@inproceedings{35791, author = {{Scheer, D. and Laubenstein, Désirée}}, location = {{Potsdam}}, title = {{{Auswirkungen der Corona-Schulschließungen auf Schülerinnen und Schüler mit und ohne Förderbedarf der emotionalen und sozialen Entwicklung (COFESE) - online.}}}, year = {{2020}}, } @misc{36373, author = {{Pöllmann, Andreas}}, booktitle = {{Compare: A Journal of Comparative and International Education}}, issn = {{0305-7925}}, keywords = {{Education}}, number = {{4}}, pages = {{629--630}}, publisher = {{Informa UK Limited}}, title = {{{Transforming study abroad: a handbook}}}, doi = {{10.1080/03057925.2020.1769882}}, volume = {{51}}, year = {{2020}}, } @inproceedings{46325, abstract = {{Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}}, isbn = {{978-3-030-43823-4}}, pages = {{137–143}}, title = {{{Towards Automated Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-43823-4_12}}, year = {{2020}}, } @inproceedings{29940, abstract = {{A full-bridge modular multilevel converter (MMC) is compared to a half-bridge-based MMC for high-current low-voltage DC-applications such as electrolysis, arc welding or datacenters with DC-power distribution. Usually, modular multilevel converters are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but to meet the needs of a high-current demand at low output voltage levels, the modular converter concept requires adaptations. In the proposed concept, the MMC is used to step-down the three-phase medium-voltage of 10 kV. Therefore, each module is extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage range of 142-220V and to provide galvanic isolation. The proposed MMC converter with full-bridge modules uses half the number of modules compared to a half-bridge-based MMC while reducing the voltage ripple by 78% and capacitor losses by 64% by rearranging the same components to ensure identical costs and volume. For additional reliability, a new robust algorithm for balancing conduction losses during the bypass phase is presented.}}, author = {{Unruh, Roland and Schafmeister, Frank and Fröhleke, Norbert and Böcker, Joachim}}, booktitle = {{PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management}}, isbn = {{978-3-8007-5245-4}}, keywords = {{Cascaded H-Bridge, Solid-State Transformer, Capacitor voltage ripple, Zero sequence voltage, Full-Bridge}}, location = {{Germany}}, publisher = {{VDE}}, title = {{{1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V) DC-Applications}}}, year = {{2020}}, } @inproceedings{23513, author = {{Gräßler, Iris and Pottebaum, Jens and Scholle, Philipp and Thiele, Henrik}}, booktitle = {{ISPIM Conference Proceedings; 7. - 10. Jun. 2020}}, pages = {{1--9}}, publisher = {{International Society for Professional Innovation Management (ISPIM)}}, title = {{{Innovation management and strategic planning of innovative self-preparednes and self-Protection services}}}, year = {{2020}}, } @inbook{48957, author = {{Schulze, Max}}, booktitle = {{Salon #15}}, editor = {{Theewen, Gerhard }}, isbn = {{978-3-89770-523-4}}, pages = {{65--74}}, publisher = {{Salon Verlag}}, title = {{{Der Wunsch zu verschwinden}}}, year = {{2020}}, } @inproceedings{20753, abstract = {{In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.}}, author = {{Ebbers, Janek and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)}}, title = {{{Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection}}}, year = {{2020}}, } @inproceedings{48849, abstract = {{One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.}}, author = {{Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Continuous optimization, Fully parallel search, One-shot optimization, Regression, Surrogate-assisted optimization}}, pages = {{111–124}}, publisher = {{Springer-Verlag}}, title = {{{Evolving Sampling Strategies for One-Shot Optimization Tasks}}}, doi = {{10.1007/978-3-030-58112-1_8}}, year = {{2020}}, } @inproceedings{48852, abstract = {{The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Evolutionary algorithms, Node weight dependent TSP, Traveling Thief Problem}}, pages = {{346–359}}, publisher = {{Springer-Verlag}}, title = {{{Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}}}, doi = {{10.1007/978-3-030-58112-1_24}}, year = {{2020}}, } @inproceedings{48897, abstract = {{In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.}}, author = {{Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from {Nature} (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Automated algorithm selection, Deep learning, Feature-based approaches, Traveling Salesperson Problem}}, pages = {{48–64}}, publisher = {{Springer-Verlag}}, title = {{{Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}}}, doi = {{10.1007/978-3-030-58112-1_4}}, year = {{2020}}, } @inbook{50354, author = {{Heitmann, Ingmar}}, booktitle = {{Neuere Entwicklungen in Produktionn und Controlling}}, editor = {{Betz, Stefan}}, isbn = {{978-3-339-11532-4}}, pages = {{249--276}}, publisher = {{Dr. Kovac}}, title = {{{Erfolgsfaktoren digitaler Transformationsprozesse in KMU}}}, year = {{2020}}, } @inbook{50374, author = {{Puls, Christoph}}, booktitle = {{Neuere Entwicklungen in Produktionn und Controlling}}, editor = {{Betz, Stefan}}, isbn = {{978-3-339-11532-4}}, pages = {{215--246}}, publisher = {{Dr. Kovac}}, title = {{{Varianten- und Komplexitätsmanagement im produzierenden Gewerbe}}}, year = {{2020}}, } @inbook{50365, author = {{Koch, Christian}}, booktitle = {{Neuere Entwicklungen in Produktionn und Controlling}}, editor = {{Betz, Stefan}}, isbn = {{978-3-339-11532-4}}, pages = {{317--347}}, publisher = {{Dr. Kovac}}, title = {{{Digitalisierung des Produktionscontrollings in KMU}}}, year = {{2020}}, } @inbook{50394, author = {{Opitz, Oliver}}, booktitle = {{Neuere Entwicklungen in Produktion und Controlling}}, editor = {{Betz, Stefan}}, isbn = {{978-3-339-11532-4}}, pages = {{11--48}}, publisher = {{Dr. Kovac}}, title = {{{Kapazitätsplanung unter besonderer Berücksichtigung produktionsseitiger Risiken}}}, year = {{2020}}, } @inproceedings{45049, author = {{Knickenberg, Margarita and Zurbriggen, Carmen and Venetz, Martin}}, location = {{Dresden}}, title = {{{ Adolescents’ engagement at school vs. leisure time and their emotional involvement.}}}, year = {{2020}}, } @inproceedings{45051, author = {{Knickenberg, Margarita}}, location = {{London}}, title = {{{Social relationships in students with special educational needs.}}}, year = {{2020}}, } @inproceedings{45052, author = {{Knickenberg, Margarita}}, location = {{Potsdam}}, title = {{{Individualisierung des Lernens im inklusiven Unterricht aus der Perspektive von Lehrkräften und Kindern.}}}, year = {{2020}}, } @inproceedings{45048, author = {{Knickenberg, Margarita and Zurbriggen, Carmen and Venetz, Martin}}, location = {{Wien}}, title = {{{Das emotionale Erleben von Jugendlichen in Abhängigkeit ihres Engagements in Schule und Freizeit.}}}, year = {{2020}}, } @inproceedings{45053, author = {{Knickenberg, Margarita and Zurbriggen, Carmen}}, location = {{Potsdam}}, title = {{{Students’ emotional experience during peer interactions in different academic tracks.}}}, year = {{2020}}, } @misc{50570, author = {{Bröker, Christina}}, booktitle = {{HsozKult}}, title = {{{Rezension zu: Peltzer, Jörg: Fürst werden. Rangerhöhungen im 14. Jahrhundert. Das römisch-deutsche Reich und England im Vergleich. Berlin-Boston 2019. ISBN 978-3-11-063902-5}}}, year = {{2020}}, }