Koldewey, C., Fichtler, T., Scholtysik, M., Biehler, J., Schreiner, N., Sommer, F., Schacht, M., Kaufmann, J., Rabe, M., Sedlmeier, J., &; Dumitrescu, R. (2024). Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice. Hawaii International Conference on System Sciences, Hawaii.
LibreCat: /record/48632

Herbert, F., Becker, S., Buckmann, A., Kowalewski, M., Hielscher, J., Acar, Y., Dürmuth, M., Sasse, M. A., &; Zou, Y. (2024). Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups. IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA.https://doi.org/10.48550/arXiv.2212.12964 
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DOI: 10.48550/arXiv.2212.12964

Afroze, L., Merkelbach, S., von Enzberg, S., &; Dumitrescu, R. (2024). Domain Knowledge Injection Guidance for Predictive Maintenance. ML4CPS 2023. ML4CPS – Machine Learning For Cyber-Physical Systems, Hamburg.
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Scholtysik, M., Rohde, M., Koldewey, C., &; Dumitrescu, R. (2024). Business strategy taxonomy and solution patterns for the circular economy.
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Krings, S. C., &; Yigitbas, E. (2024). TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR. Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to Appear).
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Dou, F., Wang, L., Chen, S., &; Liu, F. (n.d.). X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.
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Blöcher, M., Nedderhut, N., Chuprikov, P., Khalili, R., Eugster, P., &; Wang, L. (n.d.). Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.
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Hu, H., Liu, F., Pei, Q., Yuan, Y., Xu, Z., &; Wang, L. (2024). 706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. Proceedings of the ACM Web Conference (WWW). ACM Web Conference (WWW), Singapore.
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Philipp, F. M., Schaller, M., Boshoff, S., Peitz, S., Nüske, F., &; Worthmann, K. (2024). Extended Dynamic Mode Decomposition: Sharp bounds on the sample  efficiency. In arXiv:2402.02494.
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arXiv: 2402.02494

Sonntag, K., &; Peitz, S. (2024). Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications.https://doi.org/10.1007/s10957-024-02389-3 
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DOI: 10.1007/s10957-024-02389-3

Sonntag, K., Gebken, B., Müller, G., Peitz, S., &; Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. In arXiv:2402.06376.
LibreCat: /record/51334
arXiv: 	2402.06376

Peitz, S., Stenner, J., Chidananda, V., Wallscheid, O., Brunton, S. L., &; Taira, K. (2024). Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena, 461, 134096.https://doi.org/10.1016/j.physd.2024.134096 
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DOI: 10.1016/j.physd.2024.134096

Dorociak, S. (2024). Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung.
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Khedkar, M., &; Bodden, E. (2024). Toward an Android Static Analysis Approach for Data Protection. Proceedings of the 9th International Conference on Mobile Software Engineering and Systems. 9th International Conference on Mobile Software Engineering and Systems 2024, Lisbon, Portugal.
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arXiv: 2402.07889

Bodden, E., Pottebaum, J., Fockel, M., &; Gräßler, I. (2024). Evaluating Security Through Isolation and Defense in Depth. IEEE Security &; Privacy, 22(1), 69–72.https://doi.org/10.1109/msec.2023.3336028 
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DOI: 10.1109/msec.2023.3336028

Otto, S. E., Peitz, S., &; Rowley, C. W. (2024). Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories. SIAM Journal on Applied Dynamical Systems, 23(1), 885–923.https://doi.org/10.1137/22M1523601 
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DOI: 10.1137/22M1523601
arXiv: 2209.09977

Wickert, A.-K., Schlichtig, M., Vogel, M., Winter, L., Mezini, M., &; Bodden, E. (2024). Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability.
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Ahmed, Q. A., Wiersema, T., &; Platzner, M. (2024). Post-configuration Activation of Hardware Trojans in FPGAs. Journal of Hardware and Systems Security.https://doi.org/10.1007/s41635-024-00147-5 
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DOI: 10.1007/s41635-024-00147-5

Hüsing, S., Schulte, C., Sparmann, S., &; Bolte, M. (2024). Using Worked Examples for Engaging in Epistemic Programming Projects. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1.https://doi.org/10.1145/3626252.3630961 
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DOI: 10.1145/3626252.3630961

Hu, L., Habernal, I., Shen, L., &; Wang, D. (2024). Differentially Private Natural Language Models: Recent Advances and Future Directions. In Y. Graham &; M. Purver (Eds.), Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 (pp. 478–499). Association for Computational Linguistics.
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Igamberdiev, T., Vu, D. N. L., Kuennecke, F., Yu, Z., Holmer, J., &; Habernal, I. (2024). DP-NMT: Scalable Differentially Private Machine Translation. In N. Aletras &; O. De Clercq (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 94–105). Association for Computational Linguistics.
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Razavi, K., Ghafouri, S., Mühlhäuser, M., Jamshidi, P., &; Wang, L. (2024). Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece.
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Blübaum, L., &; Heindorf, S. (n.d.). Causal Question Answering with Reinforcement Learning. The World Wide Web Conference (WWW). The Web Conference, Singapore.
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De Groote, C. (2024). A Dispersion Algorithm for Robot Swarms Inside Polygonal Boundary Shapes.
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Doddegowda, R. (2024). Optimal Drone Strategies For Packet Delivery.
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Thakur, H. (2024). Evaluating the Implications.
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Preuß, O. L., Rook, J., &; Trautmann, H. (2024). On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems. In Applications of Evolutionary Computation. Springer Nature Switzerland.https://doi.org/10.1007/978-3-031-56852-7_20 
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DOI: 10.1007/978-3-031-56852-7_20

Vinzent Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., &; Trautmann, H. (2024). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI).https://doi.org/10.1109/ssci52147.2023.10372008 
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DOI: 10.1109/ssci52147.2023.10372008

Philipp, F., Schaller, M., Worthmann, K., Peitz, S., &; Nüske, F. (2024). Error bounds for kernel-based approximations of the Koopman operator. Applied and Computational Harmonic Analysis , 71, Article 101657.https://doi.org/10.1016/j.acha.2024.101657 
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DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

Lass, M., Kenter, T., Plessl, C., &; Brehm, M. (2024). Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations. Entropy, 26(4), Article 322.https://doi.org/10.3390/e26040322 
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DOI: 10.3390/e26040322

Olgu, K., Kenter, T., Nunez-Yanez, J., &; Mcintosh-Smith, S. (2024). Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures. Proceedings of the 12th International Workshop on OpenCL and SYCL.https://doi.org/10.1145/3648115.3648134 
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DOI: 10.1145/3648115.3648134

Ghafouri, S., Razavi, K., Salmani, M., Sanaee, A., Lorido Botran, T., Wang, L., Doyle, J., &; Jamshidi, P. (n.d.). IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency. Journal of Systems Research (JSys).
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Höper, L., &; Schulte, C. (2024). Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies. Informatics in Education.https://doi.org/10.15388/infedu.2024.19 
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DOI: 10.15388/infedu.2024.19

Bauer, C., Kenter, T., Lass, M., Mazur, L., Meyer, M., Nitsche, H., Riebler, H., Schade, R., Schwarz, M., Winnwa, N., Wiens, A., Wu, X., Plessl, C., &; Simon, J. (2024). Noctua 2 Supercomputer. Journal of Large-Scale Research Facilities, 9.https://doi.org/10.17815/jlsrf-8-187  
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DOI: 10.17815/jlsrf-8-187 

Dann, A. P., Hermann, B., &; Bodden, E. (2023). UpCy: Safely Updating Outdated Dependencies.
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Peitz, S., &; Bieker, K. (2023). On the Universal Transformation of Data-Driven Models to Control Systems. Automatica, 149, Article 110840.https://doi.org/10.1016/j.automatica.2022.110840 
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DOI: 10.1016/j.automatica.2022.110840

Schrader, E., Bernijazov, R., Foullois, M., Hillebrand, M., Kaiser, L., &; Dumitrescu, R. (2023). Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering. 2022 IEEE International Symposium on Systems Engineering (ISSE).https://doi.org/10.1109/isse54508.2022.10005487 
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DOI: 10.1109/isse54508.2022.10005487

Richter, C., Haltermann, J. F., Jakobs, M.-C., Pauck, F., Schott, S., &; Wehrheim, H. (2023). Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? 37th IEEE/ACM International Conference on Automated Software Engineering.https://doi.org/10.1145/3551349.3561156 
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DOI: 10.1145/3551349.3561156

Schott, S., &; Pauck, F. (2023). Benchmark Fuzzing for Android Taint Analyses. 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM).https://doi.org/10.1109/scam55253.2022.00007 
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DOI: 10.1109/scam55253.2022.00007

Pauck, F. (2023). Scaling Arbitrary Android App Analyses. 37th IEEE/ACM International Conference on Automated Software Engineering.https://doi.org/10.1145/3551349.3561339 
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DOI: 10.1145/3551349.3561339

Pilot, M. (2023). Updatable Privacy-Preserving Reputation System based on Blockchain.
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Hüsing, S., Schulte, C., &; Winkelnkemper, F. (2023). Epistemic Programming. In Computer Science Education. Bloomsbury Academic.https://doi.org/10.5040/9781350296947.ch-022 
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DOI: 10.5040/9781350296947.ch-022

Castenow, J., Harbig, J., Jung, D., Knollmann, T., &; Meyer auf der Heide, F. (2023). Gathering a Euclidean Closed Chain of Robots in Linear Time and Improved Algorithms for Chain-Formation. Theoretical Computer Science, 939, 261–291.https://doi.org/10.1016/j.tcs.2022.10.031 
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DOI: 10.1016/j.tcs.2022.10.031
arXiv: 2010.04424 

Luo, L., Piskachev, G., Krishnamurthy, R., Dolby, J., Schäf, M., &; Bodden, E. (2023). Model Generation For Java Frameworks. IEEE International Conference on Software Testing, Verification and Validation (ICST).
LibreCat: /record/41812

Shivarpatna Venkatesh, A. P., Wang, J., Li, L., &; Bodden, E. (2023). Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
LibreCat: /record/41813

Yigitbas, E., Klauke, J., Gottschalk, S., &; Engels, G. (2023). End-User Development of Interactive Web-Based Virtual Reality Scenes. Journal on Computer Languages (COLA) .
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Yigitbas, E., &; Engels, G. (2023). Enhancing Robot Programming through Digital Twin and Augmented Reality . 56th Hawaii International Conference on System Science (HICSS 2023) .
LibreCat: /record/33511

Yigitbas, E., Krois, S., Gottschalk, S., &; Engels, G. (2023). Towards Enhanced Guiding Mechanisms in VR Training through Process Mining. Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP’23) .
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Castenow, J., Harbig, J., Jung, D., Kling, P., Knollmann, T., &; Meyer auf der Heide, F. (2023). A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility . In E. Hillel, R. Palmieri, &; E. Riviére (Eds.), Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS)  (Vol. 253, p. 15:1–15:25). Schloss Dagstuhl – Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2022.15 
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DOI: 10.4230/LIPIcs.OPODIS.2022.15
arXiv: 2206.07567 

Werner, S., &; Peitz, S. (2023). Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs. In arXiv:2302.07160.
LibreCat: /record/42160
arXiv: 2302.07160