Abstract
Vulnerability scanning tools can help secure the computer networks of organisations. Triggered by the release of the Tsunami vulnerability scanner by Google, the authors analysed and compared the commonly used, free-to-use vulnerability scanners. The performance, accuracy and precision of these scanners are quite disparate and vary accordingly to the target systems. The computational, memory and network resources required be these scanners also differ. We present a recent and detailed comparison of such tools that are available for use by organisations with lower resources such as small and medium-sized enterprises.

Abstract
The Malware Information Sharing Platform (MISP) enables the sharing of cyberthreat information within a community, company or organisation. However, this platform presents limitations if its information is deemed as classified or shared only for a given period of time. This implies that this information should to be handled only in encrypted form. One solution is to use MISP with searchable encryption techniques to impose greater control over the sharing of information. In this paper, we propose a controlled information sharing functionality that features a synchronisation procedure that enables classified data exchange between MISP instances, based on policies and ensuring the required confidentiality and integrity of the shared data. Sequence charts are presented validating the configuration, the data synchronisation, and the data searching between multiple entities.

Abstract
For the regularly medicated population, the management of the posology is of utmost importance. With increasing average life expectancy, people tend to become older and more likely to have chronic medical disorders, consequently taking more medicines. This is predominant in the older population, but it’s not exclusive to this generation. It’s a common problem for all those suffering from chronic diseases, regardless of age group. Performing a correct management of the medicines stock, as well as, taking them at the ideal time, is not always easy and, in some cases, the diversity of medicines needed to treat a particular medical disorder is a proof of that. Knowing what to take, how much to take, and ensuring compliance with the medication intervals, for each medication in use, becomes a serious problem for those who experience this reality. The situation is aggravated when the posology admits variable amounts, intervals, and combinations depending on the patient’s health condition. This paper presents a solution that optimizes the management of medication of users who use the services of institutions that provide health care to the elderly (e.g., day care centers or nursing homes). Making use of the NB-IoT network, artificial intelligence algorithms, a set of sensors and an Arduino MKR NB 1500, this solution, in addition to the functionalities already described, eHealthCare also has mechanisms that allow identifying the non-adherence to medication by the elderly.

Abstract
Twitter has become a major platform to share ideas and promoting discussion on relevant topics. However, with a large number of users to resort to it as their primary source of information and with an increasing number of accounts spreading newsworthy content, a characterization of the political bias associated with the social network ecosystem becomes necessary. In this work, we aim at analyzing accounts spreading or publishing content from five different classes of the political spectrum. We also look further and study accounts who spread content from both right and left sides. Conclusions show that there is a large presence of accounts which disseminate right bias content although it is the more central classes that have a higher influence on the network. In addition, users who spread content from both sides are more actively spreading right content with opposite content associated with criticism towards left political parties or promoting right political decisions.

Abstract
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).

Abstract
In recent years we have seen a large adherence to social media by various Higher Education Institutions (HEI) with the intent of reaching their target audiences and improve their public image. These institutional publications are guided by a specific editorial strategy, designed to help them better accomplish and fulfill their mission. The current Covid-19 pandemic has had major consequences in many different fields (political, economic, social, educational) beyond the spread of the disease itself. In this paper, we attempt to determine the impact of the pandemic on the HEI content strategies by gauging if these social-economical, cultural and psychological changes that occurred during this global catastrophe are actively reflected in their publications. Furthermore, we identified the topics that emerge from the pandemic situation checking the trend changes and the concept drift that many topics had. We gathered and analyzed more than 18k Twitter publications from 12 of the top HEI according to the 2019 Center for World University Rankings (CWUR). Utilizing machine learning techniques, and topic modeling, we determined the emergent content topics for each institution before, and during, the Covid-19 pandemic to uncover any significant differences in the strategies.