@article{21298, author = {{Mirbabaie, Milad and Stieglitz, S. and Brünker, F.}}, journal = {{Information Technology & People}}, title = {{{Dynamics of Convergence Behaviour in Social Media Crisis Communication – A Complexity Perspective on Peoples’ Behaviour}}}, year = {{2021}}, } @article{21300, author = {{Brendel, A.B. and Mirbabaie, Milad and Lembcke, T.B. and Hofeditz, L.}}, journal = {{Sustainability}}, title = {{{Ethical Management of Artificial Intelligence}}}, year = {{2021}}, } @article{21301, author = {{Mirbabaie, Milad and Stieglitz, S. and Frick, N.R.J. and Möllmann, H.L.}}, journal = {{Journal of Medical Internet Research Medical Informatics }}, title = {{{Driving Digital Transformation During a Pandemic: Study of Virtual Collaboration in a German Hospital}}}, year = {{2021}}, } @inproceedings{21313, author = {{Bittner, E. and Mirbabaie, Milad and Morana, S.}}, booktitle = {{54th Hawaii International Conference System Sciences}}, title = {{{Digital Facilitation Assistance for Collaborative, Creative Design Processes}}}, year = {{2021}}, } @inproceedings{21314, author = {{Bührke, J. and Brendel, A.B. and Lichtenberg, S. and Greve, M. and Mirbabaie, Milad}}, booktitle = {{54th Hawaii International Conference System Sciences}}, title = {{{Is Making Mistakes Human? On the Perception of Typing Errors in Chatbot Communication}}}, year = {{2021}}, } @inproceedings{21326, author = {{Holtmann, Jörg and Steghöfer, Jan-Phillipp and Rath, Michael and Schmelter, David}}, booktitle = {{Software Engineering 2021}}, editor = {{Koziolek, Anne and Schaefer, Ina and Seidl, Christoph}}, location = {{Remote / Braunschweig, Germany }}, pages = {{59--60}}, title = {{{Cutting through the Jungle: Disambiguating Model-based Traceability Terminology (Extended Abstract)}}}, doi = {{10.18420/SE2021_18}}, volume = {{P-310}}, year = {{2021}}, } @article{21337, abstract = {{We present a flexible trust region descend algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective function that is computationally expensive. The method is derivative-free in the sense that neither need derivative information be available for the expensive objectives nor are gradients approximated using repeated function evaluations as is the case in finite-difference methods. Instead, a multiobjective trust region approach is used that works similarly to its well-known scalar pendants. Local surrogate models constructed from evaluation data of the true objective functions are employed to compute possible descent directions. In contrast to existing multiobjective trust region algorithms, these surrogates are not polynomial but carefully constructed radial basis function networks. This has the important advantage that the number of data points scales linearly with the parameter space dimension. The local models qualify as fully linear and the corresponding general scalar framework is adapted for problems with multiple objectives. Convergence to Pareto critical points is proven and numerical examples illustrate our findings.}}, author = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}}, issn = {{2297-8747}}, journal = {{Mathematical and Computational Applications}}, number = {{2}}, title = {{{Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models}}}, doi = {{10.3390/mca26020031}}, volume = {{26}}, year = {{2021}}, } @inproceedings{21340, author = {{Wende, Marc and Kenig, Eugeny}}, booktitle = {{Jahrestreffen der ProcessNet-Fachgruppen Fluidverfahrenstechnik und Wärme- und Stoffübertragung}}, location = {{Online-Konferenz}}, title = {{{Konzeption und Inbetriebnahme eines Versuchsstandes zur Gravidestillation}}}, year = {{2021}}, } @article{21374, abstract = {{A dark-field scanning transmission ion microscopy detector was designed for the helium ion microscope. The detection principle is based on a secondary electron conversion holder with an exchangeable aperture strip allowing its acceptance angle to be tuned from 3 to 98 mrad. The contrast mechanism and performance were investigated using freestanding nanometer-thin carbon membranes. The results demonstrate that the detector can be optimized either for most efficient signal collection or for maximum image contrast. The designed setup allows for the imaging of thin low-density materials that otherwise provide little signal or contrast and for a clear end-point detection in the fabrication of nanopores. In addition, the detector is able to determine the thickness of membranes with sub-nanometer precision by quantitatively evaluating the image signal and comparing the results with Monte Carlo simulations. The thickness determined by the dark-field transmission detector is compared to X-ray photoelectron spectroscopy and energy-filtered transmission electron microscopy measurements.}}, author = {{Emmrich, Daniel and Wolff, Annalena and Meyerbröker, Nikolaus and Lindner, Jörg and Beyer, André and Gölzhäuser, Armin}}, issn = {{2190-4286}}, journal = {{Beilstein Journal of Nanotechnology}}, pages = {{222--231}}, title = {{{Scanning transmission helium ion microscopy on carbon nanomembranes}}}, doi = {{10.3762/bjnano.12.18}}, year = {{2021}}, } @inproceedings{21376, author = {{Grabo, Matti and Kenig, Eugeny}}, location = {{Leipzig}}, title = {{{Modellierung eines Latentwärmespeichersystems in Form einer ungeordneten Schüttung makro-verkapselter PCM-Elemente}}}, year = {{2021}}, } @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}}, }