@inproceedings{20115, author = {{Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}}, pages = {{1718--1729}}, title = {{{Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}}}, year = {{2021}}, } @inproceedings{20125, abstract = {{Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}}, keywords = {{Flow scheduling, Deadlines, Reinforcement learning}}, location = {{Las Vegas, USA}}, publisher = {{IEEE Computer Society}}, title = {{{Learning Flow Scheduling}}}, doi = {{https://doi.org/10.1109/CCNC49032.2021.9369514}}, year = {{2021}}, } @inproceedings{20244, author = {{Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, location = {{Sofia}}, title = {{{Extending Business Model Development Tools with Consolidated Expert Knowledge }}}, doi = {{10.1007/978-3-030-79976-2_1}}, year = {{2021}}, } @article{28099, abstract = {{N-body methods are one of the essential algorithmic building blocks of high-performance and parallel computing. Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency.}}, author = {{Menzel, Johannes and Plessl, Christian and Kenter, Tobias}}, issn = {{1936-7406}}, journal = {{ACM Transactions on Reconfigurable Technology and Systems}}, number = {{1}}, pages = {{1--30}}, title = {{{The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations}}}, doi = {{10.1145/3491235}}, volume = {{15}}, year = {{2021}}, } @inproceedings{28350, abstract = {{In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks). In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime}}, author = {{Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike}}, booktitle = {{Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}}, publisher = {{IEEE}}, title = {{{MLCHECK–Property-Driven Testing of Machine Learning Classifiers}}}, year = {{2021}}, } @article{24143, author = {{Drees, Jan Peter and Gupta, Pritha and Hüllermeier, Eyke and Jager, Tibor and Konze, Alexander and Priesterjahn, Claudia and Ramaswamy, Arunselvan and Somorovsky, Juraj}}, journal = {{14th ACM Workshop on Artificial Intelligence and Security}}, title = {{{Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!}}}, year = {{2021}}, } @article{24169, author = {{Nüske, Feliks and Gelß, Patrick and Klus, Stefan and Clementi, Cecilia}}, issn = {{0167-2789}}, journal = {{Physica D: Nonlinear Phenomena}}, title = {{{Tensor-based computation of metastable and coherent sets}}}, doi = {{10.1016/j.physd.2021.133018}}, year = {{2021}}, } @article{24170, author = {{Klus, Stefan and Gelß, Patrick and Nüske, Feliks and Noé, Frank}}, issn = {{2632-2153}}, journal = {{Machine Learning: Science and Technology}}, title = {{{Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry}}}, doi = {{10.1088/2632-2153/ac14ad}}, year = {{2021}}, } @inproceedings{24382, author = {{Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}}, location = {{online}}, title = {{{Heated tool butt welding of two different materials – Established methods versus artificial intelligence}}}, year = {{2021}}, } @article{24719, author = {{M. Zahera, Hamada and Jalota, Rricha and Ahmed Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, journal = {{{IEEE} Access}}, pages = {{118861--118870}}, title = {{{I-AID: Identifying Actionable Information From Disaster-Related Tweets}}}, doi = {{10.1109/ACCESS.2021.3107812}}, volume = {{9}}, year = {{2021}}, } @article{24720, author = {{Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and de Melo, Gerard and Guti{\'{e}}rrez, Claudio and Kirrane, Sabrina and Emilio Labra Gayo, Jos{\'{e}} and Navigli, Roberto and Neumaier, Sebastian and Ngonga Ngomo, Axel-Cyrille and Polleres, Axel and M. Rashid, Sabbir and Rula, Anisa and Schmelzeisen, Lukas and F. Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine}}, journal = {{{ACM} Comput. Surv.}}, number = {{4}}, pages = {{71:1--71:37}}, title = {{{Knowledge Graphs}}}, doi = {{10.1145/3447772}}, volume = {{54}}, year = {{2021}}, } @inproceedings{26033, author = {{Zeipert, Henning and Johannesmann, Sarah and Nicolai, Marcel and Lugovtsova, Yevgeniya and Prager, Jens and Henning, Bernd}}, booktitle = {{Fortschritte der Akustik - DAGA 2021}}, location = {{Wien}}, title = {{{Quantifying the coupling strength of adhesively bonded materials by investigating mode repulsion regions}}}, year = {{2021}}, } @inproceedings{26050, author = {{Ködding, Patrick and Dumitrescu, Roman}}, booktitle = {{Digitalisierung souverän gestalten}}, editor = {{Hartmann, Ernst A.}}, publisher = {{Springer Vieweg}}, title = {{{Szenario-Technik mit digitalen Technologien (in Druck)}}}, year = {{2021}}, } @inproceedings{26401, author = {{Förster, Magdalena and Rabe, Martin and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Proceedings of the IEEE E-TEMS 2021}}, title = {{{Practical approach for the development of digital guidelines for smart cities}}}, year = {{2021}}, } @inproceedings{26402, author = {{Tekaat, Julian and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{ Proceedings of the 7th IEEE International Symposium on Systems Engineering}}, title = {{{The Paradigm of Design Thinking and Systems Engineering in the Design of Cyber-Physical Systems: A Systematic Literature Review}}}, year = {{2021}}, } @inproceedings{26403, author = {{Wilke, Daria and Schierbaum, Anja and Kaiser, Lydia and Dumitrescu, Roman}}, booktitle = {{Proceedings of the International Conference on Engineering Design, ICED 2021}}, location = {{Gothenburg, Sweden}}, title = {{{Need for Action for a Company-Wide Introduction of Systems Engineering in Machinery and Plant Engineering}}}, year = {{2021}}, } @inproceedings{26404, author = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 18th International Conference on Remote Engineering and Virtual Instrumentation, Online Engineering and Society 4.0}}, editor = {{Auer, Michael E. and Bhimavaram, Kalyan Ram and Yue, Xiao-Guang}}, isbn = {{978-3-030-82528-7}}, pages = {{451--463}}, publisher = {{Springer International Publishing}}, title = {{{Augmented Reality-based Product Validation to Support Collaborative Engineering of Complex Technical Systems}}}, volume = {{vol. 298}}, year = {{2021}}, } @inproceedings{26492, author = {{Gabriel, Stefan and Grauthoff, Tobias and Joppen, Robert and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, location = {{Enschede, the Netherlands}}, pages = {{241--246}}, title = {{{Analyzing socio-technical risks in implementation of Industry 4.0-use cases}}}, volume = {{vol. 100}}, year = {{2021}}, } @phdthesis{26746, abstract = {{Previous research in proof-carrying hardware has established the feasibility and utility of the approach, and provided a concrete solution for employing it for the certification of functional equivalence checking against a specification, but fell short in connecting it to state-of-the-art formal verification insights, methods and tools. Due to the immense complexity of modern circuits, and verification challenges such as the state explosion problem for sequential circuits, this restriction of readily-available verification solutions severely limited the applicability of the approach in wider contexts. This thesis closes the gap between the PCH approach and current advances in formal hardware verification, provides methods and tools to express and certify a wide range of circuit properties, both functional and non-functional, and presents for the first time prototypes in which circuits that are implemented on actual reconfigurable hardware are verified with PCH methods. Using these results, designers can now apply PCH to establish trust in more complex circuits, by using more diverse properties which they can express using modern, efficient property specification techniques.}}, author = {{Wiersema, Tobias}}, keywords = {{Proof-Carrying Hardware, Formal Verification, Sequential Circuits, Non-Functional Properties, Functional Properties}}, pages = {{293}}, publisher = {{Paderborn University}}, title = {{{Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying Hardware}}}, year = {{2021}}, } @inproceedings{26823, author = {{Kharatyan, Aschot and Jürgenhake, Christoph and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, pages = {{373--378}}, title = {{{Towards generative design in the development of mechatronic integrated devices (MID)}}}, volume = {{Vol. 100}}, year = {{2021}}, }