: *Dec 15, 2021* ? Notification of first review: March 1, 2022 ? Submission of revised manuscript: Jun 1, 2022 ? Notification of final decision: Aug 1, 2022 Guest Editors ? Jie Lu (University of Technology Sydney, Australia) ? Joao Gama (University of Porto, Portugal) ? Xin Yao (Southern University of Science and Technology, China) ? Leandro Minku (University of Birmingham, UK) Submission Instructions - Read the Information for Authors at http://cis.ieee.org/tnnls - Submit your manuscript at the TNNLS webpage (http://mc.manuscriptcentr al.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Early submissions are welcome. -------------- next part -------------- An HTML attachment was scrubbed... URL: From zheng at fias.uni-frankfurt.de Wed Oct 27 16:56:16 2021 From: zheng at fias.uni-frankfurt.de (Pengsheng Zheng) Date: Wed, 27 Oct 2021 22:56:16 +0200 Subject: Connectionists: CFP- Frontiers Research Topic: Cortico-striato-nigro-thalamo-cortical Modeling for Understanding Motor Function and Neurodegenerative Disease vol II Message-ID: <69638877-6CDD-4890-BAC5-9133672174B3@fias.uni-frankfurt.de> To whom it may concern: In collaboration with Frontiers in Computational Neuroscience, we are organizing a relaunched Research Topic titled "Cortico-striato-nigro-thalamo-cortical Modeling for Understanding Motor Function and Neurodegenerative Disease vol II?, hosted by Pengsheng Zheng, James Kozloski, Timothy Rumbell, George V. Rebec. As host editor, I would like to encourage you to contribute to this topic. Please find more information about Research Topics below, including the publishing fees that apply. You can also visit the homepage we have created on the Frontiers website, which defines the focus of the topic, and where all published articles will appear. https://www.frontiersin.org/research-topics/28267/cortico-striato-nigro-thalamo-cortical-modeling-for-understanding-motor-function-and-neurodegenerati Movement in the body is directly controlled by motor cortex, and also determined by multiple subcortical structures, such as thalamus and the basal ganglia (including striatum and dopamine neurons in the substantia nigra). Many lines of evidence have suggested the cortico-striato-nigro-thalamo-cortical circuitry plays a major role in motor learning and control. This circuitry has also been investigated for its causal role in the onset and progression of neurodegenerative diseases. Neurodegenerative diseases are often associated with movement disorders and neuronal dysfunction in degenerated brain structures, such as the striatum in Huntington?s disease and dopaminergic neurons in the substantia nigra pars compacta in Parkinson's disease. Degeneration fundamentally changes the dynamics of local neuronal circuits, and these changes then propagate through the structural connectome of whole brain circuitry, eventually altering global brain dynamics. However, our current understanding of these system dynamics in the cortico-striato-nigro-thalamo-cortical circuitry remains rudimentary. Hence, theoretical studies about critical system variables and computational principles of this circuitry, constrained by recordings throughout, will shed new light on causes of motor dysfunction and neurodegenerative diseases. This Research Topic of Frontiers in Computational Neuroscience aims to bring together recent advances in theoretical modeling of cortico-striato-nigro-thalamo-cortical network and experimental observations. Topics of interest include, but are not limited to, local brain circuit modeling, the functional role of neuronal plasticity in the local and global circuit, global circuit interactions and information exchange, new models validated by experimental observations, and dynamic disease risk analysis through perturbation studies. Best regards, Pengsheng Zheng -------------- next part -------------- An HTML attachment was scrubbed... URL: From oreilly at ucdavis.edu Thu Oct 28 01:48:10 2021 From: oreilly at ucdavis.edu (Randall O'Reilly) Date: Wed, 27 Oct 2021 22:48:10 -0700 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> Message-ID: <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit. This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it; conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity. But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts. Sometimes, it is not the basic equations etc that matter: it is the big picture vision. Cheers, - Randy > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen wrote: > > Hi, fellow artificial neural network enthusiasts! > > The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. > > Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. > > I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. > > Thank you all in advance for your help! > > J?rgen Schmidhuber > > > > > > > > From danko.nikolic at gmail.com Thu Oct 28 04:23:03 2021 From: danko.nikolic at gmail.com (Danko Nikolic) Date: Thu, 28 Oct 2021 10:23:03 +0200 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> Message-ID: Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee. Danko On Thu, 28 Oct 2021, 10:13 Randall O'Reilly wrote: > I vaguely remember someone making an interesting case a while back that it > is the *last* person to invent something that gets all the credit. This is > almost by definition: once it is sufficiently widely known, nobody can > successfully reinvent it; conversely, if it can be successfully > reinvented, then the previous attempts failed for one reason or another > (which may have nothing to do with the merit of the work in question). > > For example, I remember being surprised how little Einstein added to what > was already established by Lorentz and others, at the mathematical level, > in the theory of special relativity. But he put those equations into a > conceptual framework that obviously changed our understanding of basic > physical concepts. Sometimes, it is not the basic equations etc that > matter: it is the big picture vision. > > Cheers, > - Randy > > > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen > wrote: > > > > Hi, fellow artificial neural network enthusiasts! > > > > The connectionists mailing list is perhaps the oldest mailing list on > ANNs, and many neural net pioneers are still subscribed to it. I am hoping > that some of them - as well as their contemporaries - might be able to > provide additional valuable insights into the history of the field. > > > > Following the great success of massive open online peer review (MOOR) > for my 2015 survey of deep learning (now the most cited article ever > published in the journal Neural Networks), I've decided to put forward > another piece for MOOR. I want to thank the many experts who have already > provided me with comments on it. Please send additional relevant references > and suggestions for improvements for the following draft directly to me at > juergen at idsia.ch: > > > > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > > > The above is a point-for-point critique of factual errors in ACM's > justification of the ACM A. M. Turing Award for deep learning and a > critique of the Turing Lecture published by ACM in July 2021. This work can > also be seen as a short history of deep learning, at least as far as ACM's > errors and the Turing Lecture are concerned. > > > > I know that some view this as a controversial topic. However, it is the > very nature of science to resolve controversies through facts. Credit > assignment is as core to scientific history as it is to machine learning. > My aim is to ensure that the true history of our field is preserved for > posterity. > > > > Thank you all in advance for your help! > > > > J?rgen Schmidhuber > > > > > > > > > > > > > > > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jdc at princeton.edu Thu Oct 28 08:49:54 2021 From: jdc at princeton.edu (Jonathan D. Cohen) Date: Thu, 28 Oct 2021 12:49:54 +0000 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> Message-ID: <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> As a friendly amendment to both Randy and Danko?s comments, it is also worth noting that science is an *intrinsically social* endeavor, and therefore communication is a fundamental factor. This may help explain why the *last* person to invent or discover something is the one who gets the [social] credit. That is, giving credit to those who disseminate may even have normative value. After all, if a tree falls in the forrest? As for those who care more about discovery and invention than dissemination, well, for them credit assignment may not be as important ;^). jdc On Oct 28, 2021, at 4:23 AM, Danko Nikolic > wrote: Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee. Danko On Thu, 28 Oct 2021, 10:13 Randall O'Reilly > wrote: I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit. This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it; conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity. But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts. Sometimes, it is not the basic equations etc that matter: it is the big picture vision. Cheers, - Randy > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen > wrote: > > Hi, fellow artificial neural network enthusiasts! > > The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. > > Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. > > I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. > > Thank you all in advance for your help! > > J?rgen Schmidhuber > > > > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jose at rubic.rutgers.edu Thu Oct 28 09:27:10 2021 From: jose at rubic.rutgers.edu (=?UTF-8?Q?Stephen_Jos=c3=a9_Hanson?=) Date: Thu, 28 Oct 2021 09:27:10 -0400 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> Message-ID: <2f1d9928-543f-f4a0-feab-5a5a0cc1d4d7@rubic.rutgers.edu> Well, to? popularize is not to invent. Many of Juergen's concerns could be solved with some scholarship, such that authors look sometime before 2006 for other relevant references. This isn't a social issue.. good science writers know they didn't invent the algorithms they are describing for AI applications. OTOH, Dave Rumelhart, who introduction of the backprop learning methods, often gets confused for gradient descent and consequently, Newton *should* be referenced for gosh sakes! ?But keep in mind:? Context matters.?? The PDP framework was pretty exclusively about Cognitive Science not about how to solve multivariable engineering problems.???? The real value of Dave and PDP, was framing associative learning in networks and how that might provide a foot-hold in understanding cognitive function in the brain.?? It was no accident that before Dave became very ill, he was working in Cognitive Neuroscience and doing Brain scanning research. Sure, if we work at it everything is connected to everything, but other then historical exegesis, this is useless for paradigm change and scientific forward motion. Steve On 10/28/21 8:49 AM, Jonathan D. Cohen wrote: > As a friendly amendment to both Randy and Danko?s comments, it is also > worth noting that science is an *intrinsically social* endeavor, and > therefore communication is a fundamental factor. ?This may help > explain why the *last* person to invent or discover something is the > one who gets the [social] credit. ?That is, giving credit to those who > disseminate may even have normative value. ?After all, if a tree falls > in the forrest? As for those who care more about discovery and > invention than dissemination, well, for them credit assignment may not > be as important ;^). > > jdc > >> On Oct 28, 2021, at 4:23 AM, Danko Nikolic > > wrote: >> >> Yes Randall, sadly so. I have seen similar examples in neuroscience >> and philosophy of mind. Often, (but not always), you have to be the >> one who popularizes the thing to get the credit. Sometimes, you can >> get away, you just do the hard conceptual work and others doing for >> you the (also hard) marketing work. The best bet is doing both by >> yourself. Still no guarantee. >> >> Danko >> >> >> On Thu, 28 Oct 2021, 10:13 Randall O'Reilly > > wrote: >> >> I vaguely remember someone making an interesting case a while >> back that it is the *last* person to invent something that gets >> all the credit. This is almost by definition: once it is >> sufficiently widely known, nobody can successfully reinvent it;? >> conversely, if it can be successfully reinvented, then the >> previous attempts failed for one reason or another (which may >> have nothing to do with the merit of the work in question). >> >> For example, I remember being surprised how little Einstein added >> to what was already established by Lorentz and others, at the >> mathematical level, in the theory of special relativity.? But he >> put those equations into a conceptual framework that obviously >> changed our understanding of basic physical concepts. Sometimes, >> it is not the basic equations etc that matter: it is the big >> picture vision. >> >> Cheers, >> - Randy >> >> > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen >> > wrote: >> > >> > Hi, fellow artificial neural network enthusiasts! >> > >> > The connectionists mailing list is perhaps the oldest mailing >> list on ANNs, and many neural net pioneers are still subscribed >> to it. I am hoping that some of them - as well as their >> contemporaries - might be able to provide additional valuable >> insights into the history of the field. >> > >> > Following the great success of massive open online peer review >> (MOOR) for my 2015 survey of deep learning (now the most cited >> article ever published in the journal Neural Networks), I've >> decided to put forward another piece for MOOR. I want to thank >> the many experts who have already provided me with comments on >> it. Please send additional relevant references and suggestions >> for improvements for the following draft directly to me at >> juergen at idsia.ch : >> > >> > >> https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >> >> > >> > The above is a point-for-point critique of factual errors in >> ACM's justification of the ACM A. M. Turing Award for deep >> learning and a critique of the Turing Lecture published by ACM in >> July 2021. This work can also be seen as a short history of deep >> learning, at least as far as ACM's errors and the Turing Lecture >> are concerned. >> > >> > I know that some view this as a controversial topic. However, >> it is the very nature of science to resolve controversies through >> facts. Credit assignment is as core to scientific history as it >> is to machine learning. My aim is to ensure that the true history >> of our field is preserved for posterity. >> > >> > Thank you all in advance for your help! >> > >> > J?rgen Schmidhuber >> > >> > >> > >> > >> > >> > >> > >> > >> >> > -- -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.png Type: image/png Size: 19957 bytes Desc: not available URL: From b.ommer at lmu.de Thu Oct 28 09:59:11 2021 From: b.ommer at lmu.de (=?UTF-8?Q?Bj=c3=b6rn_Ommer?=) Date: Thu, 28 Oct 2021 15:59:11 +0200 Subject: Connectionists: PhD and PostDoc Positions in Computer Vision @ University of Munich Message-ID: <0c20e850-f6e2-c5d8-e828-7ea8e2aa1acc@lmu.de> The Machine Vision and Learning group (https://ommer-lab.com/research ) headed by *Bj?rn Ommer @ University of Munich * (previously Heidelberg University) is offering fully funded PhD and PostDoc positions. Possible research topics are for instance within the areas of *generative models for image and video synthesis, explainable AI, deep metric and representation learning, and visual retrieval and pursue the latest approaches in deep learning.* There is also the optional possibility to explore applications of this fundamental research through existing university and industry collaborations of our group. You should be curious, highly motivated, and creative and we hope you are a great person to complement our team. Strong analytical, programming, and English skills and prior practical experience in ML/CV are required. Our group offers a nice, friendly, and inspiring environment for your future research, broad experience in the latest deep learning techniques, access to high-performance GPU infrastructure, and an excellent location in downtown Munich. We provide fully funded positions with a competitive salary and benefits. Your application should include a CV, a transcript of records, two academic references, and a short (1-2 pages) statement of research, summarizing your past and future research interests. Please explain why you are passionate about these and highlight the practical experience you have gained so far. We welcome direct applications, or through the ELLIS program. Please send your PDF to applications.mvl at lrz.uni-muenchen.de . For questions,please contact Prof. Dr. Bj?rn Ommer (https://ommer-lab.com/people/ommer ). -------------- next part -------------- An HTML attachment was scrubbed... URL: From pfbaldi at ics.uci.edu Thu Oct 28 13:05:43 2021 From: pfbaldi at ics.uci.edu (Baldi,Pierre) Date: Thu, 28 Oct 2021 10:05:43 -0700 Subject: Connectionists: Fwd: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: Message-ID: <3d811a41-57bf-e5b0-ba41-7a3dc86fb313@ics.uci.edu> Besides plagiarism, this community would be well-served by taking a frank look at the remarkable levels of cronyism, collusion, and subtle--but very real--manipulation that have permeated it for several decades.? In addition to self- , cross-, and suppressive citation issues, there are many other metrics to look at. To get started, one could ask the following simple questions and compute the corresponding statistics: 1) Over the past four decades, how often has the leadership of any relevant machine learning foundation changed? 2) Over the past four decades, what is the degree of over-representation by members of any particular organization in things like: a) organizing and program committees of major machine learning conferences? b) AI/ML academic department and now also AI/ML corporate departments? c) editorial boards and other centers of power and dissemination? 3) What is the degree of over-representation of any particular organization in invited talks, workshops, tutorials, or other "special events", such as official birthday celebrations or on-stage Q&A sessions with rich and famous people, at major machine learning scientific conferences? Cronyism and collusion are nothing new in human affairs, including science, and most of the time they are even legal.? But how well do these serve science or society? The tip of the iceberg. --Pierre Baldi -------- Forwarded Message -------- Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. Date: Wed, 27 Oct 2021 07:52:09 +0000 From: Schmidhuber Juergen To: connectionists at cs.cmu.edu Hi, fellow artificial neural network enthusiasts! The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. Thank you all in advance for your help! J?rgen Schmidhuber -------------- next part -------------- An HTML attachment was scrubbed... URL: From juyang.weng at gmail.com Thu Oct 28 14:08:26 2021 From: juyang.weng at gmail.com (Juyang Weng) Date: Thu, 28 Oct 2021 14:08:26 -0400 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. Message-ID: Randall O'Reilly wrote: "how little Einstein added to what was already established by Lorentz and others". I respectfully object to this misleading statement. Much has been added to "Lorentz and others" by general relativity based on which Albert Einstein became well known. In terms of special relativity, the Lorentz transformation is very different from special relativity. I quote from wikipedia: "Some months later, FitzGerald published the conjecture that bodies in motion are being contracted, in order to explain the baffling outcome of the 1887 aether-wind experiment of Michelson and Morley. In 1892, Lorentz independently presented the same idea in a more detailed manner, which was subsequently called FitzGerald?Lorentz contraction hypothesis.[6]. Their explanation was widely known before 1905." What happened to the Turing Award 2018 is very unusual, indicating something obviously wrong at ACM that year. For example, please check the ACM Turing Award Committee that year. Anybody has published in neural networks? This is like many non-experts judged on a subject area that they were not familiar with, hyped by the media. This media hype is now challenged for protocol flaws. See A Report to *Nature* about Technical Flaws of Post-Selections Using Test Sets (PSUTS) , June 28, 2021. Currently, Nature is investigating this series of charges. See what is going on on PubPeer to the hype that resulted in Turing Aware 2018: https://pubpeer.com/publications/46ACDD49DFA0EC6C961CC22E331924#. Hinton stopped responding when things became tough. Also see challenges to Google's typical work on AI: https://pubpeer.com/publications/C1F67E615087A54B7F2F2A9FCCE33F# It seems to be rooted from a lack of due diligence and lack of account on part of ACM. This kind of accidents are unlikely in the Nobel Committee. Nobel Committee members have been careful as they must explain each Nobel Award. -John -- Juyang (John) Weng -------------- next part -------------- An HTML attachment was scrubbed... URL: From cgf at isep.ipp.pt Thu Oct 28 15:59:13 2021 From: cgf at isep.ipp.pt (Carlos) Date: Thu, 28 Oct 2021 20:59:13 +0100 Subject: Connectionists: CFP (Extended deadline: October 31, 2021): DATA STREAMS TRACK - ACM SAC 2022 Message-ID: <7d222d89-bfd2-db95-2240-2bd42f86483e@isep.ipp.pt> *ACM Symposium on Applied Computing * The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic April 25 ? April 29, 2022 https://www.sigapp.org/sac/sac2022/ *Data Streams Track * https://abifet.github.io/SAC2022/ * IMPORTANT DATES * 1. Submission deadline (Extended): October 31, 2021 2. Notification deadline: December 10, 2021 3. Camera-ready deadline: December 21, 2021 Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. Ant?nio Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From jdc at princeton.edu Thu Oct 28 14:01:30 2021 From: jdc at princeton.edu (Jonathan D. Cohen) Date: Thu, 28 Oct 2021 18:01:30 +0000 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> Message-ID: <8E01234A-03B3-492C-9DD7-B7FBD321475D@princeton.edu> The same incentive structures (and values they reflect) are not necessarily the same ? nor should they necessary be ? in commercial and academic environments. jdc On Oct 28, 2021, at 12:03 PM, Marina Meila > wrote: Since credit is a form of currency in academia, let's look at the "hard currency" rewards of invention. Who gets them? the first company to create a new product usually fails. However, the interesting thing is that society (by this I mean the society most of us we work in) has found it necessary to counteract this, and we have patent laws to protect the rights of the inventors. The point is not whether patent laws are effective or not, it's the social norm they implement. That to protect invention one should pay attention to rewarding the original inventors, whether we get the "product" directly from them or not. Best wishes, Marina -- Marina Meila Professor of Statistics University of Washington On 10/28/21, 5:59 AM, "Connectionists" > wrote: As a friendly amendment to both Randy and Danko?s comments, it is also worth noting that science is an *intrinsically social* endeavor, and therefore communication is a fundamental factor. This may help explain why the *last* person to invent or discover something is the one who gets the [social] credit. That is, giving credit to those who disseminate may even have normative value. After all, if a tree falls in the forrest? As for those who care more about discovery and invention than dissemination, well, for them credit assignment may not be as important ;^). jdc On Oct 28, 2021, at 4:23 AM, Danko Nikolic > wrote: Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee. Danko On Thu, 28 Oct 2021, 10:13 Randall O'Reilly > wrote: I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit. This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it; conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity. But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts. Sometimes, it is not the basic equations etc that matter: it is the big picture vision. Cheers, - Randy > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen > wrote: > > Hi, fellow artificial neural network enthusiasts! > > The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. > > Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. > > I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. > > Thank you all in advance for your help! > > J?rgen Schmidhuber > > > > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From li.zhaoping at tuebingen.mpg.de Thu Oct 28 14:59:44 2021 From: li.zhaoping at tuebingen.mpg.de (Li Zhaoping) Date: Thu, 28 Oct 2021 20:59:44 +0200 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: <2f1d9928-543f-f4a0-feab-5a5a0cc1d4d7@rubic.rutgers.edu> References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> <2f1d9928-543f-f4a0-feab-5a5a0cc1d4d7@rubic.rutgers.edu> Message-ID: I would find it hard to enter a scientific community if it is not scholarly. Each of us can do our bit to be scholarly, to set an example, if not a warning, to the next generation. Zhaoping On 28/10/2021 15:27, Stephen Jos? Hanson wrote: > > Well, to? popularize is not to invent. > > Many of Juergen's concerns could be solved with some scholarship, such > that authors look sometime before 2006 for other relevant references. > > This isn't a social issue.. good science writers know they didn't > invent the algorithms they are describing for AI applications. > > OTOH, Dave Rumelhart, who introduction of the backprop learning > methods, often gets confused for gradient descent and > consequently, Newton *should* be referenced for gosh sakes! > > ?But keep in mind:? Context matters.?? The PDP framework was pretty > exclusively about Cognitive Science not about how to solve > multivariable engineering problems.???? The real value of Dave and > PDP, was framing associative learning in networks and how that might > provide a foot-hold in understanding cognitive function in the > brain.?? It was no accident that before Dave became very ill, he was > working in Cognitive Neuroscience and doing Brain scanning research. > > Sure, if we work at it everything is connected to everything, but > other then historical exegesis, this is useless for paradigm change > and scientific forward motion. > > Steve > > On 10/28/21 8:49 AM, Jonathan D. Cohen wrote: >> As a friendly amendment to both Randy and Danko?s comments, it is >> also worth noting that science is an *intrinsically social* endeavor, >> and therefore communication is a fundamental factor. ?This may help >> explain why the *last* person to invent or discover something is the >> one who gets the [social] credit. ?That is, giving credit to those >> who disseminate may even have normative value. ?After all, if a tree >> falls in the forrest? As for those who care more about discovery and >> invention than dissemination, well, for them credit assignment may >> not be as important ;^). >> >> jdc >> >>> On Oct 28, 2021, at 4:23 AM, Danko Nikolic >> > wrote: >>> >>> Yes Randall, sadly so. I have seen similar examples in neuroscience >>> and philosophy of mind. Often, (but not always), you have to be the >>> one who popularizes the thing to get the credit. Sometimes, you can >>> get away, you just do the hard conceptual work and others doing for >>> you the (also hard) marketing work. The best bet is doing both by >>> yourself. Still no guarantee. >>> >>> Danko >>> >>> >>> On Thu, 28 Oct 2021, 10:13 Randall O'Reilly >> > wrote: >>> >>> I vaguely remember someone making an interesting case a while >>> back that it is the *last* person to invent something that gets >>> all the credit.? This is almost by definition: once it is >>> sufficiently widely known, nobody can successfully reinvent it;? >>> conversely, if it can be successfully reinvented, then the >>> previous attempts failed for one reason or another (which may >>> have nothing to do with the merit of the work in question). >>> >>> For example, I remember being surprised how little Einstein >>> added to what was already established by Lorentz and others, at >>> the mathematical level, in the theory of special relativity.? >>> But he put those equations into a conceptual framework that >>> obviously changed our understanding of basic physical concepts. >>> Sometimes, it is not the basic equations etc that matter: it is >>> the big picture vision. >>> >>> Cheers, >>> - Randy >>> >>> > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen >>> > wrote: >>> > >>> > Hi, fellow artificial neural network enthusiasts! >>> > >>> > The connectionists mailing list is perhaps the oldest mailing >>> list on ANNs, and many neural net pioneers are still subscribed >>> to it. I am hoping that some of them - as well as their >>> contemporaries - might be able to provide additional valuable >>> insights into the history of the field. >>> > >>> > Following the great success of massive open online peer review >>> (MOOR) for my 2015 survey of deep learning (now the most cited >>> article ever published in the journal Neural Networks), I've >>> decided to put forward another piece for MOOR. I want to thank >>> the many experts who have already provided me with comments on >>> it. Please send additional relevant references and suggestions >>> for improvements for the following draft directly to me at >>> juergen at idsia.ch : >>> > >>> > >>> https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >>> >>> > >>> > The above is a point-for-point critique of factual errors in >>> ACM's justification of the ACM A. M. Turing Award for deep >>> learning and a critique of the Turing Lecture published by ACM >>> in July 2021. This work can also be seen as a short history of >>> deep learning, at least as far as ACM's errors and the Turing >>> Lecture are concerned. >>> > >>> > I know that some view this as a controversial topic. However, >>> it is the very nature of science to resolve controversies >>> through facts. Credit assignment is as core to scientific >>> history as it is to machine learning. My aim is to ensure that >>> the true history of our field is preserved for posterity. >>> > >>> > Thank you all in advance for your help! >>> > >>> > J?rgen Schmidhuber >>> > >>> > >>> > >>> > >>> > >>> > >>> > >>> > >>> >>> >> > -- -- Li Zhaoping, Ph.D. Prof. of Cognitive Science, University of Tuebingen Head of Department of Sensory and Sensorimotor Systems, Max Planck Institute for Biological Cybernetics Author of "Understanding vision: theory, models, and data", Oxford University Press, 2014 www.lizhaoping.org -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.png Type: image/png Size: 19957 bytes Desc: not available URL: From cgf at isep.ipp.pt Thu Oct 28 17:37:55 2021 From: cgf at isep.ipp.pt (Carlos) Date: Thu, 28 Oct 2021 22:37:55 +0100 Subject: Connectionists: CFP: Special Issue on STREAM LEARNING - IEEE Transactions on Neural Networks and Learning Systems Message-ID: CALL FOR PAPERS IEEE Transactions on Neural Networks and Learning Systems Special Issue on STREAM LEARNING Deadline: 15 December 2021 Introduction In recent years, machine learning from streaming data (called Stream Learning) has enjoyed tremendous growth and exhibited a wealth of development at both the conceptual and application levels. Stream Learning is highly visible in both the machine learning and data science fields and become a new hot direction in recent years. Research developments in Stream Learning include learning under concept drift detection (whether a drift occurs), understanding (where, when, and how a drift occurs), and adaptation (to actively or passively update models). Recently we have seen several new successful developments in Stream Learning such as massive stream learning algorithms; incremental and online learning for streaming data; and streaming data-based decision-making methods. These developments have demonstrated how Stream Learning technologies can contribute to the implementation of machine learning capability in dynamic systems. We have also witnessed compelling evidence of successful investigations on the use of Stream Learning to support business real-time prediction and decision making. In light of these observations, it is instructive, vital, and timely to offer a unified view of the current trends and form a broad forum for the fundamental and applied research as well as the practical development of Stream Learning for improving machine learning, data science and practical decision support systems of business. This special issue aims at reporting the progress in fundamental principles; practical methodologies; efficient implementations; and applications of Stream Learning methods and related applications. The special issue also welcomes contributions in relation to data streams, incremental learning and reinforcement learning in data streaming situations. Scope of the Special Issue We invite submissions on all topics of Stream Learning, including but not limited to: ? Data stream prediction ? Concept drift detection, understanding and adaptation ? Recurrent concepts ? Experimental setup and Evaluation methods for stream learning ? Reinforcement learning on streaming data ? Streaming data-based real-time decision making ? Ensemble methods for stream learning ? Auto machine learning for stream algorithms ? Neural networks for big data streams ? Transfer learning for streaming data ? Real-world applications of stream learning ? Active learning for streaming data ? Online learning for streaming data ? Imbalance learning for streaming data ? Lifelong learning for streaming data ? Incremental learning for streaming data ? Continuous learning for streaming data ? Clustering for streaming data ? Audio/speech/music streams processing ? Stream learning benchmark datasets ? Multi-drift and multi-stream learning ? Stream processing platforms Timeline ? Submission deadline: Dec 15, 2021 ? Notification of first review: Feb 1, 2022 ? Submission of revised manuscript: May 1, 2022 ? Notification of final decision: July 1, 2022 Guest Editors ? Jie Lu (University of Technology Sydney, Australia) ? Joao Gama (University of Porto, Portugal) ? Xin Yao (Southern University of Science and Technology, China) ? Leandro Minku (University of Birmingham, UK) Submission Instructions - Read the Information for Authors at http://cis.ieee.org/tnnls - Submit your manuscript at the TNNLS webpage (http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Early submissions are welcome. Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. Ant?nio Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From juyang.weng at gmail.com Thu Oct 28 14:20:12 2021 From: juyang.weng at gmail.com (Juyang Weng) Date: Thu, 28 Oct 2021 14:20:12 -0400 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. Message-ID: Stephen Hanson wrote: "good science writers know they didn't invent the algorithms they are describing for AI applications." I agree. Dr. Fei-Fei Li should know that she was using other's algorithms (she cited her advisor's work but did not cite Cresceptron that should have given her directions and confidence) in her key PhD work at CalTech and the corresponding PAMI publication. However, she mis-used her advisor's concepts and algorithm: https://pubpeer.com/publications/D7C615461DEB1D1F1758FFCCA4CF4F#null So far, she did not bother to respond. -- Juyang (John) Weng -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.antonucci at idsia.ch Fri Oct 29 04:35:06 2021 From: alessandro.antonucci at idsia.ch (Antonucci Alessandro) Date: Fri, 29 Oct 2021 08:35:06 +0000 Subject: Connectionists: [UR '22 @ FLAIRS 35] First Call For Papers Message-ID: <11E71847-2AA0-422B-82C1-6345D85756B2@supsi.ch> *** Apologies for multiple postings *** [UR @ FLAIRS 35] FIRST CALL FOR PAPERS Special Track on Uncertain Reasoning at the 35th Florida Artificial Intelligence Research Society Conference (FLAIRS-35) May 15-18, 2022 - Hutchinson Island, Jensen Beach, Florida (US) Paper submission deadline: January 24, 2022 Conference website: www.flairs-35.info Track website: https://ur-flairs.github.io/2022/ ::: Call for Papers ::: Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. The objective of this track is to present and discuss a broad and diverse range of current work on uncertain reasoning, including theoretical and applied research based on different paradigms. We hope that the variety and richness of this track will help to promote cross fertilisation among the different approaches for uncertain reasoning, and in turn foster the development of new ideas and paradigms. The Special Track on Uncertain Reasoning (UR) is the oldest track in FLAIRS conferences, running annually since 1996. The UR'22 Special Track at the 35th International Florida Artificial Intelligence Research Society Conference (FLAIRS-33) is the 25th in the series. As in the past years, UR'20 track seeks to bring together researchers working on broad issues related to reasoning under uncertainty. Papers on all aspects of uncertain reasoning are invited. Papers of particular interest include, but are not limited to: - Uncertain reasoning formalisms, calculi and methodologies - Reasoning with probability, possibility, fuzzy logic, belief functions, vagueness, granularity, rough sets, and probability logics - Modelling and reasoning using imprecise and indeterminate information, such as: Choquet capacities, comparative orderings, convex sets of measures, and interval-valued probabilities - Exact, approximate and qualitative uncertain reasoning - Inference and learning with graphical models of uncertainty (e.g., Bayesian networks) - Multi-agent uncertain reasoning and decision making - Decision-theoretic planning and Markov decision processes - Temporal reasoning and uncertainty, non-monotonic reasoning, similarity-based reasoning - Conditional logics, description logic, logic programming - Argumentation - Belief change and merging - Construction of models from elicitation, data mining and knowledge discovery - Uncertain reasoning in information retrieval, filtering, fusion, diagnosis, prediction, situation assessment - Uncertain reasoning in data management - Practical applications of uncertain reasoning (e.g., machine learning, computer vision and animation) ::: Program Committee ::: : Track Chairs : - Alessandro Antonucci (IDSIA, Switzerland) - Yang Xiang (University of Guelph, Canada) : PC Members : - Mohand Said Allili (Universit? du Qu?bec en Outaouais) - Ofer Arieli (The Academic College of Tel-Aviv, Israel) - Salem Benferhat (University of Artois, France) - Ameur Bensefia (Higher Colleges of Technology, United Arab Emirates) - Stefano Bistarelli (University of Perugia, Italy) - Nizar Bouguila (Concordia University, Canada) - Cory Butz (University of Regina, Canada) - Martine Ceberio (University of Texas at El Paso, US) - S?bastien Destercke (University of Technology of Compi?gne, France) - Love Ekenberg (Stockholm University, Sweden) - Lluis Godo (Spanish National Research Council, Spain) - Christophe Gonzales (LIS, France) - Pooyan Jamshidi (University of South Caroline, US) - Mohammad Ali Javidian (Purdue University, US) - Gabriele Kern-Isberner (University of Technology Dortmund, Germany) - Vladik Kreinovich (University of Texas at El Paso, US) - Philippe Leray (University of Nantes, France) - Nicholas Mattei (Tulane University, US) - Rafael Pe?aloza (University of Milano-Bicocca, Italy) - Kamal Premaratne (University of Miami, US) - Eugene Santos (Dartmouth College, US) - Dilip Sarkar (University of Miami, US) - Kari Sentz (Los Alamos National Laboratory, US) - Karim Tabia (Artois University, France) - Carlo Taticchi (Univerity of Perugia, Italy - Choh Man Teng (Institute for Human and Machine Cognition, US) ::: Other Information ::: FLAIRS-35 is planned to run as a mild-hybrid conference. We will have a safe physical conference to the extent possible, but provide an option for online presentation for those who cannot attend the physical conference. Additional information on the venue can be found at www.flairs-35.info. Feel free to contact the organizers for any further information about the UR?22 special track. ::: Submission ::: Papers are due by January 24, 2022 (abstracts by January 17, 2022). Submissions of all papers to FLAIRS-35 is done through the EasyChair conference system: https://easychair.org/conferences/?conf=flairs35 When submitting your paper, select the Uncertain Reasoning track. Double-blind reviewing is used for research papers, so submitted papers must not reveal authors identify (for example, use your paper ID from EasyChair instead of author name in your paper). Do NOT use a fake name for your EasyChair login; your EasyChair account information is hidden from reviewers. Authors should indicate the special track "Uncertain Reasoning" for submissions. There are three kinds of submissions: * full paper - a paper of high quality, which will be published in the proceedings (up to 6 pages) and will be presented by the author in a corresponding track (20 minute oral presentation); * short paper - a paper that shows some novelty and general interest, but is more preliminary or in the early stages of development, which also get published in the proceedings (up to 4 pages) and the author presents the work in a poster session; and * poster abstract, which will be published as abstract only (up to 250 words; please follow this format for the final camera-ready poster abstract) and the author presents the work in a poster session. Rejected full papers may still be accepted as short papers or poster abstracts, if reviewers found interest in the idea, but the paper quality was not sufficient to be published in full length. There will also be a separate abstract only submission for authors who want to submit just an abstract and present it in a poster session. For each accepted paper (full, short, or poster abstract), there must be an accompanying AUTHOR REGISTRATION. A single author may have up to a maximum of two papers per AUTHOR REGISTRATION. Author names may be changed or re-ordered after reviewing; however, for budgetary reasons, registration fees will be based on the details at the time of submission and review. The accepted papers in the track will be published in the proceedings of FLAIRS-35 published by the FloridaOJ. Authors are expected to make a reasonable effort to address reviewers? comments prior to the submission of the camera ready paper. Where such expectations have been flouted, actions may be taken to preserve the quality of the conference and the expectations of conference goers. In order for a paper to be published in the proceedings, the paper must be accompanied by at least one AUTHOR REGISTRATION. It is also expected that for a full (i.e., maximum 6-page) paper, at least one of the authors will attend the conference to present their work. From marcello.pelillo at gmail.com Fri Oct 29 03:32:56 2021 From: marcello.pelillo at gmail.com (Marcello Pelillo) Date: Fri, 29 Oct 2021 09:32:56 +0200 Subject: Connectionists: Assistant Professor position @ University of Venice (Italy) Message-ID: Dear colleagues, we are looking for a highly-motivated and experienced candidate for a (non-tenured) Assistant Professor position to work within the *RePAIR* H2020 project ("Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage"): https://cordis.europa.eu/project/id/964854 The research will be conducted at the University of Venice (Project Coordinator) in collaboration with a high-profile international consortium, and will be focused on developing advanced *computer vision and machine learning algorithms for the reconstruction of large broken frescos*. >>> Deadline for application: *November 25, 2021* Detailed information about the position (and instructions on how to apply) can be found here: https://www.unive.it/data/38002/?id=2021-UNVE000-0114831 (Italian version) https://apps.unive.it/common2/file/download/concorsi/6178f699dce2c (English version) Useful information about the Italian academic system can be found here: https://www.unive.it/pag/28008/ Best regards -mp -- Marcello Pelillo, *FIEEE, FIAPR, FAAIA* Professor of Computer Science Ca' Foscari University of Venice, Italy IEEE SMC Distinguished Lecturer Specialty Chief Editor, *Computer Vision - Frontiers in Computer Science* -------------- next part -------------- An HTML attachment was scrubbed... URL: From valvilraman at yahoo.co.in Fri Oct 29 07:13:13 2021 From: valvilraman at yahoo.co.in (Anand Ramamoorthy) Date: Fri, 29 Oct 2021 11:13:13 +0000 (UTC) Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: <8E01234A-03B3-492C-9DD7-B7FBD321475D@princeton.edu> References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> <8E01234A-03B3-492C-9DD7-B7FBD321475D@princeton.edu> Message-ID: <252610507.1139182.1635505993601@mail.yahoo.com> Hi All,??????????????????? Some remarks/thoughts: 1. Juergen raises important points relevant not just to the ML folks but also the wider scientific community 2. Setting aside broader aspects of the social quality of the scientific enterprise, let's take a look at a simpler thing; individual duty. Each scientist has a duty to science (as an intellectual discipline) and the scientific community, to uphold fundamental principles informing the conduct of science. Credit should be given wherever it is due - it is a matter of duty, not preference or "strategic vale" or boosting someone because they're a great populariser.? 3. Crediting those who disseminate is fine and dandy, but should be for those precise contributions, AND the originators of an idea/method/body of work ought to be recognised - this is perhaps a bit difficult when the work is obscured by history, but not impossible. At any rate, if one has novel information of pertinence w.r.t original work, then the right action is crystal clear. 4. Academic science has loads of problems and I think there is some urgency w.r.t sorting them out. For three reasons; a) scientific duty b) for posterity and c) we now live in a world where anti-science sentiments are not limited to fringe elements and this does not bode well for humanity.? Maybe dealing with proper credit assignment as pointed out by Juergen and others in the thread could be a start. Live Long and Prosper! Best, Anand Ramamoorthy On Friday, 29 October 2021, 08:39:14 BST, Jonathan D. Cohen wrote: The same incentive structures (and values they reflect) are not necessarily the same ? nor should they necessary be ? in commercial and academic environments. jdc On Oct 28, 2021, at 12:03 PM, Marina Meila wrote: Since credit is a form of currency in academia, let's look at the "hard currency" rewards of invention. Who gets them? ?the first company to create a new product usually fails. However, the interesting thing is that society (by this I mean the society most of us we work in) has found it necessary to counteract this, and we have patent laws to protect the rights of the inventors. ? The point is not whether patent laws are effective or not, it's the social norm they implement. That to protect invention one should ?pay attention to rewarding the original inventors, whether we get the "product" directly from them or not.? ? Best wishes, ? ?????? Marina ? --?Marina Meila Professor of Statistics University of Washington ? ? On 10/28/21, 5:59 AM, "Connectionists" wrote: As a friendly amendment to both Randy and Danko?s comments, it is also worth noting that science is an *intrinsically social* endeavor, and therefore communication is a fundamental factor.??This may help explain why the *last* person to invent or discover something is the one who gets the [social] credit.??That is, giving credit to those who disseminate may even have normative value.??After all, if a tree falls in the forrest? As for those who care more about discovery and invention than dissemination, well, for them credit assignment may not be as important ;^).? jdc ? ? On Oct 28, 2021, at 4:23 AM, Danko Nikolic wrote: ? Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee.?? Danko ? ? ? ? On Thu, 28 Oct 2021, 10:13 Randall O'Reilly wrote: ? ? I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit.??This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it;??conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). ? For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity.??But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts.??Sometimes, it is not the basic equations etc that matter: it is the big picture vision. ? Cheers, - Randy ? > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen wrote: >? > Hi, fellow artificial neural network enthusiasts! >? > The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. >? > Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at?juergen at idsia.ch: >? >?https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >? > The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. >? > I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. >? > Thank you all in advance for your help!? >? > J?rgen Schmidhuber >? >? >? >? >? >? >? >? -------------- next part -------------- An HTML attachment was scrubbed... URL: From junfeng989 at gmail.com Fri Oct 29 22:56:10 2021 From: junfeng989 at gmail.com (Jun Feng) Date: Fri, 29 Oct 2021 22:56:10 -0400 Subject: Connectionists: Call for Nominations - IEEE TCSC Outstanding PhD Dissertation Award - 2021 Message-ID: Dear Colleagues: Please accept our sincere apologies if you receive multiple copies of this message. Call for Nominations - IEEE TCSC Outstanding PhD Dissertation Award - 2021 ============= The IEEE Technical Committee on Scalable Computing (TCSC) is a technical committee within IEEE Computer Society, aimed at fostering research and education in scalable computing with applications. The committee solicits nominations of Outstanding PhD Dissertation Award. Each award includes an award plaque along with a public citation for the award on the IEEE TCSC website and presented at IEEE HPCC-2021 conferemnce. The IEEE TCSC Outstanding PhD Dissertation Award is an annual award to recognize candidates that have recently received a PhD degree for no more than 2 years and have written an outstanding PhD dissertation in the field of the scalable computing with applications. This award is established to encourage doctoral research that combines theory and practice or makes in-depth technical contributions, having the potential to contribute to the IEEE TCSC. ============= Nomination Materials for Outstanding PhD Dissertation Award: Nominations must be submitted via email to the selection committee chair. A nomination application (as a single PDF file) must consist of the following materials: (1) A doctoral dissertation written by the applicant in any language, no more than 2 years prior to the submission deadline. (2) A summary of the dissertation in English of up to 2 pages in length written by the PhD candidate, highlighting the significance of the problem, the technical approach taken, and the application context and potential. (3) Sample published paper(s) in English based on the dissertation written primarily by the PhD candidate in scientific journals, especially IEEE Transactions/Journals. (4) Listing of all publications by the applicant in the related field. (5) A letter of recommendation from the applicant?s dissertation advisor that assesses the significance of the research, attests to the originality of the work, and comments on the engagement of the applicant in the TCSC field. ============= Important Dates: - Nomination Deadline: November 21, 2021 - Results Notification: December 5, 2021 ============= Award Selection Committee: - Bernady O. Apduhan, Kyushu Sangyo University, Japan bob at is.kyusan-u.ac.jp - Beniamino Di Martino, Universita' della Campania "Luigi Vanvitelli", Italy beniamino.dimartino at unicampania.it - Didier El Baz, LAAS-CNRS, France elbaz at laas.fr - Hai Jiang (Chair), Arkansas State University, USA hjiang at astate.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From oreilly at ucdavis.edu Sun Oct 31 04:44:12 2021 From: oreilly at ucdavis.edu (Randall O'Reilly) Date: Sun, 31 Oct 2021 01:44:12 -0700 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: <252610507.1139182.1635505993601@mail.yahoo.com> References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> <8E01234A-03B3-492C-9DD7-B7FBD321475D@princeton.edu> <252610507.1139182.1635505993601@mail.yahoo.com> Message-ID: I'm sure everyone agrees that scientific integrity is essential at all levels, but I hope we can avoid a kind of simplistic, sanctimonious treatment of these issues -- there are lots of complex dynamics at play in this or any scientific field. Here's few additional thoughts / reactions: * Outside of a paper specifically on the history of a field, does it really make sense to "require" everyone to cite obscure old papers that you can't even get a PDF of on google scholar? Who does that help? Certainly not someone who might want to actually read a useful treatment of foundational ideas. I generally cite papers that I actually think other people should read if they want to learn more about a topic -- those tend to be written by people who write clearly and compellingly. Those who are obsessed about historical precedents should write papers on such things, but don't get bent out of shape if other people really don't care that much about that stuff and really just care about the ideas and moving *forward*. * Should Newton be cited instead of Rumelhart et al, for backprop, as Steve suggested? Seriously, most of the math powering today's models is just calculus and the chain rule. Furthermore, the idea that gradients passed through many multiplicative steps of the chain rule tend to dissipate exponentially is pretty basic at a mathematical level, and I'm sure some obscure (or even famous) mathematician from the 1800's or even earlier has pointed this out in some context or another. For example, Lyopunov's work from the late 1800's is directly relevant in terms of iterative systems and the need to have an exponent of 1 for stability. So at some level all of deep learning and LSTM is just derivative of this earlier work (pun intended!). * More generally, each individual scientist is constantly absorbing ideas from others, synthesizing them in their own internal neural networks, and essentially "reinventing" the insights and implications of these ideas in their own mind. We all only have our own individual subjective lens onto the world, and each have to generate our own internal conceptual structures for ourselves. Thus, reinvention is rampant, and we each feel a distinct sense of ownership over the powerful ideas that we have forged in our own minds. Some people are lucky enough to be at the right place and the right time to share truly new ideas in an effective way with a large number of other people, but everyone who grasps those ideas can cherish the fact that so many people are out there in the world working tirelessly to share all of these great ideas! * To support what Pierre Baldi said: People are strongly biased to form in-group affiliations and put others into less respected (or worse) out-groups -- the power of this instinct is behind most of the evil in the world today and throughout history, and science is certainly not immune to its effects. Thus it is important to explicitly promote diversity of all forms in scientific organizations, and work against what clearly are strong "cliques" in the field, who hold longstanding and disproportionate control over important organizations. - Randy > On Oct 29, 2021, at 4:13 AM, Anand Ramamoorthy wrote: > > Hi All, > Some remarks/thoughts: > > 1. Juergen raises important points relevant not just to the ML folks but also the wider scientific community > > 2. Setting aside broader aspects of the social quality of the scientific enterprise, let's take a look at a simpler thing; individual duty. Each scientist has a duty to science (as an intellectual discipline) and the scientific community, to uphold fundamental principles informing the conduct of science. Credit should be given wherever it is due - it is a matter of duty, not preference or "strategic vale" or boosting someone because they're a great populariser. > > 3. Crediting those who disseminate is fine and dandy, but should be for those precise contributions, AND the originators of an idea/method/body of work ought to be recognised - this is perhaps a bit difficult when the work is obscured by history, but not impossible. At any rate, if one has novel information of pertinence w.r.t original work, then the right action is crystal clear. > > 4. Academic science has loads of problems and I think there is some urgency w.r.t sorting them out. For three reasons; a) scientific duty b) for posterity and c) we now live in a world where anti-science sentiments are not limited to fringe elements and this does not bode well for humanity. > > Maybe dealing with proper credit assignment as pointed out by Juergen and others in the thread could be a start. > > Live Long and Prosper! > > Best, > > Anand Ramamoorthy > > > > On Friday, 29 October 2021, 08:39:14 BST, Jonathan D. Cohen wrote: > > > The same incentive structures (and values they reflect) are not necessarily the same ? nor should they necessary be ? in commercial and academic environments. > > jdc > > > >> On Oct 28, 2021, at 12:03 PM, Marina Meila wrote: >> >> Since credit is a form of currency in academia, let's look at the "hard currency" rewards of invention. Who gets them? the first company to create a new product usually fails. >> However, the interesting thing is that society (by this I mean the society most of us we work in) has found it necessary to counteract this, and we have patent laws to protect the rights of the inventors. >> >> The point is not whether patent laws are effective or not, it's the social norm they implement. That to protect invention one should pay attention to rewarding the original inventors, whether we get the "product" directly from them or not. >> >> Best wishes, >> >> Marina >> >> -- Marina Meila >> Professor of Statistics >> University of Washington >> >> >> On 10/28/21, 5:59 AM, "Connectionists" wrote: >> >> As a friendly amendment to both Randy and Danko?s comments, it is also worth noting that science is an *intrinsically social* endeavor, and therefore communication is a fundamental factor. This may help explain why the *last* person to invent or discover something is the one who gets the [social] credit. That is, giving credit to those who disseminate may even have normative value. After all, if a tree falls in the forrest? As for those who care more about discovery and invention than dissemination, well, for them credit assignment may not be as important ;^). >> jdc >> >> >> On Oct 28, 2021, at 4:23 AM, Danko Nikolic wrote: >> >> Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee. >> Danko >> >> >> >> >> On Thu, 28 Oct 2021, 10:13 Randall O'Reilly wrote: >> >> >> I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit. This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it; conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). >> >> For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity. But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts. Sometimes, it is not the basic equations etc that matter: it is the big picture vision. >> >> Cheers, >> - Randy >> >> > On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen wrote: >> > >> > Hi, fellow artificial neural network enthusiasts! >> > >> > The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. >> > >> > Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: >> > >> > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >> > >> > The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. >> > >> > I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. >> > >> > Thank you all in advance for your help! >> > >> > J?rgen Schmidhuber >> > >> > >> > >> > >> > >> > >> > >> > > From george at cs.ucy.ac.cy Sat Oct 30 07:41:53 2021 From: george at cs.ucy.ac.cy (George A. Papadopoulos) Date: Sat, 30 Oct 2021 14:41:53 +0300 Subject: Connectionists: 9th European Conference on Service-Oriented and Cloud Computing (ESOCC 2022): Final Call for Papers for the Main Track Message-ID: *** Final Call for Papers for the Main Track *** 9th European Conference on Service-Oriented and Cloud Computing (ESOCC 2022) March 22-24, 2022, Lutherstadt Wittenberg, Germany http://www.cs.ucy.ac.cy/~george/GPLists_2021/lm.php?tk=CQlGb3JtYWwJY29ubmVjdGlvbmlzdHNAbWFpbG1hbi5zcnYuY3MuY211LmVkdQk5dGggRXVyb3BlYW4gQ29uZmVyZW5jZSBvbiBTZXJ2aWNlLU9yaWVudGVkIGFuZCBDbG91ZCBDb21wdXRpbmcgKEVTT0NDIDIwMjIpOiBGaW5hbCBDYWxsIGZvciBQYXBlcnMJMzQxCUdlb3JnZQkyMQljbGljawl5ZXMJbm8=&url=https%3A%2F%2Fwww.esocc-conf.eu (*** Submission Deadline Extended to November 21st ***) Scope Service-oriented and cloud computing have made a huge impact both on the software industry and on the research community. Today, service and cloud technologies are applied to build large-scale software landscapes as well as to provide single software services to end users. Services today are independently developed and deployed as well as freely composed while they can be implemented in a variety of technologies, a quite important fact from a business perspective. Similarly, cloud computing aims at enabling flexibility by offering a centralised sharing of resources. The industry's need for agile and flexible software and IT systems has made cloud computing the dominating paradigm for provisioning computational resources in a scalable, on- demand fashion. Nevertheless, service developers, providers, and integrators still need to create methods, tools and techniques to support cost-effective and secure development as well as use of dependable devices, platforms, services and service- oriented applications in the cloud. The European Conference on Service-Oriented and Cloud Computing (ESOCC) is the premier conference on advances in the state of the art and practice of service- oriented computing and cloud computing in Europe. The main objectives of this conference are to facilitate the exchange between researchers and practitioners in the areas of service-oriented computing and cloud computing, as well as to explore the new trends in those areas and foster future collaborations in Europe and beyond. Topics of interest ESOCC 2022 seeks original, high quality papers related to all aspects of service- oriented and cloud computing. Specific topics of interest include but are not limited to: - Service and Cloud Computing Models  ? Design patterns, guidelines and methodologies  ? Governance models  ? Architectural models  ? Requirements engineering  ? Formal Methods  ? Model-Driven Engineering  ? Quality models  ? Security, Privacy & Trust models  ? Self-Organising Service-Oriented and Cloud Architectures Models  ? Testing models - Service and Cloud Computing Engineering  ? Service Discovery, Matchmaking, Negotiation and Selection  ? Monitoring and Analytics  ? Governance and management  ? Cloud Interoperability, Multi-Cloud, Cross-Cloud, Federated Cloud solutions  ? Frameworks & Methods for Building Service and Cloud based Applications  ? Cross-layer adaptation  ? Edge/Fog computing  ? Cloud, Service Orchestration & Management  ? Service Level Agreement Management  ? Service Evolution/Optimisation  ? Service & Cloud Testing and Simulation  ? QoS for Services and Clouds  ? Semantic Web Services  ? Service mining  ? Service & Cloud Standards  ? FaaS / Serverless computing - Technologies  ? DevOps in the Cloud  ? Containerized services  ? Emerging Trends in Storage, Computation and Network Clouds  ? Microservices: Design, Analysis, Deployment and Management  ? Next Generation Services Middleware and Service Repositories  ? RESTful Services  ? Service and Cloud Middleware & Platforms  ? Blockchain for Services & Clouds  ? Services and Clouds with IoT  ? Fog Computing with Service and Cloud - Business and Social aspects  ? Enterprise Architectures for Service and Cloud  ? Service-based Workflow Deployment & Life-cycle Management  ? Core Applications, e.g., Big Data, Commerce, Energy, Finance, Health, Scientific Computing, Smart Cities  ? Business Process as a Service - BPaaS  ? Service and Cloud Business Models  ? Service and Cloud Brokerage  ? Service and Cloud Marketplaces  ? Service and Cloud Cost & Pricing  ? Crowdsourcing Business Services  ? Social and Crowd-based Cloud  ? Energy issues in Cloud Computing  ? Sustainability issues Submissions from industry are welcome (for example, use cases). Submissions ESOCC 2022 invites submissions for the main track: - Regular research papers (15 pages including references) We only accept original papers, not submitted for publication elsewhere. The papers must be formatted according to the LNCS proceedings guidelines. They must be submitted to the EasyChair site at http://www.cs.ucy.ac.cy/~george/GPLists_2021/lm.php?tk=CQlGb3JtYWwJY29ubmVjdGlvbmlzdHNAbWFpbG1hbi5zcnYuY3MuY211LmVkdQk5dGggRXVyb3BlYW4gQ29uZmVyZW5jZSBvbiBTZXJ2aWNlLU9yaWVudGVkIGFuZCBDbG91ZCBDb21wdXRpbmcgKEVTT0NDIDIwMjIpOiBGaW5hbCBDYWxsIGZvciBQYXBlcnMJMzQxCUdlb3JnZQkyMQljbGljawl5ZXMJbm8=&url=https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Desocc2022 by selecting the main track. All accepted regular research papers are expected to be published in the main conference proceedings by Springer in the Lecture Notes in Computer Science (LNCS) series (http://www.springer.com/lncs). At least one author of each accepted paper is expected to register and present the work at the conference. A journal special issue is planned, and authors of selected accepted papers will be invited to submit extended versions of their articles. Important Dates - Paper submission: 21 November 2021 - Notifications: 7 January 2022 - Camera Ready versions due: 20 January 2022 Organization General Chair ? Wolf Zimmermann (Martin Luther University Halle-Wittenberg, Germany) Programme Co-Chairs ? Fabrizio Montesi (University of Southern Denmark, Denmark) ? George A. Papadopoulos (University of Cyprus, Cyprus) -------------- next part -------------- An HTML attachment was scrubbed... URL: From junfeng989 at gmail.com Fri Oct 29 22:29:13 2021 From: junfeng989 at gmail.com (Jun Feng) Date: Fri, 29 Oct 2021 22:29:13 -0400 Subject: Connectionists: Call for Nominations (CFN) - IEEE TCSC Award for Excellence for Early Career Researchers - 2021 Message-ID: Dear Colleagues: Please accept our sincere apologies if you receive multiple copies of this message. Call for Nominations (CFN) - IEEE TCSC Award for Excellence for Early Career Researchers - 2021 ============= The IEEE TCSC (Technical Committee on Scalable Computing) Award for Excellence in Scalable Computing (Early Career Researchers) recognizes up to 5 individuals who have made outstanding, influential, and potentially long-lasting contributions in the field of scalable computing. Typically the candidates are within 5 years of receiving their PhD degree as of January 01 of the year of the award. ============= Nominations: A candidate may be nominated by members of the community.An individual may nominate at most one candidate for this award. Nomination must be submitted via email to the selection committee chair. A nomination application (as a single PDF file) should contain the following details: 1. Name/email of person making the nomination (self-nominations are not eligible); 2. Name/email of candidate for whom the award is recommended; 3. A statement by the nominator (maximum of 500 words) as to why the nominee is highly deserving of the award both on excellence and in relation to IEEE TCSC. 4. CV of the nominee; 5. Up three support letters from persons other than the nominator ? these should be collected by the nominator and included in the nomination. Members of selection committee cannot be nominators or referees. ============= Important Dates: Nomination Deadline: November 21, 2021 Results Notification: December 05, 2021 ============= Award Selection Committee: Bernady O. Apduhan, Kyushu Sangyo University, Japan, bob at is.kyusan-u.ac.jp Jinjun Chen (Chair), Swinburne University of Technology, Australia, jchen at swin.edu.au Beniamino Di Martino, Universita' della Campania Luigi Vanvitelli, Italy, beniamino.dimartino at unina.it Didier El Baz, LAAS-CNRS, France, elbaz at laas.fr ============= Award & Presentation Note: Awardees will be presented a plaque and will be recognized by IEEE TCSC in its website, newsletter and archives. The awards for 2021 will be presented at a selected IEEE TCSC sponsored conference IEEE HPCC 2021. -------------- next part -------------- An HTML attachment was scrubbed... URL: From tgd at oregonstate.edu Fri Oct 29 14:21:56 2021 From: tgd at oregonstate.edu (Dietterich, Thomas) Date: Fri, 29 Oct 2021 18:21:56 +0000 Subject: Connectionists: Fwd: Scientific Integrity, the 2021 Turing Lecture, etc Message-ID: Pierre, Regarding leadership turnover, with the exception of Terry Sejnowski, the makeup of the NIPS Foundation board turns over on a regular basis, as there are term limits for all Board members. The Board consists of previous program chairs. The IMLS Board has had term limits and regular turnover since its founding, and its members are elected. Both organizations are always seeking volunteers for the many tasks involved in running the conference, and future program chairs are drawn from the people who have served in these positions. I encourage the readers of this list who are interested to contact the members of the conference organizing committees and express your interest. I have long sought for mechanisms to broaden the set of candidates considered for leadership positions, editorial boards, etc. When people rely on their own professional networks, this naturally limits the set of names that get considered. And this combines with the "founder effect" that still exists because the field was nucleated in North America. Nonetheless, I think the machine learning community is very open compared to other fields. That said, we could do much better! --Tom Thomas G. Dietterich, Distinguished Professor Emeritus School of Electrical Engineering and Computer Science??????????????????????? US Mail: 1148 Kelley Engineering Center?????? Office: 2067 Kelley Engineering Center?????? Oregon State Univ., Corvallis, OR 97331-5501 Voice: 541-737-5559; FAX: 541-737-1300 URL: http://web.engr.oregonstate.edu/~tgd/ From stephan.chalup at newcastle.edu.au Fri Oct 29 14:06:18 2021 From: stephan.chalup at newcastle.edu.au (Stephan Chalup) Date: Fri, 29 Oct 2021 18:06:18 +0000 Subject: Connectionists: [jobs] Several continuing level B and C lecturer positions available in Newcastle, Australia In-Reply-To: <6BE4C60C-90D8-4625-A8E7-A60B40FB6BE1@newcastle.edu.au> References: <6BE4C60C-90D8-4625-A8E7-A60B40FB6BE1@newcastle.edu.au> Message-ID: <5FD78D95-A801-455F-AECD-F59B8979A7F2@newcastle.edu.au> We have several continuing level B and C lecturer positions available. Please see details at https://uniofnewcastle.secure.force.com/academicext/ts2__JobSearch Newcastle also has the best city beach in Australia. -------------------------------------------------------------------------- Associate Professor Stephan Chalup School of Information and Physical Sciences, The University of Newcastle, Australia https://www.newcastle.edu.au/profile/stephan-chalup -------------- next part -------------- An HTML attachment was scrubbed... URL: From leslie.perez at pucv.cl Fri Oct 29 15:17:32 2021 From: leslie.perez at pucv.cl (Leslie Angelica Perez Caceres) Date: Fri, 29 Oct 2021 16:17:32 -0300 Subject: Connectionists: Call for Papers (EXTENDED deadline): EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation Message-ID: (Apologies for cross-posting) News: - The submission deadline has been extended to 24 November 2021! ************************************************************************************* Third Call for Papers: EvoCOP 2022 - The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation http://www.evostar.org/2022/evocop/ April 20 - 22, 2022 held as part of EvoStar (http://www.evostar.org) Venue: Seville, Spain ** EvoCOP is now CORE Rank B ** Submission deadline: November 24, 2021 (extended) ************************************************************************************* The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation is a multidisciplinary conference that brings together researchers working on applications and theory of evolutionary computation methods and other metaheuristics for solving difficult combinatorial optimisation problems appearing in various industrial, economic, and scientific domains. Successfully solved problems include, but are not limited to, multi-objective, uncertain, dynamic and stochastic problems in the context of scheduling, timetabling, network design, transportation and distribution, vehicle routing, stringology, graphs, satisfiability, energy optimisation, cutting, packing, planning and search-based software engineering. The EvoCOP 2021 conference will be held somewhere on Earth, together with EuroGP (the 24th European Conference on Genetic Programming), EvoMUSART (the 10th European conference on evolutionary and biologically inspired music, sound, art and design) and EvoApplications (the 24th European Conference on the Applications of Evolutionary Computation), and a new special track on Evolutionary Machine Learning in a joint event collectively known as EvoStar (Evo*). Accepted papers will be published by Springer Nature in the Lecture Notes in Computer Science series. (See https://link.springer.com/conference/evocop for previous proceedings.) The best regular paper presented at EvoCOP 2022 will be distinguished with a Best Paper Award. EvoCOP conference is now ranked B in the CORE 2021 ranking: http://portal.core.edu.au/conf-ranks/2195/ **** Areas of Interest and Contributions **** EvoCOP welcomes submissions in all experimental and theoretical aspects of evolutionary computation and other metaheuristics to combinatorial optimisation problems, including (but not limited to) the following areas: * Applications of metaheuristics to combinatorial optimisation problems * Theoretical developments * Neighbourhoods and efficient algorithms for searching them * Variation operators for stochastic search methods * Constraint-handling techniques * Parallelisation and grid computing * Search space and landscape analyses * Comparisons between different (also exact) methods * Automatic algorithm configuration and design Prominent examples of metaheuristics include (but are not limited to): * Evolutionary algorithms * Estimation of distribution algorithms * Swarm intelligence methods such as ant colony and particle swarm optimisation * Artificial immune systems * Local search methods such as simulated annealing, tabu search, variable neighbourhood search, iterated local search, scatter search and path relinking * Hybrid methods such as memetic algorithms * Matheuristics (hybrids of exact and heuristic methods) * Hyper-heuristics and autonomous search * Surrogate-model-based methods Notice that, by tradition, continuous/numerical optimisation is *not* part of the topics of interest of EvoCOP. Interested authors might consider submitting to other EvoStar conferences such as EvoApplications. **** Submission Details **** Paper submissions must be original and not published elsewhere. The submissions will be peer reviewed by members of the program committee. The reviewing process will be double-blind, please omit information about the authors in the submitted paper. Submit your manuscript in Springer LNCS format: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 Page limit: 16 pages Submission link: coming soon The authors of accepted papers will have to improve their paper on the basis of the reviewers? comments and will be asked to send a camera-ready version of their manuscripts. At least one author of each accepted work has to register for the conference, attend the conference and present the work. **** Important Dates **** Submission deadline: November 24, 2021 (extended) EvoStar: April 20-22, 2022 **** EvoCOP Programme Chairs **** Leslie P?rez C?ceres Pontificia Universidad Cat?lica de Valpara?so, Chile leslie.perez at pucv.cl S?bastien Verel Universit? du Littoral C?te d'Opale (ULCO), France verel at univ-littoral.fr -- Leslie P?rez C?ceres Escuela de Ingenier?a Inform?tica Pontificia Universidad Cat?lica de Valpara?so Directora Diplomado en Inteligencia Artificial http://diplomadoia.inf.ucv.cl Co-chair , EvoCOP 2022, April 20-22 http://www.evostar.org/2022/evocop/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From junfeng989 at gmail.com Fri Oct 29 22:39:27 2021 From: junfeng989 at gmail.com (Jun Feng) Date: Fri, 29 Oct 2021 22:39:27 -0400 Subject: Connectionists: Call for Nominations - IEEE TCSC MCR Awards - 2021 Message-ID: Dear Colleagues: Please accept our sincere apologies if you receive multiple copies of this message. Call for Nominations - IEEE TCSC Middle Career Researcher (MCR) Awards - 2021 ============= The IEEE Technical Committee on Scalable Computing (TCSC) is a technical committee within IEEE Computer Society, aimed at fostering research and education in scalable computing with applications. The committee solicits nominations of Middle Career Researcher (MCR) Award. The award includes an award plaque that will be presented at the annual IEEE HPCC conference, along with a public citation for the award on the IEEE TCSC website. ============= IEEE TCSC Middle Career Researcher Award IEEE TCSC Award for Excellence in Scalable Computing (Middle Career Researcher) recognizes up to 3 individuals who have made distinguished, influential, and on-going yet towards long-lasting contributions in the field of scalable computing with applications. Typically the candidates are within 5 to 15 years of receiving their PhD degree as of January 01 of the year of the award. ============= Nomination Materials for the MCR Award: A candidate must be nominated by members of the community. Nominations must be submitted via email to the selection committee chair. A nomination application (as a single PDF file) must consist of the following materials: (1) Name/email of person making the nomination (self-nominations are not eligible) (2) Name/email of the nominee (3) A statement by the nominator (maximum of 500 words) as to why the nominee is highly deserving of the award both on excellence and in relation to IEEE TCSC. (4) CV of the nominee (5) Up to three support letters from persons other than the nominator - these should be collected by the nominator and included in the nomination. Members of selection committee cannot be nominators or referees. ============= Important Dates: - Nomination Deadline: November 21, 2021 - Results Notification: December 05, 2021 ============= Award Selection Committee: - Bernady O. Apduhan, Kyushu Sangyo University, Japan, bob at is.kyusan-u.ac.jp - Jinjun Chen (Chair), Swinburne University of Technology, Australia, jchen at swin.edu.au - Beniamino Di Martino, Universita' della Campania Luigi Vanvitelli, Italy, beniamino.dimartino at unicampania.it - Didier El Baz, LAAS-CNRS, France, elbaz at laas.fr ============= Award & Presentation Note: Awardees will be presented a plaque and will be recognized by IEEE TCSC in its website, newsletter and archives. The awards for 2021 will be presented at IEEE HPCC-2021, Dec 2021 in Hainan, China. -------------- next part -------------- An HTML attachment was scrubbed... URL: From david at irdta.eu Sat Oct 30 10:32:59 2021 From: david at irdta.eu (David Silva - IRDTA) Date: Sat, 30 Oct 2021 16:32:59 +0200 (CEST) Subject: Connectionists: DeepLearn 2022 Winter - DeepLearn 2022 Spring - DeepLearn 2022 Summer Message-ID: <667577783.578414.1635604379799@webmail.strato.com> Dear all, DeepLearn, the International School on Deep Learning, is running since 2017 successfully. Please note the next three editions of the program in 2022: https://irdta.eu/deeplearn/2022wi/ https://irdta.eu/deeplearn/2022sp/ https://irdta.eu/deeplearn/2022su/ Best regards, DeepLearn organizing team -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Sat Oct 30 04:13:40 2021 From: achler at gmail.com (Tsvi Achler) Date: Sat, 30 Oct 2021 01:13:40 -0700 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> Message-ID: Since the title of the thread is Scientific Integrity, I want to point out some issues about trends in academia and then especially focusing on the connectionist community. In general analyzing impact factors etc the most important progress gets silenced until the mainstream picks it up Impact Factiors in novel research www.nber.org/.../working_papers/w22180/w22180.pdf and often this may take a generation https://www.nber.org/.../does-science-advance-one-funeral... . The connectionist field is stuck on feedforward networks and variants such as with inhibition of competitors (e.g. lateral inhibition), or other variants that are sometimes labeled as recurrent networks for learning time where the feedforward networks can be rewound in time. This stasis is specifically occuring with the popularity of deep learning. This is often portrayed as neurally plausible connectionism but requires an implausible amount of rehearsal and is not connectionist if this rehearsal is not implemented with neurons (see video link for further clarification). Models which have true feedback (e.g. back to their own inputs) cannot learn by backpropagation but there is plenty of evidence these types of connections exist in the brain and are used during recognition. Thus they get ignored: no talks in universities, no featuring in "premier" journals and no funding. But they are important and may negate the need for rehearsal as needed in feedforward methods. Thus may be essential for moving connectionism forward. If the community is truly dedicated to brain motivated algorithms, I recommend giving more time to networks other than feedforward networks. Video: https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2 Sincerely, Tsvi Achler On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen wrote: > Hi, fellow artificial neural network enthusiasts! > > The connectionists mailing list is perhaps the oldest mailing list on > ANNs, and many neural net pioneers are still subscribed to it. I am hoping > that some of them - as well as their contemporaries - might be able to > provide additional valuable insights into the history of the field. > > Following the great success of massive open online peer review (MOOR) for > my 2015 survey of deep learning (now the most cited article ever published > in the journal Neural Networks), I've decided to put forward another piece > for MOOR. I want to thank the many experts who have already provided me > with comments on it. Please send additional relevant references and > suggestions for improvements for the following draft directly to me at > juergen at idsia.ch: > > > https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html > > The above is a point-for-point critique of factual errors in ACM's > justification of the ACM A. M. Turing Award for deep learning and a > critique of the Turing Lecture published by ACM in July 2021. This work can > also be seen as a short history of deep learning, at least as far as ACM's > errors and the Turing Lecture are concerned. > > I know that some view this as a controversial topic. However, it is the > very nature of science to resolve controversies through facts. Credit > assignment is as core to scientific history as it is to machine learning. > My aim is to ensure that the true history of our field is preserved for > posterity. > > Thank you all in advance for your help! > > J?rgen Schmidhuber > > > > > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Alberto.Paccanaro at rhul.ac.uk Sun Oct 31 07:44:35 2021 From: Alberto.Paccanaro at rhul.ac.uk (Paccanaro, Alberto) Date: Sun, 31 Oct 2021 11:44:35 +0000 Subject: Connectionists: =?iso-8859-1?q?open-rank_faculty_position_in_Data?= =?iso-8859-1?q?_Science_--_School_of_Applied_Mathematics=2C_Funda=E7=E3o_?= =?iso-8859-1?q?Getulio_Vargas=2C_Rio_de_Janeiro?= Message-ID: The School of Applied Mathematics at Funda??o Getulio Vargas (FGV EMAp) in Rio de Janeiro, Brazil, invites applications for one open-rank faculty position in Data Science to strengthen and complement our existing research activity in this area. We are looking for established researchers (associate/full professor) or outstanding young researchers (assistant professor). Applications are welcome from applicants who have demonstrated research and teaching expertise, with focus in one or more of the following areas: machine learning, artificial intelligence, natural language processing, data visualization, computer vision, and scalable computing. The successful candidate is expected to develop an externally funded research programme in data science, publish in high impact journals, supervise research (postgraduate) students, teach at both undergraduate and graduate levels, and provide service to the department and institution. Peer-reviewed external funding is expected to be obtained and sustained. Industrial partnerships are also strongly encouraged. Qualifications The successful candidate will hold a Ph.D. in Computer Science or a closely related discipline, at the time of the appointment. At the assistant professor level, the applicant is expected to have a compatible track of publications and strong potential for publishing in top ranked journals. At higher levels (associate or full professor) the applicant is expected to have an established line of research with a strong publication record, proven ability to attract external funding, and an established research network. The successful candidate will also possess excellent communication and presentation skills, being capable of teaching both undergraduate and postgraduate students across our portfolio of Data Science courses. Fluence in Portuguese is desirable, but not required. However, by the end of the second year the applicant may be required to teach in Portuguese. Start-up package A generous package is available to provide the necessary resources for starting a successful independent research programme. These include computational infrastructure, lab space as well as postdoctoral positions and graduate-student support. Also, the amount of teaching will be minimal during the first two years, and will gradually increase only in later years. There is also the possibility to apply internally for core-funded projects. Salary and Benefits We offer a very competitive salary commensurate with level of appointment. Current salaries at FGV EMAp are higher than any similar academic institution in Brazil. The benefits include health and dental plans, support for young children's education and private pension fund. Application Applicants should send their applications to: data.science at fgv.br including: 1. a cover letter describing their experience, interests, and suitability for the position; 2. a curriculum vitae; 3. teaching statement; 4. research statement; 5. The best 3 publications (assistant professor) or the best 5 publications (associate or full professor) in the past 5 years 6. Contact information for three references. To be considered at the associate or full professor level, the candidate should include evidence of experience in directing graduate students. Applications received by December 10, 2021, will receive full consideration. Selected individuals will be contacted for an interview by January 17, 2022. Further enquiries should be sent to Prof. Cesar Camacho, Head of the School, by email cesar.camacho at fgv.br. About us The Funda??o Getulio Vargas (FGV), founded in 1944 in Rio de Janeiro, Brazil, is internationally recognized as one of the 6 most important think tanks in the world and one of the best institutions of higher learning. FGV is consistently ranked as the best institution in South and Central America and the 10th leading institution in the world for the production of applied research with impact on the implementation of public policies; and the 5th in the world in the area of social policies. The School of Applied Mathematics at FGV is a growing department that aspires to be top ranked worldwide. It currently offers undergraduate, M.Sc. and PhD degrees in Applied Mathematics and Data Science. International collaborations and joint research projects are currently ongoing with several research institution in Europe and North America. FGV is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, ethnicity, sex (including gender identity), national origin, disability status, age, sexual orientation, or any other characteristic protected by law. We always welcome nominations and applications from women, members of any minority group, and others who share our passion for building a diverse community that reflects the diversity in our student population. ===================================== Alberto Paccanaro, PhD Professor in Machine Learning and Computational Biology Department of Computer Science Royal Holloway, University of London Phone: +44 1784 414239 Homepage: http://www.cs.rhul.ac.uk/home/alberto/ Lab page: http://www.paccanarolab.org This email, its contents and any attachments are intended solely for the addressee and may contain confidential information. In certain circumstances, it may also be subject to legal privilege. Any unauthorised use, disclosure, or copying is not permitted. If you have received this email in error, please notify us and immediately and permanently delete it. Any views or opinions expressed in personal emails are solely those of the author and do not necessarily represent those of Royal Holloway, University of London. It is your responsibility to ensure that this email and any attachments are virus free. -------------- next part -------------- An HTML attachment was scrubbed... URL: From levine at uta.edu Sun Oct 31 15:49:41 2021 From: levine at uta.edu (Levine, Daniel S) Date: Sun, 31 Oct 2021 19:49:41 +0000 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> Message-ID: Tsvi, While deep learning and feedforward networks have an outsize popularity, there are plenty of published sources that cover a much wider variety of networks, many of them more biologically based than deep learning. A treatment of a range of neural network approaches, going from simpler to more complex cognitive functions, is found in my textbook Introduction to Neural and Cognitive Modeling (3rd edition, Routledge, 2019). Also Steve Grossberg's book Conscious Mind, Resonant Brain (Oxford, 2021) emphasizes a variety of architectures with a strong biological basis. Best, Dan Levine ________________________________ From: Connectionists on behalf of Tsvi Achler Sent: Saturday, October 30, 2021 3:13 AM To: Schmidhuber Juergen Cc: connectionists at cs.cmu.edu Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. Since the title of the thread is Scientific Integrity, I want to point out some issues about trends in academia and then especially focusing on the connectionist community. In general analyzing impact factors etc the most important progress gets silenced until the mainstream picks it up Impact Factiors in novel research www.nber.org/.../working_papers/w22180/w22180.pdf and often this may take a generation https://www.nber.org/.../does-science-advance-one-funeral... . The connectionist field is stuck on feedforward networks and variants such as with inhibition of competitors (e.g. lateral inhibition), or other variants that are sometimes labeled as recurrent networks for learning time where the feedforward networks can be rewound in time. This stasis is specifically occuring with the popularity of deep learning. This is often portrayed as neurally plausible connectionism but requires an implausible amount of rehearsal and is not connectionist if this rehearsal is not implemented with neurons (see video link for further clarification). Models which have true feedback (e.g. back to their own inputs) cannot learn by backpropagation but there is plenty of evidence these types of connections exist in the brain and are used during recognition. Thus they get ignored: no talks in universities, no featuring in "premier" journals and no funding. But they are important and may negate the need for rehearsal as needed in feedforward methods. Thus may be essential for moving connectionism forward. If the community is truly dedicated to brain motivated algorithms, I recommend giving more time to networks other than feedforward networks. Video: https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2 Sincerely, Tsvi Achler On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen > wrote: Hi, fellow artificial neural network enthusiasts! The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. Thank you all in advance for your help! J?rgen Schmidhuber -------------- next part -------------- An HTML attachment was scrubbed... URL: From tt at cs.dal.ca Sun Oct 31 16:30:48 2021 From: tt at cs.dal.ca (Thomas Trappenberg) Date: Sun, 31 Oct 2021 17:30:48 -0300 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> Message-ID: Tsvi et al., may I add a bit (maybe we need a new thread, but found all the contributions quite stimulating). Seeing so many real world applications of connectionist neural networks is great. It is amazing what we can finally do with computer vision and NLP with complete training (e.g., I view deep networks as overtrained associative databases that are practically exhaustedly trained, and this is good for engineering solutions). However, I think many of us are also really interested in understanding how the brain thinks, and I think what Tsvi is saying is that having these scientific discussions seems to be difficult with the overwhelming Google/NeurIPS growd. There are some good computational neuroscience venues. However, the problem I have is that many of these have primarily a cellular focus and much less on the system or biological information processing principles side. Besides Cosyne, has anyone some suggestions for meetings in this area? Cheers, Thomas PS: I am on sabbatical and wonder if anyone has suggestions to further these discussions or collaborations in this area. On Sun, Oct 31, 2021, 4:16 PM Tsvi Achler wrote: > Since the title of the thread is Scientific Integrity, I want to point out > some issues about trends in academia and then especially focusing on the > connectionist community. > > In general analyzing impact factors etc the most important progress gets > silenced until the mainstream picks it up Impact Factiors in novel > research www.nber.org/.../working_papers/w22180/w22180.pdf > and > often this may take a generation > https://www.nber.org/.../does-science-advance-one-funeral... > > . > > The connectionist field is stuck on feedforward networks and variants such > as with inhibition of competitors (e.g. lateral inhibition), or other > variants that are sometimes labeled as recurrent networks for learning time > where the feedforward networks can be rewound in time. > > This stasis is specifically occuring with the popularity of deep > learning. This is often portrayed as neurally plausible connectionism but > requires an implausible amount of rehearsal and is not connectionist if > this rehearsal is not implemented with neurons (see video link for further > clarification). > > Models which have true feedback (e.g. back to their own inputs) cannot > learn by backpropagation but there is plenty of evidence these types of > connections exist in the brain and are used during recognition. Thus they > get ignored: no talks in universities, no featuring in "premier" journals > and no funding. > > But they are important and may negate the need for rehearsal as needed in > feedforward methods. Thus may be essential for moving connectionism > forward. > > If the community is truly dedicated to brain motivated algorithms, I > recommend giving more time to networks other than feedforward networks. > > Video: > https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2 > > Sincerely, > Tsvi Achler > > > > On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen > wrote: > >> Hi, fellow artificial neural network enthusiasts! >> >> The connectionists mailing list is perhaps the oldest mailing list on >> ANNs, and many neural net pioneers are still subscribed to it. I am hoping >> that some of them - as well as their contemporaries - might be able to >> provide additional valuable insights into the history of the field. >> >> Following the great success of massive open online peer review (MOOR) for >> my 2015 survey of deep learning (now the most cited article ever published >> in the journal Neural Networks), I've decided to put forward another piece >> for MOOR. I want to thank the many experts who have already provided me >> with comments on it. Please send additional relevant references and >> suggestions for improvements for the following draft directly to me at >> juergen at idsia.ch: >> >> >> https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >> >> The above is a point-for-point critique of factual errors in ACM's >> justification of the ACM A. M. Turing Award for deep learning and a >> critique of the Turing Lecture published by ACM in July 2021. This work can >> also be seen as a short history of deep learning, at least as far as ACM's >> errors and the Turing Lecture are concerned. >> >> I know that some view this as a controversial topic. However, it is the >> very nature of science to resolve controversies through facts. Credit >> assignment is as core to scientific history as it is to machine learning. >> My aim is to ensure that the true history of our field is preserved for >> posterity. >> >> Thank you all in advance for your help! >> >> J?rgen Schmidhuber >> >> >> >> >> >> >> >> >> -------------- next part -------------- An HTML attachment was scrubbed... URL: From contact at sscc.fr Sun Oct 31 15:12:32 2021 From: contact at sscc.fr (SSCC) Date: Sun, 31 Oct 2021 20:12:32 +0100 Subject: Connectionists: [SSCC-Deadline Extension] Call for Papers (Symposium on Solutions for Smart Cities Challenges) Message-ID: <002a01d7ce8b$49cb5f80$dd621e80$@sscc.fr> Symposium on Solutions for Smart Cities Challenges (SSCC 2021) Gandia, Spain. December 6-9, 2021 (Hybrid) https://www.sscc.fr/sscc2021 Internet of Things (IoT) is used to collect and exchange massive data. This technology promises an immense potential for improving the quality of life, healthcare, manufacturing, transportation, etc. The use of the IoT in smart buildings has a great importance and promising outcomes with a direct impact on our society. Researchers and industrial partners have achieved several applications where they have leveraged various enabling technologies for service enhancement. Many sectors in a smart city can benefit from an enhanced data collection and effective data analysis process done on the data gathered from these smart building devices that mainly consist of HVAC systems. However, the incremental number of connected IoT devices request a scalable and robust network. Consequently, it rises the attack surfaces of devices as well as their connections, which make them more exposed to internal and external attacks. In this context, the challenging issue is how constructing a secure IoT network and preserving its resiliency. SSCC2021 invites submissions discussing the employment of smart solutions and approaches in smart cities. Topics of either theoretical, empirical or applied interest include, but are not limited to: Safety, Security, and Resilience . Smart networks for smart cities . Security management in smart cities . Security in distributed systems . Modeling, analysis and detection of IoT attacks . Data mining for cybersecurity in smart cities . Decentralized architecture for smart cities . Consensus protocols and applications IoT & AI . IoT Indoor deployment . IoT communication protocols . Building information modeling (BIM) IoT-based HVAC control in smart buildings . Artificial Intelligence in Cyber Physical Energy Systems . Optimization for IoT and smart cities . Dynamic scheduling for IoT deployment . Autonomous and Smart decisions Edge and Cloud . Cloud-Edge for IoT and smart cities . Fog and Edge computing for smart cities . Applications/services for Edge AI . Software Platforms for Edge Social aspects and applications . Behavioral and Energy Consumption Analytics . Indoor comfort . Human factors and organizational resilience for distributed systems Important Dates . Paper Submission Date: 05 November, 2021 . Notification to Authors: 15 November, 2021 . Camera Ready Submission: 21 November 2021 Submission System https://easychair.org/conferences/?conf=sscc2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jose at rubic.rutgers.edu Sun Oct 31 16:04:28 2021 From: jose at rubic.rutgers.edu (=?UTF-8?Q?Stephen_Jos=c3=a9_Hanson?=) Date: Sun, 31 Oct 2021 16:04:28 -0400 Subject: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc. In-Reply-To: References: <33DC3654-F4D6-473C-9F95-FB99C483E89D@usi.ch> <15BAA8B8-0B89-4131-82B0-CFE4441EE55E@usi.ch> <48070117-2ABB-4CCD-ACC9-AF8C5811ED75@usi.ch> <11c3a52ca6ed4495a395ae019d8a0907@idsia.ch> <6093DADD-223B-44F1-8E8A-4E996838ED34@ucdavis.edu> <27D911A3-9C51-48A6-8034-7FF3A3E89BBB@princeton.edu> <8E01234A-03B3-492C-9DD7-B7FBD321475D@princeton.edu> <252610507.1139182.1635505993601@mail.yahoo.com> Message-ID: <089f2bf6-2d42-fb6f-c9dc-29e8c0d5bf1f@rubic.rutgers.edu> Sorry Randy. you couldn't hear my tone... or perhaps I wasn't clear enough about context.??? I was being sarcastic... Partly due to your argument (below) of what the least resistant path is for citation that has a high foot-traffic is a good practice, however it is important to do a bit of due diligence, if you are developing a new method that might have already been invented but somehow fell off the citation path, for arbitrary reasons. Of course, deep learning has raised many old ghosts (boo!), since there is a huge dependence on what happened In the 1980s and before. Getting these things as? clearly as possible? can be helpful to the newbies as they enter the field and try to navigate it based on incomplete or inaccurate information, since they are trying to avoid reinventing something that was invented 2 years or a month ago, rather then 30-40 years ago. Btw, their are plenty of good books graduate students should read that can't be found as a PDF on GS. One I highly recommend which explains how computation, brain and neural networks got tangled up .. https://press.uchicago.edu/ucp/books/book/distributed/C/bo23348570.html ??? -- But it can only be read as an actual ....book, paper glue, thread.... yikes. Steve On 10/31/21 4:44 AM, Randall O'Reilly wrote: > I'm sure everyone agrees that scientific integrity is essential at all levels, but I hope we can avoid a kind of simplistic, sanctimonious treatment of these issues -- there are lots of complex dynamics at play in this or any scientific field. Here's few additional thoughts / reactions: > > * Outside of a paper specifically on the history of a field, does it really make sense to "require" everyone to cite obscure old papers that you can't even get a PDF of on google scholar? Who does that help? Certainly not someone who might want to actually read a useful treatment of foundational ideas. I generally cite papers that I actually think other people should read if they want to learn more about a topic -- those tend to be written by people who write clearly and compellingly. Those who are obsessed about historical precedents should write papers on such things, but don't get bent out of shape if other people really don't care that much about that stuff and really just care about the ideas and moving *forward*. > > * Should Newton be cited instead of Rumelhart et al, for backprop, as Steve suggested? Seriously, most of the math powering today's models is just calculus and the chain rule. Furthermore, the idea that gradients passed through many multiplicative steps of the chain rule tend to dissipate exponentially is pretty basic at a mathematical level, and I'm sure some obscure (or even famous) mathematician from the 1800's or even earlier has pointed this out in some context or another. For example, Lyopunov's work from the late 1800's is directly relevant in terms of iterative systems and the need to have an exponent of 1 for stability. So at some level all of deep learning and LSTM is just derivative of this earlier work (pun intended!). > > * More generally, each individual scientist is constantly absorbing ideas from others, synthesizing them in their own internal neural networks, and essentially "reinventing" the insights and implications of these ideas in their own mind. We all only have our own individual subjective lens onto the world, and each have to generate our own internal conceptual structures for ourselves. Thus, reinvention is rampant, and we each feel a distinct sense of ownership over the powerful ideas that we have forged in our own minds. Some people are lucky enough to be at the right place and the right time to share truly new ideas in an effective way with a large number of other people, but everyone who grasps those ideas can cherish the fact that so many people are out there in the world working tirelessly to share all of these great ideas! > > * To support what Pierre Baldi said: People are strongly biased to form in-group affiliations and put others into less respected (or worse) out-groups -- the power of this instinct is behind most of the evil in the world today and throughout history, and science is certainly not immune to its effects. Thus it is important to explicitly promote diversity of all forms in scientific organizations, and work against what clearly are strong "cliques" in the field, who hold longstanding and disproportionate control over important organizations. > > - Randy > >> On Oct 29, 2021, at 4:13 AM, Anand Ramamoorthy wrote: >> >> Hi All, >> Some remarks/thoughts: >> >> 1. Juergen raises important points relevant not just to the ML folks but also the wider scientific community >> >> 2. Setting aside broader aspects of the social quality of the scientific enterprise, let's take a look at a simpler thing; individual duty. Each scientist has a duty to science (as an intellectual discipline) and the scientific community, to uphold fundamental principles informing the conduct of science. Credit should be given wherever it is due - it is a matter of duty, not preference or "strategic vale" or boosting someone because they're a great populariser. >> >> 3. Crediting those who disseminate is fine and dandy, but should be for those precise contributions, AND the originators of an idea/method/body of work ought to be recognised - this is perhaps a bit difficult when the work is obscured by history, but not impossible. At any rate, if one has novel information of pertinence w.r.t original work, then the right action is crystal clear. >> >> 4. Academic science has loads of problems and I think there is some urgency w.r.t sorting them out. For three reasons; a) scientific duty b) for posterity and c) we now live in a world where anti-science sentiments are not limited to fringe elements and this does not bode well for humanity. >> >> Maybe dealing with proper credit assignment as pointed out by Juergen and others in the thread could be a start. >> >> Live Long and Prosper! >> >> Best, >> >> Anand Ramamoorthy >> >> >> >> On Friday, 29 October 2021, 08:39:14 BST, Jonathan D. Cohen wrote: >> >> >> The same incentive structures (and values they reflect) are not necessarily the same ? nor should they necessary be ? in commercial and academic environments. >> >> jdc >> >> >> >>> On Oct 28, 2021, at 12:03 PM, Marina Meila wrote: >>> >>> Since credit is a form of currency in academia, let's look at the "hard currency" rewards of invention. Who gets them? the first company to create a new product usually fails. >>> However, the interesting thing is that society (by this I mean the society most of us we work in) has found it necessary to counteract this, and we have patent laws to protect the rights of the inventors. >>> >>> The point is not whether patent laws are effective or not, it's the social norm they implement. That to protect invention one should pay attention to rewarding the original inventors, whether we get the "product" directly from them or not. >>> >>> Best wishes, >>> >>> Marina >>> >>> -- Marina Meila >>> Professor of Statistics >>> University of Washington >>> >>> >>> On 10/28/21, 5:59 AM, "Connectionists" wrote: >>> >>> As a friendly amendment to both Randy and Danko?s comments, it is also worth noting that science is an *intrinsically social* endeavor, and therefore communication is a fundamental factor. This may help explain why the *last* person to invent or discover something is the one who gets the [social] credit. That is, giving credit to those who disseminate may even have normative value. After all, if a tree falls in the forrest? As for those who care more about discovery and invention than dissemination, well, for them credit assignment may not be as important ;^). >>> jdc >>> >>> >>> On Oct 28, 2021, at 4:23 AM, Danko Nikolic wrote: >>> >>> Yes Randall, sadly so. I have seen similar examples in neuroscience and philosophy of mind. Often, (but not always), you have to be the one who popularizes the thing to get the credit. Sometimes, you can get away, you just do the hard conceptual work and others doing for you the (also hard) marketing work. The best bet is doing both by yourself. Still no guarantee. >>> Danko >>> >>> >>> >>> >>> On Thu, 28 Oct 2021, 10:13 Randall O'Reilly wrote: >>> >>> >>> I vaguely remember someone making an interesting case a while back that it is the *last* person to invent something that gets all the credit. This is almost by definition: once it is sufficiently widely known, nobody can successfully reinvent it; conversely, if it can be successfully reinvented, then the previous attempts failed for one reason or another (which may have nothing to do with the merit of the work in question). >>> >>> For example, I remember being surprised how little Einstein added to what was already established by Lorentz and others, at the mathematical level, in the theory of special relativity. But he put those equations into a conceptual framework that obviously changed our understanding of basic physical concepts. Sometimes, it is not the basic equations etc that matter: it is the big picture vision. >>> >>> Cheers, >>> - Randy >>> >>>> On Oct 27, 2021, at 12:52 AM, Schmidhuber Juergen wrote: >>>> >>>> Hi, fellow artificial neural network enthusiasts! >>>> >>>> The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field. >>>> >>>> Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at juergen at idsia.ch: >>>> >>>> https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html >>>> >>>> The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned. >>>> >>>> I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity. >>>> >>>> Thank you all in advance for your help! >>>> >>>> J?rgen Schmidhuber >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> > -- -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.png Type: image/png Size: 19957 bytes Desc: not available URL: