The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict dete...The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict detection.We research the global conflict detection algorithm in this paper.We presented a semantic model that captures more complete classifications of the policy using knowledge concept in rough set.Based on this model,we presented the global conflict formal model,and represent it with OBDD(Ordered Binary Decision Diagram).Then we developed GFPCDA(Global Firewall Policy Conflict Detection Algorithm) algorithm to detect global conflict.In experiment,we evaluated the usability of our semantic model by eliminating the false positives and false negatives caused by incomplete policy semantic model,of a classical algorithm.We compared this algorithm with GFPCDA algorithm.The results show that GFPCDA detects conflicts more precisely and independently,and has better performance.展开更多
Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candid...Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candidates hold high posterior probability although these are not recognized correctly. We investigate the Word Activation Force (WAF) model that compatibly encodes syntactical and semantic information into sparse coding directed networks. A high-level confidence feature Keyword Activation Force (KAF) based on WAF is proposed. KAF can be used for detecting false alarms by considering information about the neighbors to provide a more reliable and accurate keyword affinity. Compared with the baseline system, a relative reduction of 30.94% in average error rate could be achieved when KAF is combined with the posterior probability and the language model score.展开更多
基金supported by the National Nature Science Foundation of China under Grant No.61170295 the Project of National ministry under Grant No.A2120110006+2 种基金 the Co-Funding Project of Beijing Municipal Education Commission under Grant No.JD100060630 the Beijing Education Committee General Program under Grant No. KM201211232010 the National Nature Science Foundation of China under Grant NO. 61370065
文摘The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict detection.We research the global conflict detection algorithm in this paper.We presented a semantic model that captures more complete classifications of the policy using knowledge concept in rough set.Based on this model,we presented the global conflict formal model,and represent it with OBDD(Ordered Binary Decision Diagram).Then we developed GFPCDA(Global Firewall Policy Conflict Detection Algorithm) algorithm to detect global conflict.In experiment,we evaluated the usability of our semantic model by eliminating the false positives and false negatives caused by incomplete policy semantic model,of a classical algorithm.We compared this algorithm with GFPCDA algorithm.The results show that GFPCDA detects conflicts more precisely and independently,and has better performance.
基金supported by National Natural Science Foundation of China under Grants No.61005004,No.61175011,No.61171193 the Next-Generation Broadband Wireless Mobile Communications Network Technology Key Project under Grant No.2011ZX03002-005-01+1 种基金 the 111 Project under Grant No.B08004 Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
文摘Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candidates hold high posterior probability although these are not recognized correctly. We investigate the Word Activation Force (WAF) model that compatibly encodes syntactical and semantic information into sparse coding directed networks. A high-level confidence feature Keyword Activation Force (KAF) based on WAF is proposed. KAF can be used for detecting false alarms by considering information about the neighbors to provide a more reliable and accurate keyword affinity. Compared with the baseline system, a relative reduction of 30.94% in average error rate could be achieved when KAF is combined with the posterior probability and the language model score.
