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2008 20th IEEE International Conference on Tools with Artificial Intelligence

Date 3-5 Nov. 2008

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  • [Front cover - Vol 2]

    Publication Year: 2008, Page(s): C1
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  • [Title page i - Volume 2]

    Publication Year: 2008, Page(s): i
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  • [Title page iii - Volume 2]

    Publication Year: 2008, Page(s): iii
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  • [Copyright notice - Volume 2]

    Publication Year: 2008, Page(s): iv
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  • Table of contents - Volume 2

    Publication Year: 2008, Page(s):v - xi
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  • Message from the General Chairs - Volume 2

    Publication Year: 2008, Page(s): xii
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  • Message from the Program Chair - Volume 2

    Publication Year: 2008, Page(s): xiii
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  • Conference Committees - Volume 2

    Publication Year: 2008, Page(s):xiv - xviii
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  • Visualizing Classifier Performance on Different Domains

    Publication Year: 2008, Page(s):3 - 10
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (311 KB) | HTML iconHTML

    Classifier performance evaluation typically gives rise to vast numbers of results that are difficult to interpret. On the one hand, a variety of different performance metrics can be applied; and on the other hand, evaluation must be conducted on multiple domains to get a clear view of the classifier's general behaviour. In this paper, we present a visualization technique that allows a user to stud... View full abstract»

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  • Policy Gradient Semi-markov Decision Process

    Publication Year: 2008, Page(s):11 - 18
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (326 KB) | HTML iconHTML

    This paper proposes a simulation-based algorithm for optimizing the average reward in a parameterized continuous-time, finite-state semi-Markov decision process (SMDP). Our contributions are twofold: First, we compute the approximate gradient of the average reward with respect to the parameters in SMDP controlled by parameterized stochastic policies. Then stochastic gradient ascent method is used ... View full abstract»

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  • An Empirical Analysis on the Stability of Clustering Algorithms

    Publication Year: 2008, Page(s):19 - 26
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (495 KB) | HTML iconHTML

    One of the aspects of a clustering algorithm that should be considered for choosing an appropriate algorithm in an unsupervised learning task is stability. A clustering algorithm is stable (on a dataset) if it results in the same clustering as it performed on the whole dataset, when actually performs on a (sub)sample of the dataset. In this paper, we report the results of an empirical study on the... View full abstract»

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  • Oracle Clustering: Dynamic Partitioning Based on Random Observations

    Publication Year: 2008, Page(s):27 - 34
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (474 KB) | HTML iconHTML

    In this paper, a new dynamic clustering algorithm based on random sampling is proposed. The algorithm addresses well known challenges in clustering such as dynamism, stability, and scaling. The core of the proposed method isbased on the definition of a function, named the Oracle,which can predict whether two random data points belongto the same cluster or not. Furthermore, this algorithm isalso eq... View full abstract»

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  • Moving Sensor Video Image Processing Enhanced with Elimination of Ego Motion by Global Registration and SIFT

    Publication Year: 2008, Page(s):37 - 40
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (625 KB) | HTML iconHTML

    This field of target tracking is relatively mature when the camera is stationary but the moving sensor poses uniquely challenging problems because relative to the camera, everything in the scene appears to be moving. This report will present a robust and efficient video stabilization algorithm based on the scale invariant feature transform (SIFT) algorithm and global registration. An important com... View full abstract»

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  • Handwritten Digit Segmentation in Images of Historical Documents with One-Class Classifiers

    Publication Year: 2008, Page(s):41 - 44
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (322 KB) | HTML iconHTML

    A novel method is proposed herein for handwritten digit segmentation in historical document images. It is based on one-class classifiers, which are used to distinguish isolated characters from touching characters. In contrast to other techniques based on feed forward neural networks, the proposed method does not require negative data in the training phase. Three methods for feature extraction and ... View full abstract»

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  • An Incremental Hough Transform for Detecting Ellipses in Image Data Streams

    Publication Year: 2008, Page(s):45 - 48
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (462 KB) | HTML iconHTML

    In this paper, we present a purely incremental, scalable algorithm for the detection of elliptical shapes in images. Our method uses an incremental version of the Random Hough Transform (RHT) to compute the curve parameters from sampled image points and uses a density-based robust stream clustering algorithm to discover the potential parameters from the Hough space. Finally we apply density and si... View full abstract»

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  • Automatic Image-to-Text-to-Voice Conversion for Interactively Locating Objects in Home Environments

    Publication Year: 2008, Page(s):49 - 55
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (490 KB) | HTML iconHTML

    The efficient processing and association of different multi-modal information is a very important research field with a great variety of applications, such as human computer interaction, knowledge discovery, document understanding, etc. A good approach to this important issue is the development of a common platform for converting different modalities (such as images, text, etc) into the same mediu... View full abstract»

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  • A New Exact Bit-Parallel Algorithm for SAT

    Publication Year: 2008, Page(s):59 - 65
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (382 KB) | HTML iconHTML

    This paper presents two new exact general purpose bit-parallel algorithms (BB-SAT and BBP-SAT) for the Boolean satisfiability problem (SAT). Based on the authors' recent successful bit-parallel algorithm for the maximum clique problem, the SAT formula is first reduced to a directed graph which is then bit encoded. As a result, search traverses a bit vector graph space. At present the encoding allo... View full abstract»

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  • A Unified Scoring Scheme for Detecting Essential Proteins in Protein Interaction Networks

    Publication Year: 2008, Page(s):66 - 73
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (619 KB) | HTML iconHTML

    The essentiality of a gene or protein is important for understanding the minimal requirements for cellular survival and development. Numerous computational methodologies have been proposed to detect essential proteins from large protein-protein interactions (PPI) datasets. However, only a handful of overlapping essential proteins exists between them. This suggests that the methods may be complemen... View full abstract»

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  • Information Extraction as an Ontology Population Task and Its Application to Genic Interactions

    Publication Year: 2008, Page(s):74 - 81
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB) | HTML iconHTML

    Ontologies are a well-motivated formal representation to model knowledge needed to extract and encode data from text. Yet, their tight integration with Information Extraction (IE) systems is still a research issue, a fortiori with complex ones that go beyond hierarchies. In this paper, we introduce an original architecture where IE is specified by designing an ontology, and the extraction process ... View full abstract»

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  • Definition and Extraction of Causal Relations for QA on Fault Diagnosis of Devices

    Publication Year: 2008, Page(s):82 - 85
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (315 KB) | HTML iconHTML

    Causal relations in ontology should be defined based on the inference types necessary to solve the tasks specific to application, as well as domain. In this paper, we present a model to define and to extract causal relations for application ontology, which is targeted, as a case study, to serve a question-answering (QA) system on fault-diagnosis of electronic devices. In the first phase, causal ca... View full abstract»

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  • e-Tourism: A Tourist Recommendation and Planning Application

    Publication Year: 2008, Page(s):89 - 96
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (504 KB) | HTML iconHTML

    e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a plannin... View full abstract»

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  • Rough Set Based Learning for Classification

    Publication Year: 2008, Page(s):97 - 104
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (615 KB) | HTML iconHTML

    The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relear... View full abstract»

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  • A Hybrid Self-Organizing Model for Sequence Analysis

    Publication Year: 2008, Page(s):105 - 112
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (373 KB) | HTML iconHTML

    The self-organizing hidden Markov model map (SOHMMM) constitutes a cross-section between the theoretic foundations and algorithmic realizations of the self-organizing map (SOM) and the hidden Markov model (HMM). The intimate fusion and synergy of the SOM unsupervised training and HMM dynamic programming algorithms brings forth a novel on-line gradient descent learning algorithm, which is fully int... View full abstract»

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  • Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making

    Publication Year: 2008, Page(s):113 - 120
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (573 KB) | HTML iconHTML

    Recommender systems' performance can be easily affected when there are no sufficient item preferences data provided by previous users, and it is commonly referred to as cold-start problem. This paper suggests another information source, item taxonomies, in addition to item preference data for assisting recommendation making. Item taxonomy information has been popularly applied in diverse ecommerce... View full abstract»

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  • Exploiting Syntactic and Shallow Semantic Kernels to Improve Random Walks for Complex Question Answering

    Publication Year: 2008, Page(s):123 - 130
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (401 KB) | HTML iconHTML

    We consider the problem of answering complex questions that require inferencing and synthesizing information from multiple documents and can be seen as a kind of topic-oriented, informative multi-document summarization. The stochastic, graph-based method for computing the relative importance of textual units (i.e. sentences) is very successful in generic summarization. In this method, a sentence i... View full abstract»

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