Special Issue "Selected Papers from CD-MAKE 2021 and ARES 2021"

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

Deadline for manuscript submissions: closed (7 February 2022) | Viewed by 3882

Special Issue Editors

Prof. Dr. Simon Tjoa
E-Mail Website
Guest Editor
Institute of IT Security Research St. Pölten, University of Applied Sciences, 3100 St. Pölten, Austria
Interests: artificial intelligence; trustworthy AI; high risk AI; information security; cyber resilience; information security risk analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Edgar Weippl
E-Mail Website
Guest Editor
SBA Research, University of Vienna, 1090 Vienna, Austria
Interests: fundamental and applied research on blockchain and distributed ledger technologies; security of production systems engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andreas Holzinger
E-Mail Website1 Website2
Guest Editor
Peter Kieseberg
E-Mail Website1 Website2 Website3
Guest Editor Assistant
SBA Research GmbH, St. Pölten University of Applied Sciences, 1040 Vienna, Austria
Interests: digital forensics; privacy aware machine learning; trustworthy AI; blockchain and AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of extended papers selected from papers presented at the 5th International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE 2021) as well as the 16th International Conference on Availability, Reliability and Security (ARES 2021). Please visit the conference websites for a detailed description: https://www.ares-conference.eu/ and https://cd-make.net/

Prof. Dr. Simon Tjoa
Prof. Dr. Edgar Weippl
Prof. Dr. Andreas Holzinger
Peter Kieseberg
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 submissions that pass pre-check are 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 1400 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.

Keywords

  • 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 (3 papers)

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Research

Article
VloGraph: A Virtual Knowledge Graph Framework for Distributed Security Log Analysis
Mach. Learn. Knowl. Extr. 2022, 4(2), 371-396; https://doi.org/10.3390/make4020016 - 11 Apr 2022
Cited by 1 | Viewed by 1060
Abstract
The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, [...] Read more.
The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings. Full article
(This article belongs to the Special Issue Selected Papers from CD-MAKE 2021 and ARES 2021)
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Article
Counterfactual Models for Fair and Adequate Explanations
Mach. Learn. Knowl. Extr. 2022, 4(2), 316-349; https://doi.org/10.3390/make4020014 - 31 Mar 2022
Viewed by 892
Abstract
Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they are often too complex for [...] Read more.
Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they are often too complex for humans to understand and too expensive to compute even with automated reasoning methods. Interpretability requires good explanations that humans can grasp and can compute. We take an important step toward specifying what good explanations are by analyzing the epistemically accessible and pragmatic aspects of explanations. We characterize sufficiently good, or fair and adequate, explanations in terms of counterfactuals and what we call the conundra of the explainee, the agent that requested the explanation. We provide a correspondence between logical and mathematical formulations for counterfactuals to examine the partiality of counterfactual explanations that can hide biases; we define fair and adequate explanations in such a setting. We provide formal results about the algorithmic complexity of fair and adequate explanations. We then detail two sophisticated counterfactual models, one based on causal graphs, and one based on transport theories. We show transport based models have several theoretical advantages over the competition as explanation frameworks for machine learning algorithms. Full article
(This article belongs to the Special Issue Selected Papers from CD-MAKE 2021 and ARES 2021)
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Article
An Analysis of Cholesteric Spherical Reflector Identifiers for Object Authenticity Verification
Mach. Learn. Knowl. Extr. 2022, 4(1), 222-239; https://doi.org/10.3390/make4010010 - 24 Feb 2022
Viewed by 1244
Abstract
Arrays of Cholesteric Spherical Reflectors (CSRs), microscopic cholesteric liquid crystals in a spherical shape, have been argued to become a game-changing technology in anti-counterfeiting. Used to build identifiable tags or coating, called CSR IDs, they can supply objects with unclonable fingerprint-like characteristics, making [...] Read more.
Arrays of Cholesteric Spherical Reflectors (CSRs), microscopic cholesteric liquid crystals in a spherical shape, have been argued to become a game-changing technology in anti-counterfeiting. Used to build identifiable tags or coating, called CSR IDs, they can supply objects with unclonable fingerprint-like characteristics, making it possible to authenticate objects. In a previous study, we have shown how to extract minutiæ from CSR IDs. In this journal version, we build on that previous research, consolidate the methodology, and test it over CSR IDs obtained by different production processes. We measure the robustness and reliability of our procedure on large and variegate sets of CSR IDs’ images taken with a professional microscope (Laboratory Data set) and with a microscope that could be used in a realistic scenario (Realistic Data set). We measure intra-distance and interdistance, proving that we can distinguish images coming from the same CSR ID from images of different CSR IDs. However, without surprise, images in Laboratory Data set have an intra-distance that on average is less, and with less variance, than the intra-distance between responses from Realistic Data set. With this evidence, we discuss a few requirements for an anti-counterfeiting technology based on CSRs. Full article
(This article belongs to the Special Issue Selected Papers from CD-MAKE 2021 and ARES 2021)
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