Machine Learning

Complete U-net Implementation with keras in python

U Net Lowered with Keras Complete U-net Implementation with keras The model is implemented using the original paper. But I have changed the number of filters of the layers. The implemented number of layers are reduced to 25% of the original paper. Original Model Architecture : Dataset : The dataset has been taken from kaggle . It had a specific directory tree, but it was tough to execute dataset building from it, so I prepared an usable dat directory. Link […]

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Dataset

A dataset for online Arabic calligraphy

Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy. This repository contains the dataset for the following paper : Calliar: An Online Handwritten Dataset for Arabic Calligraphy Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, Yousif Ahmed Al-Wajih https://arxiv.org/abs/2106.10745 Abstract: Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy […]

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tensorflow

Attention mechanism with MNIST dataset

MNIST_AttentionMap [TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention map, and overlapped image sequentially. Further usage The further usages. Detecting the location of digits can be conducted using an attention map. Requirements TensorFlow 2.3.0 Numpy 1.18.5 GitHub https://github.com/YeongHyeon/MNIST_AttentionMap    

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Analysis

A Scanpy extension for analyzing single-cell immune-cell receptor sequencing data

Scirpy Scirpy is a scalable python-toolkit to analyse T cell receptor (TCR) or B cell receptor (BCR) repertoires from single-cell RNA sequencing (scRNA-seq) data. It seamlessly integrates with the popular scanpy library and provides various modules for data import, analysis and visualization. Getting started Please refer to the documentation. In particular, the In the documentation, you can also learn more about our immune-cell receptor model. Case-study The case study from our preprint is available here. Installation You need to have […]

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Dataset

A Naturally-Occurring Dataset Based on Stack Exchange Data

SEDE SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description. It’s based on a real usage of users from the Stack Exchange Data Explorer platform, which brings complexities and challenges never seen before in any other semantic parsing dataset like including complex nesting, dates manipulation, numeric and text manipulation, parameters, and most importantly: under-specification and hidden-assumptions. Paper (NLP4Prog workshop at ACL2021): Text-to-SQL in the Wild: A Naturally-Occurring […]

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Machine Learning

Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting

RGBT Crowd Counting Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting” Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin. “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [PDF] Download RGBT-CC Dataset & Models: [Dropbox][BaiduYun (PW: RGBT)] Our framework can be implemented with various backbone networks. You can refer to this page for […]

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Monitoring

Splunk modular input plugin to fetch the enterprise audit log from GitHub Enterprise

GitHub Enterprise Audit Log Monitoring Splunk modular input plugin to fetch the enterprise audit log from GitHub Enterprise Support for modular inputs in Splunk Enterprise 5.0 and later enables you to add new types of inputs to Splunk Enterprise that are treated as native Splunk Enterprise inputs. This modular input makes an HTTPS request to the GitHub Enterprise’s Audit Log REST API endpoint at a definable interval to fetch audit log data. Prerequisites Splunk Heavy Forwarder v8.0+ Python 3.7+ GitHub […]

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AWS

A simple tool to check if an IP/hostname belongs to the AWS IP space or not

onaws onaws is a simple tool to check if an IP/hostname belongs to the AWS IP space or not. It uses the AWS IP address ranges data published by AWS to perform the search. Continuous recon of assets Gathering assets using a specific service (e.g. EC2) Finding region information for S3 buckets … etc. Install pip install onaws Usage Given an IP: onaws 52.219.47.34 Given a hostname: A domain or subdomain can be passed as input: onaws example.com You may […]

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Models

A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

Fold2Seq [ICML2021] Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design Environment file: Data and Feature Generation: Go to data/ and check the README there. How to train the model: python train.py –data_path $path_to_the_data_dictionary –lr $learning_rate –model_save $path_to_the_saved_model How to generate sequences: python inference.py –trained_model $path_to_the_trained_model –output $path_to_the_output_file –data_path $path_to_the_data_dictionary Fold2Seq generated structures against natural structures: GitHub https://github.com/IBM/fold2seq    

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Machine Learning

A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation Requirements python3.7 pytorch==1.5.0 cuda10.2 Datasets Cityscapes: Please follow the instructions in Cityscape to download the training set. Dark-Zurich: Please follow the instructions in Dark-Zurich to download the training/val/test set. Testing If needed, please directly download the visualization results of our method for Dark-zurich-val and Dark-zurich-test. To reproduce the reported results in our paper (on Dark-Zurich val), please follow these steps: Step1: download the [trained models](https://www.dropbox.com/s/fmlq806p2wqf311/trained_models.zip?dl=0) and put it […]

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