Stefano Marrone

User authentication is more and more crucial in everyday life, with researchers developing sophisticated, yet easy-to-use, approaches to enforce secure authentication while providing a smooth user experience. This is particularly true in mobile authentication, in which the spread of AI-capable smartphones is opening up several biometric-based authentication methods, such as face and lips recognition. A more recently emerging setup consists of the matching of a spoken passphrase with lips movements. Despite being promising and well-suited for mobility authentication, the matching operation is not trivial, and the research around this topic is limited, as no publicly available dataset is available for experimental purposes. In this paper we thus introduce BioVid , a novel privacy-aware multimodal biometric dataset for user recognition from smartphone-recorded videos in real-world conditions. BioVid consists of 650 recordings from 43 participants, where each clip captures a user pronouncing predefined passphrases while simultaneously acquiring audio and lips movements. The dataset is designed to support dual-factor biometric authentication, combining speaker-unique identity (UID) and passphrase verification, and explicitly models realistic variability in device type, recording distance, background, and ambient noise. Together with the data, we provide a detailed statistical analysis of the linguistic, visual, and acoustic properties of the data, highlighting its diversity and suitability for robust biometric system evaluation. Moreover, to test the quality of the collected dataset, we organised an international challenge on multimodal user authentication leveraging BioVid . In this paper we thus also describe the challenge protocol, baseline systems, and evaluation metrics, while also summarising the best-performing submitted approaches as measured by Equal Error Rate (EER). BioVid and the accompanying challenge aim to establish a reproducible benchmark for multimodal biometric authentication on consumer-grade smartphones and to foster further research on privacy-aware, real-world biometric systems.

In recent years, Artificial Intelligence (AI) has gained increasing traction in medical image analysis. Novel methodologies based on Deep Neural Networks (DNNs) have increasingly enabled the development of solutions that leverage data from multiple sources, including multimodal and synthetic data. Despite notable achievements, current AI systems often struggle with robustness and generalizability, particularly when deployed in real-world clinical scenarios characterized by data heterogeneity, incompleteness, and distribution shifts. This highlights the urgent need for resilient AI, that is, AI capable of maintaining consistent and reliable performance across diverse and imperfect conditions. In this paper, we explore medical image computing from a resilience-oriented perspective, focusing on two key sources of complexity: multimodal and synthetic data. For the former, we address two major challenges: (i) the design of fusion strategies that can effectively handle missing modalities; and (ii) the integration of complementary information to ensure robustness across heterogeneous subject populations. For the latter, we emphasize the critical role of biological plausibility in both the generation and evaluation of synthetic data, proposing physiologically informed pipelines that improve learning in data-scarce settings. We introduce innovative methodologies that advance the state-of-the-art, including a cross-modality calibration mechanism for incomplete acquisitions and a pharmacokinetics-driven synthetic augmentation framework. Our contributions aim to promote robustness, fairness, and generalization, ultimately fostering the development of more resilient AI systems in medical imaging.

Background/Objectives: Large Language Models (LLMs) like ChatGPT, LLAMA, and Mistral are widely used for automating tasks such as content creation and data analysis. However, due to their training on publicly available internet data, they may inherit social biases. We aimed to investigate the social biases (i.e., ethnic, gender, and disability biases) in these models and evaluate how different model versions handle them. Methods: We instruction-tuned popular models (like Mistral, LLAMA, and Gemma), and for this we curated a dataset constructed by collecting and modifying diverse data from various public datasets. Prompts were run through a controlled pipeline, and responses were categorized (e.g., biased, confused, repeated, or accurate) and analyzed. Results: We found that models responded differently to bias prompts depending on their version. Fine-tuned models showed fewer overt biases but more confusion or censorship. Disability-related prompts triggered the most consistent biases across models. Conclusions: Bias persists in LLMs despite instruction tuning. Differences between model versions may lead to inconsistent user experiences and hidden harms in downstream applications. Greater transparency and robust fairness testing are essential.

Hospital overcrowding, driven by both structural management challenges and widespread medical emergencies, has prompted extensive research into machine learning (ML) solutions for predicting patient length of stay (LOS) to optimize bed allocation. While many existing models simplify the LOS prediction problem to a classification task, predicting broad ranges of hospital days, an exact day-based regression model is often crucial for precise planning. Additionally, available data are typically limited and heterogeneous, often collected from a small patient cohort. To address these challenges, we present a novel multimodal ML framework that combines imaging and clinical data to enhance LOS prediction accuracy. Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. As a case study, we applied this framework to pneumonia patient data collected during the COVID-19 pandemic at two hospitals in Naples, Italy—one specializing in infectious diseases and the other general-purpose. Under our experimental setup, the proposed system achieved an average prediction error of only three days, demonstrating its potential to improve patient flow management in critical care environments.