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 CFL-2S, a novel two-stage clustered federated learning method designed for reliable traffic prediction in managed Wi-Fi networks. It addresses the challenge of data heterogeneity among Access Points by intelligently forming clusters, achieving superior predictive performance while minimizing communication and energy overhead among clustered approaches.

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.

This paper explores how competitive pressure in AI development influences safety choices. Through a behavioral experiment and an evolutionary model, it demonstrates that unsafe development is primarily driven by fear of falling behind and strategic responses to competitors, rather than individual risk preferences alone.

This paper introduces Cognivia, an AI therapist designed to assist with Cognitive Behavioral Therapy by identifying cognitive distortions and generating evidence-based rational responses. It addresses the scalability limitations of traditional CBT and the shortcomings of existing LLM-based mental health applications by grounding its approach in authoritative CBT literature and an expert-validated evaluation framework.