One primary objective of the HiPSTAS project is to develop the ARLO (Adaptive Recognition with Layered Optimization) software. ARLO was developed for classifying bird calls and using visualizations to help scholars classify pollen grains. ARLO has the ability to extract basic prosodic features such as pitch, rhythm and timbre for discovery (clustering) and automated classification (prediction or supervised learning), as well as visualizations. The current implementation of ARLO for modeling runs in parallel on systems at the National Center for Supercomputing Applications (NCSA). The source code for ARLO is open-source and will be made available for research purposes for this and subsequent projects. Examples for how scholars have used ARLO are linked from the Publications page.
ARLO “News Page” where developer Tony Borries posts updates to the live site.
(auto-posts to @arloproject on Twitter)
In short, Nester (built on Django) is the front-end interface and Adapt (in Java) handles the heavy lifting behind the scenes. We can spin up additional Adapt nodes as needed to run large jobs in parallel.
Main project page:
A guide to Nester’s API:
A handy class list for Nester and Adapt:
The source code for the main branch is on Bitbucket. This page also includes standalone Python scripts for mass uploading and a few other tasks, as well as an ARLO release bundled with SaltStack and Vagrant configurations to aid installation on a VM or VPS.
VM install walkthrough:
VPS install walkthrough (requires some trial and error):