Abstract
Technology for virtual reality has evolved at a fast pace and so is its affordability. Equipment such as head-mounted displays are now available for the average consumer at reasonable prices and this potentiates the use of contents such as video 360 in a more natural way. The purpose of this study was to measure the sense of presence and cybersickness comparing subjects by gender while experiencing a virtual reality application composed by type of VIDEO (360 video and 3D 360 video) using an head-mounted display. A Portuguese version of the Igroup Presence Questionnaire (IPQ) was used together with the Simulator Sickness Questionnaire (SSQ). The results have revealed that there are no significant differences across 2D and 3D videos in the sense of presence or cybersickness. © 2016 ACM.

Abstract
Modern and historical buildings may show some degree of subsidence resulting from foundation deterioration and local geological conditions. Hence, buildings stability can be affected and restoration plans must be envisaged. Resistivity methods have been used to investigate local conditions, providing 3D images of the soil under man made structures and hence contributing to the delimitation of hazardous areas and pathologies. However these techniques require the deployment of a grid of electrodes, which can be difficult to accomplish because of physical limitations and of the buildings nature that cannot be damaged. To overcome these problems special arrays have been used (L, Corner, Square arrays, etc). Here in it is proposed to use the "Odd-Even Pole-Pole Array" to study the ground under a contemporary building and under a high historical value XIV century Abbey, both showing evidence of subsidence. Field data quality is also addressed and it is proposed to identify low quality data to be expunged so that modelling is improved. It is also shown how to estimate resistivity values from data quality tests, to carry out further zonation, locate hazardous areas and to enhance modelling.

Abstract
This paper presents the game developed within the VirtualSign project. This game aims to make the process of learning sign language easier and enjoyable. In the game the player can control an avatar and interact with several objects and Non-player characters in order to obtain signs. To obtain those signs the player will have to overcome challenges and use the VirtualSign Translator. The player then has to perform the gesture himself and information is sent from the translator to the game. This improves the interactivity and makes the game more interesting and motivating. The game has as an inventory system where the signs are kept and can be checked. This lets the user visualize and learn or train the various existing configurations of gestures. There are also various checkpoints to check the player acquired knowledge, a highscore list and a continuous storyline.

Abstract

Abstract
With the realization of networks in many of the real world domains, research work in network science has gained much attention now-a-days. The real world interaction networks are exploited to gain insights into real world connections. One of the notion is to analyze how these networks grow and evolve. Most of the works rely upon the socio centric networks. The socio centric network comprises of several ego networks. How these ego networks evolve greatly influences the structure of network. In this work, we have analyzed the evolution of ego networks from a massive call network stream by using an extensive list of graph metrics. By doing this, we studied the evolution of structural properties of graph and related them with the real world user behaviors. We also proved the densification power law over the temporal call ego networks. Many of the evolving networks obey the densification power law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them and maintaining the density of graph and decreasing redundancy.

Abstract
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of sentiment analysis in microblog streams. However, one of the main challenges consists in aggregating sentiment polarity in a timely fashion that can be fed to the prediction method. We investigated a large set of sentiment aggregate functions and performed a regression analysis using political opinion polls as gold standard. Our dataset contains nearly 233 000 tweets, classified according to their polarity (positive, negative or neutral), regarding the five main Portuguese political leaders during the Portuguese bailout (2011-2014). Results show that different sentiment aggregate functions exhibit different feature importance over time while the error keeps almost unchanged. © 2016 ACM.