SARS-CoV-2 CT-scan dataset:A large dataset of real patients CT scans for SARS-CoV-2 identification

The COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. At the time of writing this paper the number of infected about 2 million people worldwide and took over 125,000 lives, the advanced public health systems of European countries as well as of USA were overwhelmed. In this paper, we propose an eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images. The rapid detection of any COVID-19 case is of supreme importance to ensure timely treatment. From a public health perspective, rapid patient isolation is also extremely important to curtail the rapid spread of the disease. From this point of view the proposed method offers an easy to use and understand tool to the front-line medics. It is of huge importance not only the statistical accuracy and other measures, but also the ability to understand and interpret how the decision was made. The results demonstrate that the proposed approach is able to surpass the other published results which were using standard Deep Neural Network in terms of performance. Moreover, it produce highly interpretable results which may be helpful for the early detection of the disease by specialists

Explainable-by-Design Deep Learning

Machine learning, and more specifically, deep learning, have attracted the attention of media and the broader public in the last decade due to its potential to revolutionize industries, public services, and society. Deep learning achieved or even surpassed human experts’ performance in terms of accuracy for different challenging problems such as image recognition, speech, and language translation. However, deep learning models are often characterized as a “black box” as these models are composed of many millions of parameters, which are extremely difficult to interpret by specialists. Complex “black box” models can easily fool users unable to inspect the algorithm’s decision, which can lead to dangerous or catastrophic events. Therefore, auditable explainable AI approaches are crucial for developing safe systems, complying with regulations, and accepting this new technology within society. This thesis tries to answer the following research question: Is it possible to provide an approach that has a performance compared to a Deep Learning and the same time has a transparent structure (non-black box)? To this end, it introduces a novel framework of explainable- by-design Deep Learning architectures that offers transparency and high accuracy, helping humans understand why a particular machine decision has been reached and whether or not it is trustworthy. Moreover, the proposed prototype-based framework has a flexible structure that allows the unsupervised detection of new classes and situations. The approaches proposed in thesis have been applied to multiple use cases, including image classification, fairness, deep recursive learning interpretation, and novelty detection

Socioenvironmental risks in the context of conservative modernization of agriculture

O artigo em debate apresenta questões fundamentais para a discussão sobre os impactos dos agrotóxicos na saúde e ambiente, a partir de uma análise crítica da política brasileira

Fair-by-design explainable models for prediction of recidivism

Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making. It can also be used for prediction of locations where crimes most occur, profiles that are more likely to commit violent crimes. While such instruments are gaining increasing popularity, their use is controversial as they may present potential discriminatory bias in the risk assessment. In this paper we propose a new fair-by-design approach to predict recidivism. It is prototype-based, learns locally and extracts empirically the data distribution. The results show that the proposed method is able to reduce the bias and provide human interpretable rules to assist specialists in the explanation of the given results

Online recruitment and selection: adaptation of R&S in the Covid-19 Era in portuguese SMEs

Projeto de mestrado em Gestão de Recursos HumanosO estudo que se segue tem como objetivo apresentar uma proposta de melhoria ao processo de recrutamento e seleção da F.RH. A F.RH é uma empresa consultora de Recursos Humanos e oferece serviços diversos na área de Recursos Humanos como Recrutamento e Seleção, entre outras. Para propor as sugestões de melhoria, foi realizada uma análise do mercado a nivel de empresas de recrutamento e de PMEs portuguesas para comparar os seus processos de R&S antes e durante a Covid-19 e analisar a sua transição digital. Para este efeito, procedeu-se a uma metodologia qualitativa com a realização de 12 entrevistas: 6 a recrutadores de empresas de recrutamento nas regiões do Porto e Lisboa; 6 a responsáveis de RH de PMEs portuguesas nas regiões do Porto e Braga. A partir da análise das entrevistas e da experiência direta no local de trabalho do autor deste projeto, foi possível criar uma proposta de melhoria direcionada para os profissionais da F.RH.The following study aims to present a proposal for improvements to the recruitment and selection process at F.RH. F.RH is a Human Resources consulting company and offers several services in the Human Resources area such as Recruitment and Selection, among others. To propose the suggestions for improvement, a market analysis was conducted at the level of Portuguese recruitment companies and SMEs to compare their R&S processes before and during Covid-19 and to analyze their digital transition. For this purpose, a qualitative methodology was used with 12 interviews: 6 to recruiters from recruitment companies in the regions of Porto and Lisbon; 6 to HR managers from Portuguese SMEs in the regions of Porto and Braga. From the analysis of the interviews and the direct experience in the workplace of the author of this project, it was possible to create a proposal for improvement directed to F.RH professionals

Uso de pesticidas en el cultivo de hortalizas : seguridad alimentaria, riesgos socioambientales y políticas publicas para la promoción de la salud

Colaboração editorial da Faculdade de Ciência da Informação (FCI) da Universidade de Brasília - UnBEste trabalho tem como objetivo apresentar o quadro de insegurança alimentar no Brasil associado à contaminação de hortaliças por agrotóxicos, e os desafios de políticas públicas para promoção da saúde por meio do incentivo ao consumo saudável. Neste sentido, foram consultados dados secundários junto ao IBGE, Sindicato das Indústrias de Defensivos Agrícolas, e do Programa de Análise de Resíduos de Agrotóxicos em Alimentos – PARA da ANVISA; bem como a legislação que trata da promulgação da Lei Orgânica de Segurança Alimentar - LOSAN. Como resultado, verificou-se que o grupo das hortaliças representa 19,75% do consumo de ingrediente ativos de fungicidas no país, demandando um consumo médio por hectare em até oito vezes, se comparado com outras culturas, como a soja. Além desse fato, nos resultados do PARA/2008, 22% das amostras em hortaliças foram consideradas insatisfatórias. Dessas, a presença de acefato, banido em vários países, foi detectada em 87% das culturas. O conceito de alimentação saudável está aquém do que preconiza a LOSAN frente ao quadro de contaminação de hortaliças por agrotóxicos no Brasil. Nesse contexto, conclui-se que o PARA deve ser considerado como um instrumento essencial às políticas públicas para promoção da segurança alimentar devendo ainda ampliar seu foco na contextualização socioambiental do risco de contaminação das hortaliças - especialmente para fungicidas -; promover o aprofundamento e ampliação da participação social em nível local e nacional; e reforçar as ações interinstitucionais direcionadas à produção de hortaliças de base agroecológica, incluindo registro de fitossanitários para produção orgânica. ____________________________________________________________________________________________________________________ ABSTRACTThis paper aims to present a picture of food insecurity in Brazil associated to vegetables contamination by pesticides, and the challenges of public policies to promote health by encouraging healthy consumption. To this effect, secondar data were consulted from IBGE, Union of Industries of Pesticides (SINDAG) and Program Analysis of Pesticide Residues in Food – (PARA) of ANVISA, as well as legislation dealing with the promulgation of the Organic Law on Food Security - LOSAN. It was found that the group of vegetables represents 19.75% of fungicide’s active ingredients in the country, requiring an average consumption per hectare up to 8 times compared to other crops such as soybeans. In addition, PARA/2008 results indicated that around 22% of vegetable samples were considered unsatisfactory. Of these, the presence of acephate, banned in several countries was recorded in 87% of the investigated vegetables. The concept of healthy eating stands short of what is advocated by LOSAN given the existing vegetable contamination by pesticides in Brazil. It is concluded that the PARA should be regarded as an essential tool of public policies to promote food safety expanding further its focus to reach social and environmental contexts of risk of vegetable contamination - especially for fungicides -; promote the deepening and expansion of social participation in local and national levels, reinforcing institutional measures aimed at the production of agroecological vegetables, including registration of pesticides for organic production. __________________________________________________________________________________________________________________ RESUMENEn este documento se presenta un panorama de la inseguridad alimentaria en el Brasil asociada a la contaminación de las hortalizas con pesticidas y los desafíos de las políticas públicas para promover la salud al fomentar el consumo saludable. Para ello, se consultaron los datos secundarios del IBGE, la Unión de Industrias de Plaguicidas, y el Programa de Análisis de Residuos de Plaguicidas en Alimentos – PARA; así como la legislación relativa a la promulgación de la Ley Orgánica de Seguridad Alimentaria - LOSAN. Como resultado, se constato que el grupo de hortalizas representa el 19,75% del ingrediente activo de fungicidas en el país, que requieren un consumo medio por hectárea hasta 8 veces mayor en comparación con otros cultivos como la soya. Además, los resultados del PARA/2008, revela que alrededor del 22% de las muestras de vegetales fueron no satisfactorias. El acetato, prohibido en varios países, se encontró en el 87% de las muestras. El concepto de alimentación saludable según la definición de la LOSAN está lejos de lograrse frente a la situación de contaminación de hortalizas con pesticidas en el Brasil. En este contexto, se concluye que el PARA debe considerarse un instrumento esencial de las políticas públicas para promover la seguridad alimentaria y debería ampliar su enfoque en el contexto social y ambiental de riesgo de contaminación de hortalizas, especialmente los fungicidas, en la promoción de la ampliación de la participación social a nivel local y nacional y fortalecer las acciones institucionales destinadas a la producción de hortalizas de cultivo orgánico, incluido el registro de pesticidas para la producción orgánica

Self-Organising and Self-Learning Model for Soybean Yield Prediction

Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods

Diversidade e estrutura da comunidade lenhosa de uma restinga no litoral de Alcântara, Maranhão, Brasil

Although Maranhão State has the second longest coastline of Brazil, phytosociological studies are necessary to expand the knowledge of the restinga vegetation therein. Thus, the present study characterizes structural parameters of the woody component of a restinga in Alcântara city, west coast of Maranhão State. The phytosociological sampling included thirteen 100 m parallel transects, totaling 50 points. The inclusion criterion established for the species was perimeter at ground level ≥ 10 cm. We sampled 34 species, 26 genera, and 17 families, totaling 200 individuals. The species with the highest importance value (IV) were Guettarda angelica Mart. ex Müll.Arg., Anacardium occidentale L., Myrcia splendens (Sw.) DC., Cenostigma bracteosum (Tul.) E. Gagnon & G.P. Lewis, Fridericia sp., Eugenia stictopetala Mart. ex DC., and Mouriri guianensis Aubl. The average height of the specimens was 4.44 m, and the average diameter was 12.6 cm. The Shannon diversity index found in the restinga was 2.92 nat. ind-1, and Pielou’s evenness was 0.83. It is worth mentioning the presence of Sapium glandulosum (L.) Morong and Manilkara bidentata (A.DC.) A.Chev, which are common species of the Cerrado and the Amazonian forest, respectively. Our findings contribute to the knowledge of diversity, generating data for the development of conservation studies, besides reinforcing the influence of the flora of neighboring ecosystems in the colonization of the restingas of Maranhão State.Apesar do Maranhão ser considerado o segundo maior litoral do país, estudos fitossociológicos são necessários para ampliar o conhecimento da vegetação das restingas do Estado. Assim, o presente estudo teve como objetivo caracterizar os parâmetros estruturais do componente lenhoso de uma restinga no município de Alcântara, Maranhão. Para a amostragem fitossociológica foram instalados 13 transectos de 100 m paralelos, totalizando 50 pontos com critério de inclusão de espécies foi o Perímetro à Altura do Solo ≥ 10 cm. Foram amostradas 34 espécies, 26 gêneros e 17 famílias, em um total de 200 indivíduos. As espécies de maior valor de importância foram Guettarda angelica Mart. ex Müll.Arg., Anacardium occidentale L., Myrcia splendens (Sw.) DC., Cenostigma bracteosum (Tul.) E. Gagnon & G.P. Lewis, Fridericia Mart. sp., Eugenia stictopetala Mart. ex DC. e Mouriri guianensis Aubl. A altura média dos espécimes foi de 4,44 m e o diâmetro médio foi de 12,6 cm. O índice de diversidade de Shannon foi de 2,92 nat. ind-1 e a equabilidade de 0,83. Cabe ressaltar a presença de Sapium glandulosum (L.) Morong e Manilkara bidentata (A.DC.) A.Chev., que são espécies comuns de áreas de Cerrado e de Floresta Amazônica, respectivamente. Os dados apresentados contribuem para o conhecimento da diversidade, gerando dados para o desenvolvimento de estudos direcionados a conservação, além de reforçar a influência da flora dos ecossistemas vizinhos na colonização das áreas de restinga do Maranhão

Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios

This paper presents an actively semi-supervised multi-layer neuro-fuzzy modeling method, ASSDRB, to classify different lighting conditions for driving scenes. ASSDRB is composed of a massively parallel ensemble of AnYa type 0-order fuzzy rules. It uses a recursive learning algorithm to update its structure when new data items are provided and, therefore, is able to cope with nonstationarities. Different lighting conditions for driving situations are considered in the analysis, which is used by self-driving cars as a safety mechanism. Differently from mainstream Deep Neural Networks approaches, the ASSDRB is able to learn from unseen data. Experiments on different lighting conditions for driving scenes, demonstrated that the deep neuro-fuzzy modeling is an efficient framework for these challenging classification tasks. Classification accuracy is higher than those produced by alternative machine learning methods. The number of algebraic calculations for the present method are significantly smaller and, therefore, the method is significantly faster than common Deep Neural Networks approaches. Moreover, DRB produced transparent AnYa fuzzy rules, which are human interpretable

Similarity-based Deep Neural Network to Detect Imperceptible Adversarial Attacks

Deep neural networks (DNN’s) have become essential for solving diverse complex problems and have achieved considerable success in tackling computer vision tasks. How-ever, DNN’s are vulnerable to human-imperceptible adversarial distortion/noise patterns that that can detrimentally impact safety-critical applications such as autonomous driving. In this paper, we introduce a novel robust-by-design deep learn-ing approach, Sim-DNN, that is able to detect adversarial attacks through its inner defense mechanism that considers the degree of similarity between new data samples and autonomously chosen prototypes. The approach benefits from the abrupt drop of the similarity score to detect concept changes caused by distorted/noise data when comparing their similarities against the set of prototypes. Due to the feed-forward prototype-based architecture of Sim-DNN, no re-training or adversarial training is required. In order to evaluate the robustness of the proposed method, we considered the recently introduced ImageNet-R dataset and different adversarial attack methods such as FGSM, PGD, and DDN. Different DNN’s methods were also considered in the analysis. Results have shown that the proposed Sim-DNN is able to detect adversarial attacks with better performance than its mainstream competitors. Moreover, as no adversarial training is required by Sim-DNN, its performance on clean and robust images is more stable than its competitors which require an external defense mechanism to improve their robustness