The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature.

Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested.

O desenvolvimento econômico associado a aspectos como a desigualdade social são processos que provocam a deterioração de recursos ambientais. São emergenciais o aperfeiçoamento das tecnologias para combater os efeitos dos desastres naturais, como a melhoria dos processos de transformação de chuva em vazão e da modelagem dos fenômenos hidrológicos em bacias hidrográficas. O objetivo desta comunicação científica foi obter, com o auxílio de técnicas de machine learning, previsões do comportamento hidrológico na Bacia do Rio Bananal. Essas informações reunidas foram submetidas aos seguintes modelos: redes neurais artificiais de aprendizado profundo (Deep Learning - InceptionTime, Resnet, FCN e ResCNN) e de aprendizado de máquina (Machine Learning - regressão linear, árvore de decisão e support-vector machine), e gradient boosting para levantar séries históricas consistentes e comparar os padrões de precipitação e da vazão (modelo chuva-vazão) do curso hídrico. Foram utilizadas as séries temporais médias diárias de 2002 a 2014 para treinamento a fim de estabelecer um comportamento otimizado para 2015. Foi possível concluir que o coeficiente R² com a melhor performance foi da arquitetura InceptionTime, com RMSE de 4.62, MAE de 2.36. O resultado com amior desempenho foi a árvore de decisão com R² de 0.73.

In recent decades, Brazilian tropical regions have experienced a significant reduction in water surface, driven by environmental and anthropogenic factors, and the state of Mato Grosso is no exception, as it hosts three biomes (Cerrado, Pantanal, and Amazon). Concerned about climate change and its effects on geo-hydrology, this study aimed to conduct a diagnosis of the water surface in the municipalities of the state of Mato Grosso. We used the AdaptaBrasil and MapBiomas platforms to identify the areas most vulnerable to geo-hydrological disasters and drought. The analysis showed that the geo-hydrological vulnerability index for the state is high (0.69), with 56% of the municipalities at high risk. On the other hand, the drought vulnerability index was low, with an average of 0.26, and 61% of the municipalities at low risk of drought. Although drought vulnerability is relatively low compared to geo-hydrological vulnerability, significant challenges persist, especially in municipalities with unregulated population growth and inadequate water resource management. Municipalities such as Colniza, Cotriguaçu, and Serra Nova Dourada are highly vulnerable to geo-hydrological conditions, while Várzea Grande, Nova Guarita, and Cuiabá are vulnerable to drought. This study emphasizes the urgent need to implement conservation measures and sustainable water resource management, including the restoration of degraded areas and the protection of remaining forests. Additionally, it is suggested that integrating state policies with federal initiatives could strengthen the state's resilience to climate change.

The aim of this study was to analyze and measure idiosyncratic differences in CO₂ emission trends over time and between the different geographical contexts of the G20 signatory countries and to assess whether these countries are fulfilling their carbon emission reduction commitments, as stipulated in the G20 sustainable development agendas. To this end, a multilevel mixed-effects model was used, considering CO₂ emissions data from 1950 to 2021 sourced from the World Bank. The research model captured approximately 93.05% of the joint variance in the data and showed (i) a positive relationship between the increase in CO₂ emissions and the creation of the G20 [CI90: +0.0080; + 0.1317]; (ii) that every year, CO₂ emissions into the atmosphere are increased by an average of 0.0165 [CI95: +0.0009; +0.0321] billion tons by the G20 countries; (iii) that only Germany, France, and the United Kingdom have demonstrated a commitment to CO₂ emissions reduction, showing a decreasing rate of CO₂ emissions into the atmosphere; and (iv) that there seems to be a mismatch between the speed at which the G20 proposes climate policies and the speed at which these countries emit CO₂.