952 resultados para monolithic reasoning


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Esta pesquisa trata do itinerário de uma estratégia de formação teológica ecumênica surgida no arcabouço de uma organização de cunho protestante. Desde as suas origens intuiu-se que esta estratégia poderia ser desenvolvida mediante o apóio a teologias contextuais incipientes na época. Esse processo compreende um período de vinte anos, entre 1980 e 2000, durante o qual é possível identificar duas grandes etapas. A primeira, sob o nome de Fundo Especial para a Educação Teológica Ecumênica na América Latina (FEPETEAL), aconteceu entre 1980 e 1988. Da sua criação participou um movimento plural, limitado ao âmbito protestante. Embora tivesse a melhor boa vontade no que se refere ao objetivo, o FEPETEAL ficou o tempo todo enredado no repasse de verbas para evitar o colapso dos seminários ecumênicos. Além disso, esta etapa ficou marcada por certo elitismo ecumênico , aliás, de feição masculinizante. A segunda etapa, herdeira orgânica da primeira, chamou-se Comunidade de Educação Teológica Ecumênica Latino-Americana e Caribenha (CETELA), de 1988-2000. Desta vez, o impacto de um movimento amplo e plural presente em várias instituições teológicas mais novas e em algumas das antigas transformou a organização a ponto de criar uma Nova CETELA a partir de 1993. No seu bojo aparecem como protagonistas diversos pensamentos teológicos comprometidos com as reivindicações de setores longamente marginalizados de Abya-Yala, por razões étnicas, culturais, sociais, religiosas e de gênero. Este movimento fazia parte de processos que a partir de horizontes pluriculturais resistiam ao pensamento único da lógica neoliberal. Da interface produzida entre estes setores emergentes, os velhos representantes da TL e a necessidade das instituições teológicas de terem uma oferta educativa encarnada na diversidade da região surgiu uma proposta ecumênica nova. Trata-se de assumir a transversalidade como instrumento hermenêutico que possibilite a transformação intersubjetiva entre saberes diferentes, como resultado de uma tarefa teológico/pedagógica. PALAVRAS/CONCEITOS-CHAVE: Instituições teológicas; sujeitos/artífices teológicos; razão monolítica; razão transversal/transversalidade; Abya-Yala; formação teológica ecumênica.

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This paper describes an extended case-based reasoning model that addresses the notion of situatedness in designing through constructive memory. The model is illustrated through an application for predicting the corrosion rate for a specific material on a specific building.

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Technical Report to accompany Ownership for Reasoning About Parallelism. Documents type system which captures effects and the operational semantics for the language which is presented as part of the paper.

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This paper has two central purposes: the first is to survey some of the more important examples of fallacious argument, and the second is to examine the frequent use of these fallacies in support of the psychological construct: Attention Deficit Hyperactivity Disorder (ADHD). The paper divides 12 familiar fallacies into three different categories—material, psychological and logical—and contends that advocates of ADHD often seem to employ these fallacies to support their position. It is suggested that all researchers, whether into ADHD or otherwise, need to pay much closer attention to the construction of their arguments if they are not to make truth claims unsupported by satisfactory evidence, form or logic.

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An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).