Joint human and autonomy teaming for defense: status, challenges, and perspectives

Abstract

Human and Autonomy Teaming (HAT) involves humans, artificial intelligence and machine learning (AI/ML), and autonomous systems (AS) working together to enhance performance and efficiency across a variety of fields. The purpose of this paper is to outline the current status, challenges, and perspectives related to HAT in the defense applications, such as multi-domain operations (MDOs). This paper emphasizes that, although ASs have numerous benefits, humans, and machines need to work together to ensure effective communication and coordination. Furthermore, this paper addresses the issues of situation awareness, HAT bias, security, privacy, confidentiality, trust, transparency, and explainability as they relate to joint HAT in MDOs. Finally, this paper explores the potential of perspectives to overcome these challenges and enhance collaboration between humans, AI/ML, and AS in the context of defense.

Improved face transformer model for facial emotion recognition in human-machine collaboration

“…Facial Emotion Recognition (FER) has emerged as a critical area of research at the intersection of computer vision and human-computer interaction as well as human-autonomy teaming. [1][2][3] Understanding and interpreting human emotions, primarily conveyed through facial expressions, are fundamental aspects of human communication and interaction. These expressions, ranging from subtle micro-expressions to overt displays of emotion, encode a rich tapestry of feelings, including six basic emotions happiness, sadness, anger, surprise, fear, and disgust.…”