Abstract
Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation/ development, and, organizational and professional changes. © 2020 IEEE.

Abstract
Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.

Abstract
In this paper it is described a low-cost robotic manipulator that was developed to be applied in the study of over-sensored systems and state estimation. The prototype was developed to be a teaching aid in advanced courses, such as a Robotics Doctoral Program, although it is also suitable to be applied in some other educational contexts, to support experiments, for graduate and undergraduate students. Its features and software implementation are described, as well as two possible approaches to the problem of estimating its pose, based on a wide variety of sensors use. © 2020 ACM.

Abstract

Abstract
Fabry disease (FD) is a rare disease commonly complicated with white matter lesions (WMLs). WMLs, which have extensively been associated with gait impairment, justify further investigation of its implication in FD. This study aims to identify a set of gait characteristics to discriminate FD patients with/without WMLs and healthy controls. Seventy-six subjects walked through a predefined circuit using gait sensors that continuously acquired different stride features. Data were normalized using multiple regression normalization taking into account the subject physical properties, with the assessment of 32 kinematic gait variables. A filter method (Mann Whitney U test and Pearson correlation) followed by a wrapper method (recursive feature elimination (RFE) for Logistic Regression (LR) and Support Vector Machine (SVM) and information gain for Random Forest (RF)) were used for feature selection. Then, five different classifiers (LR, SVM Linear and RBF kernel, RF, and K-Nearest Neighbors (KNN)) based on different selected set features were evaluated. For FD patients with WMLs versus controls the highest accuracy of 72% was obtained using LR based on 3 gait variables: pushing, foot flat, and maximum toe clearance 2. For FD patients without WMLs versus controls, the best performance was observed using LR and SVM RBF kernel based on loading, foot flat, minimum toe clearance, stride length variability, loading variability, and lift-off angle variability with an accuracy of 83%. These findings are the first step to demonstrate the potential of machine learning techniques based on gait variables as a complementary tool to understand the role of WMLs in the gait impairment of FD.

Abstract
In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.