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In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathematical morphology. We will show that this technique leads to higher quality of the recovered images and, at the same time, it delivers cleaned binary text for OCR applications. The proposed method was applied to books of XIX Century, which were cleaned in a very effective way. [ABSTRACT FROM AUTHOR]

A great deal of research has been devoted in recent years to the designing Fuzzy-Neural Networks (FNN) from input-output data. And some works were also done to analyze the performance of some methods from a rigorous mathematical point of view. In this paper, a new approximation bound for the clustering method, which is employed to design the FNN with the Gaussian Membership Function, is established. It is an improvement of the previous result in which the related approximation bound was somewhat complex. The detailed formulas of the error bound between the nonlinear function to be approximated and the FNN system designed based on the input-output data are derived. [ABSTRACT FROM AUTHOR]

Changes in a speaker's emotion are a fundamental component in human communication. Some emotions motivate human actions while others add deeper meaning and richness to human interactions. In this paper, we explore the development of a computing algorithm that uses audio and visual sensors to recognize a speaker's affective state. Within the framework of Multi-stream Hidden Markov Model (MHMM), we analyze audio and visual observations to detect 11 cognitive/emotive states. We investigate the use of individual modality confidence measures as a means of estimating weights when combining likelihoods in the audio-visual decision fusion. Person-independent experimental results from 20 subjects in 660 sequences suggest that the use of stream exponents estimated on training data results in classification accuracy improvement of audio-visual affect recognition. [ABSTRACT FROM AUTHOR]

Research on e-learner community building has attracted much attention for its effectiveness in sharing the learning experience and resources among geographically dispersed e-learners. While collaborative filtering proves its success as one of the most efficient methods in finding similar users in e-commerce domain, it does meet special challenges in e-learning areas. In this paper, we incorporate multi-agent techniques into collaborative filtering and propose a novel community building scheme. By doing so, we manage to collect useful information from the learner behaviors and thus increase the scalability and flexibility of traditional collaborative filtering methods. The experiment on a standard benchmark shows that our scheme has reasonable community building quality and e-learners can make better recommendations to each other inside the community. [ABSTRACT FROM AUTHOR]

Raster map should be corrected after scanned because of the errors caused by paper map deformation. In the paper, the deficiency of the polynomial fitting method is analyzed. The paper introduces an ANN (Artificial Neural Network) correcting method that utilizes the advantage of its function approximation ability. In the paper, two types of ANNs, BP and GRNN, are designed for the correcting. The comparing experiment is done with the same data by the polynomial fitting and ANN methods, utilizing the MALAB. The experiment results show that the ANN methods, especially the GRNN method, performances far better than the polynomial fitting method does. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The system listens to the product sound by the multimedia technology and the sound features are extracted to detect and predict faults by the neural network. The paper analyzes the reasons of timer faults and the corresponding sound features. Experiments are made in the laboratory to demonstrate the proposed method. The technology is expected to apply in factories in coming years for realizing automatic product test and improving efficiency of product inspection. [ABSTRACT FROM AUTHOR]

This chapter contains papers presented in the workshop on Text Mining and Applications (TeMA 2005), organized in the framework of the Portuguese Association for Artificial Intelligence conference (EPIA). This workshop is aimed at attracting quality papers and enhancing the knowledge in this area. 27 papers were submitted. From these, 9 papers were selected for publication in this Springer volume. These numbers show current importance of this field in AI and suggest that the organization of equivalent events in future EPIA editions should be pursued. First paper works on bilingual lexical acquisition from non parallel corpora, applicable in Machine Translation. Second paper describes work on Text Summarization. Third uses Transformation Based learning for NP Identification applied to Portuguese.Fourth uses linguistic knowledge for passage retrieval and question answering. Fifth describes the use of weakly supervised learning for extraction of semantic patterns. Sixth paper proposes a method for semantic indexing and evaluates it on traditional Information Retrieval tasks. Seventh works on unsupervised language independent extraction of multi-word terms, applicable in multiple domains and evaluates their results for Slovene and English. Eighth paper presents a variant of a known method for Anaphora resolution, adapted to Portuguese. Last paper proposes a stemmer for Brazilian Portuguese. [ABSTRACT FROM AUTHOR]

International workshop on Risk Mining (RM2006) was held in conjunction with the 20th Annual Conference of the Japanese Society for Artificial Intelligence(JSAI2005), Tokyo Japan, June 2005. The workshop aimed at sharing and comparing experiences on risk mining techniques applied to risk detection, risk clarification and risk utilization. In summary, the workshop gave a discussion forum for researchers working on both data mining and risk management where the attendees discussed various aspects on data mining based risk management. [ABSTRACT FROM AUTHOR]

Fisher's Linear Discriminant Analysis (LDA) is a traditional dimensionality reduction method that has been proven to be successful for decades. To enhance the LDA's power for high-dimensional pattern classification, such as face recognition, numerous LDA-extension approaches have been proposed in the literature. This paper proposes a new method that improves the performance of LDA-based classification by simply increasing the number of (sub)-classes through clustering a few of classes of the training set prior to the execution of LDA. This is based on the fact that the eigen space of the training set consists of the range space and the null space, and that the dimensionality of the range space increases as the number of classes increases. Therefore, when constructing the transformation matrix, through minimizing the null space, the loss of discriminative information resulted from this space can be minimized. To select the classes to be clustered, in the present paper, the intraset distance is employed as a criterion and the k-means clustering is performed to divide them. Our experimental results for an artificial data set of XOR-type samples and a well-known benchmark face database of Yale demonstrate that the classification efficiency of the proposed method could be improved. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new algorithm to compute the distance between n-dimensional histograms. There are some domains such as pattern recognition or image retrieval that use the distance between histograms at some step of the classification process. For this reason, some algorithms that find the distance between histograms have been proposed in the literature. Nevertheless, most of this research has been applied on one-dimensional histograms due to the computation of a distance between multi-dimensional histograms is very expensive. In this paper, we present an efficient method to compare multi-dimensional histograms in O(z2), where z represents the number of bins. [ABSTRACT FROM AUTHOR]

The SPAWAR Systems Center (SSC San Diego) of the U.S. Navy hosted two workshops on Active Conceptual Modeling of Learning (ACM-L). The first workshop was held at SSC San Diego on May 10-12, 2006 to introduce the Science &Technology (S&T) Initiative and identify a Research and Development agenda for the technology development investigation. Eleven invited researchers in Conceptual Modeling presented position papers on the proposed S&T Initiative. The second workshop was held on November 8, 2006 at the 25th International Conference on Conceptual Modeling, ER 2006, 6-9 November 2006, in Tucson, Arizona. Complementary to the May Workshop, the November workshop was a forum for the international researchers and practitioners to present their papers as a result of a call for papers and to exchange ideas from various perspectives of the subject. This paper describes research issues identified by participants from the two ACM-L workshops. [ABSTRACT FROM AUTHOR]

The tumor segmentation in Breast MRI image is difficult due to the complicated galactophore structure. The work in this paper attempts to accurately segment the abnormal breast mass in MRI(Magnetic resonance imaging) Images. The ROI (Region of Interest) is segmented using a novel DP (Dynamic Programming) based optimal edge detection technique. DP is an optimal approach in multistage decision-making. The method presented in this paper processes the object image to get the minimum cumulative cost matrix combining with LUM nonlinear enhancement filter, Gaussian preprocessor, non-maximum suppression and double-threshold filtering, and then trace the whole optimal edge. The experimental results show that this method is robust and efficient on image edge detection and can segment the breast tumor area more accurately. [ABSTRACT FROM AUTHOR]

This paper presents a gradient direction edge enhancement based contour (GDEEBC) detector to segment the nucleus and cytoplasm from each cervical smear image. In order to eliminate noise from the image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noise but still preserve the edge sharpness of an object. In addition, a bi-group enhancer is proposed to make a clear-cut separation for the pixels lying between two objects. Finally, a gradient direction (GD) enhancer is presented to suppress the gradients of noise and to brighten the gradients of object contours as well. The experimental results show that all the techniques proposed above have impressive performances. In addition to cervical smear images, these proposed techniques can also be utilized in object segmentation of other types of images. [ABSTRACT FROM AUTHOR]

This paper presents a contribution to programming by human demonstration, in the context of compliant motion task specification for sensor-controlled robot systems that physically interact with the environment. One wants to learn about the geometric parameters of the task and segment the total motion executed by the human into subtasks for the robot that can each be executed with simple compliant motion task specifications. The motion of the human demonstration tool is sensed with a 3D camera, and the interaction with the environment is sensed with a force sensor in the human demonstration tool. Both measurements are uncertain, and do not give direct information about the geometric parameters of the contacting surfaces, or about the contact formations encountered during the human demonstration. The paper uses a Bayesian Sequential Monte Carlo method (also known as a particle filter) to do the simultaneous estimation of the contact formation (discrete information) and the geometric parameters (continuous information). The simultaneous contact formation segmentation and the geometric parameter estimation are helped by the availability of a contact state graph of all possible contact formations. The presented approach applies to all compliant motion tasks involving polyhedral objects with a known geometry, where the uncertain geometric parameters are the poses of the objects. The approach has been verified in real world experiments, in which it is able to discriminate in realtime between 245 different contact formations of the contact state graph. [ABSTRACT FROM AUTHOR]

The major purpose of the design of distributed databases is to improve system performance and to increase system reliability. Fragmentation and allocation play important roles in the development of a cost-efficient system. This paper addresses the problem of fragmentation in the context of complex value databases, which cover the common aspects of object-oriented databases, object-relational databases and XML. In this paper, we present a cost-based approach for horizontal and vertical fragmentation. Our approach is based on a cost model that takes the structure of complex value databases into account. [ABSTRACT FROM AUTHOR]

The paper describes a collaborative assistance approach with spatio-temporal planning, which requires user's active participation in the problem solving task. The proposed collaborative assistance system operates on a region-based representation structure, which allows for partial specification of constraints at different levels of granularity. Weakly specified constraints contribute on the one hand to high computational complexity when generating alternative solutions and on the other hand to large solution spaces. The paper introduces Partial Order, Neighboring Regions and Partial Order of Neighboring Regions heuristics, which allow for pruning of significant parts of the search space, and produce hierarchical structuring of the solution space. Resulting hierarchical organization of the solution space reflects human mental processing of geographic information. To reduce cognitive load during observation of solution space, filtering of certain aspects, set-oriented structuring and case-based reasoning approaches are introduced. [ABSTRACT FROM AUTHOR]

Intelligent vehicle systems need to distinguish which objects are moving and which are static. A static concrete wall lying in the path of a vehicle should be treated differently than a truck moving in front of the vehicle. This paper proposes a new algorithm that addresses this problem, by providing dense dynamic depth information, while coping with real-time constraints. The algorithm models disparity and disparity rate pixel-wise for an entire image. This model is integrated over time and tracked by means of many pixel-wise Kalman filters. This provides better depth estimation results over time, and also provides speed information at each pixel without using optical flow. This simple approach leads to good experimental results for real stereo sequences, by showing an improvement over previous methods. [ABSTRACT FROM AUTHOR]

Data Integration in Peer-to-Peer (P2P) systems is concerned with the ability of physically connect autonomous sources (peer) for sharing and reuse information and for the creation of new information from existing one. In a P2P system a query can be posed to any peer and the answer is provided by integrating locally stored data with data provided from its neighbors. Anyhow, while collecting data for answering queries, imported data may corrupt the local database due to the violation of some integrity constraint, therefore inconsistencies have to be managed. This paper contributes to the proposal of a logic based framework for data integration and query answering in a Peer-to-Peer environment. It is based on [11,12] in which the Preferred Weak Model Semantics, capturing a different perspective for P2P data integration, has been proposed: just data not violating integrity constraints are exchanged among peers by using mapping rules. The motivation of this work stems from the observation that the complexity of computing preferred weak models in [11,12] does not let the approach to be implemented in practical applications. Therefore, a more pragmatic solution seems to be desirable for assigning semantics to a P2P system. First, the paper proposes a rewriting technique that allows modeling a P2P system, ${\mathcal {PS}}$, as a unique logic program, Rewt(${\mathcal {PS}}$), whose stable models correspond to the preferred weak models of ${\mathcal {PS}}$. Then, it presents the Well Founded Model Semantics, that allows obtaining a deterministic model whose computation is polynomial time. This is a (partial) stable model obtained by evaluating with a three-value semantics a logic program obtained from Rewt(${\mathcal {PS}})$. Finally, the paper provides results on the complexity of answering queries in a P2P system. [ABSTRACT FROM AUTHOR]

The primary aim of temporal databases is to offer a common framework to those DB applications that need to store or handle different types of temporal data from a variety of sources since they allow the concept of time to be unified from the point of view of meaning, representation and manipulation. Although at first sight the incorporation of time into a DB might appear to be a direct and even simple task, it is, however, quite complex because not only must new structures and specific operators be included but the semantics of classical manipulation sentences (insert, update or delete) must be changed when temporal data are present. In addition, temporal information is not always as precise as desired since it is affected by imprecision due to the use of natural language or to the nature of the source. In this paper, we deal with the problem of the update and query operations when time is expressed by means of a fuzzy interval of dates. Throughout the text, we will see how the delete and insert operations are particular cases of the update process and will therefore be implicitly presented in the paper. [ABSTRACT FROM AUTHOR]

In medical clinical trials, overall trial results are highlighted in the abstracts of papers/reports. These results are summaries of underlying statistical analysis where most of the time normal distributions are assumed in the analysis. It is common for clinicians to focus on the information in the abstracts in order to review or integrate several clinical trial results that address the same or similar medical question(s). Therefore, developing techniques to merge results from clinical trials based on information in the abstracts is useful and important. In reality information in an abstract can either provide sufficient details about a normal distribution or just partial information about a distribution. In this paper, we first propose approaches to constructing normal distributions from both complete and incomplete statistical information in the abstracts. We then provide methods to merge these normal distributions (or sampling distributions). Following this, we investigate the conditions under which two normal distributions can be merged. Finally, we design an algorithm to sequence the merging of trials results to ensure that the most reliable trials are considered first. [ABSTRACT FROM AUTHOR]

This paper discusses the mechanical design and simulation of a novel omnidirectional wheel based on Reuleaux-triangles. The main feature of our omniwheel is that the point of contact of the wheel with the floor is always kept at the same distance from the center of rotation by mechanical means. This produces smooth translational movement on a flat surface, even when the profile of the complete wheel assembly has gaps between the passive rollers. The grip of the wheel with the floor is also improved. The design described in this paper is ideal for hard surfaces, and can be scaled to fit small or large vehicles. This is the first design for an omnidirectional wheel without circular profile, yet capable of rolling smoothly on a hard surface. [ABSTRACT FROM AUTHOR]

This paper shows how multi-dimensional functions, describing the operation of complex equipment, can be learned. The functions are points in a shape space, each produced by morphing a prototypical function located at its origin. The prototypical function and the space's dimensions, which define morphological operations, are learned from a set of existing functions. New ones are generated by averaging the coordinates of similar functions and using these to morph the prototype appropriately. This paper discusses applying this approach to learning new functions for components of gas turbine engines. Experiments on a set of compressor maps, multi-dimensional functions relating the performance parameters of a compressor, show that it more accurately transforms old maps, into new ones, than existing methods. [ABSTRACT FROM AUTHOR]

During the early stages of architectural training, tangibility plays an important role in developing spatial awareness. In such contexts, tangible user interfaces are believed to provide a significant advantage as they combine the affordances of both the physical and the digital world. We introduce CubeExplorer, a hybrid 3D conceptual aid that combines physical interaction and digital modeling in an effort to complement conventional architectural space-training tools (such as physical materials and digital CAD programs). Using a digital pen as an input mechanism, CubeExplorer lets users perform subtractive 3D geometric operations on a simple paper based cube model while observing the resulting model on a display. The tangibility of the model simplifies navigation and command execution, while the digital interface makes it easy for users to explore multiple alternative designs. To evaluate the potential of such an approach, we conducted a user study in a normal classroom environment where students were provided with physical (wooden block), hybrid (CubeExplorer), and virtual (FormZ) interfaces to complete the same assignment. Our evaluation showed that CubeExplorer combined the advantages of both digital and tangible media. The advantages of CubeExplorer over digital media were substantiated in a follow-up study comparing CubeExplorer and SketchUp in a similar building task. [ABSTRACT FROM AUTHOR]

Ubiquitous interaction places the user in the centre of dynamic configurations of technology, where work not necessarily is performed through a single personal computer, but supported by a multiplicity of technologies and physical devices. This paper presents an activity-theoretically based framework for analyzing ubiquitous substitution, i.e. a set of mediators that are or can be continuously substituted with the purpose of highlighting expected and indented uses, and the conflicts encountered when attempting substitution between them. The paper develops a four-leveled analysis of such mediators, and point towards a minimalist approach to design of ubiquitous interaction. [ABSTRACT FROM AUTHOR]

Authoring-by-Aggregation is an authoring paradigm for creating new Learning Resources by composing several smaller ones. However finding suitable Learning Resources that can be reused in a particular course is a time-consuming task. The user has to query a repository and review the resulting list of Learning Resources whether they are applicable. In this paper we present a new method for narrowing the result set by taking the aggregation context into account. Context features are used as additional query attributes, leading to more precise query results. This paper also presents an improved Authoring-by-Aggregation process and a corresponding implementation, which facilitates the creation of new Learning Resources. [ABSTRACT FROM AUTHOR]

Recognizing that trust states are mental states, this paper presents a formal analysis of the dynamics of trust in terms of the functional roles and representation relations for trust states. This formal analysis is done both in a logical framework and in a mathematical framework based on integral and differential equations. Furthermore, the paper presents formal specifications of a number of relevant dynamic properties of trust. The specifications provided were used to perform automated formal analysis of empirical and simulated data from two case studies, one involving two experiments with humans, and one involving simulation experiments in the context of an economic game. [ABSTRACT FROM AUTHOR]

The automatic processing and management of XML-based data are ever more popular research issues due to the increasing abundant use of XML, especially on the Web. Nonetheless, several operations based on the structure of XML data have not yet received strong attention. Among these is the process of matching XML documents with XML grammars, useful in various applications such as documents classification, retrieval and selective dissemination of information. In this paper, we propose an algorithm for measuring the structural similarity between an XML document and a Document Type Definition (DTD) considered as the simplest way for specifying structural constraints on XML documents. We consider the various DTD operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes. Our approach is based on the concept of tree edit distance, as an effective and efficient means for comparing tree structures, XML documents and DTDs being modeled as ordered labeled trees. It is of polynomial complexity, in comparison with existing exponential algorithms. Classification experiments, conducted on large sets of real and synthetic XML documents, underline our approach effectiveness, as well as its applicability to large XML repositories and databases. [ABSTRACT FROM AUTHOR]

In this paper, selecting line segments as matching features in stereo vision, the orientation difference (O.D.) of the line segments is more deeply evaluated than the previous studies and two new constraints i.e. ordering and collinearity are proposed. The O.D. was supposed to be used for an indoor application, reducing candidates without elimination of the actual matches, especially if the line is horizontal or vertical with respect to the floor. The findings of this paper are as follows: 1) Applying a threshold on O.D. would result in a missing probability for the actual matches and this probability can be calculated for any given threshold, 2) The upper limit of O.D. for horizontal and vertical lines is a function of geometric parameters of the system, 3) An optimal tilt angle, whose results is the minimum upper limit for O.D., can be computed, 4) for disambiguation process, ordering and collinearity constraints are proposed. These are applied in a matching algorithm and their effectiveness is investigated on real stereo images. [ABSTRACT FROM AUTHOR]

In this paper, the line segment tracking is considered for an imaging system with translational motions (T.M.), rotational motions (R.M.) and arbitrary motions. Assuming a CCD camera with an arbitrary tilt angle installed on a mobile robot equipped with odometry system, to reduce the search space in the correspondence problem, two constraints are developed. These constraints are location and orientation differences (O.D.) for line segments in consecutive images. The findings of this paper include: 1)The development of the effect of camera tilt on the location constraint for T.M., 2)Illustrating that the upper bound of O.D. for both horizontal and vertical lines with respect to the floor, is a function of tilt in T.M., which can considerably be reduced and thus providing a tight constraint, 3)The development of the location constraint for R.M., 4) The development of the PDF and upper bound of O.D. for R.M., 5) The development of the location and O.D. constraint for arbitrary motions. Furthermore, the efficiency of these developed constraints in a line tracking algorithm was verified. [ABSTRACT FROM AUTHOR]

This paper presents a new description of image edge in the form of asymptote equation utilizing knowledge of differential geometry, and modifies a popular parlance in the traditional image processing. This paper develops the gradient concept and defines a generalized grads operator. The operator is a novel nonlinear transform, which inherits the strongpoint of noise suppression from the classical Gaussian differential filter and has a better edge extraction function. The experimental results show the proposed edge detection algorithm is powerful and effective. [ABSTRACT FROM AUTHOR]

The paper proposes a method to control the motion orbit of a cartoon object based on chaos theory. We first propose an adaptive delaying feedback controlling method which can control the Lorenz system to generate periodic, quasi-periodic and chaotic orbits. Then we apply our method to the topic of cartoon object motion controlling. It can enrich the motion type of the object thus enhances the visual effect of the cartoon video. The experiments validate the controlling results of the proposed method. Our research leads to a new area in which the chaotic theory is applied in the field of multimedia processing. [ABSTRACT FROM AUTHOR]

A Personal Assistant (PA) agent is a software agent capable of helping people to handle tasks in their workplace. The paper proposes a memory mechanism for personal assistant agents in order to enhance agent intelligence while working with the user or with other agents. Inspired by a case memory model in the domain of Case-Based Reasoning (CBR), this paper endows PA agents with a case memory mechanism, which results in improved PA agents: MemoPAs. We present the memory mechanism of MemoPA in detail, and report a first implementation of the method. Finally, future work is outlined for improving the memory mechanism. [ABSTRACT FROM AUTHOR]

A top-down experimental procedure for defect type recognition of epoxy-resin transformers by Partial Discharge (PD) is proposed. Most of the PD detection methods could be performed only at the shutdown period of equipments. By using Acoustic Emission (AE), the real-time and online detection could be reachable. Therefore, this paper conducted high voltage test of pre-faulty transformers and measured those PD signals for recognition needed. Afterward, the selected features that proposed in this paper can be extracted from these collected PD signals. According to these features, effective identification of their faulty types can be done using the proposed particle swarm optimization combined with neural network. Finally, with a view to apply in field, this research adds different noise levels to distort the original data. These distorted data are entered for subsequent testing. Research shows encouraging results that with 30% noise per discharge count, an 80% successful recognition rate can be achieved. [ABSTRACT FROM AUTHOR]

Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Because there is little research in solving the combinatorial optimization problem (COP) by EM, the paper attempts to employ the random-key concept enabling EM to solve COP in single machine scheduling problem. We present a hybrid algorithm that combines the EM methodology and genetic operators to obtain the best/optimal schedule for this single machine scheduling problem, which attempts to achieve convergence and diversity effect when they iteratively solve the problem. The objective in our problem is minimization of the sum of earliness and tardiness. This hybrid algorithm was tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm. [ABSTRACT FROM AUTHOR]

Upon close examination of a set of industrial data from a large scale power plant, time varying behavior are discovered. If a fixed model is applied to monitor this process, false alarms will be inevitable. This paper suggests the use of adaptive models to cope with such situation. A recently proposed technique, fast algorithm for Moving Window Principal Component Analysis (MWPCA) was employed because of its following strength: (i) the ability in adapting process changes, (ii) the conceptual simplicity, and (iii) its computational efficiency. Its advances in fault detection is demonstrated in the paper by comparing with the conventional PCA. In addition, this paper proposed to plot the scaled variables in conjunction with MWPCA for fault diagnosis, which is proved to be effective in this application. [ABSTRACT FROM AUTHOR]

PVM (Parallel virtual machine) library is a tool which used processes large amounts of data sets. This paper wants to achieve a high performance solution that exploits PVM library and parallel computers to solve ICA (Independent Component Analysis) problem. The paper presents parallel power ICA implementations to decomposition data sets. Power iteration (PI) is an algorithm for independent component analysis, which has some desired features. It has higher performance and data capacity than current sequential implementations. This paper, we show the power iteration algorithm which learning updating is in the form of matrix transformation . From power iteration algorithm, we develop parallel power iteration algorithm and implement parallel component decomposition solution. At last, experimental results, analysis and future plans are presented. [ABSTRACT FROM AUTHOR]

In this paper, global exponential stability of Cohen-Grossberg neural networks with reaction-diffusion and Dirichlet boundary conditions is considered by using an approach based on the delay differential inequality and the fixed-point theorem. Some sufficient conditions are obtained to guarantee that the reaction-diffusion Cohen-Grossberg neural networks are globally exponentially stable. The results presented in this paper are the improvement and extension of the existed ones in some existing works. [ABSTRACT FROM AUTHOR]

This paper analyzes global convergence and learning parameters of the back-propagation algorithm for quadratic functions. Some global convergence conditions of the steepest descent algorithm are obtained by directly analyzing the exact momentum equations for quadratic cost functions. In addition, in order to guarantee the convergence for a given learning task, the method is obtained to choose the proper learning parameters. The results presented in this paper are the improvement and extension of the existed ones in some existing works. [ABSTRACT FROM AUTHOR]

The automatic relevance determination (ARD) shows good performance in many applications. Recently, it has been applied to brain current estimation with the variational method. Although people who use the ARD tend to pay attention to one benefit of the ARD, sparsity, we, in this paper, focus on another benefit, generalization. In this paper, we clarify the generalization error of the ARD in the case that a class of prior distributions is used, and show that good generalization is caused by singularities of the ARD. Sparsity is not observed in that case, however, the mechanism that the singularities provide good generalization implies the mechanism that they also provide sparsity. [ABSTRACT FROM AUTHOR]

This paper presents iris recognition for personal identification using neural networks. Iris recognition system consists of localization of the iris region and generation of data set of iris images and then iris pattern recognition. One of the problems in iris recognition is fast and accurate localization of the iris image. In this paper, fast algorithm is used for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement it is represented by a data set. Using this data set a neural network is applied for the classification of iris patterns. Results of simulations illustrate the effectiveness of the neural system in personal identification. [ABSTRACT FROM AUTHOR]

This article deals with the identification of hinging hyperplane models. This type of non-linear black-box models is relatively new, and its identification is not thoroughly examined and discussed so far. They can be an alternative to artificial neural nets but there is a clear need for an effective identification method. This paper presents a new identification technique for that purpose based on a fuzzy clustering technique called Fuzzy c-Regression Clustering. To use this clustering procedure for the identification of hinging hyperplanes there is a need to handle restrictions about the relative location of the hyperplanes: they should intersect each other in the operating regime covered by the data points. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning of the operating region of one local linear model resuling in a binary regression tree. Hence, this paper proposes a new algorithm for the identification of tree structured piecewise linear models, where the branches correspond to linear division of the operating regime based on the intersection of two local linear models. The effectiveness of the proposed model is demonstrated by a dynamic model identification example. [ABSTRACT FROM AUTHOR]

The www is a dynamic environment and it is difficult to capture the user preferences and interests without interfering with the normal activity of the user. Hence to improve user searching in the World Wide Web, we have proposed a personalized search system that supports user searches by learning about user preferences and by observing responses to prior search experiences aided by a new index called the User Conceptual Index (UCI). This paper models every user's search behavior as a User's Search Behavior (USB) Graph. The main focus of this paper is the analysis of the USB graph and the redesign of the UCI using the results arrived from the analysis. [ABSTRACT FROM AUTHOR]

WordNet is a semantic lexicon for the English language and many countries have been developing their own WordNet. Almost, all of the WordNets are manually built and unfortunately these WordNets are not verified and are being used in many knowledge-based applications. In this paper we aimed at the clustering based verification of a manually built lexical taxonomy WordNet, namely the Korean WordNet, U-WIN. For this purpose two kinds of clustering methods are used: K-Means approach and ICA based approach. As a result the ICA based approach gives better result, and it shows very effective characteristic for extracting features. [ABSTRACT FROM AUTHOR]

With water resource allocation criteria, the three main components are economic, ecological and sociological aspects, which are often conflicting with each other. Water resource allocation problem is analyzed via the development of a multi-criteria decision support system. The main techniques used are (1) multi criteria decision making (MCDM),(2) geographic information system (GIS) (3) multi-regional computable general equilibrium (CGE) (4) Expert System (ES). The Decision Support System (DSS) proposed in this paper is an effective combination of GIS, CGE, ES, where the three modules have the joint interface and are connected by middlewares. The above three modules are united as a whole Multi-criteria Decision Support System of Water Resource (WRMCDSS). [ABSTRACT FROM AUTHOR]

Online advertisement based on the content becomes the new model as the development of web advertising, and choosing a suitable advertisement which is related to the content of a webpage turns to be the core problem. This paper presents a semantic approach, with a goal of achieving webpage-advertisement matching accurately. The method is implemented and tested, and the result shows that the proposed algorithm is promising. [ABSTRACT FROM AUTHOR]

Study of semantics in the context of Geographic Information Systems (GISs) usually focuses on association of meaning with spatial data that constitute the input to these systems. The goal is to create new data models that enable richer interaction with GISs. In this paper we widen the perspective of such studies and explore the implications of associating semantics with map styles that are present implicitly in the output of GISs. Traditionally in computer science style of rendering/presenting data has been viewed as extraneous information that does not add semantic value to data. In GIS however, several styles of rendering maps are possible, and these styles are often motivated by functionality. Thus a map style has a strong association with meaning. In this paper we explore the methods of associating semantics with the style of a map. We discuss the various levels at which semantic associations with map styles can be established, and how this lead to creation of new map rendering styles that exhibit coherence in visualization of spatial information. [ABSTRACT FROM AUTHOR]

Recent solutions to object classification have focused on the decomposition of objects into representative parts. However, the vast majority of these methods are based on single visual cue measurements. Psychophysical evidence suggests that humans use multiple visual cues to accomplish recognition. In this paper, we address the problem of integrating multiple visual information for object recognition. Our contribution in this paper is twofold. First, we describe a new probabilistic integration model of multiple visual cues at different spatial locations across the image. Secondly, we use the cue integration framework to classify images of objects by combining two-dimensional and three-dimensional visual cues. Classification results obtained using the method are promising. [ABSTRACT FROM AUTHOR]

This paper highlights key topic areas to be discussed the authors in a panel format during the Augmented Cognition thematic area paper session: "Augmented Cognition Lessons Learned and Future Directions for Enabling ‘Anyone, Anytime, Anywhere' Applications". The term "killer app" has been part of the vernacular in the commercial computer software and electronic devices industry to refer to breakthrough technologies [2]. A "killer app" generally emerges with the development of related technologies that extends over some time and involves numerous variations on a basic concept. Hypotheses may be offered with respect to the conditions that will be needed to enable a similar situation with augmented cognition technologies. This paper and resulting panel session will address the numerous concepts that have emerged from the augmented cognition field to date and postulate how and when this field's first "killer app" may emerge (e.g., 5, 10, 15, or more years from now). [ABSTRACT FROM AUTHOR]

Inspired by human's remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper "Fuzzy Sets" [1] and extended his work with his 2005 paper "Toward a Generalized Theory of Uncertainty (GTU)—An Outline" [2]. Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi's A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s. [ABSTRACT FROM AUTHOR]

Recent advances in data and knowledge engineering have emphasized the need for formal concept analysis (fca) tools taking into account structured data. There are a few adaptations of the classical fca methodology for handling contexts holding on complex data formats, e.g. graph-based or relational data. In this paper, relational concept analysis (rca) is proposed, as an adaptation of fca for analyzing objects described both by binary and relational attributes. The rca process takes as input a collection of contexts and of inter-context relations, and yields a set of lattices, one per context, whose concepts are linked by relations. Moreover, a way of representing the concepts and relations extracted with rca is proposed in the framework of a description logic. The rca process has been implemented within the Galicia platform, offering new and efficient tools for knowledge and software engineering. [ABSTRACT FROM AUTHOR]