@inproceedings{21378, author = {{Hartel, Rita and Dunst, Alexander}}, booktitle = {{MANPU 2020: The 4th International Workshop on coMics ANalysis, Processing and Understanding@Pattern Recognition. ICPR International Workshops and Challenges}}, isbn = {{9783030687793}}, issn = {{0302-9743}}, title = {{{An OCR Pipeline and Semantic Text Analysis for Comics}}}, doi = {{10.1007/978-3-030-68780-9_19}}, year = {{2021}}, } @inproceedings{21431, author = {{Chudalla, Nick and Meschut, Gerson and Bartley, Aurélie and Wibbeke, Tim Michael}}, booktitle = {{21. Kolloquium: Gemeinsame Forschung in der Klebtechnik}}, title = {{{Analyse des Versagensverhaltens geklebter Stahl Verbindungen beim werkstoffschonenden Entfügen in der Karosserieinstandsetzung}}}, year = {{2021}}, } @inproceedings{21442, author = {{Tinkloh, Steffen Rainer and Wu, Tao and Tröster, Thomas and Niendorf, Thomas}}, keywords = {{Micromechanics, Fast Fourier Transform (FFT), Reduced Order Modelling, Homogenization}}, title = {{{Development of a submodel technique for FFT-based solvers in micromechanical analysis}}}, year = {{2021}}, } @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{21478, abstract = {{In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network's performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions. We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.}}, author = {{Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC'21 - IoTSN Symposium)}}, title = {{{Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks}}}, year = {{2021}}, } @inproceedings{21479, abstract = {{Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the baseline with improvements up to 23\%, when using the trade-off objective.}}, author = {{Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{2021 IEEE 18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021)}}, keywords = {{reinforcement learning, wireless sensor networks, resource allocation, acoustic sensor networks}}, title = {{{A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks}}}, 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{21583, author = {{Lanza, Lukas Johannes}}, issn = {{1617-7061}}, journal = {{PAMM}}, title = {{{Representation and stability of internal dynamics}}}, doi = {{10.1002/pamm.202000256}}, 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}}, }