Tutor Inteligente para o Ensino em Medicina

O objectivo deste trabalho consistiu no desenvolvimento de um protótipo que possibilita a adaptação do conteúdo disponibilizado de acordo com as características pessoais e psicológicas do aluno, aplicado no ensino da Medicina, nomeadamente na componente de Desenho de Estudos da disciplina de Introdução à Medicina. Para o protótipo desenvolvido foi definida uma arquitectura constituída por três componentes: um Modelo de Aluno que engloba as características pessoais e psicológicas do aluno, um Modelo de Domínio constituído por um grafo de conceitos e um Modelo Pedagógico formado pelas regras de adaptação e mecanismos de interação utilizados para obter uma solução adaptativa. Os diferentes componentes desenvolvidos para este protótipo permitem que este apresente as seguintes funcionalidades: Acesso ao conceito adequado, tendo em consideração o nível de conhecimento do aluno; Visualização de conte udos adequados ao estilo de aprendizagem do aluno; Adaptação do percurso do aluno de acordo com os resultados obtidos; Atualização das preferências de aprendizagem, com base no comportamento demonstrado pelo aluno na interação com o sistema. A primeira versão da ferramenta j a foi implementada. No entanto ainda será realizada a avaliação do protótipo em ambiente de aprendizagem, com a maior brevidade possível.

Le transfert informel des connaissances tacites chez les gestionnaires municipaux en situation de coordination

Ce rapport de recherche porte sur une étude s’intéressant au transfert des connaissances tacites chez les gestionnaires, c’est-à-dire le partage de ces connaissances et leur utilisation informelle, durant une situation de coordination dans un service municipal. La thèse est articulée autour des questions suivantes : Quelles sont les situations de coordination vécues par les gestionnaires municipaux? Quelles sont les sources de connaissances tacites partagées et utilisées? Quelles sont les relations de connaissances mobilisées de façon informelle lors du transfert des connaissances tacites? Quels sont les facteurs encourageant ou inhibant le transfert informel des connaissances tacites? À partir d’un modèle basé sur une approche situationnelle (Taylor, 1989 et 1991), nous avons revu la documentation touchant nos questions de recherche. Nous avons défini notamment la récursivité des connaissances et le réseau de connaissances, de même que présenté le modèle de la conversion des connaissances (Nonaka, 1994) et celui de l’actualisation de soi (St-Arnaud, 1996). Nous avons questionné 22 répondants à l’aide d’instruments de mesure qui combinent les techniques de l’incident critique, de l’entrevue cognitive et réflexive, le questionnement sur les réseaux organisationnels et l’observation participante. Tels des filets, ces instruments ont permis de traquer et d’obtenir des données d’une grande richesse sur les connaissances tacites et les comportements informels durant le transfert de connaissances en situation de coordination. Ces données ont été analysées selon une approche méthodologique essentiellement qualitative combinant l’analyse de contenu, la schématisation heuristique et l’analyse des réseaux sociaux. Nos résultats montrent que la complexité d’une situation de coordination conditionne le choix des mécanismes de coordination. De plus, les sources de connaissances sont, du point de vue individuel, le gestionnaire et ses artefacts, de même que son réseau personnel avec ses propres artefacts. Du point de vue collectif, ces sources sont réifiées dans le réseau de connaissances. Les connaissances clés d’une situation de coordination sont celles sur le réseau organisationnel, le contexte, les expériences en gestion et en situation complexe de coordination, la capacité de communiquer, de négocier, d’innover et celle d’attirer l’attention. Individuellement, les gestionnaires privilégient l’actualisation de soi, l’autoformation et la formation contextualisée et, collectivement, la coprésence dans l’action, le réseautage et l’accompagnement. Cette étude fournit un modèle valide du transfert contextualisé des connaissances qui est un cas de coordination complexe d’activités en gestion des connaissances. Ce transfert est concomitant à d’autres situations de coordination. La nature tacite des connaissances prévaut, de même que le mode informel, les médias personnels et les mécanismes d’ajustement mutuel. Les connaissances tacites sont principalement transférées au début des processus de gestion de projet et continuellement durant la rétroaction et le suivi des résultats. Quant aux connaissances explicites, les gestionnaires les utilisent principalement comme un symbole à la fin des processus de gestion de projet. Parmi les personnes et les groupes de personnes d’une situation de transfert contextualisé des connaissances, 10 % jouent des rôles clés, soit ceux d’experts et d’intermédiaires de personnes et d’artefacts. Les personnes en périphérie possèdent un potentiel de structuration, c’est-à-dire de connexité, pour assurer la continuité du réseau de connaissances organisationnel. Notre étude a élargi le modèle général de la complexité d’une situation (Bystrom, 1999; Choo, 2006; Taylor, 1986 et 1991), la théorie de la coordination (Malone et Crowston, 1994), le modèle de la conversion des connaissances (Nonaka, 1994), celui de l’actualisation de soi (St-Arnaud, 1996) et la théorie des réseaux de connaissances (Monge et Contractor, 2003). Notre modèle réaffirme la concomitance de ces modèles généraux selon une approche constructiviste (Giddens, 1987) où la dualité du structurel et la compétence des acteurs sont confirmées et enrichies.

Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping

The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analyses of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections ( LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in mD. In order to perform the projection, a small number of distance calculations are necessary, and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in 2D. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high-quality methods, particularly where it was mostly tested, that is, for mapping text sets.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Knowledge extraction using visualization of hemoglobin parameters to identify thalassemia

The analysis of large amounts of data is better performed by humans when represented in a graphical format. Therefore, a new research area called the Visual Data Mining is being developed endeavoring to use the number crunching power of computers to prepare data for visualization, allied to the ability of humans to interpret data presented graphically.This work presents the results of applying a visual data mining tool, called FastMapDB to detect the behavioral pattern exhibited by a dataset of clinical information about hemoglobinopathies known as thalassemia. FastMapDB is a visual data mining tool that get tabular data stored in a relational database such as dates, numbers and texts, and by considering them as points in a multidimensional space, maps them to a three-dimensional space. The intuitive three-dimensional representation of objects enables a data analyst to see the behavior of the characteristics from abnormal forms of hemoglobin, highlighting the differences when compared to data from a group without alteration.

Histogram-based DNA analysis for the visualization of chromosome, genome and species information

We describe a novel approach to explore DNA nucleotide sequence data, aiming to produce high-level categorical and structural information about the underlying chromosomes, genomes and species. The article starts by analyzing chromosomal data through histograms using fixed length DNA sequences. After creating the DNA-related histograms, a correlation between pairs of histograms is computed, producing a global correlation matrix. These data are then used as input to several data processing methods for information extraction and tabular/graphical output generation. A set of 18 species is processed and the extensive results reveal that the proposed method is able to generate significant and diversified outputs, in good accordance with current scientific knowledge in domains such as genomics and phylogenetics.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy.

We have developed a digital holographic microscope (DHM), in a transmission mode, especially dedicated to the quantitative visualization of phase objects such as living cells. The method is based on an original numerical algorithm presented in detail elsewhere [Cuche et al., Appl. Opt. 38, 6994 (1999)]. DHM images of living cells in culture are shown for what is to our knowledge the first time. They represent the distribution of the optical path length over the cell, which has been measured with subwavelength accuracy. These DHM images are compared with those obtained by use of the widely used phase contrast and Nomarski differential interference contrast techniques.

Knowledge modeling for innovative companies: case of business incubator

The paper presents a study which is aimed at building a knowledge model for a case company – business incubator “Ingria” (St. Petersburg, Russia). The business incubator is one of its kind organization in St. Petersburg, and one of the few in Russia, providing services for innovative entrepreneurial companies at an international level. Business incubation impact is deeply researched from the point of view of knowledge engineering. The paper also provides a broad analysis of various knowledge engineering tools used for visualization of knowledge, as well as knowledge modeling techniques.

Conceptual knowledge discovery with frequent concept lattices

Knowledge discovery support environments include beside classical data analysis tools also data mining tools. For supporting both kinds of tools, a unified knowledge representation is needed. We show that concept lattices which are used as knowledge representation in Conceptual Information Systems can also be used for structuring the results of mining association rules. Vice versa, we use ideas of association rules for reducing the complexity of the visualization of Conceptual Information Systems.

CEM - visualization and discovery in Email

This paper presents a lattice-based visual metaphor for knowledge discovery in electronic mail. It allows a user to navigate email using a visual lattice metaphor rather than a tree structure. By using such a conceptual multi-hierarchy, the content and shape of the lattice can be varied to accommodate any number of queries against the email collection. The system provides more flexibility in retrieving stored emails and can be generalised to any electronic documents. The paper presents the underlying mathematical structures, and a number of examples of the lattice and multi-hierarchy working with a prototypical email collection.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Effective measures to decrease air contaminants through risk and control visualization : a study of the effective use of QR codes to facilitate safety training

Woodworking industries still consists of wood dust problems. Young workers are especially vulnerable to safety risks. To reduce risks, it is important to change attitudes and increase knowledge about safety. Safety training have shown to establish positive attitudes towards safety among employees. The aim of current study is to analyze the effect of QR codes that link to Picture Mix EXposure (PIMEX) videos by analyzing attitudes to this safety training method and safety in student responses. Safety training videos were used in upper secondary school handicraft programs to demonstrate wood dust risks and methods to decrease exposure to wood dust. A preliminary study was conducted to investigate improvement of safety training in two schools in preparation for the main study that investigated a safety training method in three schools. In the preliminary study the PIMEX method was first used in which students were filmed while wood dust exposure was measured and subsequently displayed on a computer screen in real time. Before and after the filming, teachers, students, and researchers together analyzed wood dust risks and effective measures to reduce exposure to them. For the main study, QR codes linked to PIMEX videos were attached at wood processing machines. Subsequent interviews showed that this safety training method enables students in an early stage of their life to learn about risks and safety measures to control wood dust exposure. The new combination of methods can create awareness, change attitudes and motivation among students to work more frequently to reduce wood dust. 

An online platform for real-time sensor data collection, visualization, and sharing

Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.