Security

A sample application that demonstrates integrating Firmalyzer’s IoTVAS API

This repository hosts a sample application that demonstrates integrating Firmalyzer’s IoTVAS API with the Rapid7 InsightVM platform. This integration enables InsightVM users to: accurately identify IoT/connected devices and their vulnerabilities in the firmware code level track and manage discontinued, outdated and vulnerable devices from within InsightVM platform Clone the repository content to a local folder and issue the following commands: python3 -mvenv env source env/bin/activate pip install -r requirements.txt Note: This application is based on the InsightVM API client (located […]

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Telegram

Telegram bot to stream videos in telegram voicechat for both groups and channels

Telegram bot to stream videos in telegram voicechat for both groups and channels. Supports live strams, YouTube videos and telegram media. Config Vars: Mandatory Vars API_ID : Get From my.telegram.org API_HASH : Get from my.telegram.org BOT_TOKEN : @Botfather SESSION_STRING : Generate From here CHAT : ID of Channel/Group where the bot plays Music. Optional Vars: LOG_GROUP : Group to send Playlist, if CHAT is a Group() ADMINS : ID of users who can use admin commands. STARTUP_STREAM : This will […]

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Tool

A tool was created in order to automate some basic OSINT tasks for penetration testing assingments

This tool was created in order to automate some basic OSINT tasks for penetration testing assingments. The main feature that I haven’t seen much anywhere is the downloadd google dork function where this function first perform basic google dorking to find the targets public documents. These documents will then be downloaded to the attackers computer and can be used further to identify metadata about the client. Installation Create virtual enviroment: python3 -m venv enumit Activate the virtual enviroment: source enumit/bin/activate […]

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Configuration

Configure your wallet assets and prices to watch with python

Track your favorite crypto coin price and your wallet balance. Install Create .env: ADMIN_USER=admin ADMIN_PASSWORD=admin Configure your wallet assets and prices to watch vi cryptowatch/config.json Clone this repository on your Docker host, cd into test directory and run compose up: git clone https://github.com/rafaelzimmermann/cryptowatch.git cd cryptowatch docker-compose up -d Open on your browser: http://localhost:3000/ To check the available symbols:    

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Bot

A bot which provides online/offline and player status for Thicc SMP, using Replit

A bot which provides online/offline and player status for Thicc SMP. Currently being hosted on Replit. How to use? Create a repl on Replit, put those files in. Create an environment variable, name it TOKEN, put your Discord bot token in the value box. Create another environment variable, name it ADDRESS and put your server’s IP address in. Edit your bot’s prefix if you want in main.py. Start repl. 24/7 Hosting Login to your UptimeRobot account, create a monitor (HTTP(s) […]

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Deep Learning

Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging

This repository is the code for the following paper: Zhuoyuan Wu, Jian Zhang, Chong Mou. Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging. ICCV 2021. [PDF] Introduction Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we incorporate the interpretability of model-based methods and the speed of learning-based ones and present a novel dense deep unfolding network (DUN) with 3D-CNN prior […]

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Optimization

Bunch of optimizer implementations in PyTorch

Bunch of optimizer implementations in PyTorch with clean-code, strict types. Also, including useful optimization ideas. Most of the implementations are based on the original paper, but I added some tweaks. Documentation https://pytorch-optimizers.readthedocs.io/en/latest/ Usage Install $ pip3 install pytorch-optimizer Simple Usage from pytorch_optimizer import Ranger21 … model = YourModel() optimizer = Ranger21(model.parameters()) … for input, output in data: optimizer.zero_grad() loss = loss_function(output, model(input)) loss.backward() optimizer.step() Supported Optimizers Useful Resources Several optimization ideas to regularize & stabilize the training. Most of the […]

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Deep Learning

A Simple Baseline for Bayesian Uncertainty in Deep Learning

TensorFlow implementation of “A Simple Baseline for Bayesian Uncertainty in Deep Learning” Concept Algorithm to utilize the SWAG [1]. Equation for the weight sampling from SWAG [1]. Results The red color and the blue color represent the initial state and current state respectively. Performance MNIST Method Accuracy Precision Recall F1-Score Final Epoch 0.99230 0.99231 0.99222 0.99226 Best Loss 0.99350 0.99350 0.99338 0.99344 SWAG (S = 30) 0.99310 0.99305

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GPU

High performance ptychography reconstruction python package running on GPU

Quickstart Need to install python3 to run the GUI and ptychopy, other needed library isin requirement.txt.(Tested OS RHEL 6.0, 7.0). This library could also becompiled as a CUDA-C library. Inside src folder, change build.sh with yourHDF library path. Recommend conda virtual environment, for example conda create -n py36 python=3.6 hdf5-external-filter-plugins-lz4 Activate the virtual environment source activate py36 To install and build the python package, set environment variables HDF5_BASEand CUDAHOME, which point to the installed path of the HDF5 and CUDAlibraries. […]

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PyTorch

Compact Bilinear Pooling for PyTorch

This repository has a pure Python implementation of Compact Bilinear Pooling and Count Sketch for PyTorch. This version relies on the FFT implementation provided with PyTorch 0.4.0 onward. For older versions of PyTorch, use the tag v0.3.0. Installation Run the setup.py, for instance: Usage class compact_bilinear_pooling.CompactBilinearPooling(input1_size, input2_size, output_size, h1 = None, s1 = None, h2 = None, s2 = None) Basic usage: from compact_bilinear_pooling import CountSketch, CompactBilinearPooling input_size = 2048 output_size = 16000 mcb = CompactBilinearPooling(input_size, input_size, output_size).cuda() x = […]

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