In recent years, deep learning technology has developed rapidly. In Natural Language Processing (NLP) tasks, with text representation technology rising from the word level to the document level, the unsupervised pre-training method using a large-scale corpus has been proved to be able to effectively improve the performance of models in downstream tasks. Firstly, according to the development of text feature extraction technology, typical models were analyzed from word level and document level. Secondly, the research status of the current pre-trained models was analyzed from the two stages of pre-training target task and downstream application, and the characteristics of the representative models were summed up. Finally, the main challenges faced by the development of pre-trained models were summarized and the prospects were proposed.

Focusing on the problem of nonlinear time series prediction in the field of analog circuit fault prediction and the problem of error accumulation in traditional Support Vector Regression (SVR) multi-step prediction, a new adaptive SVR prediction algorithm based on phase space reconstruction was proposed. Firstly, the significance of SVR multi-step prediction method for time series trend prediction and the error accumulation problem caused by multi-step prediction were analyzed. Secondly, phase space reconstruction technique was introduced into SVR prediction, the phase space of the time series of the analog circuit state was reconstructed, and then the SVR prediction was carried out. Thirdly, on the basis of the two SVR prediction of the error accumulated sequence generated in the multi-step prediction process, the adaptive correction of the initial prediction error was realized. Finally, the proposed algorithm was simulated and verified. The simulation verification results and experimental results of the health degree prediction of the analog circuit show that the proposed algorithm can effectively reduce the error accumulation caused by multi-step prediction, and significantly improve the accuracy of regression estimation, and better predict the change trend of analog circuit state.

In order to solve the problem of auditing the data protected by Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocol on the Internet, a plaintext gathering method for network data protected by SSL/TLS protocol based on the principles of man-in-the-middle was proposed. A data gatherer was connected between the server and the client in series, which was able to get the encryption key by modifying handshake messages during SSL/TLS handshake, so as to decrypt the secure data and then gather its plaintext. Compared with the existing gathering method based on the principles of proxy server, the proposed method has a shorter transmission delay, a larger SSL throughput and a smaller memory occupation. Compared with the existing gathering method in which the gatherer possesses the server's private key, the proposed method has a wider application scope, and also has the advantage of being unaffected by packet losses on the Internet. The experimental results show that the proposed method has a decrease in transmission delay of about 27.5% and an increase in SSL throughput of about 10.4% compared with the method based on the principles of proxy server. The experimental results also show that the SSL throughput of the proposed method approaches the ideal maximum value.

In order to solve the safety problems of privacy in the processes of authentication and communication of Radio Frequency IDentification (RFID) system, a mutual authenticate and encrypt scheme with low resource consume, high-level security and applicable for most of RFID systems was designed. This scheme combined the improved Elliptic Curve Diffie-Hellman (ECDH) algorithm and Advanced Encryption Standard (AES) algorithm to implement functions of key distribution, certification and communication encryption. It used dynamic key to enhance security. In addition, this scheme reduced the operation scale with original security strength, and saved the overhead of system resources. The measured results show that this scheme can resist replaying attacks, impersonation attacks, man-in-the-middle attacks and Denial of Service (DoS) attacks so as to save system resources. It can be applied in the field of Internet of Things (IOT) which has requirements on security and costs.

As the radio frequency communication technology gets more mature and the hardware manufacturing cost decreases, Radio Frequency IDentification (RFID) technology has been applied in the domains of real-time object monitoring, tracing and tracking. In supply chain applications, there are usually a great number of RFID objects to be monitored and traced, and objects' locations are changed essentially, so how to query the locations and the histories of location change of the RFID objects, from the huge volume of RFID data, is an urgent problem to be addressed. Concerning the characteristics of mobile RFID objects and the tracing query requirements in supply chain applications, an effective spatio-temporal index, called as CR-L, was put forward, and its structure and maintenance algorithms, including insertion, deletion, bi-splitting, and lazy splitting, were discussed in detail. In order to support object queries effectively, a new calculation principle of Minimum Bounding Rectangle (MBR), considering the three dimensional information including readers, time and objects, was presented to cluster the trajectories by the same reader at close time into the same node or the neighboring nodes. As to trajectory queries, a linked list was designed to link all trajectories belonging to the same object. The experimental results verify that CR-L has better query efficiency and lower space utilization rate than the existing method.

Real network traffic contains mass of features, and the method of anomaly detection based on feature analysis is not suitable for high-dimensional features classification. A method based on Principal Component Analysis and tabu Tabu Search (PCA-TS) decision tree classification for anomaly detection was proposed. The method reduced high-dimensional features and selected optimal feature subset which was suitable for classification through PCA-TS algorithm, then the decision tree of higher detection rate and lower false rate was used for classification and detection based on semi-supervised learning. The experiment shows that the approach has higher detection accuracy and lower false rate compared with traditional anomaly detection method, and the detection performance is less affected by sample size and is suitable for real-time detection of unknown anomalies.

For the deficiency of the existing network virus models with immunization, considering the infectious disease model in biology, a Susceptible-Infected-Recovered-Susceptible (SIRS) computer virus propagation model with stage immunization was formulated. The varying probability of being vaccinated when the threshold was reached and its impact on the spread of the virus in the network were considered. Furthermore, with the help of the theory of dynamic stability analysis, the existence and stability conditions of equilibriums were studied. The numerical simulation results illustrate that improving the rate of vaccination and setting a reasonable threshold can effectively constrain virus prevalence in the network.

The data model, business model, execution schema and performance metric of TPC-DS benchmark for next generation Decision Support System (DSS) application performance evaluation were introduced. The implementation architecture and key technologies for a configurable TPC-DS performance testing tool were put forward, including configuration file, query execution control and data maintenance mechanism. By testing practices in different Database Management Systems (DBMSs), the configurability and usability of the proposed tool for implementation strategies were verified.

Eliminating offset self-intersection could improve the ability to handle the detail of parts and the precision to make the parts. All kinds of method used around world and presented approach based on interval arithmetic and quad tree was presented. This method is good for detecting both local and global interactions. At last, gave examples for this method.