@article{31541, author = {{Kobecki, Michal and Scherbakov, Alexey V. and Kukhtaruk, Serhii M. and Yaremkevich, Dmytro D. and Henksmeier, Tobias and Trapp, Alexander and Reuter, Dirk and Gusev, Vitalyi E. and Akimov, Andrey V. and Bayer, Manfred}}, issn = {{0031-9007}}, journal = {{Physical Review Letters}}, keywords = {{General Physics and Astronomy}}, number = {{15}}, publisher = {{American Physical Society (APS)}}, title = {{{Giant Photoelasticity of Polaritons for Detection of Coherent Phonons in a Superlattice with Quantum Sensitivity}}}, doi = {{10.1103/physrevlett.128.157401}}, volume = {{128}}, year = {{2022}}, } @inbook{32792, abstract = {{Decision makers in complex business environments have different goals and constraints and therefore require tailored decision support systems (DSS). Following a low-code approach, a tailored DSS can be created by a decision maker as a process-based composition of existing, interoperable decision support services. Data incompatibilities may be introduced during the design or execution of such a process-driven DSS, e.g., when a service always generates or a decision maker selects data which violates a data constraint of a subsequent service. These incompatibilities cause interrupted or erroneous decision processes. In this paper, we contribute an approach which enables the detection of data incompatibilities in process-driven DSS during process design and execution. Our approach utilizes the JSON Schema specification to define service interfaces and associated type constraints which data produced by services or decision makers can be validated against. We demonstrate our approach in the context of decision support for energy network planning using a prototypical open-source implementation.}}, author = {{Kirchhoff, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Lecture Notes in Business Information Processing}}, isbn = {{9783031115097}}, issn = {{1865-1348}}, publisher = {{Springer International Publishing}}, title = {{{Detecting Data Incompatibilities in Process-Driven Decision Support Systems}}}, doi = {{10.1007/978-3-031-11510-3_6}}, year = {{2022}}, } @misc{33001, author = {{Linnig, Caterina and Tröster, Thomas}}, booktitle = {{CU reports}}, title = {{{Sauber recyceln- Innovative Verfahrenskombination mit Ultraschall ermöglicht sicheren Umgang mit recycelten Carbonfasern}}}, year = {{2022}}, } @misc{33033, author = {{Fehring, Lukas}}, title = {{{Combined Ranking and Regression Trees for Algorithm Selection}}}, year = {{2022}}, } @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}}, } @techreport{33070, author = {{Fuhrmann, Anika and Schoch, Rebecca and Rusam, Alexander and Bosse, Michael and Flachmann, Felix and Moritzer, Elmar and Hochrein, Thomas and Bastian, Martin}}, publisher = {{Shaker}}, title = {{{Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen}}}, 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}}, } @inproceedings{33288, author = {{Moritzer, Elmar and Bürenhaus, Franziska Isabelle}}, booktitle = {{37th International Conference of the Polymer Processing Society (PPS)}}, location = {{Fukuoka (Japan)}}, title = {{{Influence of Screw Design on Fiber Breakage of Glass Fiber Reinforced Polypropylene in the Injection Molding Process}}}, year = {{2022}}, } @misc{33286, author = {{Hopp, Matthias and Tölle, Lisa and Monsé, C. and Rosenkranz, N. and Haibel, N. and Walter, D. and Bünger, J. and Westphal, G.}}, booktitle = {{Journal of Material Science and Chemical Engineering}}, issn = {{2327-6053}}, title = {{{Characterization of Fiber Dust Resulting from Recycling of Carbon Fiber-Reinforced Thermoplastics (CFRP) and Their Cell Toxicity}}}, year = {{2022}}, } @inproceedings{33290, author = {{Moritzer, Elmar and Scheiermann, Denis and Bellusova, Denisa and Klie, Benjamin and Giese, Ulrich}}, booktitle = {{ScienceBG}}, location = {{Burgas (Bulgaria)}}, title = {{{Multimaterial Injection Moulding of Thermosets and Elastomers}}}, year = {{2022}}, } @misc{33292, author = {{Moritzer, Elmar and Scheiermann, Denis and Bellusova, Denisa and Klie, Benjamin and Giese, Ulrich}}, booktitle = {{Kunststoffland NRW}}, pages = {{34--35}}, title = {{{Spritzgießen hochtemperaturbeständiger Baugruppen aus Duromeren und Elastomeren}}}, year = {{2022}}, } @inproceedings{33296, author = {{Moritzer, Elmar and Netzband, Dennis and Ujma, Andreas }}, title = {{{Messtechnische Ermittlung der Strukurintensität bei spritzgegossenen, faserverstärkten Kunststoffen}}}, year = {{2022}}, } @article{33332, author = {{Bopp, Frederik and Rojas, Jonathan and Revenga, Natalia and Riedl, Hubert and Sbresny, Friedrich and Boos, Katarina and Simmet, Tobias and Ahmadi, Arash and Gershoni, David and Kasprzak, Jacek and Ludwig, Arne and Reitzenstein, Stephan and Wieck, Andreas and Reuter, Dirk and Müller, Kai and Finley, Jonathan J.}}, issn = {{2511-9044}}, journal = {{Advanced Quantum Technologies}}, keywords = {{Electrical and Electronic Engineering, Computational Theory and Mathematics, Condensed Matter Physics, Mathematical Physics, Nuclear and High Energy Physics, Electronic, Optical and Magnetic Materials, Statistical and Nonlinear Physics}}, publisher = {{Wiley}}, title = {{{Quantum Dot Molecule Devices with Optical Control of Charge Status and Electronic Control of Coupling}}}, doi = {{10.1002/qute.202200049}}, year = {{2022}}, } @inproceedings{33355, author = {{Klippstein, Sven Helge}}, location = {{Berlin}}, publisher = {{7. AM Forum Berlin}}, title = {{{Neue Polymerwerkstoffe - Der Schlüssel für die Industrialisierung}}}, 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}}, } @article{33836, author = {{Piskachev, Goran and Späth, Johannes and Budde, Ingo and Bodden, Eric}}, journal = {{Empirical Software Engineering}}, number = {{5}}, pages = {{1–33}}, publisher = {{Springer}}, title = {{{Fluently specifying taint-flow queries with fluentTQL}}}, volume = {{27}}, year = {{2022}}, }