TY - CHAP AU - Muschalik, Maximilian AU - Fumagalli, Fabian AU - Hammer, Barbara AU - Huellermeier, Eyke ID - 48776 SN - 0302-9743 T2 - Machine Learning and Knowledge Discovery in Databases: Research Track TI - iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams ER - TY - CHAP AU - de Camargo e Souza Câmara, Igor AU - Turhan, Anni-Yasmin ID - 52859 SN - 0302-9743 T2 - Logics in Artificial Intelligence TI - Deciding Subsumption in Defeasible $$\mathcal {ELI}_\bot $$ with Typicality Models ER - TY - CONF AU - Hansmeier, Tim AU - Platzner, Marco ID - 30971 SN - 0302-9743 T2 - Applications of Evolutionary Computation, EvoApplications 2022, Proceedings TI - Integrating Safety Guarantees into the Learning Classifier System XCS VL - 13224 ER - TY - CHAP AU - Bondarenko, Alexander AU - Fröbe, Maik AU - Kiesel, Johannes AU - Syed, Shahbaz AU - Gurcke, Timon AU - Beloucif, Meriem AU - Panchenko, Alexander AU - Biemann, Chris AU - Stein, Benno AU - Wachsmuth, Henning AU - Potthast, Martin AU - Hagen, Matthias ID - 34077 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - Overview of Touché 2022: Argument Retrieval ER - TY - CHAP AU - Wolters, Dennis AU - Engels, Gregor ED - Taibi, Davide ED - Kuhrmann, Marco ED - Mikkonen, Tommi ED - Klünder, Jil ED - Abrahamsson, Pekka ID - 34292 SN - 0302-9743 T2 - Product-Focused Software Process Improvement TI - Towards Situational Process Management for Professional Education Programmes VL - 13709 ER - TY - CHAP AU - Maack, Marten AU - Meyer auf der Heide, Friedhelm AU - Pukrop, Simon ID - 29872 SN - 0302-9743 T2 - Approximation and Online Algorithms TI - Server Cloud Scheduling ER - TY - CHAP AU - KOUAGOU, N'Dah Jean AU - Heindorf, Stefan AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille ID - 33740 SN - 0302-9743 T2 - The Semantic Web TI - Learning Concept Lengths Accelerates Concept Learning in ALC ER - TY - CHAP AU - Wohlleben, Meike Claudia AU - Bender, Amelie AU - Peitz, Sebastian AU - Sextro, Walter ID - 29727 SN - 0302-9743 T2 - Machine Learning, Optimization, and Data Science TI - Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction ER - TY - CHAP AU - Zahera, Hamada Mohamed Abdelsamee AU - Heindorf, Stefan AU - Balke, Stefan AU - Haupt, Jonas AU - Voigt, Martin AU - Walter, Carolin AU - Witter, Fabian AU - Ngonga Ngomo, Axel-Cyrille ID - 33738 SN - 0302-9743 T2 - The Semantic Web: ESWC 2022 Satellite Events TI - Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings ER - TY - CHAP AU - Zahera, H.M.A AU - Vollmers, Daniel AU - Sherif, Mohamed Ahmed AU - Ngomo, Axel-Cyrille Ngonga ID - 38506 SN - 0302-9743 T2 - The Semantic Web – ISWC 2022 TI - MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs ER - TY - CHAP AU - Feldhans, Robert AU - Wilke, Adrian AU - Heindorf, Stefan AU - Shaker, Mohammad Hossein AU - Hammer, Barbara AU - Ngonga Ngomo, Axel-Cyrille AU - Hüllermeier, Eyke ID - 29046 SN - 0302-9743 T2 - Intelligent Data Engineering and Automated Learning – IDEAL 2021 TI - Drift Detection in Text Data with Document Embeddings ER - TY - CONF AU - Hartel, Rita AU - Dunst, Alexander ID - 21378 SN - 0302-9743 T2 - MANPU 2020: The 4th International Workshop on coMics ANalysis, Processing and Understanding@Pattern Recognition. ICPR International Workshops and Challenges TI - An OCR Pipeline and Semantic Text Analysis for Comics ER - TY - CHAP AB - We construct more efficient cryptosystems with provable security against adaptive attacks, based on simple and natural hardness assumptions in the standard model. Concretely, we describe: – An adaptively-secure variant of the efficient, selectively-secure LWE- based identity-based encryption (IBE) scheme of Agrawal, Boneh, and Boyen (EUROCRYPT 2010). In comparison to the previously most efficient such scheme by Yamada (CRYPTO 2017) we achieve smaller lattice parameters and shorter public keys of size O(log λ), where λ is the security parameter. – Adaptively-secure variants of two efficient selectively-secure pairing- based IBEs of Boneh and Boyen (EUROCRYPT 2004). One is based on the DBDH assumption, has the same ciphertext size as the cor- responding BB04 scheme, and achieves full adaptive security with public parameters of size only O(log λ). The other is based on a q- type assumption and has public key size O(λ), but a ciphertext is only a single group element and the security reduction is quadrat- ically tighter than the corresponding scheme by Jager and Kurek (ASIACRYPT 2018). – A very efficient adaptively-secure verifiable random function where proofs, public keys, and secret keys have size O(log λ). As a technical contribution we introduce blockwise partitioning, which leverages the assumption that a cryptographic hash function is weak near-collision resistant to prove full adaptive security of cryptosystems. AU - Jager, Tibor AU - Kurek, Rafael AU - Niehues, David ID - 22057 SN - 0302-9743 T2 - Public-Key Cryptography – PKC 2021 TI - Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance ER - TY - CHAP AB - Verifiable random functions (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), are the public-key equivalent of pseudo- random functions. A public verification key and proofs accompanying the output enable all parties to verify the correctness of the output. How- ever, all known standard model VRFs have a reduction loss that is much worse than what one would expect from known optimal constructions of closely related primitives like unique signatures. We show that: 1. Every security proof for a VRF that relies on a non-interactive assumption has to lose a factor of Q, where Q is the number of adver- sarial queries. To that end, we extend the meta-reduction technique of Bader et al. (EUROCRYPT’16) to also cover VRFs. 2. This raises the question: Is this bound optimal? We answer this ques- tion in the affirmative by presenting the first VRF with a reduction from the non-interactive qDBDHI assumption to the security of VRF that achieves this optimal loss. We thus paint a complete picture of the achievability of tight verifiable random functions: We show that a security loss of Q is unavoidable and present the first construction that achieves this bound. AU - Niehues, David ID - 22059 SN - 0302-9743 T2 - Public-Key Cryptography – PKC 2021 TI - Verifiable Random Functions with Optimal Tightness ER - TY - CONF AB - Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression. AU - Damke, Clemens AU - Hüllermeier, Eyke ED - Soares, Carlos ED - Torgo, Luis ID - 27381 KW - Graph-structured data KW - Graph neural networks KW - Preference learning KW - Learning to rank SN - 0302-9743 T2 - Proceedings of The 24th International Conference on Discovery Science (DS 2021) TI - Ranking Structured Objects with Graph Neural Networks VL - 12986 ER - TY - CHAP AU - Götte, Thorsten AU - Kolb, Christina AU - Scheideler, Christian AU - Werthmann, Julian ID - 26888 SN - 0302-9743 T2 - Algorithms for Sensor Systems (ALGOSENSORS '21) TI - Beep-And-Sleep: Message and Energy Efficient Set Cover ER - TY - CONF AU - Bobolz, Jan AU - Eidens, Fabian AU - Krenn, Stephan AU - Ramacher, Sebastian AU - Samelin, Kai ID - 29566 SN - 0302-9743 T2 - Cryptology and Network Security TI - Issuer-Hiding Attribute-Based Credentials ER - TY - CHAP AU - Nagbøl, Per Rådberg AU - Müller, Oliver AU - Krancher, Oliver ID - 32868 SN - 0302-9743 T2 - The Next Wave of Sociotechnical Design TI - Designing a Risk Assessment Tool for Artificial Intelligence Systems ER - TY - CHAP AU - Feldhans, Robert AU - Wilke, Adrian AU - Heindorf, Stefan AU - Shaker, Mohammad Hossein AU - Hammer, Barbara AU - Ngonga Ngomo, Axel-Cyrille AU - Hüllermeier, Eyke ID - 29292 SN - 0302-9743 T2 - Intelligent Data Engineering and Automated Learning – IDEAL 2021 TI - Drift Detection in Text Data with Document Embeddings ER - TY - CONF AU - Kontinen, Juha AU - Meier, Arne AU - Mahmood, Yasir ID - 45846 SN - 0302-9743 T2 - Logical Foundations of Computer Science TI - A Parameterized View on the Complexity of Dependence Logic ER -