Shen Xiajiong

A Mandatory Access Control Model Based on Concept Lattice

Confidentiality and integrity of information are basic attributes of information security, but the existing access control models cannot unify the both well. A mandatory access control model based on access control concept lattice is proposed in this paper. This model integrates many safe sensitivity labels such as integrity, confidentiality, security category. Thus, mandatory constraints of the access of subject to the object can be realized, and Hasse diagram of lattice can be created automatically, such that security levels of the subject and object in the system can be displayed clearly.

Research on cubic convolution interpolation parallel algorithm based on GPU

The traditional cubic convolution algorithm has to confront with the problems of large operational scale and slow efficiency, when used to realize the remote sensing image magnification. In this paper, GPU, as a burgeoning high performance computing technique, is proposed to parallel processing the traditional cubic convolution, which we call the Cubic Convolution Parallel Algorithm (CCPA). This algorithm that divides the pixels points equally to each block, guarantees each pixel point is executed by a thread and threads are executed simultaneously in GPU, improving the interpolation efficiency greatly. The experimental results show that compared with the traditional cubic convolution algorithm, this algorithm not only increases the calculation speed, but also achieves high quality image after zooming. Meanwhile, with the growth of image resolution, the advantages of the algorithm become more and more obvious, for instance, to the image of 10240 * 10240 resolutions, the speed processed by GPU is 97.7% higher than that by CPU. Moreover, this algorithm also has profound practical value for remote sensing image processing under some emergency situations such as earthquakes, floods and other disasters, with the characteristic of good image quality and realtime mechanism.

A Novel Visual Programming Method Designed for Error Rate Reduction

Utilize the programming environment at present, it¿s effortless for us to design intuitionistic visual program. It only implements the visualization of programming ending. The process of programming is not fully visualization and it¿s not visual programming language in its true sense. According to the related research on visual programming languages in the past, the language is designed for special area or education, which is of pertinence and ordinary. If the programming language is made up of a series of graphic expressions it¿s under the name of visual programming language. So the goal of our research is to construct a kind of visual programming language which is propitious to program in graphic components, is convenient for comprehension to programmers and nonprogrammer and the most important is it can reduce error rate which is caused by former textual input and recede the workload in the course of lexical analysis and semantic parsing.

Adaptive Generalized Function Projective Lag Synchronization of Time-Delay Systems with Uncertain Parameters

In this paper, we consider generalized function projective lag synchronization of time-delay systems using adaptive control methods, and system parameters are uncertain. By means of the Lyapunov stability theory, an adaptive controller and parameter update rule are designed for achieving generalized function projective lag synchronization between two time-delay systems and estimating all the unknown parameters. Some numerical simulations are provided to show the effectiveness of our results. The results are helpful to make the wireless sensor network safer, and speed up the application of wireless sensor network.

Association Rules Mining Based on the Discriminative Concept Lattice

As a fundamental data mining task, frequent pattern mining has widespread applications in many different domains. Concept lattice is a very useful formal analysis tool. There is a one-to-one correspondence between concept intensions and frequent itemsets. Abundant literature has been conducted in-depth research in mining frequent itemsets and association rules based on the concept lattice. Most of them, however, did not take into account the differences of attributes when building concept lattice. In this paper, we present a new concept called discriminative attributes (discriminative intension). Every attributes has different discriminative power (DISP). In the process of building concept lattice, we can remove the attributes of low discriminative power, then speed up the step of constructing lattice. Furthermore, there has been a new method to calculate the DISP and reset the value under some conditions, but scan the database on each layer, which reduce the times of scanning the database, then decrease the time to generate association rules.

A fast batch algorithm for concept generating based on concept dictionary

The efficiency of concept lattice construction is a prevalent topic because it is necessary and important to construct concept lattice in many application fields concerning concept learning. This paper suggests a novel batch algorithm referring to the idea of isomorphic generating and closure operator. After context preprocessing in which the value and weight of objects are figured out, our algorithm could quickly generate all the possible concepts added by an object of context through using concept dictionary. Then we could obtain all the concepts after removing pseudo concepts from concept set. Our algorithm can reduce the search space when generating direct partial order relation easily between all concepts. Finally, our algorithm is completed as well as NextClosure algorithm using C# on windows platform, and comparison was made between them in the some data sets. Results showed that our algorithm performed better than NextClosure algorithm on temporal aspect.

Reconstructing concept lattices usingnth-order context kernels

To be different from traditional algorithms for concept lattice constructing, a method based onnth-order context kernel is suggested in this paper. The context kernels support generating small lattices for sub-contexts split by a given context. The final concept lattice is reconstructed by combining these small lattices. All relevant algorithms are implemented in a system IsoFCA. Test shows that the method yields concept lattices in lower time complexity than Godin algorithm in practical case.