@inproceedings{48632, abstract = {{Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services.}}, author = {{Koldewey, Christian and Fichtler, Timm and Scholtysik, Michel and Biehler, Jan and Schreiner, Nick and Sommer, Franziska and Schacht, Maximilian and Kaufmann, Jonas and Rabe, Martin and Sedlmeier, Joachim and Dumitrescu, Roman}}, keywords = {{Digital Servitization, Transformation, Capabilities, Maturity, Smart Services}}, location = {{Hawaii}}, title = {{{Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice}}}, year = {{2024}}, } @article{47275, author = {{Herbert, Franziska and Becker, Steffen and Buckmann, Annalina and Kowalewski, Marvin and Hielscher, Jonas and Acar, Yasemin and Dürmuth, Markus and Sasse, M. Angela and Zou, Yixin}}, journal = {{IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA}}, title = {{{Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups}}}, doi = {{10.48550/arXiv.2212.12964}}, year = {{2024}}, } @inproceedings{49354, author = {{Afroze, Lameya and Merkelbach, Silke and von Enzberg, Sebastian and Dumitrescu, Roman}}, booktitle = {{ML4CPS 2023}}, location = {{Hamburg}}, title = {{{Domain Knowledge Injection Guidance for Predictive Maintenance}}}, year = {{2024}}, } @inproceedings{49363, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas}}}, year = {{2024}}, } @inproceedings{49364, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Business strategy taxonomy and solution patterns for the circular economy}}}, year = {{2024}}, } @inproceedings{50476, author = {{Krings, Sarah Claudia and Yigitbas, Enes}}, booktitle = {{Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to appear)}}, publisher = {{ACM}}, title = {{{TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR}}}, year = {{2024}}, } @inproceedings{50066, author = {{Dou, Feng and Wang, Lin and Chen, Shutong and Liu, Fangming}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics}}}, year = {{2024}}, } @inproceedings{50065, author = {{Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}}}, year = {{2024}}, } @inproceedings{50807, author = {{Hu, Haichuan and Liu, Fangming and Pei, Qiangyu and Yuan, Yongjie and Xu, Zichen and Wang, Lin}}, booktitle = {{Proceedings of the ACM Web Conference (WWW)}}, location = {{Singapore}}, publisher = {{ACM}}, title = {{{𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing}}}, year = {{2024}}, } @unpublished{51160, abstract = {{We rigorously derive novel and sharp finite-data error bounds for highly sample-efficient Extended Dynamic Mode Decomposition (EDMD) for both i.i.d. and ergodic sampling. In particular, we show all results in a very general setting removing most of the typically imposed assumptions such that, among others, discrete- and continuous-time stochastic processes as well as nonlinear partial differential equations are contained in the considered system class. Besides showing an exponential rate for i.i.d. sampling, we prove, to the best of our knowledge, the first superlinear convergence rates for ergodic sampling of deterministic systems. We verify sharpness of the derived error bounds by conducting numerical simulations for highly-complex applications from molecular dynamics and chaotic flame propagation.}}, author = {{Philipp, Friedrich M. and Schaller, Manuel and Boshoff, Septimus and Peitz, Sebastian and Nüske, Feliks and Worthmann, Karl}}, booktitle = {{arXiv:2402.02494}}, title = {{{Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency}}}, year = {{2024}}, } @article{46019, abstract = {{We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.}}, author = {{Sonntag, Konstantin and Peitz, Sebastian}}, journal = {{Journal of Optimization Theory and Applications}}, publisher = {{Springer}}, title = {{{Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems}}}, doi = {{10.1007/s10957-024-02389-3}}, year = {{2024}}, } @unpublished{51334, abstract = {{The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.}}, author = {{Sonntag, Konstantin and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Volkwein, Stefan}}, booktitle = {{arXiv:2402.06376}}, title = {{{A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces}}}, year = {{2024}}, } @article{40171, abstract = {{We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.}}, author = {{Peitz, Sebastian and Stenner, Jan and Chidananda, Vikas and Wallscheid, Oliver and Brunton, Steven L. and Taira, Kunihiko}}, journal = {{Physica D: Nonlinear Phenomena}}, pages = {{134096}}, publisher = {{Elsevier}}, title = {{{Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning}}}, doi = {{10.1016/j.physd.2024.134096}}, volume = {{461}}, year = {{2024}}, } @misc{52318, author = {{Dorociak, Svitlana}}, title = {{{Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung}}}, year = {{2024}}, } @inproceedings{52235, 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 the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to write privacy-aware source code. Moreover, they have limited tool support to reason about data protection throughout their app development process. This paper motivates the need for a static analysis approach to diagnose and explain data protection in Android apps. The analysis will recognize personal data sources in the source code, and aims to further examine the data flow originating from these sources. App developers can then address key questions about data manipulation, derived data, and the presence of technical measures. Despite challenges, we explore to what extent one can realize this analysis through static taint analysis, a common method for identifying security vulnerabilities. This is a first step towards designing a tool-based approach that aids app developers and assessors in ensuring data protection in Android apps, based on automated static program analysis. }}, author = {{Khedkar, Mugdha and Bodden, Eric}}, booktitle = {{Proceedings of the 9th International Conference on Mobile Software Engineering and Systems}}, keywords = {{static program analysis, data protection and privacy, GDPR compliance}}, location = {{Lisbon, Portugal}}, title = {{{Toward an Android Static Analysis Approach for Data Protection}}}, year = {{2024}}, } @article{52587, author = {{Bodden, Eric and Pottebaum, Jens and Fockel, Markus and Gräßler, Iris}}, issn = {{1540-7993}}, journal = {{IEEE Security & Privacy}}, keywords = {{Law, Electrical and Electronic Engineering, Computer Networks and Communications}}, number = {{1}}, pages = {{69--72}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Evaluating Security Through Isolation and Defense in Depth}}}, doi = {{10.1109/msec.2023.3336028}}, volume = {{22}}, year = {{2024}}, } @article{33461, abstract = {{Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.}}, author = {{Otto, Samuel E. and Peitz, Sebastian and Rowley, Clarence W.}}, journal = {{SIAM Journal on Applied Dynamical Systems}}, number = {{1}}, pages = {{885--923}}, publisher = {{SIAM}}, title = {{{Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories}}}, doi = {{10.1137/22M1523601}}, volume = {{23}}, year = {{2024}}, } @misc{52663, abstract = {{Context Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location, and reports error chains. An error chain represents at least two interconnected errors that occur successively, thus building the connection between the fix and manifestation location. We used our tool CogniCryptSUBS for a case study on 471 GitHub repositories, a performance benchmark to compare different analysis configurations, and conducted an expert interview. Result We found that 50 % of the projects with a report had at least one error chain. Our runtime benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4 %. The results of our expert interview indicate that with our adapted version participants require fewer executions of the analysis. Conclusion Our results indicate that error chains occur frequently in real-world projects, and ignoring them can lead to imprecise evaluation results. The runtime benchmark indicates that our tool is a feasible and efficient solution for detecting error chains in real-world projects. Further, our results gave a hint that the usability of static analyses may benefit from supporting error chains.}}, author = {{Wickert, Anna-Katharina and Schlichtig, Michael and Vogel, Marvin and Winter, Lukas and Mezini, Mira and Bodden, Eric}}, keywords = {{Static analysis, error chains, false positive re- duction, empirical studies}}, title = {{{Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability}}}, year = {{2024}}, } @article{52686, author = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}}, issn = {{2509-3428}}, journal = {{Journal of Hardware and Systems Security}}, keywords = {{General Engineering, Energy Engineering and Power Technology}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Post-configuration Activation of Hardware Trojans in FPGAs}}}, doi = {{10.1007/s41635-024-00147-5}}, year = {{2024}}, } @inproceedings{52380, author = {{Sparmann, Sören and Hüsing, Sven and Schulte, Carsten}}, booktitle = {{Proceedings of the 23rd Koli Calling International Conference on Computing Education Research}}, publisher = {{ACM}}, title = {{{JuGaze: A Cell-based Eye Tracking and Logging Tool for Jupyter Notebooks}}}, doi = {{10.1145/3631802.3631824}}, year = {{2024}}, } @inproceedings{52379, author = {{Hüsing, Sven and Schulte, Carsten and Sparmann, Sören and Bolte, Mario}}, booktitle = {{Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1}}, publisher = {{ACM}}, title = {{{Using Worked Examples for Engaging in Epistemic Programming Projects}}}, doi = {{10.1145/3626252.3630961}}, year = {{2024}}, } @inproceedings{52827, author = {{Hu, Lijie and Habernal, Ivan and Shen, Lei and Wang, Di}}, booktitle = {{Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024}}, editor = {{Graham, Yvette and Purver, Matthew}}, pages = {{478–499}}, publisher = {{Association for Computational Linguistics}}, title = {{{Differentially Private Natural Language Models: Recent Advances and Future Directions}}}, year = {{2024}}, } @inproceedings{52842, abstract = {{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.}}, author = {{Igamberdiev, Timour and Vu, Doan Nam Long and Kuennecke, Felix and Yu, Zhuo and Holmer, Jannik and Habernal, Ivan}}, booktitle = {{Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations}}, editor = {{Aletras, Nikolaos and De Clercq, Orphee}}, pages = {{94–105}}, publisher = {{Association for Computational Linguistics}}, title = {{{DP-NMT: Scalable Differentially Private Machine Translation}}}, year = {{2024}}, } @inproceedings{53095, author = {{Razavi, Kamran and Ghafouri, Saeid and Mühlhäuser, Max and Jamshidi, Pooyan and Wang, Lin}}, booktitle = {{Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024}}, location = {{Athens, Greece}}, publisher = {{ACM}}, title = {{{Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling}}}, year = {{2024}}, } @inproceedings{35083, author = {{Dann, Andreas Peter and Hermann, Ben and Bodden, Eric}}, title = {{{UpCy: Safely Updating Outdated Dependencies}}}, year = {{2023}}, } @article{21199, abstract = {{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.}}, author = {{Peitz, Sebastian and Bieker, Katharina}}, journal = {{Automatica}}, publisher = {{Elsevier}}, title = {{{On the Universal Transformation of Data-Driven Models to Control Systems}}}, doi = {{10.1016/j.automatica.2022.110840}}, volume = {{149}}, 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}}, } @unpublished{38031, abstract = {{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.}}, author = {{Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl and Peitz, Sebastian and Nüske, Feliks}}, booktitle = {{arXiv:2301.08637}}, title = {{{Error bounds for kernel-based approximations of the Koopman operator}}}, 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}}, } @article{33947, author = {{Castenow, Jannik and Harbig, Jonas and Jung, Daniel and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, keywords = {{General Computer Science, Theoretical Computer Science}}, pages = {{261--291}}, publisher = {{Elsevier BV}}, title = {{{Gathering a Euclidean Closed Chain of Robots in Linear Time and Improved Algorithms for Chain-Formation}}}, doi = {{10.1016/j.tcs.2022.10.031}}, volume = {{939}}, year = {{2023}}, } @inproceedings{41812, author = {{Luo, Linghui and Piskachev, Goran and Krishnamurthy, Ranjith and Dolby, Julian and Schäf, Martin and Bodden, Eric}}, booktitle = {{IEEE International Conference on Software Testing, Verification and Validation (ICST)}}, title = {{{Model Generation For Java Frameworks}}}, year = {{2023}}, } @inproceedings{41813, author = {{Shivarpatna Venkatesh, Ashwin Prasad and Wang, Jiawei and Li, Li and Bodden, Eric}}, booktitle = {{IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}}, title = {{{Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis}}}, year = {{2023}}, } @article{34402, author = {{Yigitbas, Enes and Klauke, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, journal = {{Journal on Computer Languages (COLA) }}, publisher = {{Elsevier}}, title = {{{End-User Development of Interactive Web-Based Virtual Reality Scenes}}}, year = {{2023}}, } @inproceedings{33511, author = {{Yigitbas, Enes and Engels, Gregor}}, booktitle = {{56th Hawaii International Conference on System Science (HICSS 2023) }}, publisher = {{ScholarSpace}}, title = {{{Enhancing Robot Programming through Digital Twin and Augmented Reality }}}, year = {{2023}}, } @inproceedings{34401, author = {{Yigitbas, Enes and Krois, Sebastian and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23) }}, title = {{{Towards Enhanced Guiding Mechanisms in VR Training through Process Mining}}}, year = {{2023}}, } @inproceedings{34008, author = {{Castenow, Jannik and Harbig, Jonas and Jung, Daniel and Kling, Peter and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS) }}, editor = {{Hillel, Eshcar and Palmieri, Roberto and Riviére, Etienne}}, isbn = {{978-3-95977-265-5}}, issn = {{1868-8969}}, location = {{Brussels}}, pages = {{15:1–15:25}}, publisher = {{Schloss Dagstuhl – Leibniz Zentrum für Informatik}}, title = {{{A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility }}}, doi = {{10.4230/LIPIcs.OPODIS.2022.15}}, volume = {{253}}, year = {{2023}}, } @unpublished{42160, abstract = {{The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings.}}, author = {{Werner, Stefan and Peitz, Sebastian}}, booktitle = {{arXiv:2302.07160}}, title = {{{Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs}}}, year = {{2023}}, } @inproceedings{31872, abstract = {{Savitch's theorem states that NPSPACE computations can be simulated in PSPACE. We initiate the study of a quantum analogue of NPSPACE, denoted Streaming-QCMASPACE (SQCMASPACE), where an exponentially long classical proof is streamed to a poly-space quantum verifier. Besides two main results, we also show that a quantum analogue of Savitch's theorem is unlikely to hold, as SQCMASPACE=NEXP. For completeness, we introduce Streaming-QMASPACE (SQMASPACE) with an exponentially long streamed quantum proof, and show SQMASPACE=QMA_EXP (quantum analogue of NEXP). Our first main result shows, in contrast to the classical setting, the solution space of a quantum constraint satisfaction problem (i.e. a local Hamiltonian) is always connected when exponentially long proofs are permitted. For this, we show how to simulate any Lipschitz continuous path on the unit hypersphere via a sequence of local unitary gates, at the expense of blowing up the circuit size. This shows quantum error-correcting codes can be unable to detect one codeword erroneously evolving to another if the evolution happens sufficiently slowly, and answers an open question of [Gharibian, Sikora, ICALP 2015] regarding the Ground State Connectivity problem. Our second main result is that any SQCMASPACE computation can be embedded into "unentanglement", i.e. into a quantum constraint satisfaction problem with unentangled provers. Formally, we show how to embed SQCMASPACE into the Sparse Separable Hamiltonian problem of [Chailloux, Sattath, CCC 2012] (QMA(2)-complete for 1/poly promise gap), at the expense of scaling the promise gap with the streamed proof size. As a corollary, we obtain the first systematic construction for obtaining QMA(2)-type upper bounds on arbitrary multi-prover interactive proof systems, where the QMA(2) promise gap scales exponentially with the number of bits of communication in the interactive proof.}}, author = {{Gharibian, Sevag and Rudolph, Dorian}}, booktitle = {{14th Innovations in Theoretical Computer Science (ITCS)}}, pages = {{53:1--53:23}}, title = {{{Quantum space, ground space traversal, and how to embed multi-prover interactive proofs into unentanglement}}}, doi = {{10.4230/LIPIcs.ITCS.2023.53}}, volume = {{251}}, year = {{2023}}, } @article{27426, abstract = {{Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial but often difficult, path-following methods are used to approximate the entire regularization path, i.e., the set of all possible solutions for all regularization parameters. Due to their nature, the development of these methods requires structural results about the regularization path. The goal of this article is to derive these results for the case of a smooth objective function which is penalized by a piecewise differentiable regularization term. We do this by treating regularization as a multiobjective optimization problem. Our results suggest that even in this general case, the regularization path is piecewise smooth. Moreover, our theory allows for a classification of the nonsmooth features that occur in between smooth parts. This is demonstrated in two applications, namely support-vector machines and exact penalty methods.}}, author = {{Gebken, Bennet and Bieker, Katharina and Peitz, Sebastian}}, journal = {{Journal of Global Optimization}}, number = {{3}}, pages = {{709--741}}, title = {{{On the structure of regularization paths for piecewise differentiable regularization terms}}}, doi = {{10.1007/s10898-022-01223-2}}, volume = {{85}}, year = {{2023}}, } @unpublished{43048, author = {{Lienen, Christian and Middeke, Sorel Horst and Platzner, Marco}}, title = {{{fpgaDDS: An Intra-FPGA Data Distribution Service for ROS 2 Robotics Applications}}}, year = {{2023}}, } @article{43109, author = {{Götte, Thorsten and Kolb, Christina and Scheideler, Christian and Werthmann, Julian}}, journal = {{Theor. Comput. Sci.}}, pages = {{113756}}, title = {{{Beep-and-Sleep: Message and Energy Efficient Set Cover}}}, doi = {{10.1016/j.tcs.2023.113756}}, volume = {{950}}, year = {{2023}}, } @inproceedings{43424, author = {{Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 19th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2023)}}, publisher = {{Springer}}, title = {{{Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review}}}, year = {{2023}}, } @inproceedings{30125, abstract = {{We present an approach for guaranteed constraint satisfaction by means of data-based optimal control, where the model is unknown and has to be obtained from measurement data. To this end, we utilize the Koopman framework and an eDMD-based bilinear surrogate modeling approach for control systems to show an error bound on predicted observables, i.e., functions of the state. This result is then applied to the constraints of the optimal control problem to show that satisfaction of tightened constraints in the purely data-based surrogate model implies constraint satisfaction for the original system.}}, author = {{Schaller, Manuel and Worthmann, Karl and Philipp, Friedrich and Peitz, Sebastian and Nüske, Feliks}}, booktitle = {{IFAC-PapersOnLine}}, number = {{1}}, pages = {{169--174}}, title = {{{Towards reliable data-based optimal and predictive control using extended DMD}}}, doi = {{10.1016/j.ifacol.2023.02.029}}, volume = {{56}}, year = {{2023}}, } @article{44077, author = {{Maack, Marten}}, issn = {{0167-6377}}, journal = {{Operations Research Letters}}, keywords = {{Applied Mathematics, Industrial and Manufacturing Engineering, Management Science and Operations Research, Software}}, number = {{3}}, pages = {{220--225}}, publisher = {{Elsevier BV}}, title = {{{Online load balancing on uniform machines with limited migration}}}, doi = {{10.1016/j.orl.2023.02.013}}, volume = {{51}}, year = {{2023}}, } @misc{43374, author = {{Schürmann, Patrick}}, title = {{{ A Formal Comparison of Advanced Digital Signature Primitives}}}, year = {{2023}}, } @inproceedings{20841, author = {{Gharibian, Sevag and Watson, James and Bausch, Johannes}}, booktitle = {{Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science (STACS)}}, pages = {{54:1--54:21}}, title = {{{The Complexity of Translationally Invariant Problems beyond Ground State Energies}}}, doi = {{https://doi.org/10.4230/LIPIcs.STACS.2023.54}}, volume = {{254}}, year = {{2023}}, }