End-to-End Training of Time Domain Audio Separation and Recognition - von Neumann, Thilo; Kinoshita, Keisuke; Drude, Lukas; Boeddeker, Christoph; Delcroix, Marc; Nakatani, Tomohiro; Haeb-Umbach, Reinhold (ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multispeaker speech recognition. However, up until now, state-of-theart neural network–based time domain source separation has not yet been combined with E2E speech recognition. We here demonstrate how to combine a separation module based on a Convolutional Time domain Audio Separation Network (Conv-TasNet) with an E2E speech recognizer and how to train such a model jointly by distributing it over multiple GPUs or by approximating truncated back-propagation for the convolutional front-end. To put this work into perspective and illustrate the complexity of the design space, we provide a compact overview of single-channel multi-speaker recognition systems. Our experiments show a word error rate of 11.0% on WSJ0-2mix and indicate that our joint time domain model can yield substantial improvements over cascade DNN-HMM and monolithic E2E frequency domain systems proposed so far.

Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR - von Neumann, Thilo; Boeddeker, Christoph; Drude, Lukas; Kinoshita, Keisuke; Delcroix, Marc; Nakatani, Tomohiro; Haeb-Umbach, Reinhold (Proc. Interspeech 2020)
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database.

Gedenken an Luftangriffe auf Paderborn: "Erinnerung wird nie obsolet sein" - Hartung, Olaf (Neue Westfälische)

Workshops zur geschichtsdidaktischen Lehre in Zeiten von Corona - Hartung, Olaf (Paderborner Universitätszeitschrift)

Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network - Gburrek, Tobias; Schmalenstroeer, Joerg; Brendel, Andreas; Kellermann, Walter; Haeb-Umbach, Reinhold (European Signal Processing Conference (EUSIPCO))
We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor networks. Signal diffuseness information from acoustic signals is aggregated via the coherent-to-diffuse power ratio to obtain a distance-related feature, which is mapped to a source-to-microphone distance estimate by means of a DNN. This information is then combined with direction-of-arrival estimates from compact microphone arrays to infer the geometry of the sensor network. Unlike many other approaches to geometry calibration, the proposed scheme does only require that the sampling clocks of the sensor nodes are roughly synchronized. In simulations we show that the proposed DNN-based distance estimator generalizes to unseen acoustic environments and that precise estimates of the sensor node positions are obtained.

Corporate Entrepreneurship: Die Rolle und Bedeutung des Humankapitals - Krebs, Benjamin; Kabst, Rüdiger (PERSONALquartely)

Corporate Entrepreneurship durch die Allokation von Ressourcen - Megow, N.; Kabst, Rüdiger (Perspektiven des Entrepreneurships: Unternehmerische Konzepte zwischen Theorie und Praxis)

Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges - Haney, Julie; Furman, Susanne; Acar, Yasemin

Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges - Haney, Julie M.; Furman, Susanne M.; Acar, Yasemin (HCI for Cybersecurity, Privacy and Trust)

Cloudy with a Chance of Misconceptions: Exploring Users’ Perceptions and Expectations of Security and Privacy in Cloud Office Suites - Wermke, Dominik; Huaman, Nicolas; Stransky, Christian; Busch, Niklas; Acar, Yasemin; Fahl, Sascha (Sixteenth Symposium on Usable Privacy and Security, SOUPS 2020, August 7-11, 2020)

Listen to Developers! A Participatory Design Study on Security Warnings for Cryptographic APIs - Gorski, Peter Leo; Acar, Yasemin; Lo Iacono, Luigi; Fahl, Sascha (Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

Gier und andere Tugenden: Widersprüchliche Bewertungen der Marktwirtschaft in Oliver Stones Wall Street - Schneider, Martin (Erzähltes Geld Finanzmärkte und Krisen in Literatur, Film und Medien)

Urbane Arbeitsparadiese gestern und heute - Schneider, Martin (Urbane Kulturen und Räume Intermedial)

Personalwirtschaft - Schneider, Martin; Eisele, Simon (Handbuch Gestaltung digitaler und vernetzter Arbeitswelten)

Is Corruption Imprinted? A Study on Preconditions of Corruption in Post-Communist Countries - Knorr, Karin; Auer, Thorsten Fabian; Thommes, Kirsten (Academy of Management Proceedings)

»die reine farben- formen- und linienfreude«. Zur Vision einer ›reinen Formkunst‹ in den ornamentalen Konstellationen der Blätter für die Kunst - Gretz, Daniela (Formästhetiken und Formen der Literatur. Materialität – Ornament – Codierung)

Expertise von Lehrpersonen für inklusiven Mathematikunterricht der Sekundarstufe - Ausgangspunkte zur Professionalisierungsforschung. - Seitz, Susanne; Häsel-Weide, Uta; Wilke, Yannik; Wallner, Melina (K:ON Kölner Online-Journal für Lehrer*innenbildung)

Wolfgang Weber (1939–2019) Professor of Human Resource Management and Founding Editor of the German Journal of Human Resource Management - Festing, Marion; Iseke, Anja; Kabst, Rüdiger; Martin, Albert; Matiaske, Wenzel; Mayrhofer, Wolfgang; Nienhüser, Werner (German Journal of Human Resource Management: Zeitschrift für Personalforschung)

Intergenerational Value Similarity in Adulthood - Hoellger, Christian; Sommer, Sabrina; Albert, Isabelle; Buhl, Heike M. (Journal of Family Issues)
This investigation concentrates on value similarity between parents and their children during adulthood. The interplay between gender, age, relationship quality, and frequency of contact on value similarity was analyzed. A total of 600 adult German children (53.8% women) and their parents took part in a questionnaire study. Value orientation was measured with a short version of Schwartz’s Portrait Values Questionnaire, and relationship quality with the Network of Relationships Inventory (Furman & Buhrmeister, 1992).Value similarity was higher in mother–daughter dyads compared to mother–son dyads, but in the other dyads, no significant differences were found. Regarding relationship quality, verbal intimacy was not related to value similarity. Parental satisfaction was associated with value similarity in the father–child dyads. Satisfaction, as perceived by adult children, was linked to value similarity in mother–child and father–son dyads. Furthermore, the frequency of contact related to value similarity between mothers and sons.
Keywords: Social Sciences (miscellaneous)

Ecken, Systeme, Funktionsbereiche. Eine Medienkulturgeschichte des Filter(n)s im Kontext von Wohnräumen - Miggelbrink, Monique (Navigationen – Zeitschrift für Medien- und Kulturwissenschaften, Themenschwerpunkt: Filter(n) – Geschichte Ästhetik Praktiken)

Nash Smoothing on the Test Bench: H_alpha--Essential Equilibira - Duman, Papatya; Trockel, Walter

Mental body rotation with egocentric and object-based transformations in different postures: sitting vs. standing - Budde, Kirsten; Barela, José Angelo; Figueiredo, Gabriella Andreeta; Weigelt, Matthias (Brazilian Journal of Motor Behavior)
BACKGROUND: A detail of previous studies on mental rotation, which has not received any attention so far, relates to the testing situation of the participants. In nearly every study, participants were tested in a sitting posture (and not standing). However, when considering embodied cognition approaches on mental processes, participants may not be able to fully exploit these processes when performing mental rotation tasks in a sitting posture.
AIM: Therefore, the aim of the present study is to examine the potential influence of two different postures (sitting vs. standing), when solving mental body rotation tasks.
METHOD: Sixteen participants (6 females) were tested in two mental body-rotation tasks (MBRT), requiring either an object-based spatial transformation (based on a same-different judgment) or an egocentric transformation (based on a left-right judgment) in a sitting and in a standing posture. Reaction times and response errors were analysed in two three-way ANOVAs, with the factors orientation, task, and posture.
RESULTS: Results revealed an effect of orientation and task, indicating that participants performed better for egocentric than for object-based transformations. However, there was no effect of posture.
CONCLUSION: The different dynamics of postural control during sitting and standing do not induce different embodiment effects on mental rotation.

Hat die Körperposition (sitzen vs. stehen) einen Einfluss auf die Leistung in einer Mentalen Rotationsaufgabe? - Budde, Kirsten; Barela, José A; Weigelt, Matthias (Abstractband der 52. Jahrestagung der Arbeitsgemeinschaft für Sportpsychologie (asp))

Linguistik des Briefs - Schuster, Britt-Marie (Handbuch Brief. Von der Frühen Neuzeit bis zur Gegenwart (Bd. 1: Interdisziplinarität – Systematische Perspektiven – Briefgenres))
Keywords: Textsortengeschichte

The contribution of terminology research to the understanding of science communication - Schuster, Britt-Marie (Science Communication (Handbooks of Communication Science))
Keywords: Fachkommunikation

Was bedeutet Ehre? Diskursethische Überlegungen zu den BBC-Reden Thomas Manns - Schuster, Britt-Marie; Markewitz, Friedrich; Wilk, Nicole M. (Diskurs – ethisch.)
Keywords: Textsemantik

micro-Raman Investigations of Periodically-Poled X-Cut Thin-Film Lithium Niobate for Integrated Optics - Reitzig, Sven; Rüsing, Michael; Kirbus, Benjamin; Gossel, Joshua; Singh, Ekta; Eng, Lukas M.; Zhao, Jie; Mookherjea, Shayan (2020 Joint Conference of the IEEE International Frequency Control Symposium and International Symposium on Applications of Ferroelectrics (IFCS-ISAF))

Selbstreguliertes Lernen - Können die das überhaupt? Gestaltung von Lernprozessen in berufsvorbereitenden Bildungsgängen. - Sloane, Peter F. E.; Daniel, Desiree; Meier, Karin; Schwabl, Franziska; Volgmann, Simone

Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis - Prager, Raphael Patrick; Trautmann, Heike; Wang, Hao; Bäck, Thomas H. W.; Kerschke, Pascal (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI))
In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.

A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media - Assenmacher, D; Clever, L; Pohl, JS; Trautmann, Heike; Grimme, C (Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis)
The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020.

Towards Automated Configuration of Stream Clustering Algorithms - Carnein, Matthias; Trautmann, Heike; Bifet, Albert; Pfahringer, Bernhard (Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19))
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.

Inside the tool set of automation: Free social bot code revisited - Assenmacher, Dennis; Frischlich , Lena; Trautmann, Heike; Grimme, Christian; Adam, Lena (Disinformation in open online media)
Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code.

confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms - Carnein, Matthias; Trautmann, Heike; Bifet, Albert; Pfahringer, Bernhard (Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020))
Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.

Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection - Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Proceedings of the IEEE Congress on Evolutionary Computation (CEC))
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.

Towards Decision Support in Dynamic Bi-Objective Vehicle Routing - Bossek, Jakob; Grimme, Christian; Rudolph, Günter; Trautmann, Heike (Proceedings of the IEEE Congress on Evolutionary Computation (CEC))
We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.

Dynamic Bi-Objective Routing of Multiple Vehicles - Bossek, Jakob; Grimme, Christian; Trautmann, Heike (Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20))
In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.

Automated detection of nostalgic text in the context of societal pessimism - Lena, Clever; Frischlich, Lena; Trautmann, Heike; Grimme, Christian (Disinformation in open online media)
In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study.

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem - Seiler, Moritz V; Pohl, Janina; Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI))
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 1000 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.

Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool - Riehle, Dennis M.; Niemann, Marco; Brunk, Jens; Assenmacher, Dennis; Trautmann, Heike; Becker, Jörg (Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing)
The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation.

Demystifying Social Bots: On the Intelligence of Automated Social Media Actors - Assenmacher, Dennis; Clever, Lena; Frischlich, Lena; Quandt, Thorsten; Trautmann, Heike; Grimme, Christian (Social Media + Society)
Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.

A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms - Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Applied Soft Computing)
We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.
Keywords: Algorithm selection; Multi-objective optimization; Performance measurement; Combinatorial optimization; Traveling Salesperson Problem

Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries - Seiler, Moritz Vinzent; Trautmann, Heike; Kerschke, Pascal (Proceedings of the International Joint Conference on Neural Networks (IJCNN))
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.

Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent - Steinhoff, Vera; Kerschke, Pascal; Aspar, Pelin; Trautmann, Heike; Grimme, Christian (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI))
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.

Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision - Glockner, Max; Habernal, Ivan; Gurevych, Iryna (Findings of the Association for Computational Linguistics: EMNLP 2020)

1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V) DC-Applications - Unruh, Roland; Schafmeister, Frank; Fröhleke, Norbert; Böcker, Joachim (PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management)
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.
Keywords: Cascaded H-Bridge; Solid-State Transformer; Capacitor voltage ripple; Zero sequence voltage; Full-Bridge

Dual Study Programmes as a Design Challenge: Identifying Areas for Improvement as a Starting Point for Interventions - Mordhorst, Lisa; Gössling, Bernd (EDeR. Educational Design Research)
The proportion of freshmen enrolled in dual study programmes has steadily increased in recent years. From the perspective of potential students, these programmes are highly attractive because they combine types of learning that used to be largely separate at an institutional level: vocational and academic learning. In training-integrated dual study programmes, different institutional contexts, governance regimes, teaching styles and learning environments make bridging these two worlds of learning a challenge for both educators and learners. However, these programmes also allow leeway for didactic innovation, through the cooperation of different types of educational institutions and through new ways of using available didactic methods, and for establishing a new relationship between higher education (HE) and vocational education and training (VET). This paper positions training-integrated dual study programmes as an object of design-based research (DBR). By developing and using an extended model for the pedagogic development of HEIs, “pädagogische Hochschulentwicklung” (Brahm, Jenert, & Euler, 2016a, p. 19; Euler, 2013, p. 360), the paper systematically identifies generic educational problems in these hybrids. Based on a literature review, this paper classifies and explains the design challenges at the level of the learning environment, the study programme and the organisation. The challenges revolve mainly around the cooperation and integration of HE and VET. The paper concludes with an outlook on future DBR projects designing dual studies.
Keywords: Dual study programmes; Design challenges; Pedagogic development of HEIs; Literature review; Study programme development; DBR cycle

Heat dissipation strategies for silicon carbide power SMDs and their use in different applications - Strothmann, Benjamin; Piepenbrock, Till; Schafmeister, Frank; Böcker, Joachim (PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management)
Heat dissipation is a limiting factor in the performance of many power electronic components. Especially in the TO-263-7 package, which is used for several SiC-MOSFETs, the heat transfer must take place through the cross section of the printed circuit board (PCB) to the heatsink at the bottom side. Most commonly, thermal vias are used to form this path in a perpendicular direction through all PCB-layers. In a given soft- and hard switched example applications with the use of C3M0065090J SiC-MOSFETs, this conventional approach limited the component’s maximum heat dissipation to approx. 13 W. A recent alternative approach are massive copper blocks (”pedestals”) being integrated in PCBs and reaching from their top- to the bottom-side in relevant footprint areas under SMD-housed power semiconductors. Pedestals allowing to increase the heat dissipation in the given case to even 36 W. This step is achieved due to the clearly superior heat spreading capability of that massive thermal connection between SiC-MOSFET and heatsink. For the hard switched example application the number of switch-elements can be halved to one, by using the pedestal instead of thermal vias. Independently of optimizing the heat transfer path, the up-front avoidance of losses helps to stay within existing heat dissipation limits, of course. The dominant conduction losses of the mentioned soft-switched example application could be halved by changing to SiC-MOSFET types with significant lowered RDSon. By using pedestals and changing to SiC-MOSFETs with lowered RDSon, the number of switch-elements can also be halved for the soft switched application.

Behavioral Economics for Human-in-the-loop Control Systems Design: Overconfidence and the hot hand fallacy - Protte, Marius; Fahr, René; Quevedo, Daniel E. (IEEE Control Systems Magazine)
Successful design of human-in-the-loop control sys- tems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings. This paper addresses the following two key questions: 1) How do behavioral biases affect decision making? 2) What is the role played by feedback in human-in-the-loop control systems? Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of – or even because of – receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a “hot hand fallacy”. We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.

Birth Without a Woman: Mary Shelley's Frankenstein in the Context of Eighteenth-Century Ideas on Birth, Motherhood and Midwifery - Schneider, Julia Christina (Transient Bodies in Anglophone Literature and Culture)

Reformation und Regierungskunst: Wahrheit als Grundlage normalisierender Macht bei Nietzsche und Foucault - Corall, Niklas (Nietzsche und die Reformation)