@article{34109, author = {{Hagengruber, Ruth Edith}}, journal = {{La Lettre clandestine n° 30: Émilie Du Châtelet et la littérature clandestine. Moreau, P.F. & Seguin, M.S. (Eds.). Paris}}, pages = {{99 – 115}}, publisher = {{Classiques Garnier}}, title = {{{Du Châtelet et la tradition critique de la Bible. De la ‘philofolie’ aux Examens}}}, year = {{2022}}, } @inproceedings{32855, author = {{Clausing, Lennart and Platzner, Marco}}, booktitle = {{2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, location = {{ Lyon, France}}, pages = {{120--127}}, publisher = {{IEEE}}, title = {{{ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support}}}, doi = {{10.1109/ipdpsw55747.2022.00029}}, year = {{2022}}, } @unpublished{30791, abstract = {{We present sufficient conditions that ensure convergence of the multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of the most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling continuous action spaces: the actor-critic paradigm. In the setting considered herein, each agent observes a part of the global state space in order to take local actions, for which it receives local rewards. For every agent, DDPG trains a local actor (policy) and a local critic (Q-function). The analysis shows that multi-agent DDPG using neural networks to approximate the local policies and critics converge to limits with the following properties: The critic limits minimize the average squared Bellman loss; the actor limits parameterize a policy that maximizes the local critic's approximation of $Q_i^*$, where $i$ is the agent index. The averaging is with respect to a probability distribution over the global state-action space. It captures the asymptotics of all local training processes. Finally, we extend the analysis to a fully decentralized setting where agents communicate over a wireless network prone to delays and losses; a typical scenario in, e.g., robotic applications.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.00570}}, title = {{{Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}}}, year = {{2022}}, } @inproceedings{32590, author = {{Richter, Cedric and Wehrheim, Heike}}, booktitle = {{2022 IEEE Conference on Software Testing, Verification and Validation (ICST)}}, pages = {{162--173}}, title = {{{Learning Realistic Mutations: Bug Creation for Neural Bug Detectors}}}, doi = {{10.1109/ICST53961.2022.00027}}, year = {{2022}}, } @inproceedings{32591, author = {{Richter, Cedric and Wehrheim, Heike}}, booktitle = {{2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)}}, pages = {{418--422}}, title = {{{TSSB-3M: Mining single statement bugs at massive scale}}}, doi = {{10.1145/3524842.3528505}}, year = {{2022}}, } @misc{32398, author = {{Siek, Hanna}}, title = {{{Bringing Structure to Structure-Preserving Signatures: Overview, Implementation and Comparison of Selected SPS Schemes}}}, year = {{2022}}, } @misc{31485, author = {{Kramer, Paul}}, title = {{{On Transforming Lattice-Based Cryptography to the Ring Setting}}}, year = {{2022}}, } @inproceedings{31806, abstract = {{The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds}}, author = {{Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}}, keywords = {{2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba}}, location = {{Barcelona (Spain)}}, title = {{{ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}}}, doi = {{10.1145/3511095.3531287}}, year = {{2022}}, } @article{32854, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, journal = {{IFAC-PapersOnLine}}, number = {{13}}, pages = {{133–138}}, publisher = {{Elsevier}}, title = {{{Practical Network Conditions for the Convergence of Distributed Optimization}}}, volume = {{55}}, year = {{2022}}, } @inproceedings{33253, author = {{Hansmeier, Tim and Brede, Mathis and Platzner, Marco}}, booktitle = {{GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, location = {{Boston, MA, USA}}, pages = {{2071--2079}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{XCS on Embedded Systems: An Analysis of Execution Profiles and Accelerated Classifier Deletion}}}, doi = {{10.1145/3520304.3533977}}, year = {{2022}}, } @inproceedings{33274, author = {{Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}}, pages = {{51 -- 61}}, title = {{{Analyzing Culture-Specific Argument Structures in Learner Essays}}}, year = {{2022}}, } @inproceedings{33491, author = {{Maack, Marten and Pukrop, Simon and Rasmussen, Anna Rodriguez}}, booktitle = {{30th Annual European Symposium on Algorithms, ESA 2022, September 5-9, 2022, Berlin/Potsdam, Germany}}, editor = {{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz}}, pages = {{77:1–77:13}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{(In-)Approximability Results for Interval, Resource Restricted, and Low Rank Scheduling}}}, doi = {{10.4230/LIPIcs.ESA.2022.77}}, volume = {{244}}, year = {{2022}}, } @techreport{32106, abstract = {{We study the consequences of modeling asymmetric bargaining power in two-person bargaining problems. Comparing application of an asymmetric version of a bargaining solution to an upfront modification of the disagreement point, the resulting distortion crucially depends on the bargaining solution concept. While for the Kalai-Smorodinsky solution weaker players benefit from modifying the disagreement point, the situation is reversed for the Nash bargaining solution. There, weaker players are better off in the asymmetric bargaining solution. When comparing application of the asymmetric versions of the Nash and the Kalai-Smorodinsky solutions, we demonstrate that there is an upper bound for the weight of a player, so that she is better off with the Nash bargaining solution. This threshold is ultimately determined by the relative utilitarian bargaining solution. From a mechanism design perspective, our results provide valuable information for a social planner, when implementing a bargaining solution for unequally powerful players.}}, author = {{Haake, Claus-Jochen and Streck, Thomas}}, keywords = {{Asymmetric bargaining power, Nash bargaining solution, Kalai-Smorodinsky bargaining solution}}, pages = {{17}}, title = {{{Distortion through modeling asymmetric bargaining power}}}, volume = {{148}}, year = {{2022}}, } @article{34132, abstract = {{How can Knowledge In/Equity be addressed in qualitative research by taking the idea of Open Science into account? Two projects from the Open Science Fellows Programme by Wikimedia Deutschland will be used to illustrate how Open Science practices can succeed in qualitative research, thereby reducing In/Equity. In this context, In/Equity is considered as a fair and equal representation of people, their knowledge and insights and comprehends questions about how epistemic, structural, institutional and personal biases generate and shape knowledge as guidance. Three questions guide this approach: firstly, what do we understand by In/Equity in the context of knowledge production in these projects? Secondly, who will be involved in knowledge generation and to what extent will they be valued or unvalued? Thirdly, how can data be made accessible for re-use to enable true participation and sharing?}}, author = {{Steinhardt, Isabel and Kruschick, Felicitas}}, issn = {{2367-7163}}, journal = {{Research Ideas and Outcomes}}, keywords = {{Open Science, Knowledge Equity, Qualitative Methods}}, publisher = {{Pensoft Publishers}}, title = {{{Knowledge Equity and Open Science in qualitative research – Practical research considerations}}}, doi = {{10.3897/rio.8.e86387}}, volume = {{8}}, year = {{2022}}, } @inproceedings{34140, abstract = {{In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.}}, author = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}}, booktitle = {{2022 Kleinheubach Conference}}, keywords = {{emc, pcb, electronic system development, machine learning, neural network}}, location = {{Miltenberg, Germany}}, publisher = {{IEEE}}, title = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}}, year = {{2022}}, } @inbook{34108, author = {{Hagengruber, Ruth Edith}}, booktitle = {{Sitzungsberichte der Leibniz-Sozietät der Wissenschaften 150/151, Jahrgang 2022: „Cyberscience – Wissenschaftsforschung und Informatik. Digitale Medien und die Zukunft der Kultur wissenschaftlicher Tätigkeit. Arbeitskreis „Emergente Systeme/Informatik und Gesellschaft“ der Leibniz-Sozietät der Wissenschaften zu Berlin in Kooperation mit der Gesellschaft für Wissenschaftsforschung“}}, editor = {{Banse, Gerhard and Fuchs-Kittowski, Klaus}}, pages = {{253–256}}, title = {{{Die „dritte Wissensdimension“. Eine Epistemologie für eine neue Wissenswelt}}}, year = {{2022}}, } @inbook{32179, abstract = {{This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.}}, author = {{Kersting, Joschka and Ahmed, Mobeen and Geierhos, Michaela}}, booktitle = {{HCI International 2022 Posters}}, editor = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula}}, isbn = {{9783031064166}}, issn = {{1865-0929}}, keywords = {{On-The-Fly Computing, Chatbot, Knowledge Base}}, location = {{Virtual}}, pages = {{419----426}}, publisher = {{Springer International Publishing}}, title = {{{Chatbot-Enhanced Requirements Resolution for Automated Service Compositions}}}, doi = {{10.1007/978-3-031-06417-3_56}}, volume = {{1580}}, year = {{2022}}, } @inproceedings{34152, author = {{Otroshi, Mortaza and Meschut, Gerson}}, location = {{Rostock}}, publisher = {{Europäische Forschungsgesellschaft für Blechverarbeitung e.V. }}, title = {{{Methodenentwicklung zur Verbesserung der Schädigungsmodellierung in der numerischen 3D-Belastungssimulation mechanischer Fügeverfahren unter Berücksichtigung der fügeinduzierten Vorbeanspruchung}}}, year = {{2022}}, } @inproceedings{34155, author = {{Krauter, Stefan and Bendfeld, Jörg}}, booktitle = {{Proceedings of the 8th World Conference on Photovoltaik Energy Conversion}}, location = {{Milano / Italy}}, title = {{{Microinverter PV Systems: New Efficiency Rankings and Formula for Energy Yield Assessment for any PV Panel Size at different Microinverter types}}}, year = {{2022}}, } @inproceedings{34156, author = {{Kakande, Josephine Nakato and Philipo, Godiana Hagile and Krauter, Stefan}}, booktitle = {{Proceedings of the 8th World Conference on Photovoltaik Energy Conversion}}, location = {{Milano / Italy}}, title = {{{Optimal Design of a Semi Grid-Connected PV System for a Site in Lwak, Kenya Using HOMER}}}, year = {{2022}}, }