The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize ...The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize reasoning about context information in cloud computing environments. To formalize such context-aware reasoning, the logic LCM of context-mixture is introduced based on a Gentzen-type sequent calculus for an extended resource-sensitive logic. LCM has a specific inference rule called the context-mixture rule, which can naturally represent a mechanism for merging formulas with context information. Moreover, LCM has a specific modal operator called the sequence modal operator, which can suitably represent context information. The cut-elimination and embedding theorems for LCM are proved, and a fragment of LCM is shown to be decidable. These theoretical results are intended to provide a logical justification of context-aware cloud computing service models such as a flowable service model.展开更多
The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering bas...The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering based on General Mixture Decomposition Algorithm(GMDA-EC)is proposed.First,the belief classification of target cluster is carried out,and the sample category of target distribution overlapping region is extended.Then,on the basis of General Mixture Decomposition Algorithm(GMDA)clustering,the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target.Finally,the performance of the algorithm is verified by the synthetic dataset and the measured dataset.The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.展开更多
文摘The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize reasoning about context information in cloud computing environments. To formalize such context-aware reasoning, the logic LCM of context-mixture is introduced based on a Gentzen-type sequent calculus for an extended resource-sensitive logic. LCM has a specific inference rule called the context-mixture rule, which can naturally represent a mechanism for merging formulas with context information. Moreover, LCM has a specific modal operator called the sequence modal operator, which can suitably represent context information. The cut-elimination and embedding theorems for LCM are proved, and a fragment of LCM is shown to be decidable. These theoretical results are intended to provide a logical justification of context-aware cloud computing service models such as a flowable service model.
基金co-supported by the Youth Foundation of National Science Foundation of China(No.62001503)the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China(No.2017-JCJQ-ZQ-003)the Special Fund for Taishan Scholar Project,China(No.ts201712072)。
文摘The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering based on General Mixture Decomposition Algorithm(GMDA-EC)is proposed.First,the belief classification of target cluster is carried out,and the sample category of target distribution overlapping region is extended.Then,on the basis of General Mixture Decomposition Algorithm(GMDA)clustering,the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target.Finally,the performance of the algorithm is verified by the synthetic dataset and the measured dataset.The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.
