Tootfinder

Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
Sheikh Md Mushfiqur Rahman, Nasir Eisty
https://arxiv.org/abs/2509.23961

Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
Context: Deep Neural Networks (DNNs) are increasingly deployed in critical applications, where resilience against adversarial inputs is paramount. However, whether coverage-based or confidence-based, existing test prioritization methods often fail to efficiently identify the most fault-revealing inputs, limiting their practical effectiveness. Aims: This project aims to enhance fault detection and model robustness in DNNs by integrating Learning-Based Testing (LBT) with hypothesis and mutation t…

finding your "throughline" - prioritization for multi-passionate people
https://www.youtube.com/watch?v=Xnf_tbO0CGA

Google unveils new features for Pixel phones, including AI-powered notification summaries, notification prioritization, and AI photo edits in Messages (Ivan Mehta/TechCrunch)
https://techcrunch.com/2025/11/11/goog

Google Pixel update adds battery-saving maps mode, AI photo remixing, and smarter notifications | TechCrunch
Google says the new power saving mode for Google Maps can save you up to four hours of battery life

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Luoxi Tang, Yuqiao Meng, Ankita Patra, Weicheng Ma, Muchao Ye, Zhaohan Xi
https://arxiv.org/abs/2510.01552

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence (CTI) to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wide range of CTI tasks such as threat analysis, vulnerability detection, and intrusion defense, significant performance gaps persist in practical deployments. In this paper, we investigate the intrins…