Aiming at the photo spoofing problem that often occurs in identity verification， a face liveness detection model based on InceptionV3 and feature fusion， called InceptionV3 and Feature Fusion （InceptionV3_FF）， was proposed. Firstly， the InceptionV3 model was pretrained on ImageNet dataset. Secondly， the shallow， middle， and deep features of the image were obtained from different layers of the InceptionV3 model. Thirdly， different features were fused to obtain the final features. Finally， the fully connected layer was used to classify the features to achieve end-to-end training. The InceptionV3_FF model was simulated on NUAA dataset and self-made STAR dataset. Experimental results show that the proposed InceptionV3_FF model achieves the accuracy of 99.96% and 98.85% on NUAA dataset and STAR dataset respectively， which are higher than those of the InceptionV3 transfer learning and transfer fine-tuning models. Compared with Nonlinear Diffusion-CNN （ND-CNN）， Diffusion Kernel （DK）， Heterogeneous Kernel-Convolutional Neural Network （HK-CNN） and other models， the InceptionV3_FF model has higher accuracy on NUAA dataset and has certain advantages. When the InceptionV3_FF model recognizes a single image randomly selected from the dataset， it only takes 4 ms. The face liveness detection system consisted of the InceptionV3_FF model and OpenCV can identify real and fake faces.

Concerning the problems of artifacts and loss of image details in the analytically reconstructed image by time-domain filters， a new time-frequency domain Computed Tomography （CT） reconstruction algorithm based on Convolutional Neural Network （CNN） was proposed. Firstly， a filter network based on a convolutional neural network was constructed in the frequency domain to achieve the frequency-domain filtering of the projection data. Secondly， the back-projection operator was used to perform domain conversion on the frequency-domain filtered result to obtain a reconstructed image. A network was constructed in the image domain to process the image from the back-projection layer. Finally， a multi-scale structural similarity loss function was introduced on the basis of the minimum mean square error loss function to form a composite loss function， which reduced the blur effect of the neural network on the result image and preserved the details of the reconstructed image. The image domain network and the projection domain filter network worked together to finally get the reconstructed result. The effectiveness of the proposed algorithm was verified on the clinical dataset. Compared with the Filtered Back Projection （FBP） algorithm， the Total Variation （TV） algorithm and the image domain Residual Encoder-Decoder CNN （RED-CNN） algorithm， when the number of projections is respectively 180 and 90， the proposed algorithm achieved the reconstructed result image with highest Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity （SSIM）， and the least Normalized Mean Square Error （NMSE）.When the number of projections is 360，the proposed algorithm is second only to TV algorithm. The experimental results show that the proposed algorithm can improve the reconstructed image quality of CT image， and it is feasible and effective.

Influence maximization is one of the important issues in social network analysis， which aims to identify a small group of seed nodes. When these nodes act as initial spreaders， information can be spread to the remaining nodes as much as possible in the network. The existing heuristic algorithms based on network topology usually only consider one single network centrality， failing to comprehensively combine node characteristics and network topology； thus， their performance is unstable and can be easily affected by the network structure. To solve the above problem， an influence maximization algorithm based on Node Coverage and Structural Hole （NCSH） was proposed. Firstly， the coverages and grid constraint coefficients of all nodes were calculated. Then the seed was selected according to the principle of maximum coverage gain. Secondly， if there were multiple nodes with the same gain， the seed was selected according to the principle of minimum grid constraint coefficient. Finally， the above steps were performed repeatedly until all seeds were selected. The proposed NCSH maintains good performance on six real networks under different numbers of seeds and different spreading probabilities. NCSH achieves 3.8% higher node coverage than to the similar NCA （Node Coverage Algorithm） on average， and 43% lower time consumption than the similar SHDD （maximization algorithm based on Structure Hole and DegreeDiscount）. The experimental results show that the NCSH can effectively solve the problem of influence maximization.

Concerning the problem of insufficient image feature extraction and ignorance of single-modal internal relations and the interactions between single-modal and multi-modal， a text and image information based Multi-Modal Deep Fusion （MMDF） model was proposed. Firstly， the Bi-Gated Recurrent Unit （Bi-GRU） was used to extract the rich semantic features of the text， and the multi-branch Convolutional-Recurrent Neural Network （CNN-RNN） was used to extract the multi-level features of the image. Then the inter-modal and intra-modal attention mechanisms were established to capture the high-level interaction between the fields of language and vision， and the multi-modal joint representation was obtained. Finally， the original representation of each modal and the fused multi-modal joint representation were re-fused according to their attention weights to strengthen the role of the original information. Compared with the Multimodal Variational AutoEncoder （MVAE） model， the proposed model has the accuracy improved by 1.9 percentage points and 2.4 percentage points on the China Computer Federation （CCF） competition and the Weibo datasets respectively. Experimental results show that the proposed model can fully fuse multi-modal information and effectively improve the accuracy of false information detection.

At present， social media platforms have become the main ways for people to publish and obtain information， but the convenience of information publish may lead to the rapid spread of rumors， so verifying whether information is a rumor and stoping the spread of rumors has become an urgent problem to be solved. Previous studies have shown that people's stance on information can help determining whether the information is a rumor or not. Aiming at the problem of rumor spread， a Joint Stance Process Multi?Task Rumor Verification Model （JSP?MRVM） was proposed on the basis of the above result. Firstly， three propagation processes of information were represented by using topology map， feature map and common Graph Convolutional Network （GCN） respectively. Then， the attention mechanism was used to obtain the stance features of the information and fuse the stance features with the tweet features. Finally， a multi?task objective function was designed to make the stance classification task better assist in verifying rumors. Experimental results prove that the accuracy and Macro?F1 of the proposed model on RumorEval dataset are improved by 10.7 percentage points and 11.2 percentage points respectively compared to those of the baseline model RV?ML （Rumor Verification scheme based on Multitask Learning model）， verifying that the proposed model is effective and can reduce the spread of rumors.

Under the constrains of very short message content on social media platforms， a large number of empty forwards in the transmission structure， and the mismatch between user roles and contents， a rumor detection model based on user attribute information and message content in the propagation network， namely GMB_GMU， was proposed. Firstly， user propagation network was constructed with user attributes as nodes and propagation chains as edges， and Graph Attention neTwork （GAT） was introduced to obtain an enhanced representation of user attributes； meanwhile， based on this user propagation network， the structural representation of users was obtained by using node2vec， and it was enhanced by using mutual attention mechanism. In addition， BERT （Bidirectional Encoder Representations from Transformers） was introduced to establish the source post content representation of the source post. Finally， to obtain the final message representation， Gated Multimodal Unit （GMU） was used to integrate the user attribute representation， structural representation and source post content representation. Experimental results show that the GMB_GMU model achieves an accuracy of 0.952 on publicly available Weibo data and can effectively identify rumor events， which is significantly better than the propagation algorithms based on Recurrent Neural Network （RNN） and other neural network benchmark models.

A partitioned calibration approach was applied to electronic whiteboard based on machine vision, since its location error distribution on large screens was non-homogeneous. Based on Human Interface Device (HID)'s implementation, the specific computer software was developed and the communication between the computer and electronic whiteboard was established. Configuration of calibration points on the whiteboard, receiving coordinates of these points, and calculation of calibration coefficients were completed. Thus the whole system calibration was implemented. The experimental results indicate that after calibration, the location accuracy is about 1.2mm on average on electronic whiteboard with the size of 140cm×105cm. And basic touch operations are accurately performed on the electronic whiteboard prototype after calibration.

A lattice signature scheme was proposed and some parameter choosing rules were illustrated concerning Small Integer Solution (SIS) problem and random oracle model of lattice. Then the results of the length of the keys that were generated under different parameter circumstances were compared. Afterwards the security and efficiency with the signature scheme were verified. At last, for the purpose of fairness, and reliability in multipartite authentication, the signature scheme was combined with key distribution and escrow, a new authentication scheme with the Singular Value Decomposition (SVD) algorithm based on mathematical matrix decomposition theory was proposed.

Combining the advantages of multi-core CPU and multi-threading technology, a new Gene Expression Programming （GEP） algorithm with multi-threading evaluator was introduced, which greatly improved the efficiency of the GEP algorithm. The experimental results demonstrate that the new proposed algorithm MTEGEP is more efficient than traditional GEP. Furthermore，compared to the traditional GEP，MTEGEP achieves 1.89 times faster speed in average with a dual-core CPU, and 6.48 times faster speed with an eight-core CPU.

The communication characteristics of proxy server were investigated from the angle of remote host computer. The functions and classifications of proxy server were summarized, and its working model was proposed, together with analysis of its logic modules and factors that influenced communication. After the principle of probing characteristics of proxy server was analyzed, a probing algorithm for HTTP proxy was designed, and a probing system prototype was implemented. The system accomplished penetrating through specific authentication mechanism in Windows domain environment. At last, the system was tested and its performance was analyzed. It is concluded that it is applicable to a certain extent.

For determining the membership function in text classification with fuzzy support vector machine, a construction approach of text classifier based on fuzzy support vector machine and decision tree was proposed. The relationship between the sample and its cluster center was considered and the tangent sphere was constructed by the hyperplane that contained the support vectors and paralleled the classification hyperplane in traditional support vector machine, so to further determine the relation of all samples in the class. The membership of one sample to a class could be computed by the location of the sample in the sphere, so the efficient samples, noises and outliers could be distinguished rationally. Integrating the decision tree method, the classification of multi-classes was realized. The experimental results demonstrate the method has preferable classification effect.

In Particle Swarm Optimization (PSO) algorithm, the acting of gBest particle during evolutionary process is important for attaining convergence. A new improved PSO algorithm called SLS-PSO was proposed in this paper. Based on the structure of basic PSO algorithm, the proposed algorithm searched local optimal solutions to gBest particle by adopting Stochastic Local Search (SLS) algorithm to improve the convergence performance during evolutionary process of PSO algorithm. Four well-designed test problems were used to evaluate the proposed algorithm. Compared with the basic PSO algorithm, the proposed algorithm shows its effectiveness and efficiency.