找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復制鏈接]
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
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-11-1 10:09
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復 返回頂部 返回列表
盐城市| 抚顺县| 伊春市| 克山县| 红河县| 图片| 高淳县| 镇康县| 新巴尔虎右旗| 常熟市| 敦化市| 阜新市| 屯门区| 九龙城区| 宕昌县| 灵璧县| 武威市| 即墨市| 定日县| 龙岩市| 八宿县| 呼玛县| 渝中区| 如东县| 鲁山县| 海丰县| 察隅县| 台东县| 怀来县| 于都县| 修武县| 托克逊县| 姜堰市| 娄底市| 宁明县| 堆龙德庆县| 广宁县| 白玉县| 嵩明县| 丹寨县| 中山市|