Abstract
In this age of technology, information, innovation, and so many other features that could be associated with it, it becomes necessary to review the benefits and risks associated with this change. The question that guides this article is: Does the easiness granted to us, through the many online mechanisms, affect our ability to perceive at the moment of making a purchase decision? Rather, from a financial perspective, is the quick and easy way that the internet allows the consumer to make a payment influencing the consumers' buying act? Through the analysis of various mobile applications available for the aforementioned purpose, as well as a survey, that featured 137 answers from various age groups and occupations, we will present a brief conclusion about this theme, aiming to contribute to a better and broader understanding of the benefits, risks, advantages and disadvantages of these so well-known banking applications that increasingly stand out in the daily lives of the Portuguese. The results of this exploratory study reveal the importance of a greater and better instruction when using these innovative tools, so the user can stop being merely a hostage to their risks and begin to fully enjoy the benefits.

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A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.

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This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions. © 2018 The Authors

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