TY - CONF AU - Hüsing, Sven AU - Schulte, Carsten AU - Sparmann, Sören AU - Bolte, Mario ID - 52379 T2 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 TI - Using Worked Examples for Engaging in Epistemic Programming Projects ER - TY - CONF AU - Hu, Lijie AU - Habernal, Ivan AU - Shen, Lei AU - Wang, Di ED - Graham, Yvette ED - Purver, Matthew ID - 52827 T2 - Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 TI - Differentially Private Natural Language Models: Recent Advances and Future Directions ER - TY - CONF AB - Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. AU - Igamberdiev, Timour AU - Vu, Doan Nam Long AU - Kuennecke, Felix AU - Yu, Zhuo AU - Holmer, Jannik AU - Habernal, Ivan ED - Aletras, Nikolaos ED - De Clercq, Orphee ID - 52842 T2 - Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations TI - DP-NMT: Scalable Differentially Private Machine Translation ER - TY - CONF AU - Razavi, Kamran AU - Ghafouri, Saeid AU - Mühlhäuser, Max AU - Jamshidi, Pooyan AU - Wang, Lin ID - 53095 T2 - Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 TI - Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling ER - TY - CONF AU - Dann, Andreas Peter AU - Hermann, Ben AU - Bodden, Eric ID - 35083 TI - UpCy: Safely Updating Outdated Dependencies ER - TY - JOUR AB - As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion. AU - Peitz, Sebastian AU - Bieker, Katharina ID - 21199 JF - Automatica TI - On the Universal Transformation of Data-Driven Models to Control Systems VL - 149 ER - TY - CONF AU - Schrader, Elena AU - Bernijazov, Ruslan AU - Foullois, Marc AU - Hillebrand, Michael AU - Kaiser, Lydia AU - Dumitrescu, Roman ID - 37553 T2 - 2022 IEEE International Symposium on Systems Engineering (ISSE) TI - Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering ER - TY - CONF AU - Richter, Cedric AU - Haltermann, Jan Frederik AU - Jakobs, Marie-Christine AU - Pauck, Felix AU - Schott, Stefan AU - Wehrheim, Heike ID - 35426 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? ER - TY - CONF AU - Schott, Stefan AU - Pauck, Felix ID - 36848 T2 - 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) TI - Benchmark Fuzzing for Android Taint Analyses ER - TY - CONF AU - Pauck, Felix ID - 35427 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Scaling Arbitrary Android App Analyses ER - TY - GEN AB - We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results. AU - Philipp, Friedrich AU - Schaller, Manuel AU - Worthmann, Karl AU - Peitz, Sebastian AU - Nüske, Feliks ID - 38031 T2 - arXiv:2301.08637 TI - Error bounds for kernel-based approximations of the Koopman operator ER - TY - GEN AU - Pilot, Matthias ID - 40440 TI - Updatable Privacy-Preserving Reputation System based on Blockchain ER - TY - CHAP AU - Hüsing, Sven AU - Schulte, Carsten AU - Winkelnkemper, Felix ID - 40511 SN - 9781350296916 T2 - Computer Science Education TI - Epistemic Programming ER - TY - JOUR AU - Castenow, Jannik AU - Harbig, Jonas AU - Jung, Daniel AU - Knollmann, Till AU - Meyer auf der Heide, Friedhelm ID - 33947 JF - Theoretical Computer Science KW - General Computer Science KW - Theoretical Computer Science SN - 0304-3975 TI - Gathering a Euclidean Closed Chain of Robots in Linear Time and Improved Algorithms for Chain-Formation VL - 939 ER - TY - CONF AU - Luo, Linghui AU - Piskachev, Goran AU - Krishnamurthy, Ranjith AU - Dolby, Julian AU - Schäf, Martin AU - Bodden, Eric ID - 41812 T2 - IEEE International Conference on Software Testing, Verification and Validation (ICST) TI - Model Generation For Java Frameworks ER - TY - CONF AU - Shivarpatna Venkatesh, Ashwin Prasad AU - Wang, Jiawei AU - Li, Li AU - Bodden, Eric ID - 41813 T2 - IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) TI - Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis ER - TY - JOUR AU - Yigitbas, Enes AU - Klauke, Jonas AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34402 JF - Journal on Computer Languages (COLA) TI - End-User Development of Interactive Web-Based Virtual Reality Scenes ER - TY - CONF AU - Yigitbas, Enes AU - Engels, Gregor ID - 33511 T2 - 56th Hawaii International Conference on System Science (HICSS 2023) TI - Enhancing Robot Programming through Digital Twin and Augmented Reality ER - TY - CONF AU - Yigitbas, Enes AU - Krois, Sebastian AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34401 T2 - Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23) TI - Towards Enhanced Guiding Mechanisms in VR Training through Process Mining ER - TY - CONF AU - Castenow, Jannik AU - Harbig, Jonas AU - Jung, Daniel AU - Kling, Peter AU - Knollmann, Till AU - Meyer auf der Heide, Friedhelm ED - Hillel, Eshcar ED - Palmieri, Roberto ED - Riviére, Etienne ID - 34008 SN - 1868-8969 T2 - Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS) TI - A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility VL - 253 ER -