SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition.

This study presents a Subject-Aware Transformer-based neural network designed for the Electroencephalogram (EEG) Emotion Recognition task (SATEER), which entails the analysis of EEG signals to classify and interpret human emotional states. SATEER processes the EEG waveforms by transforming them into Mel spectrograms, which can be seen as particular cases of images with the number of channels equal to the number of electrodes used during the recording process; this type of data can thus be processed using a Computer Vision pipeline. Distinct from preceding approaches, this model addresses the variability in individual responses to identical stimuli by incorporating a User Embedder module.

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition.

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems.

MV-MS-FETE: Multi-view multi-scale feature extractor and transformer encoder for stenosis recognition in echocardiograms.

Background: aortic stenosis is a common heart valve disease that mainly affects older people in developed countries. Its early detection is crucial to prevent the irreversible disease progression and, eventually, death. A typical screening technique to detect stenosis uses echocardiograms; however, variations introduced by other tissues, camera movements, and uneven lighting can hamper the visual inspection, leading to misdiagnosis.

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization.

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope.

Affective Action and Interaction Recognition by Multi-View Representation Learning from Handcrafted Low-Level Skeleton Features.

Human feelings expressed through verbal (e.g. voice) and non-verbal communication channels (e.

SIRe-Networks: Convolutional neural networks architectural extension for information preservation via skip/residual connections and interlaced auto-encoders.

Improving existing neural network architectures can involve several design choices such as manipulating the loss functions, employing a diverse learning strategy, exploiting gradient evolution at training time, optimizing the network hyper-parameters, or increasing the architecture depth. The latter approach is a straightforward solution, since it directly enhances the representation capabilities of a network; however, the increased depth generally incurs in the well-known vanishing gradient problem. In this paper, borrowing from different methods addressing this issue, we introduce an interlaced multi-task learning strategy, defined SIRe, to reduce the vanishing gradient in relation to the object classification task.

Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.

Background: over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens.

Human Silhouette and Skeleton Video Synthesis Through Wi-Fi Signals.

The increasing availability of wireless access points (APs) is leading toward human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread visual sensors, where the signals enable to address well-known vision-related problems such as illumination changes or occlusions. Indeed, using image synthesis techniques to translate radio frequencies to the visible spectrum can become essential to obtain otherwise unavailable visual data. This domain-to-domain translation is feasible because both objects and people affect electromagnetic waves, causing radio and optical frequencies variations.

Determinants of workers' well-being during the COVID-19 outbreak: An exploratory study.

Unlabelled: Inspired by the Conservation of Resource theory (Hobfoll, 1989), this study investigated the role of a broad set of personal vulnerabilities, social, and work-related stressors and resources as predictors of workers' well-being during the COVID-19 outbreak. Participants were 594 workers in Italy. Results showed that personality predispostions, such as positivity, neuroticism and conscientiousness as well as key aspects of the individuals' relationship with their work (such as job insecurity, type of employment contract or trust in the organization) emerged as factors promoting (or hampering) workers' adjustment during the COVID -19 outbreak.

A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components.

Background And Objectives: Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts due to eye movements and blinking, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the Useful Brain Signals (UBS). Independent Component Analysis (ICA) is effective to split the signal into Independent Components (IC) whose re-projection on 2D topographies of the scalp (images also called Topoplots) allows to recognize/separate artifacts and UBS.

LieToMe: An Ensemble Approach for Deception Detection from Facial Cues.

Deception detection is a relevant ability in high stakes situations such as police interrogatories or court trials, where the outcome is highly influenced by the interviewed person behavior. With the use of specific devices, e.g.

A light CNN for detecting COVID-19 from CT scans of the chest.

Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.

Bodyprint-A Meta-Feature Based LSTM Hashing Model for Person Re-Identification.

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task.

Fusing Self-Organized Neural Network and Keypoint Clustering for Localized Real-Time Background Subtraction.

Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classification, vehicle tracking, and person re-identification. Despite the progress made in recent years, a main challenge of moving object detection still regards the management of dynamic aspects, including bootstrapping and illumination changes.

An interactive and low-cost full body rehabilitation framework based on 3D immersive serious games.

Strokes, surgeries, or degenerative diseases can impair motor abilities and balance. Long-term rehabilitation is often the only way to recover, as completely as possible, these lost skills. To be effective, this type of rehabilitation should follow three main rules.

Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation.

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments.

The rocky road to prosocial behavior at work: The role of positivity and organizational socialization in preventing interpersonal strain.

Among relevant consequences of organizational socialization, a key factor is the promotion of organizational citizenship behaviors toward individuals (i.e. OCBI).

Overall design and implementation of the virtual glove.

Post-stroke patients and people suffering from hand diseases often need rehabilitation therapy. The recovery of original skills, when possible, is closely related to the frequency, quality, and duration of rehabilitative therapy. Rehabilitation gloves are tools used both to facilitate rehabilitation and to control improvements by an evaluation system.

Customized first and second order statistics based operators to support advanced texture analysis of MRI images.

Texture analysis is the process of highlighting key characteristics thus providing an exhaustive and unambiguous mathematical description of any object represented in a digital image. Each characteristic is connected to a specific property of the object. In some cases the mentioned properties represent aspects visually perceptible which can be detected by developing operators based on Computer Vision techniques.

Visual interaction: models, systems, prototypes. The Pictorial Computing Laboratory at the University of Rome La Sapienza.

This paper reports on the research activities performed by the Pictorial Computing Laboratory at the University of Rome, La Sapienza, during the last 5 years. Such work, essentially is based on the study of humancomputer interaction, spans from metamodels of interaction down to prototypes of interactive systems for both synchronous multimedia communication and groupwork, annotation systems for web pages, also encompassing theoretical and practical issues of visual languages and environments also including pattern recognition algorithms. Some applications are also considered like e-learning and collaborative work.