Publikationen
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2024
Dumphart, B., Djordje, S., Unglaube, F., Kranzl, A., Baca, A., & Horsak, B. (2024). The impact of initial contact events on kinematics in pathological gait - Preliminary results of an ongoing study. Gait & Posture, 113, 54–55. https://doi.org/10.1016/j.gaitpost.2024.07.067
Litschka, Michael, Saurwein, Florian, & Pellegrini, Tassilo. (2024). Open Data Governance und digitale Plattformen. Ethische, ökonomische und regulatorische Herausforderungen und Perspektiven. Springer VS.
Litschka, M., Paganini, C., & Rademacher, L. (Eds.). (2024). Digitalisierte Massenkommunikation und Verantwortung. Politik, Ökonomik und Ethik von Plattformen (Vol. 22). Nomos.
Slijepcevic, D., Horst, F., Simak, M. L., Schöllhorn, W. I., Horsak, B., & Zeppelzauer, M. (2024). Decoding Gait Signatures: Exploring Individual Patterns in Pathological Gait using Explainable AI. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2024.3513893
2023
Altendorfer, K., & Felberbauer, T. (2023). Forecast and production order accuracy for stochastic forecast updates with demand shifting and forecast bias correction. Simulation Modelling Practice and Theory, 125, 102740. https://doi.org/10.1016/j.simpat.2023.102740
Belinskaya, Y. (2023). How the internet is being tamed in Russia: Chronicle of state securitization measures. Journalism Research, 6(1), 71–92. https://doi.org/10.1453/2569-152x-12023-13030-en
Belinskaya, Y. (2023). ‘Insider news’ on Russian Telegram: Resembling truth, proximity and objectivity. Journal of Applied Journalism & Media Studies. https://doi.org/10.1386/ajms_00108_1
Bruckner, Franziska, Feyersinger, Erwin, & Lechner, Patrik. (2023, June 14). AniVision: Machine Learning as a Tool for Studying Animation in Ephemeral Films [Vortrag]. Society for Animation Studies 34th Annual Conference – The Animated Environment, Online – Glassboro. https://www.sas34.org/
de Jesus Oliveira, V. A., Slijepčević, D., Dumphart, B., Ferstl, S., Reis, J., Raberger, A.-M., Heller, M., Horsak, B., & Iber, M. (2023). Auditory feedback in tele-rehabilitation based on automated gait classification. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-023-01723-2
Dumphart, B., Slijepcevic, D., Zeppelzauer, M., Kranzl, A., Unglaube, F., Baca, A., & Horsak, B. (2023). Robust deep learning-based gait event detection across various pathologies. PLOS ONE, 18(8), e0288555. https://doi.org/10.1371/journal.pone.0288555
Dumphart, B., Slijepcevic, D., Kranz, A., Zeppelzauer, M., & Horsak, B. (2023). Is it time to re-think the appropriateness of autocorrelation for gait event detection? Preliminary results of an ongoing study. Gait & Posture, 106, S50–S51. https://doi.org/10.1016/j.gaitpost.2023.07.064
Franziska, B. (2023, June 14). VRinMotion: Experimental Stop-Motion and Puppeteering in Virtual Environments [Vortrag]. Society for Animation Studies 34th Annual Conference – The Animated Environment, Online – Glassboro. https://www.sas34.org/
Ganahl, S. (2023). Foucault, the Digital Humanities, the Method. Genealogy+Critique, 9(1), 1–13. https://doi.org/10.16995/gc.10313
Horst, F., Slijepcevic, D., Simak, M., Horsak, B., Schöllhorn, W. I., & Zeppelzauer, M. (2023). Modeling biological individuality using machine learning: A study on human gait. Computational and Structural Biotechnology Journal, 21, 3414–3423. https://doi.org/10.1016/j.csbj.2023.06.009
Killian, S., Baumann, C., Delorette, M., Freisleben- Teutscher, C., Größbacher, S., Huber, A., Husinsky, M., Judmair, P., Moser, T., Pflegerl, J., Schlager, A., Schöffer, L., Taurer, F., & Vogt, G. (2023). MIRACLE - Mixed Reality und Cooperation im Lehreinsatz: Erfahrungen, Potenziale, Limitationen. In Lernen über den Tellerrand hinaus. Good Practices zu Interdisziplinarität, Internationalisierung und Future Skills (pp. 119–126). Lemberger Publishing.
Litschka, M. (2023). Classical Political Economy. In Handbook of Media and Communication Economics. Springer. https://doi.org/10.1007/978-3-658-34048-3_2-2
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy. IEEE Access, 11, 65906–65923. https://doi.org/10.1109/ACCESS.2023.3289986
Slijepcevic, D., Horst, F., Simak, M., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2023). Towards personalized gait rehabilitation: How robustly can we identify personal gait signatures with machine learning? Gait & Posture, 106, S192–S193. https://doi.org/10.1016/j.gaitpost.2023.07.232
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy. Gait & Posture, 100, 32–33. https://doi.org/10.1016/j.gaitpost.2022.11.045
Vulpe-Grigorasi, A. (2023). Multimodal machine learning for cognitive load based on eye tracking and biosensors. 2023 Symposium on Eye Tracking Research and Applications, 1–3. https://doi.org/10.1145/3588015.3589534