Engineering Commons^™

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

Employing A User-Centered Design Process For Cybersecurity Awareness In The Power Grid, Jean C. Scholtz, Lyndsey Franklin, Aditya Ashok, Katya Leblanc, Christopher Bonebrake, Eric Andersen, Michael Cassiadoro

Journal of Human Performance in Extreme Environments

In this paper, we discuss the process we are using in the design and implementation of a tool to improve the situation awareness of cyberattacks in the power grid. We provide details of the steps we have taken to date and describe the steps that still need to be accomplished. The focus of this work is to provide situation awareness of the power grid to staff from different, non-overlapping roles in an electrical transmission organization in order to facilitate an understanding of a possible occurrence of a cyberattack. Our approach follows a user-centered design process and includes determining the types …