Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is key person ReID. Considering shortcomings of existing methods extraction, method based on attention mechanism context information fusion proposed. A lightweight module introduced into ResNet50 backbone netw...

Abstract Traffic classification is widely used in network security and management. Early studies have mainly focused on mapping traffic to different unencrypted applications, but little research has been done of encrypted especially the underlying applications. To address above issues, this paper proposes a encryption model that combines attention mechanisms spatiotemporal features. The firstly...

Dam crack detection can effectively avoid safety accidents of dams. To solve the problem that dam image samples are not available and traditional algorithm detects cracks with low accuracy, we provide a model based on feature enhancement attention mechanism. Firstly, expand dataset through generative adversarial network (Cracks Enhancements GAN, CE-GAN). It fully data improve quality training d...

With the development of online educational platforms, numerous research works have focused on knowledge tracing task, which relates to problem diagnosing changing proficiency learners. Deep-neural-network-based models are used explore interaction information between students and their answer logs in current field studies. However, those ignore impact previous interactions, including exercise re...

The problem of session-aware recommendation aims to predict users’ next click based on their current session and historical sessions. Existing methods have defects in capturing complex item transition relationships. Other than that, most them fail explicitly distinguish the effects different sessions session. To this end, we propose a novel method, named Personalized Graph Neural Networks with ...