Special Issue "Selected Papers from CD-MAKE 2020 and ARES 2020"

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Edgar Weippl
Guest Editor
SBA Research & University of Vienna, Vienna, Austria
Interests: fundamental and applied research on blockchain and distributed ledger technologies; security of production systems engineering
Mr. Peter Kieseberg
Website1 Website2
Guest Editor
SBA Research GmbH—Secure Business Austria, Vienna 1040, Austria
Interests: information systems; interactive machine learning; information security; privacy
Prof. Dr. Andreas Holzinger
Website1 Website2
Guest Editor
Human-Centered AI Lab, Medical University Graz, 8036 Graz, Austria; xAI Lab, Alberta Machine Intelligence Institute, Edmonton, AB, T6G 2H1 Canada
Interests: machine learning; knowledge extraction; health informatics
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Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of extended papers selected from those presented at the 4th International Cross Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2020) as well as the 15th International Conference on Availability, Reliability and Security (ARES 2020). Please visit the conference websites for a detailed description: https://www.ares-conference.eu/ and https://cd-make.net/

Each submission to this Special Issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases and a change of title, abstract and keywords. These extended submissions will undergo a peer-review process according to the journal’s rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality reviewing process.

Prof. Dr. Edgar Weippl
Mr. Peter Kieseberg
Prof. Dr. Andreas Holzinger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • DATA – data fusion, preprocessing, mapping, knowledge representation, environments, etc.
  • LEARNING – algorithms, contextual adaptation, causal reasoning, transfer learning, etc.
  • VISUALIZATION – intelligent interfaces, human-AI interaction, dialogue systems, explanation interfaces, etc.
  • PRIVACY – data protection, safety, security, reliability, verifiability, trust, ethics and social issues, etc.
  • NETWORK – graphical models, graph-based machine learning, Bayesian inference, etc.
  • TOPOLOGY – geometrical machine learning, topological and manifold learning, etc.
  • ENTROPY – time and machine learning, entropy-based learning, etc.

Published Papers

This special issue is now open for submission.
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