@unpublished{30867, abstract = {{In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.}}, author = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}}, publisher = {{AAAI}}, title = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}}, year = {{2022}}, } @unpublished{30865, abstract = {{The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.}}, author = {{Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Machine Learning}}, title = {{{Algorithm Selection on a Meta Level}}}, year = {{2022}}, } @unpublished{33150, abstract = {{In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem.}}, author = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}}, booktitle = {{arXiv:2208.12094}}, title = {{{Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients}}}, year = {{2022}}, } @inproceedings{33230, author = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}}, booktitle = {{1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference}}, editor = {{Aspnes, James and Michail, Othon}}, pages = {{12:1–12:19}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems}}}, doi = {{10.4230/LIPIcs.SAND.2022.12}}, volume = {{221}}, year = {{2022}}, } @inproceedings{33240, author = {{Götte, Thorsten and Scheideler, Christian}}, booktitle = {{SPAA ’22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11 - 14, 2022}}, editor = {{Agrawal, Kunal and Lee, I-Ting Angelina}}, pages = {{99–101}}, publisher = {{ACM}}, title = {{{Brief Announcement: The (Limited) Power of Multiple Identities: Asynchronous Byzantine Reliable Broadcast with Improved Resilience through Collusion}}}, doi = {{10.1145/3490148.3538556}}, year = {{2022}}, } @inbook{30941, abstract = {{Decision support systems are crucial in helping decision makers to quickly identify optimal business decisions in increasingly volatile and complex business environments. However, the ideal DSS for one decision maker may not optimally address the requirements for decision support of another decision maker. This is due to differences between decision makers in business goals, regulatory restrictions or availability of resources such as data. By using a suboptimal DSS, decision makers risk implementing suboptimal decision recommendations which endanger the success of their business. This presents DSS developers with the challenge to implement a customizable DSS which can be tailored to the individual requirements for decision support of a single decision maker. In order to address this challenge, we suggest a decision support ecosystem in which DSS developers, decision makers and other domain experts collaborate using a shared platform to provide and combine reusable decision support services into a tailored DSS. The contribution of our paper is twofold: First, we define the concept of a decision support ecosystem with respect to existing digital business ecosystems and discuss expected benefits and challenges. Second, we present a reference architecture for a shared platform supporting the realization of a decision support ecosystem. We demonstrate our contributions in the example application domain of regional energy distribution network planning.}}, author = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs}}, publisher = {{Springer}}, title = {{{Decision Support Ecosystems: Definition and Platform Architecture}}}, doi = {{10.1007/978-3-031-06530-9_8}}, volume = {{447}}, year = {{2022}}, } @inproceedings{33281, abstract = {{Corporate decision makers have individual requirements for decision support influenced by business goals, regulatory restrictions or access to resources such as data. Ideally, decision makers could quickly create tailored decision support systems (DSS) themselves which optimally address their individual requirements for decision support. Although service-oriented architectures have been proposed for DSS customization, they are primarily targeting trained software developers and cannot immediately be adapted by decision makers or domain experts with little to no software development knowledge. In this paper, we therefore motivate an assisted process-based service composition approach which can be used by non-developers to create tailored DSS. For assistance during service composition, we contribute a meta-model for the formalization of both decision support requirements and functionality of decision support services. Models created according to the meta-model can be used to detect mismatches between a decision maker’s requirements for decision support and services selected in the service composition representing a DSS. Furthermore, the formalizations may even be used for automated service composition given a decision maker’s decision support requirements. We demonstrate the expressiveness of our meta-model in the domain of regional energy distribution network planning.}}, author = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, isbn = {{978-3-031-14785-2}}, pages = {{150–162}}, publisher = {{Springer International Publishing}}, title = {{{Requirements-Based Composition of Tailored Decision Support Systems}}}, doi = {{10.1007/978-3-031-14785-2_10}}, volume = {{13482}}, year = {{2022}}, } @article{6579, abstract = {{An explicit approach using symplectic time integration in conjunction with traditional finite difference spatial derivatives to solve the wave equation in moving media is presented. A simple operator split of this second order wave equation into two coupled first order equations is performed, allowing these split equations to be solved symplectically. Orders of symplectic time integration ranging from first to fourth along with orders of spatial derivatives ranging from second to sixth are explored. The case of cylindrical acoustic spreading in air under a constant velocity in a 2D square structured domain is considered. The variation of the computed time-of-flight, frequency, and wave length are studied with varying grid resolution and the deviations from the analytical solutions are determined. It was found that symplectic time integration interferes with finite difference spatial derivatives higher than second order causing unexpected results. This is actually beneficial for unstructured finite volume tools like OpenFOAM where second order spatial operators are the state-of-the art. Cylindrical acoustic spreading is simulated on an unstructured 2D triangle mesh showing that symplectic time integration is not limited to the spatial discretization paradigm and overcomes the numerical diffusion arising with the in-built numerical methods which hinder wave propagation.}}, author = {{Inguva, Venkatesh and Feldmann, Nadine and Claes, Leander and Koturbash, Taras and Hahn-Jose, Thomas and Koutcherov, Vladimir and Kenig, Eugeny}}, journal = {{Engineering Reports}}, title = {{{An explicit symplectic approach to solving the wave equation in moving media}}}, doi = {{10.1002/eng2.12573}}, year = {{2022}}, } @article{33459, author = {{Brosch, Anian and Wallscheid, Oliver and Böcker, Joachim}}, issn = {{0885-8993}}, journal = {{IEEE Transactions on Power Electronics}}, keywords = {{Electrical and Electronic Engineering}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Long-Term Memory Recursive Least Squares Online Identification of Highly Utilized Permanent Magnet Synchronous Motors for Finite-Control-Set Model Predictive Control}}}, doi = {{10.1109/tpel.2022.3206598}}, year = {{2022}}, } @inbook{29872, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, booktitle = {{Approximation and Online Algorithms}}, isbn = {{9783030927011}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Server Cloud Scheduling}}}, doi = {{10.1007/978-3-030-92702-8_10}}, year = {{2022}}, } @inproceedings{33503, author = {{Gabriel, Stefan and Aring, Theresa and Hobscheidt, Daniela and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Tagungsband des 68. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft}}, location = {{Magdeburg 02.03. - 04.03.2022}}, publisher = {{GfA-Press}}, title = {{{Handlungsfelder für die KI-Einführung in der Arbeitswelt produzierender Unternehmen}}}, year = {{2022}}, } @inproceedings{33505, author = {{Anacker, Harald and Günther, Matthias and Wyrwich, Fabian and Dumitrescu, Roman}}, booktitle = {{17th Annual System of Systems Engineering Conference (SOSE)}}, location = {{Rochester, NY, USA}}, pages = {{178--183}}, title = {{{Pattern based engineering of System of Systems - a systematic literature review}}}, doi = {{10.1109/SOSE55472.2022.9812697}}, year = {{2022}}, } @inproceedings{31188, author = {{Anacker, Harald and Dumitrescu, Roman and Könemann, Ulf and Wilke, Daria}}, booktitle = {{ Proceedings of the 16th Annual IEEE International Systems Conference}}, location = {{Montreal, Canada}}, title = {{{Identification of stakeholder-specific Systems Engineering competencies for industry}}}, year = {{2022}}, } @inproceedings{33508, abstract = {{In this work, methods will be evaluated to numerically calculate the passive electrical parameters of planar coils. These parameters can then be used to optimize inductive applications such as wireless power transmission. The focus here will be on inductive localization, which uses high-frequency magnetic fields and the resulting induced voltage to provide localization through the coupling parameter mutual inductance. To achieve localization with high accuracy and best possible operation (resonance, signal strength, etc.), the coil parameters need to be well known. For this reason, some numerical methods for the calculation of these quantities are presented and validated. In addition, the physical effects are thereby considered in more detail, allowing the localization procedure to be better optimized compared to simulative black-box methods. The goal should be a dedicated simulation platform for planar coils to be able to develop training data for neural networks and to test and optimize localization algorithms.}}, author = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Simulation Environment, Inductive Localization, Coil Parameters, Inductive Applications, Near-Field}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}}, doi = {{10.1109/ssi56489.2022.9901416}}, year = {{2022}}, } @inproceedings{33510, abstract = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}}, author = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Machine Learning, CNN, Hashing, semi-supervised learning}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}}, doi = {{10.1109/ssi56489.2022.9901433}}, year = {{2022}}, } @book{33516, author = {{Fazal-Baqaie, Masud and Linssen, Oliver and Volland, Alexander and Yigitbas, Enes and Engstler, Martin and Bertram, Martin and Kalenborn, Axel}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Projektmanagement und Vorgehensmodelle 2022. Virtuelle Zusammenarbeit und verlorene Kulturen?}}}, volume = {{P 327}}, year = {{2022}}, } @inproceedings{33553, author = {{Pfeifer, Stefan and Akgül, Didem and Röbenack, Silke and Tihlarik, Amelie and Albert, Bruno and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{DS 118: Proceedings of NordDesign 2022}}, editor = {{Mortensen, N.H. and Hansen, C.T. and Deininger, M.}}, isbn = {{9781912254170}}, location = {{Copenhagen, Denmark}}, publisher = {{The Design Society}}, title = {{{Design Decisions in the Architecture Development of Advanced Systems: Towards traceable and sustainable Documentation and Communication}}}, doi = {{10.35199/norddesign2022}}, year = {{2022}}, } @inproceedings{33552, author = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Proceedings of the IEEE}}, location = {{Nancy, France}}, title = {{{Design of an optimised value creation network for zero emission ferries}}}, year = {{2022}}, } @inproceedings{33556, author = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 5th International Conference on Information and Computer Technologies (ICICT)}}, location = {{New York City, NY, United States}}, publisher = {{IEEE}}, title = {{{Knowledge-based Interactive Configuration Tool for Industrial Augmented Reality Systems}}}, doi = {{10.1109/icict55905.2022.00021}}, year = {{2022}}, } @inproceedings{33554, author = {{Merkelbach, Silke and von Enzberg, Sebastian and Kuhn, Arno and Dumitrescu, Roman}}, booktitle = {{Proceedings of the IEEE}}, publisher = {{IEEE}}, title = {{{Towards a Process Model to Enable Domain Experts to Become Citizen Data Scientists for Industrial Applications}}}, doi = {{10.1109/icps51978.2022.9816871}}, year = {{2022}}, }