O ensino da digestão-nutrição na era das refeições rápidas e do culto ao corpo

Mudanças notáveis nos hábitos alimentares, acompanhadas de práticas fisioculturistas intensas, são a tônica dos dias atuais, com registros de carências nutricionais e de obesidade bastante preocupantes, do ponto de vista da saúde pública, entre crianças, jovens e adultos. Mas o que conhecem as crianças e os adolescentes sobre o processo de digestão-nutrição, os conceitos básicos envolvidos e as condutas alimentares adequadas à boa saúde humana? Como é desenvolvido esse tema nas escolas públicas e particulares de ensino? Tais questionamentos desencadearam um estudo sobre a natureza das práticas desenvolvidas por professores de ciências e biologia e o conhecimento apresentado por alunos de escolas públicas e particulares. Os resultados revelaram inadequação no tratamento metodológico de ensino do processo de digestão e conceitos envolvidos nesse tema, que levam os alunos ao desinteresse e a manterem praticamente inalterados os conhecimentos ordinários que possuem. O processo de digestão e nutrição, bem como suas implicações para a saúde, configuraram-se como fenômenos desvinculados do aluno, à semelhança do que observamos nos livros didáticos por eles utilizados. A dinâmica das inter-relações alimentares entre seres vivos são superficialmente consideradas em ecologia e passam ao largo das adaptações comportamentais, morfológicas e fisiológicas envolvidas. Considerando esses resultados, propõe-se conteúdo baseado em abordagem ecológica, voltado para determinadas atividades experimentais, jogos e interações coevolutivas de seres vivos - aspectos biológicos e sociais, para o despertar de posturas reflexivas e críticas diante das transformações sociais em curso e de nossas necessidades biológicas no que se refere à alimentação e saúde.

Análise de formas planas em imagens digitais: uma nova abordagem baseada na Transformada de Hough

Pós-graduação em Ciência da Computação - IBILCE

Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Inclusão de alunos com deficiência na aula de educação física: identificando dificuldades, ações e conteúdos para prover a formação do professor

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

Monitoramento hidrossedimentlógico da bacia hidrográfica do alto rio Sucuriú (MS) contribuinte da PCH Costa Rica

The growing demand for electricity in Brazil has stimulated the implementation of Small Hydro Power (PCH) in various regions of the country. However, the silting of reservoirs is a major problem faced by power plants and power plants. In this context, this study aimed to evaluate the data hydrosedimentological strategic points of the watershed of the Alto Rio Sucuriú (MS) to identify the possible causes siltation of the reservoir PCH Costa Rica and suggest mitigation measures. Hydrosedimentological surveys were conducted during the rainy season (February / March 2012) and drought (August 2012), and obtained data flow, discharge liquid, suspended solids and bottom and organic matter content. Based on these results it was determined that the points 2, 4, 7 and 9 are the largest contributors to sedimentation, and point 4 got most liquid discharge (38,20 m3s-1), point 7 largest discharge of solid suspension (906,3 mg L-1), points 2 and 4 major discharges solid totals (231,59 t dia-1 and 238,185 t dia-1 respectively) and point 9 higher organic matter content (22,18%) . Found greater fraction of fine sand and very fine suspended solids and solid medium sand in the background. As mitigation measures for the process of silting of the reservoir PCH Costa Rica (MS) highlights the orientation of landowners in adopting conservation measures and planting of leguminous species native to the region in symbiosis with nitrogen-fixing bacteria

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

Gamificação: abordagem e construção conceitual para aplicativosem TV Digital Interativa

The deployment of Digital TV in Brazil opens up space for the development of educative content based on the concepts of t-learning and edutertainment. The study proposes the application of the gamification as a link of communication to encourage and modify the users’ behavior. However, bumps into itself on the conceptual problem, once the literature brings several definitions that vary according to the application context. The objective this study, exploratory, is to propose a conceptual approach, in order to build a delimited concept that substantiates the gamification system in Interactive Digital TV.

Mineralogia da fração argila dos solos do Planalto Ocidental Paulista

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Process of construction of an educational booklet for health promotion of pregnant women

This study aimed to describe the production process of an educational booklet focusing on health promotion of pregnant women. The action research method was used in this process composed of the following steps: choice of the content based on the needs of pregnant women, creation of illustrations, content preparation based on scientific literature, validation of the material by experts and pregnant women. This work resulted in the final version of the booklet, which was entitled "Celebrating life: our commitment with the health promotion of pregnant women". Active participation of health professionals and pregnant women through dialogue and collective strategy permeated the process of development of the booklet. The opinions of pregnant women and experts who considered the booklet enriching and enlightening justify the use of it as an additional resource of educational activities carried out during the prenatal care.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

User interaction widgets for interactive theorem proving

Matita (that means pencil in Italian) is a new interactive theorem prover under development at the University of Bologna. When compared with state-of-the-art proof assistants, Matita presents both traditional and innovative aspects. The underlying calculus of the system, namely the Calculus of (Co)Inductive Constructions (CIC for short), is well-known and is used as the basis of another mainstream proof assistant—Coq—with which Matita is to some extent compatible. In the same spirit of several other systems, proof authoring is conducted by the user as a goal directed proof search, using a script for storing textual commands for the system. In the tradition of LCF, the proof language of Matita is procedural and relies on tactic and tacticals to proceed toward proof completion. The interaction paradigm offered to the user is based on the script management technique at the basis of the popularity of the Proof General generic interface for interactive theorem provers: while editing a script the user can move forth the execution point to deliver commands to the system, or back to retract (or “undo”) past commands. Matita has been developed from scratch in the past 8 years by several members of the Helm research group, this thesis author is one of such members. Matita is now a full-fledged proof assistant with a library of about 1.000 concepts. Several innovative solutions spun-off from this development effort. This thesis is about the design and implementation of some of those solutions, in particular those relevant for the topic of user interaction with theorem provers, and of which this thesis author was a major contributor. Joint work with other members of the research group is pointed out where needed. The main topics discussed in this thesis are briefly summarized below. Disambiguation. Most activities connected with interactive proving require the user to input mathematical formulae. Being mathematical notation ambiguous, parsing formulae typeset as mathematicians like to write down on paper is a challenging task; a challenge neglected by several theorem provers which usually prefer to fix an unambiguous input syntax. Exploiting features of the underlying calculus, Matita offers an efficient disambiguation engine which permit to type formulae in the familiar mathematical notation. Step-by-step tacticals. Tacticals are higher-order constructs used in proof scripts to combine tactics together. With tacticals scripts can be made shorter, readable, and more resilient to changes. Unfortunately they are de facto incompatible with state-of-the-art user interfaces based on script management. Such interfaces indeed do not permit to position the execution point inside complex tacticals, thus introducing a trade-off between the usefulness of structuring scripts and a tedious big step execution behavior during script replaying. In Matita we break this trade-off with tinycals: an alternative to a subset of LCF tacticals which can be evaluated in a more fine-grained manner. Extensible yet meaningful notation. Proof assistant users often face the need of creating new mathematical notation in order to ease the use of new concepts. The framework used in Matita for dealing with extensible notation both accounts for high quality bidimensional rendering of formulae (with the expressivity of MathMLPresentation) and provides meaningful notation, where presentational fragments are kept synchronized with semantic representation of terms. Using our approach interoperability with other systems can be achieved at the content level, and direct manipulation of formulae acting on their rendered forms is possible too. Publish/subscribe hints. Automation plays an important role in interactive proving as users like to delegate tedious proving sub-tasks to decision procedures or external reasoners. Exploiting the Web-friendliness of Matita we experimented with a broker and a network of web services (called tutors) which can try independently to complete open sub-goals of a proof, currently being authored in Matita. The user receives hints from the tutors on how to complete sub-goals and can interactively or automatically apply them to the current proof. Another innovative aspect of Matita, only marginally touched by this thesis, is the embedded content-based search engine Whelp which is exploited to various ends, from automatic theorem proving to avoiding duplicate work for the user. We also discuss the (potential) reusability in other systems of the widgets presented in this thesis and how we envisage the evolution of user interfaces for interactive theorem provers in the Web 2.0 era.

Geophotohunt: Studio di algoritmi di confronto di immagini per realizzare una caccia al tesoro fotografica

Questo studio si propone di realizzare un’applicazione per dispositivi Android che permetta, per mezzo di un gioco di ruolo strutturato come caccia al tesoro, di visitare in prima persona città d’arte e luoghi turistici. Gli utenti finali, grazie alle funzionalità dell’app stessa, potranno giocare, creare e condividere cacce al tesoro basate sulla ricerca di edifici, monumenti, luoghi di rilevanza artistico-storica o turistica; in particolare al fine di completare ciascuna tappa di una caccia al tesoro il giocatore dovrà scattare una fotografia al monumento o edificio descritto nell’obiettivo della caccia stessa. Il software grazie ai dati rilevati tramite GPS e giroscopio (qualora il dispositivo ne sia dotato) e per mezzo di un algoritmo di instance recognition sarà in grado di affermare se la foto scattata rappresenta la risposta corretta al quesito della tappa. L’applicazione GeoPhotoHunt rappresenta non solo uno strumento ludico per la visita di città turistiche o più in generale luoghi di interesse, lo studio propone, infatti come suo contributo originale, l’implementazione su piattaforma mobile di un Content Based Image Retrieval System (CBIR) del tutto indipendente da un supporto server. Nello specifico il server dell’applicazione non sarà altro che uno strumento di appoggio con il quale i membri della “community” di GeoPhotoHunt potranno pubblicare le cacce al tesoro da loro create e condividere i punteggi che hanno totalizzato partecipando a una caccia al tesoro. In questo modo quando un utente ha scaricato sul proprio smartphone i dati di una caccia al tesoro potrà iniziare l’avventura anche in assenza di una connessione internet. L’intero studio è stato suddiviso in più fasi, ognuna di queste corrisponde ad una specifica sezione dell’elaborato che segue. In primo luogo si sono effettuate delle ricerche, soprattutto nel web, con lo scopo di individuare altre applicazioni che implementano l’idea della caccia al tesoro su piattaforma mobile o applicazioni che implementassero algoritmi di instance recognition direttamente su smartphone. In secondo luogo si è ricercato in letteratura quali fossero gli algoritmi di riconoscimento di immagini più largamente diffusi e studiati in modo da avere una panoramica dei metodi da testare per poi fare la scelta dell’algoritmo più adatto al caso di studio. Quindi si è proceduto con lo sviluppo dell’applicazione GeoPhotoHunt stessa, sia per quanto riguarda l’app front-end per dispositivi Android sia la parte back-end server. Infine si è passati ad una fase di test di algoritmi di riconoscimento di immagini in modo di avere una sufficiente quantità di dati sperimentali da permettere di effettuare una scelta dell’algoritmo più adatto al caso di studio. Al termine della fase di testing si è deciso di implementare su Android un algoritmo basato sulla distanza tra istogrammi di colore costruiti sulla scala cromatica HSV, questo metodo pur non essendo robusto in presenza di variazioni di luminosità e contrasto, rappresenta un buon compromesso tra prestazioni, complessità computazionale in modo da rendere la user experience quanto più coinvolgente.