Call for Papers: Special Issue on Discovery Science
CFP: Special Issue on Discovery Science
Springer Machine Learning journal
Scope and topics
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
- Machine learning: supervised learning (including ranking, multi-target prediction and structured prediction), unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, transfer learning, etc.
- 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
- Machine learning and cybersecurity
- 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 2021 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 (based on the reviews) 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.
Paper submission: July 3rd, 2022
• First notifications: Sep 4th, 2022
• Deadline for revised submissions: October 16, 2022
• Second round of notifications: Nov 27, 2022
• Publication (online): March 2023
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” 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, smallcondensed). 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).
Paula Branco, Ottawa University, Canada
Vitor Cerqueira, Dalhousie University, Canada
Carlos Soares, University of Porto, Portugal
Luis Torgo, Dalhousie University, Canada
Queries relating to the special issue should be sent to the guest editors at email@example.com and firstname.lastname@example.org
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