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Titled Documents (TD) are short text documents that are segmented into two parts: Heading Part and Excerpt Part. With the development of the Internet, TDs are widely used as papers, news, messages, etc. In this paper we discuss the problem of automatic TDs categorization. Unlike traditional text documents, TDs have short headings which have less useless words comparing to their excerpts. Though headings are usually short, their words are more important than other words. Based on this observation we propose a titled document classification framework using the widely used MNB classifier. This framework puts higher weight on the heading words at the cost of some excerpt words. By this means heading words play more important roles in classification than the traditional method. According to our experiments on four datasets that cover three types of documents, the performance of the classifier is improved by our approach. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive motion mode selection method in H.264 frame skipping transcoding. In order to reduce the high complexity arising from variable block sizes in H.264, the proposed method exploits original motion information from incoming bitstreams. In addition, the paper also adopts Forward Dominant Vector Selection approach in MV composition of H.264 transcoding, in comparison with Bilinear Interpolation method. The simulation results show that the proposed method achieves good trade-off between computational complexity and video quality. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of global stability for a class of neural networks with time-varying delays. A new sufficient condition for global stability is proposed by using some slack matrix variables to express the relationship between the system matrices. The restriction on the derivative of the delay function to be less than unit is removed. A numerical example shows that the result obtained in this paper improves the upper bound of the delay over some existing ones. [ABSTRACT FROM AUTHOR]

This paper describes one aspect of our approach in developing an intelligent off-road autonomous vehicle, the Intelligent Off-road Navigator (ION), as team Desert Buckeyes from the Ohio State University for the DARPA Grand Challenge 2005. The real-time navigation is one of the critical components in an autonomous ground vehicle system. In this paper, we focus on the navigation module, whose main responsibility is to generate smooth and obstacle-free local paths with appropriate speed setpoints. For the smooth path generation, we introduce a polynomial interpolation method. To generate obstacle-free paths, a steering controller utilizing a fuzzy obstacle avoidance algorithm is presented. A speed fuzzy controller is introduced to generate the speed setpoints. These two fuzzy controllers collaborate with each other to guide our vehicle ION to the goal safely. The obstacle avoidance algorithm proposed in this paper was also tested in simulations and on small robots successfully. Other issues related to the navigation module are discussed in the paper as well, such as the vehicle's system structure and its finite state machine. As a result, ION achieved great performance in the National Qualification Event (NQE), covered about 30miles in the Nevada Desert with complete autonomous operations, and finished 10th in the Grand Challenge 2005. [ABSTRACT FROM AUTHOR]

This paper presents a multi-dependency language modeling approach to information retrieval. The approach extends the basic KL-divergence retrieval approach by introducing the hybrid dependency structure, which includes syntactic dependency, syntactic proximity dependency and co-occurrence dependency, to describe dependencies between terms. Term and dependency language models are constructed for both document and query. The relevant between a document and a query is then evaluated by using the KL-divergence between their corresponding models. The new dependency retrieval model has been compared with other traditional retrieval models. Experiment results indicate that it produces significant improvements in retrieval effectiveness. [ABSTRACT FROM AUTHOR]

Usability is a feature of software quality that has traditional significance in the Human Computer Interaction (HCI) community. Recent works that have been proposed by the Software Engineering (SE) community are intended to improve the usability of software applications. This paper combines aspects that are defined in both these communities to produce usable web applications. To achieve this goal, a well-known strategy to improve usability is used: usability patterns. However, many usability patterns and guidelines could only be applied when the final system is implemented. In this work, STATUS patterns have been chosen because they solve usability issues at conceptual level. The main purpose of this paper is to improve the usability of Web Applications automatically generated by OOWS (a model-based web engineering method) applying the STATUS patterns. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss the level of compliance for timetabling research to two important research qualities, namely measurability and reproducibility, analyzing what we believe are the most important contributions in the literature. Secondly, we discuss some practices that, in our opinion, could contribute to the improvement on the two aforementioned qualities for future papers in timetabling research. For the sake of brevity, we restrict our scope to university timetabling problems (exams, courses, or events), and thus we leave out other equally important timetabling problems, such as high-school, employee, and transportation timetabling. [ABSTRACT FROM AUTHOR]

This paper extends a survey on Networked Control Systems (NCSs) published in previous papers. General frameworks and some fundamental issues for the study of NCSs are proposed. A significant emphasis has been on developing control methodologies to study the character of NCSs. This survey paper presents recent NCSs control methodologies, some promising research directions are also covered. [ABSTRACT FROM AUTHOR]

Various organizations use the RFID technology for linking, tracking and identifying objects in their operative context. Nevertheless, the RFID components cannot yet be considered as mobile, intelligent and communicating elements of the organization's information infrastructure. They are not always used for accomplishing the linkage between the physical world and the adopted Information Technologies (IT) solutions. Therefore, they are not used for enabling organizations to automatically monitor, decide, and take actions. Heterogeneous networked devices and services installed within the organizations, often work independently instead of collaborating for offering a better quality of the daily activities to the end-user. This paper discusses the adoption of a Smart Ubiquitous Platform (SUP) for monitoring and managing the documents circulation by using the RFID technology. Advantages of the adoption of RFID are discussed too. [ABSTRACT FROM AUTHOR]

In Mizar, unlike in most other proof assistants, the types are not part of the foundations of the system. Mizar is based on untyped set theory, which means that in Mizar expressions are typed but the values of those expressions are not. In this paper we present the Mizar type system as a collection of type inference rules. We will interpret Mizar types as soft types, by translating Mizar's type judgments into sequents of untyped first order predicate logic. We will then prove that the Mizar type system is correct with respect to this translation in the sense that each derivable type judgment translates to a provable sequent. [ABSTRACT FROM AUTHOR]

This paper proposes a neural-based method to map facial expressions between human and robot. We applied the method to a sensitivity communication robot, Ifbot, which has been developed by our industry-university joint research project. The method enables the robot to imitate an arbitrary human facial expression. The paper describes the feature extraction from face image, and proposes neural network based parameter matching between human facial expression and Ifbot's facial expression mechanism. This paper also reports the evaluation of the facial expression transmission performance of Ifbot with the proposed system. The evaluation shows the effectiveness of emphasizing emotional expressions and the possibility of using Ifbot as an agent for distance communication. [ABSTRACT FROM AUTHOR]

This paper proposes an association method using Concept-base. Concept-base is a large-scale relational database constructed by some electronic dictionaries and some newspapers. Each concept which is defined in Concept-base has attributes which characterize the concept and weights which show each attribute's importance. Concept-base enables to calculate the depth of relationship (Degree of Association) of two any concepts using attributes and weights. This paper proposes One Word Association Method which associates one word (concept) with some relative words (concepts). Also, a threshold of Degree of Association is defined using the evaluation result of One Word Association Method. Finally, Two Words Association Method is proposed based on the threshold of Degree of Association. [ABSTRACT FROM AUTHOR]

In this paper, we propose gesture recognition in multiple people environment. Our system is divided into two modules: Segmentation and Recognition. In segmentation part, we extract foreground area from input image, and we decide the closest person as a recognition subject. In recognition part, firstly we extract feature point of subject's both hands using contour based method and skin based method. Extracted points are tracked using Kalman filter. We use trajectories of both hands for recognizing gesture. In this paper, we use the simple queue matching method as a recognition method. We also apply our system as an animation system. Our method can select subject effectively and recognize gesture in multiple people environment. Therefore, proposed method can be used for real world application such as home appliance and humanoid robot. [ABSTRACT FROM AUTHOR]

Web-based Geographic Information System(WebGIS) is a key direction towards GIS development. By analysing of the deficiency in the design of conventional WebGIS, this paper proposes an architecture of the intelligent WebGIS based on Multi-Agent, which has been applied in a agricultural WebGIS. In order to solve the key problem of Agent communication, the paper also presents an Agent communication model, which decreases the working capacity to maintain the communication protocl and enhances the system's adaptability and stability. [ABSTRACT FROM AUTHOR]

This paper surveys first-order probabilistic languages (FOPLs), which combine the expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data. [ABSTRACT FROM AUTHOR]

We consider some problems about the computation time of evolutionary algorithms in this paper. First, some exact analytic expressions of the mean first hitting times of general evolutionary algorithms in finite search spaces are obtained theoretically by using the properties of Markov chain associated with evolutionary algorithms considered here. Then, by introducing drift analysis and applying Dynkin's Formula, the general upper and lower bounds of the mean first hitting times of evolutionary algorithms are estimated rigorously under some mild conditions listed in the paper. Those results in this paper are commonly useful. Also, the analytic techniques adopted in the paper are widely instructive for analyzing the computation time of evolutionary algorithms in a given search space as long as some specific mathematical arts are introduced accordingly. [ABSTRACT FROM AUTHOR]

Manual curation of biological databases is an expensive and labor-intensive process in Genomics and Systems Biology. We report the implem-entation of a state-of-the-art, rule-based Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from abstracts and full-text papers. We evaluate its output against a manually-curated standard database, and test the possibilities and limitations of automatic and semi-automatic curation of the so-called biobibliome. We also propose a novel Regulatory Interaction Mining Markup Language suited for representing this data, useful both for biologists and for text-mining specialists. [ABSTRACT FROM AUTHOR]

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

Process models play important role in computer aided pro- cess engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor. [ABSTRACT FROM AUTHOR]

Traditional indexing methods face the difficulty of"curse of dimensionality" at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval. [ABSTRACT FROM AUTHOR]

This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profilesThis research was partially funded by the EC under the 6th FP IST IP VIKEF No. 507173, Priority 2.3.1.7 Semantic Based Knowledge Systems (www.vikef.net) and under the DELOS NoE on Digital Libraries, Priority: Technology-enhanced Learning and Access to Cultural Heritage - Contract n. G038-507618 (www.delos.info). The work was supported also by the research project "Modelli avanzati di personalizzazione per biblioteche elettroniche", year 2005, funded by the University of Bari.. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the "conference participant advisor" service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3). [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm of mining multidimensional association rules in relational database is proposed. First, design a particular structure including many indexes to save multidimensional itemsets, which makes time of finding itemsets decrease. Basing on statistic idea, the algorithm saves all frequent 1-itemsets and their support members at scanning the database for the first time. Frequent k-itemsets will be generated with no necessary to scan the database more times because of using these support members. Compared with some traditional algorithms of mining association rules, the algorithm presented in this paper has better executive efficiency and expansibility, which is proved in our experiments. [ABSTRACT FROM AUTHOR]

Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. One major challenge for its real deployment is its QoS issue. This is a rich area because sensor deaths and sensor replenishments make it difficult to specify the optimum number of sensors (this being the service quality that we address in this paper) that should be sending information at any given time. Through literature survey, we discover that current solution towards this problem remains some unreasonable assumptions in practice. So we proposed a new control schema allowing the control method to be more feasible in real environment. A distributed control schema is introduced in this paper. Every sensor node runs a control algorithm in a distributed fashion. This distributed QoS control schema can handle limitations exist in current QoS control methods. [ABSTRACT FROM AUTHOR]

This paper proposed a new framework for color texture segmentation which integrated the color and texture features. Quaternion-Gabor filter was first introduced in this paper for color texture segmentation. The algorithm achieved color texture multichannel Gabor filtering through DRBFT and IDRBFT. And the quaternion-Gabor filter extracted the input color image's color features and texture features at the same time. The proposed method was tested using different mosaic and natural images. Despite the simplicity of the whole algorithm, the segmentation results were rather encouraging. Keywords: DRBFT, RBs, MCF, k-means. [ABSTRACT FROM AUTHOR]

Engineering component interactions is a major challenge in the development of large-scale, open systems. In the realm of multiagent system research, organizational abstractions have been proposed to overcome the complexity of this task. However, the gap between these modeling abstractions, and the constructs provided by todays agent-oriented software frameworks is still rather big. This paper reports on the multiagent programming framework, which provides executable constructs for each of the organizational, ACL-based modeling abstractions of the theory. Setting out from a components and connectors perspective on the elements of the metamodel, their executions semantics is defined and instrumented on top of the JADE platform. Moreover, a systematic reuse approach to the engineering of interactions is put forward. [ABSTRACT FROM AUTHOR]

This paper proposes the canonical representation of multiplication operation on trapezoidal fuzzy numbers using the L-1-R-1 Inverse Function Arithmetic Representation method. Finally, the canonical representation proposed in this paper is applied to solve a fuzzy multiple criteria decision making problem of selection of plant location. [ABSTRACT FROM AUTHOR]

This paper presents an economical inspired intelligent approach for modeling learners in learning systems. Decision making in complex systems like e-learning systems requires processing of large amounts of heterogeneous data and information from dispread sources. Moreover, most of the decision parameters are incomplete and uncertain. Lacking of a complete model of learner is the prominent problem of current learning systems. In this paper, a market based method for describing Learner's preferences to the learning system is provided. The proposed approach strives for applying a Dempster-Shafer decision making over a society of self motivated agents. It tries to present a final learner agent with a high degree of similarity to the user for the purpose that it can act as a model of learner through the system. An implicit learning is also implemented by the idea of Stocks in real markets which can improve decision making efficiently. Keywords: Intelligent agent, personalizing, user modeling, Marker based systems. [ABSTRACT FROM AUTHOR]

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements. [ABSTRACT FROM AUTHOR]

The inherent nonlinear of switched reluctance motor (SRM) makes it hard to get a good performance by using the conventional PID controller to the speed control of SRM. This paper develops a radial basis function (RBF) artificial neural network (ANN) nonlinear prediction model based adaptive PID controller for SRM. ANN, under certain condition, can approximate any nonlinear function with arbitrary precision. It also has a strong ability of adaptive, self-learning and self-organization. So, combining it with the conventional PID controller, a neural network based adaptive PID controller can be developed. Appling it to the speed control of SRM, a good control performance can be gotten. At the same time, the nonlinear mapping property and high parallel operation ability of ANN make it suitable to be applied to establish nonlinear prediction model performing parameter prediction. In this paper, two ANN - NNC and NNI are employed. The former is a back propagation (BP) ANN with sigmoid activation function. The later is an ANN using RBF as activation function. The former is used to adaptively adjust the parameters of the PID controller on line. The later is used to establish nonlinear prediction model performing parameter prediction. Compared with BP ANN with sigmoid activation function, the RBF ANN has a more fast convergence speed and can avoid getting stuck in a local optimum. Through parameter prediction, response speed of the system can be improved. To increase the convergence speed of ANN, an adaptive learning algorithm is adopted in this paper that is to adjust the learning rate according to the error. This can increase the convergence speed of ANN and make the system response quick. The experimental results demonstrate that a high control performance is achieved. The system responds quickly with little overshoot. The steady state error is zero. The system shows robust performance to the load torque disturbance. [ABSTRACT FROM AUTHOR]

This paper proposes a method for sensitivity communication robots which infer their dialogist's emotion. The method is based on the Bayesian approach: by using a Bayesian modeling for prosodic features. In this research, we focus the elements of emotion included in dialogist's voice. Thus, as training datasets for learning Bayesian networks, we extract prosodic feature quantities from emotionally expressive voice data. Our method learns the dependence and its strength between dialogist's utterance and his emotion, by building Bayesian networks. Bayesian information criterion, one of the information theoretical model selection method, is used in the building Bayesian networks. The paper finally proposes a reasoner to infer dialogist's emotion by using a Bayesian network for prosodic features of the dialogist's voice. The paper also reports some empirical reasoning performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a state transition model using context based approach for gesture analysis. This method defines the analysis situation as five different states; NULL, OBJECT, POSTURE, LOCAL, and GLOBAL. We first infer the situation of the system by estimating the transition of the state model, and then apply different analysis algorithms according to the system state. Gestures are analyzed with the queue-based matching method which is newly proposed in the paper instead of general gesture spotting algorithms. In the algorithm, movement of feature points; face and both hands, is compared directly, so gesture can be recognized quickly without applying any constraints for real world application. The transition of the states can be interpreted as the context of motion and that is estimated with conditional probability between the states in the algorithm. [ABSTRACT FROM AUTHOR]

In WWW information integration, eliminating semantic hetero-geneity and implementing semantic combination is one of the key problems. This paper introduces classification ontology into WWW information integration to solve the semantic combination problem of heterogeneity classification architecture in Web information integration. However, there may be many kinds of ontology in a specific domain due to the structure of the websites, domain experts and different goals. So we have to combine all these kinds of ontology into logically unified integrated classification ontology in order to solve the problem of semantic heterogeneity commendably. This paper primarily discusses the method of building integrated classification ontology based on individual ontology, presents the definition of classification ontology, analyses the conceptual mapping and relational mapping between ontologies and solves the level conflict in the equivalent concepts. Keywords: Information integration, Classification ontology, Semantic, Similarity. [ABSTRACT FROM AUTHOR]

This paper extends the upright face detection framework proposed by Viola et al. 2001 to handle in-plane rotated faces. These haar-like features work inefficiently on rotated faces, so this paper proposes a new set of ±26.565 ° haar-like features which can be calculated quickly to represent the features of rotated faces. Unlike previous face detection techniques in training quantities of samples to build different rotated detectors, with these new features, we address to build different rotated detectors by rotating an upright face detector directly so as to achieve in-plane rotated face detection. This approach is selected because of its computational efficiency, simplicity and training time saving. This proposed method is tested on CMU-MIT rotated test data and yields good results in accuracy and maintains speed advantage. [ABSTRACT FROM AUTHOR]

This paper addresses a capacitated production planning (CPP) problem with outsourcing. Specially, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production, inventory, outsourcing, and sale loss without postponement or backlog; (2) The goal of the solution to this problem is to minimize the total cost covering the production, setup, inventory holding, sale loss, and outsourcing activities; (3) different from previous studies which usually involved some special assumptions on the cost function for these activities, the cost function structure we consider in this paper are arbitrary and time-varying; and (4) we consider a general problem situation where the production, inventory, and outsourcing have a capacity limitation. It is likely that there is no solution in the model due to the consideration of such general cost functions and many constraints, hence we employ a Bellman/Ford algorithm for checking feasible solutions of the problem and resulting in a feasible solution region. After that, a proposed algorithm is applied for searching a global optimal solution with in the feasible solution region. [ABSTRACT FROM AUTHOR]

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects (in Java), freed from the intricacies of the PDF file. The second stage consists of discovering simple graphics entities which we call graphemes, e.g., a pair of primitive graphic objects satisfying certain geometric constraints. The third stage uses machine learning to classify figures using grapheme statistics as attributes. A boosting-based learner (LogitBoost in the Weka toolkit) was able to achieve 100% classification accuracy in hold-out-one training/testing using 16 grapheme types extracted from 36 figures from BioMed Central journal research papers. The approach can readily be adapted to raster graphics recognition. Keywords: Graphics Recognition, PDF, Graphemes, Vector Graphics, Machine Learning, Boosting. [ABSTRACT FROM AUTHOR]

This paper discusses the AgentAssebly architecture and specifically the agent framework platform component of the architecture. An introduction is given to the work covered in the paper and previous work (see [1]) this paper is based upon. Then an in-depth discussion is rendered on the agent framework platform provided by the AgentAssembly architecture. The paper concludes with some remarks on the research presented and future aims of the ongoing research project. Keywords: Agent programming languages, frameworks, and toolkits meta-modeling and meta reasoning. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-hard, and is an often-used benchmark for new optimization techniques. This paper pro- poses an improved multi-agent approach for solving large TSP. This proposed approach mainly includes three kinds of agents with different function. The first kind of agent is conformation agent and its function is generating the new solution continuously. The second kind of agent is optimization agent and its function is optimizing the current solutions group. The third kind of agent is refining agent and its function is refining the best solution from the beginning of the trial. At same time, there are many sub-agents in each kind of agent. These sub-agents accomplish the task of its superior agent cooperatively. At the end of this paper, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation. [ABSTRACT FROM AUTHOR]

The use of an ontology in order to provide a mechanism to enable machine reasoning has continuously increased during the last few years. This paper proposed an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontology-based document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web documents are classified in real time not with experimental data or a learning process, but by similarity calculations between the terminology information extracted from Web documents and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. [ABSTRACT FROM AUTHOR]

There are several researches on 2D gaze tracking techniques for the 2D screen for the Human-Computer Interaction. However, the researches for the gaze-based interaction to the stereo images or contents are not reported. The 3D display techniques are emerging now for the reality service. Moreover, the 3D interaction techniques are much more needed in the 3D contents service environments. This paper addresses the gaze-based 3D interaction techniques. It is well known that there are characteristics of eye vergence according to the depth of object to be focused by human. This paper presents our experimental research results about the characteristics of eye vergence according to stereo image depth. Our research result is evaluated and applied to the gaze-based 3D interaction with the parallax barrier stereo image display system. [ABSTRACT FROM AUTHOR]

In this paper, a framework for individualizing the learning material structure in adaptive learning system is introduced. It aims to utilize the learning characteristics and provide a personalized learning environment that exploit pedagogical model and fuzzy logic techniques. The learning material consists of 4 structures; 1) theory, 2) examples 3) exercises and 4) activities. The pedagogical model and learning characteristics are based on the student's personality factor (Myers-Briggs Type Indicator (MBTI)), whilst the fuzzy logic techniques are used to classify the structure of learning material which is based on student's personality factors. This paper tend to illustrate the construction of fuzzy set and fuzzy rules to find the best rules based on combination of two approaches; learning style approach and fuzzy logic approach for adapting the content to the user, allowing a learning system to dynamically adapt the choice of possible learning structure through the learning material based on the user's personality factor. [ABSTRACT FROM AUTHOR]

Due to the advance of e-community, collaborative learning becomes popular on the Web. Among the learning models for collaborative learning on the Web, there are seminar-based model and project-based model. The objective of this paper is to compare the two learning models by analyzing learners' activities. Among learners' activities, we focus on learners' interaction level, their collaborability, and their accountability. Also, we propose new assessment items for evaluating learners fairly. In this paper, the existing evaluation items in traditional classroom-based collaborative learning are firstly analyzed in order to develop new evaluation criteria for the Web. Based on these analyzed items, we propose new items for assessing learners' activities in Web-based collaborative learning. After two Web-based classes, each of which has a different learning model, are performed, the proposed items are applied to compare the two learning models in terms of learners' interaction level, collaborability, and accountability. By performing this research, we can understand learners' activities clearer and can develop a better instructional technique to make improved academic achievement, improved attendance, and increased self-confidence in collaborative e-learning. [ABSTRACT FROM AUTHOR]

Over the last decades, more and more visually handicapped students have attempted post-secondary studies. This situation has created many new challenges. One of them is the need to study text and electronic documents in depth and in a reasonable time. Blind students cannot flip through the pages of a book, skim through the text or use a highlighter. In this paper, we propose a solution in the form of an experimental prototype and show how natural language processing techniques can profitably assist blind students in meeting their academic objectives. The techniques used include the automatic creation of indices, passage retrieval and the use of WordNet for query rewriting. The paper presents a technology application of a practically usable software. The system was evaluated quantitatively and qualitatively. The evaluation is very encouraging and supports further investigation. [ABSTRACT FROM AUTHOR]

The research work in this paper focuses on solving the critical problems in Web Service discovery such as how to locate functionality-desired Web Services and how to select the best one from large numbers of functionality-similar Web Services. The semantic description and quality description of Web Service based on ontology proposed in this paper, provides a consistent description of different kinds of Web Service, which is used as the basis of service matching. Then the matching model of Web Service is discussed in detail, which consists of semantic similarity matching according to functionality of Web Service, and semantic filtering according to quality of Web Service. Using the descriptions and matching approach, the performance of Web Service discovery can be improved by increasing the precision and recall of Web Service searching. [ABSTRACT FROM AUTHOR]

This paper illustrates an algorithm for osteosarcoma segmentation, using vectorial fuzzy-connectedness segmentation, and coming up with a methodology which can be used to segment some distinct tissues of osteosarcoma such as tumor, necrosis and parosteal sarcoma from 3D vectorial images. However, fuzzy-connectedness segmentation can be successfully used only in connected regions. In this paper, some improvements have been made to segment the interested tissues which are distributed in disconnected regions. And the paper speeds up the process of segmentation by segmenting two osteosarcoma tissues simultaneously. The methology has been applied to a medical image analysis system of osteosarcoma segmentation and 3D reconstruction, which has been put into practical use in some hospitals. [ABSTRACT FROM AUTHOR]

Automated timetabling is a research domain that has occupied many researchers over the last 50 years. Several algorithms have proven to be applicable to timetabling but they are nearly all designed to address specific problems. The framework presented in this paper is a step towards a generic semi-automatic timetabling tool. The basis of the framework is an ontology for timetabling that we designed after having investigated different types of timetabling problem. A first step towards solving general problems consists of mapping their data representation to the ontology. That is carried out in such a way that existing data that is already available in databases can further be used throughout the application. In the next step, the tool assists in determining the constraints and objectives of the problem. The semantic components have three sources of information: meta-data about the database, domain knowledge about timetabling problems and external, non-domain-specific knowledge. [ABSTRACT FROM AUTHOR]

This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontologybased document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web documents are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web texts and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. Keywords: Document classification, Ontology, Web Page classification. [ABSTRACT FROM AUTHOR]