Social-aware Self-organizing Networks for Aging Well: A Distributed Model for Human-centric Support

Carlotta Conversi¹ and Vittorianna Perrotta², ¹Department of Social Science, University of Urbino Carlo Bo, Urbino, Italy, ²Department of Economics, University Tor Vergata, Rome, Italy

ABSTRACT

This paper explores the potential of Social-Aware Self-Organizing Networks (SA-SONs) as an adaptive model to support psychosocial well-being in aging populations. By connecting young volunteers, smart nodes, and local environments, SA-SONs dynamically match relational needs and social opportunities through lightweight, decentralized mechanisms. This approach enables responsive and human-centered coordination of low-intensity care and community engagement. The paper introduces a conceptual architecture, discusses key challenges such as trust, privacy, and variability of human nodes, and suggests future directions for research and pilot implementation in socially diverse environments.

Keywords

Human Nodes, Social Awareness, Network Protocols, Well-Being.

No Masks Needed: Explainable Ai for Deriving Segmentation From Classification

Mosong Ma¹, Tania Stathaki¹ and Michalis Lazarou², ¹Department of Electrical and Electronic Engineering, Imperial College London, London, UK. ²Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK.

ABSTRACT

Medical image segmentation is vital for modern healthcare and is a key element of computer-aided diagnosis. While recent advancements in computer vision have explored unsupervised segmentation using pre-trained models, these methods have not been translated well to the medical imaging domain. In this work, we introduce a novel approach that fine-tunes pre-trained models specifically for medical images, achieving accurate segmentation with extensive processing. Our method integrates Explainable AI to generate relevance scores, enhancing the segmentation process. Unlike traditional methods that excel in standard benchmarks but falter in medical applications, our approach achieves improved results on datasets like CBIS-DDSM, NuInsSeg and Kvasir-SEG.

Keywords

Medical Image Segmentation, Explainable AI, Transfer Learning.