Abstract
Scientific research has always been done and is done in a collaborative manner. Today we have available the internet that facilitates this process through communication tools, data sharing, management tasks, among others. However, qualitative research has taken timid steps towards a truly collaborative work. This paper presents the 4C collaborative work model as well as the collaboration features available in the current version (2.0) of the qualitative analysis software webQDA. The paper is based on a questionnaire sought to understand the views of a random sample of users in Brazil, Spain and Portugal about these features. The results achieved demonstrate that the communications capabilities, cooperation and coordination are not yet fully explored by researchers. We hope that the development of version 3.0 of webQDA, can be benefited by the identification of the features most exploited by researchers and point to desired new features to be made available.

Abstract

Abstract
There are many open issues and challenges in the multi-agent reward-based learning field. Theoretical convergence guarantees are lost, and the complexity of the action-space is also exponential to the amount of agents calculating their optimal joint-action. Function approximators, such as deep neural networks, have successfully been used in singleagent environments with high dimensional state-spaces. We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi-agent Q-learning are used to formally describe our proposal, and are tested in a Foraging Task and a Pursuit Game. We also demonstrate how they can generalize to similar tasks and to larger teams, due to the strength of deep-learning techniques, and their viability for transfer learning approaches. With only a small fraction of the initial task's training, we adapt to longer tasks, and we accelerate the task completion by increasing the team size, thus empirically demonstrating a solution to the complexity issues of the multi-agent field.

Abstract
Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks.

Abstract
With the rise in wearable technology and "health culture", we are seeing a rising interest and affordances in studying how to not only prolong life expectancy but also in how to improve individuals' quality of life. On one hand, this attempts to give meaning to the increasing life expectancy, as living above a certain threshold of pain and lack of autonomy or mobility is both degrading and unfair. On the other hand, it lowers the cost of continuous care, as individuals with high quality of life indexes tend to have lower hospital readmissions or secondary complications, not to mention higher physical and mental health. In this paper, we evaluate the current state of the art in physiological therapy (biofeedback) along with the existing medical grade and consumer grade hardware for physiological research. We provide a comparative analysis between these two device grades and also discuss the finer details of each consumer grade device in terms of functionality and adaptability for controlled (laboratory) and uncontrolled (field) studies.

Abstract
This research aims to initially address the Digital Mourning concept under the Digital Heritage Management, investigating to what extent we can "Managing the Digital Heritage", namely the content we create on a daily basis on social networks and the Internet.. The ultimate goal is to understand to what extent this issue is important for users and present results showing that items needed to design a system that is accepted and used in practice by users. To assess the need for platforms for the Digital Heritage Management (PGHDs) and what is the desirable content was conducted a questionnaire on the subject. Analyzing the results of this questionnaire it appears that most respondents did not think about the future of your Digital Heritage is not aware about the policies of online services they use. But the evidence is that the higher the technological maturity of most individuals is likely to be future users of this type of platforms. It also notes that the vast majority of users consider necessary technologically developed using PGHDs in the future.