Identifying social relationships from images is a challenging yet promising research area with great potential for improving human health and enhancing our understanding of social networks. However, present endeavors in this field tend to concentrate on leveraging visual features for the exploration of social relationships, while disregarding certain concealed information that lies beneath these features, such as interpersonal similarity. These methodologies may result in inadequate visual data encoding, thereby imposing limitations on the accuracy of social relationship recognition. In light of this, we propose a novel framework that utilizes interpersonal similarities within images to provide more information for identifying social relationships, thereby mitigating the issue of insufficient feature exploration. Furthermore, our proposed framework incorporates an innova-tive CF-Loss function that effectively incentivizes the identifica-tion of accurate social relationships while penalizing incorrect identifications, ultimately bolstering the model's capacity to dis-criminate between distinct social relationships. Our experimental findings demonstrate the superiority of our proposed framework over state-of-the-art methods on public datasets, confirming its effectiveness and accuracy in identifying social relationships.

Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels from different label groups, including murmur's timing, pitch, grading, quality, and shape. Our experiment results show that the proposed method has achieved outstanding performance on the holdout data for the multi-labelling task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level, and an overall accuracy=0.969 at the patient's recording level.