Call for Papers

Scope

The international conference on Discovery Science provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The conference focus is on the use of artificial intelligence methods in science. Its scope includes 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.

Topics

We invite submissions of research papers addressing all aspects of discovery science. We encourage 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. We also encourage papers in the fields of computational scientific discovery, mining scientific data, computational creativity and discovery informatics. We welcome papers 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.

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

Submission Guidelines

Papers must be written in English and formatted according to the Springer LNCS guidelines. Papers should be submitted in PDF form via the DS 2020 Online Submission System at EasyChair. Once a paper has been submitted to the conference, changes to the author list are not permitted.

Submitted papers should not exceed 15 pages (long papers) and 10 pages (short ones), in total (including references). All submissions will be subject to review by the DS 2020 Program Committee. The Program Committee reserves the right to offer acceptance as Short Papers (10 pages in the Proceedings) to some Long Paper submissions. All accepted papers will appear in the conference proceedings published by Springer LNCS series and will have allocated time for oral presentation in the conference.

The reviews are single-blind. Authors do not need to anonymize their submission. Submitted papers may not have appeared in or be under consideration for another workshop, conference or journal. They may not be under review or submitted to another forum during the DS 2020 review process.

Guidelines for Accepted Papers

Prepare the final version of your paper according to reviewers’ comments and suggestions, and strictly using the Springer guidelines for authors of proceedings and templates. Due to space limitations in the Proceedings, the camera-ready versions of all accepted papers is limited to 15 pages. Please note that Springer encourages the inclusion of ORCIDs in the publication.

Download the partially completed copyright form, where the conference name and the names of the volume editors have been entered in advance. The corresponding author, who must match the corresponding author marked on the paper, should enter the missing information (“Title of the Contribution”, “Author(s) Full Name(s)”, “Corresponding Author’s Name”, “Affiliation Address, and Email”, Date) and sign the filled-out copyright form on behalf of all of the authors. Please note that Springer does not accept digital signatures on the copyright right forms at present.

Prepare the updated version of your final paper as a zip folder with: (1) PDF, as well as source files, either Latex or Word and (2) the filled-out and signed copyright, and send the zip folder by email to discoveryscience20@gmail.com

Authors interested in Open Access or Open Choice, please refer to the corresponding webpage of Springer. In this case, the address for the invoice and the CC-BY Consent-to-Publish form should be submitted along with the camera-ready files. Again, the corresponding author signing the form should match the corresponding author marked on the paper.

For a paper to appear in the proceedings, at least one of the authors must register for the conference by the early registration deadline and present the paper at the conference.

Special Issue

The authors of a number of selected papers presented at DS 2020 will be invited to submit extended versions of their papers for possible inclusion in a special issue of  Machine Learning journal (published by Springer) on Discovery Science. Fast-track processing will be used to have them reviewed and published.