NIMBUS: A Hybrid Cloud-Crowd Realtime Architecture for Visual Learning in Interactive Domains
This addresses the feasibility of cloud-crowd architectures for visual learning in interactive domains, but it appears incremental as it builds on existing concepts with specific enhancements.
The paper tackles the problem of balancing quality and response time in cloud-crowd robotic architectures by proposing a novel method that trades quality for speed, with empirical results from Amazon Mechanical Turk showing improvements in quality in exchange for response time.
Robotic architectures that incorporate cloud-based resources are just now gaining popularity. However, researchers have very few investigations into their capabilities to support claims of their feasibility. We propose a novel method to exchange quality for speed of response. Further, we back this assertion with empirical findings from experiments performed with Amazon Mechanical Turk and find that our method improves quality in exchange for response time in our cognitive architecture.