Benzinga.com

Mastering the Earnings API: Earnings Calendars and Surprise Detection with Python

Earnings announcements are one of the few scheduled events that consistently move markets. Prices react not just to the reported numbers, but to how those numbers compare with expectations. A small ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

07.2025: Dinomaly has been integrated in Intel open-edge Anomalib in v2.1.0. Great thanks to the contributors for the nice reproduction and integration. Anomalib is a comprehensive library for ...
IEEE

Anomaly Detection Model in Network Security Situational Awareness Based on Machine Learning: Limitation, Techniques, and Future Trends

Abstract: This study focuses on the anomaly detection problem in Network Security Situational Awareness (NSSA). We systematically review traditional approaches and recent advancements based on Machine ...
InfoWorld

PythoC: A new way to generate C code from Python

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...
Hackaday

Simple Tricks To Make Your Python Code Faster

Python has become one of the most popular programming languages out there, particularly for beginners and those new to the hacker/maker world. Unfortunately, while it’s easy to get something up and ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

[CVPR 2025] Official Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection". The first multi-class UAD model that can compete with single-class SOTAs - ...
IEEE

Multimodal Industrial Anomaly Detection via Attention-Enhanced Memory-Guided Network

Abstract: Anomaly detection is a key technology in quality control for automated production lines. Currently, 2D-based anomaly detection methods fail to identify geometric structure anomalies in ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
Frontiers

Anomaly detection in netflow traffic: workflow for dataset preparation and analysis

Information and communication technology (ICT) is crucial for maintaining efficient communications, enhancing processes, and enabling digital transformation. As ICT becomes increasingly significant in ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
maketecheasier.com

Google Colab Allows You to Write and Run Python Code in Your Browser – Getting Started Guide

Google Colab, also known as Colaboratory, is a free online tool from Google that lets you write and run Python code directly in your browser. It works like Jupyter Notebook but without the hassle of ...
sei.cmu

Detection of Malicious Code (DMC) Tool

The DMC tool detects two types of malicious code: (1) exfiltration of sensitive data and (2) timebombs, logic bombs, remote-access Trojans (RATs), and similar malicious code. The goal for the DMC tool ...