Fuzzy Case-Based Reasoning in Product Style Acquisition Incorporating Valence-Arousal-Based Emotional Cellular Model

Emotional cellular (EC), proposed in our previous works, is a kind of semantic cell that contains kernel and shell and the kernel is formalized by a triple- L = <P, d, δ>, where P denotes a typical set of positive examples relative to word-L, d is a pseudodistance measure on emotional two-dimensional space: valence-arousal, and δ is a probability density function on positive real number field. The basic idea of EC model is to assume that the neighborhood radius of each semantic concept is uncertain, and this uncertainty will be measured by one-dimensional density function δ. In this paper, product form features were evaluated by using ECs and to establish the product style database, fuzzy case based reasoning (FCBR) model under a defined similarity measurement based on fuzzy nearest neighbors (FNN) incorporating EC was applied to extract product styles. A mathematical formalized inference system for product style was also proposed, and it also includes uncertainty measurement tool emotional cellular. A case study of style acquisition of mobile phones illustrated the effectiveness of the proposed methodology

Camada prototípica de um conceito: O tipo de cultura inglesa "detetive particular"

The theory of culture types has been developing within the cultural studies for the past decade.&nbsp; Сonsiderable scholarship has been developed regarding the concept of “culture type” which is understood as a generalized recognizable representative of society, whose behavior reflects his attitudes and values of a particular group of people or society. Сulture types emerge on the basis of real or fictional individuals. In&nbsp;English language consciousness the analyzed type PRIVATE DETECTIVE is represented by masculine and feminine characters. The given research explores the prototypical layer of the masculine culture type PRIVATE DETECTIVE on the material of the English detective novels by Conan Doyle. Materials and Methods: methodological base of the research comprises theory of prototype according to which the prototype is considered as the best representative of the category corresponds to the model personality which possesses a set of unique characteristics inherent in this culture. Applying such investigation methods as contextual, conceptual and cognitive analysis we have determined the &nbsp;professionally marked cognitive features of masculine prototypes of the mentioned culture type&nbsp;represented by the fictional character of Sherlock Holmes and its real models – Joseph Bell and&nbsp;Conan Doyle. Results: considering the culture type as a fieldeffect structured concept the research resulted in description of the nuclear and perinuclear zone. The nuclear contains prototypical features of the culture type PRIVATE DETECTIVE that are typical to its real models. Perinuclear covers fictional characteristics related to Sherlock&nbsp;Holmes as the basic model. Conclusions: the analyses of 98 fragments from English detective novels and social essays has revealed that the culture type PRIVATE DETECTIVE is mainly formed on the basis of Sherlock Holmes (80 - 82%), Joseph Bell (8 –8%) and Conan Doyle (10 – 10%). The area of convergence of prototypical and fictional characters amounted up to 66% considered as prototypical cognitive framework reflecting the embodiment of cognitive features of counterparts in the culture type PRIVATE DETECTIVE. The results of the done&nbsp;investigation can be included to the course on cultural studies, conceptology, personology.La teoría de los tipos de cultura se ha estado&nbsp;desarrollando dentro de los estudios culturales&nbsp;durante la última década. Se ha desarrollado una&nbsp;beca considerable sobre el concepto de "tipo de&nbsp;cultura" que se entiende como un representante&nbsp;reconocible generalizado de la sociedad, cuyo&nbsp;comportamiento refleja sus actitudes y valores&nbsp;de un grupo particular de personas o sociedad.Los tipos de cultura surgen a partir de individuos&nbsp;reales o ficticios. En la conciencia de la lengua&nbsp;inglesa, el tipo analizado DETECTIVE PRIVADO&nbsp;está representado por caracteres masculinos y&nbsp;femeninos. La investigación dada explora la capa&nbsp;prototípica del tipo de cultura masculina&nbsp;DETECTIVE PRIVADO en el material de las&nbsp;novelas de detectives inglesas de Conan Doyle.Materiales y Métodos: la base metodológica de la&nbsp;investigación comprende la teoría del prototipo&nbsp;según la cual el prototipo considerado como el&nbsp;mejor representante de la categoría&nbsp;corresponde a la personalidad modelo que posee&nbsp;un conjunto de características únicas inherentes&nbsp;a esta cultura. Aplicando tales métodos de&nbsp;investigación como análisis contextual,&nbsp;conceptual y cognitivo, hemos determinado las&nbsp;características cognitivas marcadas&nbsp;profesionalmente de los prototipos masculinos&nbsp;del tipo de cultura mencionado representado&nbsp;por el personaje ficticio de Sherlock Holmes y&nbsp;sus modelos reales: Joseph Bell y Conan Doyle.Resultados: considerando el tipo de cultivo como&nbsp;un concepto estructurado de efecto de campo,&nbsp;la investigación resultó en la descripción de la&nbsp;zona nuclear y perinuclear. La nuclear contiene&nbsp;características prototípicas del tipo de cultivo&nbsp;DETECTIVE PRIVADO que son típicas de sus&nbsp;modelos reales. Perinuclear cubre características&nbsp;ficticias relacionadas con Sherlock Holmes como&nbsp;el modelo básico.Conclusiones: el análisis de 98 fragmentos de&nbsp;novelas de detectives inglesas y ensayos sociales&nbsp;ha revelado que el tipo de cultura DETECTIVE&nbsp;PRIVADO se forma principalmente sobre la base&nbsp;de Sherlock Holmes (80 - 82%), Joseph Bell (8 -8%) y Conan Doyle ( 10 - 10%). El área de&nbsp;convergencia de personajes prototípicos y de&nbsp;ficción ascendió hasta un 66% considerado&nbsp;como un marco cognitivo prototípico que refleja&nbsp;la incorporación de las características cognitivas&nbsp;de las contrapartes en el tipo de cultura&nbsp;DETECTIVE PRIVADO. Los resultados de la&nbsp;investigación realizada pueden incluirse en el&nbsp;curso de estudios culturales, conceptología,&nbsp;personología.A teoria dos tipos de cultura foi desenvolvida nos estudos culturais durante a última década. Uma bolsa deestudos considerável foi desenvolvida sobre o conceito de "tipo de cultura" que é entendido como umrepresentante reconhecível generalizado da sociedade, cujo comportamento reflete as atitudes e valoresde um determinado grupo de pessoas ou da sociedade. Os tipos de cultura surgem de indivíduos reais oufictícios. Na consciência da língua inglesa, o tipo analisado DETETIVE PRIVADO é representado porpersonagens masculinos e femininos. Esta pesquisa explora a camada prototípica da cultura masculinaDETETIVE PRIVADO no material dos romances policiais ingleses de Conan Doyle.Materiais e Métodos: A base metodológica da pesquisa inclui a teoria protótipo segundo a qual o protótipoconsiderado o melhor representante da categoria corresponde à personalidade modelo que tem umconjunto de inerente a esta cultura características únicas. Aplicação de métodos de pesquisa, comocontextual, análise conceitual e cognitiva, nós determinamos profissionalmente características cognitivasmarcados de protótipos masculinos tipo de cultura representado pelo personagem fictício SherlockHolmes e modelos reais mencionados: Joseph Bell e Conan Doyle.Resultados: considerando o tipo de cultura como um conceito estruturado de efeito de campo, ainvestigação resultou na descrição da zona nuclear e perinuclear. Nuclear contém característicasprototípicas do tipo de cultura PRIVATE DETECTIVE que são típicas de seus modelos reais. Perinuclearaborda características fictícias relacionadas a Sherlock Holmes como modelo básico.Conclusões: análise de 98 fragmentos de romances de detetives britânicos e estudos sociais revelou que otipo de DETETIVE cultura privado é formado principalmente com base em Sherlock Holmes (80-82%),Joseph Bell (8-8%) e Conan Doyle (10 - 10%). A área de convergência de personagens fictícios protótipose atingiu 66% consideraram a estrutura cognitiva prototípica que reflete a incorporação de características&nbsp;cognitivas do tipo de contrapartes cultura DETETIVE PARTICULAR. Os resultados da pesquisa realizadapodem ser incluídos no curso de estudos culturais, conceptologia, personologia

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering

Approximate model composition for explanation generation

This thesis presents a framework for the formulation of knowledge models to sup¬ port the generation of explanations for engineering systems that are represented by the resulting models. Such models are automatically assembled from instantiated generic component descriptions, known as modelfragments. The model fragments are of suffi¬ cient detail that generally satisfies the requirements of information content as identified by the user asking for explanations. Through a combination of fuzzy logic based evidence preparation, which exploits the history of prior user preferences, and an approximate reasoning inference engine, with a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬ dled. Model fragments, each representing structural or behavioural aspects of a com¬ ponent of the domain system of interest, are organised in a library. Those fragments that represent the same domain system component, albeit with different representation detail, form parts of the same assumption class in the library. Selected fragments are assembled to form an overall system model, prior to extraction of any textual infor¬ mation upon which to base the explanations. The thesis proposes and examines the techniques that support the fragment selection mechanism and the assembly of these fragments into models. In particular, a Bayesian network-based model fragment selection mechanism is de¬ scribed that forms the core of the work. The network structure is manually determined prior to any inference, based on schematic information regarding the connectivity of the components present in the domain system under consideration. The elicitation of network probabilities, on the other hand is completely automated using probability elicitation heuristics. These heuristics aim to provide the information required to select fragments which are maximally compatible with the given evidence of the fragments preferred by the user. Given such initial evidence, an existing evidence propagation algorithm is employed. The preparation of the evidence for the selection of certain fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬ rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence is propagated, a single fragment is selected for each of the domain system compo¬ nents and hence, the final model of the entire system is constructed. Finally, a highly configurable XML-based mechanism is employed to extract explanation content from the newly formulated model and to structure the explanatory sentences for the final explanation that will be communicated to the user. The framework is illustratively applied to a number of domain systems and is compared qualitatively to existing compositional modelling methodologies. A further empirical assessment of the performance of the evidence propagation algorithm is carried out to determine its performance limits. Performance is measured against the number of frag¬ ments that represent each of the components of a large domain system, and the amount of connectivity permitted in the Bayesian network between the nodes that stand for the selection or rejection of these fragments. Based on this assessment recommenda¬ tions are made as to how the framework may be optimised to cope with real world applications

Semantic vector representations of senses, concepts and entities and their applications in natural language processing

Representation learning lies at the core of Artificial Intelligence (AI) and Natural Language Processing (NLP). Most recent research has focused on develop representations at the word level. In particular, the representation of words in a vector space has been viewed as one of the most important successes of lexical semantics and NLP in recent years. The generalization power and flexibility of these representations have enabled their integration into a wide variety of text-based applications, where they have proved extremely beneficial. However, these representations are hampered by an important limitation, as they are unable to model different meanings of the same word. In order to deal with this issue, in this thesis we analyze and develop flexible semantic representations of meanings, i.e. senses, concepts and entities. This finer distinction enables us to model semantic information at a deeper level, which in turn is essential for dealing with ambiguity. In addition, we view these (vector) representations as a connecting bridge between lexical resources and textual data, encoding knowledge from both sources. We argue that these sense-level representations, similarly to the importance of word embeddings, constitute a first step for seamlessly integrating explicit knowledge into NLP applications, while focusing on the deeper sense level. Its use does not only aim at solving the inherent lexical ambiguity of language, but also represents a first step to the integration of background knowledge into NLP applications. Multilinguality is another key feature of these representations, as we explore the construction language-independent and multilingual techniques that can be applied to arbitrary languages, and also across languages. We propose simple unsupervised and supervised frameworks which make use of these vector representations for word sense disambiguation, a key application in natural language understanding, and other downstream applications such as text categorization and sentiment analysis. Given the nature of the vectors, we also investigate their effectiveness for improving and enriching knowledge bases, by reducing the sense granularity of their sense inventories and extending them with domain labels, hypernyms and collocations

Semantic Decision Support for Information Fusion Applications

La thèse s'inscrit dans le domaine de la représentation des connaissances et la modélisation de l'incertitude dans un contexte de fusion d'informations. L'idée majeure est d'utiliser les outils sémantiques que sont les ontologies, non seulement pour représenter les connaissances générales du domaine et les observations, mais aussi pour représenter les incertitudes que les sources introduisent dans leurs observations. Nous proposons de représenter ces incertitudes au travers d'une méta-ontologie (DS-ontology) fondée sur la théorie des fonctions de croyance. La contribution de ce travail porte sur la définition d'opérateurs d'inclusion et d'intersection sémantique et sur lesquels s'appuie la mise en œuvre de la théorie des fonctions de croyance, et sur le développement d'un outil appelé FusionLab permettant la fusion d'informations sémantiques à partir du développement théorique précédent. Une application de ces travaux a été réalisée dans le cadre d'un projet de surveillance maritime.This thesis is part of the knowledge representation domain and modeling of uncertainty in a context of information fusion. The main idea is to use semantic tools and more specifically ontologies, not only to represent the general domain knowledge and observations, but also to represent the uncertainty that sources may introduce in their own observations. We propose to represent these uncertainties and semantic imprecision trough a metaontology (called DS-Ontology) based on the theory of belief functions. The contribution of this work focuses first on the definition of semantic inclusion and intersection operators for ontologies and on which relies the implementation of the theory of belief functions, and secondly on the development of a tool called FusionLab for merging semantic information within ontologies from the previous theorical development. These works have been applied within a European maritime surveillance project.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies