Tool

All In One Tools For Cryptology in python

Written by TMRSWRRVersion 1.0.0All in one tools for CRYPTOLOGY. Screenshots 📹 How to use 📹 Click on the image… Features This tool include: :round_pushpin: HASH :round_pushpin: RSA :round_pushpin: XOR :round_pushpin: AES (ECC) :round_pushpin: AES (CBC) :round_pushpin: DES (ECB) :round_pushpin: FERNET :round_pushpin: RC2 :round_pushpin: RC4 :round_pushpin: CHACHA20POLY1305 :round_pushpin: TRANSPOSITION :round_pushpin: DIFFIE HELMAN :round_pushpin: IMAGE ENCRYPT/DECRYPT :round_pushpin: FILE ENCRYPT/DECRYPT 📀 Installation 📀 Installation with requirements.txt git clone https://github.com/capture0x/cypher cd cypher pip3 install -r requirements.txt Usage python3 cryptot.py HASH Encryption and decryption algorithms […]

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HTTP

A screaming-fast, scalable, asynchronous Python 3.5+ HTTP toolkit integrated

Japronto There is no new project development happening at the moment, but it’s not abandoned either. Pull requests and new maintainers are welcome. If you are a novice Python programmer, you don’t like plumbing yourself or you don’t have basic understanding of C, this project is not probably what you are looking for. Japronto (from Portuguese “já pronto” /ˈʒa pɾõtu/ meaning “already done”) is a screaming-fast, scalable, asynchronous Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop […]

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Json

Declaratively specify how to extract elements from a JSON document

JMESPath JMESPath (pronounced “james path”) allows you to declaratively specify how to extract elements from a JSON document. For example, given this document: {“foo”: {“bar”: “baz”}} The jmespath expression foo.bar will return “baz”. JMESPath also supports: Referencing elements in a list. Given the data: {“foo”: {“bar”: [“one”, “two”]}} The expression: foo.bar[0] will return “one”. You can also reference all the items in a list using the * syntax: {“foo”: {“bar”: [{“name”: “one”}, {“name”: “two”}]}} The expression: foo.bar[*].name will return [“one”, […]

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Excel

A Python module for creating Excel XLSX files

XlsxWriter XlsxWriter is a Python module for writing files in the Excel 2007+ XLSX file format. XlsxWriter can be used to write text, numbers, formulas and hyperlinks to multiple worksheets and it supports features such as formatting and many more, including: 100% compatible Excel XLSX files. Full formatting. Merged cells. Defined names. Charts. Autofilters. Data validation and drop down lists. Conditional formatting. Worksheet PNG/JPEG/GIF/BMP/WMF/EMF images. Rich multi-format strings. Cell comments. Integration with Pandas. Textboxes. Support for adding Macros. Memory optimization […]

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Python

Exploring the Python math Module

In this course, you’ll learn all about Python’s math module. Mathematical calculations are an essential part of most Python development. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math. For straightforward mathematical calculations in Python, you can use the built-in mathematical operators, such as addition (+), subtraction (-), division (/), and multiplication (*). But more advanced operations, such as exponential, logarithmic, trigonometric, or power […]

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PyTorch

PyTorch DepthNet Training on Still Box dataset

DepthNet training on Still Box This code can replicate the results of our paper that was published in UAVg-17.If you use this repo in your work, please cite us with the following bibtex : @Article{isprs-annals-IV-2-W3-67-2017, AUTHOR = {Pinard, C. and Chevalley, L. and Manzanera, A. and Filliat, D.}, TITLE = {END-TO-END DEPTH FROM MOTION WITH STABILIZED MONOCULAR VIDEOS}, JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences}, VOLUME = {IV-2/W3}, YEAR = {2017}, PAGES = {67–74}, URL […]

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PyTorch

Enhanced Deep Residual Networks for Single Image Super-Resolution

EDSR-PyTorch PyTorch version of the paper ‘Enhanced Deep Residual Networks for Single Image Super-Resolution’ (CVPRW 2017) About PyTorch 1.1.0 There have been minor changes with the 1.1.0 update. Now we support PyTorch 1.1.0 by default, and please use the legacy branch if you prefer older version. This repository is an official PyTorch implementation of the paper “Enhanced Deep Residual Networks for Single Image Super-Resolution” from CVPRW 2017, 2nd NTIRE.You can find the original code and more information from here. If […]

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PyTorch

Embed to Control implementation in PyTorch

e2c-pytorch Paper can be found here: https://arxiv.org/abs/1506.07365 You will need a patched version of OpenAI Gym in order to generate the dataset. See https://github.com/ethanluoyc/gym/tree/pendulum_internal For the planar task, we use code from. The source code of the repository has been modified for our needs and included under e2c/e2c_tf. What’s included ? E2C model, VAE and AE baselines. Allow configuration for different network architecture for the different setups (see Appendix of the paper). TODO Documentation, tests… (Soon to follow) GitHub https://github.com/ethanluoyc/e2c-pytorch […]

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PyTorch

PyTorch implementation of Advantage Actor Critic

pytorch-a2c-ppo-acktr PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL). Please use hyper parameters from this readme. With other hyper parameters things might not work (it’s RL after all)! This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version of A3C Proximal Policy Optimization PPO Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation ACKTR Generative […]

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Computer Vision

TOOD: Task-aligned One-stage Object Detection

TOOD TOOD: Task-aligned One-stage Object Detection (ICCV 2021 Oral) One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between […]

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