为机器学习驱动的智能系统量化不确定性，提升活动识别可靠性。
Quantifying Uncertainty in Machine Learning-Based Pervasive Systems: Application to Human Activity Recognition
- 结合多种技术实时评估模型预测可信度。
- 在复杂多变的人体活动识别场景中验证有效。
- 帮助领域专家判断模型决策是否可信。
pervasive computing 与 machine learning 的融合催生了众多服务，深刻影响经济与社会各个领域。然而，AI 技术的使用使得传统软件开发中的严格测试和规范定义难以实施，因为机器学习模型是基于大量高维数据训练而非手动编码。因此，其运行边界不明确，无法保证零错误。本文提出对机器学习系统的不确定性进行量化。为此，我们适配并联合使用一系列选定技术，在运行时评估模型预测的相关性。该方法应用于高度异构且动态变化的人体活动识别（HAR）领域。结果表明该方法具有实际价值，并详细讨论了对领域专家的辅助作用。
原文摘要 · Abstract (English)
The recent convergence of pervasive computing and machine learning has given rise to numerous services, impacting almost all areas of economic and social activity. However, the use of AI techniques precludes certain standard software development practices, which emphasize rigorous testing to ensure the elimination of all bugs and adherence to well-defined specifications. ML models are trained on numerous high-dimensional examples rather than being manually coded. Consequently, the boundaries of their operating range are uncertain, and they cannot guarantee absolute error-free performance. In this paper, we propose to quantify uncertainty in ML-based systems. To achieve this, we propose to adapt and jointly utilize a set of selected techniques to evaluate the relevance of model predictions at runtime. We apply and evaluate these proposals in the highly heterogeneous and evolving domain of Human Activity Recognition (HAR). The results presented demonstrate the relevance of the approach, and we discuss in detail the assistance provided to domain experts.
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