The use of serious games for the treatment of people with autism is currently considered a promising approach due to its positive effects on promoting learning through playful and motivating experiences. In recent years, increased research has focused on serious games utilizing Immersive Virtual Reality (IVR) technologies, such as large-scale projection-based systems and head-mounted displays. The high level of immersion provided by IVR has been found to benefit learning outcomes, as it reduces environmental distractions and helps individuals focus on learning tasks while also addressing social anxiety. Researchers have conducted significant work in this field over the past decade, yielding promising results. However, the development of these learning interventions comes with methodological challenges and issues, especially in how to conduct the development process and design IVR-based serious games for the learning of people with autism. Based on these premises, this systematic review thoroughly analyzes the literature on developing IVR-based serious games for individuals with autism, discussing inherent shortcomings and reflecting on them. Then, twenty IVR-based serious games for people with autism developed between 2009 and mid-2021 are selected and analyzed, focusing on the people engaged in the development process, the design methodology adopted, and the serious game design framework employed. From this analysis, a set of recommendations are proposed to support anyone interested in developing IVR-based serious games for people with autism. In addition, the gaps left unsolved in the autism literature are highlighted, upon which a research agenda is grounded.

Deep learning models are known for being large and computationally expensive. It is a challenge to fit these models into edge devices which usually have frugal memory. A striking feature about neural networks is their enormous size. Embedded devices in edge computing scenario typically cannot handle large neural networks. We present two models of Convolutional Neural Networks (VGG16 type), with a modification in the prediction layer, potentially suitable for edge computing devices. Both networks have been designed for the classification of a human face by gender and age. The first one (VGG16/10) considers gender and age as two related characteristics and the final neurons have been conceived to hold these aspects together at the same time. The second one (VGG16/8+1) in the prediction layer has got a neuron for the prediction of gender and another eight for the prediction of age (according to the Adience benchmark). Such networks have been conceived to provide simultaneously information on both the gender and age of the person identified in the image, without the need to build two dedicated networks. The aim is to develop a solution that can be suitable in edge-computing scenario.

The cybersecurity domain faces a critical disparity between the escalating demand for skilled professionals and the limited talent pool. This gap is primarily driven by the surge in cyberattacks perpetrated by malicious actors seeking financial gain or disruption. These attacks, encompassing tactics like Distributed Denial-of-Service (DDoS) and ransomware, pose significant threats to data security and public safety. To bridge this gap, several challenges must be addressed. We need to prioritize initiatives that enhance people’s awareness and education in cybersecurity. Additionally, exploring innovative training methods and leveraging technology to equip cybersecurity professionals with the necessary skillsets is crucial. Like the last edition, the goal of this workshop remains to empower individuals to make informed decisions regarding cybersecurity education, navigate diverse career paths, and recognize the alignment between their acquired skills and industry demands. These novel educational methodologies will also equip professionals with a deeper understanding of cybersecurity risks and the expertise required to develop robust and secure technological solutions. Thinking about students, teachers, and professionals as the people who need to learn about cybersecurity, the aim of this workshop is to talk about how we can better match the companies’ demand (workplace, recruitment) and supply (qualification, training). We aim to raise awareness among these learners, help identify the crucial skills that are required for their work and encourage a more unified approach to teaching and training in cybersecurity.