Focusing on the problems of low prediction accuracy and slow convergence speed of network security situation prediction model, a prediction method based on Improved Particle Swarm Optimization Extreme Learning Machine (IPSO-ELM) algorithm was proposed. Firstly, the inertia weight and learning factor of Particle Swarm Optimization (PSO) algorithm were improved to realize the adaptive adjustment of the two parameters with the increase of iteration times, so that PSO had a large search range and fast speed at the initial stage, strong convergence ability and stability at the later stage. Secondly, aiming at the problem that PSO is easy to fall into the local optimum, a particle stagnation disturbance strategy was proposed to re-guide the particles trapped in the local optimum to the global optimal flying. The Improved Particle Swarm Optimization (IPSO) algorithm obtained in this way ensured the global optimization ability and enhanced the local search ability. Finally, IPSO was combined with Extreme Learning Machine (ELM) to optimize the initial weights and thresholds of ELM. Compared with ELM, the ELM combining with IPSO had the prediction accuracy improved by 44.25%. Experimental results show that, compared with PSO-ELM, IPSO-ELM has the fitting degree of prediction results reached 0.99, and the convergence rate increased by 47.43%. The proposed algorithm is obviously better than the comparison algorithms in the prediction accuracy and convergence speed.

For the images with dense repetitive structure, the common algorithms will lead to a large number of false matches, resulting in obvious ghosting in final image and high time consumption. To solve the above problems, a fast stitching method for dense repetitive structure images was proposed based on Grid-based Motion Statistics (GMS) algorithm and optimal seam algorithm. Firstly, a large number of coarse matching points were extracted from the overlapping regions. Then, the GMS algorithm was used for precise matching, and the transformation model was estimated based on the above. Finally, the dynamic-programming-based optimal seam algorithm was adopted to complete the image stitching. The experimental results show that, the proposed method can effectively stitch images with dense repetitive structures. Not only ghosting is effectively suppressed, but also the stitching time is significantly reduced, the average stitching speed is 7.4 times and 3.2 times of the traditional Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) algorithms respectively, 4.1 times as fast as the area-blocking-based SIFT algorithm, 1.4 times as fast as the area-blocking-based SURF algorithm. The proposed algorithm can effectively eliminate the ghosting of dense repetitive structure splicing and shorten the stitching time.

A common spatial query often leads to the problem of multiple query results because a spatial database usually contains large size of data. To deal with this problem, a new categorization approach for spatial database query results was proposed. The solution consists of two steps. In the offline step, the coupling relationship between spatial objects was evaluated by considering the location proximity and semantic similarity between them, and then a set of clusters over the spatial objects could be generated by using probability density-based clustering method, where each cluster represented one type of user requirements. In the online query step, for a given spatial query, a category tree for the user was dynamically generated by using the modified C4.5 decision tree algorithm over the clusters, so that the user could easily select the subset of query results matching his/her needs by exploring the labels assigned on intermediate nodes of the tree. The experimental results demonstrate that the proposed spatial object clustering method can efficiently capture both the semantic and location relationships between spatial objects. The query result categorization algorithm has good effectiveness and low search cost.

The current Advanced Marking Scheme (AMS) algorithm is a relatively efficient algorithm for tracing IP addresses of Distributed Denial of Service (DDoS) attackers. However, as using hash functions to achieve compression of edge address, the AMS algorithm has many defects such as high complexity, poor confidentiality and a high ratio of false positives. In order to improve the efficiency of AMS, the AMS algorithm based on multidimensional pseudo-random sequences was designed. On one hand, replacing original hash functions, an edge sampling matrix was constructed with a full hardware device in a router to achieve the compression coding of IP address. On the other hand, combined with the compressed code of edge address and the calculation process of edge weight in the victim's side, the output of DDoS attack path graph was realized. In the simulation experiments, the performance of the AMS algorithm based on multidimensional pseudo-random sequences is basically the same as the original algorithm, which can effectively reduce misjudgment and quickly judge forged paths. The experimental results show that the proposed algorithm has high security, fast computation and strong anti-attack ability.

Traditional security testing methods (such as symbolic execution, fuzz testing, and taint analysis) cannot obtain high coverage of Graph User Interface (GUI) for Android programs. To solve this problem, an Android program testing method combining both static and dynamic analysis was proposed. Based on the static analysis of data flow of Android applications, activity translation graph and function call graph were constructed, and the GUI elements of the program were parsed, then scripts were written to dynamically traverse GUI elements of applications. This method was applied to the testing of the applications including Booking Calendar, Wifi Master Key and 360 Weather, the result showed that the average coverage of activity reached 76%, which was significantly higher than that of manual testing (30.08%) as well as GUI tree traversal (42.05%-61.29%). Experimental result demonstrate that the method can effectively traverse GUI of Android applications.

Since the single-criterion-based tracker can not adapt to the complex environment, a tracking approach based on adaptive fusion of dual-criteria was proposed. In the method, the second-order spatiogram was employed to represent the target, the similarity between the target candidate and the target model as well as the contrast between the target candidate and its neighboring background were used to evaluate its reliability, and the objective function (or likelihood function) was established by weighted fusion of the two criteria. The particle filter procedure was used to search the target, and the fuzzy logic was applied to adaptively adjust the weights of the similarity and contrast. Experiments were carried out on several challenging sequences such as person, animal, and the results show that, compared with other trackers such as incremental visual tracker, ι₁ tracker, the proposed algorithm obtains better comprehensive performance in handling occlusion, deformation, rotation, and appearance change, and its success rate and average overlap ratio are respectively more than 80% and 0.76.

Due to the complexity and the dynamic changes of the coal mine environment, the concentrations of harmful gases are difficult to be accurately monitored. The traditional monitoring methods use a single sensor to pick-up information, and the collected data have simple data form, low reliability, big error and so on. Concerning these problems, a new method was proposed in this paper, that is, sampling a variety of heterogeneous gases sources, and then taking advantage of the strong classification algorithm to filter, lastly fusing the above obtained information. As experiments state, the new method significantly improve the reliability of the mine monitoring system.