Abstract
This paper proposes a methodology to detect early signs of Parkinson's disease (PD) through free-speech in uncontrolled background conditions. The early detection mechanism uses signal and speech processing techniques integrated with machine learning algorithms. Three distinct speech databases containing patients' recordings at different stages of the PD are used for estimation of the parameters during the training and evaluation stages. The results reveal the potential in using Random Forest (RF) or Support Vector Machine (SVM) techniques. Once tuned, these algorithms provide a reliable computational method for estimating the presence of PD with a very high accuracy.

Abstract
Background and Purpose: The purpose of this study was to review the methods used to analyze the kinematics of upper limbs (ULs) of healthy and poststroke adults, namely specificities of sampling and motor tasks. Summary of review: A database of articles published in the last decade was compiled using the following search terms combinations: ("upper extremity" OR "upper limb" OR arm) AND (kinematics OR motion OR movement) AND (analysis OR assessment OR measurement). The articles included in this review (1) had the purpose to analyze objectively a three-dimension kinematics of ULs, (2) studied functional movements or activities of daily living (ADL) involving uppers limbs, and (3) studied healthy and/or poststroke adults. Fourteen articles were included (four studied a healthy sample, three analyzed poststroke patients, and seven examined both poststroke and healthy participants). Conclusion: Most of the recommended demographic and stroke information, such as some preexisting conditions to stroke, initial stroke severity, and stroke location, were not collected by all or most of the articles. Time poststroke onset was presented in all articles but showed great variability. Few articles identified anthropometric characteristics and adjusted task environment to them. Most of the samples were composed mainly by males and had a low mean age, which does not represent poststroke population. Most articles analyzed "functional movements", namely simulations of ADL. Implication of key findings: Future research should identify the recommended information to allow an adequate stratification. Acute phase after stroke, real ADL with different complexities, and ipsilesional UL should be studied.

Abstract
Building upon the success of the first edition, we organize the second edition of the Text2Story Workshop on Narrative Extraction from Texts in conjunction with the 41^st European Conference on Information Retrieval (ECIR 2019) on April 14, 2019. Our objective is to further consolidate the efforts of the community and reflect upon the progress made since the last edition. Although the understanding of natural language has improved over the last couple of years – with research works emerging on the grounds of information extraction and text mining – the problem of constructing consistent narrative structures is yet to be solved. It is expected that the state-of-the-art has been advancing in pursuit of methods that automatically identify, interpret and relate the different elements of narratives which are often spread among different sources. In the second edition of the workshop, we foster the discussion of recent advances in the link between Information Retrieval (IR) and formal narrative representations from text.

Abstract
In this article we present a game interface, using audio input and output, aiming to provide the concept of interactive narrative to users with visual or motor disability. The solution lets users choose the direction of the story, triggering several alternate endings and thus creating a dynamic and creative narrative. The application development process is described here from the design, implementation and evaluation. In the evaluation phase, we performed user tests with five participants with visual and motor disability. Thus, we record three metrics: effectiveness, success of the task (reaching one of the possible endings); efficiency, time needed to complete the story; and satisfaction, comfort and wellbeing of the user during the interaction. The result was positive, all participants successfully completed the application, and there were no withdrawals. Four in five wanted to repeat the experience and try to reach another end of the story.

Abstract

Abstract
Data confidentiality in cloud services is commonly ensured by encrypting information before uploading it. However, this approach limits the use of content-aware functionalities, such as deduplication and compression. Although this issue has been addressed individually for some of these functionalities, no unified framework for building secure storage systems exists that can leverage such operations over encrypted data. We present TRUSTFS, a programmable and modular stackable file system framework for implementing secure content-aware storage functionalities over hardware-assisted trusted execution environments. This framework extends the original SAFEFS architecture to provide the isolated execution guarantees of Intel SGX. We demonstrate its usability by implementing an SGX-enabled stackable file system prototype while a preliminary evaluation shows that it incurs reasonable performance overhead when compared to conventional storage systems. Finally, we highlight open research challenges that must be further pursued in order for TRUSTFS to be fully adequate for building production-ready secure storage solutions.