Search Results for "network data speed"

Efficiently integrate secure, performant, and cost-effective Generative AI workflows into business value. Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines. Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows. A four-step assessment for grading generative AI systems around performance, features, trustworthiness and enterprise-grade readiness.

multiOTP is a PHP class, a powerful command line utility and a web interface developed by SysCo systèmes de communication sa in order to provide a completely free and easy operating system independent server side implementation for strong two factors authentication solution. multiOTP supports hardware and software tokens with different One-Time Password algorithms like OATH/HOTP, OATH/TOTP and mOTP (Mobile-OTP). QRcode generation is also embedded in order to support provisioning of Google...

...CityHash offers multiple hash sizes—32-bit, 64-bit, 128-bit, and 256-bit variants—with the CRC-based versions leveraging hardware acceleration on CPUs that support SSE4.2 CRC32 instructions. The library emphasizes hashing performance and uniformity rather than cryptographic security, making it ideal for use in data structures like hash tables and distributed systems requiring rapid key lookups. CityHash has been rigorously tested using tools like SMHasher to ensure high-quality mixing and collision resistance across a wide range of inputs. Its speed and portability have made it a popular choice for developers needing dependable, lightweight hash functions.

cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that...

Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with...