Abstract
Virtual worlds, particularly those able to provide a three-dimensional physical space, have features that make them suitable to support collaborative activities. These features distinguish virtual worlds from other collaboration tools, but current taxonomies of the field of Computer-Supported Cooperative Work do not account for several distinctive features of virtual worlds, namely those related with non-verbal communication. We intended to find out how the use of an avatar, gestures, spatial sounds, etc., influence collaboration in order to be able to include non-verbal communication in taxonomies of the field Computer-Supported Cooperative Work. Several cases of collaboration in virtual worlds are analysed, to find the impact of these non-verbal characteristics of virtual worlds. We proposed adding the concept of Presence to taxonomies of Computer-Supported Cooperative Work and contribute with guidance for future taxonomy development that includes it as a new dimension. This new dimension of Presence is subdivided into "avatar" and "physical space" subdimensions. In turn, these are divided into "physical appearance", "gestures, sounds and animations" and "focus, nimbus and aura"; "environment" and "objects / artefacts". This new taxonomy-development proposal may contribute to inform better design of virtual worlds in support of cooperative work.

Abstract
Understanding the intellectual landscape of scientific communities and their collaborations has become an indispensable part of research per se. In this regard, measuring similarities among scientific documents can help researchers to identify groups with similar interests as a basis for strengthening collaboration and university-industry linkages. To this end, we intend to evaluate the performance of hybrid crowd-computing methods in measuring the similarity between document pairs by comparing the results achieved by crowds and artificial intelligence (AI) algorithms. That said, in this paper we designed two types of experiments to illustrate some issues in calculating how similar an automatic solution is to a given ground truth. In the first type of experiments, we created a crowdsourcing campaign consisting of four human intelligence tasks (HITs) in which the participants had to indicate whether or not a set of papers belonged to the same author. The second type involves a set of natural language processing (NLP) processes in which we used the TF-IDF measure and the Bidirectional Encoder Representation from Transformers (BERT) model. The results of the two types of experiments carried out in this study provide preliminary insight into detecting major contributions from human-AI cooperation at similarity calculation in order to achieve better decision support. We believe that in this case decision makers can be better informed about potential collaborators based on content-based insights enhanced by hybrid human-AI mechanisms.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
Physical and cognitive rehabilitation based on natural interaction and VR has been on our horizon for several years, and we have been conducting experimentation towards that goal through several exploratory research initiatives.This article addresses some aspects of the state-of-the-art of VR in healthcare and well-being, with opportunities in the domain of rehabilitation based on natural interaction and VR being analyzed and put in perspective with the SmartAL ecosystem roadmap.

Abstract
As life expectancy grows and the population requirements for satisfactory health and wellness levels increases, there is a clear opportunity for the incorporation of alternative techniques and tools for promoting health and preventing disease. One of these tools, is the use of thermal water-based treatments, commonly known as thermalism, as tools to trigger patients’ overall wellness. Despite the collective assumption of the effects of thermalism, there is little to no scientific evidence of these treatments, thus impairing the potential of this activity to become more widespread in society. Thus, with this paper, we present a conceptual model for a non-invasive sensing system based on wearables that can monitor a set of patients’ biomarkers which will serve as the basis for the validation of the effects of thermalism. This system will also serve as a management and operation control tool for thermal SPA managers and technical directors.

Abstract
In recent years there has been a marked increase in bank fraud by SMS (Short Messaging System) and voice. One of the factors contributing to increase in cases of SMS fraud is the low cost of acquiring large volumes of messages, the reliability (the message will reach the recipient) and the fact that it does not need the Internet to reach the victim. In relation to financial fraud by voice, these can be used to persuade victims to make bank transfers to fraudulent accounts, with the promise of receiving large sums in prizes. The prevention of these types of fraud is not a trivial task, as it requires the application of appropriate techniques and methods depending on their nature. This article presents a Systematic Literature Review (SLR) from 2015 to 2020, with the aim of analyzing the state of the art on bank frauds committed by SMS or voice. The SLR allowed the identification of the most common types of bank fraud by SMS or voice, and the respective detection techniques.