The office of Pennsylvania’s attorney general has confirmed that the ransomware gang behind an August 2025 cyberattack stole files containing personal and medical information. […] Read More
Researchers discovered a security weakness in the AI-powered coding tool that allows malicious MCP server to hijack Cursor’s internal browser. Read More
Microsoft is working to resolve a known issue preventing users from installing the Microsoft 365 desktop apps on Windows devices. […] Read More
Your complete guide to Amazon Quick Suite at AWS re:Invent 2025Artificial Intelligence This year, re:Invent will be held in Las Vegas, Nevada, from December 1 to December 5, 2025, and this guide will help you navigate our comprehensive session catalog and plan your week. The sessions cater to business and technology leaders, product and engineering teams, and data and analytics teams interested in incorporating agentic AI capabilities across their teams and organization.
This year, re:Invent will be held in Las Vegas, Nevada, from December 1 to December 5, 2025, and this guide will help you navigate our comprehensive session catalog and plan your week. The sessions cater to business and technology leaders, product and engineering teams, and data and analytics teams interested in incorporating agentic AI capabilities across their teams and organization. Read More
Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2Towards Data Science In part 1, we showed how we could leverage HTMX to add interactivity to our HTML elements. In other words, Javascript without Javascript. To illustrate that, we began building a simple chat that would return a simulated LLM response. In this article, we will extend the capabilities of our chatbot and add several features, among
The post Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2 appeared first on Towards Data Science.
In part 1, we showed how we could leverage HTMX to add interactivity to our HTML elements. In other words, Javascript without Javascript. To illustrate that, we began building a simple chat that would return a simulated LLM response. In this article, we will extend the capabilities of our chatbot and add several features, among
The post Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2 appeared first on Towards Data Science. Read More
Understanding Convolutional Neural Networks (CNNs) Through ExcelTowards Data Science Deep learning is often seen as a black box. We know that it learns from data, but we rarely stop to ask how it truly learns.
What if we could open that box and watch each step happen right before our eyes?
With Excel, we can do exactly that, see how numbers turn into patterns, and how simple calculations become the foundation of what we call “deep learning.”
In this article, we will build a tiny Convolutional Neural Network (CNN) directly in Excel to understand, step by step, how machines detect shapes, patterns, and meaning in images.
The post Understanding Convolutional Neural Networks (CNNs) Through Excel appeared first on Towards Data Science.
Deep learning is often seen as a black box. We know that it learns from data, but we rarely stop to ask how it truly learns.
What if we could open that box and watch each step happen right before our eyes?
With Excel, we can do exactly that, see how numbers turn into patterns, and how simple calculations become the foundation of what we call “deep learning.”
In this article, we will build a tiny Convolutional Neural Network (CNN) directly in Excel to understand, step by step, how machines detect shapes, patterns, and meaning in images.
The post Understanding Convolutional Neural Networks (CNNs) Through Excel appeared first on Towards Data Science. Read More
Cybersecurity researchers have discovered malware campaigns using the now-prevalent ClickFix social engineering tactic to deploy Amatera Stealer and NetSupport RAT. The activity, observed this month, is being tracked by eSentire under the moniker EVALUSION. First spotted in June 2025, Amatera is assessed to be an evolution of ACR (short for “AcridRain”) Stealer, which was available […]
A Princeton University database was compromised in a cyberattack on November 10, exposing the personal information of alumni, donors, faculty members, and students. […] Read More
BARD10: A New Benchmark Reveals Significance of Bangla Stop-Words in Authorship Attributioncs.AI updates on arXiv.org arXiv:2511.08085v1 Announce Type: cross
Abstract: This research presents a comprehensive investigation into Bangla authorship attribution, introducing a new balanced benchmark corpus BARD10 (Bangla Authorship Recognition Dataset of 10 authors) and systematically analyzing the impact of stop-word removal across classical and deep learning models to uncover the stylistic significance of Bangla stop-words. BARD10 is a curated corpus of Bangla blog and opinion prose from ten contemporary authors, alongside the methodical assessment of four representative classifiers: SVM (Support Vector Machine), Bangla BERT (Bidirectional Encoder Representations from Transformers), XGBoost, and a MLP (Multilayer Perception), utilizing uniform preprocessing on both BARD10 and the benchmark corpora BAAD16 (Bangla Authorship Attribution Dataset of 16 authors). In all datasets, the classical TF-IDF + SVM baseline outperformed, attaining a macro-F1 score of 0.997 on BAAD16 and 0.921 on BARD10, while Bangla BERT lagged by as much as five points. This study reveals that BARD10 authors are highly sensitive to stop-word pruning, while BAAD16 authors remain comparatively robust highlighting genre-dependent reliance on stop-word signatures. Error analysis revealed that high frequency components transmit authorial signatures that are diminished or reduced by transformer models. Three insights are identified: Bangla stop-words serve as essential stylistic indicators; finely calibrated ML models prove effective within short-text limitations; and BARD10 connects formal literature with contemporary web dialogue, offering a reproducible benchmark for future long-context or domain-adapted transformers.
arXiv:2511.08085v1 Announce Type: cross
Abstract: This research presents a comprehensive investigation into Bangla authorship attribution, introducing a new balanced benchmark corpus BARD10 (Bangla Authorship Recognition Dataset of 10 authors) and systematically analyzing the impact of stop-word removal across classical and deep learning models to uncover the stylistic significance of Bangla stop-words. BARD10 is a curated corpus of Bangla blog and opinion prose from ten contemporary authors, alongside the methodical assessment of four representative classifiers: SVM (Support Vector Machine), Bangla BERT (Bidirectional Encoder Representations from Transformers), XGBoost, and a MLP (Multilayer Perception), utilizing uniform preprocessing on both BARD10 and the benchmark corpora BAAD16 (Bangla Authorship Attribution Dataset of 16 authors). In all datasets, the classical TF-IDF + SVM baseline outperformed, attaining a macro-F1 score of 0.997 on BAAD16 and 0.921 on BARD10, while Bangla BERT lagged by as much as five points. This study reveals that BARD10 authors are highly sensitive to stop-word pruning, while BAAD16 authors remain comparatively robust highlighting genre-dependent reliance on stop-word signatures. Error analysis revealed that high frequency components transmit authorial signatures that are diminished or reduced by transformer models. Three insights are identified: Bangla stop-words serve as essential stylistic indicators; finely calibrated ML models prove effective within short-text limitations; and BARD10 connects formal literature with contemporary web dialogue, offering a reproducible benchmark for future long-context or domain-adapted transformers. Read More
The police in the Netherlands have seized around 250 physical servers powering a bulletproof hosting service in the country used exclusively by cybercriminals for providing complete anonymity. […] Read More
