@inproceedings{28462, author = {{Arias Cabarcos, Patricia and Habrich, Thilo and Becker, Karen and Becker, Christian and Strufe, Thorsten}}, booktitle = {{30th {USENIX} Security Symposium, {USENIX} Security 2021, August 11-13, 2021}}, editor = {{Bailey, Michael and Greenstadt, Rachel}}, pages = {{55--72}}, publisher = {{{USENIX} Association}}, title = {{{Inexpensive Brainwave Authentication: New Techniques and Insights on User Acceptance}}}, year = {{2021}}, } @misc{29540, abstract = {{Autonomous mobile robots are becoming increasingly more capable and widespread. Reliable Obstacle avoidance is an integral part of autonomous navigation. This involves real time interpretation and processing of a complex environment. Strict time and energy constraints of a mobile autonomous system make efficient computation extremely desirable. The benefits of employing Hardware/Software co-designed applications are obvious and significant. Hardware accelerators are used for efficient processing of the algorithms by exploiting parallelism. FPGAs are a class of hardware accelerators, which can contain hundreds of small execution units, and can be used for Hardware/Software co-designed application. However, there is a reluctance when it comes to adoption of these devices in well established application domains, such as Robotics, due to a steep learning curve needed for FPGA application design. ReconROS has successfully bridged the gap between robotic and FPGA application development, by providing an intuitive, common development platform for robotic application development for FPGA. It does so by integrating Robotics Operating System(ROS) which is an industry and academia standard for robotics application development, with ReconOS, an operating system for re-configurable hardware. In this thesis an obstacle avoidance system is designed and implemented for an autonomous vehicle using ReconROS. The objectives of the thesis is to demonstrate and explore ReconROS integration within the ROS ecosystem and explore the design process within ReconROS framework, and to demonstrate the effectiveness of Hardware Acceleration in Robotics, by analysing the resulting architectures for Latency and Power Consumption.}}, author = {{Sheikh, Muhammad Aamir}}, publisher = {{Paderborn University}}, title = {{{Design and Implementation of a ReconROS-based Obstacle Avoidance System}}}, year = {{2021}}, } @unpublished{22764, abstract = {{Robotics applications process large amounts of data in real-time and require compute platforms that provide high performance and energy-efficiency. FPGAs are well-suited for many of these applications, but there is a reluctance in the robotics community to use hardware acceleration due to increased design complexity and a lack of consistent programming models across the software/hardware boundary. In this paper we present ReconROS, a framework that integrates the widely-used robot operating system (ROS) with ReconOS, which features multithreaded programming of hardware and software threads for reconfigurable computers. This unique combination gives ROS2 developers the flexibility to transparently accelerate parts of their robotics applications in hardware. We elaborate on the architecture and the design flow for ReconROS and report on a set of experiments that underline the feasibility and flexibility of our approach.}}, author = {{Lienen, Christian and Platzner, Marco}}, booktitle = {{arXiv:2107.07208}}, pages = {{19}}, title = {{{Design of Distributed Reconfigurable Robotics Systems with ReconROS}}}, year = {{2021}}, } @inproceedings{29707, author = {{Bechinie, Dominik and Eilerts, Katja and Huhmann, Tobias and Lenke, Michael and Schulte, Carsten and Winkelnkemper, Felix}}, booktitle = {{Beiträge zum Mathematikunterricht 2021}}, publisher = {{WTM Verlag, Münster}}, title = {{{Geometrielernen digital unterstützen - Räumliche Kompetenzen und individuelle Lernwege mittels adaptierbarer algorithmischer Rückmeldemöglichkeiten fördern}}}, year = {{2021}}, } @inproceedings{22482, author = {{Yigitbas, Enes and Klauke, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Proceedings of the 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) }}, publisher = {{IEEE}}, title = {{{VREUD - An End-User Development Tool to Simplify the Creation of Interactive VR Scenes}}}, year = {{2021}}, } @article{29708, author = {{Gerstenberger, Dietrich Karl-Heinz and Winkelnkemper, Felix and Schulte, Carsten}}, journal = {{9. Fachtagung Hochschuldidaktik Informatik (HDI)}}, pages = {{49}}, title = {{{Nutzung der Personas-Methode zum Umgang mit der Heterogenität von Informatik-Studierenden}}}, year = {{2021}}, } @phdthesis{27499, author = {{Budde, Lea}}, publisher = {{University of Paderborn, Germany}}, title = {{{Entwicklung und Rekonstruktion einer interaktionsgeprägten Sichtweise auf das komplementäre Mensch-Maschine-Verhältnis}}}, year = {{2021}}, } @inbook{29720, author = {{Passey, Don and Brinda, Torsten and Cornu, Bernard and Holvikivi, Jaana and Lewin, Cathy and Magenheim, Johannes and Morel, Raymond and Osorio, Javier and Tatnall, Arthur and Thompson, Barrie and Webb, Mary}}, booktitle = {{Advancing Research in Information and Communication Technology}}, editor = {{Goedicke, Michael and Neuhold, Erich and Rannenberg, Kai}}, isbn = {{978-3-030-81700-8}}, issn = {{1868-422X}}, keywords = {{Educational technologies, Education and technologies, Digital technologies and education, Information technologies, Communication technologies, Educational technologies and research, Educational technologies and pedagogical practices, Educational technologies and policy, Educational management and technologies, Professional development and educational technologies}}, pages = {{129--152}}, publisher = {{Springer, Cham}}, title = {{{Computers and Education – Recognising Opportunities and Managing Challenges}}}, doi = {{10.1007/978-3-030-81701-5_5}}, volume = {{AICT-600}}, year = {{2021}}, } @inproceedings{25295, author = {{Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{67 -- 77}}, title = {{{Assessing the Sufficiency of Arguments through Conclusion Generation}}}, year = {{2021}}, } @inproceedings{23709, author = {{Chen, Wei-Fan and Al Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2021}}, pages = {{2683 -- 2693}}, title = {{{Controlled Neural Sentence-Level Reframing of News Articles}}}, year = {{2021}}, } @inproceedings{22229, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}}, location = {{Online}}, pages = {{1816–1827}}, publisher = {{Association for Computational Linguistics}}, title = {{{Argument Undermining: Counter-Argument Generation by Attacking Weak Premises}}}, doi = {{10.18653/v1/2021.findings-acl.159}}, year = {{2021}}, } @inproceedings{28988, author = {{Kirchhoff, Jonas}}, booktitle = {{The 1st Early Career Researchers Workshop Co-Located with ECSS 2021}}, location = {{Madrid}}, title = {{{Providing Decision Makers with Tailored Decision Support Systems}}}, year = {{2021}}, } @inproceedings{21198, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, location = {{Delhi, India}}, title = {{{Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}}}, year = {{2021}}, } @inproceedings{21813, author = {{Hansmeier, Tim and Platzner, Marco}}, booktitle = {{GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-8351-6}}, location = {{Lille, France}}, pages = {{1639–1647}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS}}}, doi = {{10.1145/3449726.3463159}}, year = {{2021}}, } @unpublished{27778, abstract = {{Consider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the Server Cloud Scheduling problem, in which the jobs have to be processed either on a single local machine or on one of many cloud machines. Both the source and the sink have to be scheduled on the local machine. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server, the other in the cloud. The server can process jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS with respect for the makespan objective for a fairly general case and strong hardness for the case with unit processing times and delays.}}, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, booktitle = {{arXiv:2108.02109}}, title = {{{Full Version -- Server Cloud Scheduling}}}, year = {{2021}}, } @misc{27058, author = {{Wiecher, Carsten and Fischbach, Jannik and Greenyer, Joel and Vogelsang, Andreas and Wolff, Carsten and Dumitrescu, Roman}}, booktitle = {{arXiv preprint}}, title = {{{Integrated and Iterative Requirements Analysis and Test Specification: A Case Study at Kostal}}}, year = {{2021}}, } @inbook{29292, author = {{Feldhans, Robert and Wilke, Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}}, booktitle = {{Intelligent Data Engineering and Automated Learning – IDEAL 2021}}, isbn = {{9783030916077}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Drift Detection in Text Data with Document Embeddings}}}, doi = {{10.1007/978-3-030-91608-4_11}}, year = {{2021}}, } @inproceedings{29287, abstract = {{Knowledge graph embedding research has mainly focused on the two smallest normed division algebras, $\mathbb{R}$ and $\mathbb{C}$. Recent results suggest that trilinear products of quaternion-valued embeddings can be a more effective means to tackle link prediction. In addition, models based on convolutions on real-valued embeddings often yield state-of-the-art results for link prediction. In this paper, we investigate a composition of convolution operations with hypercomplex multiplications. We propose the four approaches QMult, OMult, ConvQ and ConvO to tackle the link prediction problem. QMult and OMult can be considered as quaternion and octonion extensions of previous state-of-the-art approaches, including DistMult and ComplEx. ConvQ and ConvO build upon QMult and OMult by including convolution operations in a way inspired by the residual learning framework. We evaluated our approaches on seven link prediction datasets including WN18RR, FB15K-237 and YAGO3-10. Experimental results suggest that the benefits of learning hypercomplex-valued vector representations become more apparent as the size and complexity of the knowledge graph grows. ConvO outperforms state-of-the-art approaches on FB15K-237 in MRR, Hit@1 and Hit@3, while QMult, OMult, ConvQ and ConvO outperform state-of-the-approaches on YAGO3-10 in all metrics. Results also suggest that link prediction performances can be further improved via prediction averaging. To foster reproducible research, we provide an open-source implementation of approaches, including training and evaluation scripts as well as pretrained models.}}, author = {{Demir, Caglar and Moussallem, Diego and Heindorf, Stefan and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The 13th Asian Conference on Machine Learning, ACML 2021}}, title = {{{Convolutional Hypercomplex Embeddings for Link Prediction}}}, year = {{2021}}, } @inproceedings{29294, author = {{Nickchen, Tobias and Heindorf, Stefan and Engels, Gregor}}, booktitle = {{2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}}, publisher = {{IEEE}}, title = {{{Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts}}}, doi = {{10.1109/wacv48630.2021.00204}}, year = {{2021}}, } @inproceedings{26407, author = {{Piskachev, Goran and Krishnamurthy, Ranjith and Bodden, Eric}}, booktitle = {{2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)}}, title = {{{SecuCheck: Engineering configurable taint analysis for software developers}}}, year = {{2021}}, }