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Computational Intelligence and Security, 2007 International Conference on

Date 15-19 Dec. 2007

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  • 2007 International Conference on Computational Intelligence and Security - Cover

    Publication Year: 2007
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  • 2007 International Conference on Computational Intelligence and Security - Title

    Publication Year: 2007 , Page(s): i - iii
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  • 2007 International Conference on Computational Intelligence and Security - Copyright

    Publication Year: 2007 , Page(s): iv
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  • 2007 International Conference on Computational Intelligence and Security - Table of contents

    Publication Year: 2007 , Page(s): v - xv
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  • Preface

    Publication Year: 2007 , Page(s): xvi
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  • Organizing Committee

    Publication Year: 2007 , Page(s): xvii
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  • Program Committee

    Publication Year: 2007 , Page(s): xviii
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  • list-reviewer

    Publication Year: 2007 , Page(s): xx
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  • DRM Interoperability

    Publication Year: 2007 , Page(s): xxiii
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (32 KB) |  | HTML iconHTML  

    The use of Digital Rights Management (DRM) technologies for the enforcement of digital media usage models is currently subject of a heated debate. Consumer organizations and national governments claim that DRM technology interferes with basic personal rights, such as the right to make copies for personal use or the right to use content on any platform of choice. This issue has lately gained increased attention by a trend in some European countries to force DRM vendors and online media stores to open up their respective DRM technologies, i.e. make them interoperable. In the first part of this talk we discuss the many obstacles to DRM interoperability, both technological, legal and business wise. In the second part we discuss discuss some potential solutions to the DRM interoperability problem. In particular, we present the Coral DRM interoperability framework that allows multiple DRM systems to seamlessly work together while at the same time requiring minimal modification to existing DRMs. View full abstract»

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  • Towards an Understanding of the Brain via Microscopic and Macroscopic Studies

    Publication Year: 2007 , Page(s): xxiv
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  • Mining with Noise Knowledge: Error Aware Data Mining

    Publication Year: 2007 , Page(s): xxix
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (33 KB) |  | HTML iconHTML  

    Real-world data are dirty, and therefore, noise handling is a defining characteristic for data mining research and applications. This talk will review existing research efforts on data cleansing and classifier ensembling in dealing with random noise, and then present our recent research on an error aware data mining design to process structured noise. This error aware data mining framework makes use of error information (such as noise level, noise distribution, and data corruption rules) to improve data mining results. Experimental comparisons on real-world datasets will demonstrate the effectiveness of this design. View full abstract»

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  • Modeling Network Security Services in Tactical Networks

    Publication Year: 2007 , Page(s): xxx
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    Summary form only given. Tactical networks frequently need to be set up without adequate infrastructure in place or where infrastructure elements can be destroyed easily and, moreover, must themselves be mobile and extensible. It is therefore desirable to provide mechanisms based on mobile ad hoc networks, which have been studied intensely in recent years. However, the specific requirements for tactical networks differ from civilian networks in that robustness in the face of direct attacks and compromised nodes leading to Byzantine behavior must be maintained and that above and beyond the need for energy efficiency it is necessary to limit radio frequency emanations both in terms of output power and in the area covered by the signals. In this paper we therefore describe several techniques for modeling the specific requirements underlying tactical networks and provide selected models for efficient distributed computation using the example of trust authority services, which form part of the core infrastructure in an ad hoc network as these services are required to establish trust relations and key exchanges efficiently where pre-shared keying is not desirable or appropriate. View full abstract»

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  • Mixing Matrix Recovery of Underdetermined Source Separation Based on Sparse Representation

    Publication Year: 2007 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1434 KB) |  | HTML iconHTML  

    This paper presents a new algorithm for recovering of the mixing matrix A of underdetermined source separation. Most of the existing algorithms for SCA assume that souce signals are strictly sparse, but the condition in this paper has been relaxed, i.e., there could be at most m-1 nonzero elements of the source signals in each time. Firstly, we can find that all m-1 linearly independent column vectors of observed signals X, which can span different hyperplanes, and then cluster the normal vectors of the hyperplanes in- stead of the hyperplanes themselves. Secondly, we deter- mine the hyperplanes by maximum analysis of the number of the observed signals, which are located the same hyper- plane. Finally, the mixing matrix is identified from the in- tersection lines of the hyperplanes. The simulation results have shown the effectiveness of the proposed algorithm. View full abstract»

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  • Region Assessment of Soil Erosion Based on Naive Bayes

    Publication Year: 2007 , Page(s): 6 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (317 KB) |  | HTML iconHTML  

    Since soil erosion is a serious environmental problem, regional-scale soil erosion assessment is important. However, it is limited by the development of soil erosion mode by far. This paper presents a region- scaled soil erosion qualitative evaluation model based on naive Bayes. Firstly, the model takes the E'Dong Mountain as case study area, chooses the four factor indexes affected erosion intensity, and then calculates the naive Bayes probability of each index of soil erosion based on the observed plot sample data. Secondly, the model abstracts affected factors parameters of soil erosion by RS(Remote Sensing) and GIS(Geography Information System) in study region. Finally, the erosion intensity of region is classified into six classes in terms of the naive Bayes probability: extreme, very high, high, moderate, low, very low. Therefore, the model provides a new method for assessing the soil erosion in regional scale. View full abstract»

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  • A Bayesian Network Based Approach for Root-Cause-Analysis in Manufacturing Process

    Publication Year: 2007 , Page(s): 10 - 14
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (323 KB) |  | HTML iconHTML  

    We describe an Early Warning System (EWS) which enables the root cause analysis for initiating quality improvements in the manufacturing shop floor and process engineering departments, at product OEMs as well as their tiered suppliers. The EWS combines the use of custom designed domain ontology of manufacturing processes and failure related knowledge, innovative application of domain knowledge in the form of probability constraints and a novel two step constrained optimization approach to causal network construction. Probabilistic reasoning is the main vehicle for inference from the causal network. This inference engine provides the capability to do a root cause analysis in manufacturing scenarios, and is thus a powerful weapon for an automotive EWS. This technique is widely applicable and can be used in various contexts in the broader manufacturing industry as well. View full abstract»

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  • Step-Size Optimization EASI Algorithm for Blind Source Separation

    Publication Year: 2007 , Page(s): 15 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1272 KB) |  | HTML iconHTML  

    Aiming at the problem of blind source separation of the communication signals, we propose a step size optimization equivariant adaptive source separation via independence (SO-EASI) algorithm basing on the EASI block based algorithm. This algorithm adjusts the step-size by the steepest descent method and thereby greatly increases its convergence speed whatever value the step-size is initialized. Simulation results show that SO-EASI algorithm can effectively blindly separate the communication signals and these results also support the expected improvement in convergence speed of the approach. View full abstract»

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  • Variable Step-Size Online Algorithm for Blind Separation Based on the Extended Infomax

    Publication Year: 2007 , Page(s): 19 - 22
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1411 KB) |  | HTML iconHTML  

    A novel variable step-size online algorithm for mixed signals with sub- and super-Gaussian source distributions based on the extended infomax is present. The extended infomax algorithm usually separates the sources by batch processing and it requires adequate samples to estimate the kurtosis of the output signals so the algorithm will be invalid when the channel matrix is changed. An improved online estimation model of the kurtosis is introduced in this paper, we interpose a detection machine-made to judge whether the channel matrix is changed or not in separation process. In order to solve the ambivalent tradeoff between convergence rate and steady-state error, a variable step-size online algorithm is proposed. The step-size updating regulation is controlled by the kurtosis variance of the signals because the kurtosis fluctuation can describe the state of separation. This online algorithm accelerated the convergence rate and reduced the steady-state error efficiently. View full abstract»

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  • A Novel Learning Method for ANFIS Using EM Algorithm and Emotional Learning

    Publication Year: 2007 , Page(s): 23 - 27
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (358 KB) |  | HTML iconHTML  

    It is very difficult for the adaptive neuro-fuzzy interference system (ANFIS) using conventional training methods to converge while the samples space distribution is more complex, the desired results for that couldn't be achieved. To change the situation and improve the learning behavior of ANFIS, in this paper we propose a new self-adaptive learning algorithm for ANFIS differently from conventional training methods. The method firstly adopts the EM algorithm to learning fuzzy parameters of the ANFIS, and then applies emotion learning to learn the Takagi-Sugeno-Kang (TSK) parameters of the linear TSK functions of the ANFIS. The relevant researches indicate that the proposed learning method possesses faster training speed and better adaptability, and is more ubiquitous. In the end, a simulation example shows the availability of the proposed method. View full abstract»

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  • A Novel Method for Intelligence of Sensors Modeling

    Publication Year: 2007 , Page(s): 28 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    For the complexity of the automation system roll on, sensors should have to be more intelligent. Recently, Neural Network is widely used to intelligentize sensors for its well performance on capturing the information of the data. But due to its intrinsic linear character, it doesn't perform well in nonlinear data processing. In this paper, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then an experimental system is set up with pressure sensor. By examining the data of the example, it is shown that the proposed methods can both achieve good performance comparing with NN method. And the KPCA method performs better than the PCA method. View full abstract»

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  • An Effective Feature-Weighting Model for Question Classification

    Publication Year: 2007 , Page(s): 32 - 36
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1227 KB) |  | HTML iconHTML  

    Question classification is one of the most important sub- tasks in Question Answering systems. Now question tax- onomy is getting larger and more fine-grained for better answer generation. Many approaches to question classifi- cation have been proposed and achieve reasonable results. However, all previous approaches use certain learning al- gorithm to learn a classifier from binary feature vectors, extracted from small size of labeled examples. In this pa- per we propose a feature-weighting model which assigns different weights to features instead of simple binary val- ues. The main characteristic of this model is assigning more reasonable weight to features: these weights can be used to differentiate features each other according to their contri- bution to question classification. Furthermore, features are weighted depending on not only small labeled question col- lection but also large unlabeled question collection. Exper- imental results show that with this new feature-weighting model the SVM-based classifier outperforms the one with- out it to some extent. View full abstract»

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  • P2P Traffic Identification Technique

    Publication Year: 2007 , Page(s): 37 - 41
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2164 KB) |  | HTML iconHTML  

    Accurate traffic classification for different P2P applications is fundamental to numerous network activities, from security monitoring, capacity planning and provisioning to service differentiation. However, current P2P applications use dynamic port numbers, HTTP masquerading and inaccessible payload to prevent being identified. The paper proposed an accurate P2P identification system using Decision Tree algorithms (J48 and REPTree) on the basis of effective feature selection. The experimental results show that our scheme is of better accuracy, less computational complexity and it is robust enough to deal with the unknown P2P traffic. With the merits, the scheme can suit the real-time active detection environment, such as monitoring network attacks camouflaged with P2P traffic and service differentiation. View full abstract»

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  • Study on PIGA Test Method on Centrifuge

    Publication Year: 2007 , Page(s): 42 - 47
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    The experiment design of PIGA test on centrifuge has been studied. Based on the identification method for high-order coefficients of PIGA (pendulous integrating gyro accelerometer) on precision centrifuge with counter-rotating platform, which can isolate the rotary movement caused by centrifuge arm so as to improve the environment of PIGA test on centrifuge, the method of the D-optimal designs is used in the data processing for separating the error model coefficients to optimize the test plans. The relation between the different values of the factors taken in the testing procedures and the estimated accuracy is discussed according to the D-criterion by way of simulation analysis. The results of the simulation analysis show that by optimizing the values of the factors in a test plan, the calibrating accuracy can be greatly improved. View full abstract»

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  • Parameters Selection for SVR Based on the SCEM-UA Algorithm and its Application on Monthly Runoff Prediction

    Publication Year: 2007 , Page(s): 48 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    Support Vector Machines (SVMs) have become one of the most popular methods in Machine Learning during the last few years, but its performance mainly depends on the selection of optimal parameters which is very complex. In this study, the SCEM-UA algorithm developed by Vrugt is employed for parameters selection of Support Vector Regression (SVR). The SCEM-UA algorithm, which operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling, can avoid the tendency of falling into local minima. The proposed method was tested on a complicated nonlinearly runoff forecasting. The results illustrated that SCEM-UA algorithm can successfully identify the optimal parameters of SVR than grid search method, and can achieve an accurate prediction. Keywords: Support Vector Machines; Optimization; SCEM-UA; Time series; Forecasting View full abstract»

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  • Estimate and Track the PN Sequence of Weak DS-SS Signals

    Publication Year: 2007 , Page(s): 52 - 56
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1297 KB) |  | HTML iconHTML  

    This paper proposes a modified Sanger's generalized Hebbian neural network method to estimate and track the pseudo noise sequence of weak direct sequence spread spectrum signals. The proposed method is based on eigen-analysis of received signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. The pseudo noise sequence can be estimated and tracked by the principal eigenvector of the matrix in the end. Because the eigen-analysis method becomes inefficiency when the estimated pseudo noise sequence becomes longer or the estimated pseudo noise sequence becomes time varying, we use a modified Sanger's generalized Hebbian neural network to realize the pseudo noise sequence estimation and tracking from weak input signals adaptively and effectively. View full abstract»

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  • A Smoothing Support Vector Machine Based on Quarter Penalty Function

    Publication Year: 2007 , Page(s): 57 - 589
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1247 KB) |  | HTML iconHTML  

    It is very important to find out a smoothing support vec- tor machine. This paper studies a smoothing support vec- tor machine (SVM) by using quarter penalty function. We introduce the optimization problem of SVM with an uncon- strained and nonsmooth optimization problem via quarter penalty function. Then, we define a one-order differentiable function to approximately smooth the penalty function, and get an unconstrained and smooth optimization problem. By error analysis, we may obtain approximate solution of SVM by solving its approximately smooth penalty optimization problem without constraints. The numerical experiment shows that our smoothing SVM is efficient. View full abstract»

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