In order to improve the recommendation effect of social events in Event-based Social Network (EBSN), a personalized social event recommendation method combining historical behaviors and social relationships of users was proposed. Firstly, deep learning technology was used to build a user model from two aspects:the user's historical behaviors and the potential social relationships between users. Then, when modeling user preferences, the negative vector representation of user preferences was introduced, and the attention weight layer was used to assign different weights to different events in the user's historical behaviors and different friends in the user's social relationships according to different candidate recommendation events, at the same time, the various characteristics of events and groups were considered. Finally, a lot of experiments were carried out on the real datasets. Experimental results show that this personalized social event recommendation method is better than the comparative Deep User Modeling framework for Event Recommendation (DUMER) and DIN (Deep Interest Network) model combined with attention mechanism in terms of Hits Ratio (HR), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) evaluation indicators.

Aiming at the problem of reachable region search problem in urban computing, a method based on time segment tree was developed. In the method, a time segment tree structure was designed to store the local reachable regions, and a dynamic adaptive search algorithm was proposed, so as to improve the efficiency and accuracy of reachable region search. The method includes four steps. Firstly, the probability time weights of road segments were constructed on the basis of road speed distribution model and the trajectory data. Then, the short-term reachable regions were queried and stored by using the hierarchical skip list algorithm. After that, an efficient index structure for the hierarchical reachable region was built by the use of the time segment tree. Finally, the iterative search in the road network was carried out by using the time segment tree index, and the reachable region set was obtained. Extensive experiments were conducted on Beijing road network and taxi trajectory datasets. The results show that the proposed method improves the efficiency and accuracy by 18.6% and 25% respectively compared with the state-of-the-art Single-location reachability Query Maximum/minimum Bounding region search (SQMB) method.

Concerning the high time and space complexity caused by using high-dimensional tenuous vectors to represent network information in tenuous subgraph finding problem, a Tenuous subGraph Finding (TGF) algorithm based on network embedding was proposed. Firstly, the network structure was mapped to the low-dimensional space by the network embedding method in order to obtain the low-dimensional vector representation of nodes. Then, the tenuous subset finding problem in the vector space was defined, and the tenuous subgraph finding problem was transformed into the tenuous subset finding problem. Finally, the sample points with lowest local density were searched iteratively and expanded to figure out the largest tenuous subset satisfying the given conditions. Experimental results on Synthetic_1000 dataset show that, the search efficiency of TGF algorithm is 1 353 times that of Triangle and Edge Reduction Algorithm (TERA) and 4 times of that Weight of K-hop (WK) algorithm, and it achieves better results in k-line, k-triangle and k-density indexes

The moving object detection with moving camera has the problems that it is difficult to model the background and the computation cost is usually high. In order to solve the problems, a method for detecting moving object with moving camera based on motion saliency was proposed, which realized accurate moving object detection and avoided complex background modeling. The moving objects were detected according to the saliency of the video scene, which was computed based on the simulation of the attention mechanism in human vision system and the moving properties of background and foreground when the camera moved in translation. Firstly, the motion features of object were extracted by optical flow method and the background motion texture was suppressed by 2-D Gaussian convolution. Then the global saliency of motion features was measured by counting the histogram. According to the temporal salient map, the color information of foreground and background was extracted respectively. Finally, Bayesian model was used to deal with temporal salient map for extracting salient moving objects. The experimental results on the public video datasets show that the proposed method can suppress background motion noise, while detecting the moving object distinctly and accurately in the dynamic scene with moving camera.

The certificate path of the inner-realm is described in its subject alternative name, and the certificate path of the inter-realm was implemented by its proxy. In the same realm, the shortest path can be acquired by the sponsor with comparing the path in the subject alternative name of the target’s certificate and the sponsor’s trusted anchors. In the different realm, the path of the inter-realm can be acquired by requesting the proxy of the construction and concatenating the certificate path described in the subject alternative name, thus the construction of the whole certificate path can be implemented.