Showing 1 - 20 results of 575 for search 'rate based computer interaction', query time: 0.15s Refine Results
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    Deep Neural Network-Based Modeling of Multimodal Human–Computer Interaction in Aircraft Cockpits by Li Wang, Heming Zhang, Changyuan Wang

    Published 2025-03-01
    “…Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. …”
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    Edge Computing-Based Video Action Recognition Method and Its Application in Online Physical Education Teaching by Jinzhu Han, Jinjin Zhao, Yan Yue, Xinrui Che

    Published 2024-01-01
    “…Experimental results show that the proposed LWV-ViT network achieves the best recognition rates for both behavior detection (96.5%, 95.73%) and action recognition (97.9%, 88.3%, 79.9%) tasks, and has the fewest trainable parameters (2.7 M), which means it performs well in edge computing-based online PE teaching systems.…”
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    Enhanced Brain Functional Interaction Following BCI-Guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery by Shuaifei Huang, Yuan Liu, Zhuang Wang, Wenlai Wu, Jun Guo, Weiguo Xu, Dong Ming

    Published 2025-01-01
    “…During the testing phase before and after 4 weeks of training, all participants were tested for SRF-finger opposition sequence behavior, resting state fMRI (rs-fMRI), and task-based fMRI (tb-fMRI). The results showed that compared with the Sham group, the BCI-SRF group improved the accuracy rate of the SRF-finger opposition sequence by 132%. …”
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    How positive and negative feedback following real interactions changes subsequent sender ratings by Antje Peters, Jendrik Witte, Hanne Helming, Robert Moeck, Thomas Straube, Sebastian Schindler

    Published 2025-03-01
    “…In addition, we find rapid behavioral changes in the ratings for the senders based on their feedback behavior.…”
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    Priority-aware task offloading for LEO satellite edge computing network: a multi-agent deep reinforcement learning-based approach by Juan Chen, Jie Zhong, Zongling Wu, Di Tian, Yujie Chen

    Published 2025-08-01
    “…Existing task scheduling algorithms often fail to fully consider task deadlines and priority orders, leading to deficiencies in handling urgent tasks and optimizing overall computational efficiency. In response to these issues, we propose a multi-agent advantage actor-critic (MA2C) algorithm based on deep reinforcement learning, aiming to effectively address the task scheduling challenges in SatEC. …”
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