Interfaces inovadores para a aprendizagem musical

Mestrado em Engenharia Informática

Integração e contextualização de modelos 3D no ensino da área da saúde

Mestrado em Engenharia Informática. Sistemas Gráficos e Multimédia.

Tecnologias Multimédia no Apoio ao Ensino da Assistência Pré-Hospitalar

Vivemos num mundo em constante mudança, onde a evolução tecnológica está cada vez mais presente no nosso quotidiano e as suas influências são inúmeras nas nossas vidas. Por outro lado, a vida humana é algo extremamente valioso e único pelo que a sociedade, também nessa área, tem procurado evoluir e dotar-se de novos meios e mecanismos de atuação, que possibilitem um socorro rápido e adequado em situações que possam em algum momento pôr em perigo a vida do ser humano. Pretendemos com o presente estudo, correlacionar estas duas vertentes, a do socorro á vítima para apoio á vida humana e a tecnologia no sentido de contribuir para uma formação mais distribuída mas ao mesmo tempo capaz de transmitir conhecimentos necessários á formação dos que socorrem. Para o efeito, planeamos o desenvolvimento de um Objeto de Aprendizagem (OA) denominado CiTAT (Curso Interativo de Tripulante de Ambulância de Transporte) que tem como objetivo acrescentar valor a todos os que são obrigados a frequentar o curso em regime presencial de Tripulante de Ambulância de Transporte (TAT). Após uma fase de análise do estado da arte relacionada com este tipo de formação, e após termos percebido como funciona e que tipos de recursos utilizam, analisámos temas relacionados com este tipo de formação como o “Sistema Integrado de Emergência Médica” o “Exame á Vítima”, o “Suporte Básico de Vida” e as “Emergências de Trauma”. Percebemos que a mudança de paradigma de formação das pessoas passou a ter novas formas de distribuir conhecimento em formato digital e que proporciona aos formandos um ensino distribuído em formatos de e-learning ou de b-learning. Os Objetos de Aprendizagem (OA) parecem assumir um relevo especial no ensino da área da saúde, abordando áreas temáticas e proporcionando aos seus utilizadores mecanismos de autoavaliação após a visualização dos conteúdos pedagógicos. Após o desenho do modelo concetual do CiTAT, avançamos para a produção de recursos necessários para a sua integração no OA. Após uma fase de testes e ajustes, avançamos para a sua avaliação final por parte dos utilizadores e preparamos um questionário para aferir o potencial de utilização deste tipo de soluções no ensino de TAT, atendendo ao facto de ser uma formação obrigatória e cuja recertificação é feita de três em três anos. O passo final foi a sua distribuição ao nível global, sendo o CiTAT catalogado com metadados e colocado no repositório MERLOT.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

A learning environment for robotic production line simulation

This paper presents a framework for a robotic production line simulation learning environment using Autonomous Ground Vehicles (AGV). An eLearning platform is used as interface with the simulator. The objective is to introduce students to the production robotics area using a familiar tool, an eLearning platform, and a framework that simulates a production line using AGVs. This framework allows students to learn about robotics but also about several areas of industrial management engineering without requiring an extensive prior knowledge on the robotics area. The robotic production line simulation learning environment simulates a production environment using AGVs to transport materials to and from the production line. The simulator allows students to validate the AGV dynamics and provides information about the whole materials supplying system which includes: supply times, route optimization and inventory management. The students are required to address several topics such as: sensors, actuators, controllers and an high level management and optimization software. This simulator was developed with a known open source tool from robotics community: Player/Stage. This tool was extended with several add-ons so that students can be able to interact with a complex simulation environment. These add-ons include an abstraction communication layer that performs events provided by the database server which is programmed by the students. An eLearning platform is used as interface between the students and the simulator. The students can visualize the effects of their instructions/programming in the simulator that they can access via the eLearning platform. The proposed framework aims to allow students from different backgrounds to fully experience robotics in practice by suppressing the huge gap between theory and practice that exists in robotics. Using an eLearning platform eliminates installation problems that can occur from different computers software distribution and makes the simulator accessible by all students at school and at home.

Accounting education in an action-based learning environment: lessons learned

In the context of the Bologna Declaration a change is taking place in the teaching/learning paradigm. From teaching-centered education, which emphasizes the acquisition and transmission of knowledge, we now speak of learning-centered education, which is more demanding for students. This paradigm promotes a continuum of lifelong learning, where the individual needs to be able to handle knowledge, to select what is appropriate for a particular context, to learn permanently and to understand how to learn in new and rapidly changing situations. One attempt to face these challenges has been the experience of ISCAP regarding the teaching/learning of accounting in the course Managerial Simulation. This paper describes the process of teaching, learning and assessment in an action-based learning environment. After a brief general framework that focuses on education objectives, we report the strengths and limitations of this teaching/learning tool. We conclude with some lessons from the implementation of the project.

Teaching and learning mathematics using moodle

This paper summarizes a project that is contributing to a change in the way of teaching and learning Mathematics. Mathematics is a subject of the Accounting and Administration course. In this subject we teach: Functions and Algebra. The aim is that the student understand the basic concepts and is able to apply them in other issues, when possible, establishing a bridge between the issues that they have studied and their application in Accounting. As from this year, the Accounting course falls under in Bologna Process. The teacher and the student roles have changed. The time for theoretical and practical classes has been reduced, so it was necessary to modify the way of teaching and learning. In the theoretical classes we use systems of multimedia projection to present the concepts, and in the practical classes we solve exercises. We also use the Excel and the mathematical open source software wxMaxima. To supplement our theoretical and practical classes we have developed a project called MatActiva based on the Moodle platform offered by PAOL - Projecto de Apoio Online (Online Support Project). With the creation of this new project we wanted to take advantage already obtained results with the previous experiences, giving to the students opportunities to complement their study in Mathematics. One of the great objectives is to motivate students, encourage them to overcome theirs difficulties through an auto-study giving them more confidence. In the MatActiva project the students have a big collection of information about the way of the subject works, which includes the objectives, the program, recommended bibliography, evaluation method and summaries. It works as material support for the practical and theoretical classes, the slides of the theoretical classes are available, the sheets with exercises for the students to do in the classroom and complementary exercises, as well as the exams of previous years. Students can also do diagnostic tests and evaluation tests online. Our approach is a reflexive one, based on the professional experience of the teachers that explore and incorporate new tools of Moodle with their students and coordinate the project MatActiva.

Improving management skills through business simulation

A survey was conducted among students of the Accounting and Administration undergraduate degree at ISCAP – IPP (School of Accounting and Administration of Polytechnic Institute of Porto) in order to understand their perception value of their course Business Simulation (BS). This course is provided in a business environment where students can learn by doing through the management of a company as they were in the real life, but risk-free. The learning tasks are provided in an action-oriented format to maximize the learning process. Students learn by doing a set of tasks every session and have also to produce reports and presentations during the course. BS is part of the undergraduate degree of Accounting and Administration at ISCAP – IPP since the beginning of 2003. The questionnaire we used captured the students’ perception about general and specific skills and competencies considered important for managers and accountants in the real life, about the methodology used in the course, which is totally different from the traditional form, and also about the adequacy of the course included as part of the undergraduate degree. The results showed that students’ perception is highly positive and almost all of them think they improve the skills needed for a job during the course. These results are consistent with [1] Adler and Milne’s research in which the authors found that students agree with the use of action-oriented learning tasks in order to provide them the needed attitudes, skills, and knowledge. The improvement of group skills is the most important issue for students, which can be understandable as BS is the only course from the degree in Accounting and Administration they really have to work in groups.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Code offloading on real-time multimedia systems: a framework for handling code mobility and code offloading in a QoS aware environment

Actualmente, os smartphones e outros dispositivos móveis têm vindo a ser dotados com cada vez maior poder computacional, sendo capazes de executar um vasto conjunto de aplicações desde simples programas de para tirar notas até sofisticados programas de navegação. Porém, mesmo com a evolução do seu hardware, os actuais dispositivos móveis ainda não possuem as mesmas capacidades que os computadores de mesa ou portáteis. Uma possível solução para este problema é distribuir a aplicação, executando partes dela no dispositivo local e o resto em outros dispositivos ligados à rede. Adicionalmente, alguns tipos de aplicações como aplicações multimédia, jogos electrónicos ou aplicações de ambiente imersivos possuem requisitos em termos de Qualidade de Serviço, particularmente de tempo real. Ao longo desta tese é proposto um sistema de execução de código remota para sistemas distribuídos com restrições de tempo-real. A arquitectura proposta adapta-se a sistemas que necessitem de executar periodicamente e em paralelo mesmo conjunto de funções com garantias de tempo real, mesmo desconhecendo os tempos de execução das referidas funções. A plataforma proposta foi desenvolvida para sistemas móveis capazes de executar o Sistema Operativo Android.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.