@article{34295, author = {{Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, year = {{2022}}, } @article{44636, author = {{Hoppe, Julia A. and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, volume = {{9}}, year = {{2022}}, } @article{32273, author = {{Hoppe, Julia Amelie and Melkas, Helinä and Pekkarinen, Satu and Tuisku, Outi and Hennala, Lea and Johansson-Pajala, Rose-Marie and Gustafsson, Christine and Thommes, Kirsten}}, issn = {{1044-7318}}, journal = {{International Journal of Human–Computer Interaction}}, keywords = {{Computer Science Applications, Human-Computer Interaction, Human Factors and Ergonomics}}, pages = {{1--17}}, publisher = {{Informa UK Limited}}, title = {{{Perception of Society’s Trust in Care Robots by Public Opinion Leaders}}}, doi = {{10.1080/10447318.2022.2081283}}, year = {{2022}}, } @article{46290, author = {{Caruso, Carina and Drossel, Kerstin and Heldt, M}}, journal = {{Lehrerbildung auf dem Prüfstand}}, number = {{2}}, pages = {{347--361}}, title = {{{Zum Ausmaß und Zusammenhang der unterrichtsbezogenen Nutzung digitaler Medien mit der Medienkompetenz von Lehrkräften unter Berücksichtigung von Hintergrundmerkmalen}}}, volume = {{15}}, year = {{2022}}, } @techreport{47094, author = {{Bartels, Lara and Kesternich, Martin}}, issn = {{1556-5068}}, keywords = {{General Earth and Planetary Sciences, General Environmental Science}}, publisher = {{ZEW Discussion Paper 22-040}}, title = {{{Motivate the Crowd or Crowd-Them Out? The Impact of Local Government Spending on the Voluntary Provision of a Green Public Good}}}, doi = {{10.2139/ssrn.4251592}}, year = {{2022}}, } @techreport{47096, author = {{Chlond, Bettina and Goeschl, Timo and Kesternich, Martin}}, issn = {{1556-5068}}, keywords = {{General Earth and Planetary Sciences, General Environmental Science}}, publisher = {{ZEW Discussion Paper 22-020}}, title = {{{More Money or Better Procedures? Evidence From an Energy Efficiency Assistance Program}}}, doi = {{10.2139/ssrn.4151557}}, year = {{2022}}, } @techreport{47092, author = {{Kesternich, Martin and Osberghaus, Daniel and Botzen, Willem Jan Wouter}}, issn = {{1556-5068}}, keywords = {{General Earth and Planetary Sciences, General Environmental Science}}, publisher = {{ZEW Discussion Paper 22-055}}, title = {{{The Intention-Behavior Gap in Climate Change Adaptation}}}, doi = {{10.2139/ssrn.4288341}}, year = {{2022}}, } @techreport{47080, author = {{Kesternich, Martin and von Graevenitz, Kathrine}}, publisher = {{ifo Schnelldienst 11/2022, 21-24}}, title = {{{Gas- statt Preisbremse: Wie die Umsetzung von Unterstützungsprogrammen zum Gassparen für Haushalte und Unternehmen gelingen kann}}}, year = {{2022}}, } @techreport{47097, author = {{Chlond, Bettina and Goeschl, Timo and Kesternich, Martin}}, publisher = {{ZEW Policy Brief 22-01}}, title = {{{Wie lässt sich die Energieeffizienz in einkommensschwachen Haushalten steigern?}}}, year = {{2022}}, } @inproceedings{30883, author = {{Krings, Sarah Claudia and Yigitbas, Enes and Biermeier, Kai and Engels, Gregor}}, booktitle = {{Proceedings of the 14th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2022)}}, title = {{{Design and Evaluation of AR-Assisted End-User Robot Path Planning Strategies}}}, year = {{2022}}, } @proceedings{41170, editor = {{Hartung, Olaf}}, location = {{Paderborn}}, publisher = {{Universität Paderborn, Historisches Institut, Abt. Theorie und Didaktik der Geschichte}}, title = {{{Konzeptpapier zur Konferenz "Geschichte im digitalen Wandel? Geschichtskultur – Erinnerungspraktiken – Historisches Lernen"}}}, year = {{2022}}, } @article{49554, author = {{Steigerwald, Jörn}}, journal = {{Romanistische Zeitschrift für Literaturgeschichte}}, number = {{3/4}}, pages = {{355--370}}, title = {{{Post-rousseauistische Kulturanthropologie: Francois-René de Chateaubriands Erzählung "Atala" }}}, volume = {{46}}, year = {{2022}}, } @inproceedings{48861, abstract = {{Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-9237-2}}, keywords = {{instance features, instance generation, quality diversity, TSP}}, pages = {{186–194}}, publisher = {{Association for Computing Machinery}}, title = {{{Exploring the Feature Space of TSP Instances Using Quality Diversity}}}, doi = {{10.1145/3512290.3528851}}, year = {{2022}}, } @inproceedings{48868, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, pages = {{824–842}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}}}, doi = {{10.1145/3520304.3533626}}, year = {{2022}}, } @inproceedings{48882, abstract = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}}, author = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, doi = {{10.1007/978-3-031-14714-2_14}}, year = {{2022}}, } @inproceedings{48894, abstract = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}}, author = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}}, pages = {{237–249}}, publisher = {{Springer International Publishing}}, title = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}}, doi = {{10.1007/978-3-031-14714-2_17}}, year = {{2022}}, } @article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @inproceedings{48896, abstract = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}}, author = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, keywords = {{configuration, multi-modality, multi-objective optimization}}, pages = {{356–359}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}}, doi = {{10.1145/3520304.3528998}}, year = {{2022}}, } @inbook{49627, author = {{Schulte Eickholt, Swen and Perfölz, René}}, booktitle = {{Jahrbuch für Interantionale Germanistik. Wege der Germanistik in transkultureller Perspektive. Akten des XIV. Kongresses der Interantionelen Vereinigung für Germanistik (IVG) (Bd. 11)}}, editor = {{Auteri, Laura and Barrale, Natascia and Die Bella, Arianna }}, pages = {{468--508}}, title = {{{ Interkulturelle Gattungstransfers und - transformationen zwischen Europa und der Türkei am Beispiel des Schelmenromans}}}, year = {{2022}}, } @techreport{35799, author = {{Koch, Reinald and Holtmann, Svea and Giese, Henning}}, issn = {{1556-5068}}, title = {{{Losses Never Sleep - The Effect of Tax Loss Offset on Stock Market Returns during Economic Crises}}}, doi = {{10.2139/ssrn.4096944}}, volume = {{269}}, year = {{2022}}, }