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Computational Intelligence and Security (CIS), 2010 International Conference on

Date 11-14 Dec. 2010

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Displaying Results 1 - 25 of 161
  • [Front cover]

    Publication Year: 2010 , Page(s): C1
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  • [Title page i]

    Publication Year: 2010 , Page(s): i
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  • [Title page iii]

    Publication Year: 2010 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2010 , Page(s): iv
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  • Table of contents

    Publication Year: 2010 , Page(s): v - xv
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  • Preface

    Publication Year: 2010 , Page(s): xvi
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  • Conference Organization

    Publication Year: 2010 , Page(s): xvii
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  • Program Committee and Additional Reviewers

    Publication Year: 2010 , Page(s): xviii - xix
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  • Chinese Sentiment Orientation Analysis

    Publication Year: 2010 , Page(s): 1 - 5
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    In this paper, we present one new method to analyze and classify the sentiment orientation of merchandise comments into three categories: neutral, positive and negative. Nowadays, many methods can be used to achieve this goal, however, we find that those methods may work well in dividing the polarity sentences into positive and negative but may not have a good result on neutral sentences, so a divide and conquer strategy is applied to firstly classify the texts into two parts as neutral and polarity texts. Then the polarity texts are divided into positive part and negative part. In the first step, TSVM tool is used to achieve the neutrality and polarity classification, but the training data used in our work is very special, which contains many polarity sentences but very few neutral sentences, so the strategy is adopted to divide the polarity data into several small parts, and each part polarity data is combined with all neutral data as training data, by this way several TSVM classifiers can be obtained, and by voting scheme the final result can be gained. In the second step, we propose an algorithm to achieve positive and negative classification. Firstly, a method is designed to re-evaluate each sentiment word and divide the dictionary into two parts based on confidence, which can reduce the negative impact of low-confidence words. Then an orientation analysis and classification algorithm is proposed to classify the polarity sentences step by step. Meanwhile, a set of rules is also built to classify those sentences which contain sentiment words that appear not in our sentiment dictionary. View full abstract»

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  • A Scenario-Based Architecture for Reliability Design of Artificial Intelligent Software

    Publication Year: 2010 , Page(s): 6 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (487 KB) |  | HTML iconHTML  

    Artificial Intelligent is well invested both in industry and academic fields. However, the software reliability design for the applications is a big challenge. In this paper, we present a scenario-based architecture for reliability design of artificial intelligent Software. Scenarios are divided into environmental scenarios and structured scenarios. Both of the two kinds of scenarios are considered in the framework. The quantitative reliability of the software based on the framework could be evaluated and predicted. View full abstract»

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  • The Analysis of Yeast Cell Morphology Using a Robot Scientist

    Publication Year: 2010 , Page(s): 10 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (662 KB) |  | HTML iconHTML  

    We have developed image analysis methods to analyse the morphology of the budding yeast (Saccharomyces cerevisiae) cell. Experiments were performed on four deletant strains: ΔYLR371w, ΔYDR349c, ΔYLR192c, and ΔYDR414c. Our results show that our image analysis software provides an efficient way to automatically obtain quantitative morphology features of yeast cells. Our research show significant differences from those previously published for these strains. These differences may be due to different growth conditions or the use of unfixed cells. More research is required to understand the complex relationship between genotype and environment in yeast morphology. View full abstract»

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  • IMP-Filters of R0-Algebras

    Publication Year: 2010 , Page(s): 15 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (265 KB) |  | HTML iconHTML  

    The notion of implication MP-filters (briefly, IMP-filters) in R0-algebras is introduced. The characteristic properties and extension property of IMP-filters are obtained. The relations among IMP-filters, PIMP-filters and NMP-filters of Fo-algebras are established. Finally, the implicative R0-algebra is completely described by its IMP-filters. View full abstract»

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  • Intelligent Digital Photo Management System Using Ontology and SWRL

    Publication Year: 2010 , Page(s): 18 - 22
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (982 KB) |  | HTML iconHTML  

    The number of digital photos in the personal computer is exploding. Existing photo annotation and management systems suffer from some problems (some among them are quite serious), which discussed at the beginning of the full paper. Aiming at these problems, this paper proposed a solution based on ontology. First of all, the Family Album ontology is built to organize the domain knowledge and provide formal, explicit and conceptual annotation. Then we annotate preliminary semantic information for digital photos by automated extraction of semantic concepts from file titles, photo description texts, EXIF metadata and visual features of images etc.. Furthermore, the SWRL (Semantic Web Rule Language) rules are written and executed to extend previous semantic annotations and support knowledge inference. Finally, an Onto Album prototype system is developed for verifying the validity of the proposed approach. Experimental results show that the proposed approach is very effective and promising. View full abstract»

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  • New Results on H∞ Filter Design for Continue-Time Systems with Time-Varying Delay

    Publication Year: 2010 , Page(s): 23 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (327 KB) |  | HTML iconHTML  

    This paper proposes a class of H filter design for continue-time systems with time-varying delay. By using the Lyapunov functional function approach, some delay-dependent stability conditions can be obtained for the asymptotical stability of the H filter systems, which are expressed as a set of Linear Matrix Inequalities (LMIs). Finally, a numerical example is given to demonstrate the effectiveness and the merit of the proposed method. View full abstract»

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  • Synchronization Algorithm for Multi-hop in Wireless Sensor Networks

    Publication Year: 2010 , Page(s): 28 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (687 KB) |  | HTML iconHTML  

    Time synchronization possesses wide range of applications. There are already a variety of time synchronization methods. In wireless sensor networks, its equipment, limitations of resources and the environment, require time synchronization with high efficiency, while saving resources. This article describes the time synchronization algorithm using the time stamp of the effectiveness of the inspection stage, to simply send a small packet that is able to achieve the synchronization functions. The simulation results show that our algorithm can makes the system has lower synchronization errors and better performance. View full abstract»

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  • Tool Wear Detection Based on Wavelet Packet and BP Neural Network

    Publication Year: 2010 , Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (457 KB) |  | HTML iconHTML  

    Based on wavelet packet decomposition and the BP neural network of pattern recognition theory, this article puts forward the theory that can identify the different tool wear conditions during the cutting process, and thus we can use this theory to forecast the tool breakage accurately. The main thinking of this article is that decomposing tool acoustic emission signal by using wavelet packet to get spectrum coefficient as eigenvector, and then putting it into the BP neural network to be trained in order to accomplish the final pattern recognition of tool wear conditions by making use of BP algorithm. By testing the samples of well-trained network, it is proved that the BP neural network constructed has good generalization ability which can identify tool conditions accurately. View full abstract»

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  • Neural Network Method for Solving Linear Fractional Programming

    Publication Year: 2010 , Page(s): 37 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB) |  | HTML iconHTML  

    This paper presents a neural network method for solving a class of linear fractional optimization problems with linear equality constraints. The proposed neural network model have the following two properties. First, it is demonstrated that the set of optima to the problems coincides with the set of equilibria of the neural network models which means the proposed model is complete. Second, it is also shown that the model globally converges to an exact optimal solution for any starting point from the feasible region. View full abstract»

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  • Semantic Modeling: Computational Models of the Concepts

    Publication Year: 2010 , Page(s): 42 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (570 KB) |  | HTML iconHTML  

    As known, computing allows maintenance of the constructions corresponding to the needs in comprehended models which enable the computational experiment. Computing takes the enabling of such a model which corresponds to a 'theory'. The parameters of the model are systematically changing and correspond to 'entries' for the inputs. The parameterized family of its 'behaviors' is used as the 'outputs' of the model. These -- being computed, -- behaviors are matched with the real behavior of the problem domain object under the real inputs -- prototypes of model inputs. In case of conclusion that the model satisfactory reflects the features of the real object-prototype, then the real experiment can be replaced by the computational one, which is executed using the model-image. View full abstract»

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  • A Fast HITON_PC Algorithm

    Publication Year: 2010 , Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (403 KB) |  | HTML iconHTML  

    The HITON_PC algorithm which is a state-of-the-art local causal discovery algorithm can deal with a dataset with a very small sample-to-variable ratio efficiently. But it cannot perform inefficiently on a dataset with a very large sample. To address this problem, a fast HITON_PC algorithm is presented which uses a new yet simple search strategy from high order to low order to improve the efficiency of HITON-PC. Experimental results show our fast HITON_PC outperforms the HITON_PC algorithm. Moreover, we also apply the new search strategy to MMPC algorithm. Our method also is superior to MMPC. View full abstract»

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  • An Algorithm of Dynamic Associate Rule Based on Sliding Windows

    Publication Year: 2010 , Page(s): 51 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (441 KB) |  | HTML iconHTML  

    It is well-known that the traditional association rules with time can't be found out in many algorithms. First, according to concept of dynamic association rules, this paper analyzes the disadvantages of traditional association rules. Then, the concepts of dynamic association rules based on sliding windows and the definition of time vector representation of dynamic association rules are put forward. So, the dynamic association rules can measure out how association rules change over time. Next, a kind of algorithm to mine dynamic association rules on sliding window is proposed. Finally, numerical experiments show that the proposed algorithm is efficient for finding out dynamic association rules with time. View full abstract»

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  • Online Segmentation Algorithm for Time Series Based on BIRCH Clustering Features

    Publication Year: 2010 , Page(s): 55 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (453 KB) |  | HTML iconHTML  

    Online time series data representation is one of important problems of time series data mining. The adjacent points of time series are inherently depended and hence have similar clustering features. Based on BIRCH clustering features, we present a new kind of OSBC algorithm for time series segmentation in this paper. Using cluster features, OSBC algorithm can find easily the changing patterns of time series and achieve better segmentation results. The time complexity of OSBC algorithm is linear and its space complexity is also much smaller. The experiment results on time series benchmark show the effectiveness of our method. View full abstract»

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  • A New Clustering Algorithm Based on Normalized Signal for Sparse Component Analysis

    Publication Year: 2010 , Page(s): 60 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (370 KB) |  | HTML iconHTML  

    To the underdetermined sparse component analysis (SCA) model with noise, a new robust clustering algorithm based on normalized signal for mixture matrix estimation is addressed in this paper. This approach consists of two parts: signal clustering and matrix recovery. In the first step, according to the feature of normal signals clustering intensively on the unit observed signal hyper-sphere, we propose a criterion to detect and cluster dense observed signal sets, which is the conclusion of deduction from a fit mathematical statistics model. To the second stage for estimating the mixture matrix, Principal Component Analysis is introduced to process dense signal sets. Experiment simulations illustrate that new clustering algorithm's performance on determination of the source numbers and precision of mixing matrix recovery. View full abstract»

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  • Dynamic Fluzzy Clustering Algorithm for Web Documents Mining

    Publication Year: 2010 , Page(s): 64 - 67
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (446 KB) |  | HTML iconHTML  

    This paper first studies the methods of web documents mining and text clustering, and summaries the fuzzy clustering algorithms and similarity measure functions, then proposes a modified similarity function which can solve the problems of feature selection and feature extraction in high-dimensional space. Finally, this paper puts forward to a dynamic fluzzy clustering algorithm(DCFCM) by combining the proposed similarity function with approximated C-mediods. The experiments show that DCFCM can effectively improve he precision of web documents clustering, the method is feasible in web documents mining. View full abstract»

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  • A Novel GEP-Based Multiple-Layers Association Rule Mining Algorithm

    Publication Year: 2010 , Page(s): 68 - 72
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    To mine popular accessed Web pages items and find out their association rule from the Web server Log database for junior users providing recommendation service. A novel GEP-based algorithm for mining multiple-layers association rules was presented. Firstly, takes generalizing technology as a way to value fitness function in GEP (Gene Expression Programming). Then, relying on the significant self-search function of GEP, the most optional species was evolved. The frequent items and association rules in the next deeper layers can be mined by using traditional support-confidence method in sub-database. The algorithm improves on the frame of traditional association rule mining and uses a new evolutionary algorithm for mining association rules. Finally, the validity and efficiency of the method are presented by the application in the paper. View full abstract»

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  • Credit Card Customer Segmentation and Target Marketing Based on Data Mining

    Publication Year: 2010 , Page(s): 73 - 76
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit cards holders. Conclusively, we obtain some useful information of decision tree regulation by the best model among the four. The information is not only helpful for the bank to understand related characteristics of different customers, but also marketing representatives to find potential customers and to implement target marketing. View full abstract»

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