TY - GEN A1 - Weber, Karsten T1 - Künstliche Intelligenz als vielschichtige Herausforderung Y1 - 2025 ER - TY - RPRT A1 - Bhardwaj, Prakhar A1 - Hauer, Franziska A1 - Mücke, Vanessa A1 - Dotter, Caroline A1 - Haug, Sonja A1 - Weikl, Simone A1 - Weber, Karsten T1 - Recording and understanding multi- and intermodal mobility - a review N2 - The transition to sustainable mobility requires detailed knowledge of actual mobility behavior. Recording mobility behavior through conventional, aggregated, or retrospective survey methods (e.g., counting stations, travel diaries) may yield incomplete insights. In addition, these methods are often expensive, prone to error, or not scalable. The aim of this literature review is to provide a systematic overview of the current state of the literature on recording and analysis of individual mobility data and, based on this, to identify possible research gaps and potential. The exploratory literature review includes social science surveys on multi- and intermodal mobility behavior, as well as an overview of possible ways to analyze travel data. The focus of the latter is on identifying relevant data sources and methodological approaches for segmentation, identification of transport mode, and trip purpose determination. Results from recent studies show that sensor-based data collection alone is not sufficient to fully and accurately map mobility behavior. One possible solution is a “best-of-both-worlds” approach that combines passive, automatic mobility data collection via smartphone sensors with targeted user corrections and additions (“human-in-the-loop”) within an app. The literature review shows that this approach yields the best data quality. Combining mobility surveys with passive collection of mobility data provides a comprehensive, accurate, and complete overview of mobility behavior. KW - multimodal mobility KW - mobility Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87427 CY - Regensburg ER - TY - GEN A1 - Hauer, Franziska A1 - Haug, Sonja A1 - Scharf, Anna A1 - Schultz, Maximilian A1 - Weber, Karsten T1 - Quantitative Befragung zu Künstlicher Intelligenz für Nichtregierungsorganisationen -Fragebogen und Codebuch N2 - Künstliche Intelligenz (KI) stellt ein wichtiges Thema der Digitalisierung dar und hat das Potenzial viele Arbeitsfelder grundlegend zu verändern. Mit dem Einsatz von KI wird die Hoffnung verbunden durch Automatisierung Zeit und Geld zu sparen. Dies könnte besonders interessant für Organisationen sein, die wenig Ressourcen zur Verfügung haben, da sie nicht gewinnorientiert oder ehrenamtlich arbeiten. Dazu zählen Nichtregierungsorganisationen (NRO), die einen wichtigen Bestandteil der Zivilgesellschaft darstellen. Aufgrund der komplexer werdenden Arbeit von NROs scheint KI Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Über den Einsatz von KI in NROs ist derzeit jedoch wenig bekannt. Es wurde eine quantitative Querschnittstudie deutscher Nichtregierungsorganisationen (NRO) zum Thema KI durchgeführt. Der Web-Survey baut auf den Erkenntnissen des Scoping Reviews und der qualitativen Interviews auf und vertieft den Einblick in die Bereiche aktuelle Nutzung von KI, Wissenstand in den Organisationen sowie vorhandene und noch benötigte Ressourcen. Dafür wurden 343 NROs verschiedener Größen und Handlungsfelder befragt und die Ergebnisse quantitativ analysiert. KW - Künstliche Intelligenz KW - NGO Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87066 N1 - Hauer, Franziska; Haug, Sonja; Scharf, Anna; Schultz, Maximilian; Weber, Karsten (2024): Künstliche Intelligenz für Nichtregierungsorganisationen - Quantitative Erforschung der Umsetzung in deutschen NROs. 3. Arbeitspapier. OTH Regensburg. Regensburg. DOI: 10.13140/RG.2.2.11094.66883 CY - Regensburg ER - TY - RPRT A1 - Bhardwaj, Prakhar A1 - Hauer, Franziska A1 - Mücke, Vanessa A1 - Dotter, Caroline A1 - Haug, Sonja A1 - Weikl, Simone A1 - Weber, Karsten T1 - Multi- und intermodale Mobilität erfassen und verstehen - eine Literaturübersicht N2 - Die Transformation zu nachhaltiger Mobilität erfordert eine detaillierte Erfassung des tatsächlichen Mobilitätsverhaltens, was durch herkömmliche, aggregierte oder retrospektive Erhebungsmethoden (z.B. Zählstellen, Wegetagebücher) nicht vollständig gelingt. Zudem sind diese Methoden oft teuer, fehleranfällig oder nicht nachhaltig skalierbar. Ziel dieser Literaturübersicht ist es, einen systematischen Überblick über den aktuellen Forschungsstand zur Erhebung und Analyse individueller Mobilitätsdaten zu geben und darauf aufbauend mögliche Forschungslücken sowie Potentiale zu identifizieren. Die explorative Literaturrecherche umfasst sozialwissenschaftliche Erhebungen zu multi- und intermodalem Mobilitätsverhalten, sowie einen Überblick über Analysemöglichkeiten von Wegedaten. Der Fokus liegt auf der Identifikation relevanter Datenquellen und methodischer Ansätze zur Segmentierung, Verkehrsmittelerkennung und Wegezweckbestimmung. Ergebnisse aus aktuellen Studien verdeutlichen, dass ausschließlich sensorgestützte Datenerhebungen bisher nicht ausreichen, um Mobilitätsverhalten vollumfänglich und präzise abzubilden. Eine mögliche Lösung ist ein "Best-of-both-worlds-Ansatz, der die passive, automatische Mobilitätsdatenerfassung via Smartphone-Sensorik mit gezielten Nutzerkorrekturen und Ergänzungen ("Human-in-the-Loop") innerhalb einer App kombiniert. Die Literaturauswertung zeigt, dass auf diesem Weg die beste Datenqualität gewonnen werden kann. Durch die Kombination von Mobilitätsbefragung mit passiver Erhebung von Mobilitätsdaten kann ein umfassender, möglichst vollständiger und korrekter Überblick über das Mobilitätsverhalten gewonnen werden. KW - KI KW - Mobilität Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87415 CY - Regensburg ER - TY - CHAP A1 - Franz, Maja A1 - Winker, Tobias A1 - Groppe, Sven A1 - Mauerer, Wolfgang T1 - Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation T2 - 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada N2 - Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-76877 N1 - Corresponding author der OTH Regensburg: Maja Franz SP - 409 EP - 420 PB - IEEE ER - TY - JOUR A1 - Krüger, Tom A1 - Mauerer, Wolfgang T1 - Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation JF - Quantum N2 - The Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the QAOA optimisation landscape from solution space structures, we derive a new algorithmic variant of unit-depth QAOA for two-level Hamiltonians (including all problems in NP): Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth QAOA for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in QAOA. By ensuring generality, we link existing empirical observations on QAOA parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation. Y1 - 2025 U6 - https://doi.org/10.22331/q-2025-11-06-1903 N1 - Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/2/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/Out+of+the+Loop/docId/7938 VL - 9 PB - Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften ER - TY - CHAP A1 - Schönberger, Manuel A1 - Trummer, Immanuel A1 - Mauerer, Wolfgang T1 - Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing T2 - Proceedings of the ACM on Management of Data N2 - Multiple-query optimization (MQO) seeks to reduce redundant work across query batches. While MQO offers opportunities for dramatic performance improvements, the problem is NP-hard, limiting the sizes of problems that can be solved on generic hardware. We propose to leverage specialized hardware solvers for optimization, such as Fujitsu's Digital Annealer (DA), to scale up MQO to problem sizes formerly out of reach. We present a novel incremental processing approach that combines classical computation with DA acceleration. By efficiently partitioning MQO problems into sets of partial problems, and by applying a dynamic search steering strategy that reapplies initially discarded information to incrementally process individual problems, our method overcomes capacity limitations, and scales to extremely large MQO instances (up to νm1000 queries). A thorough and comprehensive empirical evaluation finds our method substantially outperforms existing approaches. Our generalisable framework lays the ground for other database use-cases on quantum-inspired hardware, and bridges towards future quantum accelerators. Y1 - 2025 U6 - https://doi.org/10.1145/3749171 VL - 3 IS - 4 PB - ACM ER - TY - INPR A1 - Köster, Dominik A1 - Porkert, Florian A1 - Volbert, Klaus T1 - Multi-Day Scheduling for Electric Vehicle Routing: A Novel Model and Comparison Of Metaheuristics N2 - The increasing use of electric vehicles (EVs) requires efficient route planning solutions that take into account the limited range of EVs and the associated charging times, as well as the different types of charging stations. In this work, we model and solve an electric vehicle routing problem (EVRP) designed for a cross-platform navigation system for individual transport. The aim is to provide users with an efficient route for their daily appointments and to reduce possible inconveniences caused by charging their EV. Based on these assumptions, we propose a multi-day model in the form of a mixed integer programming (MIP) problem that takes into account the vehicle's battery capacity and the time windows of user's appointments. The model is solved using various established metaheuristics, including tabu search (TS), adaptive large neighborhood search (ALNS), and ant colony optimization (ACO). Furthermore, the performance of the individual approaches is analyzed using generated ensembles to estimate their behavior in reality and is compared with the exact results of the Google OR-Tools solver. 6 pages, 5 figures KW - Charging stations KW - Electric vehicles KW - Metaheuristics KW - Smart cities KW - Vehicle routing Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2512.21346 ER - TY - INPR A1 - Krüger, Tom A1 - Mauerer, Wolfgang T1 - Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation N2 - The Quantum Approximate Optimisation Algorithm (qaoa) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While qaoa targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the qaoa optimisation landscape from solution space structures, we derive a new algorithmic variant: Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth qaoa for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in qaoa. By ensuring generality, we link existing empirical observations on qaoa parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation. Y1 - 2024 N1 - er Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/3/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/out+of+the+loop/docId/8843 ER - TY - CHAP A1 - Mauerer, Wolfgang ED - Ali, Shaukat ED - Barzen, Johanna ED - Delgado, Andrea ED - Müller, Hausi A. ED - Murillo, Juan Manuel T1 - Quantum Software = Quantum minus Software? T2 - Dagstuhl Reports : Quantum Software Engineering : Report from Dagsthul Seminar 24512 N2 - The Dagstuhl Seminar 24512 on "Quantum Software Engineering" was held from December 15 to 20, 2024. It brought together 26 participants from industry and academia from 13 different countries, including senior and junior researchers as well as practitioners in the field of Quantum Software Engineering. The aim of the seminar was to advance software engineering methods and tools for the engineering of hybrid quantum systems by promoting personal interaction and open discussion among researchers who are already working in this emerging area of knowledge. The first day of the seminar was devoted to the topic "When software engineering meets quantum mechanics", while the second day focused on "Quantum software engineering and its challenges." During both days, 16 invited presentations were given. The rest of the seminar was organized into three working groups to address the topics "Quantum Software Design, Modelling and Architecturing", "Adaptive Hybrid Quantum Systems", and "Quantum Software Quality Assurance". The seminar was a very fruitful experience for all participants both in terms of scientific outcomes and in terms of the personal relationships that were generated to jointly address future experiences. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-230469 VL - 14 IS - 12 SP - 73 EP - 74 PB - Dagstuhl Publishing ER - TY - INPR A1 - Cenko, Egidia A1 - Weimann, Thure Georg A1 - Raptis, Georgios T1 - Navigating the DiGA Jungle: A Taxonomy and Archetypal Framework of the German Digital Therapeutics Landscape N2 - Digital therapeutics (DTx) are patient-facing apps designed to support individuals in their daily lives. Therefore, they have thepotential to revolutionize healthcare by empowering and engaging patients to become active players in their own care. Despitethe increasing adoption of DTx in national healthcare systems, research on their design remains limited. The present studyintroduces “DiGATax”, a taxonomy designed to categorize and analyze DTx, including perspectives on content, interventiondelivery logic and technology, as well as the patient’s interface, consolidating and expanding upon prior taxonomic work.Based on n = 44 applications retrieved from the German DiGA directory that demonstrated positive health outcomes, thetaxonomy is supported by empirical evidence. Additionally, the study contributes by presenting an archetype frameworkof DTx derived from a taxonomy-based cluster analysis. Further analyses offer insights into specific combinations of DTxcharacteristics across archetypes, the user interface as a key factor in their acceptance, and potential links between DTxdesign and health-related and user engagement outcomes. By offering new insights into DTx design, this study contributestowards more organized research and reporting, ultimately paving the way for the development of effective solutions. It alsomarks a further step towards Meta-DTx, which aim to align patient care for multimorbid patients under one umbrella. KW - Archetypes KW - Digital Behavior Change Interventions KW - Digital Therapeutics KW - mHealth KW - Taxonomy Y1 - 2025 U6 - https://doi.org/10.64898/2025.12.30.25343225 ER - TY - CHAP A1 - Stadler, Sebastian A1 - Seitz, Marie Sophie T1 - When algorithms design: a comparative study pits an AI-generated application against a manual application in a competition of user experience and user-friendliness T2 - Proceedings of 2025 11th international HCI and UX conference in Indonesia, 5 + 6. December 2025, Bali (CHIuXiD) N2 - The rapid growth of artificial intelligence has enabled automated website creation, including design, content, and SEO. This study compares an AI-generated application with a manually designed one in terms of usability and user experience. Forty-two participants evaluated efficiency, effectiveness, satisfaction, emotional responses, and behavioural patterns. The manual application outperformed the AI-generated one in most datasets, particularly in clarity, supportiveness, and comprehensibility, while speed, learnability, activation, creativity, and security showed no significant differences. Users reported more positive emotions and confident interactions with the manual application, whereas the AI application elicited uncertainty and frustration. These behavioural differences were also reflected in processing times and task completion rates, suggesting that AI-generated solutions currently struggle to meet established UX standards. Nevertheless, the AI builder demonstrated potential for rapid prototyping and low-effort website creation, offering advantages for users with limited technical skills or resources. The findings highlight both the opportunities and limitations of current AI-based web design tools and underline the importance of human-centered design expertise in achieving high-quality user experiences. Despite existing shortcomings, AI-supported tools may become increasingly viable as technology advances. KW - user experience KW - usability KW - artificial intelligence KW - user experience questionnaire Y1 - 2025 U6 - https://doi.org/10.1109/CHIuXiD68326.2025.11323819 SP - 55 EP - 60 PB - IEEE ER - TY - CHAP A1 - Dünnweber, Jan A1 - Lorenz, Marcel A1 - Bräu, Fabian A1 - Saito, Hiroshi A1 - Melzer, Matthias T1 - Driving out the Bears from the Suburbs by Means of Location Theory and Intelligent Agents T2 - Proceedings of the ACM 12th International Conference on Animal-Computer Interaction, ACI 2025: The International Conference on Animal-Computer Interaction ; Indiana USA ; 01-04.12.2025 N2 - Due to the recent surge in bear attacks, affected municipalities have taken various measures to drive out the intruders. Political considerations include, in many places, the expansion of shooting permits. More and more regional governmental authorities invest in technologies for efficient bear hunting, such as IP cameras and computer vision. From an ecological perspective, such procedures, which are all solely aimed at decreasing the bear population, are questionable, since ecosystems can benefit from a peaceful coexistence of bears with humans: Bears remove animal carcasses and thus return vital nutrients to the soil and prevent the spread of diseases. Moreover, the bears keep the deer population in check, which is advantageous for the forest vegetation. Conflicts start, once the bears intrude populated areas, which are often rural villages and suburbs with a low population density. Naturally, bears do not see humans as prey and respect their size. When the animal attacks, this happens rather for territorial or protective reasons, e. g., after an unexpected encounter with humans unsettled it. In this work, we analyze means to eradicate the danger: We dynamically apply Weiszfeld’s algorithm for finding the shifting geometric median between time-windowed black bear sightings and use the results for implementing an early warning system. In addition, we weigh the spotted locations and predict the expected migration behavior between bear habitats using a simulation with intelligent agents. As a case study, we apply our software to data from Romania and Fukushima and identify the positions where bear repelling facilities should be placed most effectively. Y1 - 2025 SN - 9798400721236 U6 - https://doi.org/10.1145/3768539.3768546 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Gutbrod, Max A1 - Rauber, David A1 - Weber Nunes, Danilo A1 - Palm, Christoph T1 - OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection T2 - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10.-17. June 2025, Nashville N2 - The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, nearOOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OODdetection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at https://github.com/remic-othr/OpenMIBOOD. KW - Benchmark testing KW - Reliability KW - Trustworthiness KW - out-of-distribution Y1 - 2025 UR - https://openaccess.thecvf.com/content/CVPR2025/html/Gutbrod_OpenMIBOOD_Open_Medical_Imaging_Benchmarks_for_Out-Of-Distribution_Detection_CVPR_2025_paper.html SN - 979-8-3315-4364-8 U6 - https://doi.org/10.1109/CVPR52734.2025.02410 N1 - Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/8059 SP - 25874 EP - 25886 PB - IEEE ER - TY - JOUR A1 - Heinz, Florian A1 - Schildgen, Johannes T1 - Experience Report: Hey LLM, Generate SQL! JF - Datenbank-Spektrum N2 - Large Language Models have advanced to a resourceful tool with many applications. One particularly interesting use case is the LLM-aided generation of ad-hoc database queries and the possibility of subsequent processing of the results in a way suiting the users intents. In this article, practical ways and experiences are described on how to effectively use LLMs to map a natural-language user query to an SQL query conforming to a specific database schema and post-processing the results of this query in order to, for example, create an appealing visualization. Best results are achieved under favorable circumstances, as, for example, a clean and meaningful named database schema. Y1 - 2025 U6 - https://doi.org/10.1007/s13222-025-00512-w N1 - Corresponding author der OTH Regensburg: Florian Heinz VL - 25 IS - 2 SP - 95 EP - 101 PB - Springer CY - Wiesbaden ER - TY - CHAP A1 - Preiß, Niklas A1 - Westner, Markus T1 - From Agents to Copilots: a Systematic Review of Digital Assistant Technology Adoption in Proprietary Productivity Software T2 - Annals of Computer Science and Information Systems N2 - This study provides a systematic review of how the impact and adaptation of digital assistant technologies (DATs) are defined, operationalized, and studied, synthesizing key domains where DATs generate or are expected to generate value. Based on an analysis of 61 articles published since 2013, it identifies five main areas of impact: productivity and efficiency, business development, resource optimization, quality enhancement, and the promotion of learning and creativity. The review highlights DAT adoption across various disciplines and industries, while revealing limited longitudinal research on benefits and adaptation. Key gaps remain in understanding strategic use and sustained impact. Future research should explore longitudinal comparisons of recently introduced generative AI-driven DATs and their organizational implications. This review contributes to information systems research by structuring current knowledge on DAT adoption and outcomes, and by proposing a research agenda to support deeper exploration of their value and long-term integration. KW - Digital Assistant Technology KW - Copilot KW - Generative AI KW - Technology Adoption KW - Productivity Y1 - 2025 U6 - https://doi.org/10.15439/2025F3271 SN - 2300-5963 VL - 43 SP - 565 EP - 576 PB - Polish Information Processing Society ER - TY - CHAP A1 - Köhler, Jessica A1 - Harl, Maximilian Victor A1 - Westner, Markus A1 - Strahringer, Susanne T1 - Can AI be a Scholar? A Systematic Review on the Role of Generative AI in Systematic Literature Reviews T2 - 2025 27th International Conference on Business Informatics (CBI), 09-12. September 2025, Lisbon, Portugal N2 - Systematic literature reviews (SLRs) are foundational for research but resource-intensive to conduct. With the rise of large language models (LLMs) such as ChatGPT, generative AI (GenAI) tools are being increasingly explored for their potential to support and transform the SLR process. This study presents a systematic review of peerreviewed articles that examine how LLM-based GenAI tools are used in different SLR phases. Following the PRISMA 2020 guidelines, we screened 1,846 publications published since January 2021 until April 2025 and selected 54 for in-depth analysis. Each study was coded by review phase, prompting approach, automation level, validation type and challenges. Our findings show that GenAI is most often used to support in the screening, search, and writing phases, typically through Basic Prompting and under human oversight. While many studies report efficiency gains, concerns remain regarding validity, transparency, and methodological rigor. Moreover, GenAI is frequently applied to isolated tasks but is rarely embedded in a structured, methodologically guided review processhighlighting the need for clearer phase-specific guidance and standards. We offer a structured, phase-specific synthesis that highlights both the promise and the current limitations of GenAI in literature reviews and thereby offer practical recommendations for the responsible use of GenAI in literature reviews. KW - Generative AI KW - Large Language Models KW - ChatGPT KW - Systematic Literature Review KW - Evidence Synthesis KW - AI-assisted Literature Review Y1 - 2025 U6 - https://doi.org/10.1109/CBI68102.2025.00012 PB - IEEE ER - TY - CHAP A1 - Westner, Markus ED - Hofbauer, Günter T1 - Generative AI in Business BT - Opportunities, Challenges, and Strategic Implications Authors Markus Westner T2 - Artificial Intelligence in Business and Engineering N2 - Generative Artificial Intelligence (GenAI) is transforming industries at an unprecedented rate, offering novel opportunities for productivity and innovation. This article explores the adoption of GenAI, highlighting its accelerated uptake compared to previous technologies. Key topics include productivity gains through GenAI tools (e.g., ChatGPT, Klarna’s AI assistant), challenges such as data quality and organizational readiness, and the implications for business strategy. Practical recommendations for managing generative AI adoption and maximizing its impact on both individual and organizational levels are also provided. The analysis underscores the necessity of aligning AI capabilities with customer needs and creating data-driven, adaptable business models. KW - Generative AI KW - Productivity KW - Technology Adoption Y1 - 2025 SN - 978-3-17-046742-2 SP - 38 EP - 50 PB - Kohlhammer CY - Stuttgart ER - TY - INPR A1 - Rückert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Klausmann, Leonard A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Nunes, Danilo Weber A1 - Moreno, Alvaro Fernandez A1 - Luengo, Imanol A1 - Stoyanov, Danail A1 - Toussaint, Nicolas A1 - Cho, Enki A1 - Kim, Hyeon Bae A1 - Choo, Oh Sung A1 - Kim, Ka Young A1 - Kim, Seong Tae A1 - Arantes, Gonçalo A1 - Song, Kehan A1 - Zhu, Jianjun A1 - Xiong, Junchen A1 - Lin, Tingyi A1 - Kikuchi, Shunsuke A1 - Matsuzaki, Hiroki A1 - Kouno, Atsushi A1 - Manesco, João Renato Ribeiro A1 - Papa, João Paulo A1 - Choi, Tae-Min A1 - Jeong, Tae Kyeong A1 - Park, Juyoun A1 - Alabi, Oluwatosin A1 - Wei, Meng A1 - Vercauteren, Tom A1 - Wu, Runzhi A1 - Xu, Mengya A1 - an Wang, A1 - Bai, Long A1 - Ren, Hongliang A1 - Yamlahi, Amine A1 - Hennighausen, Jakob A1 - Maier-Hein, Lena A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Hirasawa, Kousuke A1 - Yang, Shu A1 - Wang, Yihui A1 - Chen, Hao A1 - Rodríguez, Santiago A1 - Aparicio, Nicolás A1 - Manrique, Leonardo A1 - Lyons, Juan Camilo A1 - Hosie, Olivia A1 - Ayobi, Nicolás A1 - Arbeláez, Pablo A1 - Li, Yiping A1 - Khalil, Yasmina Al A1 - Nasirihaghighi, Sahar A1 - Speidel, Stefanie A1 - Rückert, Daniel A1 - Feussner, Hubertus A1 - Wilhelm, Dirk A1 - Palm, Christoph T1 - Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge N2 - Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. Y1 - 2025 N1 - Der Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/10.1016%2Fj.media.2026.103945/docId/8846 ER - TY - BOOK A1 - Westner, Markus A1 - Strasser, Artur T1 - Objectives and Key Results verstehen und anwenden N2 - Dieses essential gibt eine strukturierte und kompakte Einführung in die Objectives and Key Results-Managementmethode. Es erläutert Herkunft, Definition und Einsatzzweck von Objectives and Key Results (OKR) und beschreibt deren zentrale Bestandteile. Leser:innen erfahren, wie gute Objectives und Key Results formuliert werden und welche Strategien sich zur erfolgreichen Implementierung eignen. Zudem werden praxisrelevante Erfolgs- und Misserfolgsfaktoren analysiert, die den Einsatz von OKR maßgeblich beeinflussen. Ein fundierter Leitfaden für alle, die OKR als Steuerungsinstrument in Organisationen verstehen und wirksam einsetzen möchten. KW - Objectives and Key Results (OKR) KW - Zielmanagement KW - Performance Management KW - Strategieumsetzung KW - Kennzahlen / KPI Y1 - 2025 SN - 978-3-658-50381-9 U6 - https://doi.org/10.1007/978-3-658-50382-6 PB - Springer Gabler CY - Wiesbaden ER - TY - CHAP A1 - Sachenbacher, Martin A1 - Leucker, Martin T1 - Towards Monitoring and Diagnosis of Quantum Digital Twins T2 - Lecture Notes in Informatics (LNI) N2 - As access to larger-scale quantum computers is still limited, it is useful to simulate quantum algorithms on specialized programmable logic hardware such as FPGAs to exploit analogies between quantum and digital circuits, and allow for analysis and efficient debugging of quantum programs. Such simulations can be viewed as digital representations of actual physical systems, commonly referred to as Digital Twins (DTs). In previous work, we applied formal methods from runtime verification to synthesize monitors capable of real-time analysis of conventional DTs. Specifically, these monitors supervise alignment with their physical counterparts and support model-based diagnosis to identify potential root causes of observed deviations. In this short paper, we present ongoing work aimed at adapting and extending our approach to hardware-based simulation and analysis of quantum software on FPGAs, with the objective of improving both accuracy and resilience. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87546 VL - P-366 SP - 1767 EP - 1772 PB - Gesellschaft für Informatik e.V. CY - Bonn ER -