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The 3D platform for the Yellow River Basin is an infrastructure for the construction of Digital Yellow River Programme. The platform inputs the Yellow River Basin visually, vividly and systematically into computer by adopting the advanced computer techniques, such as GIS (Geographical Information System), RS (Remote Sensing), 3D Modeling, Virtual Reality (VR) etc., and constructs virtual corresponding state for actual Yellow River basin, which is "3D Visualization Platform for the Yellow River Basin". On the platform, the basin planning, management and multiple applicable systems can be developed and articulated, which is convenient for modeling, analyzing and studying the natural phenomena of the Yellow River, exploring the internal rule and providing powerful 3D visualized decision-making support environment for each plan in the Yellow River regulation, development and management. [ABSTRACT FROM AUTHOR]

During the process large-scale XPath queries against fast XML streams on Internet, a bottleneck occurs due to the lack of memory for filtering. This paper presents an effective automaton method to reduce the memory need by diminishing the tedious operators (such as "//" and "*") in XPath expressions. The method contains a product automata to convert XPath to the actual path; thus to reduce the complexity in search space. The proposed method was implemented in MFSA (Multi- Finite States Automata) system for filtering queries of subscribers. The empirical evidence shows its efficiency and stability when the scale of queries is large. [ABSTRACT FROM AUTHOR]

XML becomes a standard format for data interchanges on Internet, especially in E-commerce. Although XML technology has been widely used, the research and development on XML security is still at the early stage. The control of XML access is important for protecting XML documents from being illegally modified or accessed. Most of available models utilize a single level check point. In this paper, we proposed an access control model with dual level access control: file-level and element-level (or attribute-level). The model allows adopt the XBLP policy with file-level security, while employs Hide-Node View for element-level security. The architecture framework of the access control model and implementation are briefly described. [ABSTRACT FROM AUTHOR]

The paper presents a new automated pattern classification method. At first original data points are partitioned by unsupervised self-organizing map network (SOM). Then from the above clustering results, some labelled points nearer to each clustering center are chosen to train supervised generalization regression neural network model (GRNN). Then utilizing the decided GRNN model, we reclassify these original data points and gain new clustering results. At last from new clustering results, we choose some labelled points nearer to new clustering center to train and classify again, and so repeat until clustering center no longer changes. Experimental results for Iris data, Wine data and remote sensing data verify the validity of our method. [ABSTRACT FROM AUTHOR]

The paper presents an input-expansion-based improved method for general regression neural network (GRNN) and BP network. Using second-order inner product function or Chebyshev polynomial function to expand input vector of original samples, which makes input vector mapped into a higher-dimension pattern space and thus leads to the samples data more easily separable. The classification results for both Iris data and remote sensing data show that general regression neural network is superior to Levenberg-Marquardt BP network (LMBPN) and moreover input-expansion method may efficiently enhance classification accuracy for neural network models. [ABSTRACT FROM AUTHOR]

The paper proposes an edge-effect training multi-class fuzzy support vector machine (EFSVM). It treats the training data points with different importance in the training process, and especially emphasizes primary contribution of these points distributed in edge area of data sets for classification, and then assigns them greater fuzzy membership degrees, thus assures that the nearer these points are away from edge area of training sets and the greater their contribution are. At the same time EFSVM is systematically compared to two other fuzzy support vector machines and a Levenberg-Marquardt-based BP algorithm (LMBP). The classification results for both Iris data and remote sensing image show that EFSVM is the best and may effectively enhance pattern classification accuracy. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) and semantic-based image retrieval (SBIR) have attracted great research attentions. However, they all have disadvantages. This paper proposes a retrieval method trying to overcome them. To achieve this we first introduce an approach of rough set-based low-level features selection. We propose an approach of feedback-based semantic-level features annotation. We also introduce a corresponding computing technology of similarity. We then build a model of combination of these two kinds of methods. Experimental results show that our method is more user-adaptive, and can achieve better performance compared with another retrieval method which is only based on low-level features. [ABSTRACT FROM AUTHOR]

As large corporations and organizations increasingly exploit the Internet as a means of improving business-transaction efficiency and productivity, it is increasingly common to find operational data and other business information in XML format. Access control for XML database is non-trivial subjects. A number of recent research efforts have considered access control models for XML data . Our first contribution is a novel model for specifying XML security access control. Given an XML document accompanied by a document DTD, we allow a two-stage access control policies to pledge to security access XML document at file-level and element-level respectively. On the element-level access control, our approach for these access control policies is based on the novel notion of hide-node views. While the hide-node view DTD is exposed to authorized users, neither the internal XPath annotations nor the full document DTD is visible. Authorized users can only operate data over the hide-node view, making use of the exposed view DTD to access data. Our hide-node view mechanism guarantees that unauthorized user cannot access sensitive data and protects the schema information from access by unauthorized users. We think that the schema information also is sensitive data and should be protected from gain through the data accessing. [ABSTRACT FROM AUTHOR]