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(fù)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(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-11-2 02:44
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(fù) 返回頂部 返回列表
辽宁省| 介休市| 清镇市| 缙云县| 宝山区| 丰顺县| 勐海县| 融水| 桂林市| 邯郸县| 长宁区| 方正县| 牙克石市| 铜陵市| 仙居县| 澎湖县| 萨迦县| 景德镇市| 澄迈县| 襄城县| 靖宇县| 望谟县| 襄汾县| 梁河县| 定南县| 九寨沟县| 响水县| 宿松县| 义马市| 万盛区| 科技| 五台县| 清丰县| 工布江达县| 贵定县| 房产| 澳门| 红河县| 太原市| 平罗县| 星座|