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This study aimed to develop an advance precision three-dimensional (3-D) image segmentation algorithm to enhance the blurred edges clearly and then introduce the result onto the intensity modulated radiotherapy (IMRT) for tumor target volume definition. This will achieve what physicians usually demand that tumor doses escalation characteristics of IMRT. A proposed algorithm flowchart designed for this precision 3-D treatment targeting was introduced in this paper. Different medical images were used to test the validity of the proposed method. The 3-D wavelet based targeting preprocessing segmentation allows physicians to improve the traditional treatments or IMRT much more accurately and effectively. This will play an important role in image-guided radiotherapy (IGRT) and many other medical applications in the future. [ABSTRACT FROM AUTHOR]

On the basis of Markov Random Field (MRF), which uses context information, in this paper, a robust image segmentation method is proposed. The relationship between observed pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function, which described the probability of pixels being classified into one class. To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number. The K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori (MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is adopted with K-means, traditional Expectation-Maximization (EM) and MRF image segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results and the histogram of signal to noise ratio (SNR)-miss classification ratio (MCR) showed that the proposed algorithm is the better choice. [ABSTRACT FROM AUTHOR]

Blur identification is usually necessary in image restoration. In this paper, a novel blur identification algorithm based on Support Vector Machines (SVM) is proposed. In this method, blur identification is considered as a multi-classification problem. First, Sobel operator and local variance are used to extract feature vectors that contain information about the Point Spread Functions (PSF). Then SVM is used to classify these feature vectors. The acquired mapping between the vectors and corresponding blur parameter provides the identification of the blur. Meanwhile, extension of this method to blind super-resolution image restoration is achieved. After blur identification, a super-resolution image is reconstructed from several low-resolution images obtained by different foci. Simulation results demonstrate the feasibility and validity of the method. [ABSTRACT FROM AUTHOR]

Super-resolution image reconstruction estimates a high-resolution image from a sequence of low-resolution, aliased images. The estimation is an inverse problem and is known to be ill-conditioned, in the sense that small errors in the observed images can cause large changes in the reconstruction. The paper discusses application of existing regularization techniques to super-resolution as an intelligent means of stabilizing the reconstruction process. Some most common approaches are reviewed and experimental results for iterative reconstruction are presented. [ABSTRACT FROM AUTHOR]

Inspired by studies of cognitive psychology, we proposed a new dynamic similarity kernel for visual recognition. This kernel has great consistency with human visual similarity judgement by incorporating the perceptual distance function. Moreover, this kernel can be seen as an extension of Gaussian kernel, and therefore can deal with nonlinear variations well like the traditional kernels. Experimental results on natural image classification and face recognition show its superior performance compared to other kernels. [ABSTRACT FROM AUTHOR]

In this paper, we present an extended boids model for simulating the aggregate moving of fish schools in a complex environment. Three behavior rules are added to the extended boids model: following a feed; avoiding obstacle; avoiding enemy boids. The moving vector is a linear combination of every behavior rule vector, and the coefficients should be optimized. We also proposed a genetic algorithm to optimize the coefficients. Experimental results show that by using the GA-based optimization, the aggregate motions of fish schools become more realistic and similar to behaviors of real fish world. [ABSTRACT FROM AUTHOR]

Automated segmentation of MR images is a difficult problem due to complexity of the images. In this paper, we proposed a new method based on independent component analysis (ICA) for segmentation of MR images. We first extract thee independent components from the T1-weighted, T2-weighted and PD images by using ICA and then the extracted independent components are used for segmentation of MR images. Since ICA can enhance the local features, the MR images can be transformed to contrast-enhanced images by ICA. The effectiveness of the ICA-based method has been demonstrated. [ABSTRACT FROM AUTHOR]

We consider the problem of partitioning a data set of n data objects into c homogeneous subsets (that is, data objects in the same subset should be similar to each other), such that each subset is of approximately the same size. This problem has applications wherever a population has to be distributed among a limited number of resources and the workload for each resource shall be balanced. We modify an existing clustering algorithm in this respect, present some empirical evaluation and discuss the results. [ABSTRACT FROM AUTHOR]

The Fisher kernel, which refers to the inner product in the feature space of the Fisher score, has been known to be a successful tool for feature extraction using a probabilistic model. If an appropriate probabilistic model for given data is known, the Fisher kernel provides a discriminative classifier such as support vector machines with good generalization. However, if the distribution is unknown, it is difficult to obtain an appropriate Fisher kernel. In this paper, we propose a new nonparametric Fisher-like kernel derived from fuzzy clustering instead of a probabilistic model, noting that fuzzy clustering methods such as a family of fuzzy c-means are highly related to probabilistic models, e.g., entropy-based fuzzy c-means and a Gaussian mixture distribution model. The proposed kernel is derived from observing the last relationship. Numerical examples show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate that simple fuzzy rule-based classification systems with high interpretability are obtained through multiobjective genetic rule selection. In our approach, first a prespecified number of candidate fuzzy rules are extracted from numerical data in a heuristic manner using rule evaluation criteria. Then multiobjective genetic rule selection is applied to the extracted candidate fuzzy rules to find a number of non-dominated rule sets with respect to the classification accuracy and the complexity. The obtained non-dominated rule sets form an accuracy-complexity tradeoff surface. The performance of each non-dominated rule set is evaluated in terms of its classification accuracy and its complexity. Computational experiments show that our approach finds simple fuzzy rules with high interpretability for some benchmark data sets in the UC Irvine machine learning repository. [ABSTRACT FROM AUTHOR]

When the data is given as mixed data, that is, the attributes take the values in mixture of binary and continuous, a clustering method based on k-means algorithm has been discussed. The binary part is transformed into the directional data (spherical representation) by a weight transformation which is induced from the consideration of the similarity between binary objects and of the natural definition of descriptive measures. At the same time, the spherical representation of the continuous part is given by the use of multidimensional scaling on the sphere. Combining the binary part and continuous part, like the latitude and longitude, we obtained a spherical representation of mixed data. Using the descriptive measures on a sphere, we obtain the clustering algorithm for mixed data based on k-means method. Finally, the performance of this clustering is evaluated by actual data. [ABSTRACT FROM AUTHOR]

This paper presents several fuzzy clustering methods based on self-organized similarity (or dissimilarity). Self-organized similarity (or dissimilarity) has been proposed in order to consider not only the similarity (or dissimilarity) between a pair of objects but also the similarity (or dissimilarity) between the classification structures of objects. Depending on how the similarity (or dissimilarity) of the classification structures cope with the fuzzy clustering methods, the results will be different from each other. This paper discusses this difference and shows several numerical examples. [ABSTRACT FROM AUTHOR]

The amount of data is growing at an exponential rate. We are faced with a challenge to analyze, process and extract useful information from the vast amount of data. Traditional data analysis techniques have contributed immensely in the area of data analysis but we believe that the soft data analysis techniques, based on soft computing techniques, can be complementary and can process complicated data sets. This paper provides an introduction to the soft data analysis paradigms. It summarizes the successful and possible applications of the soft computing analysis paradigms. The merits and demerits of these paradigms are included. A number of resources available are listed and the future vision is discussed. This paper also provides a brief summary of the papers included in the session on "Innovation in Soft Data Analysis". [ABSTRACT FROM AUTHOR]

In our previous studies, the immunity-based diagnostic model has been used by stationary agents in linked networks or by mobile agents on wired computer networks. We have not yet analyzed the performance of the diagnosis in wireless network where agents can move freely. In this paper, the diagnosis is applied to static and mobile sensor nodes in a 2-dimensional lattice space for wireless sensor network. Some simulation results show the strategy of going straight in the different direction can have the best detection rate. In addition, when the fraction of mobile nodes is changed, the transitions of the detection rate for the migration strategies are different. [ABSTRACT FROM AUTHOR]

This paper reports a multiagent approach to a basic model inspired by the asymmetric war between HIV and T-cells. The basic model focuses on the asymmetric interaction between two types of agents: Virus Agents (abstracted from HIV) and Immune Agents (abstracted from T-cells). Virus Agents and Immune Agents, characterized respectively as "generalists" and "specialists", may be compared with asymmetric wars between computer viruses and antivirus programs, between guerrillas and armed forces, and so on. It has been proposed that antigenic diversity determines the war between HIV and T-cells. We also formalize the diversity of "generalists" that would determine whether generalists or specialists won. The multiagent simulations also suggest that there is a diversity threshold over which the specialist cannot control the generalist. In multiagent approaches, two spaces, Agent Space and Shape Space, are used to observe not only the spatial distribution of agent populations but also the distribution of antigenic profiles expressed by a bit string. [ABSTRACT FROM AUTHOR]

This paper reports on the construction and use of a dynamic relational network that has been studied as an immunity-based system based on the concept of immune networks. The network is constructed by an immunological algorithm that tunes the network not to react to normal sensor data, but to react to abnormal data thereafter. The tuning is not straightforward, since even normal sensor data involve many situations such as accelerating phase and cruise phase; on-road and off-road; and running at altitude. A case study on sensor systems for the combustion control system of an automobile engine is presented. [ABSTRACT FROM AUTHOR]

We introduce an interaction scenario in which users of public places can see relevant information items on public displays as they move. Public displays can dynamically collaborate and group with each other so as to minimize information clutter and redundancy. We analyse the usability constraints of this scenario in terms of information layout on the screens. This allows us to introduce a decentralized architecture in which information screens as well as users are modeled by software agents. We then present a simulator that implements this system. [ABSTRACT FROM AUTHOR]

This paper addresses the extraction of small group configurations and activities in an intelligent meeting environment. The proposed approach takes a continuous stream of observations coming from different sensors in the environment as input. The goal is to separate distinct distributions of these observations corresponding to distinct group configurations and activities. In this paper, we explore an unsupervised method based on the calculation of the Jeffrey divergence between histograms over observations. The obtained distinct distributions of observations can be interpreted as distinct segments of group configuration and activity. To evaluate this approach, we recorded a seminar and a cocktail party meeting. The observations of the seminar were generated by a speech activity detector, while the observations of the cocktail party meeting were generated by both the speech activity detector and a visual tracking system. We measured the correspondence between detected segments and labelled group configurations and activities. The obtained results are promising, in particular as our method is completely unsupervised. [ABSTRACT FROM AUTHOR]

This paper presents an architecture to help the decision-making process of disaster managers. Here we focus on a core aspect of this process which is taking decisions in the presence of conflicting options. We exemplify this problem with three simple scenarios related to diverse contexts and provide an explanation on how our system will advice in all these cases. [ABSTRACT FROM AUTHOR]

In this paper a system is presented able to reproduce the actions of multiple moving objects into a 3D model. A multi-camera system is used for automatically detect, track and classify the objects. Data fusion from multiple sensors allows to get a more precise estimation of the position of detected moving objects and to solve occlusions problem. These data are then used to automatically place and animate objects avatars in a 3D virtual model of the scene, thus allowing users connected to this system to receive a 3D guide into the monitored environment. [ABSTRACT FROM AUTHOR]

The subject of this paper is fuzzy propositional calculus. The proposed approach is related to the basic fuzzy propositional logics, i.e. to each of the following three most important such systems (in short: BL): Łukasiewicz's, Gödel's, and product logic. The logical calculi considered here are based on a system of rules that define the methods used in proofs from assumptions. To simplify the considered proofs some set of laws called also ‘primitive rules' is next introduced. It was shown that any fuzzy propositional formula provable under Hájek's axioms of the logic BL is also provable under the above-proposed approach. [ABSTRACT FROM AUTHOR]

New designs of the Web switches must incorporate a client-and-server-aware adaptive dispatching algorithm to be able to optimize multiple static and dynamic services providing quality of service and service differentiation. This paper presents such an algorithm called FNRD (Fuzzy-Neural Request Distribution) which operates at layer-7 of the OSI protocol stack. This algorithm assigns each incoming request to the server with the least expected response time estimated using the fuzzy approach. FNRD has ability for learning and adaptation by means of a neural network feedback loop. We demonstrate through the simulations that our dispatching policy is more effective than state-of-the-art layer-7 reference dispatching policies CAP (Client-Aware Policy) and LARD (Locality Aware Request Distribution). [ABSTRACT FROM AUTHOR]

A multiple watermarking algorithm based on the Code Division Multiple Access (CDMA) spread spectrum technique is presented. Multiple copyright identifiers are convolutional encoded and block interleaved, and the orthogonal Gold sequences are used to spread spectrum of the copyright messages. The CDMA encoded copyright messages are embedded into the wavelet sub-bands. As a blind watermarking algorithm, the copyright identifiers are extracted without the original image. The experimental results show that the proposed algorithm improves the detection BER, and the multiple copyright identifiers have preferable robustness and invisibility. [ABSTRACT FROM AUTHOR]

In general, avatar-based communication has a merit that it can represent non-verbal information. The simplest way of representing the non-verbal information is to capture the human action/motion by a motion capture system and to visualize the received motion data through the avatar. However, transferring raw motion data often makes the avatar motion unnatural or unrealistic because the body structure of the avatar is usually a bit different from that of the human beings. We think this can be solved by transferring the meaning of motion, instead of the raw motion data, and by properly visualizing the meaning depending on characteristics of the avatar's function and body structure. Here, the key issue is how to symbolize the motion meanings. Particularly, the problem is what kind of motions we should symbolize. In this paper, we introduce an algorithm to decide the symbols to be recognized referring to accumulated communication data, i.e., motion data. [ABSTRACT FROM AUTHOR]

Embodied Conversational Agents (ECAs) are computer generated life-like characters that interact with human users in face-to-face conversations. To achieve natural multi-modal conversations, ECA systems are very sophisticated and require many building assemblies and thus are difficult for individual research groups to develop. This paper proposes a generic architecture, the Universal ECA Framework, which is currently under development and includes a blackboard-based platform, a high-level protocol to integrate general purpose ECA components and ease ECA system prototyping. [ABSTRACT FROM AUTHOR]

In this paper a flexible method for a distance measure between communicative agents' stored perceptions is proposed. It is assumed that each agent observes the states of external objects that are remembers them in a private temporal database in the form of base profiles. Distance measure is applied by the agent in the algorithm for the messages generation or during integration of other agents' opinions collected during communication activities in order to create objective picture of the current state's of objects. Proposed distance measure between base profiles is based on a computing of the costs of transformation one base profile into other. The objects' movements hierarchy is introduced and mathematically explained using expected value and random variable. [ABSTRACT FROM AUTHOR]

It is considered a problem of harmonisation of diagnostic rules used in a distributed set of diagnostic centres, the rules being based on a k-most-similar-cases approach. Harmonisation is reached due to an exchange of diagnostic cases among the reference sets stored in the centres. The method is based on general concepts of similarity, semi-similarity and structural compatibilitymeasures used to evaluation of adequacy of records in remote data files to the requirements connected with supporting decision making in a given, local diagnostic centre. The procedure of local reference set extension by diagnostic cases selection and acquisition is described. [ABSTRACT FROM AUTHOR]

A paper proposes a formal framework for communication patterns' uncertainty assessment within a distributed multiagent IDS architecture. The role of the detection of communication anomalies in IDS is discussed then it is shown how sequences of detectable patterns like fan-in, fan-out values for given network node and clustering coefficients can be used to detect network anomalies caused by security incidents (worm attack, virus spreading). It is defined how to use the proposed techniques in distributed IDS and backtrack the incidents. [ABSTRACT FROM AUTHOR]

This paper presents a consensus-based algorithm for resolving inconsistency in Web-based recommendation systems. In such systems it is possible that a user have his own usage path (the way of usage of the system) and system can recommend individual usage path for new user. The problem of determining a new path for a new user is the subject of this paper. An algorithm for solving this problem will be described below and some results of the experiments with this algorithm will be presented. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed class code and data propagation with Java. Traditional approach based on problem-oriented structures and on predefined task language is not suitable for universal grid programming. The main contribution is the development of an automatic framework for efficient propagation of class package and data. We examine two problems suitable for code and data distribution: large n-merge sorting and document indexing. Thanks to the use reflection mechanism, we show that Java is adequate for defining new tasks on grid elements without any language extension. Relation between number of component nodes of the structure and total processing time has been checked. Furthermore the framework is fault-tolerant when some nodes fail. [ABSTRACT FROM AUTHOR]

Ontology can be treated as the background of an information system. If integration of some systems has to be performed, their ontologies must also be integrated. In this process it is often needed to resolve conflicts between ontologies. In this paper conflicts of ontologies are classified into classes and for each of them a method for conflict solving is proposed. [ABSTRACT FROM AUTHOR]

Question and answering (QA) systems in the CRM scheme require both the quality relating user's satisfaction and the amount of questions to be managed, that is to say, it depends on the cost. This paper presents an estimation method of the FAQ service by introducing the following measurements: 1) user's disrepute for products which defined by four types of classifying questions; 2) kindness for solutions replied which defined by four types of classifying answers; 3) comprehension for answers which defined by semantic expressions of questions and answers; 4) sufficiency and quality for the whole FAQ service that introduced by the 1), 2) and 3). This approach is evaluated by the FAQ data with 4,538 questions and 5,356 answers and the real time simulation to estimate user's sufficiency is computed. From this evaluation, it is verified that the presented approach is useful and effectiveness. [ABSTRACT FROM AUTHOR]

We propose reverse-query mechanism to deliver broadband contents over unstructured overlay network. Due to the nature of scale-free network, newly defined reverse-query message will cover more than 80% of all the clients, with relaying reverse-query message under probability as low as 10%, being effective to reduce the total traffic generated by query propagation. This platform is built on percolation theory to propagate the message and contents, which each agent on P2P client relays reverse-query to randomly selected peers. This can be applied for quasi-broadcast platform on P2P network, and for business application, to flyer delivery over the Internet targeting common attributes such as residential area. [ABSTRACT FROM AUTHOR]

This paper proposes a method for development of adequate requirement specifications in the Plant Control Software (PCSW). Before we propose this method, we have analyzed this domain and developed the components as parameter-customized-style in order to facilitate the customization. In the proposed method, PCSW requirement specification is developed from information that is used to customize components. We applied it to five development cases, and achieved 91[%] of Requirement Coverage and 94 [%] of the Requirement Conformity Rate. This result indicates that proposed method have sufficient capabilities to develop exhaustive and adequate PCSW requirement specification. [ABSTRACT FROM AUTHOR]

For the business communication by email with cellular phones, it has an important weak point. That is to hard to tell to be utterance speed and the pitch of sounds involved in the sentences, because it communicate by letters only. Emoticons are often used to make up for this weak point. This paper describes techniques to predict emotions of sentences in Japanese emails and give an emoticon to end of a sentence automatically. This is achieved by learning information of emotions with emoticons used and analyzing the text of email with cellular phone by collecting and analyzing our corpus of emails. We also examined consistency evaluation with real email sentences input by cellular phones and emoticons automatically generated by this technique. We could get correct answer rate of 87.7%. Keywords: Prediction of emotions, Morpheme analysis, Emoticons. [ABSTRACT FROM AUTHOR]

Large collections of full-text document are now commonly used in automated information retrieval Readers generally identify the subject of a text when they notice specific terms, calledField Association (FA) terms, in that text. Previous researches showed that evidence from passage can improve retrieval results by dividing documents into coherent units with each unit corresponding to a subtopic. Moreover, many current researchers are extracting FA terms candidates from the whole documents to build FA term dictionary automatically. This paper proposes a method for automatically building new FA term dictionary from documents after using passage retrieval. A WWW search engine is used to extract FA terms candidates from passage document corpora. Then, new FA terms candidates in each field are automatically compared with previously determined FA terms dictionary. Finally, new FA terms from extracted term candidates are appended automatically to the existence FA terms dictionary. From experimental results the new technique using passage documents can automatically append about 15% of FA terms from terms candidates to the existence FA term dictionary over the old method. Moreover, Recall and Precision significantly improved by 20% and 32% over the traditional method. The proposed methods are applied to 38,372 articles from the large tagged corpus. [ABSTRACT FROM AUTHOR]

In finance task domain, it is indispensable to get and analyze information as quickly as possible. Analyst's reports are one of the important information in asset management, and these include a large amount of text information. However, it is very difficult to handle text information of analyst's reports, few research and development have been conducted. In [5] and [6] we explored the feasibility to extract valuable knowledge for asset management through text mining using analyst's reports as text data. And we found the effectiveness of keyword information. In this paper we make further research of analyst's reports. From empirical study on the practical data, we have confirmed the effectiveness of using keyword information and numerical information together: (1) the effectiveness of keyword information is different by the direction of change of earning estimate; (2) the keyword of "Upward (or Downward) surprise in forecast" has strong effect to stock price return. [ABSTRACT FROM AUTHOR]

Large collections of full-text document are now commonly used in automated information retrieval. When the stored document texts are long, the retrieval of complete documents may not be in the users' best interest and extract Filed Association (FA) words is not accurate. In such circumstances, efficient and effective retrieval FA words may be obtained by using passage retrieval strategies designed to retrieve text excerpts of varying size in response to statements of user interest. New approaches are described in this study for implementing selective passage retrieval systems, and identifying text passage response to particular user needs. Moreover an automated system is using for extract accurate FAwords from that passage and evaluate the usefulness of the proposed method. From the experimental results, when passage retrieval are accessible leading to the retrieval of additional extracted relevant FA word with corresponding improvements in Recall and Precision. Therefore, Recall and Precision improved by 30% than using whole texts and traditional methods. [ABSTRACT FROM AUTHOR]

With increasing popularity of the Internet and tremendous amount of on-line text, automatic document classification is important for organizing huge amounts of data. Readers can know the subject of many document fields by reading only some specific Field Association (FA) words. Document fields can be decided efficiently if there are many FA words and if the frequency rate is high. This paper proposes a method for automatically building new FA words. A WWW search engine is used to extract FA word candidates from document corpora. New FA word candidates in each field are automatically compared with previously determined FA words. Then new FA words are appended to an FA word dictionary. From the experiential results, our new system can automatically appended around 44% of new FA words to the existence FA word Dictionary. Moreover, the concentration ratio 0.9 is also effective for extracting relevant FA words that needed for the system design to build FA words automatically. [ABSTRACT FROM AUTHOR]

A simple method for reducing undesirable distortion effects of mixed noise filters for digital images is presented. The method is based on a simple 2-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated on a computer. The method can be used with any type of mixed noise filters since its operation is completely independent of the filter. The proposed method is applied to two representative mixed noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of mixed noise filters to effectively reduce their distortion effects. [ABSTRACT FROM AUTHOR]

In this paper we propose a split and merge texture segmentation method. The presented approach is characterised by the introduction of a novel operator, the Local Fuzzy Pattern for texture discrimination, and the employment of a neuro-fuzzy decision support strategy, which supervises the overall split and merge procedure. The effectiveness of the proposed approach is evaluated on a set of artificial and natural texture images. [ABSTRACT FROM AUTHOR]

We proposed the use of Lukasiewicz's operators for lineal image compression. These operators have been applied to the approximation of piecewise linear functions. In this sense we showed two basic piecewise lineal static image compression techniques in which the use of this operators lets to reduce hardware resources. [ABSTRACT FROM AUTHOR]

In this paper we model the concept of "coarseness", typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these "presence degrees", we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set "coarseness" is modelled. Keywords: Image features, texture features, fuzzy texture, visual coarseness. [ABSTRACT FROM AUTHOR]

A motion adaptive algorithm for video de-interlacing is presented in this paper. It is based on a fuzzy inference system, which performs an interpolation between two linear techniques as a function of the motion level. Fuzzy systems with different number of 'if-then' rules have been analyzed and compared in terms of complexity as well as efficiency in de-interlacing benchmark video sequences. Keywords: Video De-interlacing, Motion Adaptive, Fuzzy Inference Systems, Supervised Learning Algorithms. [ABSTRACT FROM AUTHOR]

The correct web site text content must be help to the visitors to find what they are looking for. However, the reality is quite different, many times the web page text content is ambiguous, without meaning and worst, it don't have relation with the topic that is shown as the main theme. One reason to this problem is the lack of contents with concept meaning in the web page, i.e., the utilization of words and sentences that show concepts, which finally is the visitor goal. In this paper, we introduce a new approach for improving the web site text content by extracting Concept-Based Knowledge from data originated in the web site itself. By using the concepts, a web page can be rewrite for showing more relevant information to the eventual visitor. This approach was tested in a real web site, showing its effectiveness [ABSTRACT FROM AUTHOR]

Where customers with different membership and position, use computers as in the university network systems, it often takes much time and efforts for them to cope with the change of the system management. This is because the requirements for the respective computer usage are different in the network and security policies. In this paper, a new destination addressing control system (DACS) scheme for the university network services is proposed. The DACS Scheme performs the network services efficiently through the communication management of a client. As the characteristic of DACS Scheme, only the setup modification is required by a system administrator, when the configuration change is needed in the network server. Then, the setup modification is unnecessary by a customer, which shows a merit for both a system administrator and a customer. This paper describes the instruction and the prototype for DACS Protocol as the implementation of DACS Scheme. Then, the simplicity of the system management in DACS Scheme, is examined from the customer and the system administrator viewpoints. [ABSTRACT FROM AUTHOR]

Text classification is a key technique for handling and organizing text data. The support vector machine(SVM) is shown to be better for the classification among well-known methods. In this paper, the grouping method of the similar words, is proposed for the classification of documents, which is applied to Reuters news and it is shown that the grouping of words has equivalent ability to the Latent Semantic Analysis(LSA) in the classification accuracy. Further, a new combining method is proposed for the classification, which consists of Grouping, LSA followed by the k-Nearest Neighbor classification ( k-NN ). The combining method proposed here, shows the higher accuracy in the classification than the conventional methods of the kNN, and the LSA followed by the kNN. Then, the combining method shows almost same accuracies as SVM. [ABSTRACT FROM AUTHOR]

Object tracking is an important topic in computer vision and image recognition. The probabilistic approach using the particle filter has been recently used for the tracking of moving objects. Based on our trajectory recording system of the soccer scene with multiple video cameras at one view point, we propose the extended approach to increase the tracking robustness and accuracy using the particle filter. The proposed approach makes it possible to pass the necessary particle information using the color histogram and other key factors from one image to the next image, which are taken through the different camera scene with one PC. The performance of the proposed approach is evaluated in the experiments with real video sequence. It is shown that one PC can handle two video images in real-time. [ABSTRACT FROM AUTHOR]

In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The paper describes the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods are inspected in terms of data, problem domains and inner structure and principles. [ABSTRACT FROM AUTHOR]

The problem of social capital in context of the online social networks is presented in the paper. Not only the specific elements, which characterize the single person and influence the individual's social capital like static social capital, activity component, and social position, but also the ways of stimulation of the social capital are described. [ABSTRACT FROM AUTHOR]