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Dagstuhl Reports, Volume 13, Issue 12
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Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep)
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- published at: 2024-04-24
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Complete Issue
Dagstuhl Reports, Volume 13, Issue 12, December 2023, Complete Issue
Abstract
Dagstuhl Reports, Volume 13, Issue 12, December 2023, Complete Issue
Cite as
Dagstuhl Reports, Volume 13, Issue 12, pp. 1-49, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.13.12,
title = {{Dagstuhl Reports, Volume 13, Issue 12, December 2023, Complete Issue}},
pages = {1--49},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {12},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12},
URN = {urn:nbn:de:0030-drops-198514},
doi = {10.4230/DagRep.13.12},
annote = {Keywords: Dagstuhl Reports, Volume 13, Issue 12, December 2023, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 13, Issue 12, 2023
Abstract
Dagstuhl Reports, Table of Contents, Volume 13, Issue 12, 2023
Cite as
Dagstuhl Reports, Volume 13, Issue 12, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.13.12.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 13, Issue 12, 2023}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {12},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12.i},
URN = {urn:nbn:de:0030-drops-198528},
doi = {10.4230/DagRep.13.12.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Scalable Graph Mining and Learning (Dagstuhl Seminar 23491)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23491 "Scalable Graph Mining and Learning". The event brought together leading researchers and practitioners to discuss cutting-edge developments in graph machine learning, massive-scale graph analytics frameworks, and optimization techniques for graph processing. Besides the executive summary, the report contains abstracts of the 18 scientific talks presented, descriptions of three open problems, and preliminary results of three working groups formed during the seminar. In summary, the seminar successfully fostered discussions that bridged theoretical research and practical applications in scalable graph learning, mining, and analytics. Several potential outcomes include writing position and research papers as well as joint grant submissions.
Cite as
Danai Koutra, Henning Meyerhenke, Ilya Safro, and Fabian Brandt-Tumescheit. Scalable Graph Mining and Learning (Dagstuhl Seminar 23491). In Dagstuhl Reports, Volume 13, Issue 12, pp. 1-23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{koutra_et_al:DagRep.13.12.1,
author = {Koutra, Danai and Meyerhenke, Henning and Safro, Ilya and Brandt-Tumescheit, Fabian},
title = {{Scalable Graph Mining and Learning (Dagstuhl Seminar 23491)}},
pages = {1--23},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {12},
editor = {Koutra, Danai and Meyerhenke, Henning and Safro, Ilya and Brandt-Tumescheit, Fabian},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12.1},
URN = {urn:nbn:de:0030-drops-198530},
doi = {10.4230/DagRep.13.12.1},
annote = {Keywords: Graph mining, Graph machine learning, (hyper)graph and network algorithms, high-performance computing for graphs, algorithm engineering for graphs}
}
Document
Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492)
Abstract
The term of a model has different meanings in different communities, e.g., in psychology, computer science, and human-computer interaction, among others. Well-defined models and specifications are the bottleneck of rigorous analysis techniques in practice: they are often non-existent or outdated. The constructed models capture various aspects of system behaviours, which are inherently heterogeneous in nature in contemporary autonomous systems. Once these models are in place, they can be used to address further challenges concerning autonomous systems, such as validation and verification, transparency and trust, and explanation. The seminar brought together the best experts in a diverse range of disciplines such as artificial intelligence, formal methods, psychology, software and systems engineering, and human-computer interaction as well as others dealing with autonomous systems. The goal was to consolidate these understanding of models in order to address three grand challenges in trustworthiness and trust: (1) understanding and analysing the dynamic relationship of trustworthiness and trust, (2) the understanding of mental modes and trust, and (3) rigorous and model-based measures for trustworthiness and calibrated trust.
Cite as
Ellen Enkel, Nils Jansen, Mohammad Reza Mousavi, and Kristin Yvonne Rozier. Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492). In Dagstuhl Reports, Volume 13, Issue 12, pp. 24-47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{enkel_et_al:DagRep.13.12.24,
author = {Enkel, Ellen and Jansen, Nils and Mousavi, Mohammad Reza and Rozier, Kristin Yvonne},
title = {{Model Learning for Improved Trustworthiness in Autonomous Systems (Dagstuhl Seminar 23492)}},
pages = {24--47},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {12},
editor = {Enkel, Ellen and Jansen, Nils and Mousavi, Mohammad Reza and Rozier, Kristin Yvonne},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.12.24},
URN = {urn:nbn:de:0030-drops-198543},
doi = {10.4230/DagRep.13.12.24},
annote = {Keywords: artificial intelligence, automata learning, autonomous systems, cyber-physical systems, formal methods, machine learning, safety, safety-critical systems, self-adaptive systems, software evolution, technology acceptance, trust}
}