Benchmarking sobre t��cnicas de otimiza����o para modelos de apoio �� decis��o na medicina intensiva

Disserta����o de mestrado integrado em Engenharia e Gest��o de Sistemas de Informa����o

Personalized-based dietary recommendation for weightlifting

Disserta����o de mestrado integrado em Engenharia Eletr��nica Industrial e Computadores

O professor como construtor do curr��culo: integra����o da tecnologia em atividades de aprendizagem de matem��tica

O desenvolvimento profissional dos professores de matem��tica, por meio de progra��mas nacionais e forma����es cont��nuas, deve proporcionar experi��ncias que envolvam investiga����o, pensamento, planeamento, pr��tica e reflex��o. No caso da tecnologia, n��o nos devemos focar nas ferramentas em si, mas no modo como s��o usadas pelos docentes em contexto de sala de aula. Existem taxonomias de atividades de apren��dizagem baseadas no conte��do assentes na ideia do professor como construtor do curr��culo, que, para integrar com sucesso a tecnologia educativa nas aulas, desenvolve o conhecimento pedag��gico e tecnol��gico do conte��do (TPACK), e apresenta-se a de matem��tica. Desse modo, reflete-se, por meio de v��rios estudos nacionais e internacionais, que as tecnologias dever��o ser usadas pelos professores de acordo com objetivos, conte��dos e pedagogias espec��ficas para terem um efeito positivo na aprendizagem dos alunos sobre as atividades baseadas no conte��do que melhor se enquadram com essas tecnologias.

Alunos em risco na flu��ncia da leitura: o uso da monitoriza����o com base no curr��culo no contexto de um modelo de resposta �� interven����o

Disserta����o de mestrado em Educa����o Especial (��rea de especializa����o em Dificuldades de Aprendizagem Espec��ficas)

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

A constru����o de um curr��culo de project-based learning: o curr��culo ideal e o curr��culo formal

O objetivo deste trabalho �� apresentar os resultados da an��lise das concep����es de dois protagonistas de uma reforma curricular que est�� sendo implementada numa escola de engenharia. A principal caracter��stica do novo curr��culo �� o uso de projetos e oficinas como atividades complementares a serem realizadas pelos estudantes. As atividades complementares acontecer��o em paralelo ao trabalho realizado nas disciplinas sem que haja uma rela����o de interdisciplinaridade. O novo curr��culo est�� sendo implantado desde fevereiro de 2015. Segundo Pacheco (2005) h�� dois momentos, dentre outros, no processo de mudan��a curricular, o curr��culo ���ideal���, determinado por dimens��es epistemol��gica, pol��tica, econ��mica, ideol��gica, t��cnica, est��tica, e hist��rica e, que recebe influ��ncia direta daquele que idealiza e cria o novo curr��culo e, o curr��culo ���formal��� que se traduz na pr��tica implementada na escola. S��o essas duas etapas estudadas nesta pesquisa. Para isso ser��o considerados como fontes de dados dois protagonistas, um mais ligado �� concep����o do curr��culo e outro da sua implementa����o, a partir dos quais se busca compreender as motiva����es, cren��as e percep����es que, por sua vez, determinam a reforma curricular. Entrevistas semiestruturadas foram utilizadas como t��cnica de pesquisa, com o prop��sito de se entender a g��nese da proposta e as mudan��as entre essas duas etapas. Os dados revelam que mudan��as aconteceram desde a idealiza����o at�� a formaliza����o do curr��culo, motivadas por demandas do processo de implementa����o, revela ainda diferen��as na vis��o de curr��culo e a motiva����o para romper com padr��es na forma����o de engenheiros no Brasil.

Gamification e game-based learning: uma abordagem l��dica �� aprendizagem

Relat��rio de est��gio de mestrado em Ensino de Inform��tica

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletr��nica e de Computadores

O desenvolvimento do pensamento abstrato atrav��s de game-based learning e do Scratch

Relat��rio de est��gio de mestrado em Ensino de Inform��tica