Demo Data from EDBT 2026 / reviewers in the wild / expert
Jake Fountain
dblp:143/7433
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
tracking |
0.2 | 1 | 2016 | Automatic identification of rigidly linked 6DoF sensors · VR 2016 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.2sensor fusion · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Optimization of Robot Movements Using Genetic Algorithms and Simulation
Brandon Zahn, Jake Fountain, Trent Houliston, Alex Biddulph, Stephan K. Chalup, Alexandre Mendes |
RoboCup | 2 |
| 2017 | Training Deep Neural Networks for Detecting Drinking Glasses Using Synthetic Images
Luke Farrawell, Jake Fountain, Stephan K. Chalup |
ICONIP (2) | 3 |
| 2016 | Automatic identification of rigidly linked 6DoF sensorsabstractWe present techniques for automatically identifying relationships between rigidly linked 6DoF and 3DoF sensors belonging to different sensor systems. The techniques allow for subsequent automatic alignment of the sensor systems, increasing the usability of modular sensor systems. Two techniques are presented and analysed in simulation and a case study for performance under varying noise and latency conditions. Good results were achieved, with each sensor identified correctly in at least 60% of estimates, or 4 times greater than random selection. After a sample collection period of around 5 seconds, the matching is performed in less than 5ms and is scalable to noisier systems by using more samples. Our methods represent a key step in creating highly accessible modular multi-device 3D systems. Jake Fountain, Shamus P. Smith |
VR | 1 |
| 2013 | Motivated Reinforcement Learning for Improved Head Actuation of Humanoid Robots
Jake Fountain, Josiah Walker, David M. Budden, Alexandre Mendes, Stephan K. Chalup |
RoboCup | 1 |