Albert Y. Zomaya

TL;DR: This paper introduces some novel algorithms for deriving resource placement schemes in product networks based on the assumed perfect resource placement in their underling basic graphs.

TL;DR: Zhang et al. as discussed by the authors explored the vulnerability of DRL-based schedulers to job perturbations without accessing the information of the DRL models used in the scheduler and proposed an adversarial training framework to force the neural model to adapt to the perturbation patterns during training.

TL;DR: Various aspects of advances in Computational Intelligence with Internet of Things (IoT) including collaborative filtering for recommender systems, agent-based approaches to IoT smart homes, location-based access schemes, event-based reasoning, support vector machines, swarm optimization, mining algorithms and machine to machine communications and interfaces are covered.

TL;DR: The workshop aims to provide a forum for scientists, engineers, practitioners, and computer users to share their experience and report state-of-the-art and in-progress research in this research field.