Search

This paper presents a novel application of a semantic annotation system, named Cerno, to analyze research publications in electronic format. Specifically, we address the problem of providing automatic support for authors who need to deal with large volumes of research documents. To this end, we have developed Biblio, a user-friendly tool based on Cerno. The tool directs the user's attention to the most important elements of the papers and provides assistance by generating automatically a list of references and an annotated bibliography given a collection of published research articles. The tool performance has been evaluated on a set of papers and preliminary evaluation results are promising. The backend of Biblio uses a standard relational database to store the results. [ABSTRACT FROM AUTHOR]

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBASCuBA: Subspace Clustering Based Analysis.) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. [ABSTRACT FROM AUTHOR]

Privacy-preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix this paper proposes a novel privacy-preserving classification mining algorithm which suits all data types, arbitrary probability distribution of original data, and perturbing all attributes (including label attribute). Experimental results demonstrate that decision tree built using this algorithm on perturbed data has comparable classifying accuracy to decision tree built using un-privacy-preserving algorithm on original data. [ABSTRACT FROM AUTHOR]

We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81. [ABSTRACT FROM AUTHOR]

In this paper we present our work about personalized search in digital libraries. Unlike other researches which use content-based methods, we focus on citation-based methods for this purpose. We propose a practical approach to estimate the co-citation relatedness between scientific papers using the Google search engine. We conducted some experiments to evaluate performance of different citation-based methods. The experimental results show that our approach is promising and applicable for personalized search in digital libraries. [ABSTRACT FROM AUTHOR]

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

Typically, personalized information recommendation services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. We present an approach based on Word Sense Disambiguation (WSD) for the extraction of user profiles from documents. This approach relies on a knowledge-based WSD algorithm, called JIGSAW, for the semantic indexing of documents: JIGSAW exploits the WordNet lexical database to select, among all the possible meanings (senses) of a polysemous word, the correct one. Semantically indexed documents are used to train a naïve Bayes learner that infers "semantic", sense-based user profiles as binary text classifiers (user-likes and user-dislikes). Two empirical evaluations are described in the paper. In the first experimental session, JIGSAW has been evaluated according to the parameters of the Senseval-3 initiative, that provides a forum where the WSD systems are assessed against disambiguated datasets. The goal of the second empirical evaluation has been to measure the accuracy of the user profiles in selecting relevant documents to be recommended. Performance of classical keyword-based profiles has been compared to that of sense-based profiles in the task of recommending scientific papers. The results show that sense-based profiles outperform keyword-based ones. [ABSTRACT FROM AUTHOR]

Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm -ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine-CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests. [ABSTRACT FROM AUTHOR]

The tenth year of the annual International Conference on Research in Computational Biology (RECOMB) provides an opportunity to reflect on its history. RECOMB has been held across the world, including 6 different countries spanning 3 continents (Table 1). Over its 10 year history, RECOMB has published 373 papers and 170 individuals have served on its various committees. While there are many new faces in RECOMB each year, a significant number of researchers have participated over many years forming the core of the RECOMB community. [ABSTRACT FROM AUTHOR]

Organic computing is an upcoming research area with strong relationships to the ideas and concepts of agent-based systems. In this paper, we therefore will have a closer look at agent systems, organic computing systems (as well as autonomic computing systems) and state commonalities and divergences between them. We then propose a common view on these technologies and show, how they can benefit from each other with regard to software engineering. [ABSTRACT FROM AUTHOR]

Capturing and understanding mathematics from print form is an important task in translating written mathematical knowledge into electronic form. While the problem of syntactically recognising mathematical formulas from scanned images has received attention, very little work has been done on semantic validation and correction of recognised formulas. We present a first step towards such an integrated system by combining the Infty system with a semantic analyser for matrix expressions. We applied the combined system in experiments on the semantic analysis of matrix images scanned from textbooks. While the first results are encouraging, they also demonstrate many ambiguities one has to deal with when analysing matrix expressions in different contexts. We give a detailed overview of the problems we encountered that motivate further research into semantic validation of mathematical formula recognition. [ABSTRACT FROM AUTHOR]

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]

Today the proliferation of the availability of the information of scientific events on the Web has created the necessity to offer a quickly access to up-to-date information about the quality of these events. This requirement demands for (semi) automatic tools to speedily provide this information. The human-performed activity of the information quality evaluation is extremely time consuming and easily leads to failures. The application OntoQualis here described was motivated to support the quality evaluation of Scientific Conferences, in the Computer Science area, based on the graduated programs evaluation protocol of the Brazilian agency CAPES. The evaluation mechanism is specified in the QUALIS document specifically designed to assess journals and conferences ranking. This paper presents a brief vision of the ongoing process of domain analysis and ontology prototyping aiming to classify Scientific Conferences: the OntoQualis project. Some results of OntoQualis preliminary evaluation have shown a satisfactory classification level in comparison with CAPES-QUALIS ranking. [ABSTRACT FROM AUTHOR]

Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers' and ‘authors' exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers'), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems. [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]

Nowadays amount of information on Internet is dramatically increasing. The ability of facilitating users to achieve useful information is more and more important for search field. CDSE, a model for the domain-based intelligent search engine is proposed in this paper. The model can help users to retrieve what they need by combining text classification with keywords extraction. Several algorithms that use key technologies are proposed, such as statistics, data mining and agents. Then a new criterion named ranking error is contributed to solve the problem of evaluation ranking inefficiency in traditional performance evaluation methodologies. The experimental results indicate that the proposed model can effectively improve retrieval precision and solve the problem of relevant document ranking behind in current search engine. [ABSTRACT FROM AUTHOR]

Request Processing Systems should exhibit predictable behavior by guaranteeing Quality-of-Service parameters. One of the important requirements of predictability is that requests should have maximum acceptable response time thresholds, denoted as deadlines in this paper. In order to provide QoS, the system should try to guarantee these deadlines. This way, it is desirable to use a load-balancing algorithm that tries to both maximize the throughput and minimize the missed deadlines. Assuming that all requests durations are known a priori, this paper shows, with the help of a simulator, how the control over the big tasks plays a crucial role in the pursuit of the objective. Finally, this paper presents a derived form of the traditional Least-Work-Remaining algorithm, named Task Assignment by Isolating Big Tasks, that proved to be a better alternative in such scenarios. [ABSTRACT FROM AUTHOR]

UML is being increasing used for the analysis and design of Web Information Systems. At the same time, many XML-based languages are cornerstones in the development of this kind of system. As a consequence of the predominance of these languages, there are many works in the literature devoted to exploring the relationships between UML and XML. In this paper we present a survey of current approaches to the transformation of UML models into XML schemas. The study is focused on the case of transformation of UML class diagrams to XML schemas, since we have not found any proposal regarding other kinds of UML diagrams. [ABSTRACT FROM AUTHOR]

Reading and literature exploration are important tasks of scientific research. However, conventional retrieval systems provide limited support for these tasks by concentrating on identifying relevant materials. New generation systems should provide additional support functionality by focusing on analyzing and organizing the retrieved materials. A framework of literature exploration support systems is proposed. Techniques of granular computing are used to construct granular knowledge structures from the contents, structures, and usages of scientific documents. The granular knowledge structures provide a high level understanding of scientific literature and hints regarding what has been done and what needs to be done. As a demonstration, we examine granular knowledge structures obtained from an analysis of papers from two rough sets related conferences. [ABSTRACT FROM AUTHOR]

TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among N cities and turns back to a staring city. At this time, the condition is to visit N cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NP-Hard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods. Keywords: Genetic Algorithm, GA, Optimization, Initialization. [ABSTRACT FROM AUTHOR]

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated. [ABSTRACT FROM AUTHOR]

It is well known that connectivity analysis of linked documents provides significant information about the structure of the document space for unsupervised learning tasks. However, the ability to identify distinct clusters of documents based on link graph analysis is proportional to the density of the graph and depends on the availability of the linking and/or linked documents in the collection. In this paper, we present an information theoretic approach towards measuring the significance of individual words based on the underlying link structure of the document collection. This enables us to generate a non-uniform weight distribution of the feature space which is used to augment the original corpus-based document similarities. The experimental results on the collection of scientific literature show that our method achieves better separation of distinct groups of documents, yielding improved clustering solutions. [ABSTRACT FROM AUTHOR]

Object-level vertical search engine has been the research focus recently where the resource collecting problem is still an open area. It is difficult to adapt the traditional link-based web crawler for this task because of the sparse linkage and data-centered webpage of the relevant resources. In this paper, we propose a meta-search based method enhanced with auxiliary crawling to address the problem caused by sparse linkage of the relevant resources. And to retrieve the data-centered webpages efficiently, domain schema is defined to describe the target resource, and representative data instances are selected for meta-search query composing. Moreover, evaluation criteria for the domain resource survey are also proposed as the guideline for query composing and auxiliary crawling, which enable the resource discovery to be automatically performed by computers. Experiment results on real-world data show that our method is effective and efficient. [ABSTRACT FROM AUTHOR]

A method based on multi-feature and multi-stage verification is proposed in this paper. The direction and texture features obtained by Gabor filter are used for the first-stage verification. Then the sham dynamic features representing the writing force are used for the second-stage verification. Considering the number of samples for training is limited, the support vector machine (SVM) is selected as the classifier. The experimental results show that this method can not only increase the accuracy, but also satisfy the requirement of speed in real-time system as well. [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]

This paper proposes a novel algorithm for nonnegative independent component analysis, which is based on minimizing the mutual information of the separated signals, and is truly insensitive to the particular underlying distribution of the source data. The unmixing system culminates to a novel neural network model. Compared with other algorithms for nonnegative ICA, the method proposed in this paper can work efficiently even in the case that the source signals are not well grounded, and that pre-whiting process is not needed. Finally, the experiments were performed on both simulating signals and mixtures of image data, the results indicate that the algorithm is efficient and effective. [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]

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]

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

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

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

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

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

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

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

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]

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

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

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

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]

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]

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]

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]

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]

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]

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]