The recognition of emotions and feelings through computer technology and devices has been widely explored in recent years. Social networks have become a natural environment in which users express their feelings and opinions through social media, and this includes their Facebook reactions. The aim of this study was to investigate whether the emoticons have chosen by users in social network news actually express the emotions they wish to express, having as indicative, the polarity of the emotions, and the six basic emotions. The data collection was carried out following three courses of action: 1) survey of the posts with higher reactions rates of popular news pages; 2) selection of news by a panel of experts to verify its reliability; and 3) identification of reactions, polarity, and basic emotions flagged by Facebook users for each news item. Finally, an Expectation-Maximization algorithm was deployed to find the relationship between the reactions and the basic emotions signaled. The results made it possible to determine the polarity and the correlation of the reactions with the emotional expressions. This suggests that the use of reactions in feelings analysis algorithms can increase the confidence in determining the emotion that the content reflects and the emotional state of the social network users.

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The wildfires caused by human occupation is one of the factors that most contributes to deforestation of conservation areas, resulting in a number of issues for ecological systems. Premature fire detections lead to the elimination or reduction of the damage that will be caused by a fire incident. Wireless sensor networks have been shown to be a good alternative for environmental monitoring applications, as they can collect and send information in real time, such as humidity, wind, and temperature of various parts of the forest. Due to problems such as power limitation, communication failure, and loss of nodes, the network topology is constantly changing, requiring mechanisms to achieve self-organization and fault tolerance. This article proposes the development of a model and application in self-organizing and fault-tolerant wireless sensor networks for fire detection in conservation areas. To achieve self-organization and fault tolerance, we rely on encouraged local interactions between neighboring nodes that monitor the same region and the coordination of tasks, through a supervisor node, equipped with a framework for developing component-based fault-tolerant applications. For the analysis and validation of both model and application, it was simulated 60 events in the network of sensors in a real scenario using the simulator Sinalgo. The results were classified as True (partial or absolute) or False (partial or absolute). In all, 45% of consensus identified a possible fault in the application and in only 35% there was absolute consensus.

In recent decades, the challenge of managing the development teams spread across different time zones has become increasingly common, raising the importance of the development of Global Software Development (GSD) techniques, in order to tackle its particular problems. This work discuss these issues in the context of Sidia, an R&D institute which implements technological solutions for global companies. The main partner of Sidia is a mobile multinational company located in overseas. The development team must cooperate with the overseas team, even though there are no overlapping working hours between both teams. Besides, the teams have a different set of skills regarding design, quality assurance, and software engineering. In order to address theses problems, we propose the Blueprint model, a Kanban and Scrum based model that supports the development of GSD systems, the allocation of tasks and teams, and the efficient communication. Finally, we discuss the aspects and lessons learned of development of project and deploy of a new model for systems development on a real-word project.