June 30, 2021 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 […]

June 30, 2021 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 […]

June 30, 2021 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 […]

June 30, 2021 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

June 30, 2021 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 […]

June 30, 2021 Python

Beautiful Soup: Build a Web Scraper With Python

The incredible amount of data on the Internet is a rich resource for any field of research or personal interest. To effectively harvest that data, you’ll need to become skilled at web scraping. The Python libraries requests and Beautiful Soup are powerful tools for the job. If you like to learn with hands-on examples and you have a basic understanding of Python and HTML, then this tutorial is for you. In this tutorial, you’ll learn how to: Use requests and […]

June 30, 2021 Beginner, Machine Learning, NLP, Project, Python, Text

Beginner Projects to Learn Natural Language Processing using Python !

This article was published as a part of the Data Science Blogathon Machines understanding language fascinates me, and that I often ponder which algorithms Aristotle would have accustomed build a rhetorical analysis machine if he had the possibility. If you’re new to Data Science, getting into NLP can seem complicated, especially since there are many recent advancements within the field. it’s hard to grasp where to begin. Table of Contents 1.What can Machines Understand? 2.Project 1:Word Cloud 3.Project 2:Spam Detection 4.Project […]

June 30, 2021 Beginner, Libraries, NLP, Python, Text, Unstructured Data

Beginner’s Guide To Natural Language Processing Using SpaCy

This article was published as a part of the Data Science Blogathon Pre-requisites Basic Knowledge of Natural Language Processing Hands-on practice of Python Introduction As we know data has some kind of meaning in its position. For every moment, mostly text data is getting generated in different formats like SMS, reviews, Emails, and so on. The main purpose of this article is to understand the basic idea of NLP using the library- SpaCy. So let’s go ahead. In this article, we […]

June 30, 2021 Uncategorized Leave a comment

New Future of Work: Meeting and collaborating in a remote and hybrid world with Jaime Teevan and Abigail Sellen

Episode 126 | June 30, 2021 For Microsoft researchers, COVID-19 was a call to action. The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions. Teams from across the Microsoft organizational chart pooled their unique

June 30, 2021 Python

Measuring the memory usage of a Pandas DataFrame

How much memory are your Pandas DataFrame or Series using? Pandas provides an API for measuring this information, but a variety of implementation details means the results can be confusing or misleading. Consider the following example: >>> import pandas as pd >>> series = pd.Series([“abcdefhjiklmnopqrstuvwxyz” * 10 … for i in range(1_000_000)]) >>> series.memory_usage() 8000128 >>> series.memory_usage(deep=True) 307000128 Which is correct, is memory usage 8MB or 300MB? Neither! In this special case, it’s actually 67MB, at least with the default […]

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