Aiming at the existing problems in user trajectory data modeling such as the sparsity of check-in points, long-term dependencies and complex moving patterns, a social network user trajectory prediction model based on self-supervised learning, called SeNext, was proposed to model and train the user trajectory to predict the next Point Of Interest (POI) of the user. First, data augmentation was utilized to expand the training trajectory samples, which solved the problem of the deficiency of model generalization capability caused by insufficient data and too few footprints of some users. Second, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and attention mechanism were adopted into the modeling of current and historical trajectories respectively, so as to extract effective representations from high-dimensional sparse data to match the most similar moving patterns of users in the past. Finally, SeNext learned the implicit representations in the latent space by combining self-supervised learning and introducing contrastive loss Noise Contrastive Estimation (InfoNCE) to predict the next POI of the user. Experimental results show that compared to the state-of-the-artVariational Attention based Next (VANext)model, SeNext improves the prediction accuracy about 11% on Top@1.

To solve the traffic load-imbalance problem in electric power communication networks based on Open Shortest Path First (OSPF) protocol, an efficient Two-Step Optimized OSPF Protocol (TSO-OSPF) algorithm was proposed to balance the traffic in intra-area and inter-area of OSPF respectively. The bandwidth utilization and delay were adopted as link weights, the inward and outward traffic of a router was considered, the overloaded branches were decomposed into multiple routers to minimize the maximum traffic flow, thus the traffic-imbalance problem of internal and boundary router in the electric power communication networks was solved. The simulation results show that the TSO-OSPF algorithm can effectively balance the traffic in the network and reduce the packet loss rate by about 10% compared with the OSPF algorithm.

Concerning the shortage of the tamper localization accuracy and tamper recovery performance in the existing image tampers localization and recovery algorithms, the authors proposed a precise three-watermarking algorithm. It generated three types of watermarks such as detection watermark, localization watermark and recovery watermark by binary coding based on Least Significant Bit (LSB). The watermarks were imbedded into the low bits of image. Tamper detection and recovery were implemented by detection watermark and recovery watermark based on blocks, and precise localization was implemented by localization watermark based on single pixel. The simulation results show that the proposed algorithm has precise tamper localization to any size of brightness images and RGB images, and has good tamper recovery performance.

Based on SAML2.0 standard,a scheme of supporting trust transfer of mobile equipment was presented.The architecture and the flow of this system were described. Several problems and relative countermeasures about security and deployment were analyzed and proposed.