@article{21254, author = {{Balakrishna, Praneeth and Book, Gerrit and Kirchgässner, Wilhelm and Schenke, Maximilian and Traue, Arne and Wallscheid, Oliver}}, issn = {{2475-9066}}, journal = {{Journal of Open Source Software}}, title = {{{gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems}}}, doi = {{10.21105/joss.02498}}, year = {{2021}}, } @article{25031, author = {{Schenke, Maximilian and Wallscheid, Oliver}}, issn = {{2644-1284}}, journal = {{IEEE Open Journal of the Industrial Electronics Society}}, pages = {{388--400}}, title = {{{A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors}}}, doi = {{10.1109/ojies.2021.3075521}}, year = {{2021}}, } @article{29662, author = {{Schenke, Maximilian and Wallscheid, Oliver}}, journal = {{arXiv preprint arXiv:2105.08990}}, title = {{{Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning}}}, year = {{2021}}, } @article{21557, author = {{Brosch, Anian and Wallscheid, Oliver and Böcker, Joachim}}, issn = {{1551-3203}}, journal = {{IEEE Transactions on Industrial Informatics}}, title = {{{Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors}}}, doi = {{10.1109/tii.2021.3060469}}, year = {{2021}}, } @inproceedings{25297, author = {{Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{184 -- 189}}, title = {{{Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}}}, year = {{2021}}, } @inproceedings{25294, author = {{Nouri, Zahra and Prakash, Nikhil and Gadiraju, Ujwal and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021}}, title = {{{iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{30217, author = {{Coy, Sam and Czumaj, Artur and Feldmann, Michael and Hinnenthal, Kristian and Kuhn, Fabian and Scheideler, Christian and Schneider, Philipp and Struijs, Martijn}}, booktitle = {{25th International Conference on Principles of Distributed Systems, OPODIS 2021, December 13-15, 2021, Strasbourg, France}}, editor = {{Bramas, Quentin and Gramoli, Vincent and Milani, Alessia}}, pages = {{11:1–11:23}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Near-Shortest Path Routing in Hybrid Communication Networks}}}, doi = {{10.4230/LIPIcs.OPODIS.2021.11}}, volume = {{217}}, year = {{2021}}, } @inproceedings{30340, author = {{Hagemeyer, Marc and Wallmeier, Peter and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{Proc. 36th IEEE Applied Power Electronics Conference (APEC)}}, location = {{Phoenix, AZ, USA}}, pages = {{569 -- 576}}, publisher = {{IEEE}}, title = {{{Comparison of unidirectional Three- and Four-Wire based Boost PFC-Rectifier Topologies for Non-Isolated Three-Phase EV On-Board Chargers under Common-Mode Aspects}}}, year = {{2021}}, } @misc{30348, author = {{Schafmeister, Frank}}, booktitle = {{Power System Design (PSD) Web Magazine}}, title = {{{Transformerless On-Board Chargers at Three- and Single-Phase Operation: Compensation of LF Common-Mode Noise by the Internal DC/DC-Stage}}}, year = {{2021}}, } @inproceedings{21593, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Engels, Gregor}}, booktitle = {{Proceedings of the 18th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2021) }}, publisher = {{Springer}}, title = {{{Simplifying Robot Programming using Augmented Reality and End-User Development}}}, year = {{2021}}, } @inproceedings{21707, author = {{Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 13th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2021)}}, publisher = {{ACM}}, title = {{{Using Augmented Reality for Enhancing Planning and Measurements in the Scaffolding Business}}}, year = {{2021}}, } @inproceedings{22706, author = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}}, booktitle = {{Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS'21) }}, publisher = {{ACM/IEEE}}, title = {{{Collaborative Software Modeling in Virtual Reality}}}, year = {{2021}}, } @inproceedings{21598, abstract = {{Static analysis is used to automatically detect bugs and security breaches, and aids compileroptimization. Whole-program analysis (WPA) can yield high precision, however causes long analysistimes and thus does not match common software-development workflows, making it often impracticalto use for large, real-world applications.This paper thus presents the design and implementation ofModAlyzer, a novel static-analysisapproach that aims at accelerating whole-program analysis by making the analysis modular andcompositional. It shows how to computelossless, persisted summaries for callgraph, points-to anddata-flow information, and it reports under which circumstances this function-level compositionalanalysis outperforms WPA.We implementedModAlyzeras an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications. At analysis time,ModAlyzermodularly and losslessly summarizesthe analysis effect of the library code those applications share, hence avoiding its repeated re-analysis.The experimental results show that the reuse of these summaries can save, on average, 72% ofanalysis time over WPA. Moreover, because it is lossless, the module-wise analysis fully retainsprecision and recall. Surprisingly, as our results show, it sometimes even yields precision superior toWPA. The initial summary generation, on average, takes about 3.67 times as long as WPA.}}, author = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric}}, booktitle = {{European Conference on Object-Oriented Programming (ECOOP)}}, title = {{{Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis}}}, year = {{2021}}, } @inproceedings{27381, abstract = {{Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.}}, author = {{Damke, Clemens and Hüllermeier, Eyke}}, booktitle = {{Proceedings of The 24th International Conference on Discovery Science (DS 2021)}}, editor = {{Soares, Carlos and Torgo, Luis}}, isbn = {{9783030889418}}, issn = {{0302-9743}}, keywords = {{Graph-structured data, Graph neural networks, Preference learning, Learning to rank}}, location = {{Halifax, Canada}}, pages = {{166--180}}, publisher = {{Springer}}, title = {{{Ranking Structured Objects with Graph Neural Networks}}}, doi = {{10.1007/978-3-030-88942-5}}, volume = {{12986}}, year = {{2021}}, } @unpublished{30866, abstract = {{Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. While AutoML offers many prospects, it is also known to be quite resource-intensive, which is one of its major points of criticism. The primary cause for a high resource consumption is that many approaches rely on the (costly) evaluation of many machine learning pipelines while searching for good candidates. This problem is amplified in the context of research on AutoML methods, due to large scale experiments conducted with many datasets and approaches, each of them being run with several repetitions to rule out random effects. In the spirit of recent work on Green AI, this paper is written in an attempt to raise the awareness of AutoML researchers for the problem and to elaborate on possible remedies. To this end, we identify four categories of actions the community may take towards more sustainable research on AutoML, i.e. Green AutoML: design of AutoML systems, benchmarking, transparency and research incentives.}}, author = {{Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{arXiv:2111.05850}}, title = {{{Towards Green Automated Machine Learning: Status Quo and Future Directions}}}, year = {{2021}}, } @phdthesis{27284, author = {{Wever, Marcel Dominik}}, title = {{{Automated Machine Learning for Multi-Label Classification}}}, doi = {{10.17619/UNIPB/1-1302}}, year = {{2021}}, } @article{30906, abstract = {{Abstract Background Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training. Methods In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback. Results The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7). Conclusion The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development. }}, author = {{Boschmann, Alexander and Neuhaus, Dorothee and Vogt, Sarah and Kaltschmidt, Christian and Platzner, Marco and Dosen, Strahinja}}, issn = {{1743-0003}}, journal = {{Journal of NeuroEngineering and Rehabilitation}}, keywords = {{Health Informatics, Rehabilitation}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis}}}, doi = {{10.1186/s12984-021-00822-6}}, volume = {{18}}, year = {{2021}}, } @article{30907, author = {{Rodriguez, Alfonso and Otero, Andres and Platzner, Marco and De la Torre, Eduardo}}, issn = {{0018-9340}}, journal = {{IEEE Transactions on Computers}}, keywords = {{Computational Theory and Mathematics, Hardware and Architecture, Theoretical Computer Science, Software}}, pages = {{1--1}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Exploiting Hardware-Based Data-Parallel and Multithreading Models for Smart Edge Computing in Reconfigurable FPGAs}}}, doi = {{10.1109/tc.2021.3107196}}, year = {{2021}}, } @phdthesis{30849, author = {{Henkenius, Carsten}}, title = {{{Entwurf netzfreundlicher Synchrongleichrichter mit integriertem Synchronwandler}}}, doi = {{10.17619/UNIPB/1-1109}}, year = {{2021}}, } @article{22925, author = {{Claes, Leander and Chatwell, René Spencer and Baumhögger, Elmar and Hetkämper, Tim and Zeipert, Henning and Vrabec, Jadran and Henning, Bernd}}, issn = {{0263-2241}}, journal = {{Measurement}}, title = {{{Measurement procedure for acoustic absorption and bulk viscosity of liquids}}}, doi = {{10.1016/j.measurement.2021.109919}}, year = {{2021}}, }