Tackling Crises Together? - An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic - Althaus, Maike; Poniatowski, Martin; Kundisch, Dennis

Key Properties of Sustainable Business Ecosystem Relationships - Vorbohle, Christian; Kundisch, Dennis

Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (Proceedings of the 58th Allerton Conference on Communication, Control, and Computing)
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 $Δ(n)$, 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 $Δ(n)$ is reset to one; otherwise, $Δ(n)$ 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 $α$-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 $Δ(n)$ is itself $α$-mixing from which we conclude a strong law of large numbers for $Δ(n)$. 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.

Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (Proceedings of the 14th International Conference on Agents and Artificial Intelligence)

To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation - Stahl, Maja; Spliethöver, Maximilian; Wachsmuth, Henning (Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science)
Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.

Distributed gradient-based optimization in the presence of dependent aperiodic communication - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (arXiv:2201.11343)
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 $α$-mixing with $n^{p-1}α(n)$ 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 $α(n)$ is summable.

Du Châtelet et la tradition critique de la Bible. De la ‘philofolie’ aux Examens - Hagengruber, Ruth Edith (La Lettre clandestine n° 30: Émilie Du Châtelet et la littérature clandestine. Moreau, P.F. & Seguin, M.S. (Eds.). Paris)

ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support - Clausing, Lennart; Platzner, Marco (2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))

Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (arXiv:2201.00570)
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.

Learning Realistic Mutations: Bug Creation for Neural Bug Detectors - Richter, Cedric; Wehrheim, Heike (2022 IEEE Conference on Software Testing, Verification and Validation (ICST))

TSSB-3M: Mining single statement bugs at massive scale - Richter, Cedric; Wehrheim, Heike (2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR))

Bringing Structure to Structure-Preserving Signatures: Overview, Implementation and Comparison of Selected SPS Schemes - Siek, Hanna

On Transforming Lattice-Based Cryptography to the Ring Setting - Kramer, Paul

ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning - Dreßler, Kevin; Sherif, Mohamed; Ngonga Ngomo, Axel-Cyrille (Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia)
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
Keywords: 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba

Practical Network Conditions for the Convergence of Distributed Optimization - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (IFAC-PapersOnLine)

XCS on Embedded Systems: An Analysis of Execution Profiles and Accelerated Classifier Deletion - Hansmeier, Tim; Brede, Mathis; Platzner, Marco (GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion)

Analyzing Culture-Specific Argument Structures in Learner Essays - Chen, Wei-Fan; Chen, Mei-Hua; Mudgal, Garima; Wachsmuth, Henning (Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022))

(In-)Approximability Results for Interval, Resource Restricted, and Low Rank Scheduling - Maack, Marten; Pukrop, Simon; Rasmussen, Anna Rodriguez (30th Annual European Symposium on Algorithms, ESA 2022, September 5-9, 2022, Berlin/Potsdam, Germany)

Distortion through modeling asymmetric bargaining power - Haake, Claus-Jochen; Streck, Thomas
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.
Keywords: Asymmetric bargaining power; Nash bargaining solution; Kalai-Smorodinsky bargaining solution

Knowledge Equity and Open Science in qualitative research – Practical research considerations - Steinhardt, Isabel; Kruschick, Felicitas (Research Ideas and Outcomes)
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?
Keywords: Open Science; Knowledge Equity; Qualitative Methods