@inproceedings{60506, author = {{Adler, Enno and Böttcher, Stefan and Hartel, Rita}}, booktitle = {{2025 Data Compression Conference (DCC)}}, publisher = {{IEEE}}, title = {{{String Partition for Building Long BWTs}}}, doi = {{10.1109/dcc62719.2025.00044}}, year = {{2025}}, } @article{60508, author = {{Höper, Lukas and Schulte, Carsten}}, issn = {{0899-3408}}, journal = {{Computer Science Education}}, pages = {{1--33}}, publisher = {{Informa UK Limited}}, title = {{{ReVEAL model and its application to revealing viewpoints on educational approaches to learning about data and AI}}}, doi = {{10.1080/08993408.2025.2516957}}, year = {{2025}}, } @inproceedings{60813, author = {{Seiler, Moritz and Preuß, Oliver Ludger and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}}, editor = {{Filipic, Bogdan}}, pages = {{76–84}}, publisher = {{ACM}}, title = {{{RandOptGen: A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces}}}, doi = {{10.1145/3712256.3726478}}, year = {{2025}}, } @inproceedings{60812, author = {{Preuß, Oliver Ludger and Mensendiek, Carolin and Rook, Jeroen and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}}, editor = {{Filipic, Bogdan}}, pages = {{58–66}}, publisher = {{ACM}}, title = {{{Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes}}}, doi = {{10.1145/3712256.3726481}}, year = {{2025}}, } @inproceedings{60814, author = {{Schede, Elias and Seiler, Moritz and Tierney, Kevin and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}}, editor = {{Filipic, Bogdan}}, pages = {{1190–1198}}, publisher = {{ACM}}, title = {{{Deep reinforcement learning for instance-specific algorithm configuration (GECCO Best Paper Award)}}}, doi = {{10.1145/3712256.3726480}}, year = {{2025}}, } @inproceedings{60914, author = {{Neumayr, Thomas and Yigitbas, Enes and Augstein, Mirjam and Herder, Eelco and Stojko, Laura and Strecker, Jannis and Seitz, Julia}}, booktitle = {{Proceedings of the Mensch & Computer (2025)}}, title = {{{ABIS 2025 – 29th International Workshop on Personalization and Recommendation}}}, year = {{2025}}, } @inproceedings{60915, author = {{Krings, Sarah Claudia and Yigitbas, Enes and Sauer, Stefan}}, booktitle = {{Proceedings of the 29th International Workshop on Personalization and Recommendation}}, title = {{{Perspectives on Contexts and Adaptations for Cross-Reality}}}, year = {{2025}}, } @inproceedings{58657, abstract = {{The rapid growth of 3D printing technology has transformed a wide range of industries, enabling the on-demand production of complex objects, from aerospace components to medical devices. However, this technology also introduces significant security challenges. Previous research highlighted the security implications of G-Codes—commands used to control the printing process. These studies assumed powerful attackers and focused on manipulations of the printed models, leaving gaps in understanding the full attack potential. In this study, we systematically analyze security threats associated with 3D printing, focusing specifically on vulnerabilities caused by G-Code commands. We introduce attacks and attacker models that assume a less powerful adversary than traditionally considered, broadening the scope of potential security threats. Our findings show that even minimal access to the 3D printer can result in significant security breaches, such as unauthorized access to subsequent print jobs or persistent misconfiguration of the printer. We identify 278 potentially malicious G-Codes across the attack categories Information Disclosure, Denial of Service, and Model Manipulation. Our evaluation demonstrates the applicability of these attacks across various 3D printers and their firmware. Our findings underscore the need for a better standardization process of G-Codes and corresponding security best practices. }}, author = {{Rossel, Jost and Mladenov, Vladislav and Wördenweber, Nico and Somorovsky, Juraj}}, booktitle = {{Proceedings of the 34th USENIX Security Symposium}}, location = {{Seattle, WA, USA}}, pages = {{1867 -- 1885}}, title = {{{Security Implications of Malicious G-Codes in 3D Printing}}}, year = {{2025}}, } @inproceedings{61090, author = {{Adler, Enno and Böttcher, Stefan and Hartel, Rita and Steininger, Cedric Alexander}}, booktitle = {{23rd International Symposium on Experimental Algorithms}}, pages = {{2:1----2:18}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{IBB: Fast Burrows-Wheeler Transform Construction for Length-Diverse DNA Data}}}, volume = {{228}}, year = {{2025}}, } @article{61108, abstract = {{Greybox fuzzing is used extensively in research and practice. There are umpteen publications that improve greybox fuzzing. However, to what extent do these improvements affect the internal components or internals of a given fuzzer is not yet understood as the improvements are mostly evaluated using code coverage and bug finding capability. Such an evaluation is insufficient to understand the effect of improvements on the fuzzer internals. Some of the literature visualizes the outcomes of fuzzing to enhance the understanding. However, they only focus on high-level information and no previous research on visualization has been dedicated to understanding fuzzing internals. To close this gap, we propose the first step towards development of a fuzzing-specific visualization framework: a taxonomy of visualization analysis tasks that fuzzing experts desire to help them understand the fuzzing internals. Our approach involves conducting interviews with fuzzing experts and using qualitative data analysis to systematically extract the task taxonomy from the interview data. We also evaluate the support of existing fuzzing visualization tools through the lens of our taxonomy. In our study, we have conducted 33 interviews with fuzzing practitioners and extracted a taxonomy of 120 visualization analysis tasks. Our evaluation shows that the existing fuzzing visualization tools only provide aids to support 10 of them.}}, author = {{Kummita, Sriteja and Miao, Miao and Bodden, Eric and Wei, Shiyi}}, issn = {{1049-331X}}, journal = {{ACM Transactions on Software Engineering and Methodology}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Visualization Task Taxonomy to Understand the Fuzzing Internals}}}, doi = {{10.1145/3718346}}, year = {{2025}}, } @inproceedings{61144, author = {{Kablo, Emiram and Kleber, Melina and Arias Cabarcos, Patricia}}, booktitle = {{34th USENIX Security Symposium (USENIX Security 25)}}, pages = {{1531–1548}}, title = {{{PrivaCI in VR: Exploring Perceptions and Acceptability of Data Sharing in Virtual Reality Through Contextual Integrity}}}, year = {{2025}}, } @article{61123, abstract = {{Knowledge graphs are used by a growing number of applications to represent structured data. Hence, evaluating the veracity of assertions in knowledge graphs—dubbed fact checking—is currently a challenge of growing importance. However, manual fact checking is commonly impractical due to the sheer size of knowledge graphs. This paper is a systematic survey of recent works on automatic fact checking with a focus on knowledge graphs. We present recent fact-checking approaches, the varied sources they use as background knowledge, and the features they rely upon. Finally, we draw conclusions pertaining to possible future research directions in fact checking knowledge graphs.}}, author = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, issn = {{0360-0300}}, journal = {{ACM Computing Surveys}}, keywords = {{fact checking, knowledge graphs, fact-checkers, check worthiness, evidence retrieval, trust, veracity.}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Fact Checking Knowledge Graphs -- A Survey}}}, doi = {{10.1145/3749838}}, volume = {{58}}, year = {{2025}}, } @article{59912, abstract = {{Abstract We study the expressivity and the complexity of various logics in probabilistic team semantics with the Boolean negation. In particular, we study the extension of probabilistic independence logic with the Boolean negation, and a recently introduced logic first-order theory of random variables with probabilistic independence. We give several results that compare the expressivity of these logics with the most studied logics in probabilistic team semantics setting, as well as relating their expressivity to a numerical variant of second-order logic. In addition, we introduce novel entropy atoms and show that the extension of first-order logic by entropy atoms subsumes probabilistic independence logic. Finally, we obtain some results on the complexity of model checking, validity and satisfiability of our logics.}}, author = {{Hannula, Miika and Hirvonen, Minna and Kontinen, Juha and Mahmood, Yasir and Meier, Arne and Virtema, Jonni}}, issn = {{0955-792X}}, journal = {{Journal of Logic and Computation}}, number = {{3}}, publisher = {{Oxford University Press (OUP)}}, title = {{{Logics with probabilistic team semantics and the Boolean negation}}}, doi = {{10.1093/logcom/exaf021}}, volume = {{35}}, year = {{2025}}, } @inproceedings{59054, author = {{Firmansyah, Asep Fajar and Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{ESWC2025}}, isbn = {{978-3-031-94575-5}}, keywords = {{firmansyah mousallem ngonga sherif zahera}}, pages = {{133----151}}, publisher = {{pringer Nature Switzerland}}, title = {{{ANTS: Abstractive Entity Summarization in Knowledge Graphs}}}, doi = {{10.1007/978-3-031-94575-5_8}}, year = {{2025}}, } @unpublished{61066, abstract = {{Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.}}, author = {{Fichte, Johannes and Fröhlich, Nicolas and Hecher, Markus and Lagerkvist, Victor and Mahmood, Yasir and Meier, Arne and Persson, Jonathan}}, booktitle = {{arXiv:2505.10982}}, title = {{{Facets in Argumentation: A Formal Approach to Argument Significance}}}, year = {{2025}}, } @unpublished{65018, abstract = {{Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since in 2018 the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to integrate privacy-aware practices into source code development. Despite these legal obligations, developers have limited tool support to reason about data protection throughout their app development process. This paper explores the use of static program slicing and software visualization to analyze privacy-relevant data flows in Android apps. We introduce SliceViz, a web tool that analyzes an Android app by slicing all privacy-relevant data sources detected in the source code on the back-end. It then helps developers by visualizing these privacy-relevant program slices. We conducted a user study with 12 participants demonstrating that SliceViz effectively aids developers in identifying privacy-relevant properties in Android apps. Our findings indicate that program slicing can be employed to identify and reason about privacy-relevant data flows in Android applications. With further usability improvements, developers can be better equipped to handle privacy-sensitive information.}}, author = {{Khedkar, Mugdha and Schlichtig, Michael and Mohan, Santhosh and Bodden, Eric}}, booktitle = {{arXiv:2503.16640}}, title = {{{Visualizing Privacy-Relevant Data Flows in Android Applications}}}, year = {{2025}}, } @inproceedings{65038, abstract = {{The rapid advancements in digital transformation have led to the emergence of dataspaces as a pivotal element for industry-wide and cross-industry data integration and interoperability across various businesses. Despite their potential, the adoption and effective utilization of dataspaces by business stakeholders remain challenging. This paper aims to address this gap by developing a comprehensive learning environment tailored for business stakeholders. Through an interview study and an analysis of the current state of research, we identify problem fields and derive key requirements for the development of the learning environment. The proposed environment includes a demonstrator and a training concept designed to enhance stakeholders' understanding and capabilities in managing and leveraging dataspaces. Our findings contribute to the body of knowledge by providing practical guidance through learning environments for the deployment of dataspaces in business contexts and highlighting areas for future research.}}, author = {{Lick, Jonas and Lamarz, Jessica and Dohmann, Friederike and Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian}}, booktitle = {{2024 6th International Conference on Control and Robotics (ICCR)}}, location = {{Yokohama, Japan }}, publisher = {{IEEE}}, title = {{{Guidance on Dataspaces: Development of a Learning Environment for Industrial SMEs}}}, doi = {{10.1109/iccr64365.2024.10927580}}, year = {{2025}}, } @inproceedings{65102, abstract = {{Efficient graph processing is essential for a wide range of applications. Scalability and memory access patterns are still a challenge, especially with the Breadth-First Search algorithm. This work focuses on leveraging HPC systems with multiple GPUs available in a single node with peer-to-peer functionality of the Intel oneAPI implementation of SYCL. We propose three GPU-based load-balancing methods: work-group localisation for efficient data access, even workload distribution for higher GPU occupancy, and a hybrid strided-access approach for heuristic balancing. These methods ensure performance, portability, and productivity with a unified codebase. Our proposed methodologies outperform state-of-the-art single-GPU implementations based on CUDA on synthetic RMAT graphs. We analysed BFS performance across NVIDIA A100, Intel Max 1550, and AMD MI300X GPUs, achieving a peak performance of 153.27 GTEPS on an RMAT25-64 graph using 8 GPUs on the NVIDIA A100. Furthermore, our work demonstrates the capability to handle RMAT graphs up to scale 29, achieving superior performance on synthetic graphs and competitive results on real-world datasets.}}, author = {{Olgu, Kaan and Kenter, Tobias and Nunez-Yanez, Jose and McIntosh-Smith, Simon and Deakin, Tom}}, booktitle = {{Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis}}, publisher = {{ACM}}, title = {{{Towards Efficient Load Balancing BFS on GPUs: One Code for AMD, Intel & Nvidia}}}, doi = {{10.1145/3731599.3767570}}, year = {{2025}}, } @techreport{62981, abstract = {{Otus is a high-performance computing cluster that was launched in 2025 and is operated by the Paderborn Center for Parallel Computing (PC2) at Paderborn University in Germany. The system is part of the National High Performance Computing (NHR) initiative. Otus complements the previous supercomputer Noctua 2, offering approximately twice the computing power while retaining the three node types that were characteristic of Noctua 2: 1) CPU compute nodes with different memory capacities, 2) high-end GPU nodes, and 3) HPC-grade FPGA nodes. On the Top500 list, which ranks the 500 most powerful supercomputers in the world, Otus is in position 164 with the CPU partition and in position 255 with the GPU partition (June 2025). On the Green500 list, ranking the 500 most energy-efficient supercomputers in the world, Otus is in position 5 with the GPU partition (June 2025). This article provides a comprehensive overview of the system in terms of its hardware, software, system integration, and its overall integration into the data center building to ensure energy-efficient operation. The article aims to provide unique insights for scientists using the system and for other centers operating HPC clusters. The article will be continuously updated to reflect the latest system setup and measurements. }}, author = {{Ehtesabi, Sadaf and Hossain, Manoar and Kenter, Tobias and Krawinkel, Andreas and Ostermann, Lukas and Plessl, Christian and Riebler, Heinrich and Rohde, Stefan and Schade, Robert and Schwarz, Michael and Simon, Jens and Winnwa, Nils and Wiens, Alex and Wu, Xin}}, keywords = {{Otus, Supercomputer, FPGA, PC2, Paderborn Center for Parallel Computing, Noctua 2, HPC}}, pages = {{33}}, publisher = {{Paderborn Center for Parallel Computing (PC2)}}, title = {{{Otus Supercomputer}}}, doi = {{10.48550/ARXIV.2512.07401}}, volume = {{1}}, year = {{2025}}, } @inproceedings{65466, author = {{Bäumer, Fabian and Brinkmann, Marcus and Radoy, Maximilian and Schwenk, Jörg and Somorovsky, Juraj}}, booktitle = {{Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security}}, publisher = {{ACM}}, title = {{{On the Security of SSH Client Signatures}}}, doi = {{10.1145/3719027.3765079}}, year = {{2025}}, }