Abstract
The Internet of Things (IoT) is a new model that integrates physical objects and Internet and became one of the principal technological evolutions of computing. It is estimated that a trillion of physical objects will be connected to the Internet until 2022. The low accessibility and the lack of interoperability of many of these devices in a vast heterogenous landscape will make it very hard to design specific security measures and apply specific security mechanism. Moreover, IoT networks still exposed and vulnerable to attacks aimed to disrupt the network. Therefore, additional security tools specific to IoT are needed. Intrusion Detection System (IDS) could fulfill this purpose. In this paper, we present a literature review on the IDS in IoT topic, mainly focusing on the current state of research by examining the literature, identifying current trends and presenting open issues and future directions.

Abstract
Several Heart Rate Variability (HRV) based novel methodologies for describing heart rate dynamics have been proposed in the literature with the aim of risk assessment. One such methodology is ARFIMA-EGARCH modeling which allows the quantification of long range dependence and time-varying volatility with the aim of describing non-linear and complex characteristics of HRV. This study applies the ARFIMA-EGARCH modeling of HRV recordings from 30 patients of the Noltisalis database to investigate the discrimination power of a set of features comprising currently used linear HRV features (low and high frequency components) and new measures obtained from the modeling such as, long memory in the mean, and persistence and asymmetry in volatility. A subset of the multidimensional HRV features is selected in a two-step procedure using Principal Components Analysis (PCA). Additionally, supervised classification by quadratic discriminant analysis achieves 93.3% of discrimination accuracy between the groups using the new feature set created by PCA.

Abstract
The purpose of this study was to measure the subject's sense of presence while they performed a task (riding a bicycle downhill) in a virtual reality (VR) environment and to compare it by body position (standing vs. sitting) and gender. The sample consisted of 35 subjects (19 male and 16 female) between 17 and 33 years of age. A translated and validated Portuguese version of the lgroup Presence Questionnaire (IPQp) and the Reflexive Motor Acts (RMAs), based on direct observation, were used as metrics. The results showed significant differences between body position at the level of Experienced Realism, Spatial Presence and Overall Sense of Presence. When measuring RMAs, it was demonstrated that people in the sitting position presented a higher frequency. We concluded that body position influences perceptions of credibility, which has an impact on the sense of presence. No differences were identified between the genders.

Abstract
Due to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network (ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.

Abstract
A new integrated approach is required for planning and rethinking mobility issues in European Regions, towards allowing them to become better prepared into near future challenges. In this paper, a set of recommendations for future mobility planning in the Alto Minho Region, in Northern Portugal, will be proposed taking into consideration social inclusion and environmental concerns into accessibility planning. The conducted research shows that despite different framework methods and approaches, similar trends emerge in future mobility, reinforcing that cooperative actions to share information and encourage dialogue between all stakeholders (population, politicians and operators) in long term plans is essential. Based on Gini-coefficients assessment and its resulting spider diagrams, a population profile is created aiming to explore the impacts in future mobility solutions. As the result of this approach, this paper presents some key recommendations for future mobility planning, based on an integrated delivery of policies for social and environmental equity within the transportation sector.

Abstract