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Although studies regarding the classification and analysis of source-level vulnerabilities in operating systems are not direct and practical solutions to the exploits with which computer systems are attacked, it is important that these studies supply the elementary technology for the development of effective security mechanisms. Linux systems are widely used on the Internet and in intra-net environments. However, researches regarding the fundamental vulnerabilities in the Linux kernel have not been satisfactorily conducted. In this paper, characteristic classification and correlation analysis of source-level vulnerabilities in the Linux kernel, open to the public and listed on the SecurityFocus site for the 6 years from 1999 to 2004, are presented. This study will enable Linux kernel maintenance groups to understand the wide array of vulnerabilities, to analyze the characteristics of the attack abusing vulnerabilities, and to prioritize their development effort according to the impact of these vulnerabilities on the Linux systems. [ABSTRACT FROM AUTHOR]

This paper considers a real-time risk assessment method for information systems and networks based on observations from networks sensors such as intrusion detection systems. The system risk is dynamically evaluated using hidden Markov models, providing a mechanism for handling data from sensors with different trustworthiness in terms of false positives and negatives. The method provides a higher level of abstraction for monitoring network security, suitable for risk management and intrusion response applications. [ABSTRACT FROM AUTHOR]

Nematodes are primitive organisms which nonetheless devour many of the essential resources that are critical for human beings. For effective and quick inspection and quarantine, we propose a general image based system for quantitatively characterizing and identifying nematodes. We also describe the key methods ranging from gray level image acquisition and processing to information extraction for automated detection and identification. The main contributions of this paper are not only presenting a framework of the system architecture, but also giving detail analysis and implementation of each system component with instance of Caenorhabditis elegans. Therefore with a little modification, this system can be applied to other nematode species discrimination and analysis. [ABSTRACT FROM AUTHOR]

In this paper, we presented a new wavelet-based digital image stabilization (DIS) algorithm on the motion estimation for the stabilization of the 2-axes rotation sight system. In proposed algorithm, we first estimate the local motion defined in terms of translational motion vector by using fine-to-coarse (FtC) multi-resolution motion estimation (MRME). Next, we estimate the global motion defined as the rotational motion parameters such as the rotational center and angular frequency by using the vertical and horizontal component in wavelet domain. The rotational center and angular frequency are estimated from the local motion field and the special subset of the motion vector respectively. The experimental results show the improved stabilization performance compared with the conventional digital image stabilization algorithm. [ABSTRACT FROM AUTHOR]

This paper suggests a prototype system for visualization and analysis of anatomic shape and functional features of the hippocampus. Based on the result of MR-SPECT multi-modality image registration, anatomical and functional features of hippocampus are extracted from MR and registered SPECT images, respectively. The hippocampus is visualized in 3D by applying volume rendering to hippocampus volume data extracted from the MR image with color coded by registered SPECT image. In order to offer the objective and quantitative data concerning to the anatomic shape and functional features of the hippocampus, the geometric volume and the SPECT intensity histogram of hippocampus regions are automatically measured based on the MR and the registered SPECT image, respectively. We also propose a new method for the analysis of hippocampal shape using an integrated Octree-based representation, consisting of meshes, voxels, and skeletons. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of vector-valued multiresolution analysis. We discuss the existence of biorthogonal multiple vector-valued wavelets. An algorithm for constructing a class of compactly supported biorthogonal multiple vector-valued wavelets associated with the biorthogonal multiple vector-valued scaling functions is presented by using multiresolution analysis and matrix theory. [ABSTRACT FROM AUTHOR]

We propose an extension of sequential point trees, a data structure that allows adaptive rendering of point clouds completely on the graphics processor (GPU). Using a sequential level-of-detail selection, we exploited the programmable graphics pipeline for rendering of large data sets. By adding position and radius of parent node to all points in the sequence, hole and overdraw problems of the sequential point trees technique were resolved, and as a result better image quality and rendering performance were achieved. [ABSTRACT FROM AUTHOR]

A new hiding scheme of digital image is introduced in this paper, which is robust to image processing such as JPEG compression, median filtering, adding Gauss noise, cropping, and histogram equalization. In this paper, we embed watermark into DWT domain, using integer modulo, retrieval of embedded watermark does not need original image. [ABSTRACT FROM AUTHOR]

A new scheme integrating variable-rate channel coding with the multilayered space-time trellis codes (ML-STTC), is proposed to compensate for the performance loss due to the limited diversity gain of the firstly-detected layer in the group interference suppression and cancellation algorithm at the receiver. In the proposed scheme, the code rate of the channel coding in each layer is adaptive to the diversity gain of this layer, with intent to guarantee the performance of the first layer which suffers from diversity loss. In this way, the error propagation is reduced and the overall performance is improved. [ABSTRACT FROM AUTHOR]

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method. [ABSTRACT FROM AUTHOR]

In this paper, we will present the design and implementation of the crypto coprocessors which can be used for providing the RFID security. By analyzing the power consumption characteristics of our crypto coprocessors, we can see which crypto algorithms can be used for highly resource constrained applications such as RFID. To provide the security mechanism such as integrity and authentication between the RFID tag and the RFID reader, crypto algorithm or security protocol should be implemented at the RFID tag and reader. Moreover, the security mechanism in the RFID tag should be implemented with low power consumption because the resources of RFID tag are highly constrained, that is, the RFID tag has low computing capability and the security mechanism which operates in the RFID tag should be low power consumed. After investigating the power consumption characteristics of our designed crypto processors, we will conclude which crypto algorithm is suitable for providing the security of RFID systems. Keywords: Crypto Coprocessor, Crypto Algorithm, Power Consumption. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel solution called joint encryption, in which audio and video data are scrambled efficiently by using modified phase scrambling, modified motion vector, and wavelet tree shuffling. Experimental results indicate that the proposed joint encryption technique is very simple to implement, has no adverse impact on error resiliency and coding efficiency, and produces level of security. [ABSTRACT FROM AUTHOR]

In this paper, a new public-key broadcast encryption scheme, which is asymmetric and without involvement of trusted third parties, is proposed by using the "subset-cover" framework and the identity-based encryption scheme of Boneh and Franklin. This scheme is the first concrete construction of an asymmetric identity-based public-key broadcast encryption that does not rely on trusted agents and the costly Oblivious Polynomial Evaluation mechanism. Moreover, this novel work contains other desirable features, such as efficient encryption and decryption, low memory requirements, traitor tracing, asymmetry and non-repudiation. The tracing algorithm of this system is more efficient than that of the previous ones. [ABSTRACT FROM AUTHOR]

Fingerprinting schemes use digital watermarks to determine originators of unauthorized/pirated copies. Multiple users may collude and collectively escape identification by creating an average or median of their individually watermarked copies. Previous fingerprint code design including ACC (anti-collusion code) cannot support large number of users, and we present a practical solution, which defines scalability over existing codebook generation scheme. To increase the robustness over average and median attack, we design a scalable ACC scheme using a Gaussian distributed random variable. We experiment with our scheme using human visual system based watermarking scheme, and the simulation results with standard test images show good collusion detection performance over average and median collusion attacks. [ABSTRACT FROM AUTHOR]

Fingerprinting schemes are important techniques for protection of intellectual property. However, the previous schemes have some trust assumption made on the registration center, meanwhile they always consider the collusion between the registration and the merchant, also the buyer. To overcome those disadvantages, an anonymous fingerprinting scheme using a modification of Schnorr ring signature is presented. When a buyer wants to buy digital goods, he can sign the text m that describes the deal on behalf of the ring, which can bring fully anonymity and unlinkability. Once redistributing fingerprinted copies, a merchant still could trace the buyer with a slightly different version. [ABSTRACT FROM AUTHOR]

Accurate spatial prediction of mineral deposit locations is essential for the mining industry. The integration of Geographic Information System (GIS) data with soft computing techniques can improve the accuracy of the prediction of mineral prospectivity. But uncertainty still exists. Uncertainties always exist in GIS data and in the processing required to make predictions. Quantification of uncertainty in mineral prospectivity prediction can support decision making in regional-scale mineral exploration. This research deals with these uncertainties. In this study, interval neutrosophic sets are combined with existing soft computing techniques to express uncertainty in the prediction of mineral deposit locations. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the application of a two weights neural network (TWNN) to the channel equalization problem. In particular, the purpose of the paper is to improve the previously developed TWNN equalizer with training using K-means and LMS methods; reducing the TWNN network size by considering a lesser number of TWNN kernels, and developing new techniques for determining channel order which is required to specify the structure of an TWNN equalizer. A linear regression model was used for estimating the channel order. The basic idea of reducing the network size is to select the centers, based on the channel lag. This work includes the comparison of the limits of mean square error (MSE) convergence of both a linear equalizer and an TWNN equalizer. mean square error. [ABSTRACT FROM AUTHOR]

Character skeleton plays a significant role in character recognition. This paper presents a novel algorithm based on multiscale approach to extract skeletons of printed and hand-written characters. The development of the method is inspired by some desirable characteristics of the modulus minima of wavelet transform. Namely, the local minima of wavelet transform are scale-independent and locate at the medial axis of the symmetrical contours of character stroke. Thus it is particularly suitable for characterizing the inherent skeletons of character strokes. The proposed skeletonization algorithm contains two major steps. First, by thresholding for the modulus minima of wavelet transform, the modulus minima points underlying the character strokes are extracted as the primary skeletons. Based on these primary skeletons, the modulus minima points are being eventually computed as the final skeleton by iteratively performing wavelet transform. The skeleton form the proposed method can be exactly located on the central line of the stroke, and the artifacts and branches of skeletons from traditional methods can be avoided. We tested the algorithm on handwritten and printed character images. Experimental results indicate that the proposed algorithm is applicable to not only binary image but also gray-level image. [ABSTRACT FROM AUTHOR]

Network worms, very similar to viruses, are malicious programs that use vulnerabilities in software to spread between computers that are somehow connected using a computer network. We remind that a computer should be understood broadly - in the near future, a worm might infect a mobile phone as easily as it now infects a personal desktop computer. We have proposed a modular architecture for an early warning system. We also implemented a prototype system consisting of several detection and analysis modules. While the prototype was limited in nature, it meets the most of the requirements we have set for such a system. Keywords: Network worm, early warning, malicious code. [ABSTRACT FROM AUTHOR]

Next generation military communication system (NGMCS) is described as a kind of typical information communication networks (ICN), and primary requirement and evaluation criterion of NGMCN architecture are summarized briefly. Then a general architecture model for NGMCN, composed of four sections, application, system, technology, and equipment, is set up based on vector space and Fuzzy Sets theory. Further the modeling method is presented with the discussions in detail on fuzzy sets and fuzzy relations associated with the architecture model. [ABSTRACT FROM AUTHOR]

This paper presents an efficient control approach for the elevator group control system. The essential of the method used is based on an operation strategy with a talented algorithm. In order to analyze the performance of the presented system, the control method was evaluated by considering different performance characteristics in the elevator group control system. The results of the presented method compared with the results of the area weight algorithm. [ABSTRACT FROM AUTHOR]

To protect the privacy of proxy blind signature holders from dissemination of signatures by verifiers, this paper introduces universal designated-verifier proxy blind signatures. In a universal designated-verifier proxy blind signature scheme, any holder of a proxy blind signature can convert the proxy blind signature into a designated-verifier signature. Given the designated-signature, only the designated-verifier can verify that the message was signed by the proxy signer, but is unable to convince anyone else of this fact. This paper also proposes an ID-based universal designated-verifier proxy blind signature scheme from bilinear group-pairs. The proposed scheme can be used in E-commerce to protect user privacy. [ABSTRACT FROM AUTHOR]

Many secure electronic payment protocols have been proposed, most of which are based on public key cryptograph. These protocols, however, are not suitable for mobile network due to the limitations of mobile environments, such as limited computation capability of mobile devices, limited bandwidth, etc. In this paper, we propose a private and efficient payment protocol for mobile network, which only involves symmetric key algorithm, such as symmetric encryption, hash function and keyed hash function. All these operations can be implemented on mobile devices feasibly. The proposed protocol also achieves completely privacy protection of buyers, which is one of the important requirements in mobile commerce. First, the identity of the buyer is protected from the merchant. Second, the transaction privacy of the buyer, such as what the buyer buys, and whom the buyer buys from, are also protected from any other parties and financial institutions. By giving a security analysis, we show that our protocol satisfies all security requirements in electronic payment. [ABSTRACT FROM AUTHOR]

An automatic classification system of Korean traditional music is proposed using robust multi-feature clustering method. The system accepts query sound and automatically classifies input query into one of the six Korean traditional music categories. This paper focuses on the feature optimization method to alleviate system uncertainty problem due to the different query patterns and lengths, and consequently increase the system stability and performance. In order to fit this needs, a robust feature optimization method called multi-feature clustering (MFC) based on VQ and SFS feature selection is proposed. Several pattern classification algorithms are tested and compared in terms of the system stability and classification accuracy. [ABSTRACT FROM AUTHOR]

The issues of trust are especially of great importance in peer-to-peer electronic online communities [5]. One way to address these issues is to use community-based reputations to help estimate the trustworthiness of peers. This paper presents a reputation-based trust supporting framework which includes a mathematical trust model, a decentralized trust data dissemination scheme and a distributed implementation algorithm of the model over a structured P2P network. In our approach, each peer is assigned a unique trust value, computed by aggregating the similarity-filtered recommendations of the peers who have interacted with it. The similarity between peers is computed by a novel simplified method. We also elaborate on decentralized trust data management scheme ignored in existing solutions for reputation systems. Finally, simulation-based experiments show that the system based on our algorithm is robust even against attacks from groups of malicious peers deliberately cooperating to subvert it. [ABSTRACT FROM AUTHOR]

Taxonomies are exploited to generate improved decision trees. Experiments show very considerable improvements in tree simplicity can be achieved with little or no loss of accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of robust reliable control design for a class of fuzzy uncertain systems with time-varying delay. The system under consideration is more general than those in other existent works. A reliable fuzzy control design scheme via state feedback is proposed in terms of linear matrix inequality (LMI). The asymptotic stability of the closed-loop system is achieved for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration. [ABSTRACT FROM AUTHOR]

Network forensic plays an important role in the modern network environment for computer security, but it has become a time-consuming and daunting task due to the sheer amount of data involved. This paper proposes a new method for constructing incremental fuzzy decision trees based on network service type to reduce the human intervention and time-cost, and to improve the comprehensibility of the results. At the end of paper, we discuss the performance of the forensic system and present the result of experiments. [ABSTRACT FROM AUTHOR]

Adaptive sampling is an interesting tool to eliminate noise, which is one of the main problems of Monte Carlo global illumination algorithms. We investigate the Tsallis entropy to do adaptive sampling. Implementation results show that adaptive sampling based on Tsallis entropy consistently outperforms the counterpart based on Shannon entropy. [ABSTRACT FROM AUTHOR]

Compared with spatial domain motion compensated temporal filtering (MCTF) scheme, in-band MCTF scheme needs more coding bits for motion information since the motion estimation (ME) and motion compensation (MC) are implemented on each spatial subband. Therefore, how to employ motion prediction and coding is a key problem to improve the coding efficiency of in-band MCTF. In this paper, we proposed an efficient level-by-level modebased motion prediction and coding scheme for in-band MCTF. In our scheme, three motion prediction and coding modes are introduced to exploit the subband motion correlation at different resolution as well as the spatial motion correlation in the high frequency subband. To tradeoff the complexity and the accuracy of block-based motion search, a jointly rate-distortion criterion is proposed to decide a set of optimized motion vector for three spatial high frequency subbands at the same level. By the rate-distortion optimized mode selection engine, the proposed scheme can improve the coding efficiency about 0.6db for 4CIF sequence. [ABSTRACT FROM AUTHOR]

A new method for digital image scrambling transformation, namely Magic Cube Transformation, was proposed and its principle was introduced. Combined with the Logistic mapping in non-linear dynamics system, an image encrypting and decrypting algorithm based on Magic Cube Transformation was designed. A natural chaotic sequence was created with the key. The image matrix was transformed with this chaotic sequence, using the Magic Cube Transformation. Then an encrypted image was resulted. The decrypting operation was the reverse process of encryption. The experimental results indicate that the proposed algorithm can get satisfying effect. Finally, the characteristics of the algorithm were summarized and the aspects of the subsequent work were prospected. [ABSTRACT FROM AUTHOR]

The scaling behavior has been discovered in the past decade, which has provided hope that mathematical models can be found to describe the nature of the traffic. Similarly to long-range dependence (LRD), nonstationarity is also one of vital characteristics of network traffic. In this paper, a novel traffic model is proposed based on that the traffic aggregation behavior is abstracted in hierarchical way. The traffic model is focused on the burst traffic rate. Firstly, the burst size of output aggregated flow by edge device of a network domain is derived by pseudo queue system methods. And, the nonstationarity of input traffic is developed by a generalized fractal Gaussian noise process, which is constructed by a large number of train traffic series. They are Poisson arrival and their lifetime is exponential distribution. The model has a good performance of fitting to real traffic data within multi-scale for long time, which is illuminated by simulated results. [ABSTRACT FROM AUTHOR]

Phase field models provide a well-established framework for the mathematical description of free boundary problems for image segmentation. In phase field models interfaces represent edges of jacquard images and the determination of the edges of jacquard images is the main goal of image segmentation. In this paper, the phase field model was applied to segment and recognize pattern structures of jacquard images. The segmentation was performed in two major steps. Firstly, a pattern extraction and representation was performed by an adaptive mesh generation scheme. For the conjugate gradient method has been successfully used in solving the symmetric and positive definite systems obtained by the finite element approximation of energy functionals, a novel conjugate gradient algorithm was adapted to the minimization of energy functional of discrete phase model. Experimental results show efficiency of our approach. [ABSTRACT FROM AUTHOR]

We describe a novel technique called Analytic Boosted Cascade Detector (ABCD) to automatically locate features on the human face. ABCD extends the original Boosted Cascade Detector (BCD) in three ways: (i) a probabilistic model is included to connect the classifier responses with the facial features; (ii) a features location method based on the probabilistic model is formulated; (iii) a selection criterion for face candidates is presented. The new technique melts face detection and facial features location into a unified process. It outperforms Average Positions (AVG) and Boosted Classifiers + best response (BestHit). It also shows great speed superior to the methods based on nonlinear optimization, e.g. AAM and SOS. [ABSTRACT FROM AUTHOR]