Abstract
Decision making is an important role performed by managers. This chapter will analyze the importance of information systems (IS) on the decision-making process at rural organizations in Portugal's Algarve region. Managers' perceptions were analyzed and compared with the decision-making process model proposed in this chapter, which was based on the models of Simon (1977) and Mintzberg, Raisinghani, and Theorêt (1976). This chapter will discuss the capacity of rural tourism organizations to solve problems, as well as review the time needed to solve problems through the use of IS. This chapter will conclude that IS in the organizational decision-making process is positively related to the identification of the decision-making problem and time needed to solve the problems. This investigation will allow other sectors the opportunity to discuss decision process models based on technology, information capability, and organizational competitiveness.

Abstract
Educational Robotics has been presented as a great pedagogical tool because it demonstrates an attractive way of working the theoretical knowledge put into practice. Thus, several educational technologies have emerged with different approaches, with the purpose of applying robotics in the educational area in a more attractive and playful way. This article presents the conduction of a Systematic Review of Literature (SRL), whose objective is to identify the teaching approaches used with educational robotics. With this, we present experiences reports, and at the same time show the skills and competencies that are explored through robotics and education. This review uses scientific papers published in the period from 2011 to 2016.

Abstract

Abstract
LoRa and its upper layers definition LoRaWAN is one of the most promising LPWAN technologies for implementing the Internet of Things (IoT). Although being a popular technology, several works in the literature have revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft). By using all these recommendations from the academia and industry, the LoRa-Alliance has worked on the v1.0 to develop an enhanced version and provide more secure and trustable architecture. The result of these efforts ended-up with LoRaWAN v1.1, which was released on Oct 11, 2017. This manuscript aims at demystifying the security aspects and provide a comprehensive Security Risk Analysis related to latest version of LoRaWAN. Besides, it provides several remedies to the recognized vulnerabilities. To the best of authors’ knowledge, this work is one of its first kind by providing a detailed security analysis related to latest version of LoRaWAN. According to our analysis, end-device physical capture, rogue gateway and replay attacks are found to be threating for safety operation of the network. Eventually, v1.1 of LoRaWAN is found to be less vulnerable to attacks compared to v1.0, yet possesses several security implications that need to be addressed and fixed for the upcoming releases.

Abstract
Social media platforms are having a profound impact on the so-called information ecosystem, specifically on how information is produced, distributed and consumed. Social media in particular has contributed to the rise of user generated content and consequently to a greater diversity in online content. On the other hand, social media networks, such as Twitter or Facebook, have become information management tools that allow users to setup and configure information sources to their particular interests. A Twitter user can handpick the sources he wishes to follow, thus creating a custom information channel. However, this opportunity to create personalized information channels effectively results in different consumption profiles? Is the information consumed by users through social media networks distinct from the information consumed though traditional mainstream media? In this work, we set out to investigate this question using Twitter as a case study. We prepare two samples of users, one based on a uniform random selection of user IDs, and another one based on a selection of mainstream media followers. We analyze the home timelines of the users in each sample, focusing on characterizing information consumption habits. We find that information consumption volume is higher, while diversity is consistently lower, for mainstream media followers when compared to random users. When analyzing daily behavior, however, the samples slightly approximate, while clearly maintaining a lower diversity for mainstream media followers and a higher diversity for random users.

Abstract
In multi-label classification tasks, instances are simultaneously associated with multiple labels, representing different and, possibly, related concepts from a domain. One characteristic of these tasks is a high class-label imbalance. In order to obtain improved predictive models, several algorithms either have explored the label dependencies or have dealt with the problem of imbalanced labels. This work proposes a label expansion approach which combines both alternatives. For such, some labels are expanded with data from a related class label, making the labels more balanced and representative. Preliminary experiments show the effectiveness of this approach to improve the Binary Relevance strategy. Particularly, it reduced the number of labels that were never predicted in the test instances. Although the results are preliminary, they are potentially attractive, considering the scale and consistency of the improvement obtained, as well as the broad scope of the proposed approach.