Recent progress in recognizing emotions through gait analysis has attracted substantial interest. Spatial temporal graph convolutional networks (ST-GCN) have been applied to extract gait features effectively, enabling enhanced emotion recognition. However, existing methods do not account for subtle movement cues that are intricately linked to human emotions, resulting in a lack of representation of emotion intensity. Additionally, these methods fail to consider the cyclic nature of gait, focusing only on local dependencies in the temporal domain. To tackle these limitations, we propose an innovative three-streams graph neural network model PMF-GCN (Posture-Movement-Frequency-enhanced Graph Convolutional Network). First, we introduce for the first time in gait emotion recognition, the integration of movement features from translational and rotational perspectives, combined with frequency-domain data, to capture emotion intensity and global dependencies. Then, we devise a novel adaptive feature fusion mechanism (TR-AFM), which achieves effective extraction of spatial-temporal-specific emotion features from gaits through temporal and spatial attention mechanisms, as well as gated units. Comprehensive experiments on two public datasets show that PMF-GCN achieves leading performance in gait emotion recognition, and achieves state-of-the-art performance.

Emotion Recognition in Conversations (ERC) plays a crucial role in the development of human-machine interaction. Conversations are a multi-party, multi-emotion, and multi-turn process of information propagation. However, existing works, which focus on designing models and algorithms for better learning representations of dialogue context and speakers, but rarely care about the key element of the strong correlation and inseparable interdependence of emotional states on the sentiment polarity in the process. To address this issue, we propose a novel model, named S2D-ERC (Sentiment-Speaker-Dependency for Emotion Recognition in Conversation), for ERC task. The proposed model constructs a conversation as a directed acyclic graph and represents both speaker- and sentiment- dependencies between utterances with heterogeneous edges. Additionally, to capture the information interaction dynamics in conversation context, we employ a cross-attention mechanism where latent representations of speaker and sentiment are learned with two different directions of information flow. The experimental results on two benchmarks, compared with state-of-the-art models, demonstrate the superiority and effectiveness of our model.

Drug-target interaction prediction is an important research field in computer-aided drug discovery. The data involved in drug-target interaction prediction are characterized by noise, high dimensionality, and sparseness, which leads to poor prediction performance of traditional machine learning methods. Matrix factorization methods are often used to predict unknown or missing data, and can deal with data with the above characteristics. Therefore, a drug-target interaction prediction model based on non-negative and self-representative matrix factorization is proposed in this study. The proposed model performs matrix factorization based on the topological structure of the drug-target interaction data, and focuses on capturing the internal structural information of the drug-target data for representation learning. At the same time, it introduces nonnegative and non-trivial solution constraints to optimize the representation learning results, and integrates the graphs regularization method to optimize the low-dimensional key latent factor matrix, and finally realizes the prediction of drug-target interactions. Experimental results show that the model effectively mines the structural information of drug-target interactions, and is superior to other benchmark methods in the prediction performance.