@inproceedings{53362, author = {{Amft, Sabrina and Höltervennhoff, Sandra and Huaman, Nicolas and Krause, Alexander and Simko, Lucy and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26-30, 2023}}, editor = {{Meng, Weizhi and Jensen, Christian Damsgaard and Cremers, Cas and Kirda, Engin}}, pages = {{3138–3152}}, publisher = {{ACM}}, title = {{{"We’ve Disabled MFA for You": An Evaluation of the Security and Usability of Multi-Factor Authentication Recovery Deployments}}}, doi = {{10.1145/3576915.3623180}}, year = {{2023}}, } @inproceedings{49438, author = {{Krüger, Stefan and Reif, Michael and Wickert, Anna-Katharina and Nadi, Sarah and Ali, Karim and Bodden, Eric and Acar, Yasemin and Mezini, Mira and Fahl, Sascha}}, booktitle = {{2023 IEEE Secure Development Conference (SecDev)}}, publisher = {{IEEE}}, title = {{{Securing Your Crypto-API Usage Through Tool Support - A Usability Study}}}, doi = {{10.1109/secdev56634.2023.00015}}, year = {{2023}}, } @article{53368, author = {{Fourné, Marcel and Wermke, Dominik and Fahl, Sascha and Acar, Yasemin}}, journal = {{IEEE Security & Privacy}}, number = {{6}}, pages = {{59–63}}, publisher = {{IEEE}}, title = {{{A Viewpoint on Human Factors in Software Supply Chain Security: A Research Agenda}}}, volume = {{21}}, year = {{2023}}, } @inproceedings{53366, author = {{Tran, Mindy and Munyendo, Collins W and Sri Ramulu, Harshini and Rodriguez, Rachel Gonzalez and Schnell, Luisa Ball and Sula, Cora and Simko, Lucy and Acar, Yasemin}}, booktitle = {{2024 IEEE Symposium on Security and Privacy (SP)}}, pages = {{4–4}}, title = {{{Security, Privacy, and Data-sharing Trade-offs When Moving to the United States: Insights from a Qualitative Study}}}, year = {{2023}}, } @article{53348, author = {{Fourné, Marcel and Wermke, Dominik and Fahl, Sascha and Acar, Yasemin}}, journal = {{IEEE Secur. Priv.}}, number = {{6}}, pages = {{59–63}}, title = {{{A Viewpoint on Human Factors in Software Supply Chain Security: A Research Agenda}}}, doi = {{10.1109/MSEC.2023.3316569}}, volume = {{21}}, year = {{2023}}, } @article{53352, author = {{Simko, Lucy and Sri Ramulu, Harshini and Kohno, Tadayoshi and Acar, Yasemin}}, journal = {{Proc. ACM Hum. Comput. Interact.}}, number = {{CSCW2}}, pages = {{1–54}}, title = {{{The Use and Non-Use of Technology During Hurricanes}}}, doi = {{10.1145/3610215}}, volume = {{7}}, year = {{2023}}, } @article{46310, abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}}, author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, keywords = {{Feature normalization, Algorithm selection, Traveling salesperson problem}}, pages = {{123--145}}, title = {{{A study on the effects of normalized TSP features for automated algorithm selection}}}, doi = {{https://doi.org/10.1016/j.tcs.2022.10.019}}, volume = {{940}}, year = {{2023}}, } @inproceedings{48898, abstract = {{Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.}}, author = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{361 -- 368}}, title = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}}, doi = {{10.1109/SSCI52147.2023.10372008}}, year = {{2023}}, } @inproceedings{54838, author = {{Boshoff, Septimus and Stenner, Jan and Weber, Daniel and Meyer, Marvin and Chidananda, Vikas and Peitz, Sebastian and Wallscheid, Oliver}}, booktitle = {{IEEE Power and Energy Student Summit (PESS)}}, isbn = {{978-3-8007-6318-4}}, pages = {{124--129}}, publisher = {{VDE}}, title = {{{Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches}}}, year = {{2023}}, } @inproceedings{54839, author = {{Meyer, Marvin and Weber, Daniel and Chidananda, Vikas and Schweins, Oliver and Stenner, Jan and Boshoff, Septimus and Peitz, Sebastian and Wallscheid, Oliver}}, booktitle = {{IEEE Power and Energy Student Summit (PESS)}}, isbn = {{978-3-8007-6318-4}}, pages = {{112--117}}, publisher = {{VDE}}, title = {{{ElectricGrid.jl – Automated modeling of decentralized electrical energy grids}}}, year = {{2023}}, } @article{46467, author = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}}, journal = {{Distributed Comput.}}, number = {{2}}, pages = {{159–192}}, title = {{{The canonical amoebot model: algorithms and concurrency control}}}, doi = {{10.1007/s00446-023-00443-3}}, volume = {{36}}, year = {{2023}}, } @book{45863, abstract = {{In the proposal for our CRC in 2011, we formulated a vision of markets for IT services that describes an approach to the provision of such services that was novel at that time and, to a large extent, remains so today: „Our vision of on-the-fly computing is that of IT services individually and automatically configured and brought to execution from flexibly combinable services traded on markets. At the same time, we aim at organizing markets whose participants maintain a lively market of services through appropriate entrepreneurial actions.“ Over the last 12 years, we have developed methods and techniques to address problems critical to the convenient, efficient, and secure use of on-the-fly computing. Among other things, we have made the description of services more convenient by allowing natural language input, increased the quality of configured services through (natural language) interaction and more efficient configuration processes and analysis procedures, made the quality of (the products of) providers in the marketplace transparent through reputation systems, and increased the resource efficiency of execution through reconfigurable heterogeneous computing nodes and an integrated treatment of service description and configuration. We have also developed network infrastructures that have a high degree of adaptivity, scalability, efficiency, and reliability, and provide cryptographic guarantees of anonymity and security for market participants and their products and services. To demonstrate the pervasiveness of the OTF computing approach, we have implemented a proof-of-concept for OTF computing that can run typical scenarios of an OTF market. We illustrated the approach using a cutting-edge application scenario – automated machine learning (AutoML). Finally, we have been pushing our work for the perpetuation of On-The-Fly Computing beyond the SFB and sharing the expertise gained in the SFB in events with industry partners as well as transfer projects. This work required a broad spectrum of expertise. Computer scientists and economists with research interests such as computer networks and distributed algorithms, security and cryptography, software engineering and verification, configuration and machine learning, computer engineering and HPC, microeconomics and game theory, business informatics and management have successfully collaborated here.}}, author = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{247}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}}, doi = {{10.17619/UNIPB/1-1797}}, volume = {{412}}, year = {{2023}}, } @inproceedings{55406, abstract = {{Metaphorical language, such as {“}spending time together{”}, projects meaning from a source domain (here, $money$) to a target domain ($time$). Thereby, it highlights certain aspects of the target domain, such as the $effort$ behind the time investment. Highlighting aspects with metaphors (while hiding others) bridges the two domains and is the core of metaphorical meaning construction. For metaphor interpretation, linguistic theories stress that identifying the highlighted aspects is important for a better understanding of metaphors. However, metaphor research in NLP has not yet dealt with the phenomenon of highlighting. In this paper, we introduce the task of identifying the main aspect highlighted in a metaphorical sentence. Given the inherent interaction of source domains and highlighted aspects, we propose two multitask approaches - a joint learning approach and a continual learning approach - based on a finetuned contrastive learning model to jointly predict highlighted aspects and source domains. We further investigate whether (predicted) information about a source domain leads to better performance in predicting the highlighted aspects, and vice versa. Our experiments on an existing corpus suggest that, with the corresponding information, the performance to predict the other improves in terms of model accuracy in predicting highlighted aspects and source domains notably compared to the single-task baselines.}}, author = {{Sengupta, Meghdut and Alshomary, Milad and Scharlau, Ingrid and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2023}}, editor = {{Bouamor, Houda and Pino, Juan and Bali, Kalika}}, pages = {{4636–4659}}, publisher = {{Association for Computational Linguistics}}, title = {{{Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms}}}, doi = {{10.18653/v1/2023.findings-emnlp.308}}, year = {{2023}}, } @inproceedings{54997, author = {{Turhan, Anni-Yasmin}}, booktitle = {{Proceedings of the 36th International Workshop on Description Logics {(DL} 2023) co-located with the 20th International Conference on Principles of Knowledge Representation and Reasoning and the 21st International Workshop on Non-Monotonic Reasoning {(KR} 2023 and NMR 2023)., Rhodes, Greece, September 2-4, 2023}}, editor = {{Kutz, Oliver and Lutz, Carsten and Ozaki, Ana}}, publisher = {{CEUR-WS.org}}, title = {{{Brushing-up DLs to Cope with Imperfect Data (Abstract of Joint DL+NMR Invited Talk)}}}, volume = {{3515}}, year = {{2023}}, } @inproceedings{37553, author = {{Schrader, Elena and Bernijazov, Ruslan and Foullois, Marc and Hillebrand, Michael and Kaiser, Lydia and Dumitrescu, Roman}}, booktitle = {{2022 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering}}}, doi = {{10.1109/isse54508.2022.10005487}}, year = {{2023}}, } @inproceedings{35426, author = {{Richter, Cedric and Haltermann, Jan Frederik and Jakobs, Marie-Christine and Pauck, Felix and Schott, Stefan and Wehrheim, Heike}}, booktitle = {{37th IEEE/ACM International Conference on Automated Software Engineering}}, publisher = {{ACM}}, title = {{{Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?}}}, doi = {{10.1145/3551349.3561156}}, year = {{2023}}, } @inproceedings{36848, author = {{Schott, Stefan and Pauck, Felix}}, booktitle = {{2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM)}}, publisher = {{IEEE}}, title = {{{Benchmark Fuzzing for Android Taint Analyses}}}, doi = {{10.1109/scam55253.2022.00007}}, year = {{2023}}, } @inproceedings{35427, author = {{Pauck, Felix}}, booktitle = {{37th IEEE/ACM International Conference on Automated Software Engineering}}, publisher = {{ACM}}, title = {{{Scaling Arbitrary Android App Analyses}}}, doi = {{10.1145/3551349.3561339}}, year = {{2023}}, } @misc{40440, author = {{Pilot, Matthias}}, title = {{{Updatable Privacy-Preserving Reputation System based on Blockchain}}}, year = {{2023}}, } @inbook{40511, author = {{Hüsing, Sven and Schulte, Carsten and Winkelnkemper, Felix}}, booktitle = {{Computer Science Education}}, isbn = {{9781350296916}}, publisher = {{Bloomsbury Academic}}, title = {{{Epistemic Programming}}}, doi = {{10.5040/9781350296947.ch-022}}, year = {{2023}}, }