The widespread adoption of masks and respirators has significantly influenced various aspects of society, driving technological advances to improve comfort, efficiency, and sustainability. The COVID-19 pandemic underscored their essential role in the protection of public health, with continued relevance in the industrial, environmental, and hygiene-critical sectors. Recent developments in deep learning offer promising approaches for building automated systems that can detect mask and respirator usage. In this regard, this paper first aims to present an improved version of the Facial Masks and Respirators Database (FMR-DB), which can be used to create such systems. New features include a significant increase in available images, which has been expanded from 2565 to 4200 images, and the addition of You Only Look Once (YOLO), PASCAL Visual Object Classes (PascalVOC), and Common Objects in Context (COCO) labeling for image detection tasks. Furthermore, image classification and object detection tests were conducted using Convolutional Neural Networks (CNNs), Transformers, and YOLO to determine the types of masks and respirators accurately. Finally, to the best of the authors' knowledge, these tools were used for the first time to analyze the protection levels of respirators automatically. The results provide valuable insights for developing efficient and reliable automatic recognition systems.

The Morra game is quite old. Back in time, traces of it can be found in ancient Egypt, ancient Rome, and even China. It involves two players who, for a limited number of turns, must try to suppose the sum of the number personally chosen with the number chosen by the opponent. The rules are simple, but it is rather difficult to play at a high level as there are multiple cognitive, motor, and perceptual processes involved.The goal of this paper is to illustrate the process of implementing a quantum random player for the Morra game and some of its variants. This can be done by using a quantum number generator circuit to generate two numbers and a quantum adder to obtain the supposed sum. The advantage of this proposal is that, unlike the implementations of the Morra game on classical computers, which only allow the generation of pseudo-random numbers, true randomness can be obtained through quantum computing.In addition to the description of the entire algorithms, the source code of the implementations is provided to give everyone the freedom to easily test both the quantum implementation of the Morra game and the variants discussed in the paper.

In role-playing games (RPGs), players are called upon to assume the role of a character moving in an imaginary environment and facing several challenges. Their success or failure often depends on randomizers like cards or dice. Regarding the latter, the most commonly used in RPGs are the Platonic solids with the addition of the ten-sided die. They are commonly simulated through classical computers, however, since true randomness is not in their nature, they can only generate pseudorandom numbers. On the contrary, quantum computers exploit the nondeterministic nature of quantum mechanics, so they are perfect candidates for truly random simulations in games of chance. For this reason, this paper proposes and tests various quantum circuits for sampling uniformly distributed discrete values within a fixed range, corresponding to the number of faces of the dice. The simulations reveal the pure randomness of the output of the implemented circuits. They were then used to generate random numbers within a three-dimensional dice-rolling game.

During the coronavirus pandemic, the mask detection problem has become of particular interest. Usually, the goal is to create a system that can detect whether or not a person is wearing a mask or respirator. However, this tends to trivialize a problem that hides a greater complexity. In fact, people wear masks or respirators in various ways, many of which are incorrect. This makes the problem ill-conditioned and creates a bias compared to training cases, with the consequence that these systems have a considerably lower accuracy when used in practice. We claim that focusing on the ways in which a mask can be worn and classifying the problem not as binary but at least as ternary, thus adding an intermediate class containing all those ways in which a mask or respirator can be worn incorrectly, could help address this problem. For this reason, this paper describes and puts to the proof the Ways to Wear a Mask or a Respirator Database (WWMR-DB). It has a fine classification of the most common ways in which a mask or respirator is worn, which can be used to test how mask detection systems work in cases that resemble the real ones more. It was used to test a neural network, the ResNet-152, which was trained on less fine databases, like the Face-Mask Label Dataset and the MaskedFace-Net. The mixed results denote the shortcomings of these databases and the need to enhance them or resort to finer databases.

Since the onset of the coronavirus pandemic, researchers from all over the world have been working on projects aimed at countering its advance. The authors of this paper want to go in this direction through the study of a system capable of recognizing the type of mask or respirator worn by a person. It can be used to implement automatic entry controls in high protection areas, where people can feel comfortable and safe. It can also be used to make sure that people who work daily in contact with particles, chemicals, or other impurities wear appropriate respiratory protection. In this paper, a proof-of-concept of this system will be presented. It has been realized by using a state-of-the-art Convolutional Neural Network (CNN), EfficientNet, which was trained on a novel database, called the Facial Masks and Respirators Database (FMR-DB). Unlike other databases released so far, it has an accurate classification of the most important types of facial masks and respirators and their degree of protection. It is also at the complete disposal of the scientific community.

For some years now, the idea of a fully autonomous driving car has been monopolizing the attention of the entire automotive sector. The main motivation behind all this interest is that autonomous driving cars could potentially improve road safety simply by subtracting human error, which causes thousands of road fatalities worldwide every year. One of the biggest challenges, for which researchers are working hard to give answers, concerns the search of solutions to improve the people’s response to this vehicles once they are put on the road: an incorrect answer can lead to their unsuccess, with a great monetary loss for all the companies that have invested in these technologies. Recently, we worked on a project that went in this direction and which regarded the possibility to change the driving style of the autonomous driving cars based on the passengers’ facial expressions. This is particularly useful in the event that they experience fear: the car may react to these feelings by adopting a careful driving style and stopping if these feelings do not improve within a certain period. In this paper, we want to discuss about the improvements we have made to this project.