Abstract
The implementation of traffic differentiation measures by internet service providers (ISPs) has raised concerns regarding net neutrality, potentially leading to discriminatory practices that challenge existing regulatory frameworks. The complexity of this issue intensifies with the advent of 5G networks as they dynamically assemble elements of the physical infrastructure to create logically segregated domains customised to accommodate usage scenarios with specific requirements, resulting in the categorisation of users, applications, and services into distinct groups which possess the capacity to disrupt the non-discriminatory treatment of data flows. Within this context, a pivotal question arises: how can regulatory authorities effectively evaluate traffic differentiation in 5G networks? In response, this paper proposes an innovative application of the standardised network data analytics function (NWDAF) to facilitate the assessment of internet traffic differentiation. We introduce this novel concept and demonstrate its implementation through a proof -of -concept prototype. By leveraging the NWDAF, regulators may obtain direct and automatic access to performance metrics of 5G networks, enabling the analysis of the traffic management mechanisms employed by ISPs.

Abstract
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient's travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient's body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.

Abstract
It is well known that the performance of Machine Learning techniques, notably when applied to Computer Vision (CV), depends heavily on the amount and quality of the training data set. However, large data sets lead to time-consuming training loops and, in many situations, are difficult or even impossible to create. Therefore, there is a need for solutions to reduce their size while ensuring good levels of performance, i.e., solutions that obtain the best tradeoff between the amount/quality of training data and the model's performance. This paper proposes a dataset reduction approach for training data used in Deep Learning methods in Facial Recognition (FR) problems. We focus on maximizing the variability of representations for each subject (person) in the training data, thus favoring quality instead of size. The main research questions are: 1) Which facial features better discriminate different identities? 2) Will it be possible to significantly reduce the training time without compromising performance? 3) Should we favor quality over quantity for very large datasets in FR? This analysis uses a pipeline to discriminate a set of features suitable for capturing the diversity and a cluster-based sampling to select the best images for each training subject, i.e., person. Results were obtained using VGGFace2 and Labeled Faces in the Wild (for benchmarking) and show that, with the proposed approach, a data reduction is possible while ensuring similar levels of accuracy.

Abstract
We introduce a novel method for movie genre classification, capitalizing on a diverse set of readily accessible pretrained models. These models extract high-level features related to visual scenery, objects, characters, text, speech, music, and audio effects. To intelligently fuse these pretrained features, we train small classifier models with low time and memory requirements. Employing the transformer model, our approach utilizes all video and audio frames of movie trailers without performing any temporal pooling, efficiently exploiting the correspondence between all elements, as opposed to the fixed and low number of frames typically used by traditional methods. Our approach fuses features originating from different tasks and modalities, with different dimensionalities, different temporal lengths, and complex dependencies as opposed to current approaches. Our method outperforms state-of-the-art movie genre classification models in terms of precision, recall, and mean average precision (mAP). To foster future research, we make the pretrained features for the entire MovieNet dataset, along with our genre classification code and the trained models, publicly available.

Abstract
Indoor air quality (IAQ) problems in school environments are very common and have significant impacts on students' performance, development and health. Indoor air conditions depend on the adopted ventilation practices, which in Mediterranean countries are essentially based on natural ventilation controlled through manual window opening. Citizen science projects directed to school communities are effective strategies to promote awareness and knowledge acquirement on IAQ and adequate ventilation management. Our multidisciplinary research team has developed a framework-SchoolAIR-based on low-cost sensors and a scalable IoT system architecture to support the improvement of IAQ in schools. The SchoolAIR framework is based on do-it-yourself sensors that continuously monitor air temperature, relative humidity, concentrations of carbon dioxide and particulate matter in school environments. The framework was tested in the classrooms of University Fernando Pessoa, and its deployment and proof of concept took place in a high school in the north of Portugal. The results obtained reveal that CO2 concentrations frequently exceed reference values during classes, and that higher concentrations of particulate matter in the outdoor air affect IAQ. These results highlight the importance of real-time monitoring of IAQ and outdoor air pollution levels to support decision-making in ventilation management and assure adequate IAQ. The proposed approach encourages the transfer of scientific knowledge from universities to society in a dynamic and active process of social responsibility based on a citizen science approach, promoting scientific literacy of the younger generation and enhancing healthier, resilient and sustainable indoor environments.

Abstract
We spend about one-third of our life either sleeping or attempting to do so. Sleeping is a key aspect for most human body processes, affecting physical and mental health and the ability to fight diseases, develop immunity and control metabolism. Therefore, monitoring human sleep quality is extremely important for the detection of possible sleep disorders. Several technologies exist to achieve this goal, however, most of them are expensive proprietary systems, some require hospitalization and many use intrusive equipment that can, by itself, affect sleep quality. This paper presents an intelligent system, a complete low-cost hardware and software solution, for monitoring the sleep quality of an individual in a home environment. User privacy is guaranteed as all processing is done at the edge and no audio or video is stored. This system monitors several fundamental aspects of sleeping periods in real-time using a low cost single-board computer for processing, a camera for body motion detection (MD module) and for eye/sleep status detection (SSD module), and a microphone for audio recognition (AUDR module) of breath pattern analysis and snore detection. It can be strategically placed near the bed to avoid interfering with the natural sleep pattern. For each sleeping period, the system produces a final report that can be a valuable aid for improving the sleeping health of the monitored person. Functional unitary tests were carried successfully on the selected, low-cost, hardware platform (Raspberry Pi). The entire process was validated by an expert clinical psychologist, ensuring the reliability and effectiveness of the system. The visual and sound modules use sophisticated computer vision and machine learning techniques suitable for edge computing devices. Each of the system’s features have been independently tested, using properly organized audio and video datasets and the well established metrics of precision, recall and F1 score, to evaluate the binary classifiers in each of the three modules. The accuracy values obtained where 90.2% (MD), 79.1% (SSD) and 81.3% (AUDR), demonstrating the great application potential of our solution. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.