@inproceedings{23511, author = {{Gräßler, Iris and Roesmann, Daniel and Pottebaum, Jens}}, booktitle = {{14th CIRP Conference on Intelligent Computation in Manufacturing Engineering; 15. - 17. Jul. 2020}}, pages = {{57--62}}, publisher = {{Elsevier B. V.}}, title = {{{Model based Integration of Human Characteristics in Production Systems: A Literature Survey}}}, doi = {{https://doi.org/10.1016/j.procir.2021.03.010}}, year = {{2021}}, } @article{23390, author = {{Gräßler, Iris and Pottebaum, Jens}}, journal = {{ Applied Sciences}}, pages = {{4516}}, title = {{{Generic Product Lifecycle Model: A Holistic and Adaptable Approach for Multi-Disciplinary Product-Service Systems}}}, doi = {{10.3390/app11104516}}, volume = {{11}}, year = {{2021}}, } @techreport{36060, abstract = {{Merging a sample of 492 merger and acquisition (M&A) announcements from 284 acquiring firms across Europe and North America with data from 5-year single-name credit default swaps (CDSs) written on stock-listed acquiring firms between 2005 and 2018, the paper at hand empirically analyzes the CDS investors’ risk perceptions of M&A announcements using event study methodologies. As a baseline result, we provide evidence for significantly positive cumulative average abnormal CDS spread changes for both, European and North American acquirers suggesting that CDS investors perceive an increase in the acquiring firms’ credit risk exposures due to M&A announcements. Our baseline finding holds under several robustness checks, especially when controlling for the robustness of the empirical design. Moreover, results from a large variety of sensitivity analyses reveal a number of deal and firm characteristics that may explain why CDS investors from our sample expect an increase in the acquirers’ credit risk exposures due to forthcoming M&A transactions. }}, author = {{Hippert, Benjamin and Uhde, André}}, keywords = {{credit default swaps, risk perception of CDS investors, mergers and acquisitions, event study}}, title = {{{CDS Investors’ Risk Perceptions of M&A Announcements}}}, year = {{2021}}, } @techreport{36063, abstract = {{This paper empirically investigates determinants of the outstanding net notional amount of credit default swaps (CDSs) contracts written on banks. We extend and complement the previous literature dealing with CDS trading by analyzing a comprehensive set of CDS tradingspecific, bank-fundamental, macroeconomic and bank-institutional determinants. We find that risk hedging clearly dominates an investor’s speculation and arbitrage motive, while the latter, however, exhibits the strongest impact on the outstanding net notional amount of bank CDSs. Furthermore, being classified as a G-SIB, being a constituent of the main CDS index and the equity trading volume may significantly explain changes in the outstanding CDS net notional on banks. The analysis at hand provides important implications for both academics and practitioners, since understanding the trading motives of bank CDS investors provides a deeper insight into the opaque CDS market. }}, author = {{Hippert, Benjamin and Uhde, André and Wengerek, Sascha Tobias}}, keywords = {{banking, outstanding CDS net notional, determinants of bank CDS trading}}, title = {{{Determinants of CDS Trading on Major Banks}}}, year = {{2021}}, } @inproceedings{29308, abstract = {{In this paper we present our system for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments, where it scored the fourth rank. Our presented solution is an advancement of our system used in the previous edition of the task.We use a forward-backward convolutional recurrent neural network (FBCRNN) for tagging and pseudo labeling followed by tag-conditioned sound event detection (SED) models which are trained using strong pseudo labels provided by the FBCRNN. Our advancement over our earlier model is threefold. First, we introduce a strong label loss in the objective of the FBCRNN to take advantage of the strongly labeled synthetic data during training. Second, we perform multiple iterations of self-training for both the FBCRNN and tag-conditioned SED models. Third, while we used only tag-conditioned CNNs as our SED model in the previous edition we here explore sophisticated tag-conditioned SED model architectures, namely, bidirectional CRNNs and bidirectional convolutional transformer neural networks (CTNNs), and combine them. With metric and class specific tuning of median filter lengths for post-processing, our final SED model, consisting of 6 submodels (2 of each architecture), achieves on the public evaluation set poly-phonic sound event detection scores (PSDS) of 0.455 for scenario 1 and 0.684 for scenario as well as a collar-based F1-score of 0.596 outperforming the baselines and our model from the previous edition by far. Source code is publicly available at https://github.com/fgnt/pb_sed.}}, author = {{Ebbers, Janek and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021)}}, isbn = {{978-84-09-36072-7}}, pages = {{226–230}}, title = {{{Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments}}}, year = {{2021}}, } @inproceedings{29306, abstract = {{Recently, there has been a rising interest in sound recognition via Acoustic Sensor Networks to support applications such as ambient assisted living or environmental habitat monitoring. With state-of-the-art sound recognition being dominated by deep-learning-based approaches, there is a high demand for labeled training data. Despite the availability of large-scale data sets such as Google's AudioSet, acquiring training data matching a certain application environment is still often a problem. In this paper we are concerned with human activity monitoring in a domestic environment using an ASN consisting of multiple nodes each providing multichannel signals. We propose a self-training based domain adaptation approach, which only requires unlabeled data from the target environment. Here, a sound recognition system trained on AudioSet, the teacher, generates pseudo labels for data from the target environment on which a student network is trained. The student can furthermore glean information about the spatial arrangement of sensors and sound sources to further improve classification performance. It is shown that the student significantly improves recognition performance over the pre-trained teacher without relying on labeled data from the environment the system is deployed in.}}, author = {{Ebbers, Janek and Keyser, Moritz Curt and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the 29th European Signal Processing Conference (EUSIPCO)}}, pages = {{1135–1139}}, title = {{{Adapting Sound Recognition to A New Environment Via Self-Training}}}, year = {{2021}}, } @misc{49145, abstract = {{Auch in diesem Semester finden Veranstaltungen im Fach Philosophie an den meisten Universitäten vor allem online statt; die Pandemie-Lage lässt eine Öffnung der Unis für Präsenzveranstaltungen kaum zu. Die folgenden Überlegungen hat Sebastian Luft, Professor an der Marquette University in Milwaukee/WI, aus aktuellem Anlass verfasst. 2019 erschien sein Buch »Philosophie lehren« zur philosophischen Hochschuldidaktik. Der folgende Text bietet eine aktuelle Ergänzung zur dortigen Handreichung für die philosophische Lehre.}}, author = {{Luft, Sebastian}}, pages = {{7}}, publisher = {{Meiner Telegramm}}, title = {{{»Wir hören Dich nicht, schalte bitte Dein Mikro an ! « Einige Gedanken zur digitalen Lehre in der Pandemie.}}}, year = {{2021}}, } @article{49149, author = {{Luft, Sebastian}}, journal = {{Information Philosophie}}, number = {{4}}, publisher = {{Claudia Moser Verlag}}, title = {{{In Amerika promovieren? Hinweise von Sebastian Luft}}}, year = {{2021}}, } @inbook{47957, author = {{Schneider, Jennifer Nicole}}, booktitle = {{Fostering Digitisation and Industry 4.0: Education – Vocation - Industry – Future. New Opportunities and Challenges for European VET. Insights in the DigI-VET Project}}, editor = {{Beutner, Marc and Pechuel, Rasmus and Schneider, Jennifer }}, pages = {{57 -- 62 }}, title = {{{Digital transformation in industry}}}, year = {{2021}}, } @inbook{47966, author = {{Schneider, Jennifer }}, booktitle = {{Fostering Digitisation and Industry 4.0: Education – Vocation - Industry – Future. New Opportunities and Challenges for European VET. Insights in the DigI-VET Project}}, editor = {{Beutner, Marc and Pechuel, Rasmus and Schneider, Jennifer}}, pages = {{150 -- 165}}, title = {{{Teaching and Learning Materials}}}, year = {{2021}}, } @article{24456, abstract = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}}, author = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}}, issn = {{2379-8920}}, journal = {{IEEE Transactions on Cognitive and Developmental Systems}}, keywords = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}}, number = {{3}}, pages = {{717--728}}, title = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}}, doi = {{10.1109/tcds.2020.3044366}}, volume = {{13}}, year = {{2021}}, } @inbook{49487, author = {{Malancu, Natalia and Florea, Alexandra}}, booktitle = {{Handbook of Citizenship and Migration}}, editor = {{Giugni, Marco and Grasso, Maria}}, title = {{{Chapter 5: Quantitative methodological approaches to citizenship and migration}}}, doi = {{https://doi.org/10.4337/9781789903133.00011}}, year = {{2021}}, } @inproceedings{48853, abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}}, pages = {{556–564}}, publisher = {{Association for Computing Machinery}}, title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}}, doi = {{10.1145/3449639.3459364}}, year = {{2021}}, } @inproceedings{48855, abstract = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Learning and Intelligent Optimization}}, isbn = {{978-3-030-92120-0}}, keywords = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}}, pages = {{40–54}}, publisher = {{Springer-Verlag}}, title = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}}, doi = {{10.1007/978-3-030-92121-7_4}}, year = {{2021}}, } @inproceedings{48860, abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}}, pages = {{198–206}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}}, doi = {{10.1145/3449639.3459363}}, year = {{2021}}, } @inbook{48862, abstract = {{Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time.}}, author = {{Bossek, Jakob and Sudholt, Dirk}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{evolutionary algorithms, pseudo-boolean functions, runtime analysis, theory}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Do Additional Optima Speed up Evolutionary Algorithms?}}}, year = {{2021}}, } @inbook{48881, abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}}, author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{automated algorithm selection, graph theory, instance features, normalization, traveling salesperson problem (TSP)}}, pages = {{1–15}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Normalized TSP Features for Automated Algorithm Selection}}}, year = {{2021}}, } @inproceedings{48876, abstract = {{In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.}}, author = {{Bossek, Jakob and Wagner, Markus}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-8351-6}}, keywords = {{evolutionary algorithms, evolving instances, fitness function, instance hardness, traveling thief problem (TTP)}}, pages = {{1423–1432}}, publisher = {{Association for Computing Machinery}}, title = {{{Generating Instances with Performance Differences for More than Just Two Algorithms}}}, doi = {{10.1145/3449726.3463165}}, year = {{2021}}, } @inproceedings{48893, abstract = {{Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, high-order entropy, traveling salesperson problem}}, pages = {{600–608}}, publisher = {{Association for Computing Machinery}}, title = {{{Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}}}, doi = {{10.1145/3449639.3459384}}, year = {{2021}}, } @inproceedings{48891, abstract = {{Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.}}, author = {{Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, sub-modular functions}}, pages = {{261–269}}, publisher = {{Association for Computing Machinery}}, title = {{{Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}}}, doi = {{10.1145/3449639.3459385}}, year = {{2021}}, }