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Titlebook: Intelligent Technologies for Interactive Entertainment; 14th EAI Internation Martin Clayton,Mauro Passacantando,Marcello Sangui Conference

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31#
發(fā)表于 2025-3-26 21:15:44 | 只看該作者
32#
發(fā)表于 2025-3-27 04:39:35 | 只看該作者
Conference proceedings 2024N 2023 which was held in Lucca, Italy, during November 27, 2023..The 15 full papers presented in this book were selected from 56 submissions. They present novel and innovative work in areas of methods (machine learning, movement), computer-based systems (architectures, software, algorithms), and dev
33#
發(fā)表于 2025-3-27 05:22:14 | 只看該作者
Artificial Intelligence in?Video Games 101: An Easy Introductionh and video game AI. In summary, this paper delivers a solid but easy-to-understand introductory guide to this interesting and long-running research and industrial field without sacrificing mathematical formalism but not going into every detail, as the most industry-used books and academic references will be provided.
34#
發(fā)表于 2025-3-27 12:45:22 | 只看該作者
35#
發(fā)表于 2025-3-27 13:44:22 | 只看該作者
1867-8211 ), and devices (digital cameras, smartphones).. The papers are grouped in sessions of thematic issues on Games and Game-Based learning; Motion Capture; Sports and Competitions; and Interfaces and Applications..978-3-031-55721-7978-3-031-55722-4Series ISSN 1867-8211 Series E-ISSN 1867-822X
36#
發(fā)表于 2025-3-27 19:34:00 | 只看該作者
37#
發(fā)表于 2025-3-28 00:57:10 | 只看該作者
Advancing Multi-actor Graph Convolutions for?Skeleton-Based Action Recognitionlicate diverse human data to fill and interlink multiple subgraphs. Empirically, our proposed methods applied to the NTU RGB+D 120 dataset significantly enhanced the performance of the base model MSG3D, demonstrating the effectiveness of our approach in handling multi-actor scenarios.
38#
發(fā)表于 2025-3-28 02:51:45 | 只看該作者
39#
發(fā)表于 2025-3-28 09:11:02 | 只看該作者
40#
發(fā)表于 2025-3-28 14:04:10 | 只看該作者
A PLS-SEM Approach for?Composite Indicators: An Original Application on?the?Expected Goal Modelr sample balanced techniques. Results seem to be interesting in terms of sensitivity, F1 and AUC metrics, compared with a benchmark. In addition, some original performance composites and tracking variables introduced are significant for the classification model.
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