This work concludes the first study on mouth-based emotion recognition while adopting a transfer learning approach. Transfer learning results are paramount for mouth-based emotion emotion recognition, because few datasets are available, and most of them include emotional expressions simulated by actors, instead of adopting real-world categorisation. Using transfer learning, we can use fewer training data than training a whole network from scratch, and thus more efficiently fine-tune the network with emotional data and improve the convolutional neural network’s performance accuracy in the desired domain. The proposed approach aims at improving emotion recognition dynamically, taking into account not only new scenarios but also modified situations to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in a healthcare management environment, and portable applications supporting disabled users having difficulties in seeing or recognising facial emotions. This achievement takes advantage of previous preliminary works on mouth-based emotion recognition using deep-learning, and has the further benefit of having been tested and compared to a set of other networks using an extensive dataset for face-based emotion recognition, well known in the literature. The accuracy of mouth-based emotion recognition was also compared to the corresponding full-face emotion recognition; we found that the loss in accuracy is mostly compensated by consistent performance in the visual emotion recognition domain. We can, therefore, state that our method proves the importance of mouth detection in the complex process of emotion recognition.

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of men at sea, lead us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features to be applied to various contexts.

Recent global advancements in ICT technologies have motivated our research to assess the impact of Virtual and Augmented Reality on teaching and learning. In particular, we conducted a rigorous study on the effects on learning by subjecting classes of students to an experience of these emerging technologies and measuring their feelings before and after the experiment through questionnaires. We carried out the research involving three high schools and a total of 162 students aged between 15 and 20. During the meetings, we made available two applications that we developed. The first is a Virtual Reality environment that enables the user to explore a room in which the system spawns three-dimensional objects defined by the teacher. The objects may represent mathematical functions, physics simulations, scenes relating to historical re-enactments, etc. The second is an Augmented Reality smartphone application that allows three-dimensional figures to be observed via the smartphone screen by framing a marker printed on paper. Both applications are realised with free software, trying to minimise the technical requirements for their operation and guaranteeing reliability and usability. The work concludes by analysing the results obtained and comparing the effectiveness of the two technologies tested. Results of tests conducted in three Italian schools show that Augmented Reality is seen as a useful tool in education by about 94.43%. Importantly, the use of these technologies requires an inclusive design that involves all students, regardless of their level of familiarity with the technology. The overall analysis reveals that both technologies performed well, and students value them for their intuitiveness and level of immersion. The in presence experience is more effective for using immersive applications fostered by student collaboration.

The COVID-19 pandemic caused despair, poverty and, above all, pain and death across the planet. Nevertheless, there is no doubt that it has also given a strong global impetus to the digital world, asserting its importance, sustainability and richness of perspectives. With our work we intend to establish a set of best practices aimed at defining methods and technologies that will enable those fascinated by digital technologies to contribute effectively to the digitisation of cultural heritage on a large scale. Various aspects that play a crucial role in the digitisation of artifacts will be discussed, with a focus on the issues involved in the manual realisation of works using Blender and Unity software. For demonstration purposes, two very popular use cases in the Umbria region of Italy are presented: “Piazza IV Novembre” in Perugia with the magnificent “Fontana Maggiore”, the “Palazzo dei Priori” and the Duomo on one side and the Republic square in Foligno on the other, with the Duomo, the Bishop’s house and the Diocesan Museum, the Town Hall and “Palazzo Trinci”. The first realization was carried out using photogrammetry techniques, the software Blender and Unity, while the second was carried out exclusively with Blender and Unity.The theme is highly relevant in Europe, particularly Italy, where the topic is part of the post-COVID-19 National Recovery and Resilience Plan (NRRP).

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 s even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.