Giuseppe Polese

Although emotions play a fundamental role in modulating pain perception, their objective assessment in clinical contexts remains challenging. Recent advances in artificial intelligence (AI) have opened new opportunities to measure emotional states through facial expression analysis, physiological signal modeling, natural language processing (NLP), and multimodal data integration. In affective computing, the field that focuses on technologies designed to recognize, interpret, process, and simulate human emotions, facial expression-based emotion recognition has progressed from traditional machine learning methods to advanced deep learning approaches, including convolutional neural networks (CNNs), attention-based hybrid models, and transformer architectures. Similarly, recurrent neural networks and self-supervised learning methods have been implemented for developing models from physiological signals such as electrocardiography, photoplethysmography, galvanic skin response, and related biosignals. Additionally, NLP systems can extract affective information from naturalistic text, using both lexicon-based and transformer-based models. Finally, multimodal fusion and alignment techniques allow the integration of heterogeneous data streams, providing richer and more ecologically valid emotion representations. Collectively, these strategies offer powerful tools for advancing automatic pain assessment (APA) in cancer care, with the potential to support personalized, emotion-aware therapeutic approaches. However, from an AI perspective, several open challenges remain, including multimodal representation learning under weak supervision, robustness to missing or degraded modalities, limited explainability of affective inference models, lack of standardized benchmarking protocols, and the presence of bias and domain shift in emotion datasets. Given the inherently subjective, context-dependent, and culturally mediated features of the emotional experience, further research is needed to address these technical limitations, integrating technological advances with the intrinsic complexity of emotion interpretation.

AI-assisted tools support developers in performing cognitively demanding tasks such as bug detection and code readability assessment. Despite the advancements in the technical characteristics of these tools, little is known about how developers mentally model them and how mismatches affect trust, control, and adoption. We conducted six co-design workshops with 58 developers to elicit their mental models about AI-assisted bug detection and readability features. It emerged that developers conceive bug detection tools as \textit{bug detectives}, which warn users only in case of critical issues, guaranteeing transparency, actionable feedback, and confidence cues. Readability assessment tools, on the other hand, are envisioned as \textit{quality coaches}, which provide contextual, personalized, and progressive guidance. Trust, in both tasks, depends on the clarity of explanations, timing, and user control. A set of design principles for Human-Centered AI in IDEs has been distilled, aiming to balance disruption with support, conciseness with depth, and automation with human agency.

AI-assisted tools support developers in performing cognitively demanding tasks such as bug detection and code readability assessment. Despite the advancements in the technical characteristics of these tools, little is known about how developers mentally model them and how mismatches affect trust, control, and adoption. We conducted six co-design workshops with 58 developers to elicit their mental models about AI-assisted bug detection and readability features. It emerged that developers conceive bug detection tools as \textit{bug detectives}, which warn users only in case of critical issues, guaranteeing transparency, actionable feedback, and confidence cues. Readability assessment tools, on the other hand, are envisioned as \textit{quality coaches}, which provide contextual, personalized, and progressive guidance. Trust, in both tasks, depends on the clarity of explanations, timing, and user control. A set of design principles for Human-Centered AI in IDEs has been distilled, aiming to balance disruption with support, conciseness with depth, and automation with human agency.

Although passwords remain the primary defense against unauthorized access, users often tend to use passwords that are easy to remember. This behavior significantly increases security risks, also due to the fact that traditional password strength evaluation methods are often inadequate. In this discussion paper, we present SODA ADVANCE, a data reconstruction tool also designed to enhance evaluation processes related to the password strength. In particular, SODA ADVANCE integrates a specialized module aimed at evaluating password strength by leveraging publicly available data from multiple sources, including social media platforms. Moreover, we investigate the capabilities and risks associated with emerging Large Language Models (LLMs) in evaluating and generating passwords, respectively. Experimental assessments conducted with 100 real users demonstrate that LLMs can generate strong and personalized passwords possibly defined according to user profiles. Additionally, LLMs were shown to be effective in evaluating passwords, especially when they can take into account user profile data.