With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Link age Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%-17.76% and 5.80%-8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC).

During the regularized COVID-19 epidemic control, the regional TCM intelligent cloud system is extended to the isolated medical observation sites through 5G technology. Combined with the advantages of local Chinese medicine hospitals, TCM services such as TCM preparations and TCM remote diagnosis and treatment were provided to personnel at isolated medical observation sites. These services not only reduce the risk of COVID-19 infection, but also relieve the anxiety of those under isolation and medical observation. “5G + TCM services” gives full play to the important role of TCM in the prevention and control of epidemics and provides epidemic prevention and control solutions suitable for China.