The Development of Connective Action during Social Movements on Social Media - Mirbabaie, Milad; Brünker, F.; Wischnewski, M.; Meinert, J. (ACM Transactions on Social Computing)

Affording Technology in Crisis Situations: The Occurrence of Rumor Sense-Making Processes - Mirbabaie, Milad; Stieglitz, Stefan; Amojo, Ireti (Journal of Database Management)

Understanding Collaboration with Virtual Assistants – The Role of Social Identity and Extended Self - Mirbabaie, Milad; Stieglitz, S.; Brünker, F.; Hofeditz, L.; Ross, B.; Frick, N.R.J. (Business & Information Systems Engineering)

Digital Nudging in Social Media Disaster Communication - Mirbabaie, Milad; Ehnis, C.; Stieglitz, S.; Bunker, D.; Rose, T. (Information Systems Frontiers)

Dynamics of Convergence Behaviour in Social Media Crisis Communication – A Complexity Perspective on Peoples’ Behaviour - Mirbabaie, Milad; Stieglitz, S.; Brünker, F. (Information Technology & People)

Ethical Management of Artificial Intelligence - Brendel, A.B.; Mirbabaie, Milad; Lembcke, T.B.; Hofeditz, L. (Sustainability)

Driving Digital Transformation During a Pandemic: Study of Virtual Collaboration in a German Hospital - Mirbabaie, Milad; Stieglitz, S.; Frick, N.R.J.; Möllmann, H.L. (Journal of Medical Internet Research Medical Informatics)

Digital Facilitation Assistance for Collaborative, Creative Design Processes - Bittner, E.; Mirbabaie, Milad; Morana, S. (54th Hawaii International Conference System Sciences)

Is Making Mistakes Human? On the Perception of Typing Errors in Chatbot Communication - Bührke, J.; Brendel, A.B.; Lichtenberg, S.; Greve, M.; Mirbabaie, Milad (54th Hawaii International Conference System Sciences)

Cutting through the Jungle: Disambiguating Model-based Traceability Terminology (Extended Abstract) - Holtmann, Jörg; Steghöfer, Jan-Phillipp; Rath, Michael; Schmelter, David (Software Engineering 2021)

Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models - Berkemeier, Manuel Bastian; Peitz, Sebastian (Mathematical and Computational Applications)
We present a flexible trust region descend algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective function that is computationally expensive. The method is derivative-free in the sense that neither need derivative information be available for the expensive objectives nor are gradients approximated using repeated function evaluations as is the case in finite-difference methods. Instead, a multiobjective trust region approach is used that works similarly to its well-known scalar pendants. Local surrogate models constructed from evaluation data of the true objective functions are employed to compute possible descent directions. In contrast to existing multiobjective trust region algorithms, these surrogates are not polynomial but carefully constructed radial basis function networks. This has the important advantage that the number of data points scales linearly with the parameter space dimension. The local models qualify as fully linear and the corresponding general scalar framework is adapted for problems with multiple objectives. Convergence to Pareto critical points is proven and numerical examples illustrate our findings.

Konzeption und Inbetriebnahme eines Versuchsstandes zur Gravidestillation - Wende, Marc; Kenig, Eugeny (Jahrestreffen der ProcessNet-Fachgruppen Fluidverfahrenstechnik und Wärme- und Stoffübertragung)

Scanning transmission helium ion microscopy on carbon nanomembranes - Emmrich, Daniel; Wolff, Annalena; Meyerbröker, Nikolaus; Lindner, Jörg; Beyer, André; Gölzhäuser, Armin (Beilstein Journal of Nanotechnology)
A dark-field scanning transmission ion microscopy detector was designed for the helium ion microscope. The detection principle is based on a secondary electron conversion holder with an exchangeable aperture strip allowing its acceptance angle to be tuned from 3 to 98 mrad. The contrast mechanism and performance were investigated using freestanding nanometer-thin carbon membranes. The results demonstrate that the detector can be optimized either for most efficient signal collection or for maximum image contrast. The designed setup allows for the imaging of thin low-density materials that otherwise provide little signal or contrast and for a clear end-point detection in the fabrication of nanopores. In addition, the detector is able to determine the thickness of membranes with sub-nanometer precision by quantitatively evaluating the image signal and comparing the results with Monte Carlo simulations. The thickness determined by the dark-field transmission detector is compared to X-ray photoelectron spectroscopy and energy-filtered transmission electron microscopy measurements.

Modellierung eines Latentwärmespeichersystems in Form einer ungeordneten Schüttung makro-verkapselter PCM-Elemente - Grabo, Matti; Kenig, Eugeny

An OCR Pipeline and Semantic Text Analysis for Comics - Hartel, Rita; Dunst, Alexander (MANPU 2020: The 4th International Workshop on coMics ANalysis, Processing and Understanding@Pattern Recognition. ICPR International Workshops and Challenges)

Analyse des Versagensverhaltens geklebter Stahl Verbindungen beim werkstoffschonenden Entfügen in der Karosserieinstandsetzung - Chudalla, Nick; Meschut, Gerson; Bartley, Aurélie; Wibbeke, Tim Michael (21. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

Development of a submodel technique for FFT-based solvers in micromechanical analysis - Tinkloh, Steffen Rainer; Wu, Tao; Tröster, Thomas; Niendorf, Thomas
Keywords: Micromechanics; Fast Fourier Transform (FFT); Reduced Order Modelling; Homogenization

Maneuvering through the stormy seas of digital transformation: the impact of empowering leadership on the AI readiness of enterprises - Frick, Nicholas R. J.; Mirbabaie, Milad; Stieglitz, Stefan; Salomon, Jana (Journal of Decision Systems)

Nonlinear Bicolor Holography Using Plasmonic Metasurfaces - Frese, Daniel; Wei, Qunshuo; Wang, Yongtian; Cinchetti, Mirko; Huang, Lingling; Zentgraf, Thomas (ACS Photonics)

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks - Afifi, Haitham; Ramaswamy, Arunselvan; Karl, Holger (2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC'21 - IoTSN Symposium))
In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network's performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions. We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.

A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks - Afifi, Haitham; Ramaswamy, Arunselvan; Karl, Holger (2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (CCNC 2021))
Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1%. Meanwhile, the RL solution is better than the baseline with improvements up to 23%, when using the trade-off objective.
Keywords: reinforcement learning; wireless sensor networks; resource allocation; acoustic sensor networks

The Fate of the App: Economic Implications of Updating under Reputation Resetting - Gutt, Dominik; Neumann, Jürgen; Jabr, Wael; Kundisch, Dennis

“What’s the Point of the Task?” Exploring the Influence of Task Meaning on Creativity in Crowdsourcing - Görzen, Thomas (International Journal of Innovation Management)

Preference-based Online Learning with Dueling Bandits: A Survey - Bengs, Viktor; Busa-Fekete, Róbert; El Mesaoudi-Paul, Adil; Hüllermeier, Eyke (Journal of Machine Learning Research)

Distributed Online Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Qarawlus, Haydar; Karl, Holger (IEEE International Conference on Distributed Computing Systems (ICDCS))
Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).
Keywords: network management; service management; coordination; reinforcement learning; distributed

On the forward simulation and cost functions for the ultrasonic material characterization of polymers - Itner, Dominik; Gravenkamp, Hauke; Dreiling, Dmitrij; Feldmann, Nadine; Henning, Bernd

Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung - Gottschalk, Sebastian; Yigitbas, Enes
Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.

Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance - Tornede, Tanja; Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Proceedings of the Genetic and Evolutionary Computation Conference)

Stofftransportmessung und -visualisierung am ruhenden und bewegten Einzeltropfen - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Jörg

Numerische Untersuchung der Marangonikonvektion in Flüssig-Flüssig-Systemen: Von der Tropfenbildung bis zur Tropfeninteraktion - Wecker, Christian; Schulz, Andreas; Heine, Jens; Bart, Hans-Jörg; Kenig, Eugeny

Numerische Untersuchungen zu Fluiddynamik und Stofftransport binärer Tropfeninteraktion unter Berücksichtigung von Marangonikonvektion - Wecker, Christian; Hoppe, Anna; Schulz, Andreas; Heine, Jens; Bart, Hans-Jörg; Kenig, Eugeny

Mehrkomponenten-Stofftransport an bewegten Phasengrenzflächen unter Berücksichtigung von Diffusionskreuzeffekten - Schulz, Andreas; Wecker, Christian; Kenig, Eugeny

Representation and stability of internal dynamics - Lanza, Lukas Johannes (PAMM)

Using Architectural Runtime Verification for Offline Data Analysis - Stockmann, Lars; Laux, Sven; Bodden, Eric (Journal of Automotive Software Engineering)

A Systematic Hardening of Java's Information Hiding - Holzinger, Philipp; Bodden, Eric (International Symposium on Advanced Security on Software and Systems (ASSS))

Dealing with Variability in API Misuse Specification - Bonifacio, Rodrigo; Krüger, Stefan; Narasimhan, Krishna; Bodden, Eric; Mezini, Mira (European Conference on Object-Oriented Programming (ECOOP))

Distributed merchandise management system - Göllner, Thomas; Schwarz, Jan-Hendrik; Gottschalk, Sebastian; Sauer, Stefan
The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105 a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).

LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021)

Optical secret sharing with cascaded metasurface holography - Georgi, Philip; Wei, Qunshuo; Sain, Basudeb; Schlickriede, Christian; Wang, Yongtian; Huang, Lingling; Zentgraf, Thomas (Science Advances)
Secret sharing is a well-established cryptographic primitive for storing highly sensitive information like encryption keys for encoded data. It describes the problem of splitting a secret into different shares, without revealing any information to its shareholders. Here, we demonstrate an all-optical solution for secret sharing based on metasurface holography. In our concept, metasurface holograms are used as spatially separable shares that carry encrypted messages in the form of holographic images. Two of these shares can be recombined by bringing them close together. Light passing through this stack of metasurfaces accumulates the phase shift of both holograms and optically reconstructs the secret with high fidelity. In addition, the hologram generated by each single metasurface can uniquely identify its shareholder. Furthermore, we demonstrate that the inherent translational alignment sensitivity between two stacked metasurface holograms can be used for spatial multiplexing, which can be further extended to realize optical rulers.

Innovative Measurement Of Stress Superposed Steel Strip For Straightening Machines - Bader, Fabian; Bathelt, Lukas; Djakow, Eugen; Homberg, Werner; Henke, Christian; Trächtler, Ansgar (ESAFORM 2021)
Higher quality requirements by customers demand higher precision and accuracy from manufacturing processes. Application oriented preparation of semi-finished materials is key for subsequent forming operations, therefore, straightening machines are employed. Straightening strengthens the material by increasing plastic deformation by means of strain hardening, resulting in undesirable reduction in formability when processing high strength materials, in particular. Conventional roll-type straightening machines process either bars or strips. This is achieved upon passing material between rolls arranged in two staggered rows. However, conventional straightening processes do not adapt to the local varying distortion of coiled strips. Innovative, self-correcting process control techniques, which adapt to the initial geometric characteristics of the strip, present a promising approach to fix this issue through optimization of the leveling process. Here, an innovative strategy to improve straightening of high strength steel materials (1.4310) is presented. This implements optimized leveling, adding minimal plastic deformation and, thus, strain hardening. To operate an intelligent straightening machine, a reliable online measurement of the surface defects is fundamentally essential. The MagnaTest, which is developed for material testing, is made feasible for such purposes after calibrating for curvature measurement. Preliminary results are promising in regards to measuring the curvature online, so that the following straightening process can be close loop controlled. The bending measurement is linked to open/closed loop control, therefore providing an optimal straightening result in regards to formability, leveling, and reduced strain hardening.

Instance weighting through data imprecisiation - Lienen, Julian; Hüllermeier, Eyke (International Journal of Approximate Reasoning)

From Label Smoothing to Label Relaxation - Lienen, Julian; Hüllermeier, Eyke (Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI)

Situation-specific Business Model Development Methods for Mobile App Developers - Gottschalk, Sebastian; Yigitbas, Enes; Nowosad, Alexander; Engels, Gregor (Enterprise, Business-Process and Information Systems Modeling)
The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, situational method engineering is used for software projects to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the knowledge of the domain expert and stores it in the method base as elements, building blocks, and patterns. The expert knowledge is derived from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform's business model development method.
Keywords: Business Model Development; Situational Method Engineering; Mobile App; Business Model Development Tools

Determination of local fluid dynamic parameters in structured packings through X-ray tomography: overcoming image resolution restrictions - Bolenz, Lukas; Fischer, Florian; Toye, Dominique; Kenig, Eugeny (Chemical Engineering Science)

Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks - Kersting, Joschka; Geierhos, Michaela (Natural Language Processing in Artificial Intelligence -- NLPinAI 2020)
This chapter concentrates on aspect-based sentiment analysis, a form of opinion mining where algorithms detect sentiments expressed about features of products, services, etc. We especially focus on novel approaches for aspect phrase extraction and classification trained on feature-rich datasets. Here, we present two new datasets, which we gathered from the linguistically rich domain of physician reviews, as other investigations have mainly concentrated on commercial reviews and social media reviews so far. To give readers a better understanding of the underlying datasets, we describe the annotation process and inter-annotator agreement in detail. In our research, we automatically assess implicit mentions or indications of specific aspects. To do this, we propose and utilize neural network models that perform the here-defined aspect phrase extraction and classification task, achieving F1-score values of about 80% and accuracy values of more than 90%. As we apply our models to a comparatively complex domain, we obtain promising results.

Wiedereinführung der Vermögensteuer – eine ökonomische Analyse - Maiterth, Ralf; Sureth-Sloane, Caren (Steuer und Wirtschaft)
Die Diskussion über die adäquate Besteuerung von „Reichen“ bzw. „Superreichen“ hat durch die Schulden, die im Zusammenhang mit der Coronakrise zu finanzieren sind, neuen Schwung bekommen. Als ein Instrument der adäquaten „Reichenbesteuerung“ gilt eine jährliche Vermögensteuer. Den fiskalischen und verteilungspolitischen Argumenten zugunsten einer Vermögensbesteuerung stehen negative ökonomische Wirkungen entgegen. In diesem Beitrag soll zur Versachlichung der Debatte ein genauerer Blick auf die zu erwartenden Belastungswirkungen einer Vermögensteuer geworfen werden. Dabei wird deutlich, dass die (Wieder-)Einführung der Vermögensteuer nicht nur ungelöste Probleme der Bewertung von Sachvermögen mit sich bringt, sondern die Steuerbelastungswirkungen in der jetzigen Niedrigzinsphase zu erheblichen Nebenwirkungen führen. Berücksichtigt man eine Inflationsrate von 2 %, liegt die zur unternehmerischen Realkapitalerhaltung erforderliche Mindestrendite vor Steuern selbst bei einer lediglich 1 %igen Vermögensteuer mit 5,63 % weit über der Inflationsrate. Dies verdeutlicht, dass eine Vermögensteuer in Zeiten niedriger Renditen für Unternehmen eine zukunftssichernde Eigenkapitalerhaltung oder gar -bildung und zugleich wichtige Investitionen erheblich erschwert. Dies gilt auch für private Anlagen in Aktien und Immobilien. Darüber hinaus ist die Vermögensteuer auch in Verlustperioden zu bezahlen, so dass diese Steuer krisenverschärfend wirkt. Auch dies dürfte erhebliche negative Investitionswirkungen nach sich ziehen. Zudem ist damit zu rechnen, dass die Vermögensteuer zumindest in Teilen überwälzt wird, so dass letztlich auch Verbraucher, Arbeitnehmer und Mieter diese tragen werden. Diese und weitere Nebenwirkungen einer Vermögensteuer kommen in der Vermögensteuerdebatte oftmals zu kurz.

CFD-basierte Untersuchung von Zweiphasenströmungen in strukturierten Packungen - Bertling, René; Hack, Mathias; Ausner, Ilja; Horschitz, Bernhardt; Kenig, Eugeny

Engineering in Deutschland – Status quo in Wirtschaft und Wissenschaft. Ein Beitrag zum Advanced Systems Engineering - Dumitrescu, Roman; Albers, Albert; Gausemeier, Jürgen; Riedel, Oliver; Stark, Rainer

Computing Literary Surplus Value: Alan Moore and the Density of the Comic Book as Graphic Novel - Dunst, Alexander; Hartel, Rita (Anglia)
Abstract The term graphic novel has increasingly functioned as a catalyst for understanding comic books as an emergent literary genre. This article focuses on one specific element within this historical process: the claim, made by artists such as Alan Moore, that graphic novels are characterized by greater formal complexity, or density, than serial comics. These claims are evaluated by combining computational text and image recognition of a corpus of 131 graphic narratives with sociological metadata on production and circulation. The results show that Moore’s own book-length comics, in particular Watchmen and V for Vendetta , rank among the densest graphic narratives in the sample in both their visual and textual content. Graphic memoirs, in contrast, only show an increase in textual complexity. With Pierre Bourdieu, the article understands complexity as a social and aesthetic strategy that aims at increasing the cultural capital of comics creators. At the same time, the article contextualizes computational results against the background of a changing marketplace for comics, in particular the decline of serial comics, the shift towards digital printing, and increased access to book distribution. This analysis shows that graphic narratives pursue both literary and popular aesthetic strategies, challenging Bourdieu’s account of a clear opposition between profit and prestige in cultural production.

Two-period duopolies with forward markets - Cox, Caleb; Karam, Arzé; Pelster, Matthias (Review of Industrial Organization)
We experimentally consider a dynamic multi-period Cournot duopoly with a simultaneous option to manage financial risk and a real option to delay supply. The first option allows players to manage risk before uncertainty is realized, while the second allows managing risk after realization. In our setting, firms face a strategic dilemma: They must weigh the advantages of dealing with risk exposure against the disadvantages of higher competition. In theory, firms make strategic use of the hedging component, enhancing competition. Our experimental results support this theory, suggesting that hedging increases competition and negates duopoly profits even in a simultaneous setting.