@article{SchleifHammerVillmann, author = {Schleif, Frank-Michael and Hammer, Barbara and Villmann, Thomas}, title = {Margin-based active learning for LVQ networks}, series = {Neurocomputing}, volume = {70}, journal = {Neurocomputing}, number = {7-9}, doi = {10.1016/j.neucom.2006.10.149}, pages = {1215 -- 1224}, language = {en} } @article{HillebrechtBraun, author = {Hillebrecht, Steffen and Braun, Ellen}, title = {Klotzen oder kapitulieren - erfolgreiches Personalmarketing im Web}, series = {Personal - Zeitschrift f{\"u}r Human Resource Management}, volume = {63}, journal = {Personal - Zeitschrift f{\"u}r Human Resource Management}, number = {6}, publisher = {Verl.-Gruppe Handelsblatt}, address = {D{\"u}sseldorf}, issn = {2365-8614}, pages = {34 -- 35}, language = {de} } @article{HillebrechtBraun, author = {Hillebrecht, Steffen and Braun, Ellen}, title = {Betriebliche Wahrnehmung des Burnouts}, series = {Der Betriebswirt}, volume = {54}, journal = {Der Betriebswirt}, number = {3}, publisher = {Deutscher Betriebswirte Verlag}, address = {Gernsbach}, issn = {0172-6196}, pages = {16 -- 22}, language = {de} } @inproceedings{FetzerBullmannEbneretal., author = {Fetzer, Toni and Bullmann, Markus and Ebner, Markus and Kastner, Steffen and Deinzer, Frank and Grzegorzek, Marcin}, title = {Interacting Multiple Model Particle Filter for Indoor Positioning Applications}, series = {Proceedings of the 2023 International Technical Meeting of The Institute of Navigation}, booktitle = {Proceedings of the 2023 International Technical Meeting of The Institute of Navigation}, language = {en} } @article{MeissnerDillmann, author = {Meißner, Pascal and Dillmann, R{\"u}diger}, title = {Implicit Shape Model Trees - Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots}, series = {robotics}, volume = {12}, journal = {robotics}, number = {158}, publisher = {MDPI}, doi = {10.3390/robotics12060158}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-53649}, pages = {33}, abstract = {This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed "Implicit Shape Model (ISM) trees" as a scene model to solve these two tasks together. This article presents novel algorithms for ISM trees to recognize scenes and predict object poses. For us, scenes are sets of objects, some of which are interrelated by 3D spatial relations. Yet, many false positives may occur when using single ISMs to recognize scenes. We developed ISM trees, which is a hierarchical model of multiple interconnected ISMs, to remedy this. In this article, we contribute a recognition algorithm that allows the use of these trees for recognizing scenes. ISM trees should be generated from human demonstrations of object configurations. Since a suitable algorithm was unavailable, we created an algorithm for generating ISM trees. In previous work, we integrated the object search and scene recognition into an active vision approach that we called "Active Scene Recognition". An efficient algorithm was unavailable to make their integration using predicted object poses effective. Physical experiments in this article show that the new algorithm we have contributed overcomes this problem.}, language = {en} } @inproceedings{BruecknerDeinzerDenzler, author = {Br{\"u}ckner, Marcel and Deinzer, Frank and Denzler, Joachim}, title = {Temporal Estimation of the 3d Guide-Wire Position Using 2d X-ray Images}, series = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009}, volume = {5761}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009}, editor = {Yang, Guang-Zhong and Hawkes, David and Rueckert, Daniel and Noble, Alison and Taylor, Chris}, isbn = {978-3-642-04267-6}, pages = {386 -- 393}, language = {en} } @inproceedings{WackerDeinzer, author = {Wacker, Matthias and Deinzer, Frank}, title = {Automatic Robust Medical Image Registration Using a New Democratic Vector Optimization Approach with Multiple Measures}, series = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009}, volume = {5761}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009}, editor = {Yang, Guang-Zhong and Hawkes, David and Rueckert, Daniel and Noble, Alison and Taylor, Chris}, isbn = {978-3-642-04267-6}, pages = {590 -- 597}, language = {en} } @inproceedings{KoepingMuehsamOfenbergetal., author = {K{\"o}ping, Lukas and M{\"u}hsam, Thomas and Ofenberg, Christian and Czech, Bernhard and Bernard, Michael and Schmer, Jens and Deinzer, Frank}, title = {Indoor Navigation Using Particle Filter and Sensor Fusion}, series = {Proceedings of the European Navigation Conference 2012}, booktitle = {Proceedings of the European Navigation Conference 2012}, language = {en} } @inproceedings{KoepingGrzegorzekDeinzer, author = {K{\"o}ping, L. and Grzegorzek, M. and Deinzer, Frank}, title = {Probabilistic Step and Turn Detection in Indoor Localisation}, series = {10th IET Data Fusion Target Tracking Conference (DF TT 2014): Algorithms and Applications}, booktitle = {10th IET Data Fusion Target Tracking Conference (DF TT 2014): Algorithms and Applications}, pages = {1 -- 7}, language = {en} } @article{SerondeCarvalhoSauletal., author = {Ser{\´o}n, Francisco and de Carvalho, Carlos Vaz and Saul, Melanie and Deinzer, Frank and Guerrero, Roberto and Parra, Juan Carlos and Costa, Cristiano and Rigo, Sandro}, title = {Realidades Virtuales/Aumentadas para el Desarrollo Social: Experiencias Entre Europa y Latinoamerica}, isbn = {978-950-757-046-9}, language = {es} } @article{KoepingMuehsamOfenbergetal., author = {K{\"o}ping, Lukas and M{\"u}hsam, Thomas and Ofenberg, Christian and Czech, Bernhard and Bernard, Michael and Schmer, Jens and Deinzer, Frank}, title = {Indoor Navigation Using Particle Filter and Sensor Fusion}, series = {Annual of Navigation}, volume = {19}, journal = {Annual of Navigation}, number = {2}, pages = {31 -- 40}, language = {en} } @article{KoepingDeinzer, author = {K{\"o}ping, Lukas and Deinzer, Frank}, title = {Realidades Virtuales/Aumentadas para el Desarrollo Social: Experiencias Entre Europa y Latinoamerica}, isbn = {978-950-757-046-9}, language = {es} } @techreport{BenkertSchmidtRose, type = {Working Paper}, author = {Benkert, Marius and Schmidt, Jochen and Rose, Lennard}, title = {Question-Answering System for Coffee Machines}, organization = {Center for Artificial Intelligence and Robotics, THWS}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-43657}, pages = {12}, abstract = {Our study presents a comprehensive Question Answering (QA) system for coffee machine related questions. The system covers a wide range of topics such as maintenance, usage, and troubleshooting of coffee machines, offering quick and accurate answers to the users through its intuitive interface and natural language processing capabilities. The system provides a seamless experience for coffee machine owners and users to access the information they need, ensuring smooth operation of their machines and allowing them to enjoy their favorite beverages with ease. To evaluate the performance of these models, we fine-tuned a range of BERT-based Transformers on a manually created dataset of 653 question-answer pairs. In conclusion, our findings demonstrate the feasibility of using NLP Question Answering models to deliver technical answers about coffee machines, and highlight the importance of fine-tuning these models on task-specific data.}, language = {en} } @misc{Bea, type = {Master Thesis}, author = {Bea, Michelle}, title = {Automatisiert und rechtskonform - Best-Practice-Strategien von KMU zur Umsetzung des EU-AI-Acts im Online-Marketing}, doi = {10.57714/26pn-b418}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-63905}, school = {Technische Hochschule W{\"u}rzburg-Schweinfurt}, pages = {107}, abstract = {In den vergangenen Jahren hat K{\"u}nstliche Intelligenz (KI) das Online-Marketing grundlegend ver{\"a}ndert. Inhalte werden zunehmend automatisiert erstellt, Kampagnen datenbasiert gesteuert und Kundeninteraktionen durch lernende Systeme personalisiert. Was lange als Innovationsvorteil galt, wird durch den EU-AI-Act nun auch zu einer rechtlichen Herausforderung: Unternehmen m{\"u}ssen sicherstellen, dass der Einsatz von KI-Systemen nicht nur effizient, sondern auch regelkonform erfolgt. Besonders kleine und mittlere Unternehmen (KMU) stehen vor der Aufgabe, komplexe rechtliche Anforderungen mit ihren begrenzten Ressourcen in Einklang zu bringen. Die vorliegende Masterarbeit untersucht, wie KMU im Online-Marketing die Anforderungen des EU-AI-Acts umsetzen k{\"o}nnen, um rechtliche Konformit{\"a}t sicherzustellen und zugleich die Leistungsf{\"a}higkeit ihrer digitalen Prozesse zu erhalten. KI-Systeme sind bereits fester Bestandteil von Marketingprozessen, doch bislang fehlen strukturierte Ans{\"a}tze, um die rechtlichen Vorgaben praxisnah umzusetzen. Zur Beantwortung der Forschungsfrage wurde ein Design-Science-Research-Ansatz (DSR) nach Kuechler \& Vaishnavi (2015) gew{\"a}hlt, der Forschung und Gestaltung in einem iterativen F{\"u}nf-Phasen-Prozess verbindet. Auf dieser Grundlage wurde die Logik des EU-AI-Acts systematisch analysiert, ein praxisorientiertes Artefakt entwickelt und dessen Anwendung und Umsetzbarkeit im Rahmen von sieben Experteninterviews aus Recht, Wissenschaft und unternehmerischer Praxis evaluiert. Als zentrales Ergebnis entstand eine Brosch{\"u}re als Artefakt, die den Umsetzungsprozess des EU-AI-Acts in einem 6-Schritte-Prozess abbildet. Sie umfasst ein KI-Inventar, eine Pr{\"u}fung, ob es sich um ein KI-System handelt, eine Rollenbestimmung, eine Risikoklassifizierung anhand einer Evidenz-Matrix sowie daraus abgeleitete Handlungsempfehlungen und Hinweise f{\"u}r den Umgang mit Hochrisiko-Systemen. Die Brosch{\"u}re unterst{\"u}tzt KMU bei der strukturierten Selbstbewertung und bietet einen kompakten, pr{\"u}ff{\"a}higen Rahmen f{\"u}r AI-Compliance im Online-Marketing (Anhang H). Die Ergebnisse zeigen, dass KMU den EU-AI-Act als grunds{\"a}tzlich realisierbar einstufen, in der Umsetzung jedoch klare Regeln und Strukturen ben{\"o}tigen. Fr{\"u}hzeitige Befassung mit der Regulierung, interdisziplin{\"a}re Zusammenarbeit und die Nutzung offizieller Leitf{\"a}den f{\"o}rdern eine nachhaltige Compliance-Kultur. Der EU-AI-Act erweist sich insgesamt als transformatorisches Steuerungsinstrument, das rechtliche, strategische und ethische Dimensionen verbindet. Zuk{\"u}nftige Arbeiten sollten die Wirksamkeit der Brosch{\"u}re empirisch pr{\"u}fen, sie an entstehende Normen anpassen, digitale Tool-Unterst{\"u}tzungen entwickeln und ihre {\"U}bertragbarkeit auf andere Unternehmensbereiche untersuchen.}, language = {de} } @inproceedings{HolomjovaStarkeyMeissner, author = {Holomjova, Valerija and Starkey, Andrew J. and Meißner, Pascal}, title = {GSMR-CNN: An End-to-End Trainable Architecture for Grasping Target Objects from Multi-Object Scenes}, series = {Proceedings of IEEE International Conference on Robotics and Automation (ICRA)}, booktitle = {Proceedings of IEEE International Conference on Robotics and Automation (ICRA)}, doi = {10.1109/ICRA48891.2023.10161009}, abstract = {We present an end-to-end trainable multi-task model that locates and retrieves target objects from multi-object scenes. The model is an extension of the Siamese Mask R-CNN, which combines the components of Siamese Neural Networks (SNNs) and Mask R-CNN for performing one-shot instance segmentation. The proposed network, called Grasping Siamese Mask R-CNN (GSMR-CNN), extends Siamese Mask R-CNN by adding an additional branch for grasp detection in parallel to the previous object detection head branches. This allows our model to identify a target object with a suitable grasp simultaneously, as opposed to other approaches that require the training of separate models to achieve the same task. The inherent SNN properties enable the proposed model to generalize and recognize new object categories that were not present during training, which is beyond the capabilities of standard object detectors. Moreover, an end-to-end solution uses shared features entailing less model parameters. The model achieves grasp accuracy scores of 92.1 \% and 90.4\% on the OCID grasp dataset on image-wise and object-wise splits. Physical experiments show that the model achieves a grasp success rate of 76.4 \% when correctly identifying the object. Code and models are available at https://github.com/valerijah/grasping_siamese_mask_rcnn}, language = {en} } @inproceedings{TillyNeebBhairapuetal., author = {Tilly, Julian and Neeb, Christopher and Bhairapu, Chandu and Mohamed, Fatima and Kachana, Indrasena Reddy and Saravanan, Mahesh and Nguyen Pham, Phuoc}, title = {Navigating the Future: an approach of autonomous indoor vehicles}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-43401}, pages = {12}, abstract = {In this project, we explored the ability of Reinforcement learning (RL) in driving an indoor car autonomously. RL has proven its good performance in solving challenging decision-making problems. Therefore, RL can be a promising solution for autonomous car to deal with complex driving scenarios. As hardware a model car eqipped with sensors and powerful computational unit has been used. We also utilized SLAM for environment mapping and a combination of lidar data and Wi-Fi technology for localization. The experiment showed that the model can perform very well in simulation. Although the model lacks the ability to drive the car as smoothly along a route, the car is still able to avoid obstacles and walls in an unknown real-world environment.}, language = {en} } @misc{Kumar, type = {Master Thesis}, author = {Kumar, Dibyanshu}, title = {A Comparative Analysis of Loss Formulations in Diffusion Models}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-62929}, pages = {86}, abstract = {Diffusion models have established themselves as highly effective generative frameworks, inspiring significant research into their underlying mechanisms. An important aspect of these models lies in the choice of loss functions, which directly influences their training and performance. Over recent years, multiple formulations have been proposed [20, 56, 27, 49], each with distinct characteristics and theoretical foundations. This thesis provides a comprehensive exploration of these loss functions, systematically analyzing their theoretical relationships and unifying them under the framework of the variational lower bound objective. We complement this analysis with empirical studies that examine the conditions under which different objectives yield varying performance and provide insights into the factors driving these discrepancies. Additionally, we assess the impact of loss function selection on the model's ability to achieve specific objectives, such as producing high-quality samples or precisely estimating data likelihoods. By presenting a unified perspective, this study advances the understanding of loss functions in diffusion models, contributing to more efficient and goal-oriented model designs in future research.}, language = {en} } @misc{Sprenger, type = {Master Thesis}, author = {Sprenger, Axel Bernhard Martin}, title = {Automation Bias bei Kl-gest{\"u}tzten F{\"u}hrungsentscheidungen in der IT Branche}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-63941}, school = {Technische Hochschule W{\"u}rzburg-Schweinfurt}, pages = {92}, abstract = {In vielen Organisationen werden F{\"u}hrungsentscheidungen zunehmend durch KI-Systeme vorbereitet oder getroffen. Diese Studie untersucht, ob und wie sich die Akzeptanz von F{\"u}hrungsentscheidungen ver{\"a}ndert, wenn sie einer KI statt einer menschlichen F{\"u}hrungskraft zugeschrieben werden. Auf Basis des Forschungsstands zu Automation Bias, kognitiver Belastung und organisationaler Fairness wurden zun{\"a}chst qualitative Interviews mit IT-F{\"u}hrungskr{\"a}ften durchgef{\"u}hrt, um realistische Entscheidungsszenarien (z. B. Bewerberauswahl, Ressourcenpriorisierung) zu identifizieren. Diese Szenarien dienten als Stimuli f{\"u}r ein onlinebasiertes Umfrage-Experiment mit IT-Besch{\"a}ftigten (N = 69; 328 szenariobasierte Bewertungen), in dem die Zuschreibung der Entscheidung („KI" vs. „Mensch") experimentell variiert wurde. Die Ergebnisse zeigen, dass das KI-Label zwar zu einer h{\"o}heren Wahrnehmung von Neutralit{\"a}t f{\"u}hrt, zugleich aber die Identifikation mit der entscheidenden Instanz verringert. Ein direkter Automation Bias im Sinne einer pauschal h{\"o}heren Akzeptanz von KI-Entscheidungen ließ sich nicht nachweisen. Stattdessen wird Akzeptanz {\"u}berwiegend durch wahrgenommene Fairness, Zufriedenheit und Identifikation erkl{\"a}rt, auf die wiederum Kompetenz, Erkl{\"a}rbarkeit und Neutralit{\"a}t einwirken. Damit r{\"u}cken die Qualit{\"a}t und Transparenz des Entscheidungsprozesses st{\"a}rker in den Vordergrund als die bloße Etikettierung als KI-Entscheidung. Praktisch folgt daraus, dass KI in F{\"u}hrungskontexten nur als unterst{\"u}tzendes, erkl{\"a}rbares und jederzeit vom Menschen {\"u}bersteuerbares Instrument eingesetzt werden sollte, um sowohl Objektivit{\"a}t als auch soziale Anschlussf{\"a}higkeit zu sichern.}, language = {de} } @article{YamshchikovKoltun, author = {Yamshchikov, Ivan and Koltun, Vladyslav}, title = {Pump It - Twitter Sentiment Analysis for Cryptocurrency Price Prediction}, series = {Risks}, volume = {11}, journal = {Risks}, number = {159}, publisher = {MDPI}, doi = {10.3390/risks11090159}, pages = {14}, language = {en} } @inproceedings{JohnSilva, author = {John, Isabel and Silva, Adeline}, title = {Evaluating variability instantiation strategies for product lines}, series = {VaMoS '11: Proceedings of the 5th International Workshop on Variability Modeling of Software-Intensive Systems}, booktitle = {VaMoS '11: Proceedings of the 5th International Workshop on Variability Modeling of Software-Intensive Systems}, doi = {10.1145/1944892.19449}, pages = {105 -- 113}, abstract = {Explicit variability management is essential for large product lines and requires explicit strategies for instantiating the managed variabilities during application engineering. An instantiation strategy proposes a certain order for the resolution of variabilities during application engineering or for testing. If an alphabetical strategy is used, for instance, the variabilities are resolved in alphabetical order, from A to Z. In this paper, we motivate the necessity of strategies for large variability models, which help to identify starting points and guide the resolution of variability models. We sketch the application of the strategies in a tool and give the results of an experiment performed to compare the strategies in different situations. The experiment showed that the efficiency of instantiation differs by more than 35\% between different strategies. Additionally, the meaningfulness of the instantiation was perceived differently for the various strategies and the strategies were all perceived as being easy to resolve. With the experiment, we managed to demonstrate that the effectiveness of instantiation strategies differs, which motivates the need for different variability instantiation strategies in different situations.}, language = {en} } @inproceedings{CalleaJohnMatriscianoetal., author = {Callea, Viviana and John, Isabel and Matrisciano, Apollonia and Ursache, Mihai}, title = {Coaching and Engineering Synergy for Effective Vocational Tools: The COACH_ING Model}, series = {2025 IEEE Global Engineering Education Conference (EDUCON)}, booktitle = {2025 IEEE Global Engineering Education Conference (EDUCON)}, publisher = {IEEE}, isbn = {979-8-3315-3949-8}, issn = {2165-9567}, doi = {10.1109/EDUCON62633.2025.11016626}, abstract = {Today, more than ever, in a market oriented towards sustainability, inclusion, and new technologies, it is necessary to integrate educational and guidance systems with tools that enable future professionals to choose their career path and later contribute to the collective good. To foster good career choices for engineers we have developed the COACH_ING model presented in earlier papers. In this paper we want to present a project and an experiment, conducted at an engineering faculty of a European university as part of a country wide Guidance and Tutoring Project. In our COACH_ING model, used by the faculty and professional associations for over seven years, new tools have been implemented for conscious career choice guidance and the enhancement of one's professional potential, via future-oriented educational concepts. The activities took place in structured workshops during which, assessments and questionnaires were systematically administered to gather useful research data and monitor the development of reference skills drawn from coaching and engineering. Specifically, the tool DIRECTING, presented at the EFEA2021, now called DIRECTINGVer2, has been enriched, as a questionnaire, with new elements based on empirical validations performed in the meantime. The collected data provided new insights into the level of learning of specific reference parameters related to the COACH_ING model intersecting Dublin Descriptors as a broadly known framework for qualification in degree programs. In the individual interventions, classroom techniques specific to new guidance models were respected, with the aim of developing an open mindset in students, through conditions initially defined based on the mastery of 16 skills and 8 MCs (Macro Capabilities) of the COACH_ING model. During our not-traditional lab, data emerged regarding eight MCs and four clusters of the questionnaire. Participants were involved in group work to bring out their knowledge and potential (as presented at ICL2024). Through focus groups and 1-to-1 sessions, we verified the actual level of confidence and acquisition of knowledge related to both the MCs and the dimensions identified in the Clusters: communication, teamwork, goal and project, relationship. The model finds its foundations in coaching process engineering, building a life project and choosing one's personalized future path. There emerges a strong need to focus on the advancement of engineering education and the concepts to be conveyed to future engineers so that they can act for their own benefit and that of the surrounding world, making conscious choices.}, language = {en} }