All the forest's a bug's stage

Review: Cirque du Soleil's OVO

Network traffic clustering using random forest proximities

The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for the task, which performs clustering based on Random Forest (RF) proximities instead of Euclidean distances. The approach consists of two steps. In the first step, we derive a proximity measure for each pair of data points by performing a RF classification on the original data and a set of synthetic data. In the next step, we perform a K-Medoids clustering to partition the data points into K groups based on the proximity matrix. Evaluations have been conducted on real-world Internet traffic traces and the experimental results indicate that the proposed approach is more accurate than the previous methods.

Spatial ecology of an apex predator over an urban to forest gradient

This research focused on predicting habitat suitability for the Powerful Owl over an urbanization gradient. Zones along the gradient provided different proportions of habitat for this species and tied in with availability of key resources. Urban environments provided limited habitat for the Powerful Owl and may potentially be acting as ecological traps.

Forest of gestures : video installation and performance work

Invitation to contribute to Alexander Harrisions' program 'What's Coming' resulted in 'Forest of Gestures' a 3 channel video projection and installation with a live feed component