62228300
SVM Based Classification Mapping for User Navigation
In order to implement active service, a kind of user navigation based on Support Vector Machine (SVM) classification was proposed which solved many traditional problems such as lack of rules and adaptability and so on. Especially, this paper completed a dual classified coordination between user attribute classification and SVM user classification using mapping matrix. In our SVM based user navigation, we first regard User interest Set as input, then apply the SVM classifier to sort out users on the basis of their interests or attributes, and what is more, combine the merit that user attributes classification promise better service being offered to users whose using frequency is not high, a dual-classified collaborating navigation between user attribute classification, and SVM user classification by using mapping matrix is being proposed. We use the mapping method to mix their advantages of common classification methods which lead them to collaborate and to provide the new and old custom with navigation. Using Classification T to statistic the latest and the most frequently used demands of each user type, then applying a mapping from user attribute class (A) to user interest class (T) with the purpose to judge what kind of user type in Classification T has the most possibility the user might belong, to and then to provide the user with the latest services according to their interests. From the experiments, the fitting degree of A and T classification and the true personality of users are derived by tracking the demand of 50 users. We can see that with the increase of frequency in use, the matching degree between Users Attribute Classification (A) and real types of user are essentially the same, and User Personalized Classification (T) is gradually reaching saturation. Regarding the certain point as a dividing line, after using classification mapping, the user's true extent of matching has a broader applicability. SVM have excellent generalization performance and minimum structural risk, which is a good machine learning method that elicits objective regulation from complex statistical dates. So we have improved the user model of active service navigation system and user navigation combined with User Attribute Classification A and SVM User Interest Classification T by mapping matrix. Classification A and T using mapping matrix to bounding them can satisfy the requirement of a different user. The records in the Classified Statistic Library can provide feedback date and research information for further study between a personalized user and user attribute.
16908353
The Dynamic Grey Radial Basis Function Prediction Model and its Applications
The dynamic gray radial basis function (DGRBF) prediction model is the improvement of traditional dynamic grey prediction model GM (1,1). It gives the dynamic algorithm of acquiring optimized initial conditions and identifying parameters like metabolism, and then the model combines the characters of RBF neural networks, and therefore has the ability of dynamic prediction on small volume of samples. The DGRBF prediction model has been applied in the practical engineering successfully, and the experiment results demonstrate that model and its intelligent optimizing algorithm are capable of predicting in a long term and the desired data could be acquired accurately, easily and conveniently
9176644
Automated and safe vulnerability assessment
As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%
12506111
A Case Study in Integrating Multiple E-commerce Standards via Semantic Web Technology
Internet business-to-business transactions present great challenges in merging information from different sources. In this paper we describe a project to integrate four representative commercial classification systems with the Federal Cataloging System (FCS). The FCS is used by the US Defense Logistics Agency to name, describe and classify all items under inventory control by the DoD. Our approach uses the ECCMA Open Technical Dictionary (eOTD) as a common vocabulary to accommodate all different classifications. We create a semantic bridging ontology between each classification and the eOTD to describe their logical relationships in OWL DL. The essential idea is that since each classification has formal definitions in a common vocabulary, we can use subsumption to automatically integrate them, thus mitigating the need for pairwise mappings. Furthermore our system provides an interactive interface to let users choose and browse the results and more importantly it can translate catalogs that commit to these classifications using compiled mapping results.
9006284
A workflow model based on fuzzy-timing Petri nets
Time information management in workflow has been recognized as one of the most significant tasks in workflow management. The uncertainties in time and time-related constraints in workflow models should be taken into consideration. Based on introducing time constraints on elements in fuzzy-timing Petri nets, this paper propose a new workflow model named fuzzy temporal workflow nets (FTWF-nets). The calculation of temporal elements in FTWF-nets is given. Then time modeling and time possibility analysis of temporal phenomena in FTWF-nets are investigated. Finally an example is given to illustrate how to use these methods. Research results show that FTWF-nets can be used to model time information in those workflows, which have time uncertainty and have time constraints on resources and activities, and analyze the time possibility on some constraints.
20364817
Log-Concave Functions And Poset Probabilities
elements of some (finite) poset , write for the probability that precedes in a random (uniform) linear extension of . For define where the infimum is over all choices of and distinct .Addressing an issue raised by Fishburn [6], we give the first nontrivial lower bounds on the function . This is part of a more general geometric result, the exact determination of the function where the infimum is over chosen uniformly from some compact convex subset of a Euclidean space.These results are mainly based on the Brunn–Minkowski Theorem and a theorem of Keith Ball [1], which allow us to reduce to a 2-dimensional version of the problem.
14798479
Learning the Semantic Meaning of a Concept from the Web
Many researchers have used text classification method in solving the ontology mapping problem. Their mapping results heavily depend on the availability of quality exemplars used as training data. However, manual preparation of exemplars is costly. In this work, we propose to automatically extract text from web pages returned by a search engine. Search queries are formed according to the semantic information given in the ontology. We have implemented a prototype system that automates the entire process (from search query formation to conditional probability calculation) and conducted a series of experiments. We assessed the effectiveness of our approach by comparing the obtained conditional probabilities with human expectations. Our main contribution is that we explored the possibilities of utilizing web information for text classification based ontology mapping and made several valuable discoveries on its usefulness for future research.