Orientador: Caio Lucidius Naberezny Azevedo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-23T07:11:52Z (GMT). No. of bitstreams: 1 Nogarotto_DaniloCovaes_M.pdf: 12817108 bytes, checksum: 0e5e0de542d707f4023f5ef62dc40a82 (MD5) Previous issue date: 2013 / Resumo: No presente trabalho desenvolvemos ferramentas de inferência bayesiana para modelos de regressão beta e beta inflacionados, em relação à estimação paramétrica e diagnóstico. Trabalhamos com modelos de regressão beta não inflacionados, inflacionados em zero ou um e inflacionados em zero e um. Devido à impossibilidade de obtenção analítica das posteriores de interesse, tais ferramentas foram desenvolvidas através de algoritmos MCMC. Para os parâmetros da estrutura de regressão e para o parâmetro de precisão exploramos a utilização de prioris comumente empregadas em modelos de regressão, bem como prioris de Jeffreys e de Jeffreys sob independência. Para os parâmetros das componentes discretas, consideramos prioris conjugadas. Realizamos diversos estudos de simulação considerando algumas situações de interesse prático com o intuito de comparar as estimativas bayesianas com as frequentistas e também de estudar a sensibilidade dos modelos _a escolha de prioris. Um conjunto de dados da área psicométrica foi analisado para ilustrar o potencial do ferramental desenvolvido. Os resultados indicaram que há ganho ao se considerar modelos que contemplam as observações inflacionadas ao invés de transformá-las a fim de utilizar modelos não inflacionados / Abstract: In the present work we developed Bayesian tools, concerning parameter estimation and diagnostics, for noninflated, zero inflated, one inflated and zero-one inflated beta regression models. Due to the impossibility of obtaining the posterior distributions of interest, analytically, all these tools were developed through MCMC algorithms. For the regression and precision parameters we exploited the using of prior distributions commonly considered in regression models as well as Jeffreys and independence Jeffreys priors. For the parameters related to the discrete components, we considered conjugate prior distributions. We performed simulation studies, considering some situations of practical interest, in order to compare the Bayesian and frequentist estimates as well as to evaluate the sensitivity of the models to the prior choice. A psychometric real data set was analyzed to illustrate the performance of the developed tools. The results indicated that there is an overall improvement in using models that consider the inflated observations compared to transforming these observations in order to use noninflated models / Mestrado / Estatistica / Mestre em Estatística

Le travail présenté dans cette thèse a pour objectif de proposer un algorithme de classification supervisée d’images texturées basée sur la modélisation multivariée de champs texturaux. Inspiré des algorithmes de classification dits à « Sac de Mots Visuels » (SMV), nous proposons une extension originale au cas des descripteurs paramétriques issus de la modélisation multivariée des coefficients des sous-bandes d’une décomposition en ondelettes. Différentes contributions majeures de cette thèse peuvent être mises en avant. La première concerne l’introduction d’une loi a priori intrinsèque à l’espace des descripteurs par la définition d’une loi gaussienne concentrée. Cette dernière étant caractérisée par un barycentre ¯_ et une varianceσ2, nous proposons un algorithme d’estimation de ces deux quantités. Nous proposons notamment une application au cas des modèles multivariés SIRV ( Spherically Invariant Random Vector ), en séparant le problème complexe d’estimationdu barycentre comme la résolution de deux problèmes d’estimation plus simples ( un sur la partie gaussienne et un surle multiplieur ). Afin de prendre en compte la diversité naturelle des images texturées ( contraste, orientation, . . . ), nousproposons une extension au cas des modèles de mélanges permettant ainsi de construire le dictionnaire d’apprentissage.Enfin, nous validons cet algorithme de classification sur diverses bases de données d’images texturées et montrons de bonnes performances de classification vis-à-vis d’autres algorithmes de la littérature. / The prime objective of this thesis is to propose an unsupervised classification algorithm of textured images based on multivariate stochastic models. Inspired from classification algorithm named "Bag of Words" (BoW), we propose an original extension to parametric descriptors issued from the multivariate modeling of wavelet subband coefficients. Some major contributions of this thesis can be outlined. The first one concerns the introduction of an intrinsic prior on the parameter space by defining a Gaussian concentrated distribution. This latter being characterized by a centroid ¯_ and a variance _2,we propose an estimation algorithm for those two quantities. Next, we propose an application to the multivariate SIRV (Spherically Invariant Random Vector) model, by resolving the difficult centroid estimation problem as the solution of two simpler ones (one for the Gaussian part and one for the multiplier part). To handle with the intra-class diversity of texture images (scene enlightenment, orientation . . . ), we propose an extension to mixture models allowing the construction of the training dictionary. Finally, we validate this classification algorithm on various texture image databases and show interesting classification performances compared to other state-of-the-art algorithms.

To study whether the curiosity can decline or not for a certain task could help us understand how to keep students both interested and engaged in all the different subjects that the education system has to offer. This study aimed to first find a way to measure curiosity, to then see if it changes over time, and if it shows to be different between low performing people and high performing people. 20 people participated at two different sessions. At the first session uncertainty was measured in form of Shannon’s entropy. At the second session participants got to choose between more or less informative options, and then gain feedback depending on the choice. The entropy proved to be a valid predictor for information choice and was used as curiosity measurement in form of a time cost by expected information gain. Patterns in curiosity change over time was found for the sample, low performing participants and high performing participants, where the sample and high performing people showed a significant effect of curiosity decline. / Att studera huruvida nyfikenhet kan avtaga eller ej för en särskild uppgift kan hjälpa oss förstå hur man kan hålla studenter både intresserade och engagerade i de olika ämnena som utbildningssystemet erbjuder. Den här studien siktade på att först finna ett sätt at mäta nyfikenhet, för att sedan se om förändras över tid, samt om det är någon skillnad för låg och högpresterande personer. 20 studenter deltog vid två separata tillfällen. Vid första tillfället mättes osäkerhet i form av Shannon’s entropi. Vid det andra tillfället fick deltagarna välja mellan mer eller mindre informativa val, och få feedback utifrån detta. Entropin visade sig kunna förutsäga om deltagarna valde feedback, och användes därför som mått på nyfikenhet i form av tidskostnad per förväntad informationsvinst. Mönster för nyfikenhetsförändring över tid kunde ses hos populationen, de lågpresterande samt högpresterande deltagarna, där både urvalsgruppen samt de högpresterande deltagarna visade en signifikant effekt av avtagande nyfikenhet.

Magister Educationis - MEd / Recognition of Prior Learning (RPL) is the process of recognizing and crediting a person for his/her knowledge and experience however attained and promoting that person along a development pathway. In South Africa RPL has been promoted for social justice purposes related to access and redress. However these intentions have been lost within current educational discourses despite being rooted in several policies. Recently the role of vocational education has received increased prominence as a means to provide skills development. However there is often a disjuncture between policy formulation and implementation and this has given rise to this study of how RPL policy has been implemented within public Further Education and Training (FET) colleges. This paper investigates the RPL policies and practices in two public FET colleges and analyses how these employ social justice intentions of access and redress. The study reveals that there are similar conceptions of RPL amongst lecturers but varying RPL practices in these colleges. / South Africa

Magister Curationis - MCur / The nursing profession is the backbone of the healthcare system glob-ally. However, due to the ongoing shortage of nurses there is a growing demand for nurses across the world. This demand puts pressure on the continued recruitment of new nursing students. The factors that influence students’ reasons for entering nursing vary and require investigation to improve recruitment practices.

Les véhicules autonomes, qualifiés aussi de véhicules sans conducteur, deviennent dans certains contextes une réalité tangible et partageront très bientôt nos routes avec d’autres véhicules classiques. Pour qu’un véhicule autonome se déplace de manière sécurisée, il doit savoir où il se trouve et ce qui l’entoure dans l’environnement. Pour la première tâche, pour déterminer sa position dans l’environnement, il doit se localiser selon six degrés de liberté (position et angles de rotation). Alors que pour la deuxième tâche, une bonne connaissance de cet environnement « proche » est nécessaire, ce qui donne lieu à une solution sous forme de cartographie. Par conséquent, pour atteindre le niveau de sécurité souhaité des véhicules autonomes, une localisation précise est primordiale. Cette localisation précise permet au véhicule non seulement de se positionner avec précision, mais également de trouver sa trajectoire optimale et d’éviter efficacement les collisions avec des objets statiques et dynamiques sur son trajet. Actuellement, la solution la plus répandue est le système de positionnement (GPS). Ce système ne permet qu’une précision limitée (de l’ordre de plusieurs mètres) et bien que les systèmes RTK (RealTime Kinematic) et DGPS (Differential GPS) aient atteint une précision bien plus satisfaisante, ces systèmes restent sensibles au masquage des signaux, et aux réflexions multiples, en particulier dans les zones urbaines denses. Toutes ces déficiences rendent ces systèmes inadaptés pour traiter des tâches critiques telles que l’évitement des collisions. Une alternative qui a récemment attiré l’attention des experts (chercheurs et industriels), consiste à utiliser une carte à priori pour localiser la voiture de l’intérieur de celui-ci. En effet, les cartes facilitent le processus de navigation et ajoutent une couche supplémentaire de sécurité et de compréhension. Le véhicule utilise ses capteurs embarqués pour comparer ce qu’il perçoit à un moment donné avec ce qui est stocké dans sa mémoire. Les cartes à priori permettent donc au véhicule de mieux se localiser dans son environnement en lui permettant de focaliser ses capteurs et la puissance de calcul uniquement sur les objets en mouvement. De cette façon, le véhicule peut prédire ce qui devrait arriver et voir ensuite ce qui se passe réellement en temps réel, et donc peut prendre une décision sur ce qu’il faut faire.Cette thèse vise donc à développer des outils permettant une localisation précise d’un véhicule autonome dans un environnement connu à priori. Cette localisation est déterminée par appariement (Map-matching) entre une carte de l’environnement disponible a priori et les données collectées au fur et à mesure que le véhicule se déplace. Pour ce faire, deux phases distinctes sont déployées. La première permet la construction de la carte, avec une précision centimétrique en utilisant des techniques de construction de cartes statiques ou dynamiques. La seconde correspond à la capacité de localiser le véhicule dans cette carte 3D en l’absence d’infrastructures dédiées comprenant le système GPS, les mesures inertielles (IMU) ou des balises.Au cours de ce travail, différentes techniques sont développées pour permettre la réalisation des deux phases mentionnées ci-dessus. Ainsi, la phase de construction de cartes, qui consiste à recaler des nuages de points capturés pour construire une représentation unique et unifiée de l’environnement, correspond au problème de la localisation et de la cartographie simultanée (SLAM). Afin de faire face à ce problème, nous avons testé et comparé différentes méthodes de recalage. Cependant, l’obtention de cartes précises nécessite des nuages de points très denses, ce qui les rend inefficaces pour une utilisation en temps réel. Dans ce contexte, une nouvelle méthode de réduction des points est proposée. (...) / The concept of self-driving vehicles is becoming a happening reality and will soon share our roads with other vehicles –autonomous or not-. For a self-driving car to move around in its environment in a securely, it needs to sense to its immediate environment and most importantly localize itself to be able to plan a safe trajectory to follow. Therefore, to perform tasks suchas trajectory planning and navigation, a precise localization is of upmost importance. This would further allow the vehicle toconstantly plan and predict an optimal path in order to weave through cluttered spaces by avoiding collisions with other agentssharing the same space as the latter. For years, the Global Positioning System (GPS) has been a widespread complementary solution for navigation. The latter allows only a limited precision (range of several meters). Although the Differential GPSand the Real Time Kinematic (RTK) systems have reached considerable accuracy, these systems remain sensitive to signal masking and multiple reflections, offering poor reliability in dense urban areas. All these deficiencies make these systems simply unsuitable to handle hard real time constraints such as collision avoidance. A prevailing alternative that has attracted interest recently, is to use upload a prior map in the system so that the agent can have a reliable support to lean on. Indeed,maps facilitate the navigation process and add an extra layer of security and other dimensions of semantic understanding. The vehicle uses its onboard sensors to compare what it perceives at a given instant to what is stored in the backend memory ofthe system. In this way, the autonomous vehicle can actually anticipate and predict its actions accordingly.The purpose of this thesis is to develop tools allowing an accurate localization task in order to deal with some complex navigation tasks outlined above. Localization is mainly performed by matching a 3D prior map with incoming point cloudstructures as the vehicle moves. Three main objectives are set out leading with two distinct phases deployed (the map building and the localization). The first allows the construction of the map, with centimeter accuracy using static or dynamic laser surveying technique. Explicit details about the experimental setup and data acquisition campaigns thoroughly carried outduring the course of this work are given. The idea is to construct efficient maps liable to be updated in the long run so thatthe environment representation contained in the 3D models are compact and robust. Moreover, map-building invariant on any dedicated infrastructure is of the paramount importance of this work in order to rhyme with the concept of flexible mapping and localization. In order to build maps incrementally, we rely on a self-implementation of state of the art iterative closest point (ICP) algorithm, which is then upgraded with new variants and compared to other implemented versions available inthe literature. However, obtaining accurate maps requires very dense point clouds, which make them inefficient for real-time use. Inthis context, the second objective deals with points cloud reduction. The proposed approach is based on the use of both colorinformation and the geometry of the scene. It aims to find sets of 3D points with the same color in a very small region and replacing each set with one point. As a result, the volume of the map will be significantly reduced, while the proprieties of this map such as the shape and color of scanned objects remain preserved.The third objective resort to efficient, precise and reliable localization once the maps are built and treated. For this purpose, the online data should be accurate, fast with low computational effort whilst maintaining a coherent model of the explored space. To this end, the Velodyne HDL-32 comes into play. (...)

Introduction and aim: Culturally and socially conditioned prior knowledge of text content can constitute a significant factor for teaching reading comprehension. This relationship is made visible and has gotten more consideration in education. Therefore, this essay has aimed to investigate how teachers in the subject of teaching Swedish perceive and draw attention to the importance of students 'socio-culturally conditioned prior knowledge, with a focus on students' reading comprehension. Method: When conducting this study, interviews and observation studies have been carried out at two different schools where four interviews have been conducted with primary school teachers teaching pupils participating in grade 4-6. Results: The results show that the teachers notice that the lessons and the students' reading comprehension are positively affected by the fact that they have a certain prior knowledge about the text content. Therefore, teachers choose to work towards supporting students to link the text content to something they recognize. The teachers emphasize that vocabulary and language skills can complement reading comprehension if students don’t have enough prior knowledge, thus stating that a text can be understood by combining the linguistic knowledge they have, to gain a greater understanding of the text. The study results from the observations showed that all teachers review the students need for reading comprehension, which their respective lessons were based on. Conclusion: Teachers perceive socio-culturally conditioned prior knowledge as an important part of teaching reading comprehension. The teachers' working methods showed several support structures such as guidance and modeling of strategies through communication and interaction in teaching. It also showed that they are teaching the application of different strategies for reading and understanding fiction.

Discusses how to ascertain the meaning and implications of Right to Free Prior and Informed Consent (FPIC). Discusses the difference between meaningful participation of FPIC and the relationship between ‘national interest’ and the right to FPIC. Also analyses the protection of the rights of indigenous peoples, including mainly the right to FPIC in Ethiopia. Introduces recommendations concerning the middle ground between ‘national interest’ and the right to FPIC. Discusses how the right to FPIC can be legally recognised in Ethiopia and Africa in general, including particularly by the African Commission, and outlines specific recommendations on the relevant policies of the World Bank and African Development Bank. / Dissertation submitted to the Faculty of Law University of Pretoria, in partial fulfilment of the requirements for the degree Masters of Law (LLM in Human Rights and Democratisation in Africa). Prepared under the supervision of Odile Lim Tung, Faculty of Law and Management, University of Mauritius. / Mini Dissertation (LLM (Human Rights and Democratisation in Africa))--University of Pretoria, 2009. / http://www.chr.up.ac.za/ / Centre for Human Rights / LLM

Récemment, la grande complexité des applications modernes, par exemple dans la génétique, l’informatique, la finance, les sciences du climat, etc. a conduit à la proposition des nouveaux modèles qui peuvent décrire la réalité. Dans ces cas,méthodes MCMC classiques ne parviennent pas à rapprocher la distribution a posteriori, parce qu’ils sont trop lents pour étudier le space complet du paramètre. Nouveaux algorithmes ont été proposés pour gérer ces situations, où la fonction de vraisemblance est indisponible. Nous allons étudier nombreuses caractéristiques des modèles complexes: comment éliminer les paramètres de nuisance de l’analyse et faire inférence sur les quantités d’intérêt,dans un cadre bayésienne et non bayésienne et comment construire une distribution a priori de référence. / Recently, the great complexity of modern applications, for instance in genetics,computer science, finance, climatic science etc., has led to the proposal of newmodels which may realistically describe the reality. In these cases, classical MCMCmethods fail to approximate the posterior distribution, because they are too slow toinvestigate the full parameter space. New algorithms have been proposed to handlethese situations, where the likelihood function is unavailable. We will investigatemany features of complex models: how to eliminate the nuisance parameters fromthe analysis and make inference on key quantities of interest, both in a Bayesianand not Bayesian setting, and how to build a reference prior.

As states, districts, and teachers work to make science classes more about doing the work of science and less about remembering science facts, research is needed to show what doing science looks like. This is especially needed for the youngest students, since much of the current research studies examine the upper part of the K-12 grade range. Having been an early elementary science teacher, my work in this dissertation and beyond is focused on making the doing of science accessible to young children. One way to do science is to collect and interpret data – to measure something and make sense of changes in measurement over time. Kindergarten teachers already do this with the weather as called for in math curriculums and science standards, albeit in simplified forms with words like hot, cold, sunny, cloudy, etc. I was curious if the children could understand more complex ways of measuring the weather, using quantitative measurements with the help of a thermometer designed for young children. Over the course of three interviews for each child, I asked six kindergarteners to show illustrate different temperatures, read thermometers, and interpret graphs of changing temperatures. Based on my analysis of the interviews, my findings indicate that the six kindergarteners could all read the specialized thermometer and four of them demonstrated an understanding of how the measurements related to air temperature. This work may help with the planning of future science classes.