As Intelligent Transport Systems (ITS) advance, the Internet of Vehicles (IoV) improves traffic efficiency and safety. Vehicle Edge Computing (VEC) provides strong computing and storage capabilities. However, high vehicle speeds require efficient service migration to maintain service continuity. This study proposes a real-time service migration strategy that optimizes both Quality of Service (QoS) and response latency. To support dynamic decision-making in the VEC framework, this study introduces Priority Experience Replay and Four-Trajectory exploration (PERFT), Recurrent Neural Networks (RNNs) and attention mechanisms into the Proximal Policy Optimization (PPO) algorithm. This leads to the PERFT-PPO, which addresses issues such as long sequence handling, sparse reward problems, and problems of limited exploration depth of long trajectories in single-agent scenarios. To address multi-agents resource scheduling in VEC, this study integrates Centralized Training and Distributed Execution (CTDE) into PERFT-PPO, creating the Prioritized Experience Replay Four-Trajectory Multi-Agent Proximal Policy Optimization (PERFT-MAPPO). The simulation results show that PERFT-MAPPO addresses the challenges of real-time decision-making and resource scheduling, achieving lower system cost. Compared to PERFT-PPO, PERFT-MAPPO demonstrates superior adaptability in dynamic network conditions, further optimizing system efficiency and Qos.

Learning fashion compatibility is of great significance to both academic research and industry, which serves as a key technique for many real applications like online shopping recommendation and clothing generation. In previous studies, user-generated data (e.g. outfits from social media platform) are usually used for learning item embeddings and further modeling the compatibility. However, due to the noisy and messy nature of such data, one can hardly learn a representation that can clearly characterize the fashion-related attributes (e.g. color, material). In this paper, we propose an Attention-based Dataset Distillation Graph Neural Network (ADD-GNN) to leverage the designer-generated data as a guidance on modeling the outfit compatibility. Specifically, we jointly optimize two components which distill knowledge from fashion designers for feature representation learning and model the overall compatibility through attention-based graph neural network. Experimental results on real world fashion datasets clearly demonstrate the superiority of our proposed ADD-GNN against several competitive baselines in outfit compatibility tasks, which proves the effectiveness of distilling knowledge from designers.

Semantic segmentation of high-resolution remote sensing (HRRS) images has been a long-term research topic in the field of remote sensing. Nowdays, many excellent networks based on deep learning have been applied in various remote sensing fields. However, these networks always have a large number of network parameters and rely on extensive computing resources. To solve the above problems, we propose a lightweight dual branches network with the attention modules and the feature fusion module. The backbone networks of dual branches, which have fewer parameters, are used to obtain the detail information and the context information respectively. The attention modules are used to establish full-image dependencies over the local feature representations. The feature fusion module is used to fuse the low-leve features and the high-leve features effectively. Compared to other popular networks, our network has better results evaluated on ISPRS Vaihingen Dataset while with fewer parameters(8M).

User emotion modeling is a vital problem of social media analysis. In previous studies, content and topology information of social networks have been considered in emotion modeling tasks, but the inflence of current emotion states of other users was not considered. We define emotion influence as the emotional impact from user’s friends in social networks, which is determined by both network structure and node attributes (the features of friends). In this paper, we try to model the emotion influence to help analyze user’s emotion. The key challenges to this problem are: 1) how to combine content features and network structures together to model emotion influence; 2) how to selectively focus on the major social network information related to emotion influence. To tackle these challenges, we propose an attention-based graph convolutional recurrent network to bring in emotion influence and content data. Firstly, we use an attention-based graph convolutional network to selectively aggregate the features of the user’s friends with specific attention. Then an LSTM model is used to learn user’s own content features and emotion influence. The model we proposed is more capable of quantifying the emotion influence in social networks as well as combining them together to analyze the user emotion status. We conduct emotion classification experiments to evaluate the effectiveness of our model on a real world dataset called Sina Weibo1. Results show that our model outperforms several state-of-the-art methods.