@inbook{22306, author = {{Koldewey, Christian and Gausemeier, Jürgen and Dumitrescu, Roman and Evers, Hans-Heinrich and Frank, Maximilian and Reinhold, Jannik}}, booktitle = {{Digitalization}}, editor = {{Schallmo, Daniel R. and Tidd, Joseph}}, pages = {{205--237}}, publisher = {{Springer Nature}}, title = {{{Development Process for Smart Service Strategies: Grasping the Potentials of Digitalization for Servitization}}}, doi = {{https://doi.org/10.1007/978-3-030-69380-0_12#DOI}}, year = {{2021}}, } @inproceedings{22307, author = {{Eckertz, Daniel and Möller, Marus and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{ Proceedings of the International Conference on Information and Computer Technologies}}, location = {{Kahului, Hawaii, United States of America}}, title = {{{Digital Knowledge Base for Industrial Augmented Reality Systems Based on Semantic Technologies}}}, year = {{2021}}, } @inproceedings{22309, abstract = {{Approximate computing (AC) has acquired significant maturity in recent years as a promising approach to obtain energy and area-efficient hardware. Automated approximate accelerator synthesis involves a great deal of complexity on the size of design space which exponentially grows with the number of possible approximations. Design space exploration of approximate accelerator synthesis is usually targeted via heuristic-based search methods. The majority of existing frameworks prune a large part of the design space using a greedy-based approach to keep the problem tractable. Therefore, they result in inferior solutions since many potential solutions are neglected in the pruning process without the possibility of backtracking of removed approximate instances. In this paper, we address the aforementioned issue by adopting Monte Carlo Tree Search (MCTS), as an efficient stochastic learning-based search algorithm, in the context of automated synthesis of approximate accelerators. This enables the synthesis frameworks to deeply subsamples the design space of approximate accelerator synthesis toward most promising approximate instances based on the required performance goals, i.e., power consumption, area, or/and delay. We investigated the challenges of providing an efficient open-source framework that benefits analytical and search-based approximation techniques simultaneously to both speed up the synthesis runtime and improve the quality of obtained results. Besides, we studied the utilization of machine learning algorithms to improve the performance of several critical steps, i.e., accelerator quality testing, in the synthesis framework. The proposed framework can help the community to rapidly generate efficient approximate accelerators in a reasonable runtime.}}, author = {{Awais, Muhammad and Platzner, Marco}}, booktitle = {{Proceedings of IEEE Computer Society Annual Symposium on VLSI}}, keywords = {{Approximate computing, Design space exploration, Accelerator synthesis}}, location = {{Tampa, Florida USA (Virtual)}}, pages = {{384--389}}, publisher = {{IEEE}}, title = {{{MCTS-Based Synthesis Towards Efficient Approximate Accelerators}}}, year = {{2021}}, } @inproceedings{22448, author = {{Kiesel, Johannes and Spina, Damiano and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2021 Conversational User Interfaces Conference}}, pages = {{1--5}}, title = {{{The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases}}}, year = {{2021}}, } @inproceedings{22480, abstract = {{In this publication important aspects for the implementation of inductive locating are explained. The miniaturized sensor platform called Sens-o-Spheres is used as an application of this locating method. The sensor platform is applied in bioreactors in order to obtain the environmental parameters, which makes a localization by magnetic fields necessary. Since the properties of magnetic fields in the localization area are very different from the wave characteristics, the principle of inductive localization is investigated in this publication and explained by using electrical equivalent circuit diagrams. Thereby, inductive localization uses the coupling or the mutual inductivities between coils, which is noticeable by an induced voltage. Therefore some properties and procedures are explained to extract the location of Sens-o-Spheres or other industrial sensor platforms from the couplings of the coils. One method calculates the location from an adapted ratio calculation and the other method uses neural networks and stochastic filters to obtain the results. In the end, these results are evaluated and compared.}}, author = {{Lange, Sven and Schröder, Dominik and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}}, booktitle = {{22nd IEEE International Conference on Industrial Technology (ICIT)}}, isbn = {{9781728157306}}, keywords = {{Location awareness, Coils, Couplings, Nonuniform electric fields, Magnetic separation, Neural networks, Training data}}, location = {{Valencia, Spain }}, publisher = {{IEEE}}, title = {{{Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}}}, doi = {{10.1109/icit46573.2021.9453609}}, year = {{2021}}, } @inproceedings{22481, abstract = {{During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.}}, author = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}}, booktitle = {{22nd IEEE International Conference on Industrial Technology (ICIT)}}, isbn = {{9781728157306}}, keywords = {{Image Processing, Defect Detection, wooden surfaces, Machine Learning, Neural Networks}}, location = {{Valencia, Spain }}, publisher = {{IEEE}}, title = {{{Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}}}, doi = {{10.1109/icit46573.2021.9453646}}, year = {{2021}}, } @misc{22483, abstract = {{This bachelor thesis presents a C/C++ implementation of the XCS algorithm for an embedded system and profiling results concerning the execution time of the functions. These are then analyzed in relation to the input characteristics of the examined learning environments and compared with related work. Three main conclusions can be drawn from the measured results. First, the maximum size of the population of the classifiers influences the runtime of the genetic algorithm; second, the size of the input space has a direct effect on the execution time of the matching function; and last, a larger action space results in a longer runtime generating the prediction for the possible actions. The dependencies identified here can serve to optimize the computational efficiency and make XCS more suitable for embedded systems.}}, author = {{Brede, Mathis}}, publisher = {{Paderborn University}}, title = {{{Implementation and Profiling of XCS in the Context of Embedded Systems}}}, year = {{2021}}, } @article{22510, abstract = {{Over the past decades, the Gathering problem, which asks to gather a group of robots in finite time given some restrictions, has been intensively studied. In this paper, we are given a group of n autonomous, dimensionless, deterministic, and anonymous robots, with bounded viewing range. Assuming a continuous time model, the goal is to gather these robots into one point in finite time. We introduce a simple convergence criterion that defines a new class of algorithms which perform gathering in O(nd) time, where d is the diameter of the initial robot configuration. We show that some gathering algorithms in the literature belong to this class and propose two new algorithms that belong to this class and have quadratic running time, namely, Go-To-The-Relative-Center algorithm (GTRC) and Safe-Go-To-The-Relative-Center algorithm (S-GTRC). We prove that the latter can perform gathering without collision by using a slightly more complex robot model: non oblivious, chiral, and luminous (i.e. robots have observable external memory, as in [8]). We also consider a variant of the Gathering problem, the Near-Gathering problem, in which robots must get close to each other without colliding. We show that S-GTRC solves the Near-Gathering problem in quadratic time and assumes a weaker robot model than the one assumed in the current state-of-the-art.}}, author = {{Li, Shouwei and Markarian, Christine and Meyer auf der Heide, Friedhelm and Podlipyan, Pavel}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, keywords = {{Local algorithms, Distributed algorithms, Collisionless gathering, Mobile robots, Multiagent system}}, pages = {{41--60}}, title = {{{A continuous strategy for collisionless gathering}}}, doi = {{10.1016/j.tcs.2020.10.037}}, volume = {{852}}, year = {{2021}}, } @article{22511, abstract = {{In this paper, we reconsider the well-known discrete, round-based Go-To-The-Center algorithm due to Ando, Suzuki, and Yamashita [2] for gathering n autonomous mobile robots with limited viewing range in the plane. Remarquably, this algorithm exploits the fact that during its execution, many collisions of robots occur. Such collisions are interpreted as a success because it is assumed that such collided robots behave the same from now on. This is acceptable under the assumption that each robot is represented by a single point. Otherwise, collisions should be avoided. In this paper, we consider a continuous Go-To-The-Center algorithm in which the robots continuously observe the positions of their neighbors and adapt their speed (assuming a speed limit) and direction. Our first results are time bounds of O(n2) for gathering in two dimensions Euclidean space, and Θ(n) for the one dimension. Our main contribution is the introduction and evaluation of a continuous algorithm which performs Go-To-The-Center considering only the neighbors of a robot with respect to the Gabriel subgraph of the visibility graph, i.e. Go-To-The-Gabriel-Center algorithm. We show that this modification still correctly executes gathering in one and two dimensions, with the same time bounds as above. Simulations exhibit a severe difference of the behavior of the Go-To-The-Center and the Go-To-The-Gabriel-Center algorithms: Whereas lots of collisions occur during a run of the Go-To-The-Center algorithm, typically only one, namely the final collision occurs during a run of the Go-To-The-Gabriel-Center algorithm. We can prove this “collisionless property” of the Go-To-The-Gabriel-Center algorithm for one dimension. In two-dimensional Euclidean space, we conjecture that the “collisionless property” holds for almost every initial configuration. We support our conjecture with measurements obtained from the simulation where robots execute both continuous Go-To-The-Center and Go-To-The-Gabriel-Center algorithms. }}, author = {{Li, Shouwei and Meyer auf der Heide, Friedhelm and Podlipyan, Pavel}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, keywords = {{Local algorithms, Distributed algorithms, Collisionless gathering, Mobile robots, Multiagent system}}, pages = {{29--40}}, title = {{{The impact of the Gabriel subgraph of the visibility graph on the gathering of mobile autonomous robots}}}, doi = {{10.1016/j.tcs.2020.11.009}}, volume = {{852}}, year = {{2021}}, } @inproceedings{22532, abstract = {{In this publication, further elements of the newly developed inductive localization in the near field are presented. The advantage of inductive localization is the usage of the magnetic fields, which have a very low influence of non-metallic materials in the environment and thus follows good applications in the area of medicine and biochemistry. This allows a precise localization of sensor platforms in inhomogeneous mixtures of materials, where classical methods have major problems with inhomogeneous dielectric conductivity or density. The calculation of the localization of the searched object differs from other methods such as ultrasound or electromagnetic waves due to the source-free propagation of the magnetic field. Therefore, new mathematical evaluation methods and systematic adaptations are necessary, which are presented in this paper in circuit analysis. For this purpose, the exact circuit influences of one coil and the influence of another coil are investigated and which resonance circuit should be selected for both coils for a inductive localization with optimized signal strength.}}, author = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}}, booktitle = {{2021 Smart Systems Integration (SSI)}}, isbn = {{9781665440929}}, keywords = {{Electrotechnical Characteristics of Real Coils, Inductive Localization, Resonant Circuit, Mutual Inductance, Near-Field}}, location = {{Grenoble, France }}, publisher = {{IEEE}}, title = {{{Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field}}}, doi = {{10.1109/ssi52265.2021.9466958}}, year = {{2021}}, } @article{22814, author = {{Weidmann, Nils and Salunkhe, Shubhangi and Anjorin, Anthony and Yigitbas, Enes and Engels, Gregor}}, issn = {{1660-1769}}, journal = {{The Journal of Object Technology}}, title = {{{Automating Model Transformations for Railway Systems Engineering.}}}, doi = {{10.5381/jot.2021.20.3.a10}}, year = {{2021}}, } @inproceedings{22819, author = {{Yigitbas, Enes and Karakaya, Kadiray and Jovanovikj, Ivan and Engels, Gregor}}, booktitle = {{2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}}, title = {{{Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces}}}, doi = {{10.1109/seams51251.2021.00015}}, year = {{2021}}, } @inproceedings{22913, author = {{Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}}, location = {{Bilbao (Virtual)}}, title = {{{Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}}}, year = {{2021}}, } @inproceedings{22914, author = {{Mohr, Felix and Wever, Marcel Dominik}}, location = {{Virtual}}, title = {{{Replacing the Ex-Def Baseline in AutoML by Naive AutoML}}}, year = {{2021}}, } @inproceedings{22927, author = {{Derrick, John and Doherty, Simon and Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}}, booktitle = {{Proceedings of the 35th International Symposium on Distributed Computing (DISC)}}, publisher = {{Schloß Dagstuhl}}, title = {{{On Strong Observational Refinement and Forward Simulation}}}, year = {{2021}}, } @inproceedings{22959, author = {{Weidmann, Nils and Engels, Gregor}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, location = {{Lille, France}}, title = {{{Concurrent model synchronisation with multiple objectives}}}, doi = {{10.1145/3449639.3459283}}, year = {{2021}}, } @inproceedings{23374, author = {{Kummita, Sriteja and Piskachev, Goran and Spath, Johannes and Bodden, Eric}}, booktitle = {{2021 International Conference on Code Quality (ICCQ)}}, title = {{{Qualitative and Quantitative Analysis of Callgraph Algorithms for Python}}}, doi = {{10.1109/iccq51190.2021.9392986}}, year = {{2021}}, } @inproceedings{21727, abstract = {{Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems. }}, author = {{Vorbohle, Christian and Gottschalk, Sebastian}}, booktitle = {{Proceedings of the 29th European Conference on Information Systems (ECIS)}}, keywords = {{Platform Ecosystems, Platform Ecosystem Modeling Language, Platform Ecosystem Development Tool, Business Models, Design Science}}, location = {{Virtual Conference/Workshop}}, publisher = {{AIS}}, title = {{{Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems }}}, year = {{2021}}, } @article{21808, abstract = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements 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 DeepCoord, 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 on 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, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}}, author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}}, journal = {{Transactions on Network and Service Management}}, keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}}, publisher = {{IEEE}}, title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}}, doi = {{10.1109/TNSM.2021.3076503}}, year = {{2021}}, } @article{21820, abstract = {{The reduction of high-dimensional systems to effective models on a smaller set of variables is an essential task in many areas of science. For stochastic dynamics governed by diffusion processes, a general procedure to find effective equations is the conditioning approach. In this paper, we are interested in the spectrum of the generator of the resulting effective dynamics, and how it compares to the spectrum of the full generator. We prove a new relative error bound in terms of the eigenfunction approximation error for reversible systems. We also present numerical examples indicating that, if Kramers–Moyal (KM) type approximations are used to compute the spectrum of the reduced generator, it seems largely insensitive to the time window used for the KM estimators. We analyze the implications of these observations for systems driven by underdamped Langevin dynamics, and show how meaningful effective dynamics can be defined in this setting.}}, author = {{Nüske, Feliks and Koltai, Péter and Boninsegna, Lorenzo and Clementi, Cecilia}}, issn = {{1099-4300}}, journal = {{Entropy}}, title = {{{Spectral Properties of Effective Dynamics from Conditional Expectations}}}, doi = {{10.3390/e23020134}}, year = {{2021}}, }