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.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Avaliação do desempenho de indicadores com base na metodologia ROC (Receiver Operating Characteristic)

Dissertação de mestrado em Bioinformática

Perchlorate and chlorate reduction by the Crenarchaeon Aeropyrum pernix and two thermophilic Firmicutes

This study reports the ability of one hyperthermophile and two thermophilic microorganisms to grow anaerobically by the reduction of chlorate and perchlorate. Physiological, genomic and proteome analyses suggest that the Crenarchaeon Aeropyrum pernix reduces perchlorate with a periplasmic enzyme related to nitrate reductases, but that it lacks a functional chlorite-disproportionating enzyme (Cld) to complete the pathway. A. pernix, previously described as a strictly aerobic microorganism, seems to rely on the chemical reactivity of reduced sulfur compounds with chlorite, a mechanism previously reported for perchlorate-reducing Archaeoglobus fulgidus. The chemical oxidation of thiosulfate (in excessive amounts present in the medium) and the reduction of chlorite result in the release of sulfate and chloride, which are the products of a biotic-abiotic perchlorate reduction pathway in A. pernix. The apparent absence of Cld in two other perchlorate-reducing microorganisms, Carboxydothermus hydrogenoformans and Moorella glycerini strain NMP, and their dependence on sulfide for perchlorate reduction is consistent with observations made on A. fulgidus. Our findings suggest that microbial perchlorate reduction at high temperature differs notably from the physiology of perchlorate- and chlorate-reducing mesophiles and that it is characterized by the lack of a chlorite dismutase and is enabled by a combination of biotic and abiotic reactions.

A aplicação do LibQual aos utilizadores das bibliotecas dos Serviços de Documentação da Universidade do Minho: dados de 2015

No período de 16 a 30 de novembro de 2015, os Serviços de Documentação da Universidade do Minho realizaram um inquérito por questionário aos utilizadores das suas bibliotecas: Biblioteca Geral da Universidade do Minho (U.M.) em Gualtar (BGUM), Biblioteca da U.M. em Guimarães (BPG), Biblioteca da Escola de Ciências da Saúde (BECS) e Biblioteca Nuno Portas (BNP). Dado que se pretendia avaliar a satisfação dos utilizadores das bibliotecas da Universidade do Minho, o questionário aplicado foi o LibQual, adaptação do ServQual ao contexto das bibliotecas e utilizado por muitas em todo o mundo. Este questionário utiliza uma escala de 1 a 9 (valor mínimo, valor observado e valor desejado) e avalia os serviços por dimensões (valor afetivo do serviço, biblioteca como espaço e controlo da informação) e adequação do serviço (adequação do serviço, superioridade do serviço). O questionário foi disponibilizado via web através do sistema LimeSurvey e o acesso ao mesmo exigiu autenticação. As respostas obtidas foram exportadas para Excel.

A aplicação de um questionário sobre a qualidade do atendimento aos utilizadores das bibliotecas dos Serviços de Documentação da Universidade do Minho: dados de 2015

No período de 16 a 30 de novembro de 2015, os Serviços de Documentação da Universidade do Minho realizaram um inquérito por questionário aos utilizadores das suas bibliotecas: Biblioteca Geral da Universidade do Minho (U.M.) em Gualtar (BGUM), Biblioteca da U.M. em Guimarães (BPG), Biblioteca da Escola de Ciências da Saúde (BECS) e Biblioteca Nuno Portas. Dado que se pretendia avaliar a qualidade do atendimento, a aplicação do questionário foi feita por amostra aleatória nos balcões de atendimento das referidas bibliotecas. Após atos de atendimento, os funcionários dos respetivos balcões enviaram o questionário online aos utilizadores solicitando a sua colaboração. O questionário foi disponibilizado via web através do sistema LimeSurvey e o acesso ao mesmo exigiu autenticação. As respostas obtidas foram exportadas para Excel.

Observation of top-quark pair production in association with a photon and measurement of the t t ¯ γ production cross section in pp collisions at s =7 TeV using the ATLAS detector

A search is performed for top-quark pairs (tt¯) produced together with a photon (γ) with transverse momentum >20 GeV using a sample of tt¯ candidate events in final states with jets, missing transverse momentum, and one isolated electron or muon. The dataset used corresponds to an integrated luminosity of 4.59 fb−1 of proton--proton collisions at a center-of-mass energy of 7 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. In total 140 and 222 tt¯γ candidate events are observed in the electron and muon channels, to be compared to the expectation of 79±26 and 120±39 non-tt¯γ background events respectively. The production of tt¯γ events is observed with a significance of 5.3 standard deviations away from the null hypothesis. The tt¯γ production cross section times the branching ratio (BR) of the single-lepton decay channel is measured in a fiducial kinematic region within the ATLAS acceptance. The measured value is σfidtt¯γ=63±8(stat.)+17−13(syst.)±1(lumi.) fb per lepton flavor, in good agreement with the leading-order theoretical calculation normalized to the next-to-leading-order theoretical prediction of 48±10 fb.

Search for H→γγ produced in association with top quarks and constraints on the Yukawa coupling between the top quark and the Higgs boson using data taken at 7 TeV and 8 TeV with the ATLAS detector

A search is performed for Higgs bosons produced in association with top quarks using the diphoton decay mode of the Higgs boson. Selection requirements are optimized separately for leptonic and fully hadronic final states from the top quark decays. The dataset used corresponds to an integrated luminosity of 4.5 fb−1 of proton--proton collisions at a center-of-mass energy of 7 TeV and 20.3 fb−1 at 8 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess over the background prediction is observed and upper limits are set on the tt¯H production cross section. The observed exclusion upper limit at 95% confidence level is 6.7 times the predicted Standard Model cross section value. In addition, limits are set on the strength of the Yukawa coupling between the top quark and the Higgs boson, taking into account the dependence of the tt¯H and tH cross sections as well as the H→γγ branching fraction on the Yukawa coupling. Lower and upper limits at 95% confidence level are set at −1.3 and +8.0 times the Yukawa coupling strength in the Standard Model.

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.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Search for W'→tb- in the lepton plus jets final state in proton-proton collisions at a centre-of-mass energy of s=8 TeV with the ATLAS detector

A search for new charged massive gauge bosons, called W′, is performed with the ATLAS detector at the LHC, in proton--proton collisions at a centre-of-mass energy of s√ = 8 TeV, using a dataset corresponding to an integrated luminosity of 20.3 fb−1. This analysis searches for W′ bosons in the W′→tb¯ decay channel in final states with electrons or muons, using a multivariate method based on boosted decision trees. The search covers masses between 0.5 and 3.0 TeV, for right-handed or left-handed W′ bosons. No significant deviation from the Standard Model expectation is observed and limits are set on the W′→tb¯ cross-section times branching ratio and on the W′-boson effective couplings as a function of the W′-boson mass using the CLs procedure. For a left-handed (right-handed) W′ boson, masses below 1.70 (1.92) TeV are excluded at 95% confidence level.

Search for Higgs bosons decaying to aa in the μμττ final state in pp collisions at s√= 8 TeV with the ATLAS experiment

A search for the decay to a pair of new particles of either the 125 GeV Higgs boson (h) or a second CP-even Higgs boson (H) is presented. The dataset correspods to an integrated luminosity of 20.3 fb−1 of pp collisions at s√= 8 TeV recorded by the ATLAS experiment at the LHC in 2012. The search was done in the context of the next-to-minimal supersymmetric standard model, in which the new particles are the lightest neutral pseudoscalar Higgs bosons (a). One of the two a bosons is required to decay to two muons while the other is required to decay to two τ-leptons. No significant excess is observed above the expected backgrounds in the dimuon invariant mass range from 3.7 GeV to 50 GeV. Upper limits are placed on the production of h→aa relative to the Standard Model gg→h production, assuming no coupling of the a boson to quarks. The most stringent limit is placed at 3.5% for ma= 3.75 GeV. Upper limits are also placed on the production cross section of H→aa from 2.33 pb to 0.72 pb, for fixed ma = 5 GeV with mH ranging from 100 GeV to 500 GeV.