La cartographie du couvert végétal est un outil essentiel au suivi et à la gestion et des milieux « naturels ». Des cartes caractérisant les essences forestières à l'échelle régionale sont nécessaires pour la gestion des milieux forestiers. Les séries temporelles d'images satellitaires optiques à moyenne résolution spatiale, peuvent permettre de satisfaire ce besoin. L'objectif de cette thèse est d'améliorer la classification supervisée d'une série temporelle afin de produire des cartes à l'échelle régionale détaillant la composition en essences de la végétation forestière. Nous avons d'abord évalué l'apport de la stratification du site d'étude pour améliorer les résultats de la classification d'une série temporelle d'images MODIS. Le recours à une stratification à partir d'une segmentation orientée objet améliore la classification supervisée, avec une augmentation de la valeur de Kappa et du taux de rejet des pixels à classer. Un seuil minimal et un seuil maximal de la surface de végétation à classer ont été identifiés, correspondant respectivement à un taux de rejet trop élevé et à une absence d'effet de la stratification. Nous avons ensuite évalué l'influence de l'organisation de la série temporelle d'images à moyenne résolution spatiale et du choix de l'algorithme de classification. Cette évaluation a été effectuée pour trois algorithmes (maximum de vraisemblance, Support Vector Machine, Random Forest) en faisant varier les caractéristiques de la série temporelle. On observe un effet de la temporalité et de la radiométrie sur la précision de la classification particulièrement significatif et la supériorité de l'algorithme Random Forest. Sur le plan thématique, des confusions subsistent et certains mélanges d'essences sont mal distingués. Nous avons alors cherché à évaluer l'apport du changement de résolution spatiale des images composant la série temporelle pour améliorer les résultats de classification. Les conclusions effectuées précédemment avec les données MODIS sont confortées, ce qui permet de conclure qu'elles sont indépendantes des données d'entrée et de leur résolution spatiale. Une amélioration significative est apportée par le changement de résolution spatiale, avec une augmentation de l'indice de Kappa de 0,60 à 0,72 obtenue grâce à la diminution de la proportion de pixels mixtes. Quelle que soit la résolution spatiale des images utilisées, les résultats obtenus montrent que la définition d'une procédure optimale améliore sensiblement les résultats de la classification. / Forest cover mapping is an essential tool for forest management. Detailed maps, characterizing forest types at a régional scale, are needed. This need can be fulfilled by médium spatial resolution optical satellite images time sériés. This thesis aims at improving the supervised classification procédure applied to a time sériés, to produce maps detailing forest types at a régional scale. To meet this goal, the improvement of the results obtained by the classification of a MODIS time sériés, performed with a stratification of the study area, was assessed. An improvement of classification accuracy due to stratification built by object-based image analysis was observed, with an increase of the Kappa index value and an increase of the reject fraction rate. These two phenomena are correlated to the classified végétation area. A minimal and a maximal value were identified, respectively related to a too high reject fraction rate and a neutral stratification impact.We carried out a second study, aiming at assessing the influence of the médium spatial resolution time sériés organization and of the algorithm on classification quality. Three distinct classification algorithms (maximum likelihood, Support Vector Machine, Random Forest) and several time sériés were studied. A significant improvement due to temporal and radiométrie effects and the superiority of Random Forest were highlighted by the results. Thematic confusions and low user's and producer's accuracies were still observed for several classes. We finally studied the improvement brought by a spatial resolution change for the images composing the time sériés to discriminate classes of mixed forest species. The conclusions of the former study (MODIS images) were confirmed with DEIMOS images. We can conclude that these effects are independent from input data and their spatial resolution. A significant improvement was also observed with an increase of the Kappa index value from 0,60 with MODIS data to 0,72 with DEIMOS data, due to a decrease of the mixed pixels rate.

We have a wide-range of classifiers today that are employed in numerous applications, from credit scoring to speech-processing, with great technical and commercial success. No classifier, however, exists that will outperform all other classifiers on all classification tasks, and the process of classifier selection is still mainly one of trial and error. The optimal classifier for a classification task is determined by the characteristics of the data set employed; understanding the relationship between data characteristics and the performance of classifiers is therefore crucial to the process of classifier selection. Empirical and theoretical approaches have been employed in the literature to define this relationship. None of these approaches have, however, been very successful in accurately predicting or explaining classifier performance on real-world data. We use theoretical properties of classifiers to identify data characteristics that influence classifier performance; these data properties guide us in the development of measures that describe the relationship between data characteristics and classifier performance. We employ these data measures on real-world and artificial data to construct a meta-classification system. We use theoretical properties of classifiers to identify data characteristics that influence classifier performance; these data properties guide us in the development of measures that describe the relationship between data characteristics and classifier performance. We employ these data measures on real-world and artificial data to construct a meta-classification system. The purpose of this meta-classifier is two-fold: (1) to predict the classification performance of real-world classification tasks, and (2) to explain these predictions in order to gain insight into the properties of real-world data. We show that these data measures can be employed successfully to predict the classification performance of real-world data sets; these predictions are accurate in some instances but there is still unpredictable behaviour in other instances. We illustrate that these data measures can give valuable insight into the properties and data structures of real-world data; these insights are extremely valuable for high-dimensional classification problems. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

This thesis proposes a novel approach to attribute clustering. It exploits the strength of semi-supervised learning to improve the quality of attribute clustering particularly when labeled data is limited. The significance of this work derives in part from the broad, and increasingly important, usage of attribute clustering to address outstanding problems within the machine learning community. This form of clustering has also been shown to have strong practical applications, being usable in heavyweight industrial applications. Although researchers have focused on supervised and unsupervised attribute clustering in recent years, semi-supervised attribute clustering has not received substantial attention. In this research, we propose an innovative two step iterative semi-supervised attribute clustering framework. This new framework, in each iteration, uses the result of attribute clustering to improve a classifier. It then uses the classifier to augment the training data used by attribute clustering in next iteration. This iterative framework outputs an improved classifier and attribute clustering at the same time. It gives more accurate clusters of attributes which better fit the real relations between attributes. In this study we proposed two new usages for attribute clustering to improve classification: solving the automatic view definition problem for multi-view learning and improving missing attribute-value handling at induction and prediction time. The application of these two new usages of attribute clustering in our proposed semi-supervised attribute clustering is evaluated using real world data sets from different domains.

Identifying differing opinions on a given topic as expressed by multiple people (as in a set of written reviews for a given product, for example) presents challenges. Opinions about a particular subject are often nuanced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion on the subject. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating the overall ratings of such products and services. I make two useful and useable contributions in working with opinionated text. First, I present my implementation of a semi-supervised co-training machine classification method for identifying both product aspects (features of products) and sentiments expressed about such aspects. It offers better precision than fully-supervised methods while requiring much less text to be manually tagged (a time-consuming process). This algorithm can also be run in a fully supervised manner when more data is available. Second, I apply this co-training approach to reviews of restaurants and various electronic devices; such text contains both factual statements and opinions about features/aspects of products. The algorithm automatically identifies the product aspects and the words that indicate aspect-specific opinion polarity, while largely avoiding the problem of misclassifying the products themselves as inherently positive or negative. This method performs well compared to other approaches. When run on a set of reviews of five technology products collected from Amazon, the system performed with some demonstrated competence (with an average precision of 0.83) at the difficult task of simultaneously identifying aspects and sentiments, though comparison to contemporaries' simpler rules-based approaches was difficult. When run on a set of opinionated sentences about laptops and restaurants that formed the basis of a shared challenge in the SemEval-2014 Task 4 competition, it was able to classify the sentiments expressed about aspects of laptops better than any team that competed in the task (achieving 0.72 accuracy). It was above the mean in its ability to identify the aspects of restaurants about which people expressed opinions, even when co-training using only half of the labelled training data at the outset. While the SemEval-2014 aspect-based sentiment extraction task considered only separately the tasks of identifying product aspects and determining their polarities, I take an extra step and evaluate sentences as a whole, inferring aspects and the aspect-specific sentiments expressed simultaneously, a more difficult task that seems more applicable to real-world tasks. I present first results of this sentence-level task. The algorithm uses both lexical and syntactic information in a manner that is shown to be able to handle new words that it has never before seen. It offers some demonstrated ability to adapt to new subject domains for which it has no training data. The system is characterizable by very high precision and weak-to-average recall and it estimates its own confidence in its predictions; this characteristic should make the algorithm suitable for use on its own or for combination in a confidence-based voting ensemble. The software created for and described in the course of this dissertation is made available online.

Cette thèse porte sur la segmentation supervisée de flux vidéo dans un contexte applicatif lié à la reconnaissance d'actions de la vie courante.La méthode de segmentation proposée est dérivée la méthode des modèles de formes implicites (Implicit Shape Model) et s'obtient en optimisant les votes présents dans cette méthode d'élection.Nous démontrons que cette optimisation (dans un contexte de fenêtre temporelle glissante) peut être exprimée de manière équivalente dans le formalisme des SVM en imposant une contrainte de cohérence temporelle à l'apprentissage, ou, en représentant la fenêtre glissante selon une décomposition pyramidale dense.Tout ce processus est validé expérimentalement sur un jeu de données de la littérature de segmentation supervisée.Il y surpasse les autres méthodes de type modèles de formes implicites et le SVM linéaire standard.La méthode proposée est ensuite mise en œuvre dans le cadre de la segmentation supervisée d'actions.Pour cela, des primitives dédiées sont extraites des données squelette de la personne d'intérêt obtenues grâce à des logiciels standards.Ces primitives sont ensuite quantifiées puis utilisées par la méthode d'élection.Ce système de segmentation d'actions obtient les meilleurs scores de l'état de l'art sur un jeu de données de la littérature de reconnaissance d'actions, ce qui valide cette combinaison des primitives et de la méthode d'élection. / This thesis focuses on the supervised segmentation of video streams within the application context of daily action recognition.A segmentation algorithm is obtained from Implicit Shape Model by optimising the votes existing in this polling method.We prove that this optimisation can be linked to the sliding windows plus SVM framework and more precisely is equivalent with a standard training by adding temporal constraint, or, by encoding the data through a dense pyramidal decomposition. This algorithm is evaluated on a public database of segmentation where it outperforms other Implicit Shape Model like methods and the standard linear SVM.This algorithm is then integrated into a action segmentation system.Specific features are extracted from skeleton obtained from the video by standard software.These features are then clustered and given to the polling method.This system, combining our feature and our algorithm, obtains the best published performance on a human daily action segmentation dataset.

Ces dernières années, l’intérêt pour l’apprentissage automatique n’a cessé d’augmenter dans des domaines aussi variés que la reconnaissance d’images ou l’analyse de données médicales. Cependant, une limitation du cadre classique PAC a récemment été mise en avant. Elle a entraîné l’émergence d’un nouvel axe de recherche : l’Adaptation de Domaine, dans lequel on considère que les données d’apprentissage proviennent d’une distribution (dite source) différente de celle (dite cible) dont sont issues les données de test. Les premiers travaux théoriques effectués ont débouché sur la conclusion selon laquelle une bonne performance sur le test peut s’obtenir en minimisant à la fois l’erreur sur le domaine source et un terme de divergence entre les deux distributions. Trois grandes catégories d’approches s’en inspirent : par repondération, par reprojection et par auto-étiquetage. Dans ce travail de thèse, nous proposons deux contributions. La première est une approche de reprojection basée sur la théorie du boosting et s’appliquant aux données numériques. Celle-ci offre des garanties théoriques intéressantes et semble également en mesure d’obtenir de bonnes performances en généralisation. Notre seconde contribution consiste d’une part en la proposition d’un cadre permettant de combler le manque de résultats théoriques pour les méthodes d’auto-étiquetage en donnant des conditions nécessaires à la réussite de ce type d’algorithme. D’autre part, nous proposons dans ce cadre une nouvelle approche utilisant la théorie des (epsilon, gamma, tau)-bonnes fonctions de similarité afin de contourner les limitations imposées par la théorie des noyaux dans le contexte des données structurées / During the past few years, an increasing interest for Machine Learning has been encountered, in various domains like image recognition or medical data analysis. However, a limitation of the classical PAC framework has recently been highlighted. It led to the emergence of a new research axis: Domain Adaptation (DA), in which learning data are considered as coming from a distribution (the source one) different from the one (the target one) from which are generated test data. The first theoretical works concluded that a good performance on the target domain can be obtained by minimizing in the same time the source error and a divergence term between the two distributions. Three main categories of approaches are derived from this idea : by reweighting, by reprojection and by self-labeling. In this thesis work, we propose two contributions. The first one is a reprojection approach based on boosting theory and designed for numerical data. It offers interesting theoretical guarantees and also seems able to obtain good generalization performances. Our second contribution consists first in a framework filling the gap of the lack of theoretical results for self-labeling methods by introducing necessary conditions ensuring the good behavior of this kind of algorithm. On the other hand, we propose in this framework a new approach, using the theory of (epsilon, gamma, tau)- good similarity functions to go around the limitations due to the use of kernel theory in the specific context of structured data

Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.

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No. of bitstreams: 1 wallacealvescabral.pdf: 1911282 bytes, checksum: dfb9f94d95bbbd571aaeb091331fa4a2 (MD5) Previous issue date: 2015-04-14 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho, apresentamos atividades desenvolvidas com os estudantes das disciplinas de Estágio Supervisionado em Química no ano de 2013 na Universidade Federal de Juiz de Fora (UFJF), discutindo e propondo movimentos de leitura e escrita que se afastam da produção mecânica dos relatórios técnicos, possibilitando (re)pensar a linguagem e a escola. As recentes reformas que ocorreram nos cursos de licenciatura, a partir das exigências da LDB 9394/96, buscam abandonar o modelo da racionalidade técnica, presente em muitos cursos de formação de professores ainda hoje. Dentre os diversos impactos deste modelo, podemos pensar na linguagem, muitas vezes, vista como transparente, apontando que o texto possui sentidos prontos e únicos esperando para serem decifrados. Dessa forma, ancoramo-nos nos pressupostos teórico-metodológicos da Análise de Discurso de Linha Francesa, buscando o afastamento da transparência da linguagem. Por meio da revisão de literatura, buscamos a articulação entra a linguagem e a disciplina de Estágio Supervisionado, investigando as possibilidades de trabalho dentro destes dois campos. Do ponto de vista metodológico, além das discussões que foram desenvolvidas nas dependências da UFJF e observações escolares, os estudantes produziram quatro relatos, os quais foram elaborados num processo dialógico de escrita e reescrita. Tais relatos foram compilados pelos estudantes em um capítulo de um livro de estágio, entregue aos professores orientadores e à direção das escolas nas quais o Estágio foi realizado. A partir dos caminhos percorridos pelos discentes, elaboramos uma roda de conversas ao final da disciplina com o intuito de perceber a influência dessas atividades em seus hábitos de leitura e escrita. Diante da transcrição da roda de conversas, categorias de análise emergiram e algumas considerações puderam ser feitas. Percebemos, nas falas dos estudantes, a dicotomia ainda existente nos cursos de formação de professores de química, nos quais as disciplinas específicas do curso de química possibilitam pensar a linguagem enquanto ferramenta e meio para aperfeiçoar os trabalhos acadêmicos. Em contraposição a isso, os universitários apontaram as disciplinas da licenciatura em Química como meio de discutir outras questões da linguagem. Com relação aos caminhos percorridos pelos estudantes na construção dos relatos, percebemos que o movimento inicial foi o de digitar palavras-chave do tema de interesse no buscador Google e selecionar os trabalhos que poderiam fazer conexões com os seus relatos. Além disso, houve a busca em disciplinas já cursadas, apontando para relações intertextuais que foram estabelecidas pelos estudantes. De modo geral, as fontes utilizadas pelos discentes foram as acadêmicas, marcando a relação de forças existente entre os textos acadêmicos e os não acadêmicos dentro das universidades. Com relação a escrita no formato de relatório ou relatos, os estudantes apontaram a preferência dos relatos na disciplina. Apostamos, neste trabalho, no incentivo à escrita na formação inicial de professores como uma porta para mudanças na educação básica. / In this paper, we present activities developed with students of Supervised Internship in Chemistry disciplines in 2013 at Federal University of Juiz de Fora (UFJF), discussing and proposing reading and writing motions that deviate from the mechanical production of technical reports, enabling (re) think the language and the school. Recent reforms that occurred in graduation courses from the requirements of LDB, seek leave the technical rationality model, present in many teacher training courses even today. Among the many impacts of this model, we can often think of language, seen as transparent pointing out that the text contains prompts meanings and unique waiting to be deciphered. Therefore, we anchored in the theoretical and methodological assumptions of the French Discourse Analysis, seeking the removal of the language transparency. Through literature review, we seek the articulation between language and Supervised Internship discipline, investigating employment opportunities within these two fields. From a methodological point of view, beyond the discussions that have been developed in UFJF dependencies and school observations, the students produced four reports, which were prepared in a dialogic process of writing and rewriting. These reports were compiled by the students in a chapter of an internship book, delivered to mentor teachers and principals in which the Internship was performed. From the paths followed by the students, we prepared a discussion at the end of the course, in order to understand the influence of these activities in their reading and writing habits. Front of the discussion transcript, analysis categories emerged and some considerations can be made. We realized in the students speech still has the dichotomy in chemistry teacher training courses, in which te specific disciplines of such a course allow to think the language as a tool a means of improving the academic papers. In contrast to this, the university students pointed chemistry graduation disciplines as a means to discuss other language issues. Regarding the paths followed by students in building reports, we realized that the first moviment was a typing theme keywords of interest in the google search engine and select the papers that could make connections to their reports. In addiction, there was the search for subjects already processed, pointing to intertextual relations that where established by students. In general, the sources used by them were the academic, marking the relation of forces existing between the academic texts and not academics within university. With regard to the written report format, the students indicated the preference of reports in the discipline. We bet, in this paper, encouraging writing in initial teacher training as a gateway to changes in basic education.

o presente estudo teve por objetivo investigar a influência de duas diferentes resiliências de materiais de amortecimento e de dois tipos de cabedais de calçados esportivos na cinemática e cinética de membro inferior e na percepção do conforto durante a corrida. Também investigamos as possíveis relações entre o conforto percebido e as variáveis biomecânicas capturadas. Para tal, foram avaliados 42 corredores recreacionais adultos, com no mínimo de um ano de experiência em corrida de rua, com mínimo de dois treinos regulares por semana, e com volume de treino semanal superior a 5 km. Foram avaliadas quatro condições de calçados aleatorizadas para cada corredor (material de amortecimento de baixa resiliência e cabedal estruturado, material amortecimento de alta resiliência e cabedal estruturado, material de amortecimento de baixa resiliência e cabedal minimalista, e material amortecimento de alta resiliência e cabedal minimalista). Após avaliação antropométrica e postural do complexo tornozelo/pé, os corredores realizaram corridas em uma pista de 25 metros em laboratório. A avaliação biomecânica foi realizada usando seis câmeras infravermelhas (VICON T-40, Oxford, UK) a 300 Hz, sincronizadas a duas plataformas de força (AMTI BP-600600, Watertown, USA) para aquisição da força reação do solo a 1200 Hz, e palmilhas instrumentas com sensores capacitivos (Pedar X System, Novel, Munique, Alemanha) a 100 Hz. A percepção subjetiva de conforto em cada condição foi avaliada por meio de um questionário de conforto para calçados. As comparações estatísticas entre os calçados foram verificadas por meio de análises de variância (ANOVAs) para medidas repetidas, e correlação de Pearson para verificar as relações entre o conforto e as variáveis biomecânicas (a=O,05). Realizou-se uma análise de Machine Learning para capturar variáveis da série temporal completa das curvas de cinemática e cinética que discriminassem os calçados estudados. Construímos uma matriz de entrada nas dimensões 1080 x 1242 para a análise por Machine learning. Os resultados demonstram que há uma interação entre as condições de cabedal e material de amortecimento que faz com que as comparações de resiliência se comportem de forma distinta para cabedais minimalistas e para cabedais estruturados. Contrariamente ao esperado, para os calçados de cabedal estruturado, as resiliências não foram diferentes entre si, e para o cabedal minimalista, os corredores apresentaram impactos mais altos com o material de baixa resiliência. A estrutura de cabedal influenciou a absorção de impacto, onde o cabedal minimalista apresentou impactos mais altos que o cabedal estruturado. Sobre o conforto, a condição de cabedal minimalista e material de baixa resiliência obteve as piores notas em cinco de nove quesitos do questionário. Em alguns quesitos ele foi o pior avaliado dentre todas as demais condições (como no amortecimento do calcanhar e no conforto geral). O cabedal minimalista recebeu pior avaliação que os cabedais estrutura dos no quesito controle médio-lateral da avaliação de conforto. Observou-se que a correlação entre as variáveis biomecânicas e as variáveis de conforto considerando todos os calçados conjuntamente, apesar de apresentarem valores significativos para algumas associações, foram sempre correlações fracas, abaixo de 30%. Ao se analisar cada condição de calçado isoladamente, em algumas se observou correlação moderada entre as variáveis biomecânicas e o conforto (r >31%, p < O,05), o que não se verificou em outras condições de calçados. Cada calçado gera condições particulares que favorecem ou não a associação entre conforto e repostas biomecânicas. Sobre a análise de Machine Learning, a metodologia foi capaz de diferenciar com sucesso os dois materiais de resiliência diferentes utilizando 200 (16%) variáveis biomecânicas disponíveis com uma precisão de 84,8%, e os dois cabedais com uma precisão de 93,9%. A discriminação da resiliência da entressola resultou em níveis de acurácia mais baixos do que a discriminação dos cabedais de calçados. Em ambos os casos, no entanto, as forças de reação do solo estavam entre as 25 variáveis mais relevantes. As 200 variáveis mais relevantes que discriminaram as duas resiliências estavam distribuídas em curtas janelas de tempo, ao longo de toda série temporal da cinemática e força. Estas janelas corresponderam a padrões individuais de respostas biomecânicas, ou a um grupo de indivíduos que apresentaram as mesmas respostas biomecânicas frente aos diferentes materiais de amortecimento. Como conclusão, destacamos que o cabedal tem maior influência que o material de amortecimento quando se trata da biomecânica da corrida e conforto subjetivo. Nos cabedais estruturados, a resiliência do material da entressola não diferenciou a biomecânica da corrida. A resiliência do material de amortecimento causa efeitos importantes sobre o impacto do calcanhar (menores loading rate, frequência mediana, pico de pressão em retropé) durante a corrida em cabedais com pouca estrutura. Alterações biomecânicas devido à resiliência do material de amortecimento parecem ser dependentes do sujeito, enquanto as relacionadas à estrutura de cabedal parecem ser mais sujeito independente. Sugere-se ter cautela ao afirmar que um calçado mais confortável também gerará respostas positivas biomecânicas, pois as associações entre essas variáveis analisando todos os calçados conjuntamente foram sempre correlações fracas. As correlações moderadas e particulares de cada condição de calçado com determinadas variáveis de conforto nos levam a concluir que os materiais aplicados nos calçado favorecem mais ou menos a percepção de determinada característica de conforto / The aim of this study was to investiga te the influence of two cushioning materiais with different resiliencies and two types of uppers of sportive shoes on kinematics and kinetics of lower limb and on the subjective perception of comfort during running. We also investigated the potential relationship between the perceived comfort and biomechanical variables analyzed. For this purpose, 42 adult recreational runners were evaluated. lhey had at least one year of experience on running, minimum of two regular running workouts per week, and weekly training volume above 5 km. We evaluated four randomized shoes conditions for each athlete (Iow resilience cushioning material and structured upper, high resilience cushioning material and structured upper, low resilience cushioning material and minimalist upper, and high resilience cushioning material and minimalist upper). After anthropometric and postura I assessment of the foot/ankle complex, runners held trials on a 25 meters long indoor track. Biomechanical data were collected by six infrared cameras (VICON l-40, Oxford, UK) at 300 Hz, synchronized with two force platforms (AMll BP-600600, Watertown, USA) at 1200Hz, and in- shoe plantar pressure insoles (Pedar X System, Nove\" Munich, Germany) at 100 Hz. Subjective perception of comfort in each shoe condition was assessed by a questionnaire of footwear comfort. lhe statistical comparisons between the shoes were verified by analysis of variance (ANOVA) for repeated measures and Pearson\'s correlation to verify the relationship between comfort and biomechanical variables (a=0.05). We conducted a Machine Learning analysis to capture variables from the complete kinematics and kinetics time series, which would be able to discriminate the studied footwear. We build an input matrix in the dimensions of 1080 x 1242 for Machine Learning analysis. There was an interaction between the upper structure and the resilience of cushioning material that made comparisons between resiliencies to behave differently for minimal uppers and for structured uppers. Contrary to expectation, for structured uppers, resiliencies were not different from each other, and for the minimal upper, runners had higher impact with the low-resilience material. lhe upper structure influenced the absorption of impact, in which the minimalist upper presented higher impacts than the structured upper. About comfort, minimalist upper condition and low resilience materiais had the worst grades for five of nine questions of the questionnaire. In some questions it was the worst of ali conditions (such as for the comfort in the heel cushioning and overall comfort). lhe minimalist upper received worse assessment than the structured uppers in the question about the mediolateral control. It was observed that the correlation between biomechanical variables and comfort, considering ali shoe conditions together, despite having significant values for some correlations were weak correlations (r <30%, p <0.05). When each shoe condition is analyzed alone, some footwear conditions had moderate correlation between comfort and biomechanical variables (r >31%, p <0.05L although the same behavior was not observed in other shoe conditions. Each shoe represents a specific condition that favor or not the association between comfort and biomechanical responses. On Machine Learning analysis, the method was able to successfully distinguish between the two different resiliencies using 200 (16%) of available biomechanical variables with an accuracy of 84.8%, and between the 2 uppers with an accuracy of 93.9 %. Discrimination of the resiliencies resulted in lower levels of accuracy than the discrimination of shoe uppers. In both cases, however, the ground reaction forces were among the 25 most important features. The 200 most relevant features which discriminate the two resiliencies were distribuited in short time windows along the kinematic and force time series. These windows corresponded to individual biomechanical patterns, or patterns of a group of people with similar behavior. In conclusion, we emphasize that the upper has greater influence than the resilience of cushioning material when it is about biomechanics of running and subjective comfort of the shoes. In structured uppers, the biomechanics did not differenciate the resiliencies of the midsole materiais. The resilience of the cushioning material has important effects on the heel impact (Iower loading rate, median frequency, peak pressure in rearfoot) during running on shoes with little structure on the upper. Biomechanical changes due to the resilience of the cushioning material seems to be dependent on the subject, while related to the upper structure seems to be more independent of the subject. It is suggested to be cautious to affirm that more comfortable footwear will also let to positive biomechanical responses. That is because the correlations between these variables when analyzing ali the footwear together were always weak. Moderate and positive correlations of each shoe condition with some of comfort variables lead us to conclude that the materiais applied on each footwear favors more or less the comfort perception

Algoritmos de aprendizado semi-supervisionado ativo podem se configurar como ferramentas úteis em cenários práticos em que os dados são numerosamente obtidos, mas atribuir seus respectivos rótulos de classe se configura como uma tarefa custosa/difícil. A literatura em aprendizado ativo destaca diversos algoritmos, este trabalho partiu do tradicional Hierarchical Sampling estabelecido para operar sobre hierarquias de grupos. As características de tal algoritmo o coloca à frente de outros métodos ativos, entretanto o mesmo ainda apresenta algumas dificuldades. A fim de aprimorá-lo e contornar suas principais dificuldades, incluindo sua sensibilidade na escolha particular de uma hierarquia de grupos como entrada, este trabalho propôs estratégias que possibilitaram melhorar o algoritmo na sua forma original e diante de variantes propostas na literatura. Os experimentos em diferentes bases de dados reais mostraram que o algoritmo proposto neste trabalho é capaz de superar e competir em qualidade dentro do cenário de classificação ativa com outros algoritmos ativos da literatura. / Active semi-supervised learning can play an important role in classification scenarios in which labeled data are laborious and/or expensive to obtain, while unlabeled data are numerous and can be easily acquired. There are many active algorithms in the literature and this work focuses on an active semi-supervised algorithm that can be driven by clustering hierarchy, the well-known Hierarchical Sampling (HS) algorithm. This work takes as a starting point the original Hierarchical Sampling algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification.