In the execution of method invocation sequences to test component security,abnormal or normal information is generated and recorded in a monitor log. By searching abnormal information from monitor log,the exceptions t...In the execution of method invocation sequences to test component security,abnormal or normal information is generated and recorded in a monitor log. By searching abnormal information from monitor log,the exceptions that the component has can be determined. To facilitate the searching process,string searching methods could be employed. However,current approaches are not effective enough to search long pattern string. In order to mine the specific information with less number of matches,we proposed an improved Sunday string searching algorithm in this paper. Unlike Sunday algorithm which does not make use of the already matched characters,the proposed approach presents two ideas — utilizing and recycling these characters. We take advantage of all matched characters in main string,if they are still in the matchable interval compared with pattern string,to increase the distance that pattern string moves backwards. Experimental analysis shows that,compared to Sunday algorithm,our method could greatly reduce the matching times,if the scale of character set constituting both main string and pattern string is small,or if the length of pattern string is long. Also,the proposed approach can improve the search effectiveness for abnormal information in component security testing.展开更多
In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon or...In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.展开更多
基金supported by National Natural Science Foundation of China (NSFC grant number:61202110,61401180 and 61502205)the Postdoctoral Science Foundation of China (Grant number:2015M571687 and 2015M581739)the Graduate Research Innovation Project of Jiangsu Province(KYLX15_1079 and KYLX16_0900)
文摘In the execution of method invocation sequences to test component security,abnormal or normal information is generated and recorded in a monitor log. By searching abnormal information from monitor log,the exceptions that the component has can be determined. To facilitate the searching process,string searching methods could be employed. However,current approaches are not effective enough to search long pattern string. In order to mine the specific information with less number of matches,we proposed an improved Sunday string searching algorithm in this paper. Unlike Sunday algorithm which does not make use of the already matched characters,the proposed approach presents two ideas — utilizing and recycling these characters. We take advantage of all matched characters in main string,if they are still in the matchable interval compared with pattern string,to increase the distance that pattern string moves backwards. Experimental analysis shows that,compared to Sunday algorithm,our method could greatly reduce the matching times,if the scale of character set constituting both main string and pattern string is small,or if the length of pattern string is long. Also,the proposed approach can improve the search effectiveness for abnormal information in component security testing.
文摘In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.
