This paper introduces Holonic Digital Twin Networks (HDT-Nets), a framework that transforms wireless networks into cognitive orchestrators for physical AI. It enables real-time collective reasoning by allowing holonic digital twins to actively perceive, infer, and act within dynamic physical environments.

This paper proposes "Precision Education," an AI-powered paradigm for higher education, aiming to shift from reactive to preventive student success by leveraging student digital twins, predictive analytics, and causal inference. It envisions a future where personalized academic and career pathways are dynamically optimized to enhance student outcomes.

This paper explores the significant limitations in evaluating Explainable AI (XAI) methods, particularly focusing on the scarcity of robust human-grounded evaluations and the challenges posed by evolving data streams with concept drift. It highlights the need for more systematic and interdisciplinary approaches to assess the effectiveness and trustworthiness of AI explanations.

This paper introduces a scalable web-based visual analytics system to overcome challenges in translating AI and radiomics into clinical practice for brain tumor research. It integrates cohort management, radiomic feature extraction, and guarded machine learning inference within a single, transparent interface.

This paper introduces AIGen, a modular tool that automates the generation of machine-readable AI Bills of Materials (AIBoMs) compliant with the SPDX 3.0 AI profile. AIGen integrates with MLflow and combines mining heuristics with Large Language Models to document AI system components, addressing the critical need for transparent and accountable AI supply chain governance.