Learning Concept Lengths Accelerates Concept Learning in ALC - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web)

Drift Detection in Text Data with Document Embeddings - Feldhans, Robert; Wilke, Adrian; Heindorf, Stefan; Shaker, Mohammad Hossein; Hammer, Barbara; Ngonga Ngomo, Axel-Cyrille; Hüllermeier, Eyke (Intelligent Data Engineering and Automated Learning – IDEAL 2021)

An OCR Pipeline and Semantic Text Analysis for Comics - Hartel, Rita; Dunst, Alexander (MANPU 2020: The 4th International Workshop on coMics ANalysis, Processing and Understanding@Pattern Recognition. ICPR International Workshops and Challenges)

Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance - Jager, Tibor; Kurek, Rafael; Niehues, David (Public-Key Cryptography – PKC 2021)
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.

Verifiable Random Functions with Optimal Tightness - Niehues, David (Public-Key Cryptography – PKC 2021)
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.

Ranking Structured Objects with Graph Neural Networks - Damke, Clemens; Hüllermeier, Eyke (Proceedings of The 24th International Conference on Discovery Science (DS 2021))
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.
Keywords: Graph-structured data; Graph neural networks; Preference learning; Learning to rank

Beep-And-Sleep: Message and Energy Efficient Set Cover - Götte, Thorsten; Kolb, Christina; Scheideler, Christian; Werthmann, Julian (Algorithms for Sensor Systems (ALGOSENSORS '21))

Issuer-Hiding Attribute-Based Credentials - Bobolz, Jan; Eidens, Fabian; Krenn, Stephan; Ramacher, Sebastian; Samelin, Kai (Cryptology and Network Security)

Designing a Risk Assessment Tool for Artificial Intelligence Systems - Nagbøl, Per Rådberg; Müller, Oliver; Krancher, Oliver (The Next Wave of Sociotechnical Design)

Drift Detection in Text Data with Document Embeddings - Feldhans, Robert; Wilke, Adrian; Heindorf, Stefan; Shaker, Mohammad Hossein; Hammer, Barbara; Ngonga Ngomo, Axel-Cyrille; Hüllermeier, Eyke (Intelligent Data Engineering and Automated Learning – IDEAL 2021)

A Parameterized View on the Complexity of Dependence Logic - Kontinen, Juha; Meier, Arne; Mahmood, Yasir (Logical Foundations of Computer Science)

HighPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured Grids - Alhaddad, Samer; Förstner, Jens; Groth, Stefan; Grünewald, Daniel; Grynko, Yevgen; Hannig, Frank; Kenter, Tobias; Pfreundt, Franz-Josef; Plessl, Christian; Schotte, Merlind; Steinke, Thomas; Teich, Jürgen; et al. (Euro-Par 2020: Parallel Processing Workshops)
Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell’s equation. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.
Keywords: tet_topic_hpc

Evaluating the Design Space for Offloading 3D FFT Calculations to an FPGA for High-Performance Computing - Ramaswami, Arjun; Kenter, Tobias; Kühne, Thomas; Plessl, Christian (Applied Reconfigurable Computing. Architectures, Tools, and Applications)

A Parameterized View on the Complexity of Dependence Logic - Kontinen, Juha; Meier, Arne; Mahmood, Yasir (Logical Foundations of Computer Science)

Learning Choice Functions via Pareto-Embeddings - Pfannschmidt, Karlson; Hüllermeier, Eyke (Lecture Notes in Computer Science)

Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types - Sellmann, Meinolf; Tierney, Kevin (Lecture Notes in Computer Science)
Keywords: pc2-ressources

On the Real-World Instantiability of Admissible Hash Functions and Efficient Verifiable Random Functions - Jager, Tibor; Niehues, David (Lecture Notes in Computer Science)
Verifiable random functions (VRFs) are essentially digital signatures with additional properties, namely verifiable uniqueness and pseudorandomness, which make VRFs a useful tool, e.g., to prevent enumeration in DNSSEC Authenticated Denial of Existence and the CONIKS key management system, or in the random committee selection of the Algorand blockchain. Most standard-model VRFs rely on admissible hash functions (AHFs) to achieve security against adaptive attacks in the standard model. Known AHF constructions are based on error-correcting codes, which yield asymptotically efficient constructions. However, previous works do not clarify how the code should be instantiated concretely in the real world. The rate and the minimal distance of the selected code have significant impact on the efficiency of the resulting cryptosystem, therefore it is unclear if and how the aforementioned constructions can be used in practice. First, we explain inherent limitations of code-based AHFs. Concretely, we assume that even if we were given codes that achieve the well-known Gilbert-Varshamov or McEliece-Rodemich-Rumsey-Welch bounds, existing AHF-based constructions of verifiable random functions (VRFs) can only be instantiated quite inefficiently. Then we introduce and construct computational AHFs (cAHFs). While classical AHFs are information-theoretic, and therefore work even in presence of computationally unbounded adversaries, cAHFs provide only security against computationally bounded adversaries. However, we show that cAHFs can be instantiated significantly more efficiently. Finally, we use our cAHF to construct the currently most efficient verifiable random function with full adaptive security in the standard model.
Keywords: Admissible hash functions; Verifiable random functions; Error-correcting codes; Provable security

Modeling and Analyzing Architectural Diversity of Open Platforms - Jazayeri, Bahar; Schwichtenberg, Simon; Küster, Jochen; Zimmermann, Olaf; Engels, Gregor (Advanced Information Systems Engineering)

The new V-Model of 2206 and its validation - Gräßler, Iris; Hentze, Julian (at-Automatisierungstechnik 68(5))

Using Abstract Contracts for Verifying Evolving Features and Their Interactions - Knüppel, Alexander; Krüger, Stefan; Thüm, Thomas; Bubel, Richard; Krieter, Sebastian; Bodden, Eric; Schaefer, Ina (Lecture Notes in Computer Science)
Today, software systems are rarely developed monolithically, but may be composed of numerous individually developed features. Their modularization facilitates independent development and verification. While feature-based strategies to verify features in isolation have existed for years, they cannot address interactions between features. The problem with feature interactions is that they are typically unknown and may involve any subset of the features. Contrary, a family-based verification strategy captures feature interactions, but does not scale well when features evolve frequently. To the best of our knowledge, there currently exists no approach with focus on evolving features that combines both strategies and aims at eliminating their respective drawbacks. To fill this gap, we introduce Fefalution, a feature-family-based verification approach based on abstract contracts to verify evolving features and their interactions. Fefalution builds partial proofs for each evolving feature and then reuses the resulting partial proofs in verifying feature interactions, yielding a full verification of the complete software system. Moreover, to investigate whether a combination of both strategies is fruitful, we present the first empirical study for the verification of evolving features implemented by means of feature-oriented programming and by comparing Fefalution with another five family-based approaches varying in a set of optimizations. Our results indicate that partial proofs based on abstract contracts exhibit huge reuse potential, but also come with a substantial overhead for smaller evolution scenarios.