Tootfinder

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
T. O. Abiola, K. D. Abiodun, O. E. Olumide, O. O. Adebanji, O. Hiram Calvo, Grigori Sidorov
https://arxiv.org/abs/2509.20315

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for hope speech detection using an active learning approach and transformer-based models, including mBERT and XLM-RoBERTa. Experiments were conducted on datasets in English, Spanish, German, and Urdu, including benchmark test sets from …

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi
https://arxiv.org/abs/2508.18839

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation cannot optimize when to defer decisions to manual labeling and adaptation. Modern malware detection pipelines combine classifiers with monthly active learning (AL) and rejection mechanisms to mitigate t…

"If denial of collective self-determination of a people is a sin of the modern age, as Israeli defenders often repeat, what of the self-determination of Palestinians? "
The Most Important Book of 2025 | Portside
https://portside.org/2025-08-07/most-important-book-2025

Ascent Fails to Forget
Ioannis Mavrothalassitis, Pol Puigdemont, Noam Itzhak Levi, Volkan Cevher
https://arxiv.org/abs/2509.26427 https://arxiv.org/pdf/250…

Ascent Fails to Forget
Contrary to common belief, we show that gradient ascent-based unconstrained optimization methods frequently fail to perform machine unlearning, a phenomenon we attribute to the inherent statistical dependence between the forget and retain data sets. This dependence, which can manifest itself even as simple correlations, undermines the misconception that these sets can be independently manipulated during unlearning. We provide empirical and theoretical evidence showing these methods often fail p…

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
Maybritt Schillinger, Maxim Samarin, Xinwei Shen, Reto Knutti, Nicolai Meinshausen
https://arxiv.org/abs/2509.26258

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-resolution data. Regional climate models (RCMs) provide this refinement, but are computationally expensive. To address this issue, machine learning models can learn the downscaling function, mapping coarse GCM outputs to high-resolution fields. Among these, generative approaches aim to capture the full conditional distr…