Desenvolvimento de sistema de educação continuada a distância e interconsulta em hepatites virais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Particle competition and cooperation for semi-supervised learning with label noise

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

A reumatologia em um curso de medicina com aprendizagem baseada em problemas

OBJETIVO: Apresentar a experiência de uma instituição brasileira no ensino da Reumatologia na graduação médica, cujo projeto pedagógico é estruturado em metodologias ativas de aprendizado. MÉTODOS: Realizou-se um estudo descritivo, com abordagem qualitativa dos conteúdos referentes à Reumatologia no curso de Medicina do Centro Universitário do Estado do Pará (Cesupa). RESULTADOS: O sistema musculoesquelético é abordado no segundo e sétimo semestres, nos módulos referentes ao sistema locomotor e Clínica Médica II, respectivamente, sendo que cada etapa e cada atividade apresentam objetivos específicos mínimos. Além do conteúdo teórico, no sétimo semestre, os alunos realizam atendimentos no ambulatório de Reumatologia, quando existe maior ênfase na elaboração correta de anamnese e exame físico. No internato, os alunos retornam ao ambulatório de Reumatologia e, neste momento, as habilidades de diagnóstico, de investigação e de terapêutica são as mais exigidas. CONCLUSÃO: Ainda há muito para evoluir em busca de um modelo ideal para o ensino da Reumatologia, porém, cumprindo as principais recomendações disponíveis para a boa prática do ensino na graduação, podemos proporcionar ao futuro médico conhecimento, habilidade e experiências capazes de ajudá-lo na condução desses pacientes.

What do medical students think about their quality of life? A qualitative study

Background: Medical education can affect medical students' physical and mental health as well as their quality of life. The aim of this study was to assess medical students' perceptions of their quality of life and its relationship with medical education. Methods: First-to sixth-year students from six Brazilian medical schools were interviewed using focus groups to explore what medical student's lives are like, factors related to increases and decreases of their quality of life during medical school, and how they deal with the difficulties in their training. Results: Students reported a variety of difficulties and crises during medical school. Factors that were reported to decrease their quality of life included competition, unprepared teachers, excessive activities, and medical school schedules that demanded exclusive dedication. Contact with pain, death and suffering and harsh social realities influence their quality of life, as well as frustrations with the program and insecurity regarding their professional future. The scarcity of time for studying, leisure activities, relationships, and rest was considered the main factor of influence. Among factors that increase quality of life are good teachers, classes with good didactic approaches, active learning methodologies, contact with patients, and efficient time management. Students also reported that meaningful relationships with family members, friends, or teachers increase their quality of life. Conclusion: Quality of teachers, curricula, healthy lifestyles related to eating habits, sleep, and physical activity modify medical students' quality of life. Lack of time due to medical school obligations was a major impact factor. Students affirm their quality of life is influenced by their medical school experiences, but they also reframe their difficulties, herein represented by their poor quality of life, understood as necessary and inherent to the process of becoming doctors.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Hands-on experiments in the Practicum: repositioning student teachers' autonomy.

Following a worldwide trend, the Initial Teacher Education (ITE) Programmes in Brazil are recently searching for ways of integrating practice into curriculum. It raises question about what practice must be integrated and how. Notably, university-based courses are disconnected from school and have low commitment with school issues (Zeichner, 2009).The student teacher induction into school daily life is not an easy task, mainly when the practitioners are transforming physics classroom practice toward an active learning. Drawing on cultural-historical framework (Wolff-Michael Roth & Lee, 2007; Vygotsky, 1978) this study addresses the articulation between Practicum in Physics Classes and the Hands-on Experiments (HoE) used throughout the Practicum. Although in a different level, both Practicum and HoE are linked with an idea of practice. Particularly, this study focuses on how HoE might foster student teachers' autonomy and agency in the Practicum. Data was gathered in the course Practice of Physics Teaching at University of São Paulo/Brazil in 2010; in a cohort of 60 student teachers doing a year-long Practicum in urban school in São Paulo city. Data was analysed using qualitative research methods (Roth, 2005), based on 14 interviews and video records of the student teacher preparing the HoE for Practicum we will present in general lines the role of HoE for student teacher autonomy.

The Impact of Gender and Pedagogy on the Development of Self-Regulated Learning Strategies in Undergraduate Engineering Classrooms

The purpose of this paper is to examine ways in which pedagogy and gender of instructor impact the development of self-regulated learning strategies as assessed by the Motivated Strategies for Learning Questionnaire (MSLQ) in male and female undergraduate engineering students. Pedagogy was operationalized as two general formats: lecture plus active learning techniques or problem-base/project-based learning. One hundred seventy-six students from four universities participated in the study. Within-group analyses found significant differences with regard to pedagogy, instructors’ gender, and student gender on the learning strategies and motivation subscales as operationalized by the MSLQ. Male and females students reported significant post-test differences with regard to the gender of instructor and the style of pedagogy. The results of this study showed a pattern where more positive responses for students of both genders were found with the same-gendered instructor. The results also suggested that male students responded more positively to project and problem-based courses with changes evidenced in motivation strategies and resource management. Female students showed decreases in resource management in these two types of courses. Further, female students reported increases in the lecture with active learning courses.

Internet-based exercises and mini-exams for production and operations management

The purpose of the internet-based teachware mySCM is that students of economics, informatics and industrial engineering get familiar with quantitative methods for supply chain management. Input-output-relationships of various optimization methods can be detected by sampling input values, parameters, and alternative methods for the same problem. Students can gain extra benefits by passing so-called mini-exams that motivate active learning. mySCM can be used for free, round-the-clock, and any place where access to the Internet is available.

Directional Geodesic Active Contours

We present a non-conformal metric that generalizes the geodesic active contours approach for image segmentation. The new metric is obtained by adding to the Euclidean metric an additional term that penalizes the misalignment of the curve with the image gradient and multiplying the resulting metric by a conformal factor that depends on the edge intensity. In this way, a closer fitting to the edge direction results. The provided experimental results address the computation of the geodesics of the new metric by applying a gradient descent to externally provided curves. The good performance of the proposed techniques is demonstrated in comparison with other active contours methods.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Web-based education in Spanish Universities. A Comparison of Open Source E-Learning Platforms

Web-based education or „e-learning‟ has become a critical component in higher education for the last decade, replacing other distance learning methods, such as traditional computer training or correspondence learning. The number of university students who take on-line courses is continuously increasing all over the world. In Spain, nearly a 90% of the universities have an institutional e-learning platform and over 60% of the traditional on-site courses use this technology as a supplement to the traditional face-to-face classes. This new form of learning allows the disappearance of geographical barriers and enables students to schedule their own learning process, among some other advantages. On-line education is developed through specific software called „e-learning platform‟ or „virtual learning environment‟ (VLE). A considerable number of web-based tools to deliver distance courses are currently available. Open source software packages such as Moodle, Sakai, dotLRN or Dokeos are the most commonly used in the virtual campuses of Spanish universities. This paper analyzes the possibilities that virtual learning environments provide university teachers and learners and offers a technical comparison among some of the most popular e-learning learning platforms.

Measuring the influence of Cooperative Learning and Project Based Learning on problem solvin skill.

The aim of this study is to evaluate the effects obtained after applying two active learning methodologies (cooperative learning and project based learning) to the achievement of the competence problem solving. This study was carried out at the Technical University of Madrid, where these methodologies were applied to two Operating Systems courses. The first hypothesis tested was whether the implementation of active learning methodologies favours the achievement of ?problem solving?. The second hypothesis was focused on testing if students with higher rates in problem solving competence obtain better results in their academic performance. The results indicated that active learning methodologies do not produce any significant change in the generic competence ?problem solving? during the period analysed. Concerning this, we consider that students should work with these methodologies for a longer period, besides having a specific training. Nevertheless, a close correlation between problem solving self appraisal and academic performance has been detected.

Cooperative Learning vs. Project Base Learning.

The Bologna Declaration and the implementation of the European Higher Education Area are promoting the use of active learning methodologies such as cooperative learning and project based learning. This study was motivated by the comparison of the results obtained after applying Cooperative Learning (CL) and Project Based Learning (PBL) to a subject of Computer Engineering. The fundamental hypothesis tested was whether the academic success achieved by the students of the first years was higher when CL was applied than in those cases to which PBL was applied. A practical case, by means of which the effectiveness of CL and PBL are compared, is presented in this work. This study has been carried out at the Universidad Politécnica de Madrid, where these mechanisms have been applied to the Operating Systems I subject from the Technical Engineering in Computer Systems degree (OSIS) and to the same subject from the Technical Engineering in Computer Management degree (OSIM). Both subjects have the same syllabus, are taught in the same year and semester and share also formative objectives. From this study we can conclude that students¿ academic performance (regarding the grades given) is greater with PBL than with CL. To be more specific, the difference is between 0.5 and 1 point for the individual tests. For the group tests, this difference is between 2.5 and 3 points. Therefore, this study refutes the fundamental hypothesis formulated at the beginning. Some of the possible interpretations of these results are referred to in this study.

Machine Learning in Scientometrics

El aprendizaje automático y la cienciometría son las disciplinas científicas que se tratan en esta tesis. El aprendizaje automático trata sobre la construcción y el estudio de algoritmos que puedan aprender a partir de datos, mientras que la cienciometría se ocupa principalmente del análisis de la ciencia desde una perspectiva cuantitativa. Hoy en día, los avances en el aprendizaje automático proporcionan las herramientas matemáticas y estadísticas para trabajar correctamente con la gran cantidad de datos cienciométricos almacenados en bases de datos bibliográficas. En este contexto, el uso de nuevos métodos de aprendizaje automático en aplicaciones de cienciometría es el foco de atención de esta tesis doctoral. Esta tesis propone nuevas contribuciones en el aprendizaje automático que podrían arrojar luz sobre el área de la cienciometría. Estas contribuciones están divididas en tres partes: Varios modelos supervisados (in)sensibles al coste son aprendidos para predecir el éxito científico de los artículos y los investigadores. Los modelos sensibles al coste no están interesados en maximizar la precisión de clasificación, sino en la minimización del coste total esperado derivado de los errores ocasionados. En este contexto, los editores de revistas científicas podrían disponer de una herramienta capaz de predecir el número de citas de un artículo en el fututo antes de ser publicado, mientras que los comités de promoción podrían predecir el incremento anual del índice h de los investigadores en los primeros años. Estos modelos predictivos podrían allanar el camino hacia nuevos sistemas de evaluación. Varios modelos gráficos probabilísticos son aprendidos para explotar y descubrir nuevas relaciones entre el gran número de índices bibliométricos existentes. En este contexto, la comunidad científica podría medir cómo algunos índices influyen en otros en términos probabilísticos y realizar propagación de la evidencia e inferencia abductiva para responder a preguntas bibliométricas. Además, la comunidad científica podría descubrir qué índices bibliométricos tienen mayor poder predictivo. Este es un problema de regresión multi-respuesta en el que el papel de cada variable, predictiva o respuesta, es desconocido de antemano. Los índices resultantes podrían ser muy útiles para la predicción, es decir, cuando se conocen sus valores, el conocimiento de cualquier valor no proporciona información sobre la predicción de otros índices bibliométricos. Un estudio bibliométrico sobre la investigación española en informática ha sido realizado bajo la cultura de publicar o morir. Este estudio se basa en una metodología de análisis de clusters que caracteriza la actividad en la investigación en términos de productividad, visibilidad, calidad, prestigio y colaboración internacional. Este estudio también analiza los efectos de la colaboración en la productividad y la visibilidad bajo diferentes circunstancias. ABSTRACT Machine learning and scientometrics are the scientific disciplines which are covered in this dissertation. Machine learning deals with the construction and study of algorithms that can learn from data, whereas scientometrics is mainly concerned with the analysis of science from a quantitative perspective. Nowadays, advances in machine learning provide the mathematical and statistical tools for properly working with the vast amount of scientometrics data stored in bibliographic databases. In this context, the use of novel machine learning methods in scientometrics applications is the focus of attention of this dissertation. This dissertation proposes new machine learning contributions which would shed light on the scientometrics area. These contributions are divided in three parts: Several supervised cost-(in)sensitive models are learned to predict the scientific success of articles and researchers. Cost-sensitive models are not interested in maximizing classification accuracy, but in minimizing the expected total cost of the error derived from mistakes in the classification process. In this context, publishers of scientific journals could have a tool capable of predicting the citation count of an article in the future before it is published, whereas promotion committees could predict the annual increase of the h-index of researchers within the first few years. These predictive models would pave the way for new assessment systems. Several probabilistic graphical models are learned to exploit and discover new relationships among the vast number of existing bibliometric indices. In this context, scientific community could measure how some indices influence others in probabilistic terms and perform evidence propagation and abduction inference for answering bibliometric questions. Also, scientific community could uncover which bibliometric indices have a higher predictive power. This is a multi-output regression problem where the role of each variable, predictive or response, is unknown beforehand. The resulting indices could be very useful for prediction purposes, that is, when their index values are known, knowledge of any index value provides no information on the prediction of other bibliometric indices. A scientometric study of the Spanish computer science research is performed under the publish-or-perish culture. This study is based on a cluster analysis methodology which characterizes the research activity in terms of productivity, visibility, quality, prestige and international collaboration. This study also analyzes the effects of collaboration on productivity and visibility under different circumstances.