Learning dependent sources using mixtures of Dirichlet: applications on hyperspectral unmixing

This paper is an elaboration of the DECA algorithm [1] to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.

A remote virtual experiment in mechanical engineering

Conferência: 2nd Experiment at International Conference (Exp at)- Univ Coimbra, Coimbra, Portugal - Sep 18-20, 2013

O desenvolvimento de um modelo de ensino virtual num contexto de investimento incipiente em e - learning: progressos e desafios

Este artigo relata o desenvolvimento de um modelo de ensino virtual em curso na Universidade dos Açores. Depois de ter sido adotado na lecionação de disciplinas da área da Teoria e Desenvolvimento Curricular em regime de e-learning e b-learning, o modelo foi, no ano académico de 2014/15, estendido à lecionação de outras disciplinas. Além de descrever o modelo e explicar a sua evolução, o artigo destaca a sua adoção no contexto particular de uma disciplina cuja componente online foi lecionada em circunstâncias especialmente desafiadoras. Neste sentido, explica o processo de avaliação da experiência, discute os seus resultados e sugere pistas de melhoria. Essa avaliação enquadra-se num processo de investigação do design curricular – a metodologia que tem sido usada para estudar o desenvolvimento do modelo.

Learning and Knowledge in the Context of Absorptive Capacity

Develop a new model of Absorptive Capacity taking into account two variables namely Learning and knowledge to explain how companies transform information into knowledge

Improving Remote Voting Security with Code Voting

One of the major problems that prevents the spread of elections with the possibility of remote voting over electronic networks, also called Internet Voting, is the use of unreliable client platforms, such as the voter's computer and the Internet infrastructure connecting it to the election server. A computer connected to the Internet is exposed to viruses, worms, Trojans, spyware, malware and other threats that can compromise the election's integrity. For instance, it is possible to write a virus that changes the voter's vote to a predetermined vote on election's day. Another possible attack is the creation of a fake election web site where the voter uses a malicious vote program on the web site that manipulates the voter's vote (phishing/pharming attack). Such attacks may not disturb the election protocol, therefore can remain undetected in the eyes of the election auditors. We propose the use of Code Voting to overcome insecurity of the client platform. Code Voting consists in creating a secure communication channel to communicate the voter's vote between the voter and a trusted component attached to the voter's computer. Consequently, no one controlling the voter's computer can change the his/her's vote. The trusted component can then process the vote according to a cryptographic voting protocol to enable cryptographic verification at the server's side.

Statistical motion learning for improved transform domain Wyner-Ziv video coding

Wyner - Ziv (WZ) video coding is a particular case of distributed video coding (DVC), the recent video coding paradigm based on the Slepian - Wolf and Wyner - Ziv theorems which exploits the source temporal correlation at the decoder and not at the encoder as in predictive video coding. Although some progress has been made in the last years, WZ video coding is still far from the compression performance of predictive video coding, especially for high and complex motion contents. The WZ video codec adopted in this study is based on a transform domain WZ video coding architecture with feedback channel-driven rate control, whose modules have been improved with some recent coding tools. This study proposes a novel motion learning approach to successively improve the rate-distortion (RD) performance of the WZ video codec as the decoding proceeds, making use of the already decoded transform bands to improve the decoding process for the remaining transform bands. The results obtained reveal gains up to 2.3 dB in the RD curves against the performance for the same codec without the proposed motion learning approach for high motion sequences and long group of pictures (GOP) sizes.

Overview of the tuning template for radiography in Europe

HENRE II (Higher Education Network for Radiography in Europe)

Learning techniques for automatic email message tagging

A organização automática de mensagens de correio electrónico é um desafio actual na área da aprendizagem automática. O número excessivo de mensagens afecta cada vez mais utilizadores, especialmente os que usam o correio electrónico como ferramenta de comunicação e trabalho. Esta tese aborda o problema da organização automática de mensagens de correio electrónico propondo uma solução que tem como objectivo a etiquetagem automática de mensagens. A etiquetagem automática é feita com recurso às pastas de correio electrónico anteriormente criadas pelos utilizadores, tratando-as como etiquetas, e à sugestão de múltiplas etiquetas para cada mensagem (top-N). São estudadas várias técnicas de aprendizagem e os vários campos que compõe uma mensagem de correio electrónico são analisados de forma a determinar a sua adequação como elementos de classificação. O foco deste trabalho recai sobre os campos textuais (o assunto e o corpo das mensagens), estudando-se diferentes formas de representação, selecção de características e algoritmos de classificação. É ainda efectuada a avaliação dos campos de participantes através de algoritmos de classificação que os representam usando o modelo vectorial ou como um grafo. Os vários campos são combinados para classificação utilizando a técnica de combinação de classificadores Votação por Maioria. Os testes são efectuados com um subconjunto de mensagens de correio electrónico da Enron e um conjunto de dados privados disponibilizados pelo Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Estes conjuntos são analisados de forma a perceber as características dos dados. A avaliação do sistema é realizada através da percentagem de acerto dos classificadores. Os resultados obtidos apresentam melhorias significativas em comparação com os trabalhos relacionados.

Dynamic equilibrium through reinforcement learning

Reinforcement Learning is an area of Machine Learning that deals with how an agent should take actions in an environment such as to maximize the notion of accumulated reward. This type of learning is inspired by the way humans learn and has led to the creation of various algorithms for reinforcement learning. These algorithms focus on the way in which an agent’s behaviour can be improved, assuming independence as to their surroundings. The current work studies the application of reinforcement learning methods to solve the inverted pendulum problem. The importance of the variability of the environment (factors that are external to the agent) on the execution of reinforcement learning agents is studied by using a model that seeks to obtain equilibrium (stability) through dynamism – a Cart-Pole system or inverted pendulum. We sought to improve the behaviour of the autonomous agents by changing the information passed to them, while maintaining the agent’s internal parameters constant (learning rate, discount factors, decay rate, etc.), instead of the classical approach of tuning the agent’s internal parameters. The influence of changes on the state set and the action set on an agent’s capability to solve the Cart-pole problem was studied. We have studied typical behaviour of reinforcement learning agents applied to the classic BOXES model and a new form of characterizing the environment was proposed using the notion of convergence towards a reference value. We demonstrate the gain in performance of this new method applied to a Q-Learning agent.

Análise funcional comparativa de algoritmos de aprendizagem por reforço

De entre todos os paradigmas de aprendizagem actualmente identificados, a Aprendizagem por Reforço revela-se de especial interesse e aplicabilidade nos inúmeros processos que nos rodeiam: desde a solitária sonda que explora o planeta mais remoto, passando pelo programa especialista que aprende a apoiar a decisão médica pela experiencia adquirida, até ao cão de brincar que faz as delícias da criança interagindo com ela e adaptando-se aos seus gostos, e todo um novo mundo que nos rodeia e apela crescentemente a que façamos mais e melhor nesta área. Desde o aparecimento do conceito de aprendizagem por reforço, diferentes métodos tem sido propostos para a sua concretização, cada um deles abordando aspectos específicos. Duas vertentes distintas, mas complementares entre si, apresentam-se como características chave do processo de aprendizagem por reforço: a obtenção de experiência através da exploração do espaço de estados e o aproveitamento do conhecimento obtido através dessa mesma experiência. Esta dissertação propõe-se seleccionar alguns dos métodos propostos mais promissores de ambas as vertentes de exploração e aproveitamento, efectuar uma implementação de cada um destes sobre uma plataforma modular que permita a simulação do uso de agentes inteligentes e, através da sua aplicação na resolução de diferentes configurações de ambientes padrão, gerar estatísticas funcionais que permitam inferir conclusões que retractem entre outros aspectos a sua eficiência e eficácia comparativas em condições específicas.

Utilization of Interactive Internet in High Education

The present work reports on the practical cooperation between two Universities from Hungary and Portugal. Students from Portugal are remotely accessing an experimental facility, which is physically in Hungary. The cooperation among these Higher Education establishments allowed the development and testing of a Remote Laboratory at the BME. This paper reports on the characteristics and initial testing of the Thermocouples Rise Time Measurement System and provides information on development and students' feedback.

e-learning in the classroom?

As teachers, we are challenged everyday to solve pedagogical problems and we have to fight for our students’ attention in a media rich world. I will talk about how we use ICT in Initial Teacher Training and give you some insight on what we are doing. The most important benefit of using ICT in education is that it makes us reflect on our practice. There is no doubt that our classrooms need to be updated, but we need to be critical about every peace of hardware, software or service that we bring into them. It is not only because our budgets are short, but also because e‐learning is primarily about learning, not technology. Therefore, we need to have the knowledge and skills required to act in different situations, and choose the best tool for the job. Not all subjects are suitable for e‐learning, nor do all students have the skills to organize themselves their own study times. Also not all teachers want to spend time programming or learning about instructional design and metadata. The promised land of easy use of authoring tools (e.g. eXe and Reload) that will lead to all teachers become Learning Objects authors and share these LO in Repositories, all this failed, like previously HyperCard, Toolbook and others. We need to know a little bit of many different technologies so we can mobilize this knowledge when a situation requires it: integrate e‐learning technologies in the classroom, not a flipped classroom, just simple tools. Lecture capture, mobile phones and smartphones, pocket size camcorders, VoIP, VLE, live video broadcast, screen sharing, free services for collaborative work, save, share and sync your files. Do not feel stressed to use everything, every time. Just because we have a whiteboard does not mean we have to make it the centre of the classroom. Start from where you are, with your preferred subject and the tools you master. Them go slowly and try some new tool in a non‐formal situation and with just one or two students. And you don’t need to be alone: subscribe a mailing list and share your thoughts with other teachers in a dedicated forum, even better if both are part of a community of practice, and share resources. We did that for music teachers and it was a success, in two years arriving at 1.000 members. Just do it.

Estudo para a implementação de plataformas de e-learning no sistema de formação dos recursos humanos da saúde: o caso particular dos enfermeiros de um hospital privado

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

Experiencing flow while teaching and learning in individual tuition settings

Conferência anual da ISME

A utilização da plataforma Educast@fccn, como ferramenta de gravação de aulas no ambiente de e-learning português

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.