@article{34417, abstract = {{Given strict emission targets and legal requirements, especially in the automotive industry, environmentally friendly and simultaneously versatile applicable production technologies are gaining importance. In this regard, the use of mechanical joining processes, such as clinching, enable assembly sheet metals to achieve strength properties similar to those of established thermal joining technologies. However, to guarantee a high reliability of the generated joint connection, the selection of a best-fitting joining technology as well as the meaningful description of individual joint properties is essential. In the context of clinching, few contributions have to date investigated the metamodel-based estimation and optimization of joint characteristics, such as neck or interlock thickness, by applying machine learning and genetic algorithms. Therefore, several regression models have been trained on varying databases and amounts of input parameters. However, if product engineers can only provide limited data for a new joining task, such as incomplete information on applied joining tool dimensions, previously trained metamodels often reach their limits. This often results in a significant loss of prediction quality and leads to increasing uncertainties and inaccuracies within the metamodel-based design of a clinch joint connection. Motivated by this, the presented contribution investigates different machine learning algorithms regarding their ability to achieve a satisfying estimation accuracy on limited input data applying a statistically based feature selection method. Through this, it is possible to identify which regression models are suitable to predict clinch joint characteristics considering only a minimum set of required input features. Thus, in addition to the opportunity to decrease the training effort as well as the model complexity, the subsequent formulation of design equations can pave the way to a more versatile application and reuse of pretrained metamodels on varying tool configurations for a given clinch joining task.}}, author = {{Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro}}, issn = {{2673-2688}}, journal = {{AI}}, keywords = {{Industrial and Manufacturing Engineering}}, number = {{4}}, pages = {{990--1006}}, publisher = {{MDPI AG}}, title = {{{Estimation of Clinch Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels}}}, doi = {{10.3390/ai3040059}}, volume = {{3}}, year = {{2022}}, } @misc{34549, author = {{Schönhärl, Korinna}}, booktitle = {{Sehepunkte}}, number = {{11}}, title = {{{Review on: Daniel Benedikt Stienen: Verkauftes Vaterland}}}, volume = {{22}}, year = {{2022}}, } @misc{34554, author = {{Schönhärl, Korinna}}, booktitle = {{HSozKult}}, title = {{{Review on: Sven Steinmo: Willing to Pay? A Reasonable Choice Approach }}}, year = {{2022}}, } @unpublished{34618, abstract = {{In this article, we show how second-order derivative information can be incorporated into gradient sampling methods for nonsmooth optimization. The second-order information we consider is essentially the set of coefficients of all second-order Taylor expansions of the objective in a closed ball around a given point. Based on this concept, we define a model of the objective as the maximum of these Taylor expansions. Iteratively minimizing this model (constrained to the closed ball) results in a simple descent method, for which we prove convergence to minimal points in case the objective is convex. To obtain an implementable method, we construct an approximation scheme for the second-order information based on sampling objective values, gradients and Hessian matrices at finitely many points. Using a set of test problems, we compare the resulting method to five other available solvers. Considering the number of function evaluations, the results suggest that the method we propose is superior to the standard gradient sampling method, and competitive compared to other methods.}}, author = {{Gebken, Bennet}}, booktitle = {{arXiv:2210.04579}}, title = {{{Using second-order information in gradient sampling methods for nonsmooth optimization}}}, year = {{2022}}, } @inproceedings{29220, abstract = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}}, author = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}}, year = {{2022}}, } @article{23415, author = {{Sperling, Martina and Schryen, Guido}}, journal = {{European Journal of Operational Research (EJOR)}}, number = {{2}}, pages = {{690 -- 705}}, title = {{{Decision Support for Disaster Relief: Coordinating Spontaneous Volunteers}}}, volume = {{299}}, year = {{2022}}, } @inproceedings{29539, abstract = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{Wirtschaftsinformatik 2022 Proceedings}}, keywords = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}}, location = {{Nürnberg (online)}}, title = {{{Towards a model- and data-focused taxonomy of XAI systems}}}, year = {{2022}}, } @phdthesis{29763, abstract = {{Modern-day communication has become more and more digital. While this comes with many advantages such as a more efficient economy, it has also created more and more opportunities for various adversaries to manipulate communication or eavesdrop on it. The Snowden revelations in 2013 further highlighted the seriousness of these threats. To protect the communication of people, companies, and states from such threats, we require cryptography with strong security guarantees. Different applications may require different security properties from cryptographic schemes. For most applications, however, so-called adaptive security is considered a reasonable minimal requirement of security. Cryptographic schemes with adaptive security remain secure in the presence of an adversary that can corrupt communication partners to respond to messages of the adversaries choice, while the adversary may choose the messages based on previously observed interactions. While cryptography is associated the most with encryption, this is only one of many primitives that are essential for the security of digital interactions. This thesis presents novel identity-based encryption (IBE) schemes and verifiable random functions (VRFs) that achieve adaptive security as outlined above. Moreover, the cryptographic schemes presented in this thesis are proven secure in the standard model. That is without making use of idealized models like the random oracle model.}}, author = {{Niehues, David}}, keywords = {{public-key cryptography, lattices, pairings, verifiable random functions, identity-based encryption}}, title = {{{More Efficient Techniques for Adaptively-Secure Cryptography}}}, doi = {{10.25926/rdtq-jw45}}, year = {{2022}}, } @unpublished{31545, abstract = {{Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches on benchmark datasets. However, this effectiveness often comes with the cost of over-parameterization and increased computationally complexity. The former induces extensive hyperparameter optimization to mitigate malicious overfitting. The latter magnifies the importance of winning the hardware lottery. Here, we investigate a remedy for the first problem. We propose a technique based on Kronecker decomposition to reduce the number of parameters in a knowledge graph embedding model, while retaining its expressiveness. Through Kronecker decomposition, large embedding matrices are split into smaller embedding matrices during the training process. Hence, embeddings of knowledge graphs are not plainly retrieved but reconstructed on the fly. The decomposition ensures that elementwise interactions between three embedding vectors are extended with interactions within each embedding vector. This implicitly reduces redundancy in embedding vectors and encourages feature reuse. To quantify the impact of applying Kronecker decomposition on embedding matrices, we conduct a series of experiments on benchmark datasets. Our experiments suggest that applying Kronecker decomposition on embedding matrices leads to an improved parameter efficiency on all benchmark datasets. Moreover, empirical evidence suggests that reconstructed embeddings entail robustness against noise in the input knowledge graph. To foster reproducible research, we provide an open-source implementation of our approach, including training and evaluation scripts as well as pre-trained models in our knowledge graph embedding framework.}}, author = {{Demir, Caglar and Lienen, Julian and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{arXiv:2205.06560}}, title = {{{Kronecker Decomposition for Knowledge Graph Embeddings}}}, year = {{2022}}, } @unpublished{31546, abstract = {{In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding methods have proven effective and achieve state-of-the-art performance. However, pseudo-labels typically stem from ad-hoc heuristics, relying on the quality of the predictions though without guaranteeing their validity. One such method, so-called credal self-supervised learning, maintains pseudo-supervision in the form of sets of (instead of single) probability distributions over labels, thereby allowing for a flexible yet uncertainty-aware labeling. Again, however, there is no justification beyond empirical effectiveness. To address this deficiency, we make use of conformal prediction, an approach that comes with guarantees on the validity of set-valued predictions. As a result, the construction of credal sets of labels is supported by a rigorous theoretical foundation, leading to better calibrated and less error-prone supervision for unlabeled data. Along with this, we present effective algorithms for learning from credal self-supervision. An empirical study demonstrates excellent calibration properties of the pseudo-supervision, as well as the competitiveness of our method on several benchmark datasets.}}, author = {{Lienen, Julian and Demir, Caglar and Hüllermeier, Eyke}}, booktitle = {{arXiv:2205.15239}}, title = {{{Conformal Credal Self-Supervised Learning}}}, year = {{2022}}, } @phdthesis{31556, abstract = {{Mehrzieloptimierung behandelt Probleme, bei denen mehrere skalare Zielfunktionen simultan optimiert werden sollen. Ein Punkt ist in diesem Fall optimal, wenn es keinen anderen Punkt gibt, der mindestens genauso gut ist in allen Zielfunktionen und besser in mindestens einer Zielfunktion. Ein notwendiges Optimalitätskriterium lässt sich über Ableitungsinformationen erster Ordnung der Zielfunktionen herleiten. Die Menge der Punkte, die dieses notwendige Kriterium erfüllen, wird als Pareto-kritische Menge bezeichnet. Diese Arbeit enthält neue Resultate über Pareto-kritische Mengen für glatte und nicht-glatte Mehrzieloptimierungsprobleme, sowohl was deren Berechnung betrifft als auch deren Struktur. Im glatten Fall erfolgt die Berechnung über ein Fortsetzungsverfahren, im nichtglatten Fall über ein Abstiegsverfahren. Anschließend wird die Struktur des Randes der Pareto-kritischen Menge analysiert, welcher aus Pareto-kritischen Mengen kleinerer Subprobleme besteht. Schlussendlich werden inverse Probleme betrachtet, bei denen zu einer gegebenen Datenmenge ein Zielfunktionsvektor gefunden werden soll, für den die Datenpunkte kritisch sind.}}, author = {{Gebken, Bennet}}, title = {{{Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization}}}, doi = {{10.17619/UNIPB/1-1327}}, year = {{2022}}, } @article{31574, abstract = {{We model negative polarization, which is observed for planetary regoliths at backscattering, solving a full wave problem of light scattering with a numerically exact Discontinuous Galerkin Time Domain (DGTD) method. Pieces of layers with the bulk packing density of particles close to 0.5 are used. The model particles are highly absorbing and have irregular shapes and sizes larger than the wavelength of light. This represents a realistic analog of low-albedo planetary regoliths. Our simulations confirm coherent backscattering mechanism of the origin of negative polarization. We show that angular profiles of polarization are stabilized if the number of particles in a layer piece becomes larger than ten. This allows application of our approach to the negative polarization modeling for planetary regoliths.}}, author = {{Grynko, Yevgen and Shkuratov, Yuriy and Alhaddad, Samer and Förstner, Jens}}, issn = {{0019-1035}}, journal = {{Icarus}}, keywords = {{tet_topic_scattering}}, pages = {{115099}}, publisher = {{Elsevier BV}}, title = {{{Negative polarization of light at backscattering from a numerical analog of planetary regoliths}}}, doi = {{10.1016/j.icarus.2022.115099}}, volume = {{384}}, year = {{2022}}, } @article{32864, abstract = {{The further development of in-mold-assembly (IMA) technologies for structural hybrid components is of great importance for increasing the economic efficiency and thus the application potential. This paper presents an innovative IMA process concept for the manufacturing of bending loaded hybrid components consisting of two outer metal belts and an inner core structure made of glass mat reinforced thermoplastic (GMT). In this process, the core structure, which is provided with stiffening ribs and functional elements, is formed and joined to two metal belts in one single step. For experimental validation of the concept, the development of a prototypic molding tool and the manufacturing of hybrid beams including process parameters are described. Three-point bending tests and optical measurement technologies are used to characterize the failure behavior and mechanical properties of the produced hybrid beams. It was found that the innovative IMA process enables the manufacturing of hybrid components with high energy absorption and low weight in one step. The mass-specific energy absorption is increased by 693 % compared to pure GMT beams.}}, author = {{Stallmeister, Tim and Tröster, Thomas}}, issn = {{1662-9795}}, journal = {{Key Engineering Materials}}, keywords = {{Mechanical Engineering, Mechanics of Materials, General Materials Science}}, pages = {{1457--1467}}, publisher = {{Trans Tech Publications, Ltd.}}, title = {{{In-Mold-Assembly of Hybrid Bending Structures by Compression Molding}}}, doi = {{10.4028/p-5fxp53}}, volume = {{926}}, year = {{2022}}, } @misc{33033, author = {{Fehring, Lukas}}, title = {{{Combined Ranking and Regression Trees for Algorithm Selection}}}, 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}}, } @inbook{33306, author = {{Fiege, Amanda Sophie and Pape, Stefan}}, booktitle = {{#MoodleKannMehr – nicht nur im Distanzunterricht! }}, editor = {{Krähwinkel, Tanja}}, isbn = {{978-3-96784-19-3}}, pages = {{76 -- 78}}, publisher = {{Visual Ink Publishing}}, title = {{{Moodle in der Hochschullehre: Struktur durch HTML-Code}}}, year = {{2022}}, } @inbook{33307, author = {{Pape, Stefan and Fiege, Amanda Sophie and Mindt, Ilka}}, booktitle = {{Selected Papers from the 2021 Conference of the German Association for the Study of English}}, editor = {{Wawra, Daniela and Rose, Jonathan}}, issn = {{2625-2147}}, pages = {{15 -- 27}}, publisher = {{Universitätsverlag Winter}}, title = {{{Authentic Englishes.nrw – Developing and Using Interactive Teaching and Learning Materials on Varieties of English}}}, doi = {{10.33675/ANGL/2022/2/6}}, volume = {{33.2}}, year = {{2022}}, } @article{33371, author = {{Steinhardt, Isabel}}, issn = {{2365-3329}}, journal = {{DNGPS Working Paper}}, keywords = {{Open Science, kollaborative Autoethnographie}}, number = {{si}}, pages = {{1--11}}, publisher = {{Verlag Barbara Budrich GmbH}}, title = {{{Kollaborative Autoethnographien digitaler Praktiken. Eine konzeptionelle, methodische und theoretische Einführung zu einem Lehr-Forschungsprojekt im ersten Corona-Semester}}}, doi = {{10.3224/dngps.v8si.01}}, volume = {{8}}, year = {{2022}}, } @article{33370, abstract = {{Research that investigates respective researchers’ engagement in Open Science varies widely in the topics addressed, methods employed, and disciplines investigated, which makes it difficult to integrate and compare its results. To investigate current outcomes of Open Science research, and to get a better understanding on well-researched topics and research gaps, we aimed at providing an openly accessible overview of empirical studies that focus on different aspects of Open Science in different scientific disciplines, academic groups and geographical regions. In this paper, we describe a data set of studies about Open Science practices retrieved following a PRISMA approach to compile a literature review. We included studies from the Scopus and Web of Science databases with keywords relating to Open Science between the years 2000 and 2020, as well as a snowball search for relevant articles. Studies that did not investigate any aspect of Open Science, or weren’t peer-reviewed were excluded, resulting in a total of 695 remaining studies. The data set was collaboratively annotated to ensure intercoder reliability of the coded data.}}, author = {{Lasser, Jana and Schneider, Jürgen and Lösch, Thomas and Röwert, Ronny and Heck, Tamara and Bluemel, Clemens and Neufend, Maike and Steinhardt, Isabel and Skupien, Stefan}}, issn = {{2046-1402}}, journal = {{F1000Research}}, keywords = {{Open Science}}, publisher = {{F1000 Research Ltd}}, title = {{{MapOSR - A mapping review dataset of empirical studies on Open Science}}}, doi = {{10.12688/f1000research.121665.1}}, volume = {{11}}, year = {{2022}}, } @book{33377, abstract = {{Zur Inhaltsanalyse gibt es bereits diverse Methodenbücher. Warum sollten Sie also genau das vorliegende Methodenbuch „Qualitative und quantitative Inhaltsanalyse: digital und automatisiert “lesen? Erstens schließt dieses Buch eine Lücke, der wir in der Lehre und bei Methodenworkshops immer wieder begegnet sind. Diese Lücke besteht darin, dass selten eine Übersicht über und Gegenüberstellung verschiedener Methoden der qualitativen und quantitativen Inhaltsanalyse gegeben wird. Zudem haben bisherige Lehr-und Methodenbücher in den Sozialwissenschaften teil-und vollautomatisierte Verfahren der Textanalyse noch nicht aufgegriffen und diese mit bereits etablierten Verfahren qualitativer und quantitativer Inhaltsanalyse verknüpft. Zweitens bietet dieses Buch im ersten Teil eine systematische und anwendungsorientierte Einführung zu den Grundlagen inhaltsanalytischer empirischer Sozialforschung. Im zweiten Teil werden detaillierte Anleitungen von digital unterstützten qualitativen inhaltsanalytischen Auswertungstechniken gegeben. Den teil-und vollautomatisierten quantitativen inhaltsanalytischen Auswertungstechniken widmet sich der dritte Teil des Buches. Bei digital unterstützten Verfahren werden Sie durch Software bei Ihrer Analyse unterstützt, beispielsweise bei der digitalen Organisation Ihrer Daten und bei der digitalen Durchführung der Auswertung. Bei teilautomatisierten Verfahren der Inhaltsanalyse nehmen Ihnen Software und Tools Teile der Datenanalyse ab, wohingegen bei vollautomatisierten Verfahren der gesamte Auswertungsprozess und oftmals auch die Datenbeschaffung durch Programme und Tools durchgeführt wird.}}, author = {{Schneijderberg, Christian and Wieczorek, Oliver and Steinhardt, Isabel}}, keywords = {{qualitative Inhaltsanalyse, quantitative Inhaltsanalyse}}, publisher = {{Beltz Juventa}}, title = {{{Qualitative und quantitative Inhaltsanalyse: digital und automatisiert. Eine anwendungsorientierte Einführung mit empirischen Beispielen und Softwareanwendungen}}}, year = {{2022}}, }