Since Convolutional Neural Network （CNN） conversion method based on the weight normalization method for event stream data has a large loss of accuracy and the effective deployment of floating-point networks is difficult on hardware， a network conversion method for dynamic event stream was proposed. Firstly， the event stream data was reconstructed as the input of CNN for training. In the training process， the quantized activation function was adopted to reduce the accuracy loss， and a symmetric fixed-point quantization method was used to reduce the parameter storage. Then， instead of equivalence principle， pulse count equivalence principle was used to adapt to the sparsity of data better. Experimental results show that on three datasets N-MNIST， POKER-DVS and MNIST-DVS， compared with using the traditional activation function， Spiking Convolutional Neural Network （SCNN） using the quantized activation function has the recognition accuracy improved by 0.29 percentage points， 8.52 percentage points and 3.95 percentage points respectively， and the conversion loss reduced by 21.77%， 100.00% and 92.48% respectively. Meanwhile， the proposed quantized SCNN can effectively save 75% of storage space compared with high-precision SCNN generated on the basis of the weight normalization method， and has the conversion loss on N-MNIST and MNIST-DVS datasets reduced by 6.79% and 46.29% respectively.

Considering the demand of detecting Android malware and the redundancy of permission properties, a fast scheme was proposed to detect malware from the perspective of permission correlation. To eliminate the redundant permissions, Chi-square test was used to compute the influence of the permission on the classification results. Then some representative permissions were selected on the basis of permission clustering to further reduce redundancy. Finally an improved Naive Bayesian classification based on the weights of different permissions was proposed to classify the software. Results of the experiments conducted on 2000 software samples show that the miss rate of malware detection is 10.33% and the overall prediction accuracy is 88.98%. Experiments indicate that this scheme is capable of detecting malware on Android platform by using a few permission properties, which can provide a reference for further analysis and judgment.

Current software protection technologies generally achieve the software protection through improving the code and applying encryption scheme. To address the problem of whether the static authorized anti-attack capability of software code and the strength of the software encryption can sufficiently resist attack, the authors proposed a software protection game model based on divided-storage strategy. The strategy of divided-storage was used by the model to divide secret key into many segments, so multiple verified functions that were used to inspect and resist the cracker's attack were received. After being hidden in the program, the program was protected by multiple different verified functions during the running of the software. The model was analyzed and demonstrated from the perspective of game theory, also applied to the instances of software registration code verification. The security of the software code had been improved. The experimental results and analysis show that the proposed model is correct and effective.

To overcome the disadvantage of traditional facial expression recognition methods that generally use single feature extraction methods, which can not extract effective features currently, a new method based on fused hybrid features and discrete Hidden Markov Model （HMM） for facial expression recognition from image sequences was presented. For the same image sequence, we used Discrete Wavelet Transformation （DWT） and Orthonormal Non-Negative Matrix Factorization （ONMF） to extract texture information toward pure faces and used Active Appearance Model （AAM） to extract geometrical shaped information. In order to reduce the redundancy, Canonical Correlation Analysis （CCA） was used to fuse the two features for observation vectors of discrete HMM. Experiments show that the method can get high recognition rate after fused with fewer feature vectors.