@inproceedings{SuessMelznerDendorfer, author = {Suess, Franz and Melzner, Maximilian and Dendorfer, Sebastian}, title = {Towards ergonomics working - machine learning algorithms and musculoskeletal modeling}, series = {IOP Conference Series: Materials Science and Engineering}, volume = {1208}, booktitle = {IOP Conference Series: Materials Science and Engineering}, publisher = {IOP Publishing}, issn = {1757-899X}, doi = {10.1088/1757-899X/1208/1/012001}, abstract = {Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments.}, language = {en} } @inproceedings{ChowDoerrenbaecherPoikolainenRosenetal., author = {Chow, Rosan and D{\"o}rrenb{\"a}cher, Judith and Poikolainen Ros{\´e}n, Anton and Yoo, Daisy and Eriksson, Eva and Hassenzahl, Marc}, title = {Paradoxes, tensions and challenges in decentering the human : re-examining concepts, practices, methods and artifacts in More-Than-Human-Design}, series = {DIS'25 Companion Proceedings of the 2025 ACM Designing Interactive Systems Conference : designing for a sustainable ocean}, booktitle = {DIS'25 Companion Proceedings of the 2025 ACM Designing Interactive Systems Conference : designing for a sustainable ocean}, publisher = {ACM}, address = {New York, USA}, isbn = {9798400714863}, doi = {10.1145/3715668.3734160}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-84871}, pages = {23 -- 25}, abstract = {In recent years, More-Than-Human-Design (MTHD) has gained traction in HCI, as exemplified by a growing body of workshops and publications. As this nascent field progresses, unresolved conceptual and practical methodological challenges continue to surface. It is time for MTHD researchers to critically revisit underlying assumptions, concepts and processes that shape their work. This one-day workshop invites the DIS community to engage in two key areas: a critical discussion on anthropocentrism, a core yet multifaceted concept in MTHD, and the presentation of practical work that either reveals paradoxes within MTHD - concerning how to decenter humans and how to engage with more-than-human actors - or explores constructive ways to navigate these paradoxes. Through mapping and discussing the challenges, we aim to identify patterns and shared concerns. The workshop will culminate in an open-ended Archive of Tensions, accessible via the workshop website, serving as a collective resource for ongoing reflection and inquiry. Furthermore, the Archive will be presented in an online event and the results will be published as an article.}, subject = {More-Than-Human}, language = {en} } @inproceedings{FranzWinkerGroppeetal., author = {Franz, Maja and Winker, Tobias and Groppe, Sven and Mauerer, Wolfgang}, title = {Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation}, series = {2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada}, booktitle = {2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada}, publisher = {IEEE}, doi = {10.1109/QCE60285.2024.00055}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-76877}, pages = {409 -- 420}, abstract = {Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems.}, language = {en} }