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Although ontologies as an important component are widely used for different purpose in different communities and a number of approaches have been reported for developing ontologies, few works have been done to clarify the concept of knowledge science as far as we know. This paper presents a novel attempt to create an ontology characterizing a research program "Technology Creation Based on Knowledge Science" from a Knowledge Science perspective. We address a combination of bottom-up and top-down approaches to ontology creation, which is a first time to put forward a perspective of combining explicit knowledge with tacit, intuitive and experiential knowledge for constructing an ontology. An example of application of this ontology, related to a software tool named adaptive hermeneutic agent (AHA), is also given in the paper. [ABSTRACT FROM AUTHOR]

Knowledge visualization is a creative process, but difficult to formalize. This paper presents a system that is capable of analyzing voluminous citation data and visualizing the result. The system offers visualizations of trends by clustering scientific papers taken from the web (CiteSeer papers). Two methods are implemented: factor analysis and PFNET. An experiment has been carried out with the literature in knowledge management. A deep analysis of current trends in KM is then performed to check the relevance of these results. While the topical content is specific to knowledge engineering, semantic web, and related sub-areas, the approach could be applied to any general topic area in AI. [ABSTRACT FROM AUTHOR]

The aim of this paper is to give theoretical and experimental tools for measuring the driving force in evolving complex networks. First a discrete-time stochastic model framework is introduced to state the question of how the dynamics of these networks depend on the properties of the parts of the system. Then a method is presented to determine this dependence in the possession of the required data about the system. This measurement method is applied to the citation network of high energy physics papers to extract the in-degree and age dependence of the dynamics. It is shown that the method yields close to "optimal" results. [ABSTRACT FROM AUTHOR]

Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification. [ABSTRACT FROM AUTHOR]

Temporal difference (TD) learning is a form of approximate reinforcement learning using an incremental learning updates. For large, stochastic and dynamic systems, however, it is still on open question for lacking the methodology to analyse the convergence and sensitivity of TD algorithms. Meanwhile, analysis on convergence and sensitivity of parameters are very expensive, such analysis metrics are obtained only by running an experiment with different parameter values. In this paper, we utilise the TD(λ) learning control algorithm with a linear function approximation technique known as tile coding in order to help soccer agent learn the optimal control processes. The aim of this paper is to propose a methodology for analysing the performance for adaptively selecting a set of optimal parameter values in TD(λ) learning algorithm. [ABSTRACT FROM AUTHOR]

Description logics (DLs) are a well-known family of knowledge representation (KR) languages. The notation of DLs has the style of a variable-free first order predicate logic. In this paper a diagrammatic representation of the DL $\cal ALC$- based on Peirce's existential graphs - is presented and a set of transformation rules on these graphs provided. As the transformation rules modify the diagrammatic representation of $\cal ALC$ this produces a diagrammatic calculus. Some examples present in the paper illustrate the use and properties of this calculus. [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 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]

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]

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]

The paper proposes the use of the multilayer perceptron model to the problem of detecting ham and spam e-mail patterns. It also proposes an intensive use of data pre-processing and feature selection methods to simplify the task of the multilayer perceptron in classifying ham and spam e-mails. The multilayer perceptron is trained and assessed on patterns extracted from the SpamAssassin Public Corpus. It is required to classify novel types of ham and spam patterns. The results are presented and evaluated in the paper. [ABSTRACT FROM AUTHOR]

In singular models, the Bayes estimation, commonly, has the advantage of the generalization performance over the maximum likelihood estimation, however, its accurate approximation using Markov chain Monte Carlo methods requires huge computational costs. The variational Bayes (VB) approach, a tractable alternative, has recently shown good performance in the automatic relevance determination model (ARD), a kind of hierarchical Bayesian learning, in brain current estimation from magnetoencephalography (MEG) data, an ill-posed linear inverse problem. On the other hand, it has been proved that, in three-layer linear neural networks (LNNs), the VB approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation. In this paper, noting the similarity between the ARD in a linear problem and an LNN, we analyze a simplified version of the VB approach in the ARD. We discuss its relation to the shrinkage estimation and how ill-posedness affects learning. We also propose the algorithm that requires simpler computation than, and will provide similar performance to, the VB approach. [ABSTRACT FROM AUTHOR]

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods. [ABSTRACT FROM AUTHOR]

This paper reports a snapshot of our on-going experiments in which a common target we call a-tiny-island-in-a-huge-lake is explored with different methods ranging from a data-mining technique to an artificial immune system. Our implicit interest is a network intrusion detection, and we assume data floating in the huge lake are normal while ones found on the tiny island are abnormal. Our goal here is twofold. One is to know (i) whether or not it is possible to train a system using just normal data alone. The other is to study (ii) a limit of the size of the detectable area, when we decrease the size of the island eventually shrinking to zero, equivalently so-called a-needle-in-a-haystack which is still an open and worth while tuckling problem. To learn these two issues, a fuzzy rule extraction system - one with fixed triangle/trapezoid membership functions for our first goal, and for the second goal with Gaussian membership functions whose shape is adaptively determined for the second goal, are exploited in this paper. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster. [ABSTRACT FROM AUTHOR]

With the growth of ontology scale and complexity, the query performance of Ontology Repository Management System (ORMS) becomes more and more important. The paper proposes materialized relations technique which speeds up query processing in ORMS by making the implicit derived relations of ontology explicit. Here the selection of materialized relations is a key problem, because the materialized relations technique trades off required inference time against maintenance cost and storage space. However, the problem has not been discussed formally before. So the paper proposes a QSS model to describe the queries set of ontology formally and gives the benefit evaluation model and the selection algorithm of materialized relations based on QSS model. The method in this paper not only considers the benefit in query response of the materialization technique, but also the storage and maintenance cost of it. In the end, an application case is introduced to prove the selection method of materialized relations is effective. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to implement classical conditioning with spiking neurons instead of connectionist neural networks. The neuron model used is a leaky linear integrate-and-fire model with a learning algorithm combining spike-time dependent Hebbian learning and spike-time dependent anti-Hebbian learning. Experimental results show that the major phenomena of classical conditioning, including Pavlovian conditioning, extinction, partial conditioning, blocking, inhibitory conditioning, overshadow and secondary conditioning, can be implemented by the spiking neuron model proposed here and further indicate that spiking neuron models are well suited to implementing classical conditioning. [ABSTRACT FROM AUTHOR]

The performance of a Radial Basis Functions network (RBF) can be increased with the use of an ensemble of RBF networks because the RBF networks are successfully applied to solve classification problems and they can be trained by gradient descent algorithms. Reviewing the bibliography we can see that the performance of ensembles of Multilayer Feedforward (MF) networks can be improved by the use of the two combination methods based on Stacked Generalization described in [1]. We think that we could get a better classification system if we applied these combiners to an RBF ensemble. In this paper we satisfactory apply these two new methods, Stacked and Stacked+, on ensembles of RBF networks. Increasing the number of networks used in the combination module is also successfully proposed in this paper. The results show that training 3 MF networks to combine an RBF ensemble is the best alternative. [ABSTRACT FROM AUTHOR]

Classifier Combination has been investigated as an alternative to obtain improvements in design and/or accuracy for difficult pattern recognition problems. In the literature, many combination methods and algorithms have been developed, including methods based on computational Intelligence, such as: fuzzy sets, neural networks and fuzzy neural networks. This paper presents an evaluation of how different levels of diversity reached by the choice of the components can affect the accuracy of some combination methods. The aim of this analysis is to investigate whether or not fuzzy, neural and fuzzy-neural combination methods are affected by the choice of the ensemble members. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for directly tuning the gaussian kernel matrix for one class learning. The popular gaussian kernel includes a free parameter, σ, that requires tuning typically performed through validation. The value of this parameter impacts model performance significantly. This paper explores an automated method for tuning this kernel based upon a hill climbing optimization of statistics obtained from the kernel matrix. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative approach to parameter optimization of prototype-based learning algorithms that aim to minimize an objective function based on gradient search. The proposed approach is a stochastic optimization method called the Cross Entropy (CE) method. The CE method is used to tackle the initialization sensitiveness problem associated with the original generalized Learning Vector Quantization (GLVQ) algorithm and its variants and to locate the globally optimal solutions. We will focus our study on a variant which deals with a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, highly competitive numerical results on real world data sets are reported. [ABSTRACT FROM AUTHOR]

Multidimensional Scaling algorithms (MDS) are useful tools that help to discover high dimensional object relationships. They have been applied to a wide range of practical problems and particularly to the visualization of the semantic relations among documents or terms in textual databases. The MDS algorithms proposed in the literature often suffer from a low discriminant power due to its unsupervised nature and to the ‘curse of dimensionality'. Fortunately, textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we propose a semi-supervised version of the Torgerson MDS algorithm that takes advantage of this document classification to improve the discriminant power of the word maps generated. The algorithm has been applied to the visualization of term relationships. The experimental results show that the model proposed outperforms well known unsupervised alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary approach to deal with shortcomings on conventional focused crawling systems in semantic web environment. Thereby, meta-evolution strategy for collaboration among multiple crawlers has to be efficiently carried out. It is based on incremental aggregation of partial semantic structures extracted from web resources, which are in advance annotated with local ontologies. To do this, we employ similarity-based matching algorithm, so that fitness function is formulated by summing all possible semantic similarities. As a result, the best mapping condition (i.e., the fitness is maximized) is obtained for efficiently i) reconciling semantic conflicts between multiple crawlers, and ii) evolving semantic structures of web spaces over time. [ABSTRACT FROM AUTHOR]

In this paper, we examine analysis of clusters of labeled samples to identify their underlying hierarchical structure. The key in this identification is to select a suitable measure of dissimilarity among clusters characterized by subpopulations of the samples. Accordingly, we introduce a dissimilarity measure suitable for measuring a hierarchical structure of subpopulations that fit the mixture model. Glass identification is used as a practical problem for hierarchical cluster analysis, in the experiments in this paper. In the experimental results, we exhibit the effectiveness of the introduced measure, compared to several others. [ABSTRACT FROM AUTHOR]

In this paper we present a well designed method that makes use of edge information to extract textual blocks from gray scale document images. It aims at detecting textual regions on heavy noise infected newspaper images and separate them from graphical regions. The algorithm traces the feature points in different entities and then groups those edge points of textual regions. Finally feature based connected component merging was introduced to gather homogeneous textual regions together within the scope of its bounding rectangles. The proposed method can be used to locate text in-group of newspaper images with multiple page layouts. Initial results are encouraging, then they are experimented with considerable number of newspaper images with different layout structures and promising results were obtained. This finds its major application in digital libraries for OCR where information can be of different quality depending on the age of the scanned paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character. [ABSTRACT FROM AUTHOR]

An automated approach for computation of the frontal plane QRS vector and an important observation of its clinical significance is described in this paper. Frontal plane QRS vector is computed from the six frontal plane leads ( Standard leads I, II, III , AVR, AVL and AVF). The R-R interval of each ECG wave is detected by square derivative technique. The baseline or isoelectric level of every ECG wave is determined. After that the net positive or net negative deflection (NQD) of QRS complex is detected. Net positive or net negative deflection in any lead is obtained by subtracting the smaller deflection (+ve or -ve) from the larger deflection (-ve or +ve). An algorithm is developed for computation of the exact angle,amplitude and direction of the frontal plane QRS vector from maximum and minimum NQD. In the present work, the PTB diagnostic ECG database of normal and Myocardial Infarction (MI) subjects is used for computation of the QRS vector. An interesting clinical observation that, the rotation of QRS axis for MI data may significantly detect the region of the infarcted cardiac wall, is reported in this paper. [ABSTRACT FROM AUTHOR]

Automatic tracking of references involves aggregating and synthesizing references through World Wide Web, thereby introducing greater efficiency and granularity to the task of finding publication information. This paper discusses the design and implementation of crawler-based reference tracking system, which has the advantage of online reference filtering. The system automatically analyses the semantic relevance of the reference article by harvesting keywords and their meanings, from title and abstract of the respective article. Indirectly this attempts to improve the performance of the reference database by reducing the articles that are actually being downloaded thereby improving the performance of the system. The number of levels for recursive downloads of reference articles are specified by the user. According to user's interest the system tracks up the references required for the understanding of the seed article, stores them in the databases and projects the information by threshold based view filtering. [ABSTRACT FROM AUTHOR]

This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure. [ABSTRACT FROM AUTHOR]

This paper presents a computer vision guidance system for agricultural vehicles. This system is based on a segmentation algorithm that uses an optimum threshold function in terms of minimum quadratic value over a discriminant based on the Fisher lineal discriminant. This system has achieved not only very interesting results in the sense of segmentation, but it has also guided successfully a vehicle in a real world environment. [ABSTRACT FROM AUTHOR]

Transmission of images and video over unreliable channels produces visual artifacts due to either loss or corruption of bit streams. Error concealment schemes aim at reducing such degradation and improving the visual quality. Error concealment schemes usually conceal errors of a specific type. This points towards the possibility of combining more than one scheme, for efficient error concealment and better visual effects. In this paper, we propose a scheme for error concealment using a combination of watermarking and Projection Onto Convex Sets (POCS). Watermarking an image using the information derived from the host data itself conceals errors initially and those which are prominent even after this stage are concealed through POCS. The proposed method can be used for concealing errors in images and intra coded video by preserving average values through watermarking and edge information through POCS. Simulation results of POCS, watermarking and watermarking with POCS are given in the paper to show the advantage of combining both the methods. [ABSTRACT FROM AUTHOR]

Applications in various domains often lead to very large and frequently high-dimensional data. Successful algorithms must avoid the curse of dimensionality but at the same time should be computationally efficient. Finding useful patterns in large datasets has attracted considerable interest recently. The primary goal of the paper is to implement an efficient Hybrid Tree based clustering method based on CF-Tree and KD-Tree, and combine the clustering methods with KNN-Classification. The implementation of the algorithm involves many issues like good accuracy, less space and less time. We will evaluate the time and space efficiency, data input order sensitivity, and clustering quality through several experiments. [ABSTRACT FROM AUTHOR]

This paper addresses a service-mining technique and applies this technique to improve the services of vehicle service centers. We propose a service-mining system and its data structure to discover the most important services required through analyzing service records, feedback records and the available products. The system can improve the quality of mining automatically by updating mining strategies regularly. [ABSTRACT FROM AUTHOR]

Focusing on the application of Intelligent Security Supervisory Control System, this paper proposes a new human activity recognition approach in which the Background Subtraction and the Time-stepping are averaged by weights to implement the precise extraction of moving human contour. In this way, the incompleteness of the extracting objects contour resulting from the color comparability between the human and the background can be resolved. Moreover, an ant colony clustering algorithm is applied to estimate and classify the body posture. Finally, Discrete Hidden Markov Models is used for human posture training, modeling and activity matching to recognize the human motion. Experiment results have shown that this method gives stable performances and good robustness. [ABSTRACT FROM AUTHOR]

Visual analysis of human motion from video sequences is one of the most active research topics in the field of computer vision. This research has certain practical value and can be widely applied in some places such as: bank,hotel,garage,government department,large public facility. In this paper we present a novel method for judging irregular behavior based on treading track. Firstly, we use an object detection method in which the background subtraction method and the time difference method are averaged by weights to detect moving body, and then we judge whether someone is suspicious or not on the basis of treading track, it improves the judgment ability of irregular behavior recognition and experiment results have shown that this method gives static performances and good robustness. [ABSTRACT FROM AUTHOR]

Formalized ontology model and its semantic consistency checking has become one of the highlights in knowledge engineering research. This paper constructs an ontology model based on description logics. The term consistency checking of this model is divided into four types: term satisfiability checking, term subsumption checking, term equivalence checking and term disjointness checking. Then the proof that the four types of checking can be resolved into subsumption checking of term extension is provided in this paper. Moreover, the ontology term consistency algorithm using inference of description logics is proposed and tested by examples. [ABSTRACT FROM AUTHOR]

The emphasis nowadays on individual information needs and personalization of content as well as the level of complexity involved in some domains necessitates the use of Knowledge Engineering approaches. In particular, product and performance support is a complex and knowledge-intensive domain which despite the advances in the last few years, still suffers from the lack of information and knowledge models. This paper addresses this problem by offering definitions the for the product and performance support domain suggesting a problem-solving approach which uses the proposed knowledge models to adapt existing and construct entirely new solutions. [ABSTRACT FROM AUTHOR]

Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalizing scores based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database and do not consider the retrieval performance. In this paper, we address the problem that in peer to peer information systems and argue that the performance of search engine should also be considered. We also proposed a collection profiling strategy which can discover not only collection content but also retrieval performance. Web-based query classification and two collection fusion approaches based on the collection profiling are also introduced in this paper. Our experiments show that our merging strategies are effective in merging results on uncooperative environment. [ABSTRACT FROM AUTHOR]

The AMP-activated protein kinase (AMPK) acts as a metabolic master switch regulating several intracellular systems. The effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. With increasingly available AMPK regulation data, it is critical to develop an efficient way to analyze the data since this assists in further understanding AMPK pathways. Bayesian networks can play an important role in expressing the dependency and causality in the data. This paper aims to analyse the regulation data using B-Course, a powerful analysis tool to exploit several theoretically elaborate results in the fields of Bayesian and causal modelling, and discover a certain type of multivariate probabilistic dependencies. The identified dependency models are easier to understand in comparison with the traditional frequent patterns. [ABSTRACT FROM AUTHOR]

One of the issues for Web-based learning applications is to adaptively provide personalized instructions for different learning activities. This paper proposes a high level colored timed Petri Net based approach to providing some level of adaptation for different users and learning activities. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

It has been widely recognized that the relative priority of requirements can help developers to resolve inconsistencies and make some necessary trade-off decisions. However, for most distributed development such as Viewpoints-based approaches, different stakeholders may assign different levels of priority to the same shared requirements statement from their own perspectives. The disagreement in the local priorities assigned to the same shared requirements statement often puts developers into a dilemma during inconsistency handling process. As a solution to this problem, we present a merging-based approach to handling inconsistency in the Viewpoints framework in this paper. In the Viewpoints framework, each viewpoint is a requirements collection with local prioritization. Informally, we transform such a requirements collection with local prioritization into a stratified knowledge base. Moreover, the relationship between viewpoints is considered as integrity constraints. By merging these stratified knowledge bases, we then construct a merged knowledge base with a global prioritization, which may be viewed as an overall belief in these viewpoints. Finally, proposals for inconsistency handling are derived from the merged result. The global prioritization as well as the local prioritization may be used to argue these proposals and to help developers make a reasonable trade-off decision on handling inconsistency. [ABSTRACT FROM AUTHOR]

In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method to calculate orientation histograms of n×n subimages partitioning the 2D-camera image with the segmented face. Numerical experiments have been made utilizing the Olivetti Research Laboratory (ORL) database containing 400 images of 40 subjects. Remarkable recognition rates of 98% to 99% were achieved with this extremely simple approach. Keywords: Orientation histograms, Face recognition. [ABSTRACT FROM AUTHOR]

The two key factors to design an ensemble of neural networks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on Stacked Generalization as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of Stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on Stacked Generalization are better than classical combiners. [ABSTRACT FROM AUTHOR]

In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. In particular we compare the classical training which consist of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations. [ABSTRACT FROM AUTHOR]

The paper proposes the use of the multilayer perceptron model to the problem of detecting attack patterns in computer networks. The multilayer perceptron is trained and assessed on patterns extracted from the files of the Third International Knowledge Discovery and Data Mining Tools Competition. It is required to classify novel normal patterns and novel categories of attack patterns. The results are presented and evaluated in the paper. [ABSTRACT FROM AUTHOR]

A novel hierarchical hybrid neural model to the problem of long-term electrical load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets — one on top of the other —, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper. [ABSTRACT FROM AUTHOR]