@article{33251,
  author       = {{Robra-Bissantz, Susanne and Lattemann, Christoph and Laue, Ralf and Leonhard-Pfleger, Raphaela and Wagner, Luisa and Gerundt, Oliver and Schlimbach, Ricarda and Baumann, Sabine and Vorbohle, Christian and Gottschalk, Sebastian and Kundisch, Dennis and Engels, Gregor and Wünderlich, Nancy and Nissen, Volker and Lohrenz, Lisa and Michalke, Simon}},
  journal      = {{HMD Praxis der Wirtschaftsinformatik}},
  number       = {{5}},
  pages        = {{1227 -- 1257}},
  title        = {{{Methoden zum Design digitaler Plattformen, Geschäftsmodelle und Service-Ökosysteme}}},
  volume       = {{59}},
  year         = {{2022}},
}

@inproceedings{33502,
  author       = {{Althaus, Maike and Poniatowski, Martin and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 43rd International Conference on Information Systems (ICIS)}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Tackling Crises Together? – An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic}}},
  year         = {{2022}},
}

@inproceedings{33882,
  author       = {{Laux, Florian and Poniatowski, Martin and Kundisch, Dennis}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{May I have your attention, please? Analyzing the effects of attention screening mechanisms on crowdworking platforms}}},
  year         = {{2022}},
}

@inproceedings{33885,
  author       = {{Seutter, Janina}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Online Reviews in B2B Markets: A Qualitative Study on the Underlying Motives }}},
  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{30916,
  author       = {{Seutter, Janina}},
  booktitle    = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}},
  location     = {{Timișoara, Romania}},
  title        = {{{Online Reviews in B2B Markets: A Qualitative Study of Underlying Motivations}}},
  year         = {{2022}},
}

@inproceedings{31062,
  author       = {{Poniatowski, Martin}},
  booktitle    = {{Proceedings of the 28th Americas Conference on Information Systems (AMCIS)}},
  location     = {{Minneapolis, USA}},
  title        = {{{How the Display of the Transaction Count Affects the Purchase Intention}}},
  year         = {{2022}},
}

@inproceedings{30939,
  author       = {{Vorbohle, Christian and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}},
  location     = {{Timișoara, Romania}},
  title        = {{{Overcoming Silos: A Review of Business Model Modeling Languages for Business Ecosystems}}},
  year         = {{2022}},
}

@inproceedings{30734,
  author       = {{Althaus, Maike and Poniatowski, Martin and Kundisch, Dennis}},
  location     = {{Madrid, Spain}},
  title        = {{{Tackling Crises Together? - An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic}}},
  year         = {{2022}},
}

@inproceedings{30212,
  author       = {{Vorbohle, Christian and Kundisch, Dennis}},
  title        = {{{Key Properties of Sustainable Business Ecosystem Relationships}}},
  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}},
}