@inproceedings{34040, abstract = {{Consider the practical goal of making a desired action profile played, when the planner can only change the payoffs, bound by stringent constraints. Applications include motivating people to choose the closest school, the closest subway station, or to coordinate on a communication protocol or an investment strategy. Employing subsidies and tolls, we adjust the game so that choosing this predefined action profile becomes strictly dominant. Inspired mainly by the work of Monderer and Tennenholtz, where the promised subsidies do not materialise in the not played profiles, we provide a fair and individually rational game adjustment, such that the total outside investments sum up to zero at any profile, thereby facilitating easy and frequent usage of our adjustment without bearing costs, even if some players behave unexpectedly. The resultant action profile itself needs no adjustment. Importantly, we also prove that our adjustment minimises the general transfer among all such adjustments, counting the total subsidising and taxation.}}, author = {{Polevoy, Gleb and Dziubiński, Marcin}}, booktitle = {{Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence}}, editor = {{De Raedt, Luc}}, keywords = {{adjustment, strictly dominant, fairness, individually rational, transfer, tax, subsidy}}, location = {{Vienna}}, publisher = {{International Joint Conferences on Artificial Intelligence Organization}}, title = {{{Fair, Individually Rational and Cheap Adjustment}}}, doi = {{10.24963/ijcai.2022/64}}, year = {{2022}}, } @inproceedings{33085, author = {{Epstein, Leah and Lassota, Alexandra and Levin, Asaf and Maack, Marten and Rohwedder, Lars}}, booktitle = {{39th International Symposium on Theoretical Aspects of Computer Science, STACS 2022, March 15-18, 2022, Marseille, France (Virtual Conference)}}, editor = {{Berenbrink, Petra and Monmege, Benjamin}}, pages = {{28:1–28:15}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Cardinality Constrained Scheduling in Online Models}}}, doi = {{10.4230/LIPIcs.STACS.2022.28}}, volume = {{219}}, year = {{2022}}, } @inproceedings{33004, author = {{Wachsmuth, Henning and Alshomary, Milad}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{344 -- 354}}, title = {{{"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain}}}, year = {{2022}}, } @article{34049, author = {{Lauscher, Anne and Wachsmuth, Henning and Gurevych, Iryna and Glavaš, Goran}}, journal = {{Transactions of the Association for Computational Linguistics}}, title = {{{On the Role of Knowledge in Computational Argumentation}}}, year = {{2022}}, } @inproceedings{22157, author = {{Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{4459 -- 4471}}, title = {{{Identifying the Human Values behind Arguments}}}, year = {{2022}}, } @inproceedings{34057, author = {{Pasic, Faruk and Becker, Matthias}}, booktitle = {{2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, publisher = {{IEEE}}, title = {{{Domain-specific Language for Condition Monitoring Software Development}}}, doi = {{10.1109/etfa52439.2022.9921730}}, year = {{2022}}, } @inproceedings{34047, abstract = {{News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.}}, author = {{Spliethöver, Maximilian and Keiff, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)}}, location = {{Abu Dhabi}}, publisher = {{Association for Computational Linguistics}}, title = {{{No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media}}}, year = {{2022}}, } @inbook{34077, author = {{Bondarenko, Alexander and Fröbe, Maik and Kiesel, Johannes and Syed, Shahbaz and Gurcke, Timon and Beloucif, Meriem and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030997380}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Overview of Touché 2022: Argument Retrieval}}}, doi = {{10.1007/978-3-030-99739-7_43}}, year = {{2022}}, } @inproceedings{32602, author = {{Padalkin, Andreas and Scheideler, Christian and Warner, Daniel}}, booktitle = {{28th International Conference on DNA Computing and Molecular Programming (DNA 28)}}, editor = {{Ouldridge, Thomas E. and Wickham, Shelley F. J.}}, isbn = {{978-3-95977-253-2}}, issn = {{1868-8969}}, pages = {{8:1–8:22}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{The Structural Power of Reconfigurable Circuits in the Amoebot Model}}}, doi = {{10.4230/LIPIcs.DNA.28.8}}, volume = {{238}}, year = {{2022}}, } @inproceedings{32603, author = {{Kostitsyna, Irina and Scheideler, Christian and Warner, Daniel}}, booktitle = {{28th International Conference on DNA Computing and Molecular Programming (DNA 28)}}, editor = {{Ouldridge, Thomas E. and Wickham, Shelley F. J.}}, isbn = {{978-3-95977-253-2}}, issn = {{1868-8969}}, pages = {{9:1–9:22}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{Fault-Tolerant Shape Formation in the Amoebot Model}}}, doi = {{10.4230/LIPIcs.DNA.28.9}}, volume = {{238}}, year = {{2022}}, } @inproceedings{32811, abstract = {{The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}}, title = {{{Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}}}, year = {{2022}}, } @inproceedings{30793, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 14th International Conference on Agents and Artificial Intelligence}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}}}, doi = {{10.5220/0010845400003116}}, year = {{2022}}, } @unpublished{30790, abstract = {{Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), which measures the freshness of data received, may be large and hence hinder algorithmic convergence. In this paper, we study the convergence of general distributed gradient-based optimization algorithms in the presence of communication that neither happens periodically nor at stochastically independent points in time. We show that convergence is guaranteed provided the random variables associated with the AoI processes are stochastically dominated by a random variable with finite first moment. This improves on previous requirements of boundedness of more than the first moment. We then introduce stochastically strongly connected (SSC) networks, a new stochastic form of strong connectedness for time-varying networks. We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $\alpha$-mixing with $n^{p-1}\alpha(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment. In combination with our first contribution, this implies that distributed stochastic gradient descend converges in the presence of AoI, if $\alpha(n)$ is summable.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.11343}}, title = {{{Distributed gradient-based optimization in the presence of dependent aperiodic communication}}}, 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}}, } @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}}, }