206617892
Granular Computing
The structure and representation theories of (crisp/fuzzy) granulations are presented. The results are applied to data mining, fuzzy control, security and etc.
15123815
Chinese Wall Security Policy Models: Information Flows and Confining Trojan Horses
In 1989, Brewer and Nash (BN) presented a fascinating idea, called Chinese wall security policy model, for commercial security. Their idea was based on the analysis of the notion, Conflict of Interest binary Relation (CIR). Unfortunately, their formalization did not fully catch the appropriate properties of CIR. In this paper, we present a theory based on granulation that has captured the essence of BN’s intuitive idea. The results are more than the Chinese wall models: Malicious Trojan horses in certain DAC Model (discretionary access control) can be controlled or confined.
10629196
A New Rough Sets Model Based on Database Systems
Rough sets theory was proposed by Pawlak in the early 1980s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.
8563061
Concurrent Automata and Parallel Architecture
14241577
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX network can be manifested as jump-ahead connections in the time-unfolded network. These jump-ahead connections can propagate gradient information more efficiently, thus reducing the sensitivity of the network to long-term dependencies. This work gives empirical justification to our hypothesis that similar improvements in learning long-term dependencies can be achieved with other classes of recurrent neural network axchitectures simply by increasing the order of the embedded memory. In particular we explore the impact of learning simple long-term dependency problems on three classes of recurrent neural network architectures: globally recurrent networks, locally recurrent networks, and NARX (output feedback) networks.Comparing the performance of these architectures with different orders of embedded memory on two simple long-term dependencies problems shows that all of these classes of network architectures demonstrate significant improvement on learning long-term dependencies when the orders of embedded memory are increased. These results can be important to a user comfortable with a specific recurrent neural network architecture because simply increasing the embedding memory order of that architecture will make it more robust to the problem of long-term dependency learning.
13328190
A β-expectation tolerance interval for general balanced mixed linear models
A @b-expectation tolerance interval procedure is derived from the concept of generalized pivotal quantity, which has been frequently used to obtain confidence intervals in situations where standard procedures do not lead to useful solutions. The proposed procedure can be applied to general balanced mixed linear models. Some practical examples are given to illustrate the proposed procedure. In addition, detailed simulation studies are conducted to evaluate its performance, showing that it can be recommended for use in practical applications.
26234971
A Color Consistency Algorithm Between Different Printers
Color consistency between different color peripherals su as printers, scanners and digital cameras is a well-kno problem. In this paper, a color consistency algorith between different printers is proposed. Each printer has its own characteristics. The co behavior of a printer can be modeled by a nonline characteristic function. We approximate the characteris function by a three-dimensional look-up table. Then, transfer function between two printers can be construc based on their characteristic functions. The transfer funct is to compensate one’s color behavior and simula another’s color behavior. After applying the transfe function, the color difference ( ∆E) and hue difference can be reduced to two to three times smaller.
23641664
Mediating Organizational Innovation: A Study of Implementation of Digital Technologies in TV News
Successful implementation of IT innovations requires mutual adjustment of technical features and social practices and structures of an organization. In this paper, we develop a synthesized model for mediating organizational innovation, drawn from Roger's model for organizational diffusion of innovation and Orlikowski et al.'s (2004) model for technology use mediation, to investigate the implementation of digital technologies in TV news. Our analysis of two case studies of TV news stations highlights the phases of mediation that occurred in these organizations and points to differences in mediation styles from previous studies. We suggest that scope of the implementation and temporal pressure in the work context were important dimensions in the mediation process
31273280
Adaptive-Hierarchical-Filtering Technique for Image Enhancement
This paper describes a novel adaptive-hierarchical-filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moire pattern during the reproduction of the image by different computer peripherals such as color copier, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moire distortion may result when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moire patterns in the frequency and spatial domain. Based on the analysis, a set of hierarchical filter is developed to suppress the moire artifacts and enhance the image adaptively. The hierarchical filter consists of a set of variable-length low-pass filters and high-pass filters. The low-pass filters have a nice inheritance of canceling aliased low frequency components (moire distortion). High-pass filtering is also applied to sharpen image edges. An image classifier is developed to determine that an edge is either a global true edge (for sharpening enhancement) or a local halftone's micro-structural edge (for LPF for moire reduction), so that adaptive filter technique can be applied to achieve the smooth transition between sharp edges and smooth halftone regions. Thus, we achieves overall high-quality output images.Experimental results have been shown the effectiveness of the presented technique that works well on wide combinations of above-mentioned 6 factors, which will be explained during the following sections, for high-quality magazine image reproduction.