@inbook{62228, abstract = {{This chapter highlights the intricate nature of data and their profound social implications. It examines the acts of rendering data visible and the inherent power dynamics and imbalances that accompany such processes. Our dialogue unfolds in three interconnected parts, each focusing on the intersection of in/visibility and power. Part 1 attends to the challenges of producing knowledge about and with data, emphasizing the relativity, fluidity, and instability inherent in data. It explores frameworks that uncover the often invisible infrastructures of algorithms, rendering visible the actors, technologies, and divergent values involved in data manipulation. Part 2 presents empirical case studies that analyse the consequences of data visibility while contemplating the methodological opportunities and challenges of foregrounding the embedded values and norms within data. Part 3 discusses tool-based interventions aimed at bringing alternative data framings and narratives to the fore. It examines the complexities of tracing data across various contexts and the value, utility, and obstacles associated with creating visual representations of data and their flows. By critically engaging with the complexities of data in/visibility, this chapter challenges existing gatekeepers and fosters a deeper understanding of the multifaceted nature of data and its socio-political ramifications.}}, author = {{Fahimi, Miriam and Falk, Petter and Gray, Jonathan W. Y. and Jarke, Juliane and Kinder-Kurlanda, Katharina and Light, Evan and McGeachey, Ellouise and Perea, Itzelle Medina and Poechhacker, Nikolaus and Poirier, Lindsay and Röhle, Theo and Sharon, Tamar and Stevens, Marthe and Gastel, Bernard van and White, Quinn and Zakharova, Irina}}, booktitle = {{Dialogues in Data Power}}, isbn = {{978-1-5292-3832-7}}, pages = {{52–79}}, publisher = {{Bristol University Press}}, title = {{{In/visibilities in Data Studies: Methods, Tools, and Interventions}}}, year = {{2024}}, } @inbook{62230, abstract = {{Algorithms have risen to become one, if not the central technology for producing, circulating, and evaluating knowledge in multiple societal arenas. In this book, scholars from the social sciences, humanities, and computer science argue that this shift has, and will continue to have, profound implications for how knowledge is produced and what and whose knowledge is valued and deemed valid. To attend to this fundamental change, the authors propose the concept of algorithmic regimes and demonstrate how they transform the epistemological, methodological, and political foundations of knowledge production, sensemaking, and decision-making in contemporary societies. Across sixteen chapters, the volume offers a diverse collection of contributions along three perspectives on algorithmic regimes: the methods necessary to research and design algorithmic regimes, the ways in which algorithmic regimes reconfigure sociotechnical interactions, and the politics engrained in algorithmic regimes.}}, author = {{Kinder-Kurlanda, Katharina and Fahimi, Miriam}}, booktitle = {{Algorithmic Regimes. Methods, Interactions, and Politics.}}, editor = {{Jarke, Juliane and Prietl, Bianca and Egbert, Simon and Boeva, Yana and Heuer, Hendrik and Arnold, Maike}}, isbn = {{978-94-6372-848-5}}, pages = {{309–330}}, publisher = {{Amsterdam University Press}}, title = {{{Making Algorithms Fair: Ethnographic Insights from Machine Learning Interventions}}}, year = {{2024}}, } @misc{52671, author = {{Dumitrescu, Roman and Hölzle, K.}}, isbn = {{978-3-947647-32-3}}, title = {{{Vorausschau und Technologieplanung. 17. Symposium für Vorausschau und Technologieplanung }}}, volume = {{Band 413}}, year = {{2023}}, } @book{45863, abstract = {{In the proposal for our CRC in 2011, we formulated a vision of markets for IT services that describes an approach to the provision of such services that was novel at that time and, to a large extent, remains so today: „Our vision of on-the-fly computing is that of IT services individually and automatically configured and brought to execution from flexibly combinable services traded on markets. At the same time, we aim at organizing markets whose participants maintain a lively market of services through appropriate entrepreneurial actions.“ Over the last 12 years, we have developed methods and techniques to address problems critical to the convenient, efficient, and secure use of on-the-fly computing. Among other things, we have made the description of services more convenient by allowing natural language input, increased the quality of configured services through (natural language) interaction and more efficient configuration processes and analysis procedures, made the quality of (the products of) providers in the marketplace transparent through reputation systems, and increased the resource efficiency of execution through reconfigurable heterogeneous computing nodes and an integrated treatment of service description and configuration. We have also developed network infrastructures that have a high degree of adaptivity, scalability, efficiency, and reliability, and provide cryptographic guarantees of anonymity and security for market participants and their products and services. To demonstrate the pervasiveness of the OTF computing approach, we have implemented a proof-of-concept for OTF computing that can run typical scenarios of an OTF market. We illustrated the approach using a cutting-edge application scenario – automated machine learning (AutoML). Finally, we have been pushing our work for the perpetuation of On-The-Fly Computing beyond the SFB and sharing the expertise gained in the SFB in events with industry partners as well as transfer projects. This work required a broad spectrum of expertise. Computer scientists and economists with research interests such as computer networks and distributed algorithms, security and cryptography, software engineering and verification, configuration and machine learning, computer engineering and HPC, microeconomics and game theory, business informatics and management have successfully collaborated here.}}, author = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{247}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}}, doi = {{10.17619/UNIPB/1-1797}}, volume = {{412}}, year = {{2023}}, } @phdthesis{42344, author = {{Rüddenklau, Nico}}, title = {{{Hardware-in-the-Loop-Simulation von HD-Scheinwerfer-Steuergeräten zur Entwicklung von Lichtfunktionen in virtuellen Nachtfahrten}}}, year = {{2023}}, } @phdthesis{42345, author = {{Biemelt, Patrick}}, title = {{{Entwurf und Analyse modellprädiktiver Regelungsansätze zur Steigerung des Immersionsempfindens in interaktiven Fahrsimulationen}}}, year = {{2023}}, } @inproceedings{62231, abstract = {{Explainable artificial intelligence (XAI) is a rapidly growing research field that has received a lot of attention during the last few years. An important goal of the field is to use its methods to detect (social) bias and discrimination. Despite these positive intentions, aspects of XAI can be in conflict with feminist approaches and values. Therefore, our conceptual contribution brings forward both a careful assessment of current XAI methods, as well as visions for carefully doing XAI from a feminist perspective. We conclude with a discussion on the possibilities for caring XAI, and the challenges that might lie along the way.}}, author = {{State, Laura and Fahimi, Miriam}}, booktitle = {{Proceedings of the 2nd European Workshop on Algorithmic Fairness}}, publisher = {{CEUR Workshop Proceedings}}, title = {{{Careful Explanations: A Feminist Perspective on XAI}}}, year = {{2023}}, } @unpublished{30868, abstract = {{Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.}}, author = {{Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}}, booktitle = {{arXiv:2202.01651}}, title = {{{A Survey of Methods for Automated Algorithm Configuration}}}, year = {{2022}}, } @misc{33033, author = {{Fehring, Lukas}}, title = {{{Combined Ranking and Regression Trees for Algorithm Selection}}}, year = {{2022}}, } @unpublished{30867, abstract = {{In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.}}, author = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}}, publisher = {{AAAI}}, title = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}}, year = {{2022}}, } @unpublished{30865, abstract = {{The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.}}, author = {{Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Machine Learning}}, title = {{{Algorithm Selection on a Meta Level}}}, year = {{2022}}, } @phdthesis{52669, author = {{Javed, A. R.}}, isbn = {{978-3-947647-26-2}}, title = {{{Mixed-Signal Baseband Circuit Design for High Data Rate Wireless Communication in Bulk CMOS and SiGe BiCMOS Technologies}}}, volume = {{Band 407}}, year = {{2022}}, } @phdthesis{52668, author = {{Fechtelpeter, Christian}}, isbn = {{978-3-947647-24-8}}, title = {{{Rahmenwerk zur Gestaltung des Technologietransfers in mittelständisch geprägten Innovationsclustern}}}, volume = {{Band 405}}, year = {{2022}}, } @phdthesis{34174, abstract = {{Anforderungsänderungen sind ein wesentlicher Grund für Ineffizienzen und Projektfehlschläge in der Entwicklung komplexer technischer Systeme. Proaktives Management von Anforderungsänderungen hat das Potenzial, den Umgang mit Anforderungsänderungen effizienter zu gestalten. Dafür ist ein systematischer Ansatz erforderlich, der eine ganzheitliche Bewertung und Handhabung des Änderungsrisikos im industriellen Entwicklungskontext ermöglicht. Im Rahmen dieser Dissertation wird mit der ProMaRC-Methodik ein neuartiger Ansatz für das proaktive Management von Anforderungsänderungen vorgestellt. Die Methodik wurde in enger Zusammenarbeit mit Industrieanwender:innen aus der Automobilindustrie entwickelt und anhand von fünf Fallstudien validiert. Mittels automatisierter Abhängigkeitsanalyse auf Grundlage künstlicher Intelligenz wird der Anwendungsaufwand gegenüber bestehenden Ansätzen reduziert. Die teilautomatisierte Bewertung und Handhabung der Änderungswahrscheinlichkeit und -auswirkung erfolgt anhand eines modifizierten PageRank-Algorithmus und umfasst erstmalig alle für die Risikoanalyse relevanten Einflussfaktoren. Die Validierung belegt, dass durch die ProMaRC-Methodik eine überzeugende Kombination aus praxistauglichem Anwendungsaufwand und Vollständigkeit der Analyse erzielt wird. Damit erschließt diese Dissertation das bisher kaum beachtete Forschungsfeld des proaktiven Managements von Anforderungsänderungen und fördert eine effizientere Produktentwicklung.}}, author = {{Oleff, Christian}}, publisher = {{LibreCat University}}, title = {{{Proaktives Management von Anforderungsänderungen in der Entwicklung komplexer technischer Systeme}}}, doi = {{10.17619/UNIPB/1-1600}}, volume = {{406}}, year = {{2022}}, } @phdthesis{40518, abstract = {{In dieser Arbeit wird die Entwicklung einer dezentralen Produktionssteuerung mit digitaler Repräsentation zur Anwendung in cyber-physischen Produktionssystemen (CPPS) beschrieben. Die dezentrale Produktionssteuerung geschieht hierbei selbstorganisiert, da sie die Einlastung und Umplanung von Aufträgen sowie die Koordination zwischen den einzelnen Produktionselementen selbstständig übernimmt. Dies erlaubt die automatisierte Steuerung von CPPS. Dabei spielen die Beschäftigten in dieser Betrachtung eine wesentliche Rolle. Ihre Bedürfnisse und Wünsche werden von dem Steuerungssystem berücksichtigt. Dafür wurde das Konzept der digitalen Repräsentation von Beschäftigten entwickelt. Diese Repräsentation erlaubt den individuellen Eingriff von Beschäftigten bei Entscheidungen von dem Steuerungssystem. Das automatisierte dezentrale Steuerungssystem wurde in einer Laborumgebung, die zu einem CPPS umgebaut wurde, implementiert. Darauf basierend wurde eine zweistufige Validierung durchgeführt. Ein Teil der Validierung findet durch eine reale Umsetzung in einem Labor statt, bei der ein Vergleich zu dem vorher im Labor eingesetzten zentralen Produktionssteuerungssystem gezogen wird. Die zweite Validierung erfolgt mit Hilfe einer Simulationsumgebung. Dabei konnte die Implementierung der Selbstorganisation und eine Steigerung der Liefertreue der dezentralen Produktionssteuerung gegenüber heutzutage üblicherweise eingesetzten Verfahren nachgewiesen werden.}}, author = {{Pöhler, Alexander}}, publisher = {{LibreCat University}}, title = {{{Automatisierte dezentrale Produktionssteuerung für cyber-physische Produktionssysteme mit digitaler Repräsentation der Beschäftigten}}}, doi = {{10.17619/UNIPB/1-1642}}, year = {{2022}}, } @inproceedings{34101, author = {{Gräßler, Iris and Roesmann, Daniel}}, location = {{Padeborn}}, publisher = {{LibreCat University}}, title = {{{Menschenzentrierte Montageplanung und -steuerung durch fähigkeitsorientierte Aufgabenzuordnung}}}, year = {{2022}}, } @article{36988, author = {{Thomas, Sven}}, journal = {{Technology and Language }}, number = {{4}}, title = {{{Language in the Age of Mechanical Reproduction}}}, doi = {{10.48417/technolang.2022.04.07}}, volume = {{3}}, year = {{2022}}, } @phdthesis{28371, author = {{Drewel, Marvin}}, isbn = {{978-3-947647-16-3}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zum Einstieg in die Plattformökonomie}}}, volume = {{397}}, year = {{2021}}, } @phdthesis{28372, author = {{Frank, Maximilian}}, isbn = {{978-3-947647-17-0}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zur Planung des organisationalen Wandels zum Smart Service-Anbieter}}}, volume = {{398}}, year = {{2021}}, } @phdthesis{28374, author = {{Bretz, Lukas}}, isbn = {{978-3-947647-20-0}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Rahmenwerk zur Planung und Einführung von Systems Engineering und Model-Based Systems Engineering}}}, volume = {{401}}, year = {{2021}}, }