Tootfinder

ClickFix may be the biggest security threat your family has never heard of https://arstechnica.com/security/2025/11/clickfix-may-be-the-biggest-security-threat-your-family-has-never-heard-of/

ClickFix may be the biggest security threat your family has never heard of
Relatively new technique can bypass many endpoint protections.

Democratic whistleblower told FBI that Adam Schiff approved classified leaks to target Trump (Andrea Margolis/Fox News)
https://www.foxnews.com/politics/democratic-whistleblower-told-fbi-adam-schiff-approved-classified-leaks-target-trump
http://www.memeorandum.com/250812/p3#a250812p3

Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference
Yehudit Aperstein, Alexander Apartsin
https://arxiv.org/abs/2509.08318 https://

Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference
Real-time image classification on resource-constrained platforms demands inference methods that balance accuracy with strict latency and power budgets. Early-exit strategies address this need by attaching auxiliary classifiers to intermediate layers of convolutional neural networks (CNNs), allowing "easy" samples to terminate inference early. However, conventional training of early exits introduces a covariance shift: downstream branches are trained on full datasets, while at inference they pro…

Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
Zhiyue Liu, Fanrong Ma, Xin Ling
https://arxiv.org/abs/2509.09160 https://

Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
Target-oriented multimodal sentiment classification seeks to predict sentiment polarity for specific targets from image-text pairs. While existing works achieve competitive performance, they often over-rely on textual content and fail to consider dataset biases, in particular word-level contextual biases. This leads to spurious correlations between text features and output labels, impairing classification accuracy. In this paper, we introduce a novel counterfactual-enhanced debiasing framework …

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Alireza Sameh, Mehrdad Rostami, Mourad Oussalah, Vahid Farrahi
https://arxiv.org/abs/2509.08606

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Ra…

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Grazia Sveva Ascione, Nicol\`o Tamagnone
https://arxiv.org/abs/2509.09303

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Classifying patents by their relevance to the UN Sustainable Development Goals (SDGs) is crucial for tracking how innovation addresses global challenges. However, the absence of a large, labeled dataset limits the use of supervised learning. Existing methods, such as keyword searches, transfer learning, and citation-based heuristics, lack scalability and generalizability. This paper frames patent-to-SDG classification as a weak supervision problem, using citations from patents to SDG-tagged sci…

Classification of Driver Behaviour Using External Observation Techniques for Autonomous Vehicles
Ian Nell, Shane Gilroy
https://arxiv.org/abs/2509.09349 https://

Classification of Driver Behaviour Using External Observation Techniques for Autonomous Vehicles
Road traffic accidents remain a significant global concern, with human error, particularly distracted and impaired driving, among the leading causes. This study introduces a novel driver behavior classification system that uses external observation techniques to detect indicators of distraction and impairment. The proposed framework employs advanced computer vision methodologies, including real-time object tracking, lateral displacement analysis, and lane position monitoring. The system identif…

Cough Classification using Few-Shot Learning
Yoga Disha Sendhil Kumar, Manas V Shetty, Sudip Vhaduri
https://arxiv.org/abs/2509.09515 https://arxiv.org/pdf…

Cough Classification using Few-Shot Learning
This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data. Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches while using significantly fewer training samples. Ad…

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman
https://arxiv.org/abs/2508.07536

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physi…

Beyond Distribution Shifts: Adaptive Hyperspectral Image Classification at Test Time
Xia Yue, Anfeng Liu, Ning Chen, Chenjia Huang, Hui Liu, Zhou Huang, Leyuan Fang
https://arxiv.org/abs/2509.08436

Beyond Distribution Shifts: Adaptive Hyperspectral Image Classification at Test Time
Hyperspectral image (HSI) classification models are highly sensitive to distribution shifts caused by various real-world degradations such as noise, blur, compression, and atmospheric effects. To address this challenge, we propose HyperTTA, a unified framework designed to enhance model robustness under diverse degradation conditions. Specifically, we first construct a multi-degradation hyperspectral dataset that systematically simulates nine representative types of degradations, providing a com…