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Intelligent Computer Communication and Processing, 2009. ICCP 2009. IEEE 5th International Conference on

Date 27-29 Aug. 2009

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Displaying Results 1 - 25 of 83
  • [Front cover]

    Page(s): c1
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  • [Title page]

    Page(s): 1
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  • [Copyright notice]

    Page(s): 1
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  • [Copyright notice]

    Page(s): 1
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  • Table of contents

    Page(s): i - iv
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  • Foreword

    Page(s): v
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  • Steering Committee

    Page(s): vi
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  • Program Committee

    Page(s): vii - viii
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  • Intervehicle communications for higher safety and higher efficiency at intersections

    Page(s): ix - x
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  • ProActive Parallel Suite: Multi-cores to Clouds to autonomicity

    Page(s): xi - xii
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  • An offline system for handwritten signature recognition

    Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1211 KB) |  | HTML iconHTML  

    Automatic online and offline signature recognition and verification is becoming ubiquitous in person identification and authentication problems, in various domains requiring different levels of security. There has recently been an increasing interest in developing such systems, with several views on which are the best discriminator features. This paper presents a new offline signature verification system, which considers a new combination of previously used features and introduces two new distance-based ones. A new feature grouping is presented. We have experimented with two classification methods and two feature selection techniques. The best performance so far was obtained with the Naiumlve Bayes classifier on the reduced feature set (through feature selection). View full abstract»

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  • Evolutionary optimization in dynamic environment

    Page(s): 11 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (134 KB) |  | HTML iconHTML  

    The behavior of standard evolutionary algorithm in the case of multi-modal optimization problems meets a major difficulty. It generally converges towards a single optimum point failing to maintain in the population the multiple optima of the problem under consideration. Various methods enrich the standard algorithm to obtain efficient techniques for solving multi-modal problems. These methods mainly consist of increasing the population diversity and of maintaining the promising areas in the search space in order to finally achieve convergence of the population towards the multiple optima. The present paper introduces mmEA, an evolutionary algorithm for multimodal optimization based on multidimensional exploration of the search space. This technique doesn't require any user defined parameter except those specific to standard evolutionary algorithm. Experiments and comparisons with similar techniques from literature, for static and dynamic environment, prove that mmEA technique is promising. View full abstract»

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  • An argumentation framework for optimal repair checking

    Page(s): 19 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (160 KB) |  | HTML iconHTML  

    When a database becomes inconsistent is crucial to restore its consistency. In this context, the notions of repair and optimal repair were introduced. In this work, we use Argumentation to comprehensibly describe and show the relationship between (inconsistent) information, i.e., its conflicts and inter-dependencies. We introduce an argumentation framework that provides a comprehensive way to check and constructively prove the optimality of a relational database repair based on the notions of locally, semi-globally and globally optimal repair and with respect to Binary Denial Constraints, Inclusion Dependencies and Key-related Dependencies Integrity Constraint classes. View full abstract»

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  • Evolutional meta-learning framework for automatic classifier selection

    Page(s): 27 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (111 KB) |  | HTML iconHTML  

    Meta-learning is currently a hot research topic in machine learning, which has emerged from the need to support data mining automation in issues related to algorithm and parameter selection. Finding the best learning strategy for a new domain/problem can prove to be an expensive and time-consuming process even for the experienced analysts. This paper presents a new meta-learning system, designed to automatically discover the most reliable learning schemes for a particular dataset, based on the knowledge the system acquired about similar datasets. The novelty of the approach consists in combining dataset characterization with landmarking to increase the accuracy of the predictions. The proposed architecture is aiming to resolve the problem of selecting the best classifier for a dataset while minimizing the work done by the user but still offering flexibility. View full abstract»

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  • IPA - An intelligent personal assistant agent for task performance support

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (116 KB) |  | HTML iconHTML  

    Assisting users in performing their tasks is an important issue in human computer interaction research. A solution to deal with this challenge is to build a personal assistant agent capable to discover the user's habits, abilities, preferences, and goals, ever more accurately anticipating the user's intentions. In order to solve in an intelligent manner this problem, the assistant agent has to continuously improve its behavior based on previous experiences. By endowing the agent with the learning capability, it will become able to adapt himself to the user's behavior. This paper proposes an intelligent personal assistant agent that learns by supervision to assist users in performing specific tasks. For evaluating the performance of the agent a case study is considered, and a neural network is used by the agent to learn by supervision from its experience. We also provide a comparison of our approach with other similar existing work. View full abstract»

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  • Intelligent component for adaptive E-learning systems

    Page(s): 35 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (103 KB) |  | HTML iconHTML  

    Existing e-learning systems are based on the quantity of information. They fail to consider an important factor for the success of the system, namely the user. An intelligent adaptive system should adjust the content in order to ensure faster and better performances in the learning process. Moreover, it should help students to develop new, desirable learning abilities. This paper presents the intelligent component of an adaptive e-learning system based on Bayesian networks; such a module makes an initial user classification using a psychological test, and continuously adapts according to user's interaction with the system. View full abstract»

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  • Cognoscenti agents

    Page(s): 39 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (84 KB) |  | HTML iconHTML  

    The purpose of this paper is to introduce cognoscenti entities as reasoning individuals capable of acquiring, using, and generating knowledge. Using the ontological model proposed by Noica, the paper also covers: (i) individual' behavior - as representing its problem-solving capabilities, (ii) individual' contexts associated to various problem-solving situations, and (iii) some specific relationships notions, theoretically imposed by the new type of individuals and tuples. We introduce a special type of binary relation, the holomer, as representing the seed for any cognoscenti agent. Also, the cognoscenti agent generation process, as representing the reasoning within the individual' context, brings to our attention another concept: logical situations. But, prior to all these, the novelty of the binary relationships between cognoscenti individuals and their associated concepts, the homoomerical relations, should be explained. View full abstract»

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  • Modelling imprecise arguments in a weighted argument system

    Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (145 KB) |  | HTML iconHTML  

    The paper applies the newly proposed weighted argument systems (WAS) and the associated notion of inconsistency budget. Fuzzy theory and ontological knowledge are used to supply WAS with the required weights, whilst the weighted argument systems provide a beautiful and simple principle to decide which fuzzy logic to use for reasoning, given an argumentation set. View full abstract»

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  • Integrating agent programs with the support of formal concepts

    Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    For the development of new agent programs by reengineering available ones, we consider the artifact A&A metamodel based on the activity theory, and MADeM, using auctions for providing social feedback. The goal is to integrate two applications developed separately based on these models by using GALICIA, a tool based on formal concept analysis and relational concept analysis. View full abstract»

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  • Aligning meaning understanding between agents using different, but close ontologies

    Page(s): 51 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (134 KB) |  | HTML iconHTML  

    A method for facilitating the communication between agents which have close ontologies by constructing a model of the interlocutor agent ontology is presented. The model of the interlocutor agent understanding is gradually refined based on the information exchanged between agents. We are in the situation where only the terms communicated between agents are made public and known by the other agent and concept mapping is implemented with this restriction. View full abstract»

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  • Arguments as a form of mixing ontologies and rules

    Page(s): 55 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (162 KB) |  | HTML iconHTML  

    It is good to know, but to know how to mix what you know is even better. We propose an argumentative manner for dealing with knowledge from different sources that are using different formalisms as ontologies and rules. We introduced non standard ontology reasoning services of explanation into the mix between the inference on Horn clauses and the tableaux based reasoning over Description Logic ontologies. The proposed formalism for representing the identified interdependences follows the Argumentation Interchange Format. View full abstract»

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  • Meta-learning enhancements by data partitioning

    Page(s): 59 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (88 KB) |  | HTML iconHTML  

    In data mining, there is no learning algorithm which attains the highest accuracy on any dataset. Multilevel arbiter and combiner arbiter are presented in this paper, as techniques to integrate classifiers induced from partitioned data, having as optimization criterion the accuracy of a given dataset. Experimental evaluations have shown that an arbiter tree can be found having similar or higher predictive performance when compared to the accuracy of the individual learner, trained on the entire training set. Moreover, for multiclass dataset with unbalanced class distribution, the combiner arbiter strategy yielded a good improvement in the prediction performance level. View full abstract»

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  • Reaching consensus over contradictory interpretation of Semantic Web data for ontology mapping

    Page(s): 63 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (71 KB) |  | HTML iconHTML  

    Software agents that need to interpret the possible meaning of Semantic Web data should be able to deal with scenarios where the different agent's belief becomes contradicting. This is especially true for ontology mapping where different agents using different similarity measures create beliefs in the assessed similarities and this needs to be combined into a more coherent state. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. Typically mapping algorithms, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different sources. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities. View full abstract»

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  • A dialogue-based model for the query synchronization problem

    Page(s): 67 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (97 KB) |  | HTML iconHTML  

    The synchronization of queries is one of the schema evolution problems and it calls for the redefinition of those queries becoming undefined after a schema change, in order to keep them still working on the new schema. This problem is particularly difficult for changes that upset the schema, because it could not be possible to rewrite the queries exactly. In this paper we show that the dialogue can play an important role in these cases and we propose a model of dialogue for query synchronization based on the Hintikka interrogative logic. View full abstract»

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  • Texture analysis within contrast enhanced abdominal CT images

    Page(s): 73 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (169 KB) |  | HTML iconHTML  

    The aim of this paper is to develop an automated texture based imaging system for the abdominal tissue classification within CT images. The development of such a system implies two major steps: the first step consists in finding the best features to describe the organs and the second one suppose a correctly classification of the desired tissue. In order to find relevant features, we realize a texture analysis on abdominal CT tissues by making use of a large variety of the most used texture features, including first and second order statistics, multiresolution analysis (Gabor filters) and Laws energy measures. Features of these types are used to classify three sets of different abdominal organs: liver, kidney and spleen. We also analyze the importance of the image contrast enhancement by making experiments on three different data sets: CT images in native phase, arterial phase and venous phase. Several classifiers were employed to build the classification system. Finally, we increase the classification system for this particular requirements by taking into account the CT capabilities of performing accurate geometrical localization of the anatomical structures. View full abstract»

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