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In the process of auto-generating test-paper, the category and the difficulty of the title plays a key role in the quality of generating test-paper. It will produce low quality questions and hard to popularize when used the methods of artificial generating test-paper and random generating test-paper, because considering less on the knowledge point classification and difficulty in the subject. To improve the quality of auto-generating test-paper, this paper takes the evaluation data of ACM Online Judge system as the research object. After normalization, (1) we can get three different results by the K-means clustering analysis based on the temporal and spatial characteristics of time variance and average time; (2) On the basis of clustering, the difficulty index of each topic of all the categories is calculated by using the number of submissions and the number of submissions to solve the problem. The ratio of the two is proportional to the difficulty of the problem. In this paper, the ratio of the two to determine the degree of difficulty index; (3) The Gaussian stochastic process is used to extract numbers of questions of each knowledge point, and calculated the difficulty index which were extracted to make sure they are within range to complete the auto-generating test-paper. In the experiment, we try to train and test the automatic test paper model by the number of professional problems (about 50000 data) in the C language test question of the university OJ system. The average difficulty index of the test paper was 0.4663, which meet the requirements, and the difficulty index of the title fit in with the normal distribution. Compared with the traditional generating test-paper method, the automatic test paper model is based on the difficulty and discrimination of the subject, and it can evaluate the level of tester scientifically. The experimental results show that the proposed automatic test model is simple and effective.

The image match technology has become one of domain key technologies in pictorial information processing, and it has the widespread application background. Existed image match algorithm may divide into two kinds: one is based on image geometry characteristic match and another is based on picture element grey level match.It is mainly used correlation method of correlates to realize the template match algorithm which is based on the pixel grey level. And the key is how to reduce the computation load. The main idea of this algorithm is to find the parts of close region by gradation projecting in those searched regions first. Then makes the correlation computation in these regions in order to discover the most superior match region.

An improved mixed evolutionary algorithm is proposed, which is based on evolutionary trend, EDAs (Estimation of Distribution Algorithms) and clustering analysis. Firstly, the population is classified by clustering algorithm, then for each class, partial individuals of next generation are generated by EDAs, and the rest are supplemented by combination of extrema among classes, which can overcome the premature effectively. When the individuals in some classes converge to a small field, an exhaustive local search replaces EDAs. Simulation shows the algorithm can not only improve the global searching greatly, but also overcome premature effectively.

Face detection and location are widely used in practice. Although there are lots of related researches, the results are not satisfactory, esp. for the situation of side-face and multiscale in complex background. Among these techniques, the method based on skin-color is an important approach, so far, however, the effect of detection and location is not favorable due to the unsatisfactory segmentation. In order to improve the segmentation, we employ a new proposed classifier - support vector machine (SVM), based on the output of which we put forward a new scheme to realize the face detection and location. Experimental results indicate the algorithm can work well on wide range of color, light and size variations in still images.

Based on molecular kinematics and other related jobs, an explicit analytical mathematical model about severe acute respiratory syndrome (SARS) and the similar epidemics is put forward. New factors are considered such as resistibility of individuals and system of SARS checkpoints and health registration. Simulation based on molecular kinematics shows the analytical model is able to describe the dissemination of SARS correctly before and after the applications of various control measures, and therefore is able to predict the future epidemics. Application of the model to Beijing's epidemics presents satisfactory results. Principle of superposition is pointed out in determining the value of R in open model. Another useful conclusion is that the system of checkpoints and health registration plays critically important role. Drawbacks of the model are also discussed.