  The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines
complementary cognitive strengths of humans and machines in an intelligent
manner to tackle various inference tasks and achieves higher performance than
either humans or machines by themselves. While inference performance
optimization techniques for human-only or sensor-only networks are quite
mature, HuMaINs require novel signal processing and machine learning solutions.
In this paper, we present an overview of the HuMaINs architecture with a focus
on three main issues that include architecture design, inference algorithms
including security/privacy challenges, and application areas/use cases.
