@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}}, } @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{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}}, } @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}}, }