In recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems’ vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid QuantumClassical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models’ effectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models’ performance in terms of accuracy and consumption of computational resources.
Emotions are affective states accompanied by physiological reactions that affect cognition processes such as decision making, perception, and learning. Emotion detection can be helpful in fields like education, sports and accident prevention. In this pilot study, we used biosensors to measure heart rate and galvanic skin response of twenty-eight volunteers (fourteen male, fourteen female). They were asked to watch video clips to elicit two target emotions: amusement and anger. The purpose of this study was to determine the relationship between mean values of biosignals and emotional states (including amusement, anger and neutral state). From the analysis of variance, Fisher least significant difference and Multiple Range test, it was observed that emotions elicited with video clips influence mean values and other features of physiological signals with a confidence level of 90%.
This paper proposes an approach to facilitate the process of individualization of patients from their medical images, without compromising the inherent confidentiality of medical data. The identification of a patient from a medical image is not often the goal of security methods applied to image records. Usually, any identification data is removed from shared records, and security features are applied to determine ownership. We propose a method for embedding a QR-code containing information that can be used to individualize a patient. This is done so that the image to be shared does not differ significantly from the original image. The QR-code is distributed in the image by changing several pixels according to a threshold value based on the average value of adjacent pixels surrounding the point of interest. The results show that the code can be embedded and later fully recovered with minimal changes in the UIQI index - less than 0.1% of different.
In this article, we show the development of a low-cost hardware/software system based on close range photogrammetry to track the movement of a person performing weightlifting. The goal is to reduce the costs to the trainers and athletes dedicated to this sport when it comes to analyze the performance of the sportsman and avoid injuries or accidents. We used a web-cam as the data acquisition hardware and develop the software stack in Processing using the OpenCV library. Our algorithm extracts size, position, velocity, and acceleration measurements of the bar along the course of the exercise. We present detailed characteristics of the system with their results in a controlled setting. The current work improves the detection and tracking capabilities from a previous version of this system by using HSV color model instead of RGB. Preliminary results show that the system is able to profile the movement of the bar as well as determine the size, position, velocity, and acceleration values of a marker/target in scene. The average error finding the size of object at four meters of distance is less than 4%, and the error of the acceleration value is 1.01% in average.
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