@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}},
}

@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}},
}

@phdthesis{29769,
  abstract     = {{Wettstreit zwischen der Entwicklung neuer Hardwaretrojaner und entsprechender Gegenmaßnahmen beschreiten Widersacher immer raffiniertere Wege um Schaltungsentwürfe zu infizieren und dabei selbst fortgeschrittene Test- und Verifikationsmethoden zu überlisten. Abgesehen von den konventionellen Methoden um einen Trojaner in eine Schaltung für ein Field-programmable Gate Array (FPGA) einzuschleusen, können auch die Entwurfswerkzeuge heimlich kompromittiert werden um einen Angreifer dabei zu unterstützen einen erfolgreichen Angriff durchzuführen, der zum Beispiel Fehlfunktionen oder ungewollte Informationsabflüsse bewirken kann. Diese Dissertation beschäftigt sich hauptsächlich mit den beiden Blickwinkeln auf Hardwaretrojaner in rekonfigurierbaren Systemen, einerseits der Perspektive des Verteidigers mit einer Methode zur Erkennung von Trojanern auf der Bitstromebene, und andererseits derjenigen des Angreifers mit einer neuartigen Angriffsmethode für FPGA Trojaner. Für die Verteidigung gegen den Trojaner ``Heimtückische LUT'' stellen wir die allererste erfolgreiche Gegenmaßnahme vor, die durch Verifikation mittels Proof-carrying Hardware (PCH) auf der Bitstromebene direkt vor der Konfiguration der Hardware angewendet werden kann, und präsentieren ein vollständiges Schema für den Entwurf und die Verifikation von Schaltungen für iCE40 FPGAs. Für die Gegenseite führen wir einen neuen Angriff ein, welcher bösartiges Routing im eingefügten Trojaner ausnutzt um selbst im fertigen Bitstrom in einem inaktiven Zustand zu verbleiben: Hierdurch kann dieser neuartige Angriff zur Zeit weder von herkömmlichen Test- und Verifikationsmethoden, noch von unserer vorher vorgestellten Verifikation auf der Bitstromebene entdeckt werden.}},
  author       = {{Ahmed, Qazi Arbab}},
  keywords     = {{FPGA Security, Hardware Trojans, Bitstream-level Trojans, Bitstream Verification}},
  publisher    = {{ Paderborn University, Paderborn, Germany}},
  title        = {{{Hardware Trojans in Reconfigurable Computing}}},
  doi          = {{10.17619/UNIPB/1-1271}},
  year         = {{2022}},
}

@article{34197,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.</jats:p>}},
  author       = {{Panzner, Melina and von Enzberg, Sebastian and Meyer, Maurice and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Characterization of Usage Data with the Help of Data Classifications}}},
  doi          = {{10.1007/s13132-022-01081-z}},
  year         = {{2022}},
}

@article{34196,
  abstract     = {{<jats:p>Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.</jats:p>}},
  author       = {{Merkelbach, Silke and Afroze, Lameya and Janssen, Nils and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  issn         = {{2345-0533}},
  journal      = {{Vibroengineering PROCEDIA}},
  keywords     = {{General Medicine}},
  pages        = {{21--26}},
  publisher    = {{JVE International Ltd.}},
  title        = {{{Using vibration data to classify conditions in disk stack separators}}},
  doi          = {{10.21595/vp.2022.23000}},
  volume       = {{46}},
  year         = {{2022}},
}