Tootfinder

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen
https://arxiv.org/abs/2507.21743

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which c…

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
https://arxiv.org/abs/2506.09365

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than …

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management
Mohammad Hossein Nejati Amiri, Fawaz Annaz, Mario De Oliveira, Florimond Gueniat
https://arxiv.org/abs/2508.08132

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management
Renewable energy integration into microgrids has become a key approach to addressing global energy issues such as climate change and resource scarcity. However, the variability of renewable sources and the rising occurrence of High Impact Low Probability (HILP) events require innovative strategies for reliable and resilient energy management. This study introduces a practical approach to managing microgrid resilience through Explainable Deep Reinforcement Learning (XDRL). It combines the Proxim…