News from April 10th 2019 is the release of Google’s collaborative AI platform for Data Science teams, for execution on cloud or on premises. Google’s platform joins Alibaba‘s similar platform called PAI 2.0 announced in March 29th 2017. While comprehensive information on Alibaba’s platform is sparse in non-Chinese, the Google AI Platform does give samples and tutorials. Two others ClusterOne for the DevOps of data science and DeterminedAI for collaboration each had funding announcements earlier this year. Google and Alibaba’s platforms give a clear separation for team roles to collaborate at each stage of the process (as is indicated for the two yet-to-be-released others). The concept is well worth a mention because they are collaborative frameworks pushing forward the methodologies of data science, engineering and in essence, social intelligence..
Back on 26th August 2016 we drew to a close SAB2016, the 14th international conference of the series on bio-inspiration for robotics and algorithms from more or less any discipline at theory stages, to simulations and empirically applied. For me, nothing can take away my reverberating proud feeling I have for being a part of a diverse, kind and like-minded collection of people with a sheer fascination for nature, paired with the talent and vision to pursue it for the betterment of society and life. I mean, isn’t that the dream!?
Ever thought what kind of app you would need to survive the Zombie Apocalypse? No, well neither had I until this semester’s session began and we needed a fun project for the final year students to work through in 8 hrs of lab time. This is what I came up with along with the support of the team at Aber Comp Sci.
Box and Whisker Plotsor boxplots, are a hugely useful data visualisation tool to clearly compare algorithm configuration performance results (or experiment data with multiple dimensions). However, using Python’s Matplotlib library to implement them suitably for comparisons by groups used to be tough. To make them attractive and clear you had to stitch together documentation and examples and more examples and grids and line colours and axis labels and some very hacky legend use case, etc.. each taken from across the matplotlib site and beyond. So I wrote a couple of scripts to simplify grouped boxplots that can be directly reused..
Here’s the Grouped Boxplot (on left) and Ungrouped Boxplot (on right):
Occasionally, you will probably need to combine a set of PDF files into a single PDF file…
LaTeX can do it – But it can be a pain to get the correct appearance and find the correct API parameters for scaling, margins, number of pages per PDF, etc. Below is a quick snippet that should just work.
The Receptor Density Algorithm (RDA) is an Artificial Immune System (AIS) anomaly detection algorithm modelled upon how T-cell receptors respond to antigen, originally modelled by Owens et al in 2009. A recent small project has been to investigate its applicability as an AIS anomaly classifier for our CARDINAL-Vanilla AIS self-healing architecture.
Github repo | Version 0.1 implementation of RDA in python 2.7.