No way, it seems pretty exciting research for working on voting machines, but it is the nature of machine learning research that you get a lot of similar stuff that are really incremental.
I peeked his google scholar because I was curious and some of his papers are in intrusion detection systems, so that actually makes some sense that he’s a security dude there.
This basically explains how small CPU's could talk to each other and analyze huge amounts of data to make predictions about the data in real time.
This is every machine leaning algorithm at some level of scale (GPUs used often are really many tiny cpus, for example). Not saying I wouldn’t hire the dude to steal an election or w/e though lol
These are classifiers where a distribution (think equation) is being altered for detecting things. Finding hidden patterns in data is pretty normal, especially when there’s not a lot of the kind you’re looking for.
This ain’t it chief. The poster doesn’t know what she’s talking about. This is just a machine learning algorithm to deal with a data particularly (needle in a haystack basically).
Here’s one I’ve heard of: https://en.m.wikipedia.org/wiki/AdaBoost
And you’ll note that if you read about boosting in general, the original post is wrong on the face about this being a unique boosting algorithm. Researchers tend to oversell the novelty of their findings.
This is an algorithm used for network intrusion detection..