Publikationen

E. Slany, S. Scheele, and U. Schmid. 2024. “Hybrid Explanatory Interactive Machine Learning for Medical Diagnosis.” In Artificial Intelligence Applications and Innovations, , 105–16. http://doi.org/10.1007/978-3-031-63211-2_9.

E. Slany, S. Scheele, and U. Schmid. 2024. “Explanatory Interactive Machine Learning with Counterexamples from Constrained Large Language Models.” In Advances in Artificial Intelligence – 47th German Conference on AI, Lecture Notes in Computer Science, , 324–31. http://doi.org/10.1007/978-3-031-70893-0_26.

J. Amling, S. Scheele, E. Slany, M. Lang, and U. Schmid. 2024. “Explainable AI for Mixed Clustering.” In Explainable Artificial Intelligence – Second World Conference (XAI), , 42–62. http://doi.org/10.1007/978-3-031-63797-1_3.

J. Deuschel, A. Foltyn, K. Roscher, and S. Scheele. 2024. “The Role of Uncertainty Quantification for Trustworthy AI.” In Unlocking Artificial Intelligence: From Theory to Applications, Springer. 10.1007/978-3-031-64832-8_5.

F. Gretz, F.-J. Frosch, M. Mendler, and S. Scheele. 2024. “Synchronized Shared Memory and Black-Box Procedural Abstraction: Toward a Formal Semantics of Blech.” ACM Transactions on Embedded Computing Systems 23.5: 1–45. http://doi.org/10.1145/3571585.

E. Slany, S. Scheele, and U. Schmid. 2023. “Bayesian CAIPI: A Probabilistic Approach to Explanatory and Interactive Machine Learning.” In Artificial Intelligence. ECAI 2023 International Workshops – XAI3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Communications in Computer and Information Science, , 285–301. http://doi.org/10.1007/978-3-031-50396-2_16.

E. Slany, Y. Ott, S. Scheele, J. Paulus, and U. Schmid. 2022. “CAIPI in Practice: Towards Explainable Interactive Medical Image Classification.” In Artificial Intelligence Applications and Innovations (AIAI) 2022 IFIP WG 12.5 International Workshops – MHDW 2022, 5G-PINE 2022, AIBMG 2022, ML@HC 2022, and AIBEI 2022, IFIP Advances in Information and Communication Technology, , 389–400. http://doi.org/10.1007/978-3-031-08341-9_31.

D. Müller, M. März, S. Scheele, and U. Schmid. 2022. “An Interactive Explanatory AI System for Industrial Quality Control.” In 36th Conference on Artificial Intelligence (AAAI), 34th Conference on Innovative Applications of Artificial Intelligence (IAAI), 12th Symposium on Educational Advances in Artificial Intelligence (EAAI), AAAI Press. http://doi.org/10.1609/aaai.v36i11.21530.

M. Herchenbach, D. Müller, S. Scheele, and U. Schmid. 2022. “Explaining Image Classifications with near Misses, near Hits and Prototypes: Supporting Domain Experts in Understanding Decision Boundaries.” In Pattern Recognition and Artificial Intelligence – Third International Conference (ICPRAI), Lecture Notes in Computer Science, http://doi.org/10.1007/978-3-031-09282-4_35.

M. Mendler, S. Scheele, and L. Burke. 2021. “The Dosen Square Under Construction: A Tale of Four Modalities.” In International Conference on Automated Reasoning with Analytic Tableaux and Related Methods, Springer, 446–65. http://doi.org/10.1007/978-3-030-86059-2_26.

B. Finzel, D. E. Tafler, S. Scheele, and U. Schmid. 2021. “Explanation as a Process: User-Centric Construction of Multi-Level and Multi-Modal Explanations.” In Advances in Artificial Intelligence – 44th German Conference on AI (KI 2021), Lecture Notes in Computer Science, Springer, 80–94. http://doi.org/10.1007/978-3-030-87626-5_7.

F. Gretz, F.-J. Grosch, M. Mendler, and S. Scheele. 2020. “Synchronized Shared Memory and Procedural Abstraction: Towards a Formal Semantics of Blech.” In 2020 Forum for Specification and Design Languages (FDL), , 1–8. http://doi.org/10.1109/fdl50818.2020.9232942.

M. Mendler, and S. Scheele. 2014. “On the Computational Interpretation of CKn for Contextual Information Processing.” Fundamenta Informaticae 130.1: 125–62. http://doi.org/10.3233/fi-2014-984.

M. Mendler, and S. Scheele. 2012. “Towards a Simply Typed CALculus for Semantic Knowledge Bases.” In 3rd International Workshop on Logics, Agents, and Mobility (LAM), , 52–67. http://doi.org/10.29007/xlfw.

M. Mendler, and S. Scheele. 2011. “Cut-Free Gentzen Calculus for Multimodal CK.” Information and Computation 209.12: 1465–90. http://doi.org/10.1016/j.ic.2011.10.003.

M. Mendler, and S. Scheele. 2010. “Towards Constructive DL for Abstraction and Refinement.” Journal of Automated Reasoning 44.3: 207–43. http://doi.org/10.1007/s10817-009-9151-8.

M. Mendler, and S. Scheele. 2009. “Towards a Type System for Semantic Streams.” In Proceedings of the 1st International Workshop on Stream Reasoning, https://ceur-ws.org/Vol-466/.

L. Heidrich, E. Slany, and S. Scheele. “FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction.” Machine Learning and Knowledge Extraction 5.4. http://doi.org/10.3390/make5040076.

S. Mühlhausen, S. Gomez, N. Lauer, and T. Baumann. 2025. “Cross-Lingual Transfer Learning Does Not Improve Aphasic Speech Recognition.” In Elektronische Sprachsignalverarbeitung 2025,.

D. M. Schenke, and T. Baumann. 2025. “Controlled Diversity: Length-Optimized Natural Language Generation.” In Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models, http://doi.org/10.48550/arXiv.2502.19347.

T. Baumann, K. Eller, and N. Gagarina. 2024. “BERT-Based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives.” In Proceedings of the 6th Workshop on Narrative Understanding (WNU), Association for Computational Linguistics. http://doi.org/10.18653/v1/2024.wnu-1.16.

V. Penzkofer, and T. Baumann. 2024. “Evaluating and Fine-Tuning Retrieval-Augmented Language Models to Generate Text with Accurate Citations.” In Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024), Association for Computational Linguistic. https://aclanthology.org/2024.konvens-main.6.

C. Schuler, S. Nayak, D. Saha, and T. Baumann. 2024. “Can We See Your Response Before You Speak? Exploring Linguistic Information Found in Inter-Utterance Pauses.” In Elektronische Sprachsignalverarbeitung 2024, Tagungsband Der 35. Konferenz, http://doi.org/10.35096/othr/pub-7094.

T. Baumann, H. Hussein, and B. Meyer-Sickendiek. 2023. “Free Verse Prosodies: Identifying and Classifying Spoken Poetry Using Literary and Computational Perspectives (Rhythmicalizer).” Mixing Methods: Practical Insights from the Humanities in the Digital Age. http://doi.org/10.1515/9783839469132-018.

D. Saha, S. Nayak, and T. Baumann. 2022. “Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts.” In Proceedings of LREC, http://doi.org/10.48550/arXiv.2205.12194.

S. Nayak, C. Schuler, D. Saha, and T. Baumann. 2022. “A Deep Dive Into Neural Synchrony Evaluation for Audio-Visual Translation.” In International Conference on Multimodal Interaction, Bengaluru India: ACM. http://doi.org/10.1145/3536221.3556621.

A. Windbuhler, S. Okkesim, O. Christ, S. Mottaghi, S. Rastogi, M. Schmuker, T. Baumann, and U. G. Hofmann. 2022. “Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings.” In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom: IEEE. http://doi.org/10.1109/EMBC48229.2022.9871702.

T. Baumann, and S. Ashutosh. 2021. “Evaluating Heuristics for Audio-Visual Translation.” In Proceedings of the Conference on Computational Humanities Research 2021, Amsterdam, Netherlands. http://ceur-ws.org/Vol-2989/short_paper46.pdf.

N. Krüger, K. Fischer, P. Manoonpong, O. Palinko, L. Bodenhagen, T. Baumann, J. Kjærum, et al. 2021. “The SMOOTH-Robot: A Modular, Interactive Service Robot.” Frontiers in Robotics and AI 8. http://doi.org/10.3389/frobt.2021.645639.

S. Nayak, T. Baumann, S. Bhattacharya, A. Karakanta, M. Negri, and M. Turchi. 2020. “See Me Speaking? Differentiating on Whether Words Are Spoken On Screen or Off to Optimize Machine Dubbing.” In Companion Publication of the 2020 International Conference on Multimodal Interaction, Virtual Event Netherlands: ACM. http://doi.org/10.1145/3395035.3425640.

V. I. Schneider, B. Bugl, A. Sehr, and M. Niemetz. 2025. “An Overview for Realizing an Acoustical Assistance System for Visually Impaired Climbers.” In Proceedings DAS DAGA,.

T. Himaya, A. Sehr, T. Yoshida, and S. Hangai. 2020. “Text/Language-Independent Unknown Speaker Rejection Method Using LSP Codes.” IEEE 9th Global Conference on Consumer Electronics (GCCE). http://doi.org/10.1109/GCCE50665.2020.9291835.

J. Reschke, and A. Sehr. 2017. “Face Recognition with Machine Learning in OpenCV: Fusion of the Results with the Localization Data of an Acoustic Camera for Speaker Identification.” In Applied Research Conference, Munich. http://arxiv.org/pdf/1707.00835.

K. Kinoshita, M. Delcroix, S. Gannot, E. Habets, R. Haeb-Umbach, W. Kellermann, V. Leutnant, et al. 2016. “A Summary of the REVERB Challenge: State-of-the-Art and Remaining Challenges in Reverberant Speech Processing Research.” EURASIP Journal on Advances in Signal Processing. http://doi.org/10.1186/s13634-016-0306-6.

R. Maas, C. Huemmer, A. Sehr, and W. Kellermann. 2015. “A Bayesian View on Acoustic Model-Based Techniques for Robust Speech Recognition.” EURASIP Journal on Advances in Signal Processing. http://doi.org/10.1186/s13634-015-0287-x.

J. Wissing, T. Scholz, S. Saloman, L. F. Pelufo, S. Junger, A. Stefani, W. Tschekalinskij, S. Scheele, and U. Schmid. 2024. “SPECTRE: A Dataset for Spectral Reconstruction on Chip-Size Spectrometers with a Physics-Informed Augmentation Method.” In IEEE Sensors, The 23rd IEEE on Sensors, http://doi.org/10.1109/SENSORS60989.2024.10784898.

N. Schiering, S. Eichstädt, M. Heizmann, W. Koch, L.-S. Schneider, S. Scheele, and K.-D. Sommer. 2024. “Modelling of Measuring Systems – From White Box Models to Cognitive Approaches.” Measurement: Sensors. http://doi.org/10.1016/j.measen.2024.101503.

J. Wissing, L. Fargueta, and S. Scheele. 2024. “Spectral Reconstruction Using Neural Networks in Filterarray-Based Chip-Size Spectrometers.” tm-Technisches Messen. http://doi.org/10.1515/teme-2024-0063.

J. Wissing, and S. Scheele. 2023. “Boosting Energy Efficient Machine Learning in Smart Sensor Systems.” In SMSI 2023 – Sensor and Measurement Science International Conference, , 53–54. http://doi.org/10.5162/smsi2023/a4.1.

T. Rück, M. Müller, S. Jobst, S. Weigl, J. Pangerl, R. Bierl, and F. Matysik. 2022. “Digital Twin of a Photoacoustic Trace Gas Sensor for Monitoring Methane in Complex Gas Compositions.” Sensors and Actuators B: Chemical. http://doi.org/10.1016/j.snb.2022.133119.

J. Wissing, S. Scheele, A. Mohammed, D. Kolossa, and U. Schmid. 2022. “HiMLEdge – An Energy-Aware Optimization Framework for Hierarchical Machine Learning.” In Advanced Research in Technologies, Information, Innovation and Sustainability – Second International Conference (ARTIIS), Communications in Computer and Information Science, http://doi.org/10.1007/978-3-031-20316-9_2.

J. Weindl, M. G. Ehrnsperger, A. Prasetiadi, and T. F. Eibert. 2022. “Interdigital Resonators in Wideband Ridged-Waveguide Filters.” Advances in Radio Science 20. http://doi.org/10.5194/ars-20-29-2023.

M. G. Ehrnsperger, M. Noll, U. Siart, and T. F. Eibert. 2021. “Background and Clutter Removal Techniques for Ultra Short Range Radar.” In 17th European Radar Conference (EuRAD), Utrecht, The Netherlands, 78–81. http://doi.org/10.1109/EuRAD48048.2021.00031.

M. G. Ehrnsperger, M. Noll, S. Punzet, U. Siart, and T. F. Eibert. 2021. “Dynamic Eigenimage Based Background and Clutter Suppression for Ultra Short-Range Radar.” Advances in Radio Science 19: 71–77. http://doi.org/10.5194/ars-19-71-2021.

M. G. Ehrnsperger, T. Brenner, H. L. Hoese, U. Siart, and T. F. Eibert. 2020. “Real-Time Gesture Detection Based on Machine Learning Classification of Continuous Wave Radar Signals.” IEEE Sensors Journal 21: 8310–22. http://doi.org/10.1109/JSEN.2020.3045616.

M. G. Ehrnsperger, T. Brenner, U. Siart, and T. F. Eibert. 2020. “Real-Time Gesture Recognition with Shallow Convolutional Neural Networks Employing an Ultra Low Cost Radar System.” In German Microwave Conference., https://ieeexplore.ieee.org/document/9080241.

J. Kornprobst, T. J. Mittermaier, R. A. M. Mauermayer, G. F. Hamberger, M. G. Ehrnsperger, B. Lehmeyer, M. T. Ivrlac, et al. 2020. “Compact Uniform Circular Quarter-Wavelength Monopole Antenna Arrays with Wideband Decoupling and Matching Networks.” IEEE Transactions on Antennas and Propagation 69: 769–83. http://doi.org/10.1109/TAP.2020.3016422.

M. G. Ehrnsperger, U. Siart, M. Moosbühler, E. Daporta, and T. F. Eibert. 2019. “Signal Degradation through Sediments on Safety-Critical Radar Sensors.” Advances in Radio Science 17: 91–100. http://doi.org/10.5194/ars-17-91-2019.

M. G. Ehrnsperger, H. L. Hoese, U. Siart, and T. F. Eibert. 2019. “Performance Investigation of Machine Learning Algorithms for Simple Human Gesture Recognition Employing an Ultra Low Cost Radar System.” In Kleinheubacher Tagung, https://ieeexplore.ieee.org/document/8890169.

A. Gschossmann, S. Jobst, J. Mottok, and R. Bierl. 2019. “A Measure of Confidence of Artificial Neural Network Classifiers.” In ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8836211&isnumber=8836190.

M. Franz, T. Winker, S. Groppe, and W. Mauerer. 2024. “Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation.” In Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), http://doi.org/10.1109/QCE60285.2024.00055.

V. Tresp, S. Udluff, D. Hein, W. Hauptmann, M. Leib, C. Mutschler, D. Scherer, and W. Mauerer. 2023. “Workshop Summary: Quantum Machine Learning.” In Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), http://doi.org/10.1109/QCE57702.2023.10174.

T. Winker, S. Groppe, V. J. E. Uotila, Z. Yan, J. Lu, M. Franz, and W. Mauerer. 2023. “Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research.” In Proceedings of ACM SIGMOD/PODS International Conference on Management of Data, http://doi.org/10.1145/3555041.3589404.

W. Mauerer, and S. Scherzinger. 2022. “1-2-3 Reproducibility for Quantum Software Experiments.” Q-SANER@IEEE International Conference on Software Analysis, Evolution and Reengineering. http://doi.org/10.1109/SANER53432.2022.00148.

M. Franz, L. Wolf, M. Periyasamy, C. Ufrecht, D. D. Scherer, A. Plinge, C. Mutschler, and W. Mauerer. 2022. “Uncovering Instabilities in Variational-Quantum Deep Q-Networks.” Journal of The Franklin Institute. http://doi.org/10.1016/j.jfranklin.2022.08.021.

M. Schönberger, M. Franz, S. Scherzinger, and Wolfgang Mauerer. 2022. “Peel | Pile? Cross-Framework Portability of Quantum Software.” QSA@IEEE International Conference on Software Architecture (ICSA). http://doi.org/10.1109/ICSA-C54293.2022.00039.

A. Bayerstadler, G. Becquin, J. Binder, T. Botter, H. Ehm, T. Ehmer, M. Erdmann, et al. 2021. “Industry Quantum Computing Applications.” EPJ Quantum Technology. http://doi.org/10.1140/epjqt/s40507-021-00114-x.

W. Mauerer, S. Klessinger, and S. Scherzinger. 2022. “Beyond the Badge: Reproducibility Engineering as a Lifetime Skill.” In SEENG@International Conference on Software Engineering (ICSE), http://doi.org/10.1145/3528231.3528359.

L. Grabinger, F. Hauser, and J. Mottok. 2022. “Accessing the Presentation of Causal Graphs and an Application of Gestalt Principles with Eye Tracking.” In In Proceedings of the 29th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2022), New York, NY, USA. http://doi.org/10.1109/SANER53432.2022.00153.

W. Mauerer, M. Joblin, D. A. Tamburri, C. Paradis, R. Kazman, and Sven Apel. 2021. “In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study.” IEEE Transactions on Software Engineering. http://doi.org/10.1109/TSE.2021.3082074.

F. Hauser, S. Schreistetter, R. Reuter, J. Mottok, H. Gruber, K. Holmqvist, and N. Schorr. 2020. “Code Reviews in C++: Preliminary Results from an Eye Tracking Study.” In Proceedings of the ACM Eye Tracking Research and Application, ACM, Stuttgart, Germany. http://doi.org/10.1145/3379156.3391980.

R. Ramsauer, L. Bulwahn, D. Lohmann, and W. Mauerer. 2020. “The Sound of Silence: Mining Security Vulnerabilities from Secret Integration Channels in Open-Source Projects.” Proceedings of the 12th Cloud Computing Security Workshop (CCSW ’20). http://doi.org/10.1145/3411495.3421360.

W. Mauerer, and S. Scherzinger. 2020. “Educating Future Software Architects in the Art and Science of Analysing Software Data.” Proc. SEUH Workshop. http://ceur-ws.org/Vol-2531/paper10.pdf.

R. Ramsauer, D. Lohmann, and W. Mauerer. 2019. “The List Is the Process: Reliable Pre-Integration Tracking of Commits on Mailing Lists.” Proceedings of the 41st International Conference on Software Engineering (ICSE ’19). http://doi.org/10.1109/ICSE.2019.00088.

I. Hutzler, F. Hauser, R. Reuter, J. Mottok, and H. Gruber. 2018. “Will the Noun/Verb Analysis Be Used to Generate Class Diagrams? An Eye Tracking Study.” In 11th Annual International Conference of Education, Research and Innovation, Seville, Spain. http://doi.org/10.21125/iceri.2018.1103.

F. Hauser, M. Reiß, M. Nivala, J. Mottok, and H. Gruber. 2017. “Eye Tracking Applied: Visual Expertise in Code Reviews.” In Barcelona, Spain. http://doi.org/10.21125/edulearn.2017.1084.

M. Joblin, S. Apel, C. Hunsen, and W. Mauerer. 2017. “Classifying Developers into Core and Peripheral: An Empirical Study on Count and Network Metrics.” 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). http://doi.org/10.1109/icse.2017.23.

M. Nivala, F. Hauser, J. Mottok, and H. Gruber. 2016. “Developing Visual Expertise in Software Engineering: An Eye Tracking Study.” In Proceedings of the IEEE EDUCON 2016, Abu Dhabi, UAE. http://doi.org/10.1109/EDUCON.2016.7474614.

M. Joblin, S. Apel, and Wolfgang Mauerer. 2016. “Evolutionary Trends of Developer Coordination: A Network Approach.” Empirical Software Engineering. http://doi.org/10.1007/s10664-016-9478-9.

M. Joblin, W. Mauerer, S. Apel, J. Siegmund, and D. Riehle. 2015. “From Developer Networks to Verified Communities: A Fine-Grained Approach.” 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. http://doi.org/10.1109/icse.2015.73.

N. Weiß, E. Pozzobon, J. Mottok, and V. Matoušek. 2021. “Automated Reverse Engineering of CAN Protocols.” Neural Network World. http://doi.org/10.14311/NNW.2021.31.015.

A. Rudolph, S. Voget, and J. Mottok. 2018. “A Consistent Safety Case Argumentation for Artificial Intelligence in Safety Related Automotive Systems.” In ERTS 2018, Toulouse, France. https://hal.science/hal-02156048.

T.-J. Krause, M. Franke, I. Heigle, and M. Wagner. 2025. “Data Augmentation for Recorded Properties of Processed Materials in Industrial Production Processes for the Application of Machine Learning Models.” In International Conference of Modelling, Data Analytics and AI in Engineering (MADEAI),.

T. Lehrer, P. Stocker, F. Duddeck, and M. Wagner. 2025. “Deep-Drawn U-Shaped Sheet Metal Mesh Files Excerpted from NHTSA Crash Vehicles.” https://zenodo.org/records/14716577.

M. Franke, and M. Wagner. 2024. “Transient Simulations with Surrogate Elements.” In International Conference of Modelling, Data Analytics and AI in Engineering (MADEAI),.

T. Lehrer, A. Kaps, I. Lepenies, E. Raponi, M. Wagner, and F. Duddeck. 2024. “Complementing Drawability Assessment of Deep-Drawn Components With Surrogate-Based Global Sensitivity Analysis.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 10.3. http://doi.org/10.1115/1.4065143.

T. Lehrer, P. Stocker, F. Duddeck, and M. Wagner. 2024. “Parametric CAD Models to Create an Infinite Number of Structural U-Shaped Sheet Metal Geometries.” http://dx.doi.org/10.5281/zenodo.14191374.

M. Franke, and M. Wagner. 2024. “Transient Surrogate Modeling of Modally Reduced Structures with Discontinuous Loads and Damping.” Archive of Applied Mechanics 94.6. http://doi.org/10.1007/s00419-024-02601-w.

T. Lehrer, P. Stocker, F. Duddeck, and M. Wagner. 2023. “Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment.” In Third International Conference on Computational Science and AI in Industry (CSAI), International Centre for Numerical Methods in Engineering. http://doi.org/10.35096/othr/pub-6477.

T. Lehrer, A. Kaps, I. Lepenies, F. Duddeck, and M. Wagner. 2023. “2S-ML: A Simulation-Based Classification and Regression Approach for Drawability Assessment in Deep Drawing.” International Journal of Material Forming 16.5. http://doi.org/10.1007/s12289-023-01770-3.

A. Kaps, T. Lehrer, I. Lepenies, M. Wagner, and F. Duddeck. 2023. “Multi-Fidelity Optimization of Metal Sheets Concerning Manufacturability in Deep-Drawing Processes.” Structural and Multidisciplinary Optimization 66.8. http://doi.org/10.1007/s00158-023-03631-8.

W. Huang, M. Melzer, and J. Dünnweber. 2024. “Optimizing Smart Retail by Experiment Using an Online AI Model Exploration Interface.” In 2024 Winter Simulation Conference (WSC), http://doi.org/10.5555/3712729.3712947.

S. Galka. 2024. “Reducing Transient Behavior in Simulation-Based Digital Twins: A Novel Initialization Approach for Order Picking Systems.” In 2024 Winter Simulation Conference (WSC), http://doi.org/10.1109/WSC63780.2024.10838961.

S. Galka, and F. Schmid. 2024. “Initialization of Simulation-Based Digital Twins for Internal Transport Systems.” In IFAC Symposium on Information Control Problems in Manufacturing (INCOM), http://doi.org/10.1016/j.ifacol.2024.09.127.

L. Schuhegger, T. Armbruster, and S. Galka. 2023. “Factory Data Model for Planning Variant Sequences, Expansion Stages and Reconfigurations.” In IFAC Symposium on Information Control Problems in Manufacturing (INCOM), http://doi.org/10.1016/j.ifacol.2023.10.1573.

M. Marinkovic, S. Galka, and S. Meißner. 2023. “Digital Twins for Internal Transport Systems: Use Cases, Functions, and System Architecture.” In Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS), https://hdl.handle.net/10125/102777.

J. Schneidewind, and S. Galka. 2023. “Interactive Reinforcement Learning-Based Factory Layout Planning.” In Proceedings of the 13th Conference on Learning Factories (CLF), http://doi.org/10.2139/ssrn.4469300.

F. Schmid, T. Wild, J. Schneidewind, T. Vogl, L. Schuhegger, and S. Galka. 2022. “Simulation Based Approach for Reconfiguration and Ramp Up Scenario Analysis in Factory Planning.” In 2022 Winter Simulation Conference (WSC), Singapore: IEEE. http://doi.org/10.1109/WSC57314.2022.10015310.

B. Rolf, T. Reggelin, S. Lang, and S. Galka. 2022. “Developing a Decision Support System for Integrated Decision-Making in Purchasing and Scheduling under Lead Time Uncertainty.” In Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS), http://hdl.handle.net/10125/79579.

D. Kauke, J. Fattner, and S. Galka. 2021. “Digital Twins in Order Picking Systems for Operational Decision Support.” In Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), http://hdl.handle.net/10125/70812.

T. Reggelin, S. Galka, S. Lang, and D. Ivanov. 2021. “Introduction to the Minitrack on Modeling and Decision Making in Manufacturing and Logistics in the Age of Industry 4.0.” In Proceedings of the 54th Hawaii International Conference on System Sciences, http://hdl.handle.net/10125/70810.

R. Wöstmann, P. Schlunder, F. Temme, R. Klinkenberg, J. Kimberger, A. Spichtinger, M. Goldhacker, and J. Deuse. 2020. “Conception of a Reference Architecture for Machine Learning in the Process Industry.” In 2020 IEEE International Conference on Big Data (Big Data), , 1726–35. http://doi.org/10.1109/BigData50022.2020.9378290.

M. Weiß. 2021. “Optimization of Cartesian Tasks with Configuration Selection.” 2nd IMA Conference on Mathematics of Robotics. http://doi.org/10.1007/978-3-030-91352-6_16.

M. G. Weiß. 2019. “Optimal Object Placement Using a Virtual Axis.” International Symposium on Advances in Robot Kinematics. http://doi.org/10.1007/978-3-319-93188-3_14.

M. G. Weiß. 2015. “A Class of 6R Robots and Poses with 16 Analytical Solutions.” Proceedings of the IMA Conference on Mathematics of Robotics. https://cdn.ima.org.uk/wp/wp-content/uploads/2017/12/Weiss-A-Class-of-6R-Robots-and-Poses-with-16-Analytical-Solutions.pdf.

M. Kurze, M. G. Weiß, and M. Otter. 2006. “Methods and Tools to Design and Test Robot Control Systems.” 37th International Symposium on Robotics (ISR). https://www.researchgate.net/publication/224928821_Methods_and_Tools_to_Design_and_Test_Robot_Control_Systems.

F. Eder, S. Hoeng, M. Schmailzl, T. Linner, and M. Obergrießer. 2025. “Towards Improving Data Interoperability for the Reconstruction of Existing Buildings.” In Advances in Information Technology in Civil and Building: Proceedings of ICCCBE 2024, http://doi.org/10.1007/978-3-031-84208-5_9.

S. Hoeng, F. Eder, M. Schmailzl, T. Linner, and M. Obergrießer. 2025. “Exploring the Potential of BIM Models for Deriving Synthetic Training Data for Machine Learning Applications.” In Advances in Information Technology in Civil and Building: Proceedings of ICCCBE 2024, http://doi.org/10.1007/978-3-031-87364-5_5.

M. Melzer, J. Dunnweber, and T. Baumann. 2022. “Towards Smart Home Data Interpretation Using Analogies to Natural Language Processing.” In 2022 IEEE International Conference on Smart Internet of Things (SmartIoT), http://doi.org/10.1109/SmartIoT55134.2022.00020.

J. Reschke. 2021. “Fahrerintentionserkennung Zur Lichtbasierten Kommunikation Mit Fußgängern.” Doctoral thesis. https://publikationen.bibliothek.kit.edu/1000140184.

J. Reschke, and M. Klaußner. 2021. “Explicit and Implicit Communication for Automated Vehicles.” In Proceedings of the 14th International Symposium on Automotive Lighting (ISAL), https://www.utzverlag.de/assets/pdf/44953dbl.pdf.

J. Reschke, S. Berlitz, and C. Neumann. 2020. “Personalised Neural Networks for a Driver Intention Prediction: Communication as Enabler for Automated Driving.” Advanced Optical Technologies 9(6). doi:https://doi.org/10.1515/aot-2020-0035.

J. Reschke, T. Höß, B. Schleyer, S. Berlitz, and C. Neumann. 2019. “How Vehicles Learn to Display Symbols to Pedestrians.” In Proceedings of the 13th International Symposium on Automotive Lighting (ISAL), https://www.utzverlag.de/catalog/book/44817.

J. Reschke, S. Prösl, M. Hamm, and C. Neumann. 2018. “Assistance System for Vehicle-Pedestrian-Interaction: Deep Learning and Driver Intention Prediction.” In SIA VISION, https://wiki.unece.org/download/attachments/75531441/AVSR-02-16e.pdf?api=v2.

J. Völkl, A. Sarkar, M. Melzer, and J. Dünnweber. 2024. “Dynamic Route Planning for a Data Collecting Luggage Transport Service.” In Proceedings of the 18th IEEE International on Control & Automation (ICCA), http://doi.org/10.1109/ICCA62789.2024.10591830.

V. Dahmen, S. Weikl, and K. Bogenberger. 2024. “Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data.” Transportation Research Record: Journal of the Transportation Research Board. http://doi.org/10.1177/03611981241246973.

S. Weikl, and P. Mayer. 2023. “Data-Driven Quality Assessment of Cycling Networks.” Frontiers in Future Transportation. http://doi.org/10.3389/ffutr.2023.1127742.

M. Rostami-Shahrbabak, S. Weikl, T. Niels, and K. Bogenberger. 2023. “Modeling Vehicle Flocking in Lane-Free Automated Traffic.” Transportation Research Record: Journal of the Transportation Research Board. http://doi.org/10.1177/03611981231159405.

C. Hardt, S. Weikl, Y. Zhang, K. Lippoldt, and K. Bogenberger. 2022. “Empirical Analysis of Free-Floating Carsharing Systems in Munich and Berlin.” In Transportation Research Board 101st Annual Meeting, https://www.researchgate.net/publication/357900050_Empirical_Analysis_of_Free-Floating_Carsharing_Systems_in_Munich_and_Berlin_TRBAM-22-02706.

M. Rostami-Shahrbabak, S. Weikl, M. Akbarzadeh, and K. Bogenberger. 2022. “A Two-Layer Approach for Vehicular Flocking in Lane-Free Environment.” In 11th Triennal Symposium on Transportation Analysis (TRISTAN), https://tristan2022.org/proceedings.

S. Haimerl, C. Tschernitz, T. Schiller, C. Weig, S. Galka, and U. Briem. 2022. “Development of a Simulation Framework for Urban Ropeway Systems and Analysis of the Planned Ropeway Network in Regensburg, Germany.” In Proceedings of the Winter Simulation Conference, WSC ’22, Singapore, Singapore: IEEE Press. http://doi.org/https://dl.acm.org/doi/10.5555/3586210.3586326.

T. Stadler, A. Sarkar, and J. Dünnweber. 2021. “Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm.” Computer Information Systems and Industrial Management. http://doi.org/10.1007/978-3-030-84340-3_36.

S. Weikl, and K. Bogenberger. 2019. “Integrated Relocation Model for Free-Floating Carsharing Systems: Field Trial Results.” Transportation Research Record 2563,. http://doi.org/10.3141/2536-03.

S. Weikl, K. Bogenberger, and N. Geroliminis. 2016. “Simulation Framework for Proactive Relocation Strategies in Free-Floating Carsharing Systems.” In Transportation Research Board 95th Annual Meeting, https://mediatum.ub.tum.de/node?id=1631662.

S. Weikl. 2016. “A Mesoscopic Relocation Model for Free-Floating Carsharing Systems.” Doctoral thesis. https://athene-forschung.unibw.de/doc/112725/112725.pdf.

S. Weikl, and K. Bogenberger. 2015. “A Practice-Ready Relocation Model for Free-Floating Carsharing Systems with Electric Vehicles – Mesoscopic Approach and Field Trial Results.” Transportation Research Part C: Emerging Technologies. http://doi.org/10.1016/j.trc.2015.06.024.

S. Schmöller, S. Weikl, J. Müller, and K. Bogenberger. 2015. “Empirical Analysis of Free-Floating Carsharing Usage: The Munich and Berlin Case.” Transportation Research Part C: Emerging Technologies. http://doi.org/10.1016/j.trc.2015.03.008.

S. Schmöller, S. Weikl, J. Müller, and K. Bogenberger. 2014. “Empirical Data Analysis of Free-Floating Carsharing System.” In Transportation Research Board 93rd Annual Meeting, https://trid.trb.org/View/1289361.

S. Weikl, and K. Bogenberger. 2012. “Relocation Strategies and Algorithms for Free-Floating Car Sharing Systems.” IEEE Intelligent Transportation Systems Magazine 5. http://doi.org/10.1109/MITS.2013.2267810.

M. W. Scheppach, R. Mendel, A. Muzalyova, D. Rauber, A. Probst, S. Nagl, C. Römmele, et al. 2025. “Artificial Intelligence Improves Submucosal Vessel Detection during Third Space Endoscopy.” Endoscopy. http://doi.org/10.1055/a-2534-1164.

R. Hartmann, M. Weiherer, F. Nieberle, C. Palm, V. Brébant, L. Prantl, P. Lamby, et al. 2025. “Evaluating Smartphone-Based 3D Imaging Techniques for Clinical Application in Oral and Maxillofacial Surgery: A Comparative Study with the Vectra M5.” Oral and Maxillofacial Surgery 29. http://doi.org/10.1007/s10006-024-01322-2.

M. Weiherer, A. v. Riedheim, V. Brébant, B. Egger, and C. Palm. 2025. “IRBSM: A Deep Implicit 3D Breast Shape Model.”

E. Schulze, C. Palm, M. Kerschbaum, R. Seidel, L. Lehmann, M. Koller, and A. Pfingsten. 2024. “KI-Gestützte Untersuchung in Der Nicht-Operativen Versorgung Symptomgebender Erkrankungen Des Kniegelenks – Ein Multiprofessionelles Konzept (KINEESIO).” MSK – Muskuloskelettale Physiotherapie 28(5): 312–21. http://doi.org/10.1055/a-2402-9982.

D. Roser, M. Meinikheim, R. Mendel, C. Palm, A. Muzalyova, D. Rauber, T. Rückert, et al. 2024. “Mensch-Maschine-Interaktion: Einfluss Künstlicher Intelligenz Auf Das Diagnostische Vertrauen von Endoskopikern Bei Der Beurteilung Des Barrett-Ösophagus.” In Zeitschrift Für Gastroenterologie, , e575–76. http://doi.org/10.1055/s-0044-1789656.

L. A. Souza, A. G. C. Pacheco, G. G. d. Angelo, T. Oliveira-Santos, C. Palm, and J. P. Papa. 2024. “LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection.” In 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Manaus, Brazil, 9/30/2024 – 10/3/2024, , 1–6. http://doi.org/10.1109/SIBGRAPI62404.2024.10716324.

M. Scheppach, R. Mendel, A. Muzalyova, D. Rauber, A. Probst, S. Nagl, C. Römmele, et al. 2024. “Künstliche Intelligenz Erhöht Die Gefäßerkennung von Endoskopikern Bei Third Space Endoskopie.” In Zeitschrift Für Gastroenterologie, , e830. http://doi.org/10.1055/s-0044-1790087.

M. Scheppach, D. W. Nunes, X. Arizi, D. Rauber, A. Probst, S. Nagl, C. Römmele, et al. 2024. “Intraoperative Phasenerkennung Bei Endoskopischer Submukosadissektion Mit Hilfe von Künstlicher Intelligenz.” In Zeitschrift Für Gastroenterologie, , e828. http://doi.org/10.1055/s-0044-1790084.

L. A. de Souza, L. A. Passos, M. C. S. Santana, R. Mendel, R. Rauber, A. Ebigbo, A. Probst, et al. 2024. “Layer-Selective Deep Representation to Improve Esophageal Cancer Classification.” Medical & Biological Engineering & Computing. http://doi.org/10.1007/s11517-024-03142-8.

D. A. Roser, M. Meinikheim, R. Mendel, C. Palm, A. Probst, A. Muzalyova, M. W. Scheppach, et al. 2024. “Human-Computer Interaction: Impact of Artificial Intelligence on the Diagnostic Confidence of Endoscopists Assessing Videos of Barrett’s Esophagus.” In Endoscopy, , 79. http://doi.org/10.1055/s-0044-1782859.

M. Meinikheim, R. Mendel, C. Palm, A. Probst, A. Muzalyova, M. W. Scheppach, S. Nagl, et al. 2024. “Influence of Artificial Intelligence on the Diagnostic Performance of Endoscopists in the Assessment of Barrett’s Esophagus: A Tandem Randomized and Video Trial.” Endoscopy 56: 641–49. http://doi.org/10.1055/a-2296-5696.

M. W. Scheppach, D. W. Nunes, X. Arizi, D. Rauber, A. Probst, S. Nagl, C. Römmele, et al. 2024. “Procedural Phase Recognition in Endoscopic Submucosal Dissection (ESD) Using Artificial Intelligence (AI).” In Endoscopy, , S439. http://doi.org/10.1055/s-0044-1783804.

M. W. Scheppach, R. Mendel, D. Rauber, A. Probst, S. Nagl, C. Römmele, M. Meinikheim, et al. 2024. “Artificial Intelligence (AI) Improves Endoscopists’ Vessel Detection during Endoscopic Submucosal Dissection (ESD).” In Endoscopy, , S93. http://doi.org/10.1055/s-0044-1782891.

A. Ebigbo, D. Rauber, M. Ayoub, L. Birzle, T. Matsumura, A. Probst, I. Steinbrück, et al. 2024. “Early Esophageal Cancer and the Generalizability of Artificial Intelligence.” In Endoscopy, , S428. http://doi.org/10.1055/s-0044-1783775.

S. Zellmer, D. Rauber, A. Probst, T. Weber, G. Braun, C. Römmele, S. Nagl, et al. 2024. “Artificial Intelligence as a Tool in the Detection of the Papillary Ostium during ERCP.” In Endoscopy, , S198. http://doi.org/10.1055/s-0044-1783138.

M. Meinikheim, R. Mendel, C. Palm, A. Probst, A. Muzalyova, M. W. Scheppach, S. Nagl, et al. 2024. “Effect of AI on Performance of Endoscopists to Detect Barrett Neoplasia: A Randomized Tandem Trial.”

R. Hartmann, F. Nieberle, C. Palm, V. Brébant, L. Prantl, R. Kuehle, T. E. Reichert, J. Taxis, and T. Ettl. 2024. “Utility of Smartphone-Based Three-Dimensional Surface Imaging for Digital Facial Anthropometry.” JPRAS Open 39: 330–43. http://doi.org/10.1016/j.jpra.2024.01.014.

R. Mendel, T. Rueckert, D. Wilhelm, D. Rueckert, and C. Palm. 2024. “Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation.” 10.48550/arXiv.2403.03120.

T. Rueckert, M. Rieder, H. Feussner, D. Wilhelm, D. Rueckert, and C. Palm. 2024. “Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-Temporal Information.” In Bildverarbeitung Für Die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen, eds. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, and Thomas Tolxdorff. , 298–303. http://doi.org/10.1007/978-3-658-44037-4_78.

L. A. Souza Jr., A. G.C. Pacheco, L. A. Passos, M. C. S. Santana, R. Mendel, A. Ebigbo, A. Probst, et al. 2024. “DeepCraftFuse: Visual and Deeply-Learnable Features Work Better Together for Esophageal Cancer Detection in Patients with Barrett’s Esophagus.” Neural Computing and Applications. http://doi.org/10.1007/s00521-024-09615-z.

M. Gutbrod, B. Geisler, D. Rauber, and C. Palm. 2024. “Data Augmentation for Images of Chronic Foot Wounds.” In Bildverarbeitung Für Die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen, eds. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, and Thomas Tolxdorff. , 261–66. http://doi.org/10.1007/978-3-658-44037-4_71.

S. Hammer, D. W. Nunes, M. Hammer, F. Zeman, M. Akers, A. Götz, A. Balla, et al. 2024. “Deep Learning-Based Differentiation of Peripheral High-Flow and Low-Flow Vascular Malformations in T2-Weighted Short Tau Inversion Recovery MRI.” Clinical hemorheology and microcirculation: 1–15. http://doi.org/10.3233/CH-232071.

T. Rückert, D. Rückert, and C. Palm. 2024. “Methods and Datasets for Segmentation of Minimally Invasive Surgical Instruments in Endoscopic Images and Videos: A Review of the State of the Art.” Computers in Biology and Medicine 169. http://doi.org/10.1016/j.compbiomed.2024.107929.

C. Palm. 2023. “History, Core Concepts, and Role of AI in Clinical Medicine.” In AI in Clinical Medicine: A Practical Guide for Healthcare Professionals, eds. M. F. Byrne, N. Parsa, A. T. Greenhill, D. Chahal, O. Ahmad, and U. Bargci. https://doi.org/10.1002/9781119790686.ch5.

J. Frikel, and P. Bauer. 2023. “A Deep Learning Based ρ-Filtered Layergram Reconstruction Method for Computed Tomography.” In 21st International Conference of Numerical Analysis and Applied Mathematics,.

M. W. Scheppach, R. Mendel, A. Probst, S. Nagl, M. Meinikheim, H. C. Yip, L. H. S. Lau, et al. 2023. “Effekt Eines Künstliche Intelligenz (KI) – Algorithmus Auf Die Gefäßdetektion Bei Third Space Endoskopien.” In Zeitschrift Für Gastroenterologie, http://doi.org/10.1055/s-0043-1771980.

S. Zellmer, D. Rauber, A. Probst, T. Weber, Sandra S. Nagl, C. Römmele, E. Schnoy, et al. 2023. “Verwendung Künstlicher Intelligenz Bei Der Detektion Der Papilla Duodeni Major.” In Zeitschrift Für Gastroenterologie, http://doi.org/10.1055/s-0043-1772000.

M. Meinikheim, R. Mendel, A. Probst, M. W. Scheppach, S. Nagl, E. Schnoy, C. Römmele, et al. 2023. “Einfluss von Künstlicher Intelligenz Auf Die Performance von Niedergelassenen Gastroenterolog:Innen Bei Der Beurteilung von Barrett-Ösophagus.” In Zeitschrift Für Gastroenterologie, http://doi.org/10.1055/s-0043-1771711.

T. Rückert, M. Rieder, D. Rauber, M. Xiao, E. Humolli, H. Feussner, D. Wilhelm, and C. Palm. 2023. “Augmenting Instrument Segmentation in Video Sequences of Minimally Invasive Surgery by Synthetic Smoky Frames.” In International Journal of Computer Assisted Radiology and Surgery, , S54–56. http://doi.org/10.1007/s11548-023-02878-2.

R. Mendel, D. Rauber, and C. Palm. 2023. “Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data.” In Bildverarbeitung Für Die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2- 4, 2023, Braunschweig, , 128–13. http://doi.org/10.1007/978-3-658-41657-7.

M. W. Scheppach, D. Rauber, J. Stallhofer, A. Muzalyova, V. Otten, C. Manzeneder, T. Schwamberger, et al. 2023. “Performance Comparison of a Deep Learning Algorithm with Endoscopists in the Detection of Duodenal Villous Atrophy (VA).” In Endoscopy, http://doi.org/10.1055/s-0043-1765421.

M. W. Scheppach, R. Mendel, A. Probst, D. Rauber, T. Rueckert, M. Meinikheim, C. Palm, H. Messmann, and A. Ebigbo. 2023. “Real-Time Detection and Delineation of Tissue during Third-Space Endoscopy Using Artificial Intelligence (AI).” In Endoscopy, , S53–54. http://doi.org/10.1055/s-0043-1765128.

M. Meinikheim, R. Mendel, A. Probst, M. W. Scheppach, E. Schnoy, S. Nagl, C. Römmele, et al. 2023. “AI-Assisted Detection and Characterization of Early Barrett’s Neoplasia: Results of an Interim Analysis.” In Endoscopy, http://doi.org/10.1055/s-0043-1765437.

M. W. Scheppach, D. Rauber, J. Stallhofer, A. Muzalyova, V. Otten, C. Manzeneder, T. Schwamberger, et al. 2023. “Detection of Duodenal Villous Atrophy on Endoscopic Images Using a Deep Learning Algorithm.” Gastrointestinal Endoscopy. http://doi.org/10.1016/j.gie.2023.01.006.

R. Mendel, D. Rauber, L. A. de Souza Jr., J. P. Papa, and C. Palm. 2023. “Error-Correcting Mean-Teacher: Corrections Instead of Consistency-Targets Applied to Semi-Supervised Medical Image Segmentation.” Computers in Biology and Medicine. http://doi.org/10.1016/j.compbiomed.2023.106585.

A. Ebigbo, R. Mendel, M. W. Scheppach, A. Probst, N. Shahidi, F. Prinz, C. Fleischmann, et al. 2022. “Vessel and Tissue Recognition during Third-Space Endoscopy Using a Deep Learning Algorithm.” Gut. http://doi.org/10.1136/gutjnl-2021-326470.

C. Römmele, R. Mendel, C. Barrett, H. Kiesl, D. Rauber, T. Rückert, L. Kraus, et al. 2022. “An Artificial Intelligence Algorithm Is Highly Accurate for Detecting Endoscopic Features of Eosinophilic Esophagitis.” Scientific Reports. http://doi.org/10.1038/s41598-022-14605-z.

L. A. de Souza Jr., R. Mendel, S. Strasser, A. Ebigbo, A. Probst, H. Messmann, J. P. Papa, and C. Palm. 2021. “Convolutional Neural Networks for the Evaluation of Cancer in Barrett’s Esophagus: Explainable AI to Lighten up the Black-Box.” Computers in Biology and Medicine. http://doi.org/10.1016/j.compbiomed.2021.104578.

J. Arribas, G. Antonelli, L. Frazzoni, L. Fuccio, A. Ebigbo, F. v. d. Sommen, N. Ghatwary, et al. 2021. “Standalone Performance of Artificial Intelligence for Upper GI Neoplasia: A Meta-Analysis.” Gut. http://doi.org/10.1136/gutjnl-2020-321922.

R. Mendel, L. A. de Souza Jr., D. Rauber, J. P. Papa, and C. Palm. 2020. “Semi-Supervised Segmentation Based on Error-Correcting Supervision.” European Conference on Computer Vision (ECCV). http://doi.org/10.1007/978-3-030-58526-6_9.

A. Ebigbo, R. Mendel, A. Probst, J. Manzeneder, F. Prinz, L. A. de Souza Jr., J. P. Papa, C. Palm, and H. Messmann. 2020. “Real-Time Use of Artificial Intelligence in the Evaluation of Cancer in Barrett’s Oesophagus.” Gut. http://doi.org/10.1136/gutjnl-2019-319460.

A. Ebigbo, R. Mendel, T. Rückert, L. Schuster, A. Probst, J. Manzeneder, F. Prinz, et al. 2020. “Endoscopic Prediction of Submucosal Invasion in Barrett’s Cancer with the Use of Artificial Intelligence: A Pilot Study.” Endoscopy. http://doi.org/10.1055/a-1311-8570.

M. Melzner, C. Pfeiffer, F. Suess, and S. Dendorfer. 2022. “Musculoskeletal Simulation of Elbow Stability for Common Injury Patterns.” Journal of Orthopaedic Research. http://doi.org/10.1002/jor.25460.

M. Melzner, F. Süß, and S. Dendorfer. 2022. “The Impact of Anatomical Uncertainties on the Predictions of a Musculoskeletal Hand Model – a Sensitivity Study.” Computer Methods in Biomechanics and Biomedical Engineering. http://doi.org/10.1080/10255842.2021.1940974.

S. Auer, J. Schiebl, K. Iversen, D. S. Chander, M. Damsgaard, and S. Dendorfer. 2022. “Biomechanical Assessment of the Design and Efficiency of Occupational Exoskeletons with the AnyBody Modeling System.” Zeitschrift für Arbeitswissenschaft. http://doi.org/10.1007/s41449-022-00336-4.

M. Weiherer, A. Eigenberger, B. Egger, V. Brébant, L. Prantl, and C. Palm. 2022. “Learning the Shape of Female Breasts: An Open-Access 3D Statistical Shape Model of the Female Breast Built from 110 Breast Scans.” The Visual Computer. http://doi.org/10.1007/s00371-022-02431-3.

C. Birkenmaier, and L. Krenkel. 2021. “Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs.” Notes on Numerical Fluid Mechanics and Multidisciplinary Design. http://doi.org/10.1007/978-3-030-79561-0_43.

L. Engelhardt, M. Melzner, L. Havelkova, P. Fiala, P. Christen, S. Dendorfer, and U. Simon. 2021. “A New Musculoskeletal AnyBody Detailed Hand Model.” Computer Methods in Biomechanics and Biomedical Engineering. http://doi.org/10.1080/10255842.2020.1851367.

A. Saffert, M. Melzner, and S. Dendorfer. 2021. “Biomechanical Analysis of the Right Elevated Glenohumeral Joint in Violinists during Legato-Playing.” Technology and Health Care. http://doi.org/10.3233/THC-219001.

M. Aurbach, J. Špička, F. Süß, J. Vychytil, L. Havelková, T. Ryba, and S. Dendorfer. 2020. “Torus Obstacle Method as a Wrapping Approach of the Deltoid Muscle Group for Humeral Abduction in Musculoskeletal Simulation.” Journal of Biomechanics. http://doi.org/10.1016/j.jbiomech.2020.109864.

E. D. Pieri, F. Atzori, S. J. Ferguson, S. Dendorfer, M. Leunig, and M. Aepli. 2020. “Contact Force Path in Total Hip Arthroplasty: Effect of Cup Medialisation in a Whole-Body Simulation.” HIP International. http://doi.org/10.1177/1120700020917321.

C. Birkenmeier. 2020. “A Hybrid Computational Approach for Non-Newtonian Blood Flows Using Euler-Euler Modelling and Deep Regression.” Doctoral thesis. https://books.google.de/books?id=VA3yzgEACAAJ.

M. Aurbach, J. Špička, F. Süß, and S. Dendorfer. 2020. “Evaluation of Musculoskeletal Modelling Parameters of the Shoulder Complex during Humeral Abduction above 90°.” Journal of Biomechanics. http://doi.org/10.1016/j.jbiomech.2020.109817.

S. Wein, G. Deco, A. M. Tomé, M. Goldhacker, W. M. Malloni, M. W. Greenlee, and E. W. Lang. 2021. “Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning” ed. P. Aricò. Computational Intelligence and Neuroscience 2021. http://doi.org/10.1155/2021/5573740.

C. Ott, K. Rosengarth, C. Doenitz, J. Hoehne, C. Wendl, F. Dodoo-Schittko, E. Lang, N. O. Schmidt, and M. Goldhacker. 2021. “Preoperative Assessment of Language Dominance through Combined Resting-State and Task-Based Functional Magnetic Resonance Imaging.” Journal of Personalized Medicine 11. http://doi.org/10.3390/jpm11121342.

S. Wein, A. M. Tomé, M. Goldhacker, M. W. Greenlee, and E. W. Lang. 2020. “A Constrained ICA-EMD Model for Group Level FMRI Analysis.” Frontiers in Neuroscience 14. http://doi.org/10.3389/fnins.2020.00221.

M. Goldhacker, A. M. Tomé, M. W. Greenlee, and E. W. Lang. 2018. “Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States.” Frontiers in Human Neuroscience 12. http://doi.org/10.3389/fnhum.2018.00253.

M. Goldhacker, P. Keck, A. Igel, E. W. Lang, and A. M. Tomé. 2017. “A Multi-Variate Blind Source Separation Algorithm.” Computer Methods and Programs in Biomedicine 151. http://doi.org/10.1016/j.cmpb.2017.08.019.

M.-C. Fellner, G. Volberg, K. J. Mullinger, M. Goldhacker, M. Wimber, M. W. Greenlee, and S. Hanslmayr. 2016. “Spurious Correlations in Simultaneous EEG-FMRI Driven by in-Scanner Movement.” NeuroImage 133. http://doi.org/10.1016/j.neuroimage.2016.03.031.

M.-C. Fellner, G. Volberg, M. Wimber, M. Goldhacker, M. W. Greenlee, and S. Hanslmayr. 2016. “Spatial Mnemonic Encoding: Theta Power Decreases and Medial Temporal Lobe BOLD Increases Co-Occur during the Usage of the Method of Loci.” eNeuro 3(6). http://doi.org/10.1523/ENEURO.0184-16.2016.

K. Al-Subari, S. Al-Baddai, A. M. Tomé, M. Goldhacker, R. Faltermeier, and E. W. Lang. 2015. “EMDLAB: A Toolbox for Analysis of Single-Trial EEG Dynamics Using Empirical Mode Decomposition.” Journal of Neuroscience Methods 253. http://doi.org/10.1016/j.jneumeth.2015.06.020.

M. Goldhacker, K. Rosengarth, T. Plank, and M. W. Greenlee. 2014. “The Effect of Feedback on Performance and Brain Activation during Perceptual Learning.” Vision Research 99. http://doi.org/10.1016/j.visres.2013.11.010.

T. Plank, K. Rosengarth, C. Schmalhofer, M. Goldhacker, S. Brandl-Rühle, and M. W. Greenlee. 2014. “Perceptual Learning in Patients with Macular Degeneration.” Frontiers in Psychology 5. http://doi.org/10.3389/fpsyg.2014.01189.

D. Frommeld, S. Haug, E. Currle, and K. Weber. 2022. “Telepräsenzroboter in Der Schlaganfallrehabilitation.” Pflegezeitschrift. http://doi.org/10.1007/s41906-022-1251-7.

U. Scorna, D. Frommeld, S. Haug, and K. Weber. 2022. “Digitale Assistenzsysteme in Der Altenpflege – Fluch Oder Segen? Eine Empirische Untersuchung Zu Chancen, Risiken Und Auswirkungen.” In Organisationen in Zeiten Der Digitalisierung, https://link.springer.com/10.1007/978-3-658-36514-1_12.

U. Scorna, D. Frommeld, S. Haug, and K. Weber. 2022. “Digitale Technik in Der Pflege Als Generallösung? Neue Perspektiven Auf Altersgerechte Assistenzsysteme.” In Gegenwart Und Zukunft Sozialer Dienstleistungsarbeit. Chancen Und Risiken Der Digitalisierung in Der Sozialwirtschaft., Springer VS.

S. Haug. 2021. “Nutzung, Planung Und Bewertung Digitaler Assistenzsysteme in Der Pflege: Ergebnisse Einer Befragung von Führungskräften in Ambulanten Und Stationären Einrichtungen.” In Gute Technik Für Ein Gutes Leben Im Alter?, doi:10.1515/9783839454695-008.

S. Haug, and M. Vetter. 2021. “Altersgerechtes Wohnen Im Quartier: Das Beispiel Margaretenau Regensburg.” http://doi.org/10.1007/s00548-020-00678-3.

S. Haug, M. Vetter, and K. Weber. 2020. “Gebäudesanierung Zwischen Energieeffizienz Und Sozialverträglichkeit: Zwei Empirische Fallstudien.” TATuP – Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis. http://doi.org/10.14512/tatup.29.3.56.

U. Scorna, K. Weber, and S. Haug. 2018. “ELSI in Serious Games Für Die Technikunterstützte Medizinische Ausbildung: Das Beispiel HaptiVisT.” Technische Unterstützungssysteme, die die Menschen wirklich wollen. Konferenzband.

K. Weber, and S. Haug. 2018. “Ist Automatisiertes Fahren Nachhaltig?” Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis (TATuP). http://doi.org/10.14512/tatup.27.2.16.

J. Höcherl, S. Niedersteiner, S. Haug, C. Pohlt, T. Schlegl, K. Weber, and T. Berlehner. 2016. “Smart Workbench: Ein Multimodales Und Bidirektionales Assistenzsystem Für Den Industriellen Einsatz.” Technische Unterstützungssysteme, die die Menschen wirklich wollen, Proceedingsband. https://www.researchgate.net/publication/311699737_Smart_Workbench_Ein_multimodales_und_bidirektionales_Assistenzsystem_fur_den_industriellen_Einsatz.

D. Franz, U. Katzky, S. Neumann, J. Perret, M. Hofer, M. Huber, S. Schmitt-Rüth, et al. 2016. “Haptisches Lernen Für Cochlea Implantationen. Konzept HaptiVisT-Projekt.” 15. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC). https://curac.org/images/advportfoliopro/images/CURAC2016/CURAC%202016%20Tagungsband.pdf.

S. Haug, L. Glashauser, B. Großmann, C. Pohlt, T. Schlegl, A. Wackerbarth, and K. Weber. 2016. “Gamification Im Anlernprozess Am Industriearbeitsplatz – Ein Inklusiver Ansatz. Studie Zur Entwicklung Eines Anlerntutorials Für Ein Gestengesteuertes Teilautomatisiertes Assistenzsystem.” Technische Unterstützungssysteme, die die Menschen wirklich wollen, Proceedingsband.

D. Schneider, and K. Weber. 2024. “AI for Decision Support: What Are Possible Futures, Social Impacts, Regulatory Options, Ethical Conundrums and Agency Constellations?” Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis (TATuP) 33(1). http://doi.org/10.14512/tatup.33.1.08.

K. Weber, and N. Kleine. 2024. “Normierung, Regulierung, Governance: Wie, von Wem Und Mit Welchen Mitteln Kann Der Einsatz Künstlicher Intelligenz Gesellschaftlich Gestaltet Werden?” Künstliche Intelligenz, Mensch und Gesellschaft. http://doi.org/10.1007/978-3-658-43521-9_8.

T. Kriza. 2023. “Ethische Fragen Der Digitalisierung Und Ihre Thematisierung in Forschung Und Lehre an Hochschulen: Dimensionen von Transdisziplinarität.” Interdisziplinarität in der Hochschullehre. http://doi.org/10.3278/9783763974610.

H. Krug, H. Gerhards, U. Bittner, U. Scorna, N. Kaufner, L. Kokott, V. Rolfes, H. Fangerau, and K. Weber. 2022. “Einwilligungsfähig Oder Nicht? Zur Praxis Der Prüfung von Einwilligungsfähigkeit Im Klinischen Alltag Und Deren Herausforderungen.” Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (ZEFQ) 172. http://doi.org/10.1016/j.zefq.2022.04.030.

A. Sonar, and K. Weber. 2022. Künstliche Intelligenz Und Gesundheit ‒ Ethische, Philosophische Und Sozialwissenschaftliche Explorationen. Franz Steiner Verlag.

D. Schneider, A. Sonar, and K. Weber. 2022. “Zwischen Automatisierung Und Ethischem Anspruch – Disruptive Effekte Des KI-Einsatzes in Und Auf Professionen Der Gesundheitsversorgung.” Künstliche Intelligenz im Gesundheitswesen. http://doi.org/10.1007/978-3-658-33597-7_14.

A. Frewer, L. Bergemann, and E. Langmann. 2020. “Verunsicherung Des Ärztlichen Selbstverständnisses Durch Künstliche Intelligenz? Ein Überblick Über Potenzielle Auswirkungen Ihres Einsatzes Im Ärztlichen Alltag.” Unsicherheit in der Medizin (Jahrbuch für Ethik in der Klinik 2020).

A. Sonar, and K. Weber. 2020. “KI Gestern Und Heute. Einsichten Aus Der Frühgeschichte Der KI Für Aktuelle Ethische Überlegungen Zum Einsatz von KI in Der Medizin.” Arbeit 29 2. http://doi.org/10.1515/arbeit-2020-0009.

K. Weber. 2020. “Verunsicherung Des Ärztlichen Selbstverständnisses Durch Künstliche Intelligenz? Ein Überblick Über Potenzielle Auswirkungen Ihres Einsatzes Im Ärztlichen Alltag.” In Unsicherheit in der Medizin,.

K. Weber. 2020. “KI Gestern Und Heute: Einsichten Aus Der Frühgeschichte Der KI Für Aktuelle Ethische Überlegungen Zum Einsatz von KI in Der Medizin.” Arbeit 29(2). http://doi.org/10.1515/arbeit-2020-0009.

H. Gerhards, K. Weber, and U. Bittner. 2020. “Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach Is Needed.” The American Journal of Bioethics. http://doi.org/10.1080/15265161.2020.1820104.

K. Weber. 2018. “Autonomie Und Moralität Als Zuschreibung.” Maschinenethik. http://doi.org/10.1007/978-3-658-21083-0_12.

K. Weber. 2018. “Maschinenethik Und Technikethik.” Handbuch Maschinenethik. http://doi.org/10.1007/978-3-658-17483-5_10.

K. Weber. 2013. “Wie Es Ist Einem Autonomen Artifiziellen Agenten Zu Begegnen.” Informatik 2013: Informatik angepasst an Menschen, Organisation und Umwelt. Lecture Notes on Informatics.

K. Weber. 2013. “What Is It Like to Encounter an Autonomous Artificial Agent?” AI & Society. http://doi.org/10.1007/s00146-013-0453-3.

K. Weber. 1999. Simulation Und Erklärung.