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Computing, 2006. CIC '06. 15th International Conference on

Date 21-24 Nov. 2006

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Displaying Results 1 - 25 of 72
  • 15th International Conference on Computing - Cover

    Page(s): c1
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  • 15th International Conference on Computing-Title

    Page(s): i - iii
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  • 15th International Conference on Computing-Copyright

    Page(s): iv
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  • 15th International Conference on Computing - TOC

    Page(s): v - ix
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  • Preface

    Page(s): x - xii
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  • Program Committee

    Page(s): xiii
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  • Additional reviewers

    Page(s): xiv
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  • Organizing Committee

    Page(s): xv
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  • cc{rm T}: A Tool for Checking Advanced Correspondence Problems in Answer-Set Programming

    Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (525 KB) |  | HTML iconHTML  

    In recent work, a general framework for specifying correspondences between logic programs under the answer-set semantics has been defined. The framework allows to define different notions of equivalence, including well-known notions like strong equivalence as well as refined ones based on the projection of answer sets, where not all parts of an answer set are of relevance. In this paper, we describe a system, called ccT, to verify program correspondences in this general framework, relying on linear-time constructive reductions to quantified propositional logic using extant solvers for the latter language as back-end inference engines. We provide a preliminary performance evaluation which sheds light on some crucial design issues View full abstract»

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  • Improving Wavelet-Networks Performance with a New Correlation-based Initialisation Method and Training Algorithm

    Page(s): 11 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (233 KB) |  | HTML iconHTML  

    Wavelet-networks are inspired by both the feedforward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets, but unfortunately, most of powerful and used wavelets do not satisfy this property. This paper presents a new initialisation procedure and a new training algorithm for wavelet neural-networks that improve its performance allowing the use of different kind of wavelets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet-network View full abstract»

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  • Discrete Graphic Markov Model Selection by Multiobjective Genetic Algorithm (GMS-MGA)

    Page(s): 18 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (198 KB) |  | HTML iconHTML  

    The problem of graphic Markov model selection (GMS) is considered as a multiobjective one, where the objectives are: (1) best fitting of the model to the sample data and, (2) least possible number of edges, and the fitting criteria function is the Kullback-Leibler. The multiobjective strategy is to obtain an approximate Pareto front using a multi-starting multiobjective genetic algorithm (MGA). To test the performance of the algorithm, 48 samples of 6 different model complexities are generated using a Markov model random sampler (MMRS) and used as benchmarks. The performance is assessed through the times that the Pareto front contains the true model. As results the algorithm obtains the true models in 93% of the cases, the complexity of the model made a difference in the performance of the algorithm. The mean time of execution is least or equal to 2 minutes for 10 binary variables in a PC View full abstract»

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  • Assembly Line Reconfiguration Under Disturbances: An Evolutionary Approach to Decision Making

    Page(s): 24 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (195 KB) |  | HTML iconHTML  

    Considering the disturbances affect all production lines, this article describes a response mechanism to determine adjustments that conserve or improve profit. The modifications of the product force us to eliminate some tasks and to incorporate others, at the same time as a certain production speed must be satisfied. In order to satisfy these new conditions, the possible positions of each work element are analyzed until we are able to select an appropriate configuration that fulfills a certain goal. In this new configuration the company decides if the changes are to be carried out, after evaluating the cost of relocation of tools, devices, and materials. This focus has two advantages: first, a quick evaluation of many configurations and second, an identification of better configurations. The mechanism in question has been applied successfully in an automotive plant View full abstract»

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  • Analyzing Video Object Motion Focusing on Non-Planar Rotation for Two Video Applications

    Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB) |  | HTML iconHTML  

    In this paper an object-based non-planar rotation estimation for video analysis and video coding is presented. This method is based on a non-planar rotation model which assumes that the moving object as a planar surface. It is also assumed the considered video objects are rigid and have been previously segmented for each key instant considered. The model can be easily adjusted by choosing a suitable region of support (block or arbitrary rigid region). The presented results indicate that the proposed technique is a suitable approach for motion estimation, which can be used for applications such as MPEG-4 block based hybrid coding or MPEG-4 object based video manipulations. Experimental results have been performed on real video sequences View full abstract»

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  • Robust Estimation of Background for Fixed Cameras

    Page(s): 37 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (293 KB) |  | HTML iconHTML  

    This paper proposes a robust background estimator for fixed cameras, to be used for foreground segmentation in tracking systems. The estimator is based on a variation of Stauffer's dynamic background algorithm, where the background learning rate is spatiotemporally adapted. The adaptation is based on the position, size and velocity of the various foreground objects already detected. The evidence for the initialization and tracking of the foreground objects is obtained by combining a pixel map showing the temporal persistence of each image pixel and the edge binary image. The spatiotemporal adaptation of the learning rate overcomes the problem of fading immobile or slowly moving objects into the background encountered in all to-date variations of Stauffer's algorithm, while the combination with edge information allows for objects already present in the scene at startup time and new objects to be treated by the same image processing module View full abstract»

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  • RM L-Filters for Real-Time Imaging

    Page(s): 43 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    In this paper we present the capability and realtime processing features of the RM L-filter for the removal of impulsive and multiplicative noise in realtime image processing applications. The proposed filter uses the robust RM-estimator in the filtering scheme of L-filter. The real-time implementation of proposed algorithm was realized on the DSP TMS320C6701. Extensive simulation results have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and detail preservation View full abstract»

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  • Singular Value Conjugation: An Approach for Computing Similarity Among Rectangular Images

    Page(s): 49 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2398 KB) |  | HTML iconHTML  

    In computer sciences, matrices are widely used for representing different kinds of information. Furthermore, measuring similarity among matrices is an interesting open problem. In this paper we introduce a new similarity measure among two data matrices of the same class; the idea is based on evaluating the effect of conjugating the singular values and singular vectors of one matrix with the other matrix, and vice versa. A brief analysis of singular value conjugation (SV-conjugation) is also included in order to extend the understanding of the concept. Finally, some experimental results are presented with the aim of exemplify the usefulness of SV-conjugation as an approach for the problem of computing similarity among data matrices and also among images View full abstract»

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  • A New Classifier Based on Associative Memories

    Page(s): 55 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (250 KB) |  | HTML iconHTML  

    The Lernmatrix, which is the first known model of associative memory, is an heteroassociative memory, but it can also act as a binary pattern classifier depending on the choice of the output patterns. However, this model suffers two great problems: saturation and imperfect recall of some of the associations, even in the fundamental set, depending on the associations. In this work, a modification to the original Lernmatrix recall phase algorithm is presented. This modification improves the recalling capacity of the original model. Experimental results show this improvement View full abstract»

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  • Shape Similarity Index for Time Series based on Features of Euclidean Distances Histograms

    Page(s): 60 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (845 KB) |  | HTML iconHTML  

    Shape similarity between time series is quantified based on descriptive statistical features from histograms of Euclidean distances. The histogram shows the statistical distribution of point to point differences between two time series, using this representation of the compared time series, a shape similarity index was designed. The index quantifies the differences between histograms with respect to an ideal similarity histogram that corresponds to a reference time series, this index allows the ordering of a set of time series based on their similarity with respect to this time series. A benchmark set of time series with known similarities and dissimilarities was used in order to evaluate the performance of the proposed index. The experimental results show that this index is able to order and differentiate with good resolution the time series, by their similarity with respect to the reference time series View full abstract»

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  • Recurrence Plot Analysis and its Application to Teleconnection Patterns

    Page(s): 65 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (369 KB) |  | HTML iconHTML  

    The recurrence plot analysis provides us a way to visualize and quantify the dynamical systems behavior over the time. The construction of a recurrence plot (RP) from a time series begins with the determination of the trajectory of the system through phase-space. Seven different measures are extracted from the RP. These measures are obtained from diagonal and vertical structures of the RP. Hence, the aim of this article is to find new characteristics underneath the dynamical behavior of teleconnection patterns. Those climate patterns are represented as time series. Not only does this method help us to find more characteristics of the system than classical ones, but it also could be used with other phenomena that can be represented as time series View full abstract»

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  • Instance Selection and Feature Weighting Using Evolutionary Algorithms

    Page(s): 73 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB) |  | HTML iconHTML  

    Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a genetic algorithm (GA) and evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier View full abstract»

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  • A Reversible Automata Approach to Modeling Birdsongs

    Page(s): 80 - 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB) |  | HTML iconHTML  

    We propose a new automata-based approach to modeling birdsongs on the basis of Angluin's induction algorithm, which ensures that k-reversible languages can be learned from positive samples with polynomial time. There are similarities between Angluin's algorithm and the vocal learning of songbirds; for example, during a critical period, songbirds also learn songs from positive samples of conspecific birds. Using the proposed method, we demonstrate that the song syntaxes of the Bengalese finch can be represented as reversible automata with lower k-reversibility and that juvenile song syntaxes have two types of development. Our approach provides an effective way to understand the vocal learning of songbirds in terms of computational learning View full abstract»

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  • Modelling the Human Values Scale from Consumers Transactional Data Bases

    Page(s): 86 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (193 KB) |  | HTML iconHTML  

    The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the data base used by the recommender systems, and which allow to resolve the specific problems of prediction and recommendation. In this work, we propose to extract the user's human values scale from the data base of the users, to improve their suitability in open environments, such as the recommender systems. For this purpose, the methodology is applied with the data of the user after interacting with the system. The methodology is exemplified with a case study View full abstract»

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  • Using Grammatical Inference For Structure Induction

    Page(s): 92 - 104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    Given the huge quantity of the current available textual information, text mining process tackles the task of searching useful knowledge in a natural language document. When dealing with a free-format textual corpus (e.g. a job announcement) where the linguistic rules are not respected, the time consuming morpho-syntactic analysis is not of a great help. However, text mining techniques process may exploit linguistic sub-structures in the text. In this paper, we present an applications of grammatical inference (GI) in a machine learning system applied to a text corpus. We specify and use the process of the grammatical inference as an instance of the constraint satisfaction problem that instantiates automata in a (language inclusion) lattice View full abstract»

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  • Combined Lesk-Based Method for Words Senses Disambiguation

    Page(s): 105 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB) |  | HTML iconHTML  

    Word sense disambiguation (WSD) is a linguistically-based mechanism for defining automatically the correct sense of a word in the context. Lesk-based methods use context information for WSD. In this paper, we propose a method of word sense disambiguation based on the combination of the original Lesk method and the simplified one with additional application of large lexical resources, like synonym dictionaries, ontologies, etc., for calculations of sense plausibility. We present experimental results that show that our method has better precision than the baseline Lesk-based methods View full abstract»

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  • A Maximum Entropy Model Application on Recognition of Metaphor Phenomena

    Page(s): 109 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB) |  | HTML iconHTML  

    Metaphor happening in our daily life language is a usual phenomenon, and recognizing them by the use of computer is becoming a valuable research task in the field of natural language processing, artificial intelligence, and even applied linguistics. This paper proposes a way to recognize metaphors based on maximum entropy model after analyzing the features of metaphor, and reasons the rationality to build a recognition model using the statistical methods. The results of the experiment show that the model performs well at high precision and recall, as well as the f value, thus we come to the conclusion that such metaphor recognizing model based on maximum entropy principle has a promising future View full abstract»

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