This paper proposes a new method for estimating the reliability of machine learning predictions applied to Earth observation (EO) and health-related datasets, with a focus on forecasting mosquito abundance (MA). The authors develop a Variational Autoencoder (VAE)-based confidence metric that… Read More »A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
As 6G networks promise unparalleled connectivity with ultra-low latency, high bandwidth, and multi-domain interoperability, they also face an increasingly complex security landscape. The integration of advanced AI/ML-driven automation in network management introduces new opportunities but also heightens the potential risks… Read More »Enhancing 6G Security with Zero-Touch Automation: Insights from ROBUST-6G Deliverable 4.1
As global connectivity ushers in the next era of communication, 6G networks are poised to revolutionize technology with unprecedented capabilities. These advancements will rely heavily on Artificial Intelligence (AI) and Machine Learning (ML), which are expected to enhance network efficiency,… Read More »Strengthening 6G AI/ML Security: Insights from ROBUST-6G Threat Assessment and Prevention Report
As the world transitions towards the sixth generation (6G) of wireless networks, ensuring robust security and trustworthiness solutions becomes a critical necessity. In this vein, the ROBUST-6G project aims to address key challenges related to security and trustworthiness through a… Read More »Exploring the Future of Secure 6G Networks: Insights from ROBUST-6G Deliverable 2.2