TY - CHAP AU - Sacher, Marc AU - Bauer, Anna ED - Terkowsky, Claudius ED - May, Dominik ED - Frye, Silke ED - Haertel, Tobias ED - Ortelt, Tobias ED - Heix, Sabrina ED - Lensing, Karsten ID - 24951 T2 - Labore in der Hochschullehre. Didaktik, Digitalisierung, Organisation TI - Kompetenzförderung im Laborpraktikum ER - TY - JOUR AU - Bauer, Anna AU - Sacher, Marc AU - Brassat, Katharina ID - 24952 JF - hochschullehre TI - Studentische Akzeptanz und Relevanzwahrnehmung eines disziplinspezifischen Workshops „Wissenschaftliche Vorträge in der Physik“ VL - 6 ER - TY - JOUR AU - Bauer, Anna AU - Reinhold, Peter AU - Sacher, Marc ID - 24956 JF - Phydid B, Didaktik der Physik, Beiträge zur DPG-Frühjahrstagung TI - Entwicklung eines Bewertungsmodells zur handlungsorientierten Messung experimenteller Kompetenz (Physik)Studierender ER - TY - CHAP AU - Bauer, Anna AU - Reinhold, Peter AU - Sacher, Marc ED - Habig, Sebastian ID - 24957 T2 - Naturwissenschaftliche Kompetenzen in der Gesellschaft von morgen TI - Bewertungsmodell zur experimentellen Performanz (Physik)Studierender ER - TY - JOUR AB - Die Frage, wie sich die Weiterentwicklung der Lehre an Hochschulen systematisch verankern lässt, erfährt mit dem Auslaufen von Förderprogrammen wie dem QPL erneute Aufmerksamkeit. Bislang fehlt es an einer kontextspezifischen Theorie, die lehrbezogenen Wandel an Hochschulen analysier- und gestaltbar macht. In jedem Fall sind Change-Konzepte aus dem betriebswirtschaftlichen Bereich nur sehr beschränkt auf Hochschulen übertragbar. Demgegenüber gibt neuere Forschung Hinweise darauf, welche Kernkategorien eine hochschulspezifische Change- Theorie umfassen könnte. Darauf aufbauend schlägt der Beitrag zwei Konzepte als Kernkategorien einer Theorie lehrbezogenen Wandels an Hochschulen vor. AU - Jenert, Tobias ID - 24973 IS - 4 JF - Zeitschrift für Hochschulentwicklung KW - educational development KW - change management KW - educational innovation TI - Überlegungen auf dem Weg zu einer Theorie lehrbezogenen Wandels an Hochschulen VL - 15 ER - TY - GEN AB - 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. AU - Bode, Henrik AU - Heid, Stefan Helmut AU - Weber, Daniel AU - Hüllermeier, Eyke AU - Wallscheid, Oliver ID - 19603 T2 - arXiv:2005.04869 TI - Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control ER - TY - CONF AB - 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. AU - Gottschalk, Sebastian AU - Yigitbas, Enes AU - Schmidt, Eugen AU - Engels, Gregor ED - Bernhaupt, Regina ED - Ardito, Carmelo ED - Sauer, Stefan ID - 19606 KW - Product Configuration KW - Augmented Reality KW - Model-based KW - Tool Support T2 - Human-Centered Software Engineering. HCSE 2020 TI - ProConAR: A Tool Support for Model-based AR Product Configuration VL - 12481 ER - TY - CONF AB - 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). AU - Schneider, Stefan Balthasar AU - Klenner, Lars Dietrich AU - Karl, Holger ID - 19607 KW - distributed management KW - service coordination KW - network coordination KW - nfv KW - softwarization KW - orchestration T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Every Node for Itself: Fully Distributed Service Coordination ER - TY - CONF AB - 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. AU - Schneider, Stefan Balthasar AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 19609 KW - self-driving networks KW - self-learning KW - network coordination KW - service coordination KW - reinforcement learning KW - deep learning KW - nfv T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Self-Driving Network and Service Coordination Using Deep Reinforcement Learning ER - TY - JOUR AU - Kreusser, Lisa Maria AU - McLachlan, Robert I AU - Offen, Christian ID - 19939 IS - 5 JF - Nonlinearity SN - 0951-7715 TI - Detection of high codimensional bifurcations in variational PDEs VL - 33 ER - TY - THES AB - Ordinary differential equations (ODEs) and partial differential equations (PDEs) arise in most scientific disciplines that make use of mathematical techniques. As exact solutions are in general not computable, numerical methods are used to obtain approximate solutions. In order to draw valid conclusions from numerical computations, it is crucial to understand which qualitative aspects numerical solutions have in common with the exact solution. Symplecticity is a subtle notion that is related to a rich family of geometric properties of Hamiltonian systems. While the effects of preserving symplecticity under discretisation on long-term behaviour of motions is classically well known, in this thesis (a) the role of symplecticity for the bifurcation behaviour of solutions to Hamiltonian boundary value problems is explained. In parameter dependent systems at a bifurcation point the solution set to a boundary value problem changes qualitatively. Bifurcation problems are systematically translated into the framework of classical catastrophe theory. It is proved that existing classification results in catastrophe theory apply to persistent bifurcations of Hamiltonian boundary value problems. Further results for symmetric settings are derived. (b) It is proved that to preserve generic bifurcations under discretisation it is necessary and sufficient to preserve the symplectic structure of the problem. (c) The catastrophe theory framework for Hamiltonian ODEs is extended to PDEs with variational structure. Recognition equations for A-series singularities for functionals on Banach spaces are derived and used in a numerical example to locate high-codimensional bifurcations. (d) The potential of symplectic integration for infinite-dimensional Lie-Poisson systems (Burgers’ equation, KdV, fluid equations, . . . ) using Clebsch variables is analysed. It is shown that the advantages of symplectic integration can outweigh the disadvantages of integrating over a larger phase space introduced by a Clebsch representation. (e) Finally, the preservation of variational structure of symmetric solutions in multisymplectic PDEs by multisymplectic integrators on the example of (phase-rotating) travelling waves in the nonlinear wave equation is discussed. AU - Offen, Christian ID - 19947 TI - Analysis of Hamiltonian boundary value problems and symplectic integration ER - TY - CONF AB - Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs. AU - Damke, Clemens AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ED - Jialin Pan, Sinno ED - Sugiyama, Masashi ID - 19953 KW - graph neural networks KW - Weisfeiler-Lehman test KW - cycle detection T2 - Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) TI - A Novel Higher-order Weisfeiler-Lehman Graph Convolution VL - 129 ER - TY - CONF AU - Spliethöver, Maximilian AU - Wachsmuth, Henning ID - 20139 T2 - Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020) TI - Argument from Old Man's View: Assessing Social Bias in Argumentation ER - TY - JOUR AU - Otroshi, Mortaza AU - Meschut, Gerson ID - 20170 IS - 7/20 JF - Umformtechnik Blech Rohre Profile SN - 0300-3167 TI - Spannungszustandsabhängige Schädigungsmodellierung zum Halbhohlstanznieten ER - TY - GEN AU - Hemsen, Paul AU - Hesse, Marc AU - Löken, Nils AU - Nouri, Zahra ID - 20191 T2 - 2nd Crowdworking Symposium TI - Platform-independent Reputation and Qualification System for Crowdwork ER - TY - GEN AB - In many real-world applications, the relative depth of objects in an image is crucial for scene understanding, e.g., to calculate occlusions in augmented reality scenes. Predicting depth in monocular images has recently been tackled using machine learning methods, mainly by treating the problem as a regression task. Yet, being interested in an order relation in the first place, ranking methods suggest themselves as a natural alternative to regression, and indeed, ranking approaches leveraging pairwise comparisons as training information ("object A is closer to the camera than B") have shown promising performance on this problem. In this paper, we elaborate on the use of so-called \emph{listwise} ranking as a generalization of the pairwise approach. Listwise ranking goes beyond pairwise comparisons between objects and considers rankings of arbitrary length as training information. Our approach is based on the Plackett-Luce model, a probability distribution on rankings, which we combine with a state-of-the-art neural network architecture and a sampling strategy to reduce training complexity. An empirical evaluation on benchmark data in a "zero-shot" setting demonstrates the effectiveness of our proposal compared to existing ranking and regression methods. AU - Lienen, Julian AU - Hüllermeier, Eyke ID - 20211 T2 - arXiv:2010.13118 TI - Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model ER - TY - JOUR AB - The challenge of designing new tunable nonlinear dielectric materials with tailored properties has attracted an increasing amount of interest recently. Herein, we study the effective nonlinear dielectric response of a stochastic paraelectric-dielectric composite consisting of equilibrium distributions of circular and partially penetrable disks (or parallel, infinitely long, identical, partially penetrable, circular cylinders) of a dielectric phase randomly dispersed in a continuous matrix of a paraelectric phase. The random microstructures were generated using the Metropolis Monte Carlo algorithm. The evaluation of the effective permittivity and tunability were carried out by employing either a Landau thermodynamic model or its Johnson’s approximation to describe the field-dependent permittivity of the paraelectric phase and solving continuum-electrostatics equations using finite element calculations. We reveal that the percolation threshold in this composite governs the critical behavior of the effective permittivity and tunability. For microstructures below the percolation threshold, our simulations demonstrate a strong nonlinear behaviour of the field-dependent effective permittivity and very high tunability that increases as a function of dielectric phase concentration. Above the percolation threshold, the effective permittivity shows the tendency to linearization and the tunability dramatically drops down. The highly reduced permittivity and extraordinarily high tunability are obtained for the composites with dielectric impenetrable disks at high concentrations, in which the triggering of the percolation transition is avoided. The reported results cast light on distinct nonlinear behaviour of 2D and 3D stochastic composites and can guide the design of novel composites with the controlled morphology and tailored permittivity and tunability. AU - Myroshnychenko, Viktor AU - Smirnov, Stanislav AU - Jose, Pious Mathews Mulavarickal AU - Brosseau, Christian AU - Förstner, Jens ID - 20233 JF - Acta Materialia SN - 1359-6454 TI - Nonlinear dielectric properties of random paraelectric-dielectric composites VL - 203 ER - TY - THES AU - Homt, Martina ID - 28416 TI - Die Anbahnung einer forschenden Grundhaltung im Praxissemester – eine empirische Analyse von Bedingungen und Entwicklungsverläufen ER - TY - JOUR AU - Engels, Gregor ID - 29045 JF - Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO) SN - 2366-6145 TI - Der digitale Fußabdruck, Schatten oder Zwilling von Maschinen und Menschen ER - TY - GEN AB - Previous accounting research shows that taxes affect decision making by individuals and firms. Most studies assume that agents have an accurate perception regarding their tax burden. However, there is a growing body of literature analyzing whether taxes are indeed perceived correctly. We review 127 studies on the measurement of tax misperception and its behavioral implications. The review reveals that many taxpayers have substantial tax misperceptions that lead to biased decision making. We develop a Behavioral Taxpayer Response Model on the impact of provided tax information on tax perception. Besides individual traits, characteristics of the tax information and the decision environment determine the extent of tax misperception. We discuss opportunities for future research and methodological limitations. While there is much evidence on tax misperception at the individual level, we hardly find any research at the firm level. Little is known about the real effects of managers’ tax misperception and on how tax information is strategically managed to impact stakeholders. This research gap is surprising as a large part of the accounting literature analyzes decision making and disclosure of firms. We recommend a mixed-method approach combining experiments, surveys, and archival data analyses to improve the knowledge on tax misperception and its consequences. AU - Blaufus, Kay AU - Chirvi, Malte AU - Huber, Hans-Peter AU - Maiterth, Ralf AU - Sureth-Sloane, Caren ID - 21406 TI - Tax Misperception and Its Effects on Decision Making - a Literature Review VL - No. 39 ER -