Carlos Ferreira

Abstract

Abstract
The application of feature engineering in classification problems has been commonly used as a means to increase the classification algorithms performance. There are already many methods for constructing features, based on the combination of attributes but, to the best of our knowledge, none of these methods takes into account a particular characteristic found in many problems: causality. In many observational data sets, causal relationships can be found between the variables, meaning that it is possible to extract those relations from the data and use them to create new features. The main goal of this paper is to propose a framework for the creation of new supposed causal probabilistic features, that encode the inferred causal relationships between the target and the other variables. In this case, an improvement in the performance was achieved when applied to the Random Forest algorithm. © 2020, The Author(s).

Abstract
Peer-to-peer communication has increasingly gained prevalence in people’s daily lives, with its widespread adoption being catalysed by technological advances. Although there have been strides for the inclusion of disabled individuals to ease communication between peers, people who suffer hand/arm impairments have scarce support in regular mainstream applications to efficiently communicate privately with other individuals. Additionally, as centralized systems have come into scrutiny regarding privacy and security, development of alternative, decentralized solutions has increased, a movement pioneered by Bitcoin that culminated on the blockchain technology and its variants. Within the inclusivity paradigm, this paper aims to showcase an alternative on human-computer interaction with support for the aforementioned individuals, through the use of an electroencephalography headset and electromyography surface electrodes, for application navigation and text input purposes respectively. Users of the application are inserted in a decentralized system that is designed for secure communication and exchange of data between peers that are both resilient to tampering attacks and central points of failure, with no long-term restrictions regarding scalability prospects. Therefore, being composed of a silent speech and brain-computer interface, users can communicate with other peers, regardless of disability status, with no physical contact with the device. Users utilize a specific user interface design that supports such interaction, doing so securely on a decentralized network that is based on a distributed hash table for better lookup, insert and deletion of data performance. This project is still in early stages of development, having successfully been developed a functional prototype on a closed, testing environment. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
The literature on gait analysis in Vascular Parkinsonism (VaP), addressing issues such as variability, foot clearance patterns, and the effect of levodopa, is scarce. This study investigates whether spatiotemporal, foot clearance and stride-to-stride variability analysis can discriminate VaP, and responsiveness to levodopa. Fifteen healthy subjects, 15 Idiopathic Parkinson's Disease (IPD) patients and 15 VaP patients, were assessed in two phases: before (Off-state), and one hour after (On-state) the acute administration of a suprathreshold (1.5 times the usual) levodopa dose. Participants were asked to walk a 30-meter continuous course at a self-selected walking speed while wearing foot-worn inertial sensors. For each gait variable, mean, coefficient of variation (CV), and standard deviations SD1 and SD2 obtained by Poincaré analysis were calculated. General linear models (GLMs) were used to identify group differences. Patients were subject to neuropsychological evaluation (MoCA test) and Brain MRI. VaP patients presented lower mean stride velocity, stride length, lift-off and strike angle, and height of maximum toe (later swing) (p < .05), and higher %gait cycle in double support, with only the latter unresponsive to levodopa. VaP patients also presented higher CV, significantly reduced after levodopa. Yet, all VaP versus IPD differences lost significance when accounting for mean stride length as a covariate. In conclusion, VaP patients presented a unique gait with reduced degrees of foot clearance, probably correlated to vascular lesioning in dopaminergic/non-dopaminergic cortical and subcortical non-dopaminergic networks, still amenable to benefit from levodopa. The dependency of gait and foot clearance and variability deficits from stride length deserves future clarification. © 2019 Elsevier Ltd