Opportunities for standardization in emergency scenarios in the European Union.

Objective: Despite current standardization actions towards the unification between European Union (EU) countries, there is still much work to do. In this context, this paper aims to offer a comprehensive analysis of the limitations of the EU concerning emergency situations, specifically in cross-border, cross-hierarchical, and cross-sectorial emergencies, as well as the analysis of emergent opportunities for improvement. The final goal of this analysis is to serve as an initial step for pre-standardizing these opportunities.

Breaching Subjects' Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the study of the central nervous system response to certain stimuli being one of them, being the case of evoked potentials. However, due to the sensitivity of these data, the transmissions must be protected, with blockchain being an interesting approach to ensure the integrity of the data.

S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information.

Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems.

Twitter social bots: The 2019 Spanish general election data.

The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.

BEHACOM - a dataset modelling users' behaviour in computers.

This paper details the methodology and approach conducted to monitor the behaviour of twelve users interacting with their computers for fifty-five consecutive days without preestablished indications or restrictions. The generated dataset, called BEHACOM, contains for each user a set of features that models, in one-minute time windows, the usage of computer resources such as CPU or memory, as well as the activities registered by applications, mouse and keyboard. It has to be stated that the collected data have been treated in a privacy-preserving way during each phase of the collection and analysis.

A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms.

The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs.

UMUDGA: A dataset for profiling algorithmically generated domain names in botnet detection.

In computer security, botnets still represent a significant cyber threat. Concealing techniques such as the dynamic addressing and the domain generation algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labeled algorithmically generated domain names decorated with a feature set ready-to-use for machine learning (ML) analysis.

ReCAN - Dataset for reverse engineering of Controller Area Networks.

This article details the methodology and the approach used to extract and decode the data obtained from the Controller Area Network (CAN) buses in two personal vehicles and three commercial trucks for a total of 36 million data frames. The dataset is composed of two complementary parts, namely the raw data and the decoded ones. Along with the description of the data, this article also reports both hardware and software requirements to first extract the data from the vehicles and secondly decode the binary data frames to obtain the actual sensors' data.

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System.

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users' quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users' behaviours.