Abstract
Virtual Reality (VR) technologies have evolved to the point where it is being used in various areas (entertainment, medicine, education, etc.). One of the metrics that allow the evaluation of the virtual experience is Presence. In this work, we conduct an exploratory study that studies which factors of VR games correlate to presence. Various components of games are also shared between other VR applications allowing the results to be applicable not only in VR games. A study with 78 participants divided into 5 groups was conducted where each group played a different VR game. Presence and Game Experience were evaluated. The results indicated multiple positive correlations between subscales of Presence and Game Experience.

Abstract

Abstract

Abstract
This article presents preliminary findings on the application of both Speculative Design and Game Design towards the conception of two prototypes of serious games with focus on anorexia. The first prototype focuses on psychoeducation of school-age youth, and the second aims to support research and sharing of knowledge about the disease, able to be used in focus groups and interviews. Anorexia is a complex and often fatal disease that has no cure, and by conceiving and playing these first prototypes we were able get a glimpse of the its context, making us more ready for this research’s next stages. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © 2019 for this paper by its authors.