Focused on the issue that the traditional static detection and dynamic detection methods cannot cope with malicious PDF document attacks using a lot of obfuscation and unknown technologies， a new detection model based on system calls and data provenance， called NtProvenancer， was proposed. Firstly， the system call records during execution of the document were collected by the system call tracing tool. Then， the data provenance technology was used to establish a data provenance graph based on the system calls. After that， the feature segments of system calls were extracted for detection by using the key point algorithm of the graph. The experimental dataset consists of 528 benign PDF documents and 320 malicious ones. The test was carried out on Adobe Reader， and the Term Frequency-Inverse Document Frequency （TF-IDF） and the rarity algorithm in PROVDETECTOR were used to replace the key point algorithm of the graph to conduct the comparative study. The results show that NtProvenancer has better performance on precision and F1 score. Under the optimal parameter setting， the proposed model has the average time of document training and detection stages of 251.51 ms and 60.55 ms respectively， the false alarm rate lower than 5.22%， and the F1 score reached 0.989， showing that NtProvenancer is an efficient and practical model for PDF document detection.

Simultaneous Localization And Mapping （SLAM） is a research hotspot in robot localization and navigation. Reliable loop closure detection is critical for graph-based SLAM. However， loop closure detection by vision or Lidar is computationally expensive and has low reliability in large and complex environments. To solve this problem， a graph-based SLAM algorithm based on WiFi fingerprint sequence matching was proposed. In this algorithm， fingerprint sequences were used for loop closure detection. Since the fingerprint sequence contains data of multiple fingerprints， which is considered to be richer than a single fingerprint pair in the amount of information. Therefore， the traditional method based on single fingerprint pair matching was extended to fingerprint sequence matching， which greatly reduced the probability of false loop closure， thus ensuring the high accuracy of loop closure detection and satisfying high precision requirement of SLAM algorithm in large and complex environments. Two sets of experimental data （robots start from different starting points） were used to verify the proposed algorithm. The results show that the proposed algorithm is more accurate than Gaussian similarity method， and has the accuracy on the first and second set of data increased by 22.94% and 39.18% respectively. Experimental results fully verify the superiority of the proposed algorithm in improving the positioning accuracy and ensuring the reliability of loop closure detection

To deal with the single-image scale-up problem, a super-resolution reconstruction algorithm based on dictionary learning and non-local similarity was proposed. The difference images between the high-resolution images and results of using iterative back-projection image reconstruction were obtained, and then the high and corresponding low dictionaries could be co-generated by training difference image patches and the corresponding low-resolution image patches via using K-Singular Value Decomposition (K-SVD) algorithm which was combined with the idea that the high and low dictionaries could be co-trained for super-resolution reconstruction. In addition, a non-local similarity regularization constraint was introduced in the new algorithm to further improve the quality of the reconstructed images. The experimental results show that the proposed algorithm achieves better results than learning-based algorithms in terms of both visual perception and objective evaluation.

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.

Most training simulators of military power cannot give reasonable evaluation to the operation process of soldiers, because of being lack of scoring system. Therefore, in this paper, the scoring system for training simulator of military power based on the operating rule of actually weapon and the knowledge of large-scale system theory and expert system was analyzed. The operating rule of the military power and the key technologies of scoring system such as building up scoring rule, selecting scoring standard, selecting coefficient for the score deductions, calculation of the scoring system and program process of the scoring system were deeply analyzed in this paper. At last, a very useful scoring system for the training simulator of gas turbine generator was developed.

To study XML query with tree structure modeling, a query and detection method based on tree isomorphism was proposed, systematically enumerating the same root subtree of query pattern tree with the most right branch expansion. In the enumeration process, the Diffset data structure was used to record the query item logo of item set, and the DiffFRSTMiner mining algorithm was proposed. This item set includes the same root subtree. The experimental results prove that the algorithm is efficient, and can reduce definite memory overhead.