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.

A Conceptual Recommendation Approach and Experimental Evaluation for Search and Reuse of Object-oriented Design Patterns

Tarek Sboui^{1, 2} and Saida AISSI^{3, 41}Department of Geology, Faculty of Science of Tunis, University of Tunis El-Manar, Tunis 1068, Tunisia, ²GREEN-TEAM Laboratory, INAT, Tunis 1082, Tunisia, ³ESPRIT School of Business, Airana, Tunisia, ⁴SMART Lab laboratory, University of Tunis, Higher Institute of Management, Tunis 2000, Tunisia

ABSTRACT

A design pattern is a well-known solution to a recurring design problem in a given context. Reusing design patterns helps information system developers in saving time and gaining quality when developing object-oriented systems. However, it may be difficult to find the relevant pattern and to reuse it because the design patterns are scattered over various sources and have a high level of abstraction. In this paper, we present a novel approach that allows finding and recommending relevant design patterns relative to a particular design problem, the proposed approach is based on a new strategy for representing and indexing design patterns based on conceptual graphs, and a semantic similarity measure between concepts of these graphs. We also develop a new tool called Design Pattern Retrieval and Reuse (DePaRR) which implements the approach and automates the search and reuse of design patterns by recommending relevant design patterns to information system developers. The presented approach is described theoretically and validated by experiments.

Keywords

Design pattern, Retrieval and reuse, Conceptual graph, Semantic similarity.