@unpublished{30866, abstract = {{Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an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}}, } @inproceedings{21093, abstract = {{Requirements for energy distribution networks are changing fast due to the growing share of renewable energy, increasing electrification, and novel consumer and asset technologies. Since uncertainties about future developments increase planning difficulty, flexibility potentials such as synergies between the electricity, gas, heat, and transport sector often remain unused. In this paper, we therefore present a novel module-based concept for a decision support system that helps distribution network planners to identify cross-sectoral synergies and to select optimal network assets such as transformers, cables, pipes, energy storage systems or energy conversion technology. The concept enables long-term transformation plans and supports distribution network planners in designing reliable, sustainable and cost-efficient distribution networks for future demands.}}, author = {{Kirchhoff, Jonas and Burmeister, Sascha Christian and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Energy Informatics and Electro Mobility ICT}}, editor = {{Breitner, Michael H. and Lehnhoff, Sebastian and Nieße, Astrid and Staudt, Philipp and Weinhardt, Christof and Werth, Oliver}}, title = {{{Towards a Decision Support System for Cross-Sectoral Energy Distribution Network Planning}}}, year = {{2021}}, } @inproceedings{28199, author = {{Pauck, Felix and Wehrheim, Heike}}, booktitle = {{2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)}}, title = {{{Jicer: Simplifying Cooperative Android App Analysis Tasks}}}, doi = {{10.1109/scam52516.2021.00031}}, year = {{2021}}, } @unpublished{26645, author = {{Bobolz, Jan and Eidens, Fabian and Heitjohann, Raphael and Fell, Jeremy}}, publisher = {{IACR eprint}}, title = {{{Cryptimeleon: A Library for Fast Prototyping of Privacy-Preserving Cryptographic Schemes}}}, year = {{2021}}, } @techreport{33854, abstract = {{Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.}}, author = {{Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, keywords = {{mobility management, coordinated multipoint, CoMP, cell selection, resource management, reinforcement learning, multi agent, MARL, self-learning, self-adaptation, QoE}}, title = {{{DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}}}, year = {{2021}}, } @inproceedings{29137, author = {{Hansmeier, Tim}}, booktitle = {{HEART '21: Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies}}, location = {{Online}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Self-aware Operation of Heterogeneous Compute Nodes using the Learning Classifier System XCS}}}, doi = {{10.1145/3468044.3468055}}, year = {{2021}}, } @phdthesis{28683, abstract = {{In den letzten Jahren haben sich Software-Ökosysteme als neue, erfolgreiche Geschäftsform etabliert. Unternehmen agieren hierbei als Anbieter von Software-Plattformen, auf denen Drittanbieter Softwarelösungen für den Markt anbieten können. Etablierte Beispiele sind hierbei sogenannte App-Stores, die z.B. von Google oder Apple angeboten werden. Beim Aufbau von Software-Ökosystemen müssen vom Plattformanbieter viele architektonische Entwurfsentscheidungen getroffen werden. Bisher gibt es keine Architekturrichtlinien und -werkzeuge, die den Entwurf einer Ökosystemarchitektur unterstützen. Dadurch fehlt hier systematisches, wiederverwendbares Wissen. Plattformanbieter müssen auf ad-hoc Entscheidungen zurückgreifen. Dies kann dann zu Problemen im Betrieb der Software-Plattformen führen, zu erhöhten Ausfallrisiken und Mehrkosten. Der Mangel an Architekturwissen manifestiert sich konkret in zwei Gruppen von Herausforderungen: Erstens fehlt eine Wissensbasis zu Architekturalternativen und zweitens fehlt es an methodischem Wissen zu Entwicklung und Betrieb von Software-Ökosystemen. Eine Architekturwissensbasis würde Orientierungshilfen zu den Bestandteilen von Software-Ökosystemen und deren Abhängigkeiten geben, während methodisches Wissen die Erstellung dieser Systeme erleichtern würde. In der Dissertation werden diese Herausforderungen durch die Entwicklung des Frameworks SecoArc für die Modellierung von Software-Ökosystemen angegangen. Der Beitrag der Dissertation ist zweifach: 1. Das SecoArc-Framework umfasst eine Architekturwissensbasis, die wiederverwendbare Architekturentwurfsentscheidungen von Software-Ökosystemen enthält. Die Wissensbasis wurde entwickelt, indem das Architekturwissen bestehender Ökosysteme sowie aus existierender Fachliteratur ermittelt wurde und in einer Produktlinie für Software-Ökosysteme konsolidiert wurde. Die Produktlinie umfasst architektonische Gemeinsamkeiten und Variabilitäten von Software-Ökosystemen. 2. Das SecoArc-Framework liefert methodisches Wissen, um die Ökosystemarchitektur in Modellen zu entwerfen und zu analysieren. Dieses Wissen wurde entwickelt, indem drei Architekturmuster identifiziert wurden. Jedes Muster erfasst unterschiedliche Beziehungen zwischen architektonischen Entwurfsentscheidungen zu den Qualitätsmerkmalen einer Ökosystemgesundheit und der Erreichung von Geschäftszielen. Die Architekturmuster und die Produktlinie wurden dazu genutzt, ein Modellierungsframework zu entwickeln und in Form eines Prototypen umzusetzen, welches einen Entwurfsprozess, eine Modellierungssprache und eine Architekturanalysetechnik umfasst. Es erleichtert das Modellieren, Analysieren und Vergleichen von Ökosystemarchitekturen. Die Ergebnisse der Dissertation wurden im Rahmen von zwei Studien evaluiert. In der ersten Validierungsstudie wurden das Framework sowie der Prototyp verwendet, um zwei alternative Ökosystemarchitekturen zu entwerfen und zu analysieren. In der zweiten Studie wurde eine Analyse von existierenden Ökosystemen basierend auf den architektonischen Variabilitäten des Frameworks durchgeführt.}}, author = {{Schwichtenberg, Bahar}}, keywords = {{Enterprise Architecture, Architectural Design Decisions, Open Platforms}}, title = {{{Modeling and Analyzing Software Ecosystems}}}, doi = {{10.17619/UNIPB/1-1270 }}, year = {{2021}}, } @inproceedings{26986, author = {{Castenow, Jannik and Götte, Thorsten and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2021}}, editor = {{Johnen, C. and Schiller, E.M. and Schmid, S.}}, location = {{Online}}, pages = {{289--304 }}, publisher = {{Springer}}, title = {{{The Max-Line-Formation Problem – And New Insights for Gathering and Chain-Formation}}}, doi = {{10.1007/978-3-030-91081-5_19}}, volume = {{13046}}, year = {{2021}}, } @inproceedings{29486, author = {{Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 11th on Knowledge Capture Conference}}, isbn = {{978-1-4503-8457-5}}, pages = {{73–80}}, publisher = {{ACM}}, title = {{{GATES: Using Graph Attention Networks for Entity Summarization}}}, doi = {{10.1145/3460210.3493574}}, year = {{2021}}, } @inproceedings{29566, author = {{Bobolz, Jan and Eidens, Fabian and Krenn, Stephan and Ramacher, Sebastian and Samelin, Kai}}, booktitle = {{Cryptology and Network Security}}, isbn = {{9783030925475}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Issuer-Hiding Attribute-Based Credentials}}}, doi = {{10.1007/978-3-030-92548-2_9}}, year = {{2021}}, } @article{28463, author = {{Hanisch, Simon and Arias Cabarcos, Patricia and Parra-Arnau, Javier and Strufe, Thorsten}}, journal = {{CoRR}}, title = {{{Privacy-Protecting Techniques for Behavioral Data: A Survey}}}, volume = {{abs/2109.04120}}, year = {{2021}}, } @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}}, }