Connectionists: CFP: QUATIC'2021 Theme Track: Quality Aspects in Machine Learning, AI and Data Analytics

[Leandro Minku]

About:
Machine learning, AI and data analytics have become a major force of 
research progress in data mining and innovation across enterprises of 
all sizes. A lot of new platforms with increasingly more features for 
managing datasets have been proposed in recent years. Given that the 
datasets are frequently big, data mining is also related to the 
management of cloud and modern HPC clusters.

Quality assurance in machine learning, AI and data analytics is an 
important research and engineering challenge in today's data intensive 
computing. It can be directly related to the quality of data - quality 
of data generators, privacy, statistical considerations, etc. Given the 
whole ecosystem surrounding the application of such approaches, quality 
assurance can also relate to many other aspects, such as the quality of 
the software implementing the approaches, of the services providing the 
approaches, of the management of data-intensive computing systems 
running the approaches, of the relevant resource and data management 
tools, of the handling of ethical concerns surrounding the use of the 
approaches, etc. From the machine learning model's point of view, the 
quality aspects also include model robustness (e.g. generalization, 
model architecture, resilience to noise), controllability, 
explainability, and so on.

Topics of interest:
Papers on this track can explore any topics related to quality in 
machine learning, AI and data analytics. These include, but not limited 
to:
- Quality in data science
- Quality in deep learning
- Quality in business intelligence
- Quality in evolutionary algorithms
- Quality in fuzzy systems
- Quality in distributed machine learning systems
- Data quality in distributed and streaming analytics
- Algorithms for detecting concept drifts / changes in the underlying 
distribution of incoming data
- Algorithms and approaches for detecting outliers, duplicated data, and 
inconsistent data
- Efficiency versus accuracy trade-off
- Data governance
- Big data quality management
- Big data quality metrics
- Big data management across distributed databases and datacentres
- Big data persistence and preservation
- Big data quality in cloud systems
- Testing of machine learning and AI software systems
- Automated software testing
- Algorithms and approaches for data healing or system fault healing
- Procedures for evaluating data models
- Handling of ethical aspects in data analytics

Important dates:
Full paper submission:          April 10th, 2021
Notification for full papers:   May 7th, 2021
Camera-ready (full papers):     May 31st, 2021

Conference website: https://2021.quatic.org/calls/call-for-papers

Publication: QUATIC'2021 proceedings to be published in a volume of the 
Springer CCIS Series (Communications in Computer and Information 
Science).  CCIS is abstracted/indexed in DBLP, Google Scholar, 
EI-Compendex, Mathematical Reviews, SCImago, Scopus. CCIS volumes are 
also submitted for inclusion in the ISI Proceedings. The organizers are 
presently negotiating a journal special issue for extended versions of 
the best papers. In 2020 and 2019 there were associated special issues 
with the Software Quality Journal.

Theme chair: Dr. Shuo Wang, University of Birmingham, UK 
(s.wang.2 at bham.ac.uk)