Connectionists: Special issue of the 25th Edition of the Discovery Science conference on Machine Learning journal, Springer

[Roberto Interdonato]

Call for Papers: Special Issue on Discovery Science 2022

https://www.springer.com/journal/10994/updates/23693484


The Machine Learning journal invites submissions of research papers
addressing all aspects of discovery science – a research discipline
concerned with the development and analysis of methods for discovering
scientific knowledge, coming from machine learning, data mining,
intelligent data analysis, and big data analytics, as well as their
application in various domains.

Submissions addressing all aspects of discovery science are welcome.
Research papers that focus on the analysis of different types of massive
and complex data, including structured, spatio-temporal and network data,
as well as heterogeneous, continuous or imprecise data are encouraged.
Research papers in the fields of computational scientific discovery, mining
scientific data, computational creativity and discovery informatics are
also welcome. Finally, submissions addressing applications of artificial
intelligence in different domains of science, including biomedicine and
life sciences, materials science, astronomy, physics, chemistry, as well as
social sciences are encouraged.

Possible topics include, but are not limited to:

Artificial intelligence (machine learning, knowledge representation and
reasoning, natural language processing, statistical methods, etc.) applied
to science
Knowledge discovery and data mining
Causal modelling
AutoML, meta-learning, planning to learn
Machine learning and high-performance computing, grid and cloud computing
Literature-based discovery
Ontologies for science, including the representation and annotation of
datasets and domain knowledge
Explainable AI, interpretability of machine learning and deep learning
models
Process discovery and analysis
Computational creativity
Anomaly detection and outlier detection
Data streams, evolving data, change detection, concept drift, model
maintenance
Network analysis
Time-series analysis
Learning from complex data
Graphs, networks, linked and relational data
Spatial, temporal and spatiotemporal data
Unstructured data, including textual and web data
Multimedia data
Data and knowledge visualization
Human-machine interaction for knowledge discovery and management
Evaluation of models and predictions in discovery setting
Applications of the above techniques in scientific domains, such as
Physical sciences (e.g., materials sciences, particle physics)
Life sciences (e.g., systems biology/systems medicine)
Environmental sciences
Natural and social sciences
Papers which, at the time of submission, have appeared in the proceedings
of Discovery Science 2022 or other relevant conferences will be considered
provided that the submission constitutes a significant contribution beyond
the conference paper containing at least 30% of new material (e.g.,
extensions of the method, additional technical results, etc.) as compared
to the conference version of the paper. The guest editors (accounting for
reviewers’ comments) will make the decision on whether the difference is
significant enough to warrant publication. The journal version should
include a short paragraph explaining how it extends the previously
published conference paper.

Schedule

Paper submission: February 14, 2023

First notifications: April 28, 2023

Deadline for revised submissions: June 13, 2023

Final Notification: July 28, 2023

Expected publication (online): September/October, 2023

Submission procedure

To submit to this issue, authors have to make a journal submission to the
Springer Machine Learning journal (https://www.editorialmanager.com/MACH/)
and select the type of submission to be for the “S.I.: Discovery Science
2022” special issue. It is highly recommended that submitted papers do not
exceed 20 pages including references. Every paper may be accompanied with
unlimited appendices.

The papers should be formatted in the Springer journal style (svjour3,
small condensed). The journal requires authors to include an information
sheet as a supplementary material that contains a short summary of their
contribution and specifically address the following questions:

What is the main claim of the paper? Why is this an important contribution
to the machine learning literature? [“We are the first to have done X” is
not an acceptable answer without stating the importance of X.]

What is the evidence you provide to support your claim? Be precise. [“The
evidence is provided by experiments and/or theoretical analysis” is not an
acceptable answer without a summary of the main results and their
implications.]

What papers by other authors make the most closely related contributions,
and how is your paper related to them?

Have you published parts of your paper before, for instance in a
conference? If so, give details of your previous paper(s) and a precise
statement detailing how your paper provides a significant contribution
beyond the previous paper(s).

Guest editors

Dino Ienco, INRAE, France

Roberto Interdonato, CIRAD, France

Pascal Poncelet, University of Montpellier, France

For Queries relating to Journal Track Submissions email the journal track
chairs at dino.ienco at inrae.fr, roberto.interdonato at cirad.fr and
pascal.poncelet at lirmm.fr
-- 

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Roberto Interdonato, PhD
Research Scientist
CIRAD
UMR TETIS
Montpellier
France
email: roberto.interdonato at cirad.fr
tel: +33 (0) 467558615
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