abstract: To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple sub- networks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input. / Dissertation/Thesis / Masters Thesis Computer Science 2017

Missense mutations account for more than 50% of the mutations known to be involved in human inherited diseases. Missense classification is a challenging task that involves sequencing of the genome, identifying the variations, and assessing their deleteriousness. This is a very laborious, time and cost intensive task to be carried out in the laboratory. Advancements in bioinformatics have led to several large-scale next-generation genome sequencing projects, and subsequently the identification of genome variations. Several studies have combined this data with information on established deleterious and neutral variants to develop machine learning based classifiers. There are significant issues with the missense classifiers due to which missense classification is still an open area of research. These issues can be classified under two broad categories: (a) Dataset overlap issue - where the performance estimates reported by the state-of-the-art classifiers are overly optimistic as they have often been evaluated on datasets that have significant overlaps with their training datasets. Also, there is no comparative analysis of these tools using a common benchmark dataset that contains no overlap with the training datasets, therefore making it impossible to identify the best classifier among them. Also, such a common benchmark dataset is not available. (b) Inadequate capture of vital biological information of the protein and mutations - such as conservation of long-range amino acid dependencies, changes in certain physico-chemical properties of the wild-type and mutant amino acids, due to the mutation. It is also not clear how to extract and use this information. Also, some classifiers use structural information that is not available for all proteins. In this study, we compiled a new dataset, containing around 2 - 15% overlap with the popularly used training datasets, with 18,036 mutations in 5,642 proteins. We reviewed and evaluated 15 state-of-the-art missense classifiers - SIFT, PANTHER, PROVEAN, PhD-SNP, Mutation Assessor, FATHMM, SNPs&GO, SNPs&GO3D, nsSNPAnalyzer, PolyPhen-2, SNAP, MutPred, PON-P2, CONDEL and MetaSNP, using the six metrics - accuracy, sensitivity, specificity, precision, NPV and MCC. When evaluated on our dataset, we observe huge performance drops from what has been claimed. Average drop in the performance for these 13 classifiers are around 15% in accuracy, 17% in sensitivity, 14% in specificity, 7% in NPV, 24% in precision and 30% in MCC. With this we show that the performance of these tools is not consistent on different datasets, and thus not reliable for practical use in a clinical setting. As we observed that the performance of the existing classifiers is poor in general, we tried to develop a new classifier that is robust and performs consistently across datasets, and better than the state-of-the-art classifiers. We developed a novel method of capturing long-range amino acid dependency conservation by boosting the conservation frequencies of substrings of amino acids of various lengths around the mutation position using AdaBoost learning algorithm. This score alone performed equivalently to the sequence conservation based tools in classifying missense mutations. Popularly used sequence conservation properties was combined with this boosted long-range dependency conservation scores using AdaBoost algorithm. This reduced the class bias, and improved the overall accuracy of the classifier. We trained a third classifier by incorporating changes in 21 important physico-chemical properties, due to the mutation. In this case, we observed that the overall performance further improved and the class bias further reduced. The performance of our final classifier is comparable with the state-of-the-art classifiers. We did not find any significant improvement, but the class-specific accuracies and precisions are marginally better by around 1-2% than those of the existing classifiers. In order to understand our classifier better, we dissected our benchmark dataset into: (a) seen and unseen proteins, and (b) pure and mixed proteins, and analysed the performance in detail. Finally we concluded that our classifier performs consistently across each of these categories of seen, unseen, pure and mixed protein.

abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2018

Text classification for companies is becoming more important in a world where an increasing amount of digital data are made available. The aim is to research whether five different machine learning algorithms can be used to automate the process of classification of invoice data and see which one gets the highest accuracy. Algorithms are in a later stage combined for an attempt to achieve higher results. N-grams are used, and results are compared in form of total accuracy of classification for each algorithm. A library in Python, called scikit-learn, implementing the chosen algorithms, was used. Data is collected and generated to represent data present on a real invoice where data has been extracted. Results from this thesis show that it is possible to use machine learning for this type of problem. The highest scoring algorithm (LinearSVC from scikit-learn) classifies 86% of all samples correctly. This is a margin of 16% above the acceptable level of 70%.

Trafikverket, the agency in charge of state road maintenance in Sweden, have a number of so-called Road Weather Information Systems (RWIS). The main purpose of the stations is to provide winter road maintenance workers with information to decide when roads need to be plowed and/or salted. Each RWIS have a number of sensors which make road weather-related measurements every 30 minutes. One of the sensors is dug into the road which can cause traffic disturbances and be costly for Trafikverket. Other RWIS sensors fail occasionally. This project aims at modelling a set of RWIS sensors using supervised machine learning algorithms. The sensors that are of interest to model are: Optic Eye, Track Ice Road Sensor (TIRS) and DST111. Optic Eye measures precipitation type and precipitation amount. Both TIRS and DST111 measure road surface temperature. The difference between TIRS and DST111 is that the former is dug into the road, and DST111 measures road surface temperature from a distance via infrared laser. Any supervised learning algorithm trained to model a given measurement made by a sensor, may only train on measurements made by the other sensors as input features. Measurements made by TIRS may not be used as input in modelling other sensors, since it is desired to see if TIRS can be removed. The following input features may also be used for training: road friction, road surface condition and timestamp. Scikit-learn was used as machine learning software in this project. An experimental approach was chosen to achieve the project results: A pre-determined set of supervised algorithms were compared using different amount of top relevant input features and different hyperparameter settings. Prior to achieving the results, a data preparation process was conducted. Observations with suspected or definitive errors were removed in this process. During the data preparation process, the timestamp feature was transformed into two new features: month and hour. The results in this project show that precipitation type was best modelled using Classification And Regression Tree (CART) on Scikit-learn default settings, achieving a performance score of Macro-F1test = 0.46 and accuracy = 0.84 using road surface condition, road friction, DST111 road surface temperature, hour and month as input features. Precipitation amount was best modelled using k-Nearest Neighbor (kNN); with k = 64 and road friction used as the only input feature, a performance score of MSEtest = 0.31 was attained. TIRS road surface temperature was best modelled with Multi-Layer Perceptron (MLP) using 64 hidden nodes and DST111 road surface temperature, road surface condition, road friction, month, hour and precipitation type as input features, with which a performance score of MSEtest = 0.88 was achieved. DST111 road surface temperature was best modelled using Random forest on Scikit-learn default settings with road surface condition, road friction, month, precipitation type and hour as input features, achieving a performance score of MSEtest = 10.16.

With a predictive model that can predict failures of a manufacturing machine, many benefits can be obtained. Unnecessary downtime and accidents can be avoided. In this study a wood chipper which has 12 replaceable knives was examined. The specific task was to create a predictive model that can predict if a knife change is needed or not. To create a predictive model, supervised machine learning was used. Decision forest was the algorithm used in this study. Data samples were collected from vibration measurements. Each sample was labeled with help of ocular inspections of the knives. Microsoft Azure learning studio was the workspace used to train all models. The data set acquired consist of 106 samples, were only 9 samples belongs to the minority class. Two strategies of training a model were used, with and without oversampling. The result for the best model without oversampling obtained 87.5% precision and 77.8% recall. The best model with oversampling achieved 79% precision and 86.7% recall. This result indicates that the trained models can be useful. However, the validity of the result has been hurt by a small data set and many uncertainness of acquiring the data set.

Made available in DSpace on 2015-03-26T13:53:32Z (GMT). No. of bitstreams: 1 texto completo.pdf: 3797598 bytes, checksum: 93618b9d18c5fd42a738d4d3d1f869f6 (MD5) Previous issue date: 2013-03-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The need for recognition of the vast territory of the São Francisco valley for defining priority areas for the implementation of recovery actions and environmental preservation motivated the proposition of this work. We attempted to find low-cost methods for performing this task on submedium, which includes drainage shedding for the trough between the municipalities of Bahia Pilão Arcado and Paulo Afonso. The sub-basin of the river Pacuí was taken as a pilot area for this job. Located in northern Bahia, has 1010,1 km2 and is fully inserted in the municipality of Campo Formoso. It is a tributary of the Salitre River, which rises in the Chapada Diamantina north and empties into the São Francisco river in Juazeiro, 20 km upstream of the seat. The sub-basin has just over 10.000 inhabitants and the main town is existing is Lajes dos Negros, which is distant 96 km from the municipal seat. The region has an average annual temperature of 23,7 oC, evapotranspiration from 1000 to 1400 mm, annual rainfall between 475 and 700 mm, according to the influence of relief, characterized as semi-arid climate , the type classification BSh by Koppen-Geiger. The mountains that surround the dividers topographic northern and western reaches maximum elevation of 1275 m. The center, south and east of the basin have gentle slope and elevation of the mouth is 465 m. Was tested using TM / Landsat 5 to find the best combinations of bands for the distinction of classes of use and land use in the lower basin San Francisco through the Maximum Likelihood classifier. We used the natural sensor TM bands except 6, which deals with the thermal infrared. Were produced four bands to participate in the artificial combinations: the first three bands of the principal component analysis (PCA) and vegetation index (NDVI). We analyzed 1023 different combinations. It was noticed that the PCA3 adds no good results due to the low amount of information. The band also has 2 bad performance often, but there are some cases where benefits. As for the other, it is ideal to use all or at best remove some of the visible region. The combination of natural and artificial bands is ideal. NDVI, PCA1, bands 4, 5 and 7 are the best that deliver results. With the selected bands, recognized the evolution of land use in the sub-basin of the river Pacuí through images TM/Lansat-5 current and historical. The result was the existence of more than 28.000 ha of heavily disturbed areas in the sub-basin, with 45% of the valley and 8% of the mountains are covered with vegetation degraded. We also used the NDVIs four TM/Landsat-5 to investigate deforestation and regeneration of vegetation cover in the last two decades. We employed a subtraction method of vegetation indices. The images used were from August 1992, May 2001, October 2001 and June 2011. For the production of NDVIs were made radiometric calibration, generation of surface reflectance and standardization of means and variances. The subtraction images were divided according to the twelve classes defined for use and occupation for the oldest image of the pair comparison. Each sector was analyzed fitting image pixels difference under nine different ranges: high, medium, low and very low deforestation; remain unchanged, and very low, low, medium and high regeneration. The intervals were calculated from the average by summing up one or down one and a half, two or three times the standard deviation respectively. Were dismissed as clearing those areas that were once agricultural soils or exposed. Only regeneration areas that were considered in more recent image were not classified as agriculture and in the past were exposed soils or pastures. The result was over 1000 ha of accumulated deforestation from 1992 to 2011. We also analyzed the characteristics of the soils of sub-basin through eleven soil profiles were determined and 10 different environments occurrence. Performed analyzes were physical, chemical and morphological routine. We have found the following classes: Neossolo Litólico Eutrófico típico, Cambissolo Háplico Tb Distrófico latossólico, Latossolo Vermelho- Amarelo Distrófico típico (2 profiles), Neossolo Litólico Distrófico típico, Neossolo Quartzarênico Hidromórfico típico, Cambissolo Háplico Tb Eutrófico latossólico (2 profiles), Cambissolo Háplico Tb Eutrófico léptico, Cambissolo Háplico Carbonático saprolítico e Neossolo Quartzarênico Órtico latossólico. It was noticed that the relief is a major factor for the differentiation of environments occurring in the more hilly the Neossolos Litólicos, the intermediate parts Cambissolos and parts flatter Latossolos. Soils that occur on limestone result eutrophic and on sandstone and detrital cover, dystrophic. The portions of the landscape under influence of Formation Bebedouro have soils rich in silt, strongly alkaline and high susceptibility to erosion. Any areas within the basin where it promotes loss of permeability shows strong signs of erosion. The mountainous region of the sub-basin and some areas of sandy soils for lowland present good condition of vegetation and constitute one of the last preserved parts of the region, and may account for much of the recharge of underground springs that keeps the river Pacuí perennial. Should be preserved, along with the huge speleological site. It is necessary to change the means of production based on unbridled exploitation of natural resources to activities that approximate environmental sustainability. The activity most striking is the extensive goat and sheep. It must be recovered to cover the slopes of the river Pacuí, the main focus of erosion. It should also control the runoff of roads, settlements and vegetation cover to shedding impoverished areas of greatest slope. / A necessidade de reconhecimento do vasto território da bacia do rio São Francisco para a definição de áreas prioritárias para a aplicação de ações de recuperação e preservação ambiental motivou a proposição deste trabalho.Buscou-se encontrar métodos de baixo custo para a execução desta tarefa no trecho submédio, que engloba a drenagem que verte para a calha entre os municípios baianos de Pilão Arcado e Paulo Afonso. A sub-bacia do rio Pacuí foi tomada como área piloto para este trabalho. Localiza-se no norte da Bahia, possui 1010,1 km² e está totalmente inserida no município de Campo Formoso. É afluente do rio Salitre, o qual nasce na Chapada Diamantina norte e deságua no rio São Francisco em Juazeiro, 20 km a montante da sede. A sub-bacia tem pouco mais de 10.000 habitantes e o principal povoado existente é Laje dos Negros, que dista 96 km da sede municipal. A região possui temperatura média anual de 23,7 ºC, evapotranspiração de 1000 a 1400 mm e precipitação anual entre 475 e 700 mm, de acordo com a influência do relevo, caracterizando o clima como semiárido, do tipo BSh pela classificação de Koppen-Geiger. As serras que delimitam os divisores topográficos norte e oeste atingem cota máxima de 1275 m. O centro, sul e leste da bacia possuem relevo suave e a cota da foz é 465 m. Foi testado o uso de imagens TM/Landsat 5 para encontrar as melhores combinações de bandas para a distinção de classes de uso e ocupação dos solos no Submédio São Francisco por meio do classificador Máxima Verossimilhança. Foram utilizadas as bandas naturais do sensor TM, exceto a 6, que trata do infravermelho termal. Foram produzidas quatro bandas artificiais para participar das combinações: as três primeiras bandas da análise de componentes principais (PCAs) e o índice de vegetação por diferença normalizada (NDVI). Foram analisadas 1023 diferentes combinações. Percebeu-se que a PCA3 não agrega bons resultados devido à sua baixa quantidade de informação. A banda 2 também tem mal desempenho frequentemente, mas há alguns casos em que traz benefícios. Quanto às demais, o ideal é utilizar todas ou no máximo remover algumas da região do visível. A combinação entre as bandas naturais e artificiais é o ideal. NDVI, PCA1, bandas 4, 5 e 7 são as que agregam melhores resultados. Com as bandas escolhidas, foi reconhecida a evolução do uso do solo na sub-bacia do rio Pacuí por meio de imagens TM/Lansat-5 atuais e históricas. O resultado foi a existência de mais de 28.000 ha de áreas fortemente antropizadas na sub-bacia, sendo que 45 % do vale e 8 % das serras estão com a cobertura vegetal degradada. Foram utilizados ainda os NDVIs de quatro imagens TM/Landsat-5 para investigar desmatamento e regeneração da cobertura vegetal nas duas últimas décadas. Foi empregado o método de subtração dos índices de vegetação. As imagens utilizadas foram de agosto de 1992, maio de 2001, outubro de 2001 e junho de 2011. Para a produção dos NDVIs foram feitas calibração radiométrica, geração de reflectância de superfície e uniformização de médias e variâncias. As imagens de subtração foram divididas segundo as doze classes de uso e ocupação definidas para a imagem mais antiga do par de comparação. Cada setor foi analisado enquadrando pixels de imagens diferença segundo nove diferentes intervalos: alto, médio, baixo e muito baixo desmatamento; inalteração; e muito baixa, baixa, média e alta regeneração. Os intervalos foram calculados a partir da média somando-se ou diminuindo-se uma, uma e meia, duas ou três vezes o desvio padrão respectivamente. Foram desconsideradas como desmatamento aquelas áreas que no passado eram agricultura ou solos expostos. Só foram consideradas regeneração áreas que na imagem mais recente não foram classificadas como agricultura e na do passado eram pastagens ou solos expostos. O resultado foi mais de 1000 ha de desmatamento acumulado no período 1992 a 2011. Analisou-se também as características dos solos da sub-bacia por meio de onze perfís de solo e foram determinados 10 diferentes ambientes de ocorrência. Realizaram-se análises físicas, químicas e morfológicas de rotina. Foram encontradas as seguintes classes: Neossolo Litólico Eutrófico típico, Cambissolo Háplico Tb Distrófico latossólico, Latossolo Vermelho-Amarelo Distrófico típico (2 perfis), Neossolo Litólico Distrófico típico, Neossolo Quartzarênico Hidromórfico típico, Cambissolo Háplico Tb Eutrófico latossólico (2 perfís), Cambissolo Háplico Tb Eutrófico léptico, Cambissolo Háplico Carbonático saprolítico e Neossolo Quartzarênico Órtico latossólico. Percebeu-se que o relevo é um dos fatores principais para a diferenciação dos pedoambientes, ocorrendo nas partes mais declivosas os Neossolos litólicos, nas partes intermediárias Cambissolos e nas partes mais planas Latossolos. Solos que ocorrem sobre calcário resultam eutróficos e sobre arenito e cobertura detríticas, distróficos. As porções da paisagem sob influencia da Formação Bebedouro possuem solos ricos em silte, fortemente alcalinos e com alta suscetibilidade à erosão. Quaisquer áreas dentro da bacia onde se promova perda da permeabilidade apresenta fortes sinais de erosão. A região serrana da sub-bacia e algumas áreas de solos arenosos de baixada apresentam bom estado de conservação da cobertura vegetal e constituem uma das últimas partes preservadas da região, e podem responder por grande parte da recarga dos mananciais subterrâneos que mantém o rio Pacuí perene. Devem ser preservadas, juntamente com o enorme patrimônio espeleológico local. Se faz necessária a mudança dos meios de produção baseados na exploração desregrada dos recursos naturais, para atividades que se aproximem da sustentabilidade ambiental. A atividade mais impactante é a caprino-ovinocultura extensiva. É preciso que seja recuperada a cobertura vegetal das encostas do rio Pacuí, principal foco de processos erosivos. Deve-se ainda controlar o escoamento superficial em estradas, povoados e em áreas com cobertura vegetal empobrecida que vertem para locais de maior declividade.

Submitted by ELISANGELA VENANCIO ANANIAS null (elisangelavenancio@hotmail.com) on 2016-09-20T13:49:53Z No. of bitstreams: 1 TextoCompletoTeseElisangelaANanias.pdf: 3060058 bytes, checksum: 7e988ffe3532cb47117a76b9685c08ba (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-09-21T15:23:47Z (GMT) No. of bitstreams: 1 ananias_ev_dr_rcla.pdf: 3060058 bytes, checksum: 7e988ffe3532cb47117a76b9685c08ba (MD5) / Made available in DSpace on 2016-09-21T15:23:47Z (GMT). No. of bitstreams: 1 ananias_ev_dr_rcla.pdf: 3060058 bytes, checksum: 7e988ffe3532cb47117a76b9685c08ba (MD5) Previous issue date: 2016-07-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A pesquisa teve como foco de estudo a formação de professores de Educação Física, a partir da leitura e análise dos programas de formação, com atenção às propostas de estágio curricular supervisionado em três contextos. Os objetivos foram: (a) compreender a influências do movimento de profissionalização do ensino nas propostas de estágio curricular supervisionado no contexto português, estadunidense e brasileiro; (b) identificar e analisar a constituição dos três programas de formação de professores estudados; (c) conhecer e a analisar as propostas de estágio curricular supervisionado nas três instituições pesquisadas e; (d) verificar a influência do movimento de profissionalização do ensino nessas três propostas de estágio curricular supervisionado. Optou-se pela pesquisa qualitativa, na modalidade estudo de casos múltiplos que foram compostos pela Universidade de Towson (EUA), Instituto Universitário da Maia (Portugal) e a Universidade Estadual Paulista, Campus Rio Claro (Brasil). Os participantes foram: três coordenadores de estágio curricular supervisionado, três professores supervisores de estágio supervisionado, quatro professores colaboradores e cinco estagiários, sendo os mesmos dos diferentes contextos de estudo. Como instrumentos de coleta de dados foram utilizadas a análise documental, observação assistemática e entrevista semiestruturada in loco. Para a análise dos dados utilizou-se as etapas previstas no interior de estudos de casos múltiplos contanto com o auxílio do software NVIVO. Para todas as etapas utilizou-se o termo de consentimento livre e esclarecido aprovado pelo comitê de ética. Sendo assim, como resultados apresentou-se a composição de três casos com os quais foi possível se discutir as nuances do processo de profissionalização e a relação com o estágio para com os cursos de Licenciatura em Educação Física. Os três programas estudados foram reformados a partir da ideia de profissionalização preconizada pelos Estados Unidos com os relatórios da década de 1980. Sobre os programas de formação, considerando-se estágio curricular supervisionado, verificou-se que são predominantemente organizados e conduzidos pelos docentes do ensino superior, e assim é necessário um trabalho de contextualização da realidade das escolas, para que a prática docente, seja mais eficiente no suporte gradativo à inserção de futuros docentes nas escolas de Educação Básica. Nesta perspectiva, reforçou-se a urgência de trabalhos em colaboração entre as instâncias Universidade e Escola e, especificamente sobre profissionalização no contexto do estágio curricular supervisionado, foram possíveis avanços na compreensão do peso das regionalidades socioculturais, de cada caso estudado. Assim, pontuamos que essas características precisam ser preservadas, e a partir delas, estabelecer diálogos com outras realidades visando a dinamização das estratégias e tomadas de decisão frente aos desafios que emergem da contemporaneidade, produzindo acesso permanente aos sujeitos que fazem e refazem a profissão docente cotidianamente. / The research had at its core the Physical Education Teacher Education based on the reading and analysis of the supervised curricular traineeship programs mainly focused in three contexts. The objectives were: a) understand the influences of the professionalization movement in teacher education by three traineeship proposals in Portugal, United States and Brazil; b) identify and analyze the constitution of the three teacher education programs; c) know and analyze the three traineeship proposals in each context ; d) verify the influence of the professionalization movement in these three traineeship proposals. It is a qualitative research in the form of multiple cases that were composed by Towson University, USA, Instituto Superior da Maia, Portugal, and the the University of São Paulo State, Rio Claro, Brazil. The informants were: three coordinators of the teacher education programs, three supervisors, four menthor teachers, and five teacher candidates, all being from the different contexts. Document analysis, non-systematic observation, and semi-structured interview on the spot were used as data-collecting instruments. For the data analysis, the steps provided within the study of multiple cases, and the software NVIVO were utilized as resources. Thus, the research presented as a result the composition of three cases through which it was possible to discuss the nuances of the professionalization process, and the relationship between the teacher education traineeship programs and a degree in Physical Education. The three studied programs were reformed preceding from the idea of professionalization advocated by the United States based on reports from the 1980s. Regarding the training programs, including supervised traineeship, it was verified that they are predominantly organized and conducted by higher education teachers. Therefore, it is necessary to contextualize the reality of schools so that the teaching practice is more efficient in supporting the gradual inclusion of future educators in primary and secondary education. In this regard, the urgent need for collaborative work between schools and universities was reinforced. Also, advances were made with respect to the comprehension of the weight of sociocultural factors of each case, which need to be preserved. Through such factors, the establishment of dialogues with other realities is possible, envisioning the promotion of strategies, and legitimate decisions in face of challenges that emerge from contemporaneity – producing permanent access to those who exert the teaching profession on a daily basis.

O estágio curricular supervisionado teve seu reconhecimento ampliado na legislação de ensino brasileira, especialmente após a publicação da Lei de Diretrizes e Bases da Educação 9.394/96, pois tem se mostrado um dos elementos centrais na formação de professores. Ao mesmo tempo em que ganha o reconhecimento legal, amplia-se a necessidade de que sejam feitas reflexões aprofundadas sobre a forma como as experiências do estágio têm sido vividas por estudantes e professores dos cursos de formação. O problema de pesquisa deste estudo é: Como os saberes são mobilizados por estudantes de um curso de Licenciatura em Educação Física em suas práticas docentes durante o estágio supervisionado no Ensino Médio? Foram adotados os princípios metodológicos da pesquisa qualitativa. A pesquisa foi realizada com nove estudantes-estagiários de um curso de licenciatura em Educação Física de uma instituição comunitária de ensino superior do interior do Rio Grande do Sul. A produção das informações se deu através de entrevistas narrativas, observações, análise do projeto pedagógico do curso e grupo de discussão. O processo analítico se deu através da triangulação entre as fontes do campo, as fontes teóricas e as interpretações da autora. Os resultados da pesquisa evidenciaram que os participantes reconhecem a relevância do estágio na sua formação profissional e consideram-no tempo e espaço de muitas aprendizagens, e, ainda, que essa experiência poderia ter seu espaço ampliado no currículo do curso. Entendem que o estágio contribui para que o início de carreira seja menos impactante, para que aprendam a movimentar-se na estrutura e na cultura escolar, construam relações humanas e ocupem a posição de professor com autoridade epistemológica. Em relação aos saberes necessários para ensinar, que se constituem, ao mesmo tempo, em aprendizagens advindas com o estágio, os participantes referiram a capacidade de escuta, mediação, negociação, diálogo e acolhimento; o posicionamento como sujeito de autoridade, integrante de um coletivo que compartilha, planeja e aprende junto; o conhecimento sobre os conteúdos da Educação Física e sua trajetória histórica; a elaboração e execução do planejamento de ensino e o conhecimento sobre o contexto escolar, a vida de professor e a vida na escola. Em relação às questões da formação, os participantes referiram que as disciplinas do curso ofereceram um importante suporte para o planejamento do estágio; que as atividades realizadas em escolas básicas, tanto no estágio como em outras situações acadêmicas, foram indispensáveis para compreender melhor o trabalho e a escola; que a formação para a pesquisa contribuiu para sua autonomia e que o estágio contribuiu para a ampliação do debate em sala de aula. As relações entre teoria e prática se mostraram uma questão ainda pouco compreendida pelos estudantes, que, de certa forma, desqualificam as disciplinas compreendidas como teóricas. Os estudantes entendem que as relações entre universidade e escola precisam ser intensificadas, no sentido de constituirmos uma formação mais integrada. A pesquisa evidenciou que há um desejo dos estagiários de, através do investimento em um bom ensino da Educação Física e da ampliação de seus conteúdos na escola, contribuir para seu reconhecimento enquanto componente curricular da Educação Básica. / The supervised curricular internship has had its recognition extended within the Brazilian educational legislation, especially after the edition of the Law 9.394/96, which establishes the guidelines for the national education, because it plays a key role in the teacher’s formation process. At the time it gains legal recognition, it increases the need for in-depth reflections on how the internship experiences have been experienced by students and teacher throughout the formation courses. The issue studied in the present work was how the different knowledges are mobilized by the students of a Bachelor’s Degree in Physical Education in their teaching practices during the supervised internship in high school? The methodological principles of the qualitative research have been used. The study has been carried out with nine students/interns of a Bachelor’s Degree in Physical Education from a community institution located in the countryside of Rio Grande do Sul. The data was produced through interviews, observations, analysis of course pedagogical projects and group discussions. The analytical process performed a triangulation of the field sources, theoretical sources and author interpretations. The results showed that the participants recognized the importance of the internship in their professional formation process and considered it a time and a place for many learnings, and also that this experience could be extended in the couse curriculum. They understand that the internship helped them to reduce the difficulties faced in the beginning of the professional practice, learn how to move within the school structure, build human relations and occupy the position of teacher and authority. Regarding the knowledge required to teach, considering at the same time the outcome of the internship experience, the participants mentioned the capacity of listening, mediation, negotiation, dialogue and acceptance; the position of a subject of authority, a member of a collective that share, plan and learn together; the knowledge of the Physical Education contents and its historical path; the preparation and implementation of the learning plan and the knowledge of the school context, the teacher's life and the school life. Regarding the formation issues, the participants mention that the course disciplines offer an important support for the planning of the internship; the activities carried out in basic schools, either in the internship or in other academic situations were indispensable for a better understanding of the work and the school; the preparation for the research contributed to develop their autonomy and the internship contributed to broaden the debate in the classroom. The relation between theory and practice stood as an issue still poorly understood by students, that in some way disqualify the subjects understood as theoretical. The students understand that the relations between university and school need to be intensified in the sense of achieving an more integrated formation. The study showed that there is a desire of interns, through investing in a good PE teaching and expanding its contents in school, to contribute to its recognition as a curricular component of the Basic Education.

O monitoramento do meio ambiente e suas mudanças requer a análise de uma grade quantidade de imagens muitas vezes coletadas por satélites. No entanto, variações nos sinais devido a mudanças nas condições atmosféricas frequentemente resultam num deslocamento da distribuição dos dados para diferentes locais e datas. Isso torna difícil a distinção dentre as várias classes de uma base de dados construída a partir de várias imagens. Neste trabalho introduzimos uma nova abordagem de classificação supervisionada, chamada Classifica-Normaliza-Classifica (CNC), para amenizar o problema de deslocamento dos dados. A proposta é implementada usando dois classificadores. O primeiro é treinado em imagens não normalizadas de refletância de topo de atmosfera para distinguir dentre pixels de uma classe de interesse (CDI) e pixels de outras categorias (e.g. floresta versus não-floresta). Dada uma nova imagem de teste, o primeiro classificador gera uma segmentação das regiões da CDI e então um vetor mediano é calculado para os valores espectrais dessas áreas. Então, esse vetor é subtraído de cada pixel da imagem e portanto fixa a distribuição de dados de diferentes imagens num mesmo referencial. Finalmente, o segundo classificador, que é treinado para minimizar o erro de classificação em imagens já centralizadas pela mediana, é aplicado na imagem de teste normalizada no segundo passo para produzir a segmentação binária final. A metodologia proposta foi testada para detectar desflorestamento em pares de imagens co-registradas da Landsat 8 OLI sobre a floresta Amazônica. Experimentos usando imagens multiespectrais de refletância de topo de atmosfera mostraram que a CNC obteve maior acurácia na detecção de desflorestamento do que classificadores aplicados em imagens de refletância de superfície fornecidas pelo United States Geological Survey. As acurácias do método proposto também se mostraram superiores às obtidas pelas máscaras de desflorestamento do programa PRODES. / Monitoring natural environments and their changes over time requires the analysis of a large amount of image data, often collected by orbital remote sensing platforms. However, variations in the observed signals due to changing atmospheric conditions often result in a data distribution shift for different dates and locations making it difficult to discriminate between various classes in a dataset built from several images. This work introduces a novel supervised classification framework, called Classify-Normalize-Classify (CNC), to alleviate this data shift issue. The proposed scheme uses a two classifier approach. The first classifier is trained on non-normalized top-of-the-atmosphere reflectance samples to discriminate between pixels belonging to a class of interest (COI) and pixels from other categories (e.g. forest vs. non-forest). At test time, the estimated COI’s multivariate median signal, derived from the first classifier segmentation, is subtracted from the image and thus anchoring the data distribution from different images to the same reference. Then, a second classifier, pre-trained to minimize the classification error on COI median centered samples, is applied to the median-normalized test image to produce the final binary segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. Experiments using top-of-the-atmosphere multispectral reflectance images showed that the deforestation was mapped by the CNC framework more accurately as compared to running a single classifier on surface reflectance images provided by the United States Geological Survey (USGS). Accuracies from the proposed framework also compared favorably with the benchmark masks of the PRODES program.