Words change meanings over time, and the evolution of language provides insight into both historical and cultural events. The objective of this article is to accurately model the evolution of words from Classical Arabic.
A similar idea has been done for English, German, and French by a team over at Stanford. Here we apply the same process of using word vector embeddings learned from Word2vec on a corpus of Classical Arabic text.
This fall I started a class revisiting the basics of Arabic grammar. My notes are a mix of Arabic and English, and I prefer to have the Arabic text larger so that it’s easier to see. It’s become a painstaking routine to format each language individually.
Rather than find and edit all Arabic text embedded in the document, I built an extension to automate the effort. What I came up with is a useful extension for multilingual documents, language learning, and text manipulation in general.
TLDR: Google Apps Script w/ regexes + HMTL/CSS
The extension is…
Last year, Kettering University was one of eight universities around the world selected to participate in the Society of Automotive Engineers’ (SAE) AutoDrive Challenge — an autonomous vehicle competition. The goal of the competition is to navigate an urban driving course autonomously within three years. The introduction of the competition has brought up a host of research efforts within autonomous driving.
Traffic light recognition plays a crucial role in traffic control and collision avoidance. Intersection accidents are the second leading cause of vehicle collisions, led only by rear-end crashes. …
Graduate Student at the University of Michigan