Catálogo de publicaciones - libros

Fuzzy Cognitive Maps (FCMs) is a graphical model for causal knowledge representation. FCMs consist of nodes-concepts and weighted edges that connect the concepts and represent the cause and effect relationships among them. FCMs are used in complex problems involving causal relationships, which often include feedback, and where qualitative rather than quantitative measures of influences are available. They have used for decision support to determine a final state given a qualitative initial knowledge for nodes and weighted edges. A first study on introducing Interval analysis in the FCM framework has been attempted and it is presented in this work. Here a new structure for FCM is proposed with interval weights and a new method for processing interval data input for FCMs is proposed.

E-Learning service providers produce or collect digital learning resources, derive metadata for their description, and reuse and organize them in repositories. This paper proposes a data mining approach to discover relationships between the learning resources metadata. In particular, it presents and evaluates methods for clustering learning resources and providing controlled vocabularies for each class description. The derived classes and vocabularies contribute to the semantic interoperability in learning resource interchanges.

This paper, based on the organizational model proposed in [2], investigates the organization oriented programming paradigm. The approach proposed, in contrast to other approaches, emphasizes on group thinking. To show how the organization oriented programming paradigm is applied the paper describes the implementation of the asynchronous backtracking algorithm used in distributed CSPs.

In this paper we present a multimodal Greek Sign Language (GSL) recognizer. The system can recognize either signs or finger-spelled words of GSL, forming sentences of GSL. A vocabulary of 54 finger-spelled words together with 17 signs, giving a total of 71 signs/words, is used. The system has been tested on various grammars and the recognition rates we achieved exceeded 89% in most cases.

State of the art artificial agents rely heavily on human intervention for performing vision-language integration; apart from being cost and effort effective, this intervention deprives artificial agents from the ability to react intelligently and to show intentionality when engaged in situated multimodal communication. In this paper, we suggest an alternative way of building vision-language integration prototypes with limited human intervention. The suggestions have emerged from the development of such a prototype for the verbalisation of visual scenes in a property-surveillance task.

In this short paper we suggest that Population P Systems and Communication X-machines may be combined into one hybrid formal method which facilitates the correct specification of reconfigurable multi-agent systems.

This paper proposes an intelligent combination of neural network theory and financial statistical models for the detection of arbitrage opportunities in a group of stocks. The proposed intelligent methodology is based on a class of neural network-GARCH autoregressive models for the effective handling of the dynamics related to the statistical mispricing between relative stock prices. The performance of the proposed intelligent trading system is properly measured with the aid of profit & loss diagrams.

A faceted taxonomy is a set of taxonomies each describing the application domain from a different (preferably orthogonal) point of view. CTCA is an algebra that allows specifying the set of meaningful compound terms (meaningful conjunctions of terms) over a faceted taxonomy in a flexible and efficient manner. However, taxonomy updates may turn a CTCA expression ill-formed and may turn the compound terms specified by to no longer reflect the domain knowledge originally expressed in . This paper shows how we can revise after a taxonomy update and reach an expression ′ that is both well-formed and whose semantics (compound terms defined) is as close as possible to the semantics of the original expression before the update.

In spectral datasets, such as those consisting of MR spectral data derived from MS lesions, neighboring features tend to be highly correlated, suggesting the data lie on some low-dimensional space. Naturally, finding such low-dimensional space is of interest. Based on this real-life problem, this paper extracts an abstract problem, neighboring feature clustering (NFC). Noticeably different from traditional clustering schemes where the order of features doesn’t matter, NFC requires that a cluster consist of neighboring features, that is features that are adjacent in the original feature ordering. NFC is then reduced to a piece-wise linear approximation problem. We use minimum description length (MDL) method to solve this reduced problem. The algorithm we proposed works well on synthetic datasets. NFC is an abstract problem. With minor changes, it can be applied to other fields where the problem of finding piece-wise neighboring groupings in a set of unlabeled data arises.