Conselho Nacional de Desenvolvimento Científico e Tecnológico / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A tese pretende estabelecer o que estaria o mais próximo possível de uma lógica da descoberta para as ciências sociais. A narrativa dessas disciplinas não seria neutra nem objetiva, mas procuraria produzir, retoricamente, os efeitos de neutralidade e objetividade, evitando a heroicização, a vilanização e a vitimização dos agentes. Isso decorreria da necessidade de o cientista social validar sua narrativa perante um auditório ideal ou potencialmente universal, abrigando, em princípio, todo o tipo de valores. Essa pluralidade de visões de mundo não permite que os agentes sejam ingenuamente tratados como heróis, vilões ou vítimas. Com isso, o autor do texto de ciências sociais procuraria simular um ponto de vista de Deus, como ironista supremo, que tudo vê, acima dos participantes imperceptivos de seu relato. Foi feito um estudo de caso a partir do debate sobre populismo no Brasil, no qual foram identificados quatro pontos básicos em torno dos quais girou a controvérsia: o das estruturas prototípicas, o da estruturação imaginária, o da estrutura intersubjetiva e a dinâmica da relação entre grande teoria e pesquisa empírica. / The aim of this thesis is to get as close as possible to a logic of discovery for the social sciences. Those disciplines do not have a neutral and objective narrative, but try to achieve, rhetorically, neutrality and objectivity effects, avoiding to portrait agents as heroes, villains or victims. It follows from the need to validate scientific narratives in face of an ideally or potentially universal auditory, withholding, in principle, all kinds of values. Such plurality of world visions makes it difficult to naively treat agents as heroes, villains, or victims. As a consequence, the social scientist simulates a Gods Eye view, placing himself in a Supreme Ironist perspective, who sees everything from above, whose vision reaches beyond what nonperceptive agents can see. To make my point, I took as case study the Brazilian controversy over populism. Based on it, four main topics of development were identified: one refered to the prototypical theoretic structures, other to its imaginary structuration, another to its intersubjective structure, and a last one to the relationship of empirical research to grand theory.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

A presente tese analisa a maneira como a probabilidade influencia a formação da decisão sobre os fatos (contexto de descobrimento), bem como a motivação acerca dessas decisões (contexto justificativo). Mediante prévia análise da relevância da verdade no processo judicial, demonstra-se também que no terreno processual somente é possível falar em probabilidade, o que implica a ideia de um modelo probabilístico do juízo de fato, cujo núcleo é o módulo da escolha entre as alternativas possíveis. Portanto, são oferecidos critérios para a escolha das alternativas, os quais são denominados como diretrizes probatórias. De outro lado, a fim de que o convencimento do juiz a respeito dos fatos seja o mais racional possível, também é examinada a obrigatoriedade da motivação das decisões judiciais. Para tanto, é realizada abordagem à luz do Estado Constitucional e, ao final, demonstra-se que a probabilidade igualmente conforma o contexto justificativo, pois faz com que a motivação seja um discurso probatório racional. / This thesis examines how probability influences the fact-finding process (context of discovery) and the motivation about the trial of facts (context of justification). Considering the analysis of the relevance of truth in the judicial process, also in the procedural field only probability is taken into account, which implies the idea of a probabilistic model of factual judgment, whose core is the module of choice among the possible alternatives. Therefore, guidelines are offered for the choice of alternatives, which are called as evidentiary guidelines. On the other hand, in order to achieve the most rational conviction of the trier, mandatory legal motivation is also examined. The approach focuses on the Constitutional State and, in the end, is shown that the probability also conforms the context of justification, in order to make the legal motivation as a rational evidence speech.

O crescimento do uso de sistemas de posicionamento global (GPS) e outros sistemas de localização espacial tornaram possível o rastreamento de objetos móveis, produzindo um grande volume de um novo tipo de dado, chamado trajetórias de objetos móveis. Existe, entretanto, uma forte lacuna entre a quantidade de dados extraídos destes dispositivos, dotados de sistemas GPS, e a descoberta de conhecimento que se pode inferir com estes dados. Um tipo de descoberta de conhecimento em dados de trajetórias de objetos móveis é a classificação. A classificação de trajetórias é um tema de pesquisa relativamente novo, e poucos métodos tem sido propostos até o presente momento. A maioria destes métodos foi desenvolvido para uma aplicação específica. Poucos propuseram um método mais geral, aplicável a vários domínios ou conjuntos de dados. Este trabalho apresenta um novo método de classificação que transforma as trajetórias em séries temporais, de forma a obter características mais discriminativas para a classificação. Experimentos com dados reais mostraram que o método proposto é melhor do que abordagens existentes. / The growing use of global positioning systems (GPS) and other location systems made the tracking of moving objects possible, producing a large volume of a new kind of data, called trajectories of moving objects. However, there is a large gap between the amount of data generated by these devices and the knowledge that can be inferred from these data. One type of knowledge discovery in trajectories of moving objects is classification. Trajectory classification is a relatively new research subject, and a few methods have been proposed so far. Most of these methods were developed for a specific application. Only a few have proposed a general method, applicable to multiple domains or datasets. This work presents a new classification method that transforms the trajectories into time series, in order to obtain more discriminative features for classification. Experiments with real trajectory data revealed that the proposed approach is more effective than existing approaches.

abstract: Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

abstract: Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

abstract: Research on government innovation has focused on identifying factors that contribute to higher levels of innovation adoption. Even though various factors have been tested as contributors to high levels of innovation adoption, the independent variables have been predominantly contextual and community characteristics. Previous empirical studies shed little light on chief executive officers' (CEOs) attitudes, values, and behavior. Result has also varied with the type of innovation examined. This research examined the effect of CEOs' attitudes and behaviors, and institutional motivations on the adoption of sustainability practices in their municipalities. First, this study explored the relationship between the adoption level of sustainability practices in local government and CEOs' entrepreneurial attitudes (i.e. risk taking, proactiveness, and innovativeness) and discovery skills (i.e. associating, questioning, experimenting, observing, and networking) that have not been examined in prior research on local government innovation. Second, the study explored the impact of organizational intention to change and isomorphic pressures (i.e., coercive, mimetic, and normative pressures) and the availability and limit of organizational resources on the early adoption of innovations in local governments. Third, the study examines how CEOs' entrepreneurial attitudes and discovery skills, and institutional motivations account for high and low sustaining levels of innovation over time by tracking how much their governments have adopted innovations from the past to the present. Lastly, this study explores their path effects CEOs' entrepreneurial attitudes, discovery skills, and isomorphic pressures on sustainability innovation adoption. This is an empirical study that draws on a survey research of 134 CEOs who have influence over innovation adoption in their local governments. For collecting data, the study identified 264 municipalities over 10,000 in population that have responded to four surveys on innovative practices conducted by the International City/County Management Association (ICMA) in past eight years: the Reinventing local government survey (2003), E-government survey (2004), Strategic practice (2006), and the Sustainability survey (2010). This study combined the information about the adoption of innovations from four surveys with CEOs' responses in the current survey. Socio-economic data and information about variations in form of government were also included in the data set. This study sheds light on the discovery skills and institutional isomorphic pressures that influence the adoption of different types of innovations in local governments. This research contributes to a better understanding of the role of administrative leadership and organizational isomorphism in the dynamic of innovation adoption, which could lead to improvements in change management of organizations. / Dissertation/Thesis / Ph.D. Public Administration 2013

Filariasis and onchocerciasis are debilitating diseases affecting 120 million people globally. The massive socio-economic impact of these diseases energized the international community to declare a goal of eliminating filariasis 2020. This resulted in a dramatic increase in the efforts to eliminate filariasis and onchocerciasis, employing a strategy of mass drug administration (MDA). However, these programs rely upon the small arsenal of drugs. This leaves these programs vulnerable to failure in the face of developing resistance and local intolerance to the current drug regimens. Thus, new drugs against these infections are critically needed. A homologue of the ecdysone receptor (EcR), a master regulator of development in insects, has been identified in B. malayi. The potential of the EcR as a drug target has been underscored by work in the agricultural industry, where insecticides targeting the ecdysone developmental pathway are effective and non-toxic to non-target species. As the EcR is absent in humans, it represents an attractive potential chemotherapeutic target. The first study investigates the hypothesis that the ecdysone receptor controls the embryogenesis and molting in the filarial parasite. In-vitro embryogram and in-vivo phenotypic studies were conducted to delineate the effect of 20-hydroxyecdysone on the Brugia malayi parasites. The results suggest that the hormone accelerates embryogenesis and causes precocious molts, resulting in the death of the parasite. Further, transcriptomic and proteomic analysis of the ecdysone treated worms provided evidence that the up-regulated genes participate in embryogenesis. Based upon the validation of the ecdysone receptor as a potential drug target, subsequent studies focused on the development of a drug discovery model to screen for agonists and antagonists of the B. malayi ecdysone receptor. A stable cell line was created to aid the high throughput screening to rapidly identity agonist and antagonist compounds. A total of 7 agonists and 2 antagonists were identified. A homology model of the BmEcR ligand-binding domain was created as an alternate method for virtual screening of small molecules as well as to study the ligand-receptor interactions. The hits identified with the assay were docked in the active site of the BmEcR homology model providing an excellent correspondence of data between the molecular assay and the virtual screening method.

Talvez uma das maiores capacidades do ser humano seja a sua habilidade de aprender a partir de observações e transmitir o que aprendeu para outros humanos. Durante séculos, a humanidade vem tentado compreender o mundo em que vive e, a partir desse novo conhecimento adquirido, melhorar o mundo em que vive. O desenvolvimento da tecnologia colocou a descoberta de conhecimento em um momento ímpar na história da humanidade. Com os progressos da Ciência da Computação, e, em particular, da Inteligência Artificial - IA - e Aprendizado de Máquina -AM, hoje em dia é possível, a partir de métodos de inferência indutiva e utilizando um conjunto de exemplos, descobrir algum tipo de conhecimento implícito nesses exemplos. Entretanto, por ser uma área de pesquisa relativamente nova, e por envolver um processo tanto iterativo quanto interativo, atualmente existem poucas ferramentas que suportam eficientemente a descoberta de conhecimento a partir dos dados. Essa falta de ferramentas se agrava ainda mais no que se refere ao seu uso por pesquisadores em Aprendizado de Máquina e Aquisição de Conhecimento. Esses fatores, além do fato que algumas pesquisas em nosso Laboratório de Inteligência Computacional - LABIC - têm alguns componentes em comum, motivaram a elaboração do projeto Discover, que consiste em uma estratégia de trabalho em conjunto, envolvendo um conjunto de ferramentas que se integram e interajam, e que supram as necessidades de pesquisa dos integrantes do nosso laboratório. O Discover também pode ser utilizado como um campo de prova para desenvolver novas ferramentas e testar novas idéias. Como o Discover tem como principal finalidade o seu uso e extensão por pesquisadores, uma questão principal é que a arquitetura do projeto seja flexível o suficiente para permitir que novas pesquisas sejam englobadas e, simultaneamente, deve impor determinados padrões que permitam a integração eficiente de seus componentes. Neste trabalho, é proposto um framework de integração de componentes que tem como principal objetivo possibilitar a criação de um sistema computacional a partir das ferramentas desenvolvidas para serem utilizadas no projeto Discover. Esse framework compreende um mecanismo de adaptação de interface que cria uma camada (interface horizontal) sobre essas ferramentas, um poderoso mecanismo de metadados, que é utilizado para descrever tanto os componentes que implementam as funcionalidades do sistema quanto as configurações de experimentos criadas pelos usuário, que serão executadas pelo framework, e um ambiente de execução para essas configurações de experimentos. / One of human greatest capability is the ability to learn from observed instances of the world and to transmit what have been learnt to others. For thousands of years, we have tried to understand the world, and used the acquired knowledge to improve it. Nowadays, due to the progress in digital data acquisition and storage technology as well as significant progress in the field of Artificial Intelligence - AI, particularly Machine Learning - ML, it is possible to use inductive inference in huge databases in order to find, or discover, new knowledge from these data. The discipline concerned with this task has become known as Knowledge Discovery from Databases - KDD. However, this relatively new research area offers few tools that can efficiently be used to acquire knowledge from data. With these in mind, a group of researchers at the Computational Intelligence Laboratory - LABIC - is working on a system, called Discover, in order to help our research activities in KDD and ML. The aim of the system is to integrate ML algorithms mostly used by the community with the data and knowledge processing tools developed as the results of our work. The system can also be used as a workbench for new tools and ideas. As the main concern of the Discover is related to its use and extension by researches, an important question is related to the flexibility of its architecture. Furthermore, the Discover architecture should allow new tools be easily incorporated. Also, it should impose strong patterns to guarantee efficient component integration. In this work, we propose a component integration framework that aims the development of an integrated computational environment using the tools already implemented in the Discover project. The proposed component integration framework has been developed keeping in mind its future integration with new tools. This framework offers an interface adapter mechanism that creates a layer (horizontal interface) over these tools, a powerful metadata mechanism, which is used to describe both components implementing systems' functionalities and experiment configurations created by the user, and an environment that enables these experiment execution.

A presente tese analisa a maneira como a probabilidade influencia a formação da decisão sobre os fatos (contexto de descobrimento), bem como a motivação acerca dessas decisões (contexto justificativo). Mediante prévia análise da relevância da verdade no processo judicial, demonstra-se também que no terreno processual somente é possível falar em probabilidade, o que implica a ideia de um modelo probabilístico do juízo de fato, cujo núcleo é o módulo da escolha entre as alternativas possíveis. Portanto, são oferecidos critérios para a escolha das alternativas, os quais são denominados como diretrizes probatórias. De outro lado, a fim de que o convencimento do juiz a respeito dos fatos seja o mais racional possível, também é examinada a obrigatoriedade da motivação das decisões judiciais. Para tanto, é realizada abordagem à luz do Estado Constitucional e, ao final, demonstra-se que a probabilidade igualmente conforma o contexto justificativo, pois faz com que a motivação seja um discurso probatório racional. / This thesis examines how probability influences the fact-finding process (context of discovery) and the motivation about the trial of facts (context of justification). Considering the analysis of the relevance of truth in the judicial process, also in the procedural field only probability is taken into account, which implies the idea of a probabilistic model of factual judgment, whose core is the module of choice among the possible alternatives. Therefore, guidelines are offered for the choice of alternatives, which are called as evidentiary guidelines. On the other hand, in order to achieve the most rational conviction of the trier, mandatory legal motivation is also examined. The approach focuses on the Constitutional State and, in the end, is shown that the probability also conforms the context of justification, in order to make the legal motivation as a rational evidence speech.