The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.
In 2018, 22,000,000 Brazilian women experienced some type of violence (either physical or psychological), and 42% of these episodes occurred in the domestic environment. Therefore, government strategies have been developed to solve this problem. This study aimed to (a) carry out a survey of Brazilian government strategies for the protection of women after the creation of the Maria da Penha Law (Law No. 11.340, 2006 ) and (b) discuss these strategies from the concept of cultural design. Sixteen laws, five decrees, and two ordinances were found and analyzed. In a general way, the analysis suggests that they are not effective cultural plans because most do not show long-term results, and some of the laws that typify crimes and those that relate to the aggressor’s behavior do not specify the target behaviors, so there is no explicit contingency. Despite this, such strategies are relevant, as they end up providing the protective context for women from a social, legal, and political point of view. Finally, it is expected that the discussions raised in the present work can help prepare interventions that favor socially beneficial cultural practices.
This work analyzed the six official statements of the president of Brazil that were broadcast on radio and television during the first 4 months of COVID-19 contamination in the country, regarding the efficacy in communicating the crisis and dimensions of rules. We observed a higher frequency of ineffective excerpts in the statements, especially in the categories “effective fear incitement” and “respect.” The categories “speed” and “expression of empathy” showed high efficacy. Additionally, there was a higher recurrence of implicit and inaccurate rules and rules opposing the recommendations of experts. These results indicate that the analyzed statements were ineffective in crisis communication and control of behaviors combating the pandemic in Brazil. The analysis of governmental practices by behavioral science can be useful in the planning of public policies.
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