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Information Technology

Artificial intelligence

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Displaying 226 - 250 of 433

Trust and Artificial Intelligence (Draft)

March 2, 2021
Author(s)
Brian Stanton, Theodore Jensen
WITHDRAWN January 4, 2023 The artificial intelligence (AI) revolution is upon us, with the promise of advances such as driverless cars, smart buildings, automated health diagnostics and improved security monitoring. Many current efforts are aimed to

Towards Deep Transfer Learning in Industrial Internet of Things

February 26, 2021
Author(s)
Xing Liu, Wei Yu, Fan Liang, David Griffith, Nada T. Golmie
In this paper, we propose a general framework to adopt transfer learning in IIoT systems. Transfer learning is a machine learning technique that fully uses the knowledge from pre- trained models to reduce the computing requirements for the training process

Designing Trojan Detectors in Neural Networks Using Interactive Simulations

February 20, 2021
Author(s)
Peter Bajcsy, Nicholas J. Schaub, Michael P. Majurski
This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the

TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection

February 19, 2021
Author(s)
Ellen M. Voorhees, Ian Soboroff, Tasmeer Alam, William Hersh, Kirk Roberts, Dina Demner-Fushman, Kyle Lo, Lucy L. Wang, Steven Bedrick
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated

On Deep Reinforcement Learning Security for Industrial Internet of Things

February 15, 2021
Author(s)
Xing Liu, Wei Yu, Fan Liang, David W. Griffith, Nada T. Golmie
Industrial Internet of Things (IIoT), also known as Industry 4.0, empowers manufacturing and production processes by leveraging automation and Internet of Things (IoT) technologies. In IIoT, the information communication technologies enabled by IoT could

Predicting Flashover Occurrence using Surrogate Temperature Data

February 9, 2021
Author(s)
Andy Tam, Eugene Yujun Fu, Richard Peacock, Paul A. Reneke, Jun Wang, Grace Ngai, Hong Va Leong, Thomas Cleary
Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of

Technical Language Processing: Unlocking Maintenance Knowledge

December 11, 2020
Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. Text-based documents account for a significant portion of data collected during the life cycle of asset

Temporal Memory with Magnetic Racetracks

December 1, 2020
Author(s)
Hamed Vakili, Mohammed N. Sakib, Samiran Ganguly, Mircea Stan, Matthew Daniels, Advait Madhavan, Mark D. Stiles, Avik W. Ghosh
Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning algorithms. Skyrmions, bubbles, and domain walls are mobile magnetic

Six-sigma Quality Management of Additive Manufacturing

November 26, 2020
Author(s)
Yan Lu, Hui Yang, Paul Witherell
Quality is a key determinant in deploying new processes, products or services, and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a

On-the-fly closed-loop materials discovery via Bayesian active learning

November 24, 2020
Author(s)
Aaron Gilad Kusne, Heshan Yu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Albert Davydov, Leonid A. Bendersky, Apurva Mehta, Ichiro Takeuchi
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop