5 papers
Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
Eduardo de la Cruz Fernández, Marcelo Karanik, Sascha Ossowski
As intelligent systems become more autonomous, the scientific community focuses on creating decision-making mechanisms that include ethical and moral considerations, unlike traditi…
LSTM VS. Feed-Forward Autoencoders for Unsupervised Fault Detection in Hydraulic Pumps
P. Sánchez, K. Reyes, B. Radu +1
Unplanned failures in industrial hydraulic pumps can halt production and incur substantial costs. We explore two unsupervised autoencoder (AE) schemes for early fault detection: a…
Assesing the Viability of Unsupervised Learning with Autoencoders for Predictive Maintenance in Helicopter Engines
P. Sánchez, K. Reyes, B. Radu +1
Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive…
Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders
P. Sánchez, K. Reyes, B. Radu +1
This paper introduces an unsupervised health-monitoring framework for turbofan engines that does not require run-to-failure labels. First, operating-condition effects in NASA CMAPS…
Value Lens: Using Large Language Models to Understand Human Values
Eduardo de la Cruz Fernández, Marcelo Karanik, Sascha Ossowski
The autonomous decision-making process, which is increasingly applied to computer systems, requires that the choices made by these systems align with human values. In this context,…