@article{50356,
  author       = {{Kundisch, Heike}},
  journal      = {{Magazin des Forum Bildung Digitalisierung}},
  title        = {{{Wie wir die mittlere Führungsebene als Transformationsbegleiter:innen an Schulen stärken können}}},
  year         = {{2023}},
}

@misc{50370,
  author       = {{Eickelmann, Birgit}},
  title        = {{{KI in der Schule und Bildungsgerechtigkeit. Blog-Beitrag im fiete.ai-Blog.}}},
  year         = {{2023}},
}

@article{50407,
  abstract     = {{In the last decade, conductive domain walls (CDWs) in single crystals of the uniaxial model ferroelectric lithium niobate (LiNbO3; LNO) have been shown to reach resistances more than 10 orders of magnitude lower than the resistance of the surrounding bulk, with charge carriers being firmly confined to sheets with a width of a few nanometers. LNO is thus currently witnessing increased attention because of its potential in the design of room-temperature nanoelectronic circuits and devices based on such CDWs. In this context, the reliable determination of the fundamental transport parameters of LNO CDWs, in particular the 2D charge carrier density n2D and the Hall mobility μH of the majority carriers, is of great interest. In this contribution, we present and apply a robust and easy-to-prepare Hall-effect measurement setup by adapting the standard four-probe van der Pauw method to contact a single, hexagonally shaped domain wall that fully penetrates the 200-μm-thick LNO bulk single crystal. We then determine n2D and μH for a set of external magnetic fields B and prove the expected cosinelike angular dependence of the Hall voltage. Lastly, we present photoinduced-Hall-effect measurements of one and the same DW, by determining the impact of super-band-gap illumination on n2D.}},
  author       = {{Beccard, Henrik and Beyreuther, Elke and Kirbus, Benjamin and Seddon, Samuel D. and Rüsing, Michael and Eng, Lukas M.}},
  issn         = {{2331-7019}},
  journal      = {{Physical Review Applied}},
  keywords     = {{General Physics and Astronomy}},
  number       = {{6}},
  publisher    = {{American Physical Society (APS)}},
  title        = {{{Hall mobilities and sheet carrier densities in a single LiNbO3 conductive ferroelectric domain wall}}},
  doi          = {{10.1103/physrevapplied.20.064043}},
  volume       = {{20}},
  year         = {{2023}},
}

@misc{50403,
  author       = {{Troike, Manuel}},
  booktitle    = {{SAMPLES}},
  issn         = {{1216-8001}},
  publisher    = {{Gesellschaft für Popularmusikforschung}},
  title        = {{{Rezension zu Dagmar Abfalter und Rosa Reitsamer (Hg.) (2022) »Music as Labour – Inequalities and Activism in the Past and Present.«}}},
  volume       = {{21}},
  year         = {{2023}},
}

@inproceedings{37312,
  abstract     = {{Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.}},
  author       = {{Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}},
  booktitle    = {{Hawaii International Conference on System Sciences}},
  keywords     = {{Algorithm aversion, Time series, Decision making, Advice taking, Forecasting}},
  title        = {{{Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}}},
  year         = {{2023}},
}

@inproceedings{50121,
  abstract     = {{Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice.}},
  author       = {{Leffrang, Dirk}},
  booktitle    = {{International Conference on Information Systems}},
  keywords     = {{Artificial Intelligence, Algorithm Appreciation, Framing, Advice-taking, Expertise}},
  location     = {{Hyderabad, India}},
  number       = {{10}},
  title        = {{{AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization}}},
  year         = {{2023}},
}

@inproceedings{50118,
  abstract     = {{Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior.}},
  author       = {{Leffrang, Dirk}},
  booktitle    = {{Wirtschaftsinformatik Conference}},
  keywords     = {{Algorithm aversion, Data, Decision-making, Advice-taking, Human-Computer Interaction}},
  location     = {{Paderborn}},
  number       = {{19}},
  title        = {{{The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization}}},
  year         = {{2023}},
}

@article{50429,
  author       = {{Schroeder, René and Franzen, Katja and Reh, Anne}},
  issn         = {{2699-2477}},
  journal      = {{QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte}},
  number       = {{1}},
  pages        = {{17}},
  title        = {{{Diagnostische Potentiale von Lernaufgaben im Sachunterricht fach- und entwicklungsbezogen analysieren und nutzbar machen}}},
  doi          = {{https://doi.org/10.21248/qfi.100}},
  volume       = {{5}},
  year         = {{2023}},
}

@inproceedings{50430,
  author       = {{Görel, Gamze and Franzen, Katja and Hellmich, Frank}},
  location     = {{Universität Potsdam, Potsdam}},
  title        = {{{Quellen der Selbstwirksamkeitsüberzeugungen von Lehramtsstudierenden im Zusammenhang mit der Gestaltung von inklusivem Unterricht}}},
  year         = {{2023}},
}

@article{50428,
  author       = {{Görel, Gamze and Franzen, Katja and Hellmich, Frank}},
  issn         = {{1354-0602, 1470-1278}},
  journal      = {{Teachers and Teaching}},
  pages        = {{1--14}},
  title        = {{{Primary school teachers' perspectives on the quality of inclusive education}}},
  doi          = {{10.1080/13540602.2023.2252347}},
  year         = {{2023}},
}

@inproceedings{50431,
  abstract     = {{Recommender systems now span the entire customer journey. Amid the multitude of diversified experi- ences, immersing in cultural events has become a key aspect of tourism. Cultural events, however, suffer from fleeting lifecycles, evade exact replication, and invariably lie in the future. In addition, their low standardization makes harnessing historical data regarding event content or past patron evaluations intricate. The distinctive traits of events thereby compound the challenge of the cold-start dilemma in event recommenders. Content-based recommendations stand as a viable avenue to alleviate this issue, functioning even in scenarios where item-user information is scarce. Still, the effectiveness of content- based recommendations often hinges on the quality of the data representation they build upon. In this study, we explore an array of cutting-edge uni- and multimodal vision and language foundation models (VL-FMs) for this purpose. Next, we derive content-based recommendations through a straightforward clustering approach that groups akin events together, and evaluate the efficacy of the models through a series of online user experiments across three dimensions: similarity-based evaluation, comparison-based evaluation, and clustering assignment evaluation. Our experiments generated four major findings. First, we found that all VL-FMs consistently outperformed a naive baseline of recommending randomly drawn events. Second, unimodal text-based embeddings were surprisingly on par or in some cases even superior to multimodal embeddings. Third, multimodal embeddings yielded arguably more fine-grained and diverse clusters in comparison to their unimodal counterparts. Finally, we could confirm that cross event interest is indeed reliant on the perceived similarity of events, resonating with the notion of similarity in content-based recommendations. All in all, we believe that leveraging the potential of contemporary FMs for content-based event recommendations would help address the cold-start problem and propel this field of research forward in new and exciting ways.}},
  author       = {{Halimeh, Haya and Freese, Florian and Müller, Oliver}},
  booktitle    = {{Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference on Recommender Systems}},
  title        = {{{Event Recommendations through the Lens of Vision and Language Foundation Models}}},
  year         = {{2023}},
}

@inproceedings{45270,
  abstract     = {{Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.}},
  author       = {{Halimeh, Haya and Caron, Matthew and Müller, Oliver}},
  booktitle    = {{Hawaii International Conference on System Sciences}},
  keywords     = {{Social Media and Healthcare Technology, early depression detection, liwc, mental health, transfer learning, transformer architectures}},
  title        = {{{Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}}},
  year         = {{2023}},
}

@inproceedings{50437,
  abstract     = {{The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs.}},
  author       = {{Reimann, Raphael and Caron, Matthew}},
  booktitle    = {{Wirtschaftsinformatik}},
  location     = {{Paderborn, Germany}},
  title        = {{{Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach}}},
  year         = {{2023}},
}

@phdthesis{50448,
  abstract     = {{Hybridstrukturen bieten bei Anwendungen mit Biegebeanspruchung ein großes Leichtbaupotenzial, erfordern jedoch komplexe und zum Teil mehrschrittige Fertigungsverfahren. In dieser Arbeit wird ein Verfahren entwickelt, das auf Basis des Fließpressprozesses biegebelastbare Hybridbalken in einem Schritt herstellt. Dazu wird ein Versuchsträger entwickelt, der die Komplexität von Realbauteilen abbildet und für zerstörende sowie zerstörungsfreie Charakterisierungsmethoden geeignet ist. Der Versuchsträger besteht aus einer funktionalisierten Kernstruktur aus Glasfasermattenverstärktem Polypropylen und äußeren Metallgurten aus Stahl- und Aluminiumlegierungen, die mit einem Haftvermittlerfilm versehen sind. Anhand des Versuchsträgers wird ein Fließpresswerkzeug und eine instrumentierte Fertigungsanlage entwickelt, mit der die Hybridstrukturen prototypisch hergestellt werden. Zur Prozessoptimierung wird die Verbindung mechanisch und optisch auf Probenebene analysiert. Weiterhin erfolgen Bauteiluntersuchungen anhand von Dreipunktbiegetests, mit denen das strukturelle Verhalten der Hybridbalken charakterisiert wird. Es wird festgestellt, dass sich mit dem einstufigen Fließpressverfahren sehr gute Verbundfestigkeiten erzielen lassen. Die Temperatur- und Druckführung weisen dabei einen großen Einfluss auf das Ergebnis auf. Anhand der Bauteiluntersuchungen wird bestätigt, dass mit dem entwickelten Verfahren Hybridbalken in nur einem Schritt gefertigt werden können, die vergleichbare mechanische Eigenschaften zu Hybridstrukturkonzepten aus mehrschrittigen Fertigungsverfahren aufweisen.}},
  author       = {{Stallmeister, Tim}},
  title        = {{{Verfahrensentwicklung zur einstufigen Herstellung von biegebelastbaren Hybridstrukturen im Fließpressprozess}}},
  doi          = {{10.17619/UNIPB/1-1670}},
  year         = {{2023}},
}

@misc{48335,
  author       = {{Knorr, Lukas and Jungeilges, André and Pfeifer, Florian and Burmeister, Sascha Christian and Meschede, Henning}},
  publisher    = {{4. Aachener Ofenbau- und Thermoprozess-Kolloquium}},
  title        = {{{Regenerative Energien für einen effizienten Betrieb von Presshärtelinien}}},
  year         = {{2023}},
}

@misc{49106,
  author       = {{Jungeilges, André}},
  publisher    = {{Car Body Parts - Automotive Circle Conference}},
  title        = {{{Possibilities of Sustainable Heating Methods for Press Hardening Processes}}},
  year         = {{2023}},
}

@inproceedings{37058,
  abstract     = {{Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality.
}},
  author       = {{Brennig, Katharina and Müller, Oliver}},
  booktitle    = {{Hawaii International Conference on System Sciences}},
  keywords     = {{Digital Services, Line of Visibility, Process Transparency, Customer Preferences, Conjoint Analysis}},
  location     = {{Lāhainā}},
  title        = {{{More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency}}},
  year         = {{2023}},
}

@article{50458,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Consider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the server cloud scheduling problem, in which the jobs have to be processed either on a single local machine or on one of infinitely many cloud machines. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server and the other in the cloud. The server processes jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS for the makespan objective for graphs with a constant source and sink dividing cut and strong hardness for the case with unit processing times and delays.</jats:p>}},
  author       = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}},
  issn         = {{0178-4617}},
  journal      = {{Algorithmica}},
  keywords     = {{Applied Mathematics, Computer Science Applications, General Computer Science}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Server Cloud Scheduling}}},
  doi          = {{10.1007/s00453-023-01189-x}},
  year         = {{2023}},
}

@inproceedings{50460,
  author       = {{Deppert, Max A. and Jansen, Klaus and Maack, Marten and Pukrop, Simon and Rau, Malin}},
  booktitle    = {{2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)}},
  publisher    = {{IEEE}},
  title        = {{{Scheduling with Many Shared Resources}}},
  doi          = {{10.1109/ipdps54959.2023.00049}},
  year         = {{2023}},
}

@inbook{50450,
  author       = {{Brennig, Katharina and Benkert, Kay and Löhr, Bernd and Müller, Oliver}},
  booktitle    = {{Business Process Management Workshops}},
  isbn         = {{9783031509735}},
  issn         = {{1865-1348}},
  title        = {{{Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?}}},
  doi          = {{10.1007/978-3-031-50974-2_33}},
  year         = {{2023}},
}