Abstract
Device identity management and authentication are one of the critical and primary security challenges in IoT. In order to decrease the IoT attack surface and provide protection from security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity management systems and resilient device authentication mechanisms. Existing mechanisms for device identity management and device authentication were not designed for huge number of devices and therefore are not suitable for IoT environments. This work presents results of a blockchain-based identity management approach with consensus authentication, as a scalable solution for IoT device authentication management. Our identity management approach relies on having a blockchain secure tamper proof registry and lightweight consensus-based identity authentication.

Abstract
The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

Abstract
Nowadays, students commonly use and are assessed through an online platform. New pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be intense feedback from these activities to students, usually it is restricted to the assessments of the online set of tasks. We propose a model that informs students of abnormal deviations of a “correct” learning path. Our approach is based on the vision that, by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. In the major learning management systems available, the interaction between the students and the system, is stored in log. Our proposal uses that logged information, and new one computed by our methodology, such as the time each student spends on an activity, the number and order of resources used, to build a table that a machine learning algorithm can learn from. Results show that our model can predict with more than 86% accuracy the failing situations. Copyright

Abstract
In this article we propose an automatic system that informs students of abnormal deviations of a virtual learning path that leads to the best grades in the course. Our motivation is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student's current online actions towards the course. Our goal is therefore to prevent situations that have a significant probability to lead to a pour grade and, eventually, to failing. Our methodology can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student's evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS. Our results shown that it is possible to predict grade levels by only taking interaction patterns in consideration.

Abstract
The rise of online social networks has reshaped the way information is published and spread. Users can now post in an effortless way and in any location, making this medium ideal for searching breaking news and journalistic relevant content. However, due to the overwhelming number of posts published every second, such content is hard to trace. Thus, it is important to develop methods able to detect and analyze whether a certain text contains journalistic relevant information. Furthermore, it is also important that this detection system can provide additional information towards a better comprehension of the prediction made. In this work, we overview our system, based on an ensemble classifier that is able to predict if a certain post is relevant from a journalistic perspective which outperforms the previous relevant systems in their original datasets. In addition, we describe REMINDS: a web platform built on top of our relevance system that is able to provide users with the visualization of the system's features as well as additional information on the text, ultimately leading to a better comprehension of the system's prediction capabilities. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.

Abstract
Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results: On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.