• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 10
  • 7
  • 1
  • 1
  • 1
  • 1
  • 1
  • Tagged with
  • 27
  • 27
  • 7
  • 6
  • 5
  • 5
  • 4
  • 4
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Age and Context Dependency in Causal Learning

Lowry, Katherine Danielle 01 October 2015 (has links)
The ability to make associations between causal cues and outcomes is an important adaptive trait that allows us to properly prepare for an upcoming event. Encoding context is a type of associative processing; thus, context is also an important aspect of acquiring causal relationships. Context gives us additional information about how two events are related and allows us to be flexible in how we respond to causal cues. Research indicates that older adults exhibit an associative deficit as well as a deficit in contextual processing; therefore, it seems likely that these deficits are responsible for the deficit in older adults’ causal learning. The purpose of the current study was to more directly test how associative deficits related to older adults’ contextual processing affect their causal learning. Based on past research, it was hypothesized that older adults would be less likely than younger adults to acquire and use contextual information in causal learning. A causal learning scenario from Boddez, Baeyens, Hermans, and Beckers (2011) was used to test the hypothesis that older adults show deficits in contextual processing in a causal learning scenario. This task examined contextual processing using blocking and extinction. Participants went through eight blocks of trials in which they were exposed to various cues and outcomes. They provided expectancy ratings that indicated how likely they believed an outcome was to occur, and these ratings were used to assess age differences in use of contextual information in a causal learning scenario. As expected, both younger and older adults demonstrated blocking in that they assigned higher causal value to a previously trained target cue (A+) than to another cue (X) that was only presented in compound with cue A later in the task (i.e., AX+). Additionally, when tested in the context where the association was originally learned following extinction training (i.e., A-), the causal value of cue A decreased for all groups, even if extinction training took place in a different context. However, ratings for cue A decreased even more for younger adults whose extinction training took place in a different context when tested in their extinction context.
2

Exploração de informações contextuais para enriquecimento semântico em representações de textos / Exploration of contextual information for semantic enrichment in text representations

Ribeiro, João Vítor Antunes 14 November 2018 (has links)
Em decorrência da crescente quantidade de documentos disponíveis em formato digital, a importância da análise computacional de grandes volumes de dados torna-se ainda mais evidente na atualidade. Embora grande parte desses documentos esteja disponível em formato de língua natural, a análise por meio de processos como a Mineração de Textos ainda é um desafio a ser superado. Normalmente, abordagens tradicionais de representação de textos como a Bag of Words desconsideram aspectos semânticos e contextuais das coleções de textos analisadas, ignorando informações que podem potencializar o desempenho das tarefas realizadas. Os principais problemas associados a essas abordagens são a alta esparsidade e dimensionalidade que prejudicam consideravelmente o desempenho das tarefas realizadas. Como o enriquecimento de representações de textos é uma das possibilidades efetivas para atenuar esses tipos de problemas, nesta dissertação foi investigada a aplicação conjunta de enriquecimentos semânticos e contextuais. Para isso foi proposta uma nova técnica de representação de textos, cuja principal novidade é a abordagem utilizada para calcular a frequência dos atributos (contextos) baseando-se em suas similaridades. Os atributos extraídos por meio dessa técnica proposta são considerados dependentes já que são formados por conjuntos de termos correlacionados que podem compartilhar informações semelhantes. A efetividade da técnica foi avaliada na tarefa de classificação automática de textos, na qual foram explorados diferentes procedimentos de enriquecimento textual e versões de modelos de linguagem baseados em word embeddings. De acordo com os resultados obtidos, há evidências favoráveis a respeito da efetividade e da aplicabilidade da técnica de representação de textos proposta. Segundo os testes de significância estatística realizados, a aplicação de enriquecimentos textuais baseados em Reconhecimento de Entidades Nomeadas e em Desambiguação Lexical de Sentido pode contribuir efetivamente para o aumento do desempenho da tarefa de classificação automática de textos, principalmente nas abordagens em que também são considerados textos de fontes externas de conhecimento como a Wikipédia. Constatou-se empiricamente que a efetividade dessa técnica proposta pode ser superior às abordagens tradicionais em cenários de aplicação baseados em informações semânticas das coleções de textos, caracterizando-a como uma alternativa promissora para a geração de representações de textos com alta densidade de informações semânticas e contextuais que se destacam pela interpretabilidade. / Due to the increasing number of available documents in digital format, the importance of computational analysis of large volumes of data becomes even more evident recently. Although most of these documents are available in natural language format, analysis through processes such as text mining is still a challenge to be overcome. Normally, traditional text representation approaches such as the bag of words disregard semantic and contextual aspects of the analyzed text collections, ignoring information that can enhance the performance of the tasks performed. The main problems associated with these approaches are the high sparsity and dimensionality that considerably impair the performance of the tasks performed. As the text representations enrichment is one of the effective possibilities to attenuate these types of problems, in this dissertation the joint application of semantic and contextual enrichment was investigated. For that a new text representation technique was proposed, whose main novelty is the approach used to calculate the frequency of attributes (contexts) based on their similarities. The attributes attributes extracted by this proposed technique are considered dependent because they are formed by sets of correlated terms that can share similar information. The effectiveness of the technique was evaluated in the automatic text classification task, in which different procedures of textual enrichment and versions of language models based on word embeddings were explored. According to the results, there is favorable evidence regarding the effectiveness and applicability of the proposed text representation technique. According to the statistical significance tests, the application of textual enrichment based on named entity recognition and word sense disambiguation can effectively contribute to the increase of the performance of the automatic text classification task, especially in the approaches that are also considered texts from external knowledge sources such asWikipedia. It has been empirically verified that the effectiveness of this proposed technique can be superior to the traditional approaches in application scenarios based on semantic information of the text collections, characterizing it as a promising alternative for the generation of text representations with high density of semantic and contextual information that stand out for their interpretability.
3

Graphical and non-speech sound metaphors in email browsing : an empirical approach : a usability based study investigating the role of incorporating visual and non-speech sound metaphors to communicate email data and threads

Alharbi, Saad Talal January 2009 (has links)
This thesis investigates the effect of incorporating various information visualisation techniques and non-speech sounds (i.e. auditory icons and earcons) in email browsing. This empirical work consisted of three experimental phases. The first experimental phase aimed at finding out the most usable visualisation techniques for presenting email information. This experiment involved the development of two experimental email visualisation approaches which were called LinearVis and MatrixVis. These approaches visualised email messages based on a dateline together with various types of email information such as the time and the senders. The findings of this experiment were used as a basis for the development of a further email visualisation approach which was called LinearVis II. This novel approach presented email data based on multi-coordinated views. The usability of messages retrieval in this approach was investigated and compared to a typical email client in the second experimental phase. Users were required to retrieve email messages in the two experiments with the provided relevant information such as the subject, status and priority. The third experimental phase aimed at exploring the usability of retrieving email messages by using other type of email data, particularly email threads. This experiment investigated the synergic use of graphical representations with non-speech sounds (Multimodal Metaphors), graphical representations and textual display to present email threads and to communicate contextual information about email threads. The findings of this empirical study demonstrated that there is a high potential for using information visualisation techniques and non-speech sounds (i.e. auditory icons and earcons) to improve the usability of email message retrieval. Furthermore, the thesis concludes with a set of empirically derived guidelines for the use of information visualisation techniques and non-speech sound to improve email browsing.
4

Mining Semantics from Low-level Features in Multimedia Computing

January 2011 (has links)
abstract: Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems. / Dissertation/Thesis / Ph.D. Computer Science 2011
5

Recognition of online handwritten mathematical expressions using contextual information / Reconhecimento online de expressões matemáticas manuscritas usando informação contextual

Frank Dennis Julca Aguilar 29 April 2016 (has links)
Online handwritten mathematical expressions consist of sequences of strokes, usually collected through a touch screen device. Automatic recognition of online handwritten mathematical expressions requires solving three subproblems: symbol segmentation, symbol classification, and structural analysis (that is, the identification of spatial relations, as subscript or superscript, between symbols). A main issue in the recognition process is ambiguity at symbol or relation levels that often leads to several likely interpretations of an expression. Some methods treat the recognition problem as a pipeline process, in which symbol segmentation and classification is followed by structural analysis. A main drawback of such methods is that they compute symbol level interpretations without considering structural information, which is essential to solve ambiguities. To cope with this drawback, more recent methods adapt string parsing techniques to drive the recognition process. As string grammars were originally designed to model linear arrangements of objects (like in text, where symbols are arranged only through left-to-right relations), non-linear arrangements of mathematical symbols (given by the multiple relation types of mathematics) are modeled as compositions of production rules for linear structures. Then, parsing an expression involves searching for linear structures in the expression that are consistent with the structure of the production rules. This last step requires the introduction of constraints or assumptions, such as stroke input order or vertical and horizontal alignments, to linearize the expression components. These requirements not only limit the effectiveness of the methods, but also make difficult their extension to include new expression structures. In this thesis, we model the recognition problem as a graph parsing problem. The graph-based description of relations in the production rules allows direct modeling of non-linear mathematical structures. Our parsing algorithm determines recursive partitions of the input strokes that induce graphs matching the production rule graphs. To mitigate the computational cost, we constrain the possible partitions to graphs derived from sets of symbol and relation hypotheses, calculated using previously trained classifiers. A set of labels that indicate likely interpretations is associated to each symbol and relation hypothesis, and treatment of ambiguity at symbol and relation levels is left to the parsing process. The parsing algorithm builds a forest in which each tree corresponds to an interpretation coherent with the grammar. We define a score function, optimized through training data, that associates a cost to each tree. We then select a tree with minimum cost as result. Experimental evaluation shows that the proposed method is more accurate than several state of the art methods. Even though graph parsing is a computationally expensive process, the use of symbol and relation hypotheses to constrain the search space is able to effectively reduce complexity, allowing practical application of the process. Furthermore, since the proposed parsing algorithm does not make direct use of structural particularities of mathematical expressions, it has potential to be adapted for other two-dimensional object recognition problems. As a secondary contribution of this thesis, we have proposed a framework to automatize the process of building handwritten mathematical expression datasets. The framework has been implemented in a computer system and used to generate part of the samples used in the experimental part of this thesis. / Expressões matemáticas manuscritas online estão constituídas por sequências de traços. O reconhecimento automático de tais expressões requer a solução de três subproblemas: segmentação de símbolos, classificação de símbolos e análise estrutural (isto é, a identificação de relações espaciais, tais como sobrescrito e subscrito, entre símbolos). Uma das dificuldades principais do problema é a ambiguidade no nível de símbolos ou relações, que frequentemente sugere várias possíveis interpretações de uma mesma expressão. Alguns métodos de reconhecimento tratam o problema de maneira sequencial, onde um processo de segmentação e classificação de símbolos é seguido de análise estrutural. Um problema principal de tais métodos é que eles determinam interpretações no nível de símbolos sem considerar informação estrutural, a qual é importante para solucionar ambiguidades. Para solucionar esse problema, métodos mais recentes adaptaram técnicas de parsing de strings. Dado que gramáticas de strings foram originalmente projetadas para modelar arranjos lineares de tokens (como texto, onde símbolos são arranjados de esquerda a direita), a estrutura não linear dos símbolos matemáticos (dada pelos multiples tipos de relações espaciais) é modelada como uma composição de regras de produção de estruturas lineares. Dessa maneira, o parsing de uma expressão consiste em determinar estruturas lineares na expressão que são consistentes com as estruturas das regras de produção. Esse último passo requer a introdução de restrições, baseadas na definição de uma ordem em relação ao tempo ou espaço, para linearizar os componentes da expresão. Os requerimentos das gramáticas de strings não apenas limitam a efectividade dos métodos, mas também dificultam a extensão dos métodos na inclusão de novas estruturas. Neste trabalho, o problema de reconhecimento de expressões matemáticas é modelado como um problema de parsing de grafos. A representação por meio de grafos nas regras de produção permite uma representação direta das estruturas não lineares das expressões matemáticas. O algoritmo de parsing determina partições dos traços de entrada que induzem grafos isomorfos aos grafos das regras de produção. Para mitigar o custo computacional, restringimos as possíveis partições a aquelas derivadas de um conjunto de possíveis símbolos e relações identificados por classificadores previamente treinados. Um conjunto de rótulos que indica interpretações alternativas é associado a cada símbolo e relação; a decisão da melhor interpretação é realizada pelo parser. O parser construi uma floresta na qual uma árvore representa uma possível interpretação da entrada, e atribui um custo de interpretação para cada árvore, baseado nas relações e símbolos definidas na árvore. O resultado do reconhecimento é dado pela extração de uma árvore com custo mínimo. Resultados experimentais do método proposto mostram um melhor desempenho em comparação com vários métodos descritos na literatura. A pesar do parsing de grafos ser um processo computacionalmente caro, a restrição do espaço de busca proposto reduz a complexidade o suficiente para permitir uma aplicação prática da abordagem. Adicionalmente, dado que a abordagem não pressupõe estruturas particulares das expressões matemática, o método tem potencial para ser adaptado para o reconhecimento de outras estruturas bidimensionais. Uma contribuição secundaria deste trabalho é o desenvolvimento de uma framework para construção automática de bancos de dados de expressões matemáticas manuscritas. A framework tem sido implementada num sistema usado para criar parte das amostras de expressões usadas para avaliação do método de reconhecimento.
6

Sistema para auxílio à recuperação contextualizada de informações em soluções de apoio à gestão do conhecimento / A system to aid in contextual recovery of information in supporting solutions to knowledge management

Antonio Francisco Savi 16 October 2003 (has links)
Existem muitas ferramentas capazes de apoiar o processo de gestão do conhecimento nas organizações. A eficiência dessas ferramentas depende da capacidade de lidar com grandes massas de dados. Portanto, a recuperação é fundamental e é hoje uma área de pesquisa em ascensão, para a qual várias técnicas vêm sendo desenvolvidas, tais como sistemas sofisticados de classificação, mecanismos de busca e agentes inteligentes. Grande parte dessas tecnologias se baseia no princípio de que o usuário sabe da existência da informação e está a sua procura. Este trabalho propõe um conceito para a construção de sistemas que, ao invés de apenas disponibilizar mecanismos para a busca, procura filtrar as informações e apresentá-las em meio aos sistemas corporativos empregados pelo usuário nas suas tarefas rotineiras. Isto significa uma informação contextualizada, isto é, apresentada conforme a situação enfrentada pelo usuário e dentro da qual faz sentido e adiciona valor às decisões. O conceito proposto emprega conhecimentos sobre gestão de projetos e modelagem de empresa, de forma a proporcionar que as informações relevantes sejam oferecidas no ambiente onde o usuário interage com outros membros da equipe de projeto. Foram desenvolvidos, além do conceito, uma especificação detalhada e um protótipo da ferramenta. Os resultados demonstram que o conceito é viável e pode ser utilizado em sistemas de apoio à gestão do conhecimento. Encerra-se o trabalho apresentando as propostas de melhoria para o conceito e solução desenvolvidos. / There exist many tools able to support the knowledge management process in organizations. Due to a large quantity of information registered in these tools, they need to be efficient in the recovery of information. In this sense there exist several techniques, such as sophisticated search engines, classification and intelligent agents. These ways are by far important to the information recovery but, the user knows that exist an information and need to search. The present dissertation proposes a concept of a system which, instead of providing a form of the user to search the information, tries to find a form to present it together the functionalities the user employs when he/she performs his/her job. It means contextual information, which is presented according to the situation the user faces and which such information makes sense and adds value to the decisions. For the development of this system, concepts on project management and enterprise modeling are used. The results show that the concept is viable and can be utilized supporting systems to knowledge management. Finally the dissertation present the proposals of improvement for the concept and the solution developed.
7

Sistema para auxílio à recuperação contextualizada de informações em soluções de apoio à gestão do conhecimento / A system to aid in contextual recovery of information in supporting solutions to knowledge management

Savi, Antonio Francisco 16 October 2003 (has links)
Existem muitas ferramentas capazes de apoiar o processo de gestão do conhecimento nas organizações. A eficiência dessas ferramentas depende da capacidade de lidar com grandes massas de dados. Portanto, a recuperação é fundamental e é hoje uma área de pesquisa em ascensão, para a qual várias técnicas vêm sendo desenvolvidas, tais como sistemas sofisticados de classificação, mecanismos de busca e agentes inteligentes. Grande parte dessas tecnologias se baseia no princípio de que o usuário sabe da existência da informação e está a sua procura. Este trabalho propõe um conceito para a construção de sistemas que, ao invés de apenas disponibilizar mecanismos para a busca, procura filtrar as informações e apresentá-las em meio aos sistemas corporativos empregados pelo usuário nas suas tarefas rotineiras. Isto significa uma informação contextualizada, isto é, apresentada conforme a situação enfrentada pelo usuário e dentro da qual faz sentido e adiciona valor às decisões. O conceito proposto emprega conhecimentos sobre gestão de projetos e modelagem de empresa, de forma a proporcionar que as informações relevantes sejam oferecidas no ambiente onde o usuário interage com outros membros da equipe de projeto. Foram desenvolvidos, além do conceito, uma especificação detalhada e um protótipo da ferramenta. Os resultados demonstram que o conceito é viável e pode ser utilizado em sistemas de apoio à gestão do conhecimento. Encerra-se o trabalho apresentando as propostas de melhoria para o conceito e solução desenvolvidos. / There exist many tools able to support the knowledge management process in organizations. Due to a large quantity of information registered in these tools, they need to be efficient in the recovery of information. In this sense there exist several techniques, such as sophisticated search engines, classification and intelligent agents. These ways are by far important to the information recovery but, the user knows that exist an information and need to search. The present dissertation proposes a concept of a system which, instead of providing a form of the user to search the information, tries to find a form to present it together the functionalities the user employs when he/she performs his/her job. It means contextual information, which is presented according to the situation the user faces and which such information makes sense and adds value to the decisions. For the development of this system, concepts on project management and enterprise modeling are used. The results show that the concept is viable and can be utilized supporting systems to knowledge management. Finally the dissertation present the proposals of improvement for the concept and the solution developed.
8

Context, cognition and communication in language

Winters, James Richard January 2017 (has links)
Questions pertaining to the unique structure and organisation of language have a long history in the field of linguistics. In recent years, researchers have explored cultural evolutionary explanations, showing how language structure emerges from weak biases amplified over repeated patterns of learning and use. One outstanding issue in these frameworks is accounting for the role of context. In particular, many linguistic phenomena are said to to be context-dependent; interpretation does not take place in a void, and requires enrichment from the current state of the conversation, the physical situation, and common knowledge about the world. Modelling the relationship between language structure and context is therefore crucial for developing a cultural evolutionary approach to language. One approach is to use statistical analyses to investigate large-scale, cross-cultural datasets. However, due to the inherent limitations of statistical analyses, especially with regards to the inadequacy of these methods to test hypotheses about causal relationships, I argue that experiments are better suited to address questions pertaining to language structure and context. From here, I present a series of artificial language experiments, with the central aim being to test how manipulations to context influence the structure and organisation of language. Experiment 1 builds upon previous work in iterated learning and communication games through demonstrating that the emergence of optimal communication systems is contingent on the contexts in which languages are learned and used. The results show that language systems gradually evolve to only encode information that is informative for conveying the intended meaning of the speaker - resulting in markedly different systems of communication. Whereas Experiment 1 focused on how context influences the emergence of structure, Experiments 2 and 3 investigate under what circumstances do manipulations to context result in the loss of structure. While the results are inconclusive across these two experiments, there is tentative evidence that manipulations to context can disrupt structure, but only when interacting with other factors. Lastly, Experiment 4 investigates whether the degree of signal autonomy (the capacity for a signal to be interpreted without recourse to contextual information) is shaped by manipulations to contextual predictability: the extent to which a speaker can estimate and exploit contextual information a hearer uses in interpreting an utterance. When the context is predictable, speakers organise languages to be less autonomous (more context-dependent) through combining linguistic signals with contextual information to reduce effort in production and minimise uncertainty in comprehension. By decreasing contextual predictability, speakers increasingly rely on strategies that promote more autonomous signals, as these signals depend less on contextual information to discriminate between possible meanings. Overall, these experiments provide proof-of-concept for investigating the relationship between language structure and context, showing that the organisational principles underpinning language are the result of competing pressures from context, cognition, and communication.
9

Graphical and Non-speech Sound Metaphors in Email Browsing: An Empirical Approach. A Usability Based Study Investigating the Role of Incorporating Visual and Non-Speech Sound Metaphors to Communicate Email Data and Threads.

Alharbi, Saad T. January 2009 (has links)
This thesis investigates the effect of incorporating various information visualisation techniques and non-speech sounds (i.e. auditory icons and earcons) in email browsing. This empirical work consisted of three experimental phases. The first experimental phase aimed at finding out the most usable visualisation techniques for presenting email information. This experiment involved the development of two experimental email visualisation approaches which were called LinearVis and MatrixVis. These approaches visualised email messages based on a dateline together with various types of email information such as the time and the senders. The findings of this experiment were used as a basis for the development of a further email visualisation approach which was called LinearVis II. This novel approach presented email data based on multi-coordinated views. The usability of messages retrieval in this approach was investigated and compared to a typical email client in the second experimental phase. Users were required to retrieve email messages in the two experiments with the provided relevant information such as the subject, status and priority. The third experimental phase aimed at exploring the usability of retrieving email messages by using other type of email data, particularly email threads. This experiment investigated the synergic use of graphical representations with non-speech sounds (Multimodal Metaphors), graphical representations and textual display to present email threads and to communicate contextual information about email threads. The findings of this empirical study demonstrated that there is a high potential for using information visualisation techniques and non-speech sounds (i.e. auditory icons and earcons) to improve the usability of email message retrieval. Furthermore, the thesis concludes with a set of empirically derived guidelines for the use of information visualisation techniques and non-speech sound to improve email browsing. / Taibah University in Medina and the Ministry of Higher Education in Saudi Arabia.
10

Context-awareness for adaptive information retrieval systems

Agbele, Kehinde Kayode January 2014 (has links)
Philosophiae Doctor - PhD / This research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectiveness

Page generated in 0.1675 seconds