Predicting future heart rate (HR) not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services. Existing methods for HR prediction encounter challenges, especially concerning privacy protection and data heterogeneity. To address these challenges, this paper proposes a novel HR prediction framework, PCFedH, which leverages personalized federated learning and prototypical contrastive learning to achieve stable clustering results and more accurate predictions. PCFedH contains two core modules: a prototypical contrastive learning-based federated clustering module, which characterizes data heterogeneity and enhances HR representation to facilitate more effective clustering, and a two-phase soft clustered federated learning module, which enables personalized performance improvements for each local model based on stable clustering results. Experimental results on two real-world datasets demonstrate the superiority of our approach over state-of-the-art methods, achieving an average reduction of 3.1% in the mean squared error across both datasets. Additionally, we conduct comprehensive experiments to empirically validate the effectiveness of the key components in the proposed method. Among these, the personalization component is identified as the most crucial aspect of our design, indicating its substantial impact on overall performance.

The prediction of health metrics for drivers has become increasingly crucial due to the potential impact of drivers' health conditions on traffic accidents. Heart attack is one of the primary causes of health-related traffic tragedies. However, drivers' heart rate (HR) is considered highly-private data, which should not be collected by a centralized server for training the prediction model. To this end, we contribute FedHeart, a novel privacy-preserving federated learning (FL) system for HR prediction. We observe distinct HR changes when drivers are in steady-state and changing-state conditions, and thus we utilize FL to train two separate models for these states. To enhance the prediction accuracy, we incorporate contrastive learning to extract HR features. Through experiments on two real-world datasets, we validate the efficiency of the proposed system in accurately predicting HR during driving scenarios.

Research on designing local feature descriptors has gradually shifted to deep learning. Different from other computer vision tasks, the biggest challenge for local descriptor learning lies with the formulation of loss functions. Existing methods solve the problem by leveraging Siamese loss or triplet loss and improve the performance of the learned descriptors by a significant margin. However, the widely used Siamese loss and triplet loss cannot fully utilize the context information. In this paper, we propose a novel loss function to introduce more context information to facilitate training. After incorporating the proposed loss function into training, our learned descriptor demonstrates state-of-the-art performance in patch verification, image matching and patch retrieval benchmarks. The pretrained model will be publicly available at https://github.com/clelouch/CANet.

There are many test generation methods, but few of them consider the character strings. This paper proposes a method to generate test data for C programs with character strings and character string function calls, which is based on path oriented testing. Each character variable is viewed as an integer variable with the restriction that the value should be between 0 and 255. A character string is viewed as an array of characters with a predefined fixed length. Many commonly used character library functions are modeled by formulae in predicate logic with assignment statements. The model is then used to replace the function call in the program path, which will be solved by a path analysis tool to generate the test data. A prototype tool called StrGen is developed to illustrate the feasibility of this method. The results of some examples also show that this method is feasible and very efficient.