Authors would like to thank to Dirk Heylen, head of HMI Lab of University of Twente, for facilitating us the HMI Lab to conduct the experiments and his early feedback. Also, We thank all the participants who took part in our research. This work has been supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU). Moreover, this work has received financial support from the Spanish Ministry of Economy, Industry and Competitiveness with the Project: TIN2016-78011-C4-1-R; Council of Culture, Education and University Planning with the project ED431G/08, the European Regional Development Fund (ERDF).

Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identi...

Acknowledgments. This work has been supported by CONCYTEC - FONDECYT within the framework of the call E038-01 contract 014-2019. N. Condori Fernandez wish also to thank Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.

Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle these findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community. © 2020 IEEE.

Acknowledgment. A. Mendoza, A. Cuno, N. Condori-Fernandez and W. Ramos acknowledge financial support from the “Proyecto Concytec - Banco Mundial, Mejo-ramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE, through its executing unit FONDECYT [Contract N? 014-2019-FONDECYT-BM-INC.INV]. Also, this work has been partially supported by Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.

The present live study is proposed with the objective of investigating the influence of negative emotions (i.e., stress) in the efficiency for verifying conceptual models. To conduct this study, we use a Model-driven Testing tool, named CoSTest, and our own version of stress detector within a competition setting. The experiment design, overview of the empirical procedure, instrumentation and potential threats are presented in the proposal. © 2020 ACM.