Brain-computer interface (BCI): a methodological proposal to assess the impacts of medical applications in 2022

Based on the report for ���Project IV��� unit of the PhD programme on Technology Assessment (Doctoral Conference) at Universidade Nova de Lisboa (December 2011). This thesis research has the supervision of Ant��nio Moniz (FCT-UNL and ITAS-KIT) and Armin Grunwald (Karlsruhe Institute of Technology-ITAS, Germany). Other members of the thesis committee are M��rio Forjaz Secca (FCT-UNL) and Femke Nijboer (University of Twente, Netherlands).

Data-centric concurrency control on the java programming language

Disserta����o para obten����o do Grau de Mestre em Engenharia Inform��tica

Purification of complex biopharmaceuticals with new processes, advanced analytics and computer-aided process design tools

Disserta����o para obten����o do Grau de Mestre em Biotecnologia

Child programming: an adequate domain specific language for programming specific robots

Disserta����o para obten����o do Grau de Mestre em Engenharia Inform��tica

Machine ethics via logic programming

Machine ethics is an interdisciplinary field of inquiry that emerges from the need of imbuing autonomous agents with the capacity of moral decision-making. While some approaches provide implementations in Logic Programming (LP) systems, they have not exploited LP-based reasoning features that appear essential for moral reasoning. This PhD thesis aims at investigating further the appropriateness of LP, notably a combination of LP-based reasoning features, including techniques available in LP systems, to machine ethics. Moral facets, as studied in moral philosophy and psychology, that are amenable to computational modeling are identified, and mapped to appropriate LP concepts for representing and reasoning about them. The main contributions of the thesis are twofold. First, novel approaches are proposed for employing tabling in contextual abduction and updating ��� individually and combined ��� plus a LP approach of counterfactual reasoning; the latter being implemented on top of the aforementioned combined abduction and updating technique with tabling. They are all important to model various issues of the aforementioned moral facets. Second, a variety of LP-based reasoning features are applied to model the identified moral facets, through moral examples taken off-the-shelf from the morality literature. These applications include: (1) Modeling moral permissibility according to the Doctrines of Double Effect (DDE) and Triple Effect (DTE), demonstrating deontological and utilitarian judgments via integrity constraints (in abduction) and preferences over abductive scenarios; (2) Modeling moral reasoning under uncertainty of actions, via abduction and probabilistic LP; (3) Modeling moral updating (that allows other ��� possibly overriding ��� moral rules to be adopted by an agent, on top of those it currently follows) via the integration of tabling in contextual abduction and updating; and (4) Modeling moral permissibility and its justification via counterfactuals, where counterfactuals are used for formulating DDE.

Augmenting spaces and creating interactive experiences using video camera networks

This research addresses the problem of creating interactive experiences to encourage people to explore spaces. Besides the obvious spaces to visit, such as museums or art galleries, spaces that people visit can be, for example, a supermarket or a restaurant. As technology evolves, people become more demanding in the way they use it and expect better forms of interaction with the space that surrounds them. Interaction with the space allows information to be transmitted to the visitors in a friendly way, leading visitors to explore it and gain knowledge. Systems to provide better experiences while exploring spaces demand hardware and software that is not in the reach of every space owner either because of the cost or inconvenience of the installation, that can damage artefacts or the space environment. We propose a system adaptable to the spaces, that uses a video camera network and a wi-fi network present at the space (or that can be installed) to provide means to support interactive experiences using the visitor���s mobile device. The system is composed of an infrastructure (called vuSpot), a language grammar used to describe interactions at a space (called XploreDescription), a visual tool used to design interactive experiences (called XploreBuilder) and a tool used to create interactive experiences (called urSpace). By using XploreBuilder, a tool built of top of vuSpot, a user with little or no experience in programming can define a space and design interactive experiences. This tool generates a description of the space and of the interactions at that space (that complies with the XploreDescription grammar). These descriptions can be given to urSpace, another tool built of top of vuSpot, that creates the interactive experience application. With this system we explore new forms of interaction and use mobile devices and pico projectors to deliver additional information to the users leading to the creation of interactive experiences. The several components are presented as well as the results of the respective user tests, which were positive. The design and implementation becomes cheaper, faster, more flexible and, since it does not depend on the knowledge of a programming language, accessible for the general public.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.

Quadratic programming versus second order con programming in portfolio optimization

Despite the extensive literature in finding new models to replace the Markowitz model or trying to increase the accuracy of its input estimations, there is less studies about the impact on the results of using different optimization algorithms. This paper aims to add some research to this field by comparing the performance of two optimization algorithms in drawing the Markowitz Efficient Frontier and in real world investment strategies. Second order cone programming is a faster algorithm, appears to be more efficient, but is impossible to assert which algorithm is better. Quadratic Programming often shows superior performance in real investment strategies.

Implementa����o de um modelo de geometric semantic genetic programming para aplica����o naval

Reca�� sob a responsabilidade da Marinha Portuguesa a gest��o da Zona Econ��mica Exclusiva de Portugal, assegurando a sua seguran��a da mesma face a atividades criminosas. Para auxiliar a tarefa, �� utilizado o sistema Oversee, utilizado para monitorizar a posi����o de todas as embarca����es presentes na ��rea afeta, permitindo a r��pida interven����o da Marinha Portuguesa quando e onde necess��rio. No entanto, o sistema necessita de transmiss��es peri��dicas constantes originadas nas embarca����es para operar corretamente ��� casos as transmiss��es sejam interrompidas, deliberada ou acidentalmente, o sistema deixa de conseguir localizar embarca����es, dificultando a interven����o da Marinha. A fim de colmatar esta falha, �� proposto adicionar ao sistema Oversee a capacidade de prever as posi����es futuras de uma embarca����o com base no seu trajeto at�� �� cessa����o das transmiss��es. Tendo em conta os grandes volumes de dados gerados pelo sistema (hist��ricos de posi����es), a ��rea de Intelig��ncia Artificial apresenta uma poss��vel solu����o para este problema. Atendendo ��s necessidades de resposta r��pida do problema abordado, o algoritmo de Geometric Semantic Genetic Programming baseado em refer��ncias de Vanneschi et al. apresenta-se como uma poss��vel solu����o, tendo j�� produzido bons resultados em problemas semelhantes. O presente trabalho de tese pretende integrar o algoritmo de Geometric Semantic Genetic Programming desenvolvido com o sistema Oversee, a fim de lhe conceder capacidades preditivas. Adicionalmente, ser�� realizado um processo de an��lise de desempenho a fim de determinar qual a ideal parametriza����o do algoritmo. Pretende-se com esta tese fornecer �� Marinha Portuguesa uma ferramenta capaz de auxiliar o controlo da Zona Econ��mica Exclusiva Portuguesa, permitindo a correta interven����o da Marinha em casos onde o atual sistema n��o conseguiria determinar a correta posi����o da embarca����o em quest��o.

Aprendizagem baseada na resolu����o de problemas: uma experi��ncia pedag��gica com alunos de um Curso de Especializa����o Tecnol��gica

Relat��rio de est��gio de mestrado em Ensino de Inform��tica

Non emergency patients transport - a mixed integer linear programming

This work presents a model and a heuristic to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving problems with one vehicle was presented, and this heuristic provides good results in terms of accuracy and computation time.

The non-emergency patient transport modelled as a team orienteering problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.