Human activity data discovery based on accelerometry

Dissertation to Obtain Master Degree in Biomedical Engineering

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

Automated illustration of multimedia stories

Submitted in part fulfillment of the requirements for the degree of Master in Computer Science

The web feature service: transaction standard: an example of prototype implementation

Trabalho de Projecto apresentado como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

A proteomic approach toward the selection of proteins with enhanced intrinsic conformational stability

Journal of Proteome Research (2006)5: 2720-2726

Enterococcus and biocides: mechanisms of tolerance and selection for vancomycin resistance

Dissertation presented to obtain a Doctoral degree in Biology, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa.

Biogenic amines in wine: gene transcription as a tool for selection of Oenococcus oeni starter strains

Dissertation presented to obtain the Ph.D degree in Biology

Segregated modeling and selection of populations for polyhydroxyalkanoate production by mixed microbial cultures

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Química e Bioquímica

Selection of a co-operation agreement between EMOVE – Innovative Technologies and WavEC – Wave Energy Center

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

Market selection and market entry recommendations for a non-profit organization's first international venture

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

Food waste valorization through the production of polyhydroxyalkanoates by mixed microbial cultures

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

Calibration, selection and mosaicing of SMART-1 AMIE images

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

Production of polyhydroxyalkanoates from cheese whey - pH effect on the acidogenic fermentation stage and nutrient needs of the culture selection stage

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Return Migration, Self-Selection and Entrepreneurship in Mozambique

Does return migration affect entrepreneurship? This question has important implications for the debate on the economic development effects of migration for origin countries. The existing literature has, however, not addressed how the estimation of the impact of return migration on entrepreneurship is affected by double unobservable migrant self-selection, both at the initial outward migration and at the final inward return migration stages. This paper uses a representative household survey conducted in Mozambique in order to address this research question. We exploit variation provided by displacement caused by civil war in Mozambique, as well as social unrest and other shocks in migrant destination countries. The results lend support to negative unobservable self-selection at both and each of the initial and return stages of migration, which results in an under-estimation of the effects of return migration on entrepreneurial outcomes when using a ‘naïve’ estimator not controlling for self-selection. Indeed, ‘naïve’ estimates point to a 13 pp increase in the probability of owning a business when there is a return migrant in the household relative to non-migrants only, whereas excluding the double effect of unobservable self-selection, this effect becomes significantly larger - between 24 pp and 29 pp, depending on the method of estimation and source of variation used.