Case study: Developing digital learning at Prospects College for Advanced Technology

Case study on how a digital learning fellow at Prospects College for Advanced Technology has developed a digital learning strategy that focuses on vocational training and work-based learning.

Aprendizaje basado en proyectos en Diseño y Construcción de Sistemas Digitales

Duración (en horas): Más de 50 horas. Destinatario: Estudiante y Docente

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Disseminação de dados estatísticos georeferenciados através de WEB services e software livre.

Diferentes organizações públicas e privadas coletam e disponibilizam uma massa de dados sobre a realidade sócio-econômica das diferentes nações. Há hoje, da parte do governo brasileiro, um interesse manifesto de divulgar uma gama diferenciada de informações para os mais diversos perfis de usuários. Persiste, contudo, uma série de limitações para uma divulgação mais massiva e democrática, entre elas, a heterogeneidade das fontes de dados, sua dispersão e formato de apresentação pouco amigável. Devido à complexidade inerente à informação geográfica envolvida, que produz incompatibilidade em vários níveis, o intercâmbio de dados em sistemas de informação geográfica não é problema trivial. Para aplicações desenvolvidas para a Web, uma solução são os Web Services que permitem que novas aplicações possam interagir com aquelas que já existem e que sistemas desenvolvidos em plataformas diferentes sejam compatíveis. Neste sentido, o objetivo do trabalho é mostrar as possibilidades de construção de portais usando software livre, a tecnologia dos Web Services e os padrões do Open Geospatial Consortium (OGC) para a disseminação de dados espaciais. Visando avaliar e testar as tecnologias selecionadas e comprovar sua efetividade foi desenvolvido um exemplo de portal de dados sócio-econômicos, compreendendo informações de um servidor local e de servidores remotos. As contribuições do trabalho são a disponibilização de mapas dinâmicos, a geração de mapas através da composição de mapas disponibilizados em servidores remotos e local e o uso do padrão OGC WMC. Analisando o protótipo de portal construído, verifica-se, contudo, que a localização e requisição de Web Services não são tarefas fáceis para um usuário típico da Internet. Nesta direção, os trabalhos futuros no domínio dos portais de informação geográfica poderiam adotar a tecnologia Representational State Transfer (REST).

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Tecer cotidianos, tecendo rebeldias : narrativas femininas sobre EJA

Esta dissertação surgiu a partir do desejo de contar a minha experiência como professora de Educação de Jovens e Adultos, como eu a vivi, o que aprendi e o que ainda quero descobrir. É nesta medida que, através deste trabalho, pesquisei e ouvi os relatos dos professores, pensando em suas práticas cotidianas, suas subjetividades e suas relações com os conteúdos escolares e com os saberes dos alunos, buscando identificar práticas emancipatórias em seus processos educativos cotidianos. As vozes dos alunos, tornam-se audíveis também, através das narrativas de suas vivências, que permitem, também, identificar atividades emancipatórias em meio aos seus processos de aprendizagem. Narrativas de alunos e narrativas de professores se entrelaçam criando uma trama da memória cotidiana, sobre a qual Nilda Alves (2008) chama a atenção por ser uma narrativa não linear e sujeita a diversas interrupções e introduções de outras histórias paralelas. Dessa forma, busquei fazer um entrelaçamento entre narrativas de professores e alunos, procurando tecer uma rede a partir da narrativa da vida e da literaturização da ciência.

Modelagem e desenvolvimento de sistemas de informações geográficas para web com tecnologias de rich internet applications.

Os SIG estão se popularizando cada vez mais e isso tem se dado principalmente através da Internet. Os assim chamados SIG-Web no entanto, quando desenvolvidos com as tecnologias tradicionais de web, apresentam as mesmas fraquezas daquelas, a saber: sincronicidade e pobreza na interação com o usuário. As tecnologias usadas para Rich Internet Applications (RIA) são uma alternativa que resolvem esses problemas. Na presente dissertação será demonstrada a factibilidade do seu uso para o desenvolvimento de SIG-Web, oferecendo um conjunto de códigos e estratégias para desenvolvimentos futuros, a partir de um conjunto básico de operações a se realizar em um SIG-Web. Adicionalmente será proposta a UWE-R, uma extensão a uma metodologia de engenharia web existente, para modelagem de RIA e SIG-Web.

Invasionsoft: a web-enabled tool for invasive species colonization predictions

Predicting and averting the spread of invasive species is a core focus of resource managers in all ecosystems. Patterns of invasion are difficult to forecast, compounded by a lack of user-friendly species distribution model (SDM) tools to help managers focus control efforts. This paper presents a web-based cellular automata hybrid modeling tool developed to study the invasion pattern of lionfish (Pterois volitans/miles) in the western Atlantic and is a natural extension our previous lionfish study. Our goal is to make publically available this hybrid SDM tool and demonstrate both a test case (P. volitans/miles) and a use case (Caulerpa taxifolia). The software derived from the model, titled Invasionsoft, is unique in its ability to examine multiple default or user-defined parameters, their relation to invasion patterns, and is presented in a rich web browser-based GUI with integrated results viewer. The beta version is not species-specific and includes a default parameter set that is tailored to the marine habitat. Invasionsoft is provided as copyright protected freeware at http://www.invasionsoft.com.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

基于web的遥操作系统时延分析

采用预测控制算法给出了一种带有时延补偿器的新的控制结构,分别在前向通道和反馈通道设计补偿器对网络时延进行补偿.实验结果表明:带有预测器及补偿器的新的控制结构可以改善系统的动态性能,并且能够保证系统在具有时延和数据丢失的环境下的稳定性.

基于web的远程控制机器人研究

Internet连接了全球的计算机 ,它为人们提供了分享数据、图片、影像甚至实时影像的机会 ,但与远程地点的真实交互还是离不开象机器人这样的智能设备 .Web技术与机器人控制技术的结合 ,促成了基于 web的远程控制机器人概念的诞生 .本文将就基于 web的远程控制机器人的发展历程、研究现状、主要技术与实现、发展趋势及应用前景等做综合的介绍 .

基于WEB支持的市场客户管理系统的设计

本文阐述了采用Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术和利用ＡＳＰ实现数据的动态发布技术以及基于分布网络环境下的异地设计与制造技术，设计了基于ｗｅｂ的支持机器人异地设计制造的市场客户管理系统，从而探讨了Ｂｒｏｗｓｅｒ／Ｓｅｒｖｅｒ结构的数据库发布系统的设计方法。

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.