A real-time Artificial Intelligence system for learning Sign Language

A primary challenge for the deaf and hearing-impaired community stems from the communication gap with the hearing society, which can greatly impact their daily lives and result in social exclusion. To foster inclusivity in society, our endeavor focuses on developing a cost-effective, resource-efficient, and open technology based on Artificial Intelligence, designed to assist people in learning and using Sign Language for communication. The analysis presented in this research paper intends to enrich the recent academic scientific literature on Sign Language solutions based on Artificial Intelligence, with a particular focus on American Sign Language (ASL). This research has yielded promising preliminary results and serves as a basis for further development

Robust methods based on shrinkage

In this thesis, robust methods based on the notion of shrinkage are proposed for outlier detection and robust regression. A collection of robust Mahalanobis distances is proposed for multivariate outlier detection. The robust intensity and scaling factors, needed to define the shrinkage of the robust estimators used in the distances, are optimally estimated. Some properties are investigated, such as the affine equivariance and the breakdown value. The performance of the proposal is illustrated through the comparison to other robust techniques from the literature, in a simulation study and with a real example of breast cancer data. The robust alternatives are also reviewed, highlighting their advantages and disadvantages. The behavior when the underlying distribution is heavy-tailed or skewed, shows the appropriateness of the proposed method when we deviate from the common assumption of normality. The resulting high true positive rates and low false positive rates in the vast majority of cases, as well as the significantly smaller computational time show the advantages of the proposal. On the other hand, a robust estimator is proposed for the parameters that characterize the linear regression problem. It is also based on the notion of shrinkages. A thorough simulation study is conducted to investigate the efficiency with Normal and heavy-tailed errors, the robustness under contamination, the computational times, the affine equivariance and breakdown value of the regression estimator. It is compared to the classical Ordinary Least Squares (OLS) approach and the robust alternatives from the literature, which are also briefly reviewed in the thesis. Two classical data-sets often used in the literature and a real socio-economic data-set about the Living Environment Deprivation (LED) of areas in Liverpool (UK), are studied. The results from the simulations and the real data examples show the advantages of the proposed robust estimator in regression. Also, with the LED data-set it is also shown that the proposed robust regression method has improved performance than machine learning techniques previously used for this data, with the advantage of interpretability. Furthermore, an adaptive threshold, that depends on the sample size and the dimension of the data, is introduced for the proposed robust Mahalanobis distance based on shrinkage estimators. The cut-off is different than the classical choice of the 0.975 chi-square quantile providing a more accurate method to detect multivariate outliers. A simulation study is done to check the performance improvement of the new cut-off against the classical. The adjusted quantile shows improved performance, even when the underlying distribution is heavy-tailed or skewed. The method is illustrated using the LED data-set, and the results demonstrate the additional advantages of the adaptive threshold for the regression problem.En esta tesis, se proponen métodos robustos basados en la noción de shrinkage para la detección de atípicos y la regresión robusta. Se propone una colección de distancias de Mahalanobis robustas para la detección de outliers multivariantes. Los factores de intensidad y escala, necesarios para definir el shrinkage de los estimadores robustos utilizados en las distancias, se estiman de manera óptima. Se investigan algunas propiedades como la equivarianza afín y el breakdown value (valor de ruptura). El desempeño de la propuesta se ilustra mediante la comparación con otras técnicas robustas de la literatura, en un estudio de simulación y con un ejemplo real de datos de cáncer de mama. Las alternativas robustas también se revisan, destacando sus ventajas y desventajas. El comportamiento cuando la distribución subyacente es de cola pesada o asimétrica, muestra lo apropiado que es el método propuesto cuando nos apartamos de la suposición común de normalidad. Las altas tasas de verdaderos positivos y las bajas tasas de falsos positivos, en la gran mayoría de los casos, así como el tiempo de cómputo significativamente menor, muestran las ventajas de la propuesta. Por otro lado, se introduce un estimador robusto para los parámetros que caracterizan la regresión lineal. También se basa en la noción de shrinkage. Se lleva a cabo un estudio de simulación exhaustivo para investigar la eficiencia con errores Normales y de cola pesada, la robustez bajo contaminación, los tiempos de cómputo, la equivarianza afín y el valor de ruptura del estimador de regresión. Se compara con el método Mínimos Cuadrados Ordinarios (OLS) clásico y las alternativas sólidas de la literatura, que también se revisan brevemente en la tesis. Se estudian dos conjuntos de datos clásicos que se utilizan a menudo en la literatura y un conjunto de datos socioeconómicos reales sobre la privación del entorno vital (LED) de las áreas de Liverpool (Reino Unido). Los resultados de las simulaciones y los ejemplos de datos reales muestran las ventajas del estimador robusto propuesto para regresión. Además, con el conjunto de datos LED también se muestra que el método de regresión robusta propuesto presenta mejoras con respecto a las técnicas de aprendizaje automático utilizadas anteriormente para estos datos, con la ventaja de la interpretabilidad. Además, se introduce un recorte adaptativo, que depende del tamaño de la muestra y la dimensión de los datos, para la distancia robusta de Mahalanobis propuesta, basada en estimadores shrinkage. El valor de recorte es diferente a la opción clásica del cuantil 0.975 de la chi-cuadrado, y proporciona un método más preciso para detectar valores atípicos multivariados. Se realiza un estudio de simulación para verificar el rendimiento del nuevo punto de corte respecto al clásico. El cuantil ajustado muestra un desempeño mejorado, incluso cuando la distribución subyacente es de cola pesada o asimétrica. El método se ilustra utilizando el conjunto de datos LED y los resultados demuestran las ventajas adicionales del recorte adaptativo para el problema de regresión.I want to acknowledge the financial support received from the Spanish Ministry of Economy and Competitiveness ECO2015-66593-P and the UC3M PIF pre-doctoral scholarship.Programa de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Fco. Javier Nogales Martín.- Secretario: Julio Rodríguez Puerta.- Vocal: José Manuel Mira Mcwilliam

Shrinkage reweighted regression

A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously studied for outlier detection in multivariate data. A thorough simulation study is conducted to investigate: the efficiency with normal and heavy-tailed errors, the robustness under contamination, the computational times, the affine equivariance and breakdown value of the regression estimator. Two classical data-sets often used in the literature and a real socio-economic data-set about the Living Environment Deprivation of areas in Liverpool (UK), are studied. The results from the simulations and the real data examples show the advantages of the proposed robust estimator in regression.This research was partially supported by MINISTERIO DE ECONOMIA, INDUSTRIA Y COMPETITIVIDAD, award number: ECO2015-66593-P

Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators

A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is proposed. The procedure consists on different combinations of robust estimates for location and covariance matrix based on shrinkage. The performance of our proposal is illustrated, through the comparison to other techniques from the literature, in a simulation study. The resulting high correct classification rates and low false classification rates in the vast majority of cases, and also the good computational times shows the goodness of our proposal. The performance is also illustrated with a real dataset example and some conclusions are established.This research was partially supported by Spanish Ministry grant ECO2015-66593-P

As identidades fragmentadas no cotidiano da Feira do Produtor de Maringá

Neste artigo, em um trabalho de resgate histórico de mais de 30 anos, procuramos compreender como as práticas cotidianas dos sujeitos vão se alterando à medida que a identidade organizacional do local onde trabalham se modifica. O estudo foi feito na Feira do Produtor de Maringá, fazendo uso de observação, de entrevistas de história oral temática e da análise do discurso. Verificamos que a Feira do Produtor de Maringá apresenta uma identidade fragmentada e constatamos no seu transcurso histórico que, no início, táticas e estratégias encontravam-se ligadas a novas experiências e saberes vinculados ao ofício dos feirantes. À medida que aumenta a formalização da feira e, por sua vez, seus instrumentos disciplinares, novas táticas e estratégias são criadas para driblar esses novos instrumentos de controle, assim como para amenizar a situação das identidades reprimidas, que nesse novo contexto se acentuam. Observamos na pesquisa movimentos de resistências e pequenos protestos por parte de alguns feirantes, no sentido de defender espaços temporários de posições identitárias, muito embora essas lutas sejam ofuscadas diante de uma aparente estabilidade, que se impõe por diferentes instrumentos disciplinares e por diversos discursos que inibem comportamentos não congruentes com os prescritos na feira

CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics

The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.Comment: Presented at The KDD Workshop on Humanitarian Mapping, San Diego, California USA, August 24, 202

Onde está o Outro nos Estudos Organizacionais e da Administração?

O objetivo deste ensaio teórico é trazer uma reflexão sobre o tratamento do Outro no transcurso dos Estudos Organizacionais e da Administração. Destarte, tentamos instigar o aprofundamento na temática do Outro, assim como de sua problematização. Para tanto, partindo de uma perspectiva merleu-pontyana discutimos o problema da existência do Outro, para logo discorrer sobre o tratamento dado ao Outro nos Estudos Organizacionais e da Administração. No percurso destes Estudos, na sua maior parte, privilegiou-se a herança das filosofias ocidentais, que geralmente possuem no seu âmago um solipsismo sem saída em que o Outro é reduzido ao Mesmo. Desta forma é necessário quebrar essas correntes esmagadoras do Outro para poder falar não de processos de constituição, de definição do Outro, mas dos entrelaçamentos do Mesmo com o(s) Outro(s), que criam o social e também as organizações e que se manifestam em experiências vividas, sentidas e percebidas