A proposed three-dimensional benchmark for LLM moral reasoning that combines MFQ, WVS, and moral dilemmas, but the reported model scores are not reproducible from the paper.
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.
fields
cs.CY 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models
A proposed three-dimensional benchmark for LLM moral reasoning that combines MFQ, WVS, and moral dilemmas, but the reported model scores are not reproducible from the paper.