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Context: Data privacy and data security became a priority among the problems faced by many Brazilian organizations that should be compliant with the Lei Geral de Protecao de Dados Pessoais (LGPD). This law defines the privacy rights on user data and penalties to the ones that break it. Problem: In a compliance program, business processes are of fundamental importance since they are the most important pillar of information security. However, an approach to guide companies to assess and achieve compliance with LGPD on their business processes is missing. Objective: This work proposes the LGPD4BP (LGPD for Business Process) method, which is composed by an evaluation questionnaire and a modelling method with a modelling patterns catalog. Method: To develop LGPD4BP, we carried out a literature review, an analysis of privacy laws, in particular the LGPD, and relevant works on the area. Results: The method was applied on a case study of Colegio de Aplicacao from Federal University of Pernambuco and validated by a postgraduate class which applied the method and answered a questionnaire about easiness and completeness of the method. Conclusions: The results from students evaluations showed that the most hard step is the business process modeling and not the components from the proposed method.

An autonomous car is a driverless vehicle for the purpose of navigating autonomously and safely over the earth's surface. Interest in this type of transport has increased significantly in recent years and it is possible that the number of these vehicles on the streets will increase, changing the daily lives of cities. In this context, this work aims to understand the technological limitations and impacts of using autonomous cars, observing the feasibility, adaptation, traffic legislation, and perceived benefits. Following the method of systematic literature review proposed by Kitchenham, 69 primary studies from relevant bases in the area of Technology were analyzed. These studies showed some evidence by indicating, for example, that there will be a change in the way people relate to cars, particularly in terms of sharing them, in addition to impacts on urban mobility.

The pandemic of the new COVID-19 has raised many questions to a very connected society as to how to best respond to such a challenge at this current time. The best response so far is to call people for following the instructions from the World Health Organisation (WHO) as a way of reducing the spread of the virus and thus relieving the health system, striving to avoid a collapse. This work studies the spread of positive opinion on adhering to social distancing based on network topology and metrics using a network-oriented model for social contagion. It is shown that interventions based on social network measurements can be used to boost the spread of positive opinion about adhering to these measures. It is also shown that our model accounts for the relevance the health authorities have on encouraging people to partake in social distancing voluntarily.

The emergence of COVID-19 in China, in December of 2019 led to a local epidemic that rapidly spread to multiple countries in the world, including Brazil. Nowadays, there is an accelerated search to understand the dynamics of the spread of the disease and evaluate the effectiveness of intervention measures. Given these special socioeconomic conditions surrounding Brazil, using the predictive models developed for other countries can make a very incomplete picture of the epidemic, since these differences could result in different patterns in low income settings. The aim of this work is to simulate interventions and understand the impact to reduce the spread of COVID-19 considering the socioeconomic conditions of Brazil. With this purpose we use an agent- based model (ABM), a subarea of the Artificial Intelligence, as it allows us to treat each individual in a personalized manner, as well as the environment of which they are part. The simulations have heterogeneous populations, considering different age groups, socioeconomic differences and number of members per family, contacts and movements intra and inter the sub-populations (favelas and non-favelas), numbers of Intensive Care Unit (ICU) and study different scenarios to show how the interventions can influence the spread of the virus in the population of simulated environments.

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

Social processes play a key role in health behaviour. Understanding the underlying mechanisms of such processes is important when designing health interventions with a social component. In this work, we apply a computational model of social contagion to a data set of 2,472 users of a physical activity promotion program. We compare this model’s predictions to the predictions of a simple linear model that has been derived by a regression analysis. The results show that the social contagion model performs better at describing the pattern seen in the empirical data than the linear model, indicating that some of the dynamics of the physical activity levels in the network can be explained by social contagion processes.