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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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面具CAE:通过传感器数据重建进行自我监督的人类活动识别的面具卷积自动编码器.

Dongzhou Cheng, Lei Zhang, Lutong Qin

    IEEE journal of biomedical and health informatics
    |March 5, 2024
    PubMed
    概括

    本研究介绍了面具卷积自动编码器 (MaskCAE),用于自我监督的人类活动识别 (HAR). MaskCAE有效地重建掩盖的传感器数据,在没有数据增强的情况下超过现有方法.

    科学领域:

    • 无处不在的计算无处不在的计算
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 自主监督的人类活动识别 (HAR) 对可穿戴设备至关重要.
    • 手动标记传感器数据是具有挑战性和耗时的.
    • 目前的自我监督方法面临数据增强,模型限制和计算需求的问题.

    研究的目的:

    • 为HAR提出一种新的自我监督的方法,以应对现有的挑战.
    • 在可穿戴设备上为 HAR 引入一个计算效率高的模型.
    • 为了填补哈尔的蒙面传感器数据建模的空白.

    主要方法:

    • 开发了面具卷积自动编码器 (MaskCAE),是一种拒绝自动编码器方法.
    • 利用一个完全卷积网络来重建掩盖的时间序列传感器数据.
    • 在自我监督,完全监督和半监督设置中评估绩效.

    主要成果:

    • 在HAR任务中,MaskCAE显著超过了最先进的算法.
    • 该模型在不需要数据增强的情况下实现了高性能.
    • 通过可视化在传感器数据中有效捕获时间语义.

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    结论:

    • MaskCAE为HAR提供了一种强大而高效的自我监督解决方案.
    • 该方法显示了模拟抽象传感器数据的巨大潜力.
    • 该模型的有效性在嵌入式平台上得到验证.