In this paper, we present Neighbor Community Finder (NCF, for short), a tool for finding Web communities related to given URLs. While existing link-based methods of finding communities, such as HITS, trawling, and Companion, use algorithms running on a Web graph whose vertices are pages and edges are links on the Web, NCF uses an algorithm running on an inter-site graph whose vertices are sites and edges are global-links (links between sites). Since the phrase "Web site" is used ambiguously in our daily life and has no unique definition, NCF uses directory-based sites proposed by the authors as a model of Web sites. NCF receives URLs interested in by a user and constructs an inter-site graph containing neighbor sites of the given URLs by using a method of identifying directory-based sites from URL and link data obtained from the actual Web on demand. By computational experiments, we show that NCF achieves higher quality than Google's "Similar Pages" service for finding pages related to given URLs corresponding to various topics selected from among the directories of Yahoo! Japan.

We study the statistical multiplexing performance of self-similar traffic. We consider that input streams have different QoS (Quality of Service) requirements such as loss and delay jitter. By applying the FBM (fractal Brownian motion) model, we present methods of estimating the effective bandwidth of aggregated traffic. We performed simulations to evaluate the QoS performances and the bandwidths required to satisfy them. The comparison between the estimation and the simulation confirms that the estimation could give rough data of the effective bandwidth. Finally, we analyze the bandwidth gain with priority multiplexing against non-prioritized multiplexing and suggest how to get better performance with the right configuration of QoS parameters.

Although open defects are hard to diagnose because they are unstable, we developed a technique to diagnose completely open defects. We applied a new "segment model" that takes the coupling effects on a defective node that are caused by neighboring nodes into consideration. This technique is used to focuse not only on the behavior of the defective node, but also on the behavior of other nodes affecting its behavior. We explain the theoretical treatment of our model and present experimental results obtained from an actual chip.

Although production systems are widely used in artificial intelligence (AI) applications, they are seen to have certain disadvantages in terms of their need for special purpose assistance software to build and execute their knowledge-bases (KB), and in the fact that they will not run on any operating system (platform dependency). Furthermore, for AI applications such as learning assistance systems, there is a strong requirement for a self-adaptive function enabling a flexible change in the service contents provided, according to the user. Against such a background, a Java based production system (JPS) featuring no requirement for special purpose assistance software and no platform dependency, is proposed. Furthermore, a new self-adaptive Java production system (A-JPS) is proposed to realize the "user adaptation" requirement mentioned above. Its key characteristic is the combination of JPS with a Causal-network (CN) for obtaining a "user profile". In addition, the execution time of the JPS was studied using several benchmark problems with the aim of comparing the effectiveness of different matching algorithms in their recognize-act cycles as well as comparing their performance to that of traditional procedural programs for different problem types. Moreover, the effectiveness of the user adaptation function of the A-JPS was studied for the case of a CN with a general DAG structure, using the experimental KB of a learning assistance system.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L^*u^*v^* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L^*u^*v^* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L^*u^*v^* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.

We propose a new method of direct mode motion compensation for bi-directionally predicted pictures (B-pictures). The proposed direct mode system is utilized in extended multiple picture prediction, in which blocks in B-pictures are encoded by referring to previous B-pictures in addition to I- or P-pictures as forward reference pictures in a multiple picture prediction framework. The proposed direct mode is suitable for extended multiple picture prediction, since it always uses the immediately previous picture as the forward reference picture. In the simulation, our proposed method is implemented in the H.26L codec. The simulation results show that the extended multiple picture prediction employing the proposed direct mode can reduce the bit rate of B-pictures by up to nearly 13% compared to conventional multiple picture prediction under typical encoding conditions. We also evaluate the performance of the proposed direct mode with the extended multiple picture prediction under several different encoding conditions.

We propose a method to determine accurate motion fields and illumination conditions such as non-uniform or non-stationary illuminations. The method extends a stabilization method using reliability indices of optical flow to combine with a gradient-based approach that determines a motion field and illumination conditions simultaneously. We applied the proposed method to two synthetic image sequences and a standard image sequence. The method is effective for image sequences including poorly textured areas, edges of brightness variation, and almost dark objects.

This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.

Digital watermarks on pictures must have the ability to survive various image processing operations while not causing degradation of picture quality. Random geometric distortion is one of the most difficult kinds of image processing for a watermark to survive, and this problem has become a central issue in watermarking research. Previous methods for dealing with random geometric distortion have been based on searches, special watermark patterns, learning, or additional data such as original pictures. Their use, however, is accompanied by large computational overhead or by operational inconvenience. This paper therefore proposes a method based on embedding watermark patterns in two of the three color planes constituting a color picture so that these two planes have a specific covariance. The detection of the embedded information is based on the covariance between these two planes. Random geometric distortion distorts all the constituent color planes of a picture in the same way and thus does not affect the covariance between any two. The covariance-based detection is therefore immune to the distortion. The paper clarifies that detection error would occur whenever the inherent covariance (the covariance in the original picture) overrides the covariance made by watermarking. The two constituent planes having the minimum inherent covariance are therefore selected and their inherent covariance is reduced by shifting one of them and using a noise-reduction preprocess. Experimental evaluations using StirMark confirmed that 64 bits embedded in 256256-pixel pictures can be correctly detected without using searches, special patterns, learning, or additional data.

Even in embedded processors, the accuracy in a branch prediction significantly affects the performance. In designing a branch predictor, in addition to accuracy, microarchitects should consider area, delay and power consumption. We propose two techniques to reduce the power consumption; these techniques do not requires any additional storage arrays, do not incur additional delay (except just one MUX delay) and never deteriorate accuracy. One is to look up two predictions at a time by increasing the width (decreasing the depth) of the PHT (Prediction History Table). The other is to reduce unnecessary accesses to the BTB (Branch Target Buffer) by accessing the PHT in advance. Analysis results with Samsung Memory Compiler show that the proposed techniques reduce the power consumption of the branch predictor by 15-52%.

The purpose of this research is to show that the stress during the VDT task could be evaluated using the chaotic features for the focal accommodation system and the pupil area. The result of this experiment shows that the fractal dimension for the pupil area can be used to evaluate the stress during the VDT task. Moreover, it is shown that the chaotic property in the fixed target measurement is higher than that in the linear control and step control measurements. However, the first Lyapunov exponent hardly changed over time for all of three accommodation measurements.

We propose an improved probabilistic packet marking approach for IP traceback to reconstruct a more precise attack path in an incomplete PPM deployment environment. Moreover, this scheme may also be used with a view to reducing the deployment overhead without requiring the participation of all routers along the attack path.

This work explores generative models of handwritten digit images using natural elastic nets. The analysis aims to extract global features as well as distributed local features of handwritten digits. These features are expected to form a basis that is significant for discriminant analysis of handwritten digits and related analysis of character images or natural images.

This paper deals with the problem of multiple object tracking with the condensation algorithm, applied to tracking of soccer players. To solve the problem of failures in tracking multiple players under overlapping, we introduce occlusion alarm probability, which attracts or repels particles based on their posterior distribution of previous time step. Real experiments showed a robust performance.

Broadcasting in wireless mobile computing environments is an effective technique to disseminate information to a massive number of clients equipped with powerful, battery operated devices. To conserve the usage of energy, which is scarce resource, the information to be broadcast must be organized so that the client can selectively tune in at the desired portion of the broadcast. In this letter, the power efficient behavior of a predeclaration-based transaction processing in mobile broadcast environments is examined. The analytical studies have been performed to observe the effectiveness of predeclaration-based transaction processing combined with selective tuning ability in mobile broadcast environments.

A new mesh reconstruction method, called the shrink-wrapped boundary face (SWBF) algorithm, is proposed for approximating a surface from a set of unorganized 3D points. SWBF overcomes the genus-0 spherical topology restriction of previous shrink-wrapping based mesh generation technique. Furthermore, SWBF is much faster since it requires only local nearest-point-search in the shrinking process. Our experimental results demonstrate that SWBF is very robust and efficient, and it is expected to become a general solution for reconstructing a mesh from an unorganized points cloud.

In this paper, we propose a naïve probabilistic shift-reduce parsing model which can use contextual information more flexibly than the previous probabilistic GLR parsing models, and utilize the characteristics of agglutinative language in which the functional words are highly developed. Experimental results on Korean have shown that our model using the proposed contextual information improves the parsing accuracy more effectively than the previous models. Moreover, it is compact in model size, and is robust with a small training set.