INF - Instituto de Informática
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Navegando INF - Instituto de Informática por Autor "Amorim, Leonardo Afonso"
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Item Agente para suporte à decisão multicritério em gestão pública participativa(Universidade Federal de Goiás, 2014-09-26) Amorim, Leonardo Afonso; Patto, Vinicius Sebba; Bulcão Neto, Renato de Freitas; http://lattes.cnpq.br/5627556088346425; Bulcão Neto, Renato de Freitas; Sene Junior, Iwens Gervásio; Patto, Vinicius Sebba; Cruz Junior, Gelson daDecision making in public management is associated with a high degree of complexity due to insufficient financial resources to meet all the demands emanating from various sectors of society. Often, economic activities are in conflict with social or environmental causes. Another important aspect in decision making in public management is the inclusion of various stakeholders, eg public management experts, small business owners, shopkeepers, teachers, representatives of social and professional classes, citizens etc. The goal of this master thesis is to present two computational agents to aid decision making in public management as part of ADGEPA project: Miner Agent (MA) and Agent Decision Support (DSA). The MA uses data mining techniques and DSA uses multi-criteria analysis to point out relevant issues. The context in which this work fits is ADGEPA project. The ADGEPA (which means Digital Assistant for Participatory Public Management) is an innovative practice to support participatory decision making in public resources management. The main contribution of this master thesis is the ability to assist in the discovery of patterns and correlations between environmental aspects that are not too obvious and can vary from community to community. This contribution would help the public manager to make systemic decisions that in addition to attacking the main problem of a given region would decrease or solve other problems. The validation of the results depends on actual data and analysis of public managers. In this work, the data were simulated.Item Exploiting parallelism in document similarity tasks with applications(Universidade Federal de Goiás, 2019-09-05) Amorim, Leonardo Afonso; Martins, Wellington Santos; http://lattes.cnpq.br/3041686206689904; Martins, Wellington Santos; Vincenzi, Auri Marcelo Rizzo; Rodrigues, Cássio Leonardo; Rosa, Thierson Couto; Martins, WeberThe amount of data available continues to grow rapidly and much of it corresponds to text expressing human language, that is unstructured in nature. One way of giving some structure to this data is by converting the documents to a vector of features corresponding to word frequencies (term count, tf-idf, etc) or word embeddings. This transformation allows us to process textual data with operations such as similarity measure, similarity search, classification, among others. However, this is only possible thanks to more sophisticated algorithms which demand higher computational power. In this work, we exploit parallelism to enable the use of parallel algorithms to document similarity tasks and apply some of the results to an important application in software engineering. The similarity search for textual data is commonly performed through a k nearest neighbor search in which pairs of document vectors are compared and the k most similar are returned. For this task we present FaSSTkNN, a fine-grain parallel algorithm, that applies filtering techniques based on the most common important terms of the query document using tf-idf. The algorithm implemented on a GPU improved the top k nearest neighbors search by up to 60x compared to a baseline, also running on a GPU. Document similarity using tf-idf is based on a scoring scheme for words that reflects how important a word is to a document in a collection. Recently a more sophisticated similarity measure, called word embedding, has become popular. It creates a vector for each word that indicates co-occurrence relationships between words in a given context, capturing complex semantic relationships between words. In order to generate word embeddings efficiently, we propose a fine-grain parallel algorithm that finds the k less similar or farthest neighbor words to generate negative samples to create the embeddings. The algorithm implemented on a multi-GPU system scaled linearly and was able to generate embeddings 13x faster than the original multicore Word2Vec algorithm while keeping the accuracy of the results at the same level as those produced by standard word embedding programs. Finally, we applied our accelerated word embeddings solution to the problem of assessing the quality of fixes in Automated Software Repair. The proposed implementation was able to deal with large corpus, in a computationally efficient way, being a promising alternative to the processing of million source code files needed for this task.