System Description
System descriptions encompass the design, implementation, and evaluation of computational systems addressing diverse challenges. Current research focuses on improving system efficiency and accuracy through techniques like hybrid neural networks for optimal control, fine-tuned BERT models for question answering, and various large language model (LLM) applications for tasks ranging from automatic scoring to creative idea generation. These advancements are significant for improving automation in various fields, from energy management and disaster response to healthcare and education, and for advancing our understanding of AI capabilities and limitations.
Papers
Locomotion Mode Transitions: Tackling System- and User-Specific Variability in Lower-Limb Exoskeletons
Andrea Dal Prete, Zeynep Özge Orhan, Anastasia Bolotnikova, Marta Gandolla, Auke Ijspeert, Mohamed Bouri
Sensor-fusion based Prognostics Framework for Complex Engineering Systems Exhibiting Multiple Failure Modes
Benjamin Peters, Ayush Mohanty, Xiaolei Fang, Stephen K. Robinson, Nagi Gebraeel
Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems
Saurabh Mishra, Anand Rao, Ramayya Krishnan, Bilal Ayyub, Amin Aria, Enrico Zio
A System Level Performance Evaluation for Superconducting Digital Systems
Joyjit Kundu, Debjyoti Bhattacharjee, Nathan Josephsen, Ankit Pokhrel, Udara De Silva, Wenzhe Guo, Steven Van Winckel, Steven Brebels, Manu Perumkunnil, Quentin Herr, Anna Herr