Notes on codes, projects and everything
Implementing a Information Retrieval system is a fun thing to do. However, doing it efficiently is not (at least to me). So my first few attempts didn’t really end well (mostly uses just Go/golang with some bash tricks here and there, with or without a database). Then I jumped back to Python, which I am more familiar with and was very surprised with all the options available. So I started with Pandas and Scikit-learn combo.
Just happened to see this post a few months ago, and the author created another cloud that uses almost the same technique to ‘visualize’ a list of countries. The author uses PHP to generate the cloud originally and I thought I may be able to do in javascript. After some quick coding I managed to produce something similar to the first example, source code after the jump.
Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users [4, 5]. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications [1, 5]. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive [3]. Both content hierarchy and folksonomy can be used together to better content classification.
Recently I volunteered in building a site that reports whether certain websites are blocked locally (please don’t ask why that is happening). As it is a very simple app reporting status I wanted it to be easily scrape-able. One of the decision made was I want it to have things to see on first load, this practically removes the possibility of using react, which is my current favorite.
While the previous file structure works well, I decided to tune some details before deploying the latest WordPress release. Besides that, I also started a new theme development project after my last theme which was developed more than 2 years ago. Thankfully, everything seems to work so far.
One of my recent tasks involving crawling a lot of geo-tagged data from a given service. The most recent one is crawling files containing a point cloud for a given location. So I began by observing the behavior in the browser. After exporting the list of HTTP requests involved in loading the application, I noticed there are a lot of requests fetching resources with a common rXXX
pattern.