Publications

Furbach, U., Kitzelmann, E., Michaeli, T., & Schmid, U. (Eds.). (2024). Künstliche Intelligenz für Lehrkräfte: eine fachliche Einführung mit didaktischen Hinweisen (1st ed.). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44248-4

Fan, W., Tian, J., Troles, J., Döllerer, M., Kindu, M., & Knoke, T. (2024). Comparing Deep Learning and MCWST Approaches for Individual Tree Crown Segmentation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1–2024, 67–73. https://doi.org/10.5194/isprs-annals-x-1-2024-67-2024

Gärtig-Daugs, A., & Schmid, U. (2024). Informatische Grundbildung in der Grundschule: Kombinierter Einsatz von haptischen Spiel- und Erfahrungsmaterialien mit digitalen Übungen. In B. Brandt & L. Bröll (Eds.), Digitales Lernen in der Grundschule IV: Fachdidaktische Perspektiven auf den Einsatz digitaler Werkzeuge (1st ed., pp. 104–124). Waxmann.

Neufeld, D. (2024). Supporting Experts in Detecting and Interpreting Anomalies in Time Series: Exploring Data Science Approaches for the Monitoring of Hydraulic Test Benches [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-95413

Schmid, U., Weitz, K., & Siebers, M. (2024). Künstliche Intelligenz selber programmieren für Dummies Junior (2nd ed.). Wiley-VCH.

Schramm, S., Wehner, C., & Schmid, U. (2024). Comprehensible Artificial Intelligence on Knowledge Graphs: A survey (pp. 1–23). arXiv. https://doi.org/10.48550/arxiv.2404.03499

Schwalbe, G., & Finzel, B. (2024). A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Mining and Knowledge Discovery, 38(5), 3043–3101. https://doi.org/10.1007/s10618-022-00867-8

Troles, J., Schmid, U., Fan, W., & Tian, J. (2024). BAMFORESTS: Bamberg Benchmark Forest Dataset of Individual Tree Crowns in Very-High-Resolution UAV Images. Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring, 16(11), 1–12. https://doi.org/10.3390/rs16111935

Wehner, C., Kertel, M., & Wewerka, J. (2024). Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs. arXiv. https://doi.org/10.48550/arxiv.2402.00043

Ai, L., Langer, J., Muggleton, S. H., & Schmid, U. (2023). Explanatory machine learning for sequential human teaching. Machine Learning, 112(10), 3591–3632. https://doi.org/10.1007/s10994-023-06351-8

Eirich, J. (2023). The Creation, Formalization, and Transfer of Expert Knowledge with Visual Analytics in Industrial Manufacturing Processes of Electrical Vehicles [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-58339

Eirich, J., Jackle, D., Sedlmair, M., Wehner, C., Schmid, U., Bernard, J., & Schreck, T. (2023). ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories. IEEE Transactions on Visualization and Computer Graphics, 29(8), 3441–3457. https://doi.org/10.1109/tvcg.2023.3279857

Finzel, B., Rieger, I., Kuhn, S., & Schmid, U. M. (2023). Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition. Machine Learning and Knowledge Extraction: 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings, 31–44. https://doi.org/10.1007/978-3-031-40837-3_3

Heidrich, L., Slany, E., Scheele, S., & Schmid, U. (2023). FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction. Machine Learning and Knowledge Extraction, 5(4), 1519–1538. https://doi.org/10.3390/make5040076

Hirmer, T., Ochs, M., Stöckl, A., & Völker, A. (2023). Nutzung von Studienverlaufsdaten im Kontext eines Studienplanungsassistenten. Workshops der 21. Fachtagung Bildungstechnologien (DELFI), 177–180. https://doi.org/10.18420/wsdelfi2023-51

Kiefer, S. (2023). Human-centered Interactions with Text Classifiers: Fusing Concept-based Knowledge with Local Surrogate Explanation Models [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-89771

Münsterberg, A. V., Budde, R., Schmid, U., & Leimbach, T. (2023). Perzeptrons programmieren und explorieren im Rahmen der Open Roberta Lernumgebung. Informatikunterricht zwischen Aktualität und Zeitlosigkeit: 20.-22.09.2023, Würzburg, Deutschland, 435–436. https://doi.org/10.18420/infos2023-056

Schramm, S. G., Wehner, C., & Schmid, U. (2023). Comprehensible Artificial Intelligence on Knowledge Graphs: A survey. Web Semantics, 79(December 2023, 100806), 1–17. https://doi.org/10.1016/j.websem.2023.100806

Troles, J., Nieding, R., Simons, S., & Schmid, U. (2023). Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data. Innovations for Community Services: 23rd International Conference, I4CS 2023, Bamberg, Germany, September 11–13, 2023; Proceedings, 103–122. https://doi.org/10.1007/978-3-031-40852-6_6

Wehner, C., Kertel, M., & Wewerka, J. (2023). Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring): Proceedings. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 20-23 June 2023, Florence, Italy. https://doi.org/10.1109/vtc2023-spring57618.2023.10199563

Finzel, B., Saranti, A., Angerschmid, A., Tafler, D., Pfeifer, B., & Holzinger, A. (2022). Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs. Künstliche Intelligenz, 36(3–4), 271–285. https://doi.org/10.1007/s13218-022-00781-7

Göbel, K., Niessen, C., Seufert, S., & Schmid, U. (2022). Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations. Frontiers in Artificial Intelligence, 5(919534), 1–19. https://doi.org/10.3389/frai.2022.919534

Hepp, T., Zierk, J., Rauh, M., Metzler, M., & Seitz, S. (2022). Mixture density networks for the indirect estimation of reference intervals. BMC Bioinformatics, 23(1, 307), 1–17. https://doi.org/10.1186/s12859-022-04846-0

Kiefer, S. (2022). CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge. Information Fusion, 77, 184–195. https://doi.org/10.1016/j.inffus.2021.07.014

Kiefer, S., Hoffmann, M., & Schmid, U. (2022). Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions. Machine Learning and Knowledge Extraction, 4(4), 994–1010. https://doi.org/10.3390/make4040050

Mey, O., & Neufeld, D. (2022). Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation. Sensors, 22(23, 9037), 1–22. https://doi.org/10.3390/s22239037

Mohammed, A., Geppert, C., Hartmann, A., Kuritcyn, P., Bruns, V., Schmid, U., Wittenberg, T., Benz, M., & Finzel, B. (2022). Explaining and Evaluating Deep Tissue Classification by Visualizing Activations of Most Relevant Intermediate Layers. Current Directions in Biomedical Engineering, 8(2), 229–232. https://doi.org/10.1515/cdbme-2022-1059

Rieger, I., Pahl, J., Finzel, B., & Schmid, U. (2022). CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. 2022 26th International Conference on Pattern Recognition (ICPR): Proceedings, 798–804. https://doi.org/10.1109/ICPR56361.2022.9956319

Schmid, U. (2022). Constructing Explainability: Interdisciplinary Framework to Actively Shape Explanations in XAI. Künstliche Intelligenz, 36(3–4), 327–331. https://doi.org/10.1007/s13218-022-00767-5

Schmid, U., & Weitz, K. (2022). Künstliche Intelligenz und Psychologie - Von Kognitiver Modellierung bis Erklärbarkeit. In A. Schütz, M. Brand, & S. Steins-Löber (Eds.), Psychologie: eine Einführung in ihre Grundlagen und Anwendungsfelder (6., überarbeitete Auflage, pp. 219–231). Kohlhammer.

Schmid, U., & Wrede, B. (2022b). Explainable AI. Künstliche Intelligenz, 36(3–4), 207–210. https://doi.org/10.1007/s13218-022-00788-0

Schmid, U., & Wrede, B. (2022a). What is Missing in XAI So Far?: An Interdisciplinary Perspective. Künstliche Intelligenz, 36(3–4), 303–315. https://doi.org/10.1007/s13218-022-00786-2

Schwalbe, G. (2022). Concept Embedding Analysis Based Methods for the Safety Assurance of Deep Neural Networks: towards safe automotive computer vision applications [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-57172

Thaler, A. M., Mitrovic, A., & Schmid, U. (2022). Worked Examples as Application of Analogical Reasoning in Intelligent Tutoring and their Effects on SQL Competencies. KogWis 2022: Understanding Minds: 15th Biannual Conference of the German Society for Cognitive Science, 157–158. https://doi.org/10.6094/UNIFR/229611

Thaler, A. M., Paukner, F. K., Troles, J.-D., & Schmid, U. (2022). Individuelle Förderung von Programmierfertigkeiten im Studium am Beispiel von Intelligenten Tutor Systemen für SQL. In N. Vöing, S. Reisas, & M. Arnolds (Eds.), Scholarship of Teaching and Learning: Eine forschungsgeleitete Fundierung und Weiterentwicklung hochschul(fach)didaktischen Handelns (pp. 61–77). https://doi.org/10.57684/COS-986

Wehner, C., Powlesland, F., Altakrouri, B., & Schmid, U. (2022). Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence: 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19–22, 2022, Proceedings, 13343, 621–632. https://doi.org/10.1007/978-3-031-08530-7_52

Deuschel, J., Finzel, B., & Rieger, I. (2021). Uncovering the Bias in Facial Expressions. Kolloquium Forschende Frauen 2020 - Gender in Gesellschaft 4.0: Beiträge Bamberger Nachwuchswissenschaftlerinnen, 15–42. https://doi.org/10.20378/irb-90482

Troles, J.-D., & Schmid, U. (2021). Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. Sixth Conference on Machine Translation: Proceedings of the Conference, 531–541.

KI 2020: advances in artificial intelligence: 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020, proceedings. (2020). 43rd German Conference on AI, September 21–25, 2020, Bamberg, Germany. https://doi.org/10.1007/978-3-030-58285-2

Flener, P., & Schmid, U. M. (2020). Inductive Programming. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 658–666). Springer US. https://doi.org/10.1007/978-1-4899-7687-1_137

Wolking, M., & Schmid, U. (2017). Mental Models, Career Aspirations, and the Acquirement of Basic Concepts of Computer Science in Elementary Education. Proceedings of the 12th Workshop on Primary and Secondary Computing Education, 119–120. https://doi.org/10.1145/3137065.3137076

Approaches and Applications of Inductive Programming:  third International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009; revised Papers. (2010). Third International Workshop, AAIP 2009, September 4, 2009, Edinburgh, UK. https://doi.org/10.1007/978-3-642-11931-6

Schmid, U., & Kaup, B. (1994). Der Einfluß von Beispielähnlichkeit auf induktive Lernprozesse beim rekursiven Programmieren. Technische Universität Berlin.