Group activity recognition is a challenging task in complex scenes， which involves the interaction and the relative spatial position relationship of a group of people in the scene. The current group activity recognition methods either lack the fine design or do not take full advantage of interactive features among individuals. Therefore， a network framework based on partitioned attention mechanism and interactive position relationship was proposed， which further considered individual limbs semantic features and explored the relationship between interaction feature similarity and behavior consistency among individuals. Firstly， the original video sequences and optical flow image sequences were used as the input of the network， and a partitioned attention feature module was introduced to refine the limb motion features of individuals. Secondly， the spatial position and interactive distance were taken as individual interaction features. Finally， the individual motion features and spatial position relation features were fused as the features of the group scene undirected graph nodes， and Graph Convolutional Network （GCN） was adopted to further capture the activity interaction in the global scene， thereby recognizing the group activity. Experimental results show that this framework achieves 92.8% and 97.7% recognition accuracy on two group activity recognition datasets （CAD （Collective Activity Dataset） and CAE （Collective Activity Extended Dataset））. Compared with Actor Relationship Graph （ARG） and Confidence Energy Recurrent Network （CERN） on CAD dataset， this framework has the recognition accuracy improved by 1.8 percentage points and 5.6 percentage points respectively. At the same time， the results of ablation experiment show that the proposed algorithm achieves better recognition performance.

A fuzzy multi-objective software reliability allocation model was established, and bacteria foraging optimization algorithm based on estimation of distribution was proposed to solve software reliability redundancy allocation problem. As the fuzzy target function, software reliability and cost were regarded as triangular fuzzy members, and bacterial foraging algorithm optimization based on Gauss distribution was applied. Different membership function parameters were set up, and different Pareto optimal solutions could be obtained. The experimental results show that the proposed swarm intelligence algorithm can solve multi-objective software reliability allocation effectively and correctly, Pareto optimal solution can help the decision between software reliability and cost.

Through analyzing and simulating human visual perception of stereo image, an objective quality evaluation method of stereo image was proposed. The method combined the characteristics of Human Visual System (HVS) with Structural Similarity, using steerable pyramid to simulate multi-channel effects. Meanwhile, the proposed method used stereo matching algorithm to assess the stereo sense. The experimental results show that the proposed objective method achieves consistent stereoscopic image quality evaluation result with subjective assessment and can better reflect the level of image quality and stereo sense.

Based on the concepts of primitive production process and eigenword of sequences, the index in primitive production process (IPP) of sequences was defined for measuring the complexity of chaotic pseudorandom sequences. In comparison with bifurcation curves and approximate entropy of simulation results, the efficiency and advantages of the proposed measure are obvious. IPP is superior to approximate entropy for distinguishing sequence complexity.

Complexity stability of sequences is one of the important characteristics of chaotic pseudorandom sequences. Based on the definition of the index in primitive production process (IPP) of a sequence, the concept of weight IPP was proposed. Moreover, the absolute change and the relative change of IPP were recommended to measure the complexity stability of chaotic pseudorandom sequences and some conclusions were drawn. Numerical simulations on several chaotic pseudorandom sequences indicate that the proposed indices are effective in measuring complexity stability of short chaotic sequences.