This paper investigates patenting trends in core AI, traditional robots, and AI-enhanced robots from 1980-2019, revealing distinct growth trajectories and long-run integration patterns across major national innovation systems. It highlights how institutional contexts influence the co-evolution of AI and robotics.

This paper examines how cyber threat intelligence (CTI) needs to adapt for artificial intelligence (AI) systems, detailing AI-specific assets, vulnerabilities, and attack patterns. It reviews existing AI security knowledge, outlines requirements for an AI-oriented threat intelligence knowledge base with concrete indicators of compromise (IoC), and discusses techniques for measuring similarity between IoCs and AI artifacts.

This vision paper outlines a 10-year roadmap for co-designing AI and hardware to overcome current inefficiencies and achieve sustainable, intelligent systems. It emphasizes the need for unprecedented energy efficiency and system-level integration across algorithms, architectures, and applications.

Fusion-CAM is a novel framework that unifies gradient-based and region-based Class Activation Map (CAM) methods through a dedicated fusion mechanism. It aims to provide robust and highly discriminative visual explanations by first denoising gradient-based maps and then adaptively combining them with region-based maps to enhance class coverage and precision, outperforming existing CAM variants.

This paper explores STEM faculty perspectives on the integration of Generative AI in higher education through a focus group study. It investigates how faculty use GenAI in courses, the perceived benefits and challenges for student learning, and necessary institutional support, highlighting shifts in pedagogy, assessment, and governance.