@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}}, } @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}}, } @phdthesis{32057, abstract = {{Ein zentraler Aspekt bei der Untersuchung dynamischer Systeme ist die Analyse ihrer invarianten Mengen wie des globalen Attraktors und (in)stabiler Mannigfaltigkeiten. Insbesondere wenn das zugrunde liegende System von einem Parameter abhängt, ist es entscheidend, sie im Bezug auf diesen Parameter effizient zu verfolgen. Für die Berechnung invarianter Mengen stützen wir uns für ihre Approximation auf numerische Algorithmen. Typischerweise können diese Methoden jedoch nur auf endlich-dimensionale dynamische Systeme angewendet werden. In dieser Arbeit präsentieren wir daher einen numerischen Rahmen für die globale dynamische Analyse unendlich-dimensionaler Systeme. Wir werden Einbettungstechniken verwenden, um das core dynamical system (CDS) zu definieren, welches ein dynamisch äquivalentes endlich-dimensionales System ist.Das CDS wird dann verwendet, um eingebettete invariante Mengen, also eins-zu-eins Bilder, mittels Mengen-orientierten numerischen Methoden zu approximieren. Bei der Konstruktion des CDS ist es entscheidend, eine geeignete Beobachtungsabbildung auszuwählen und die geeignete inverse Abbildung zu entwerfen. Dazu werden wir geeignete numerische Implementierungen des CDS für DDEs und PDEs vorstellen. Für eine nachfolgende geometrische Analyse der eingebetteten invarianten Menge betrachten wir eine Lerntechnik namens diffusion maps, die ihre intrinsische Geometrie enthüllt sowie ihre Dimension schätzt. Schließlich wenden wir unsere entwickelten numerischen Methoden an einigen bekannten unendlich-dimensionale dynamischen Systeme an, wie die Mackey-Glass-Gleichung, die Kuramoto-Sivashinsky-Gleichung und die Navier-Stokes-Gleichung.}}, author = {{Gerlach, Raphael}}, title = {{{The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems}}}, doi = {{10.17619/UNIPB/1-1278}}, year = {{2021}}, } @article{32016, author = {{Delarue, Benjamin and Ramacher, Pablo}}, journal = {{Journal of Symplectic Geometry}}, number = {{6}}, pages = {{1281 -- 1337}}, title = {{{Asymptotic expansion of generalized Witten integrals for Hamiltonian circle actions}}}, doi = {{10.4310/JSG.2021.v19.n6.a1}}, volume = {{19}}, year = {{2021}}, } @inproceedings{32125, abstract = {{Fault coverage analysis and fault simulation are well-established methods for the qualification of test vectors in hardware design. However, their role in virtual prototyping and the correlation to later steps in the design process need further investigation. We introduce a metric for RISC-V instruction and register coverage for binary software. The metric measures if RISC-V instruction types are executed and if GPRs, CSRs, and FPRs are accessed. The analysis is applied by the means of a virtual prototype which is based on an abstract instruction and register model with direct correspondence to their bit level representation. In this context, we analyzed three different openly available test suites: the RISC-V architectural testing framework, the RISC-V unit tests, and programs which are automatically generated by the RISC-V Torture test generator. We discuss their tradeoffs and show that by combining them to a unified test suite we can arrive at a 100% GPR and FPR register coverage and a 98.7% instruction type coverage.}}, author = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Scheytt, Christoph}}, booktitle = {{MBMV 2021 - Methods and Description Languages for Modelling and Verification of Circuits and Systems; GMM/ITG/GI-Workshop}}, isbn = {{978-3-8007-5500-4}}, publisher = {{VDE}}, title = {{{Register and Instruction Coverage Analysis for Different RISC-V ISA Modules}}}, year = {{2021}}, } @article{34042, author = {{Li, Jiaao and Ma, Yulai and Miao, Zhengke and Shi, Yongtang and Wang, Weifan and Zhang, Cun-Quan}}, issn = {{0095-8956}}, journal = {{Journal of Combinatorial Theory, Series B}}, keywords = {{Computational Theory and Mathematics, Discrete Mathematics and Combinatorics, Theoretical Computer Science}}, pages = {{61--80}}, publisher = {{Elsevier BV}}, title = {{{Nowhere-zero 3-flows in toroidal graphs}}}, doi = {{10.1016/j.jctb.2021.11.001}}, volume = {{153}}, 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}}, } @article{28196, abstract = {{We show that narrow trenches in a high-contrast silicon-photonics slab can act as lossless power dividers for semi-guided waves. Reflectance and transmittance can be easily configured by selecting the trench width. At sufficiently high angles of incidence, the devices are lossless, apart from material attenuation and scattering due to surface roughness. We numerically simulate a series of devices within the full 0-to-1-range of splitting ratios, for semi-guided plane wave incidence as well as for excitation by focused Gaussian wave bundles. Straightforward cascading of the trenches leads to concepts for 1×M-power dividers and a polarization beam splitter.}}, author = {{Hammer, Manfred and Ebers, Lena and Förstner, Jens}}, issn = {{2578-7519}}, journal = {{OSA Continuum}}, keywords = {{tet_topic_waveguide}}, number = {{12}}, pages = {{3081}}, title = {{{Configurable lossless broadband beam splitters for semi-guided waves in integrated silicon photonics}}}, doi = {{10.1364/osac.437549}}, volume = {{4}}, 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}}, } @inproceedings{32132, abstract = {{Die Werkzeugdemonstration des QEMU Timing Analyzers (QTA) stellt eine Erweiterung des quelloffenen CPU Emulators QEMU zur Simulation von Softwareprogrammen und deren Worst-Case Zeitverhaltens vor, das durch eine statische Zeitanalyse vorher aus dem Softwareprogramm extrahiert wurde. Der Ablauf der Analyse gliedert sich in mehrere Schritte: Zunächst wird für das zu simulierende Binärprogramm eine WCET-Analyse mit aiT durchgeführt. Im Preprocessing des aiT-Reports wird daraufhin ein WCET-annotierter Kontrollflussgraph erzeugt. Dabei entsprechen die Knoten im Kontrollflussgraph den aiT-Blöcken und die Kanten dem jeweiligen Worst-Case-Zeitverbrauch, um das Programm im aktuellen Ausführungskontext vom Quell- bis zum Zielblock laufen zu lassen. Nach dem Preprocessing werden Binärprogramm und der zuvor erzeugte, zeitannotierte Kontrollflussgraph von QEMU geladen und gemeinsam simuliert. Die Implementierung des QTA basiert auf der Standard TGI Plugin API (Tiny Code Generator Plugin API), die seit Ende 2019 mit QEMU V4.2 verfügbar ist. Dieses API erlaubt die Entwicklung von versionsunabhängigen QEMU-Erweiterungen. Die QEMU-QTA-Erweiterung wird zum Zeitpunkt der Werkzeugdemonstration inklusive des ait2qta-Preprozessors unter github.com im Quellcode frei verfügbar sein. Die Demonstration geht von einer existierenden aiT-Analyse eines für TriCore© kompilierten binären Softwareprograms aus, erläutert das Kontrollflusszwischenformat und zeigt die zeitannotierte Simulation der Software.}}, author = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Scheytt, Christoph}}, booktitle = {{MBMV 2021 - Methods and Description Languages for Modelling and Verification of Circuits and Systems; GMM/ITG/GI-Workshop}}, keywords = {{QEMU, aiT, Zeitannotation, WCET}}, publisher = {{VDE}}, title = {{{QEMU zur Simulation von Worst-Case-Ausführungszeiten}}}, year = {{2021}}, }