Tamara Watson | Searcharxiv

arXiv2026

BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents

Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose a conceptual security framework that integrates a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze biometric and environmental data for context-aware security decisions. The CNN extracts spatial patterns from sensor data, while the LSTM captures temporal dynamics associated with human error susceptibility. The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework supports proactive interventions that reduce the likelihood of human-driven cyber incidents

cs.CR↗

arXiv2022

Virtual reality (VR) as a testing bench for consumer optical solutions: A machine learning approach (GBR) to visual comfort under simulated progressive addition lenses (PALS) distortions

For decades, manufacturers have attempted to reduce or eliminate the optical aberrations that appear on the progressive addition lens' surfaces during manufacturing. Besides every effort made, some of these distortions are inevitable given how lenses are fabricated, where in fact, astigmatism appears on the surface and cannot be entirely removed or where non-uniform magnification becomes inherent to the power change across the lens. Some presbyopes may refer to certain discomfort when wearing these lenses for the first time, and a subset of them might never adapt. Developing, prototyping, testing and purveying those lenses into the market come at a cost, which is usually reflected in the retail price. This study aims to test the feasibility of virtual reality for testing customers' satisfaction with these lenses, even before getting them onto production. VR offers a controlled environment where different parameters affecting progressive lens comforts, such as distortions, image displacement or optical blurring, can be analysed separately. In this study, the focus was set on the distortions and image displacement, not taking blur into account. Behavioural changes (head and eye movements) were recorded using the built-in eye tracker. Participants were significantly more displeased in the presence of highly distorted lens simulations. In addition, a gradient boosting regressor was fitted to the data, so predictors of discomfort could be unveiled, and ratings could be predicted without performing additional measurements.

cs.HC↗