Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Speed control of induction machine based on direct torque control method

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Machine learning Gaussian short rate

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco

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.

Machine diagnosis based on artificial immune systems

Nowadays, many of the manufactory and industrial system has a diagnosis system on top of it, responsible for ensuring the lifetime of the system itself. It achieves this by performing both diagnosis and error recovery procedures in real production time, on each of the individual parts of the system. There are many paradigms currently being used for diagnosis. However, they still fail to answer all the requirements imposed by the enterprises making it necessary for a different approach to take place. This happens mostly on the error recovery paradigms since the great diversity that is nowadays present in the industrial environment makes it highly unlikely for every single error to be fixed under a real time, no production stop, perspective. This work proposes a still relatively unknown paradigm to manufactory. The Artificial Immune Systems (AIS), which relies on bio-inspired algorithms, comes as a valid alternative to the ones currently being used. The proposed work is a multi-agent architecture that establishes the Artificial Immune Systems, based on bio-inspired algorithms. The main goal of this architecture is to solve for a resolution to the error currently detected by the system. The proposed architecture was tested using two different simulation environment, each meant to prove different points of views, using different tests. These tests will determine if, as the research suggests, this paradigm is a promising alternative for the industrial environment. It will also define what should be done to improve the current architecture and if it should be applied in a decentralised system.

Behavioural development in a matching-to-sample task and token use by an infant chimpanzee reared by his mother

Animal Cognition, V.6, pp. 259–267

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 António Moniz (FCT-UNL and ITAS-KIT) and Armin Grunwald (Karlsruhe Institute of Technology-ITAS, Germany). Other members of the thesis committee are Mário Forjaz Secca (FCT-UNL) and Femke Nijboer (University of Twente, Netherlands).

Customer Lifetime Value: a framework for application in the insurance industry - Building a Business Process to Generate and Maintain an Automatic Estimation Agent

Research Project submited as partial fulfilment for the Master Degree in Statistics and Information Management

Promoting Portuguese exports to Germany: An export promotion plan for the machinery & equipment sector

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Amorphous silicon e 3D sensors applied to object detection

Nowadays, existing 3D scanning cameras and microscopes in the market use digital or discrete sensors, such as CCDs or CMOS for object detection applications. However, these combined systems are not fast enough for some application scenarios since they require large data processing resources and can be cumbersome. Thereby, there is a clear interest in exploring the possibilities and performances of analogue sensors such as arrays of position sensitive detectors with the final goal of integrating them in 3D scanning cameras or microscopes for object detection purposes. The work performed in this thesis deals with the implementation of prototype systems in order to explore the application of object detection using amorphous silicon position sensors of 32 and 128 lines which were produced in the clean room at CENIMAT-CEMOP. During the first phase of this work, the fabrication and the study of the static and dynamic specifications of the sensors as well as their conditioning in relation to the existing scientific and technological knowledge became a starting point. Subsequently, relevant data acquisition and suitable signal processing electronics were assembled. Various prototypes were developed for the 32 and 128 array PSD sensors. Appropriate optical solutions were integrated to work together with the constructed prototypes, allowing the required experiments to be carried out and allowing the achievement of the results presented in this thesis. All control, data acquisition and 3D rendering platform software was implemented for the existing systems. All these components were combined together to form several integrated systems for the 32 and 128 line PSD 3D sensors. The performance of the 32 PSD array sensor and system was evaluated for machine vision applications such as for example 3D object rendering as well as for microscopy applications such as for example micro object movement detection. Trials were also performed involving the 128 array PSD sensor systems. Sensor channel non-linearities of approximately 4 to 7% were obtained. Overall results obtained show the possibility of using a linear array of 32/128 1D line sensors based on the amorphous silicon technology to render 3D profiles of objects. The system and setup presented allows 3D rendering at high speeds and at high frame rates. The minimum detail or gap that can be detected by the sensor system is approximately 350 μm when using this current setup. It is also possible to render an object in 3D within a scanning angle range of 15º to 85º and identify its real height as a function of the scanning angle and the image displacement distance on the sensor. Simple and not so simple objects, such as a rubber and a plastic fork, can be rendered in 3D properly and accurately also at high resolution, using this sensor and system platform. The nip structure sensor system can detect primary and even derived colors of objects by a proper adjustment of the integration time of the system and by combining white, red, green and blue (RGB) light sources. A mean colorimetric error of 25.7 was obtained. It is also possible to detect the movement of micrometer objects using the 32 PSD sensor system. This kind of setup offers the possibility to detect if a micro object is moving, what are its dimensions and what is its position in two dimensions, even at high speeds. Results show a non-linearity of about 3% and a spatial resolution of < 2µm.

From user browsing behaviour to user demographics

A Internet conta hoje com mais de 3 mil milhões de utilizadores e esse valor não para de aumentar. Desta forma, proporcionar uma experiência online agradável aos seus utilizadores é cada vez mais importante para as empresas. De modo a tirar partido dos benefícios deste crescimento, as empresas devem ser capazes de identificar os seus clientes-alvo dentro do total de utilizadores; e, subsequentemente, personalizar a sua experiência online. Existem diversas formas de estudar o comportamento online dos utilizadores; no entanto, estas não são ideais e existe uma ampla margem para melhoria. A inovação nesta área pode comportar um grande potencial comercial e até ser disruptiva. Com isto em mente, proponho-me a estudar a possível criacão de um sistema de aprendizagem automática (machine learning) que permita prever informa ações demográficas dos utilizadores estritamente com base no seu comportamento online. Tal sistema poderia constituir uma alternativa às atuais opções, que são mais invasivas; mitigando assim preocupações ao nível da proteção de dados pessoais. No primeiro capítulo (Introdução) explico a motivação para o estudo do comportamento dos utilizadores online por parte de empresas, e descrevo as opções disponíveis atualmente. Apresento também a minha proposta e o contexto em que assenta. O capítulo termina com a identicação de limitações que possam existir a priori. O segundo capítulo (Machine Learning) fornece uma introdução sobre machine learning, com o estudo dos algoritmos que vão ser utilizados e explicando como analisar os resultados. O terceiro capítulo (Implementação) explica a implementação do sistema proposto e descreve o sistema que desenvolvi no decorrer deste estudo, e como integra-lo em sistemas já existentes. No quarto capítulo (Análise e manipulação dos dados), mostro os dados compilados e explico como os recolhi e manipulei para testar a hipótese. No quinto capítulo (Análise de dados e discussão) vemos como e que os dados recolhidos foram usados pelos vários algoritmos para descobrir como se correlacionam com dados dos utilizadores e analiso e discuto os resultados observados. Por fim, o sexto e último capítulo apresenta as conclusões. Dependendo dos resultados, mostro como a hipótese poderia ser melhor testada, ou então discuto os próximos passos para tornar o sistema realidade.