aglaianwoman,github

strif

Tiny, useful Python lib for strings and files

striker

Striker is an offensive information and vulnerability scanner.

The Openlink Structured Data Sniffer (OSDS) is a plugin for the Chrome, Firefox and Opera browsers that detects and shows structured data embedded in web pages in either JSON-LD, Microdata, RDFa or Turtle format.

style-manor

this is the website code for a business I worked with in 2012. skills: PHP, HTML, CSS

styleguide

arrange coding rule.

sushihangover-powershell

PowerShell 3.0 My Home

swiftintern

Internship platform made on SwiftMVC framework

system-code

Code related with system programming (os, admin, etc.)

tactical-exploitation

Modern tactical exploitation toolkit.

tech-doc-ch

翻译一些技术文档和手册方便使用时查询

tekdefense-automater

Automater - IP URL and MD5 OSINT Analysis

telnetlogger

Simulates enough of a Telnet connection in order to log failed login attempts.

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners with Latest APIs

tensorflow-world-resources

:satellite: Organized & Useful Resources about Deep Learning with TensorFlow

terminals-are-sexy

💥 A curated list of Terminal frameworks, plugins & resources for CLI lovers.

text-extractor

simple web page text extractor

text-scraping-document-clustering-topic-modeling

The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.