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Trust is a vital feature for the Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension. [ABSTRACT FROM AUTHOR]

For a long time, logic programming and rule-based reasoning have been proposed as a basis for policy specification languages. However, the term "policy" has not been given a unique meaning. In fact, it is used in the literature in a broad sense that encompasses the following notions: Security Policies pose constraints on the behaviour of a system. They are typically used to control permissions of users/groups while accessing resources and services.Trust Management policy languages are used to collect user properties in open environments, where the set of potential users spans over the entire web.Action Languages are used in reactive policy specification to execute actions like event logging, notifications, etc. Authorizations that involve actions and side effects are sometimes called provisional.Business Rules are "statements about how a business is done" [25] and are used to formalize and automatize business decisions as well as for efficiency reasons. They can be formulated as reaction rules, derivation rules, and integrity constraints [142],[147]. [ABSTRACT FROM AUTHOR]

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. Keywords: Fuzzy cognitive maps, Interval analysis. [ABSTRACT FROM AUTHOR]

The role and importance of methods for lexical knowledge elicitation in the area of multilingual information processing, including machine translation, computer-aided translation and cross-lingual information retrieval is undisputable. The usefulness of such methods becomes even more apparent in cases of language pairs where no appropriate digital language resources exist. This paper presents encouraging experimental results in automatically eliciting bilingual lexica out of Greek-Turkish parallel corpora, consisting of international organizations' documents available in English, Greek and Turkish, in an attempt to aid multilingual document processing involving these languages. [ABSTRACT FROM AUTHOR]

Color plays an important role in web site design. The selection of effective chromatic combinations and the relation of color to the perceived aesthetic and emotional value of a web site is the focus of this paper. The subject of the reported research has been to define a model through which to be able to associate color combinations with specific desirable emotional and aesthetic values. The presented approach involves application of machine learning techniques on a rich data set collected during a number of empirical studies. [ABSTRACT FROM AUTHOR]

Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 e ill-formed and may turn the compound terms specified by e to no longer reflect the domain knowledge originally expressed in e. This paper shows how we can revise e after a taxonomy update and reach an expression e′ that is both well-formed and whose semantics (compound terms defined) is as close as possible to the semantics of the original expression e before the update. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual mapping framework between a formal and visual process modelling language, Fundamental Business Process Modelling Language (FBPML), and the Web Services Ontology (OWL-S), aiming to bridge the gap between Enterprise Modelling methods and Semantic Web services. The framework is divided into a data model and a process model component. An implementation and an evaluation of the process model mapping are demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Kohonen Self -Organizing maps in order to detect intrusive behaviours. The proposed approach combines machine learning and information visualization techniques to analyze network traffic and is based on classifying "normal" versus "abnormal" traffic. The results are promising as they show the ability of eSOMs to classify normal against abnormal behaviour regarding false alarms and detection probabilities. [ABSTRACT FROM AUTHOR]

Let G(V,E) denote an undirected graph, V and E being the sets of its nodes and edges, respectively. A matching in G(V,E) is a subset of edges with no common endpoints. Finding a matching of maximum cardinality constitutes the maximum cardinality matching (MCM) problem. For a thorough theoretical discussion we refer to [6]. The MCM problem is of specific interest from a Constraint Programming (CP) point of view because it can model several logical constraints (predicates) like the all_different and the symmetric all_different predicates [7]. Thus, the definition of a maximum cardinality matching constraint provides a framework encompassing other predicates. Along this line of research, we define a global constraint with respect to the MCM and address the issue of consistency. Establishing hyper-arc consistency implies the identification of edges that cannot participate in any maximum cardinality matching. Evidently, this issue (also called filtering) is related to the methods developed for solving the problem. Solving this problem for bipartite graphs was common knowledge long before Edmonds proposed an algorithm for the non-bipartite case [3]. Regarding hyper-arc consistency, the problem has been resolved only for the bipartite case [1]. [ABSTRACT FROM AUTHOR]

This paper describes two simple techniques that can greatly improve navigation and motion control of nonholonomic robots based on range sensor data. The first technique enhances sensory information by re-using recent sensor data through coordinate transformation, whereas the second compensates for errors due to long control cycle times by forward projection through the kinematic model of the robot. Both techniques have been succesfully tested on a Nomad 200 mobile robot. [ABSTRACT FROM AUTHOR]

This paper proposes and compares several data fusion techniques for robot navigation. The fusion techniques investigated here are several topologies of the Kalman filter. The problem that had been simulated is the navigation of a robot carrying two sensors, one Global Positioning System (GPS) and one Inertial Navigation System (INS). For each of the above topologies, the statistic error and its, mean value, variance and standard deviation were examined. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the discrimination of benign from malignant breast lesions using: the linear, the feedforward neural network, the k-nearest neighbor and the boosting classifiers. Nuclear morphometric parameters from cytological smears taken by Fine Needle Aspiration (FNA) of the breast, have been measured from 193 patients. These parameters undergo an appropriate transformation and then, the classifiers are performed on the raw and on the transformed data. The results show that in terms of the raw data set all classifiers exhibit almost the same performance (overall accuracy ≡ 87%), Thus the linear classifier suffices for the discrimination of the present problem. Also, based on the previous results, one can conjecture that the use of these classifiers combined with image morphometry and statistical techniques for feature transformation, may offer useful information towards the improvement of the diagnostic accuracy of breast FNA. [ABSTRACT FROM AUTHOR]

We present a novel technique for efficient Content Based Peer to Peer Image Retrieval (CBP2PIR) that employs a Content Addressable Network (CAN). A two-stage color histogram based method is described. The first stage defines mapping into the CAN by use of a single fuzzy histogram; while the second stage completes the image retrieval process through a spatially-biased histogram. The proposed system is completely decentralized, non-flooding, and promises high image recall, while minimizing network traffic. [ABSTRACT FROM AUTHOR]

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. This study indicates that a decision tree can be successfully used in the identification of FFS and underline the importance of financial ratios. [ABSTRACT FROM AUTHOR]

This paper describes an ontology data service (ODS) for supporting Search and Rescue (SaR) operations. The ontological model represents various aspects of the command, communication, and organisational structure of the SaR forces and the deployment and progress of a SaR operation. Furthermore, the ontology supports the semantic indexing of multimedia documents in the context of SaR processes and activities. This ODS supports a semantically-enhanced information and communication system for SaR forces. Modelling the spatio-temporal aspects of an operation in alignment with possibly-unreliable information automatically extracted from multimedia objects, introduces a number of challenges for the field of knowledge representation and reasoning. [ABSTRACT FROM AUTHOR]

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Nevertheless, it is based on solid mathematical formulations and is not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs (or digraphs) can assist in this affair, but their applicability is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the connections in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting their expressiveness, but also trying to counter their major disadvantage, by defining multiple node and connection types. [ABSTRACT FROM AUTHOR]

Traditional document clustering techniques rely on single-term analysis, such as the widely used Vector Space Model. However, recent approaches have emerged that are based on Graph Models and provide a more detailed description of document properties. In this work we present a novel Significance-based Graph Model for Web documents that introduces a sophisticated graph weighting method, based on significance evaluation of graph elements. We also define an associated similarity measure based on the maximum common subgraph between the graphs of the corresponding web documents. Experimental results on artificial and real document collections using well-known clustering algorithms indicate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We present a combined clinico-genomic knowledge discovery (CGKD) process suited for linking gene-expression (microarray) and clinical patient data. The process present a multi-strategy mining approach realized by the smooth integration of three distinct data-mining components: clustering (based on a discretized k-means approach), association rules mining, and feature-selection for selecting discrimant genes. The proposed CGKD process is applied on a real-world gene-expression profiling study (i.e., clinical outcome of breast cancer patients). Assessment of the results demonstrates the rationality and reliability of the approach. [ABSTRACT FROM AUTHOR]

We use decision trees and genetic algorithms to analyze the academic performance of students throughout an academic year at a distance learning university. Based on the accuracy of the generated rules, and on cross-examinations of various groups of the same student population, we surprisingly observe that students' performance is clustered around tutors. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of text classification in high dimensionality spaces by applying linear weight updating classifiers that have been highly studied in the domain of machine learning. Our experimental results are based on the Winnow family of algorithms that are simple to implement and efficient in terms of computation time and storage requirements. We applied an exponential multiplication function to weight updates and we experimentally calculated the optimal values of the learning rate and the separating surface parameters. Our results are at the level of the best results that were reported on the family of linear algorithms and perform nearly as well as the top performing methodologies in the literature. [ABSTRACT FROM AUTHOR]

This work studies the problem of violence detection in audio data, which can be used for automated content rating. We employ some popular frame-level audio features both from the time and frequency domain. Afterwards, several statistics of the calculated feature sequences are fed as input to a Support Vector Machine classifier, which decides about the segment content with respect to violence. The presented experimental results verify the validity of the approach and exhibit a better performance than the other known approaches. [ABSTRACT FROM AUTHOR]

In this paper a method for feature selection and classification of email spam messages is presented. The selection of features is performed in two steps: The selection is performed by measuring their entropy and a fine-tuning selection is implemented using a genetic algorithm. In the classification process, a Radial Basis Function Network is used to ensure robust classification rate even in case of complex cluster structure. The proposed method shows that, when using a two-level feature selection, a better accuracy is achieved than using one-stage selection. Also, the use of a lemmatizer or a stop-word list gives minimal classification improvement. The proposed method achieves 96-97% average accuracy when using only 20 features out of 15000. [ABSTRACT FROM AUTHOR]

Image fingerprinting systems aim to extract unique and robust image descriptors (in analogy to human fingerprints). They search for images that are not only perceptually similar but replicas of an image generated through mild image processing operations. In this paper, we examine the use of color descriptors based on a 24-color quantized palette for image fingerprinting. Comparisons are provided between different similarity measures methods as well as regarding the use of color-only and spatial chromatic histograms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel holistic methodology for keyword search in historical typewritten documents combining synthetic data and user's feedback. The holistic approach treats the word as a single entity and entails the recognition of the whole word rather than of individual characters. Our aim is to search for keywords typed by the user in a large collection of digitized typewritten historical documents. The proposed method is based on: (i) creation of synthetic image words; (ii) word segmentation using dynamic parameters; (iii) efficient hybrid feature extraction for each image word and (iv) a retrieval procedure that is optimized by user's feedback. Experimental results prove the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

One of the crucial tasks towards the realization of the Semantic Web vision is the efficient encoding of human knowledge in ontologies. The proper maintenance of these, usually large, structures and, in particular, their adaptation to new knowledge (ontology evolution) is one of the most challenging problems in current Semantic Web research. In this paper, we uncover a certain gap in current ontology evolution approaches and propose a novel research path based on belief change. We present some ideas in this direction and argue that our approach introduces an interesting new dimension to the problem that is likely to find important applications in the future. [ABSTRACT FROM AUTHOR]

This paper presents the main characteristics of Logic Models Creator (LMC). LMC is a new educational environment for young students to build and explore logic models in graphical form. LMC allows visual representation of logic models using IF/THEN/ELSE constructs. In this paper we provide an overview of LMC architecture and discuss briefly an example of use of LMC. As discussed, LMC users in the reported case study managed to achieve effective communication and task evaluation during exploration of problems involving decision making. [ABSTRACT FROM AUTHOR]

There exist many approaches to agent-based conflict resolution which adopts argumentation as their underlying conflict resolution machinery. In most argumentation systems, the credibility of argument sources plays a minimal role. This paper focuses on combining credibility of sources in a source sensitive argumentation. [ABSTRACT FROM AUTHOR]

Autonomy is a crucial property of an artificial agent. The type of representational structures and the role they play in the preservation of an agent's autonomy are pointed out. A framework of self-organised Peircean semiotic processes is introduced and it is then used to demonstrate the emergence of grounded representational structures in agents interacting with their environment. [ABSTRACT FROM AUTHOR]

A multi-layered biomedical literature mining approach is presented aiming to the discovery of gene-gene correlations and the construction of respective gene networks. Utilization of the Trie-memory data structure enables efficient manipulation of different gene nomenclatures. The whole approach is coupled with a texts (biomedical abstracts) classification method. Experimental validation and evaluation results show the rationality, efficiency and reliability of the approach. [ABSTRACT FROM AUTHOR]

The selection of the winding material in power transformers is an important task, since it has significant impact on the transformer manufacturing cost. This winding material selection has to be checked in every transformer design, which means that for each design, there is a need to optimize the transformer twice and afterwards to select the most economical design. In this paper, an Artificial Neural Network (ANN) is proposed for the selection of the winding material in power transformers, which significantly contributes in the reduction of the effort needed in the transformer design. The proposed ANN architecture provides 94.7% classification success rate on the test set. Consequently, this method is very suitable for industrial use because of its accuracy and implementation speed. [ABSTRACT FROM AUTHOR]

In this paper, a two-class pattern recognition problem is studied, namely the automatic detection of speech disorders such as vocal fold paralysis and edema by processing the speech signal recorded from patients affected by the aforementioned pathologies as well as speakers unaffected by these pathologies. The data used were extracted from the Massachusetts Eye and Ear Infirmary database of disordered speech. The linear prediction coefficients are used as input to the pattern recognition problem. Two techniques are developed. The first technique is an optimal linear classifier design, while the second one is based on the dual-space linear discriminant analysis. Two experiments were conducted in order to assess the performance of the techniques developed namely the detection of vocal fold paralysis for male speakers and the detection of vocal fold edema for female speakers. Receiver operating characteristic curves are presented. Long-term mean feature vectors are proven very efficient in detecting the voice disorders yielding a probability of detection that may approach 100% for a probability of false alarm equal to 9.52%. [ABSTRACT FROM AUTHOR]

In this paper we are introducing the employment of features extracted from Fujisaki's parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki's parameters benefited all prediction models with an average raise of 9,52% in the total accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment in real-time) for predator/prey games and a neuro-evolution on-line learning (i.e. during play) approach have already been reported in the literature to serve this purpose. In this paper, an alternative quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced and a comparative study of the two approaches is presented. Artificial neural networks (ANNs) and fuzzy ANNs are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment. We demonstrate that appropriate non-extreme levels of challenge and curiosity generate high values of entertainment and we discuss the extensibility of the approach to other genres of digital entertainment and edutainment. [ABSTRACT FROM AUTHOR]

This paper reports on the results of an ongoing project for the development of a platform for e-Learning, which automatically constructs curricula based on available educational resources and the learners needs and abilities. The system under development, called PASER (Planner for the Automatic Synthesis of Educational Resources), uses an automated planner, which given the initial state of the problem (learner's profile, preferences, needs and abilities), the available actions (study an educational resource, take an exam, join an e-learning course, etc.) and the goals (obtain a certificate, learn a subject, acquire a skill, etc.) constructs a complete educational curriculum that achieves the goals. PASER is compliant with the evolving educational metadata standards that describe learning resources (LOM), content packaging (CP), educational objectives (RDCEO) and learner related information (LIP). [ABSTRACT FROM AUTHOR]

Modern Power Systems often operate close to their stability limits in order to meet the continuously growing demand, due to the difficulties in expanding the generation and transmission system. An effective way to face power system contingencies that can lead to instability is load shedding. In this paper we propose a method to assess the dynamic performance of the Greek mainland Power System and to propose a load shedding scheme in order to maintain voltage stability under various loading conditions and operating states in the presence of critical contingencies including outages of one or more generating units in the south part of the system. A Self Organizing Map is utilized in order to classify the Load profiles of the Power System. With a decision tree the dynamic performance of each class is assessed. The classification of Load Profiles by the SOM, provide the load shedding scheme. Keywords: Dynamic Security,Load shedding, Self Organizing Maps, Decision Trees. [ABSTRACT FROM AUTHOR]

In this paper we present an artificial cortical network, inspired by the Human Visual System (HVS), which extracts the salient contours in color images. Similarly to the primary visual cortex, the network consists of orientation hypercolumns. Lateral connections between the hypercolumns are modeled by a new connection pattern based on co-exponentiality. The initial color edges of the image are extracted in a way inspired by the double-opponent cells of the HVS. These edges are inputs to the network, which outputs the salient contours based on the local interactions between the hypercolumns. The proposed network was tested on real color images and displayed promising performance, with execution times small enough even for a conventional personal computer. [ABSTRACT FROM AUTHOR]

The Relevance Vector Machine(RVM) is a widely accepted Bayesian model commonly used for regression and classification tasks. In this paper we propose a multikernel version of the RVM and present an alternative inference algorithm based on Fourier domain computation to solve this model for large scale problems, e.g. images. We then apply the proposed method to the object detection problem with promising results. [ABSTRACT FROM AUTHOR]

As is widely known, the popular Black & Scholes model for option pricing suffers from systematic biases, as it relies on several highly questionable assumptions. In this paper we study the ability of neural networks (MLPs) in pricing call options on the S&P 500 index; in particular we investigate the effect of the hidden neurons in the in- and out-of-sample pricing. We modify the Black & Scholes model given the price of an option based on the no-arbitrage value of a forward contract, written on the same underlying asset, and we derive a modified formula that can be used for our purpose. Instead of using the standard backpropagation training algorithm we replace it with the Levenberg-Marquardt approach. By modifying the objective function of the neural network, we focus the learning process on more interesting areas of the implied volatility surface. The results from this transformation are encouraging. [ABSTRACT FROM AUTHOR]

The prediction of the translation initiation site (TIS) in a genomic sequence is an important issue in biological research. Several methods have been proposed to deal with it. However, it is still an open problem. In this paper we follow an approach consisting of a number of steps in order to increase TIS prediction accuracy. First, all the sequences are scanned and the candidate TISs are detected. These sites are grouped according to the length of the sequence upstream and downstream them and a number of features is generated for each one. The features are evaluated among the instances of every group and a number of the top ranked ones are selected for building a classifier. A new instance is assigned to a group and is classified by the corresponding classifier. We experiment with various feature sets and classification algorithms, compare with alternative methods and draw important conclusions. [ABSTRACT FROM AUTHOR]

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a "relevance" of the predictive attributes towards target attributes. In this paper we use the Bayesian Metanetwork vision to model such context-sensitive feature relevance. Such model assumes that the relevance of predictive attributes in a Bayesian network might be a random attribute itself and it provides a tool to reason based not only on probabilities of predictive attributes but also on their relevancies. According to this model, the evidence observed about contextual attributes is used to extract a relevant substructure from a Bayesian network model and then the predictive attributes evidence is used to reason about probability distribution of the target attribute in the extracted sub-network. We provide the basic architecture for such Bayesian Metanetwork, basic reasoning formalism and some examples. [ABSTRACT FROM AUTHOR]

The innovative feature of the system presented in this paper is the use of pattern-matching techniques to retrieve translations resulting in a flexible, language-independent approach, which employs a limited amount of explicit a priori linguistic knowledge. Furthermore, while all state-of-the-art corpus-based approaches to Machine Translation (MT) rely on bitexts, this system relies on extensive target language monolingual corpora. The translation process distinguishes three phases: 1) pre-processing with ‘light' rule and statisticsbased NLP techniques 2) search & retrieval, 3) synthesising. At Phase 1, the source language sentence is mapped onto a lemma-to-lemma translated string. This string then forms the input to the search algorithm, which retrieves similar sentences from the corpus (Phase 2). This retrieval process is performed iteratively at increasing levels of detail, until the best match is detected. The best retrieved sentence is sent to the synthesising algorithm (Phase 3), which handles phenomena such as agreement. [ABSTRACT FROM AUTHOR]