In Event-Based Social Network （EBSN）， the recommendation work starts from the user historical preferences to model user preferences， which hinders the scope and ways for users to access new things. Aiming at the above problems， an unexpectedness metric-based social event recommendation model was proposed， namely UER（Unexpectedness-based Event Recommendation）. UER model included two sub-models， Base and Unexpected. Firstly， based on the interaction sequence characteristics of users， events， and user historical events， the Base sub-model used the attention mechanism to measure the weights of events in user historical preferences， and finally predicted the probabilities of users participating in events. Secondly， multiple interest representations of the user were extracted by Unexpected sub-model through the self-attention mechanism to calculate the unexpectedness of the user itself and the unexpectedness value of the candidate event to the user according to the multiple interest representations of the user， so as to measure the unexpectedness of the recommended event. Experimental results on Meetup-California dataset show that compared with Deep Interest Network （DIN） and Personalized Unexpected Recommender System （PURS）， the recommendation Hit Ratio （HR） of the UER model is increased by 22.9% and 30.3%， the Normalized Discounted Cumulative Gain （NDCG） is increased by 27.5% and 42.3%， and the unexpectedness of recommended events is increased by 54.5% and 21.4% respectively. On Meetup-NewYork dataset， the recommendation HR of the UER model is increased by 18.2% and 21.8%， the NDCG is increased by 26.9% and 32.0%， and the unexpectedness of recommended events is increased by 52.6% and 20.8% respectively.

To solve the problem that the two-tower neural network in the recommendation system is difficult to learn the interaction information between the user side and the item side and the graph connection information， a new algorithm TSN （Two-stage Siamese graph convolutional Neural network recommendation algorithm） was proposed. First， a heterogeneous graph based on user behavior was built. Then， a graph convolutional Siamese network was designed between the two-tower neural networks， so as to achieve information interaction while learning the connection information of the heterogeneous graph. Finally， by designing a special structure of two-stage information sharing mechanism， the neural networks on the user side and the item side could transmit information dynamically and bidirectionally during the training process， and neural network cascading was effectively avoided. In comparative experiments on MovieLens and Douban movie datasets， the NDCG@10， NDCG@50， NDCG@100 of the proposed algorithm are 11.39% to 23.98% higher than those of the optimal benchmark algorithm DAT （Dual Augmented Two-tower model for online large-scale recommendation）. The results show that the proposed algorithm can alleviate the problem of lack of information interaction in the two-tower neural network； and significantly improves the recommendation performance compared with the previous algorithms.

When the image is blurred due to rapid camera movement or in low-texture scenes， the Simultaneous Localization And Mapping （SLAM） algorithm using only point features is difficult to track and extract enough feature points， resulting in poor positioning accuracy and matching robustness. If it causes false matching， even the system cannot work. To solve the problem， a low-texture monocular SLAM algorithm based on point-line feature fusion was proposed. Firstly， the line features were added to enhance the system stability， and the problem of insufficient extraction of point feature algorithm in low texture scenes was solved. Then， the idea of weighting was introduced for the extraction number selection of point and line features， and the weight of point and line features were allocated reasonably according to the richness of the scene. The proposed algorithm ran in low-texture scenes， so the line features were set as the main features and the point features were set as the auxiliary features. Experimental results on the TUM indoor dataset show that compared with the existing point-line feature algorithms， the proposed algorithm can effectively improve the matching precision of the line features， has the trajectory error reduced by about 9 percentage points， and has the feature extraction time reduced by 30 percentage points. As the result， the added line features play a positive and effective role in low-texture scenes， and improve the overall accuracy and reliability of the data.

The capability of entrapment is significantly influenced by the degree of simulation in industrial control honeypots. In view of the lack of business logic simulation of existing industrial control honeypots, the high-interaction Programmable Logic Controller (PLC) honeypot design framework and implementation method based on industrial control business simulation were proposed. First, based on the interaction level of industrial control system, a new classification method of Industrial Control System (ICS) honeypots was proposed. Then, according to different simulation dimensions of ICS devices, the entrapment process in honeypot was divided into a process simulation cycle and a service simulation cycle. Finally, in order to realize the real-time response to business logic data, the process data was transferred to the service simulation cycle through a customized data transfer module. Combining typical ICS honeypot software Conpot and the modeling simulation tool Matlab/Simulink, the experiments were carried out with Siemens S7-300 PLC device as the reference, and so as to realize the collaborative work of information service simulation and control process simulation. The experimental results show that compared with Conpot, the proposed PLC honeypot system newly adds 11 private functions of Siemens S7 devices. Especially, the operating read (function code 04 Read) and write (function code 05 Write) in the new functions realize 7 channel monitoring for I area data and 1 channel control for Q area data in PLC. This new honeypot system breaks through the limitations of existing interaction levels and methods and finds new directions for ICS honeypot design.

Micro-Expression (ME) occurrence is only related to local region of face, with very short time and subtle movement intensity. There are also some unrelated muscle movements in the face during the occurrence of micro-expressions. By using existing global method of micro-expression recognition, the spatio-temporal patterns of these unrelated changes were extracted, thereby reducing the representation capability of feature vectors, and thus affecting the recognition performance. To solve this problem, the local region method was proposed to recognize micro-expression. Firstly, according to the region with the Action Units (AU) related to the micro-expression, seven local regions related to the micro-expression were partitioned by facial key coordinates. Then, the spatio-temporal patterns of these local regions were extracted and connected in series to form feature vectors for micro-expression recognition. The experimental results of leave-one-subject-out cross validation show that the micro-expression recognition accuracy of local region method is 9.878% higher than that of global region method. The analysis of the confusion matrix of each region's recognition result shows that the proposed method makes full use of the structural information of each local region of face, effectively eliminating the influence of unrelated regions of the micro-expression on the recognition performance, and its performance of micro-expression recognition can be significantly improved compared with the global region method.

To effectively solve overhead computing and bandwidth, high complexity problems about user access privileges revoking in cloud-storage service, a cloud-storage privilege revoking optimization mechanism based on dynamic re-encryption (DR-PRO) was proposed. Firstly, based on ciphertext access control scheme of Ciphertext Policy Attribute Based Encryption (CP-ABE), by using (k,n) threshold algorithm of secret sharing scheme, data information was divided into a number of blocks, and then a data information block was dynamically selected to realize re-encryption. Secondly, the user access privilege revoking was finished by the sub-algorithms, including data cutting, data reconstructing, data publishing, data extracting and data revoking. The theoretical analysis and test simulation showed that, based on high security of user information in cloud-storage service, compared with lazy re-encryption mechanism, the average computing and bandwidth decrease of user access privileges revoking was 5% when data file changed; compared with full re-encryption mechanism, the average computing and bandwidth decrease of user access privileges revoking was 20% when shared data block changed. The experimental results show that DR-PRO effectively improves the performance and efficiency of user access privileges revoking in cloud-storage service.

The virtual machines in cloud computing platform exchange data in the shared memory of physical machine. In view of the problem that the traffic cannot be captured and detected in firewall or other security components, the OpenFlow technology was analyzed, and a traffic redirection method based on OpenFlow was presented. To control traffic forwarding process and redirect it to security components, the method provided network connection for virtual machines with OpenFlow controller and virtual switches instead of physical switches, and built a traffic detection system composed of four modules including virtual switch, control unit, intrusion detection and system configuration management. The experimental results show that the proposed scheme can realize traffic redirection and the subsequent detection processing, and the system can provide switch-level and host-level control granularity. It also solves traffic detection problem under cloud computing environment in traditional scene by traffic redirection, and provides great expansion of the traffic processing based on OpenFlow.

To ensure the integrity and accuracy of the meteorological data, combined with automatic weather station's daily average temperature data which contained discontinuous noise, three types of membership functions were submitted. A compensation algorithm of Fuzzy Support Vector Machine (FSVM) based on root-mean-square membership function was designed and the compensation model was established too. Finally, the FSVM method was compared with the traditional Support Vector Machine (SVM) method. The experimental results show that the proposed algorithm has good recognition capability for noise points. After interpolation, the data precision was 1.4℃, better than 1.6℃ of the traditional SVM method. Moreover, the whole data precision was 1.13℃, superior to 1.42℃ of the traditional SVM method.

To deal with the performance bottleneck of operating the Device Independent Bitmap (DIB) in Wine, a method of client software rendering was brought forward. This method firstly analyzed GDI API for operating DIB, and then confirmed the load point of client software rendering, subsequently designed a list for linking different device context and their corresponding GDI API, at last realized the client software rendering of GDI API. The performance test shows that compared with the Wine without optimization, the average graphic performance of the Wine with the optimization of client software rendering enhances at least 10 times when it operates DIB, and is close to the performance of the local Windows XP, which effectively avoids the performance bottleneck of operating DIB.

CT image denoising restoration is a basic procedure in medical image processing. Cartoon-texture decomposition method was extended in order to resolve the problems of computational difficulty and low precision while applying cartoon-texture models in medical image denoising. First, the structure of cartoon-texture model was described by curvelet transform. Second, the texture of cartoon-texture decomposition was described using more sparse dual-tree complex wavelet transform. Third, an image cartoon images-texture model was established by combining curvelet transform and sparse representation. The algorithms of cartoon-texture model were discussed at last. The simulation experimental results show that the new method can effectively resolve the problem of large amount of iterative calculation using medical image denoising algorithm, and the image quality after processing can be improved as well.

With the rapid development of automotive electronic market, more and more Electronic Control Units (ECU) for vehicle controller appear and the functional test also becomes more complex. In order to solve the problem of ECU functional test, the ECU's automatic test method based on Controller Area Network (CAN) was studied. The system included the software and hardware platform of National Instrument (NI) and communication platform of CAN bus, by which the system and ECU formed a closed-loop structure. To transmit the test message through CAN bus, the system could achieve batch test of ECUs with the same type. By using the new test method, the system can reduce the test errors, and support assembly line test of ECU, which greatly reduces the complexity of ECU functional test and test work. At the same time, the system can also apply to other types of ECU functional test by improving the generation module of simulated signal and use case library.

A new algorithm was proposed based on the geometric symmetry to detect grid corners. The method can be utilized in camera calibration where chessboard-like patterns were often used. Based on the observation of the geometric symmetry of such patterns, the new algorithm could be regarded as a coarse-to-fine process. Coarse detection defined the corner candidates and then precise extraction was used based on symmetry analysis. Experimental results show that the algorithm assures an efficient and accurate detection and the procedure can be carried out automatically.