An interactive web app demonstrating shepherding behaviour. Interactive Demo Code and Documentation Slimmer Version for Website Integration References Implemented using the technique described in: Van Havermaet, S., Simoens, P., Landgraf, T., & Khaluf, Y. (2023). Steering herds away from dangers in dynamic environments. Royal Society Open Science, 10(5), 230015. Interface inspired by the work of Nick Frosst.
click for time travel Background The Great Canadian Hairy Star Party was an educational initiative produced by ScienceWeb in 1996. It featured observations, sketches, and other artwork from students across Canada related to Comet Hyakutake, a spectacular comet that passed close to Earth in March of that year. Centre Consolidated Elementary School in Lunenburg, Nova Scotia participated by having students submit their personal observations on a dedicated website. I was one of those students.
This project implements a Machine Learning (ML) pipeline for the Adver-City synthetic dataset. The dataset is designed for investigating cooperative perception in autonomous vehicles. Overall, this project enables efficient, reproducible modelling of the Adver-City dataset through ML workflows. We have chosen to train a model to identify weather conditions (clear, fog, rain) from nighttime images. Note: full GitRepo available here. Figure 1: Example Scenario (foggy) from the Adver-City dataset. The dataset contains images of a variety of weather conditions (clear, fog, rain) and times of day (day, night). In this project we focus on classifying weather conditions from nighttime images. Operation The project is organized into four main stages. Depending on what your goal is (data exploration, ingestion, or training), the notebooks indicated below describe how to run each stage of the workflow.
A fully open architecture implementation of modern multi-agent coordination techniques. All agents make individual decisions based on local information only. There is no global plan. This is an open project I use to explore new ideas, validate theoretical results, and produce data for my academic research.
Building on my earlier exploration of custom ChatGPTs, I tried building an AI app from scratch, primarily using vibe coding supported by GitHub Copilot in VSCode. Inspired by a recent Airbnb experience, I developed ContactsnApp: a user-friendly application designed to streamline the extraction and annotation of phone numbers from images using AI models. Comments from the Co-author I asked ChatGPT how the project went. Here it is in her own words:
Key Points Built a custom ChatGPT to assist me with my research. Trained it on actual comments from peer-review. Received feedback on my proposed revisions. Overall impressed with its initial performance. Introduction One of my holiday projects this year was to play with some of the special features that come with the OpenAI ChatGPT Plus account. In additon to gaining access to better models and more reliable performance, the Plus account allows you to create your own custom GPTs, tailored to your own needs.