Dependable decentralized storage management for cloud computing

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto.

The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Brincar de Internet: a vivência lúdica infantil em ambiente virtual

Tese de Doutoramento em Ciências da Educação (Especialidade em Tecnologia Educativa)

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Improving diagnostic yield in obscure gastrointestinal bleeding - how virtual chromoendoscopy may be the answer

Background and aim: A significant proportion of patients presenting with obscure gastrointestinal bleeding (OGIB) have negative small bowel capsule endoscopy (SBCE) examinations, and yet remain at risk of rebleeding. We aimed to evaluate whether a second-look review of SBCE images using flexible spectral color enhancement (FICE) may improve the detection of potentially bleeding lesions. Materials and methods: This was a retrospective, single-center study including consecutive patients with OGIB subjected to SBCE, whose standard white light examination was nondiagnostic. Each SBCE was reviewed using FICE 1. New findings were labeled as either P1 or P2 lesions according to bleeding potential. Patients were followed up to assess the incidence of rebleeding. Results: A total of 42 consecutive patients were included. Sixteen patients (38%) experienced rebleeding after a mean follow-up of 26 months. Review of SBCE images using FICE 1 enabled the identification of previously unrecognized P2 lesions, mainly angioectasias, in nine patients (21%) and P1 lesions, mainly erosions, in 26 patients (62%). Among patients who experienced rebleeding, 13/16 (81%) were diagnosed with P1 lesions with FICE 1 (P=0.043), whereas 3/16 (19%) had confirmed nondiagnostic SBCE and only 1/16 (6%) had newly diagnosed P2 (plus P1) lesions. An alternative source of bleeding outside the small bowel was found in only 3/16 (19%) patients with rebleeding during the follow-up. Conclusion: In a significant proportion of patients with OGIB, FICE 1 may detect potentially bleeding lesions previously missed under conventional white light SBCE. Review of nondiagnostic SBCE with FICE 1 may be a valuable strategy to obviate the need for further investigations in patients with OGIB, particularly for those who experience rebleeding.