@article{19503, author = {{Eckhoff, Marco and Blöchl, Peter E. and Behler, Jörg}}, issn = {{2469-9950}}, journal = {{Physical Review B}}, keywords = {{pc2-ressources}}, title = {{{Hybrid density functional theory benchmark study on lithium manganese oxides}}}, doi = {{10.1103/physrevb.101.205113}}, year = {{2020}}, } @article{19507, author = {{Barkhausen, Franziska and Schumacher, Stefan and Ma, Xuekai}}, issn = {{0146-9592}}, journal = {{Optics Letters}}, keywords = {{pc2-ressources}}, title = {{{Multistable circular currents of polariton condensates trapped in ring potentials}}}, doi = {{10.1364/ol.386250}}, year = {{2020}}, } @article{19511, author = {{Pukrop, Matthias and Schumacher, Stefan}}, issn = {{2470-0045}}, journal = {{Physical Review E}}, keywords = {{pc2-ressources}}, title = {{{Externally controlled Lotka-Volterra dynamics in a linearly polarized polariton fluid}}}, doi = {{10.1103/physreve.101.012207}}, year = {{2020}}, } @inbook{19521, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030582845}}, issn = {{0302-9743}}, title = {{{Learning Choice Functions via Pareto-Embeddings}}}, doi = {{10.1007/978-3-030-58285-2_30}}, year = {{2020}}, } @inproceedings{19551, author = {{Kurek, Rafael}}, booktitle = {{Information Security and Privacy - 25th Australasian Conference, {ACISP} 2020, Perth, WA, Australia, November 30 - December 2, 2020, Proceedings}}, editor = {{K. Liu, Joseph and Cui, Hui}}, pages = {{330--349}}, publisher = {{Springer}}, title = {{{Efficient Forward-Secure Threshold Public Key Encryption}}}, doi = {{10.1007/978-3-030-55304-3\_17}}, volume = {{12248}}, year = {{2020}}, } @inproceedings{19553, author = {{Kurek, Rafael}}, booktitle = {{Advances in Information and Computer Security - 15th International Workshop on Security, {IWSEC} 2020, Fukui, Japan, September 2-4, 2020, Proceedings}}, editor = {{Aoki, Kazumaro and Kanaoka, Akira}}, pages = {{239--260}}, publisher = {{Springer}}, title = {{{Efficient Forward-Secure Threshold Signatures}}}, doi = {{10.1007/978-3-030-58208-1\_14}}, volume = {{12231}}, year = {{2020}}, } @article{19560, author = {{Parreño-Torres, Consuelo and Alvarez-Valdes, Ramon and Ruiz, Rubén and Tierney, Kevin}}, issn = {{1366-5545}}, journal = {{Transportation Research Part E: Logistics and Transportation Review}}, keywords = {{pc2-ressources}}, title = {{{Minimizing crane times in pre-marshalling problems}}}, doi = {{10.1016/j.tre.2020.101917}}, year = {{2020}}, } @inbook{19561, author = {{Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030535513}}, issn = {{0302-9743}}, keywords = {{pc2-ressources}}, title = {{{Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types}}}, doi = {{10.1007/978-3-030-53552-0_12}}, year = {{2020}}, } @article{19562, author = {{Kuhlemann, Stefan and Tierney, Kevin}}, issn = {{1381-1231}}, journal = {{Journal of Heuristics}}, keywords = {{pc2-ressources}}, title = {{{A genetic algorithm for finding realistic sea routes considering the weather}}}, doi = {{10.1007/s10732-020-09449-7}}, year = {{2020}}, } @book{19568, editor = {{Eke, Norbert Otto and Besch, Werner and Geulen, Eva and Klein, Thomas and Oellers, Norbert and Peters, Ursula and Steinecke, Hartmut and Tervooren, Helmut}}, publisher = {{Erich Schmidt}}, title = {{{Zeitschrift für deutsche Philologie (Herausgebertätigkeit von Norbert Otto Eke ab Bd. 123, 2004)}}}, year = {{2020}}, } @unpublished{19603, abstract = {{Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas. Modern MSGs are largely driven by power electronic converters due to their high efficiency and flexibility. Nevertheless, controlling MSGs is a challenging task due to highest requirements on energy availability, safety and voltage quality within a wide range of different MSG topologies. This results in a high demand for comprehensive testing of new control concepts during their development phase and comparisons with the state of the art in order to ensure their feasibility. This applies in particular to data-driven control approaches from the field of reinforcement learning (RL), whose stability and operating behavior can hardly be evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package, an open-source software toolbox for the simulation and control optimization of MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG topologies and offers a Python-based interface for plug \& play controller testing. In particular, the standardized OpenAI Gym interface allows for easy RL-based controller integration. Besides the presentation of the OMG toolbox, application examples are highlighted including safe Bayesian optimization for low-level controller tuning.}}, author = {{Bode, Henrik and Heid, Stefan Helmut and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}}, booktitle = {{arXiv:2005.04869}}, title = {{{Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control}}}, year = {{2020}}, } @inproceedings{19606, abstract = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Model-based, Tool Support}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}}, doi = {{10.1007/978-3-030-64266-2_14}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{19607, abstract = {{Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{19632, author = {{Jovanovikj, Ivan and Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 8th International Working Conference on Human-Centered Software Engineering (HCSE'20)}}, pages = {{216--224}}, publisher = {{Springer}}, title = {{{Augmented and Virtual Reality Object Repository for Rapid Prototyping }}}, year = {{2020}}, } @inproceedings{19656, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS)}}, publisher = {{Springer}}, title = {{{Automatic Fairness Testing of Machine Learning Models}}}, year = {{2020}}, } @article{19679, abstract = {{In the present work, we provide an electronic structure based method for the “on-the-fly” determination of vibrational sum frequency generation (v-SFG) spectra. The predictive power of this scheme is demonstrated at the air-water interface. While the instantaneous fluctuations in dipole moment are obtained using the maximally localized Wannier functions, the fluctuations in polarizability are approximated to be proportional to the second moment of Wannier functions. The spectrum henceforth obtained captures the signatures of hydrogen bond stretching, bending, as well as low-frequency librational modes.}}, author = {{Ojha, Deepak and Kühne, Thomas D.}}, issn = {{1420-3049}}, journal = {{Molecules}}, title = {{{“On-The-Fly” Calculation of the Vibrational Sum-Frequency Generation Spectrum at the Air-Water Interface}}}, doi = {{10.3390/molecules25173939}}, volume = {{25}}, year = {{2020}}, } @article{19680, abstract = {{This is the second part of a project on the foundations of first-principle calculations of the electron transport in crystals at finite temperatures, aiming at a predictive first-principles platform that combines ab-initio molecular dynamics (AIMD) and a finite-temperature Kubo-formula with dissipation for thermally disordered crystalline phases. The latter are encoded in an ergodic dynamical system (Ω,G,dP), where Ω is the configuration space of the atomic degrees of freedom, G is the space group acting on Ω and dP is the ergodic Gibbs measure relative to the G-action. We first demonstrate how to pass from the continuum Kohn–Sham theory to a discrete atomic-orbitals based formalism without breaking the covariance of the physical observables w.r.t. (Ω,G,dP). Then we show how to implement the Kubo-formula, investigate its self-averaging property and derive an optimal finite-volume approximation for it. We also describe a numerical innovation that made possible AIMD simulations with longer orbits and elaborate on the details of our simulations. Lastly, we present numerical results on the transport coefficients of crystal silicon at different temperatures.}}, author = {{Kühne, Thomas and Heske, Julian Joachim and Prodan, Emil}}, issn = {{0003-4916}}, journal = {{Annals of Physics}}, pages = {{168290}}, title = {{{Disordered crystals from first principles II: Transport coefficients}}}, doi = {{https://doi.org/10.1016/j.aop.2020.168290}}, volume = {{421}}, year = {{2020}}, } @article{19681, author = {{Salem, M. Alaraby and Kühne, Thomas D.}}, issn = {{0026-8976}}, journal = {{Molecular Physics}}, pages = {{1--6}}, title = {{{Insight from energy decomposition analysis on a hydrogen-bond-mediated mechanism for on-water catalysis}}}, doi = {{10.1080/00268976.2020.1797920}}, year = {{2020}}, } @article{19684, abstract = {{Peer-Berater*innen nehmen während eines Beratungsgespräches teilweise intuitiv, teilweise bewusst verschiedene Rollen ein. Dieser Artikel soll einen Beitrag zur Reflexion über das Spannungsverhältnis zwischen der Experten- und Prozessberatungsrolle leisten, um die Selbstprofessionalisierung in der Rolleneinnahme zu fördern. Dafür werden auf theoretischer Ebene die konträren Arbeitsweisen beleuchtet, auf denen beide Rollen basieren. Zur Betrachtung von möglichen Einflussfaktoren auf die Rolleneinnahme wird ein selbst entwickeltes Modell präsentiert, welches zum Austausch über diese Faktoren motivieren soll. Zuletzt wird eine praktische Möglichkeit zur gezielten Reflexion über die eigene Rolleneinnahme anhand eines Fragebogens vorgeschlagen.}}, author = {{Lebock, Sarah}}, journal = {{Journal der Schreibberatung}}, keywords = {{Expertenberatung, Prozessberatung, Rolleneinnahme, Selbstprofessionalisierung}}, location = {{Köln }}, number = {{1}}, pages = {{59--70}}, publisher = {{wbv}}, title = {{{Wann bin ich wer? Peer-Berater*innen im Spannungsverhältnis}}}, doi = {{10.3278/JOS2001W008}}, volume = {{19}}, year = {{2020}}, }