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Titlebook: Artificial Intelligence in HCI; 5th International Co Helmut Degen,Stavroula Ntoa Conference proceedings 2024 The Editor(s) (if applicable)

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樓主: Diverticulum
41#
發(fā)表于 2025-3-28 14:34:21 | 只看該作者
You Got the?Feeling: Attributing Affective States to?Dialogical Social Robotsgrees of dialogical complexity), the perceived difference in emotion attribution and understanding by the human users interacting with them. In particular, in our case study, the most complex dialogical modality - using a emotional content to vehiculate its messages - has been based entirely on the
42#
發(fā)表于 2025-3-28 20:09:02 | 只看該作者
Enhancing Usability of?Voice Interfaces for?Socially Assistive Robots Through Deep Learning: A Germare the user to learn specific speech commands or sentence patterns to use them. This property does not satisfy usability heuristics and causes current language interfaces to underachieve the naturalness of language interaction. To address this issue, we developed a voice interface that is capable of
43#
發(fā)表于 2025-3-29 00:47:21 | 只看該作者
44#
發(fā)表于 2025-3-29 03:37:00 | 只看該作者
Adaptive Robotics: Integrating Robotic Simulation, AI, Image Analysis, and Cloud-Based Digital Twin ge analysis, and cloud-based storage of digital twin simulations. The primary objective is to enable robots to dynamically assess their surroundings using AI and pre-simulated data to make informed decisions in unfamiliar scenarios. An autonomous mobile robot platform capable of simulation-based nav
45#
發(fā)表于 2025-3-29 10:38:28 | 只看該作者
46#
發(fā)表于 2025-3-29 13:10:33 | 只看該作者
47#
發(fā)表于 2025-3-29 16:02:42 | 只看該作者
Enhancing Relation Extraction from?Biomedical Texts by?Large Language Modelsn biomedical relation extraction tasks. We further show that entity explanations that are generated by LLMs can improve the performance of the classification-based relation extraction in the biomedical domain. Our proposed model achieved an F-score of 85.61% on the DDIExtraction-2013 dataset, which is competitive with the state-of-the-art models.
48#
發(fā)表于 2025-3-29 19:44:38 | 只看該作者
https://doi.org/10.1007/978-1-349-99582-0adoption of a Large Language Model (i.e. chatGPT in our case) whilst the simplest one has been based on a manual simplification of the generated text. We report the obtained results based on the adoption of a number tests and standardized scales and highlight some possibile future directions.
49#
發(fā)表于 2025-3-30 00:54:45 | 只看該作者
50#
發(fā)表于 2025-3-30 04:08:09 | 只看該作者
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