@article{21460,
  author       = {{Frick, Nicholas R. J. and Mirbabaie, Milad and Stieglitz, Stefan and Salomon, Jana}},
  issn         = {{1246-0125}},
  journal      = {{Journal of Decision Systems}},
  pages        = {{1--24}},
  title        = {{{Maneuvering through the stormy seas of digital transformation: the impact of empowering leadership on the AI readiness of enterprises}}},
  doi          = {{10.1080/12460125.2020.1870065}},
  year         = {{2021}},
}

@inproceedings{21525,
  author       = {{Gutt, Dominik and Neumann, Jürgen and Jabr, Wael and Kundisch, Dennis}},
  location     = {{Virtual Conference/Workshop}},
  title        = {{{The Fate of the App: Economic Implications of Updating under Reputation Resetting}}},
  year         = {{2021}},
}

@article{21532,
  author       = {{Görzen, Thomas}},
  journal      = {{International Journal of Innovation Management}},
  number       = {{1}},
  title        = {{{“What’s the Point of the Task?” Exploring the Influence of Task Meaning on Creativity in Crowdsourcing}}},
  doi          = {{10.1142/S1363919621500079}},
  volume       = {{25}},
  year         = {{2021}},
}

@article{21535,
  author       = {{Bengs, Viktor and Busa-Fekete, Róbert and El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}},
  journal      = {{Journal of Machine Learning Research}},
  number       = {{7}},
  pages        = {{1--108}},
  title        = {{{Preference-based Online Learning with Dueling Bandits: A Survey}}},
  volume       = {{22}},
  year         = {{2021}},
}

@inproceedings{21543,
  abstract     = {{Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.

To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).}},
  author       = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}},
  booktitle    = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}},
  keywords     = {{network management, service management, coordination, reinforcement learning, distributed}},
  location     = {{Washington, DC, USA}},
  publisher    = {{IEEE}},
  title        = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}},
  year         = {{2021}},
}

@misc{21564,
  author       = {{Itner, Dominik and Gravenkamp, Hauke and Dreiling, Dmitrij and Feldmann, Nadine and Henning, Bernd}},
  title        = {{{On the forward simulation and cost functions for the ultrasonic material characterization of polymers }}},
  year         = {{2021}},
}

@techreport{21569,
  abstract     = {{Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes}},
  publisher    = {{Gesellschaft für Informatik}},
  title        = {{{Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools  und deren Datennutzung}}},
  volume       = {{1}},
  year         = {{2021}},
}

@inproceedings{21570,
  author       = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  title        = {{{Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}}},
  year         = {{2021}},
}

@inproceedings{21573,
  author       = {{Heine, Jens and Wecker, Christian and Kenig, Eugeny and Bart, Hans-Jörg}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Stofftransportmessung und -visualisierung am ruhenden und bewegten Einzeltropfen}}},
  year         = {{2021}},
}

@inproceedings{21574,
  author       = {{Wecker, Christian and Schulz, Andreas and Heine, Jens and Bart, Hans-Jörg and Kenig, Eugeny}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Numerische Untersuchung der Marangonikonvektion in Flüssig-Flüssig-Systemen: Von der Tropfenbildung bis zur Tropfeninteraktion}}},
  year         = {{2021}},
}

@inproceedings{21575,
  author       = {{Wecker, Christian and Hoppe, Anna and Schulz, Andreas and Heine, Jens and Bart, Hans-Jörg and Kenig, Eugeny}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Wärme- und Stofftransport}},
  title        = {{{Numerische Untersuchungen zu Fluiddynamik und Stofftransport binärer Tropfeninteraktion unter Berücksichtigung von Marangonikonvektion}}},
  year         = {{2021}},
}

@inproceedings{21576,
  author       = {{Schulz, Andreas and Wecker, Christian and Kenig, Eugeny}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Mehrphasenströmung}},
  title        = {{{Mehrkomponenten-Stofftransport an bewegten Phasengrenzflächen unter Berücksichtigung von Diffusionskreuzeffekten}}},
  year         = {{2021}},
}

@article{21595,
  author       = {{Stockmann, Lars and Laux, Sven and Bodden, Eric}},
  issn         = {{2589-2258}},
  journal      = {{Journal of Automotive Software Engineering}},
  title        = {{{Using Architectural Runtime Verification for Offline Data Analysis}}},
  doi          = {{10.2991/jase.d.210205.001}},
  year         = {{2021}},
}

@phdthesis{21596,
  author       = {{Fischer, Andreas}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Computing on Encrypted Data using Trusted Execution Environments}}},
  year         = {{2021}},
}

@article{21597,
  author       = {{Holzinger, Philipp and Bodden, Eric}},
  journal      = {{International Symposium on Advanced Security on Software and Systems (ASSS)}},
  title        = {{{A Systematic Hardening of Java's Information Hiding}}},
  year         = {{2021}},
}

@article{21599,
  author       = {{Bonifacio, Rodrigo and Krüger, Stefan and Narasimhan, Krishna and Bodden, Eric and Mezini, Mira}},
  journal      = {{European Conference on Object-Oriented Programming (ECOOP)}},
  title        = {{{Dealing with Variability in API Misuse Specification}}},
  year         = {{2021}},
}

@misc{21601,
  abstract     = {{The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105 a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).}},
  author       = {{Göllner, Thomas and Schwarz, Jan-Hendrik and Gottschalk, Sebastian and Sauer, Stefan}},
  title        = {{{Distributed merchandise management system}}},
  year         = {{2021}},
}

@inproceedings{21610,
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021}},
  location     = {{Virtual}},
  pages        = {{27--32}},
  publisher    = {{ACM}},
  title        = {{{LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis}}},
  doi          = {{https://doi.org/10.1145/3453688.3461506}},
  year         = {{2021}},
}

@misc{21627,
  author       = {{Liedtke, David}},
  title        = {{{Exploration and Convex Hull Construction in the Three-Dimensional Hybrid Model}}},
  year         = {{2021}},
}

@phdthesis{21628,
  abstract     = {{This thesis considers the realization of distributed data structures and the construction of distributed protocols for self-stabilizing overlay networks.

In the first part of this thesis, we provide distributed protocols for queues, stacks and priority queues that serve the insertion and deletion of elements within a logarithmic amount of rounds.
Our protocols respect semantic constraints such as sequential consistency or serializability and the individual semantic constraints given by the type (queue, stack, priority queue) of the data structure.
We furthermore provide a protocol that handles joining and leaving nodes.
As an important side product, we present a novel protocol solving the distributed $k$-selection problem in a logarithmic amount of rounds, that is, to find the $k$-smallest elements among a polynomial number of elements spread among $n$ nodes.
	
The second part of this thesis is devoted to the construction of protocols for self-stabilizing overlay networks, i.e., distributed protocols that transform an overlay network from any initial (potentially illegitimate) state into a legitimate state in finite time.
We present protocols for self-stabilizing generalized De Bruijn graphs, self-stabilizing quadtrees and self-stabilizing supervised skip rings.
Each of those protocols comes with unique properties that makes it interesting for certain distributed applications.
Generalized De Bruijn networks provide routing within a constant amount of hops, thus serving the interest in networks that require a low latency for requests.
The protocol for the quadtree guarantees monotonic searchability as well as a geometric variant of monotonic searchability, making it interesting for wireless networks or applications needed in the area of computational geometry.
The supervised skip ring can be used to construct a self-stabilizing publish-subscribe system.
}},
  author       = {{Feldmann, Michael}},
  title        = {{{Algorithms for Distributed Data Structures and Self-Stabilizing Overlay Networks}}},
  doi          = {{10.17619/UNIPB/1-1113}},
  year         = {{2021}},
}