Drawing on lessons from electronic health record implementation, we argue that AI’s ultimate impact will be determined not by use rates, but by implementation quality and fit. Poorly implemented ...

X-AnyLabeling-Server is a simple, lightweight and extensible serving framework for AI model inference, specifically designed for X-AnyLabeling. It provides a production-ready solution with pluggable ...

Abstract: This brief presents an edge-AIoT speech recognition system, which is based on a new spiking feature extraction (SFE) method and a PoolFormer (PF) neural network optimized for implementation ...

Abstract: Large language models (LLMs) have emerged as a promising tool for detecting code vulnerabilities, potentially offering advantages over traditional rule-based methods. This paper proposes an ...

This server does not require installing a new UE plugin as it uses the built-in Python remote execution protocol. Adding new tools/features is much faster to develop ...