Abstract
This paper describes an environment to assess auditory emotional recognition based on a mobile application. The primary aim of this work is to provide a valuable instrument that can be used both in research and clinical settings, responding to the strong need of validated measures of emotional processing, especially in Portugal. The secondary aim is to acquire and study the participants' interaction behavior with the technological device (e.g. touch patterns, touch intensity), in search for a relationship with medical conditions, cognitive impairments, auditory emotional recognition capacities or socio-demographic indicators. This will establish the basis for the prediction of such aspects as a function of an individual's interaction with technological devices, potentially providing new diagnostic tools.

Abstract

Abstract
A previous version of this article contained minor errors in Tables 2, 3 and 4. This has now been corrected, the publisher apologises for the error. © 2016 European Crohn's and Colitis Organisation (ECCO).

Abstract

Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state of the art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP and a Twitter temporal network with over 310 million non-zeros, where we predict the evolution of the activity of the use of political hashtags.

Abstract