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Artificial intelligence

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Displaying 151 - 175 of 273

System Explanations: A Cautionary Tale

May 8, 2021
Author(s)
Ellen M. Voorhees
There are increasing calls for systems that are able to explain themselves to their end users to increase transparency and help engender trust. But, what should such explanations contain, and how should that information be presented? A pilot study of

Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

May 7, 2021
Author(s)
Peter Bajcsy, Michael Majurski
This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing how NN accuracy responds to systematic pruning. This study leverages the NN models generated for the TrojAI challenges. Our pruning-based approach (1) detects any

Optoelectronic Intelligence

May 7, 2021
Author(s)
Jeff Shainline
To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and

Generative Adversarial Network Performance in Low-Dimensional Settings

April 20, 2021
Author(s)
Felix M. Jimenez, Amanda Koepke, Mary Gregg, Michael R. Frey
A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications

Neural Networks for Classifying Probability Distributions

April 19, 2021
Author(s)
Siham Khoussi, N. Alan Heckert, Abdella Battou, Saddek Bensalem
Probability distribution fitting of an unknown stochastic process is an important preliminary step for any further analysis in science or engineering. However, it requires some background in statistics and prior considerations of the process or phenomenon

Challenge Design and Lessons Learned from the 2018 Differential Privacy Challenges

April 12, 2021
Author(s)
Diane Ridgeway, Mary Theofanos, Terese Manley, Christine Task
The push for open data has made a multitude of datasets available enabling researchers to analyze publicly available information using various statistical and machine learning methods in support of policy development. An area of increasing interest that is

A Machine Learning Based Scheme for Dynamic Spectrum Access

March 30, 2021
Author(s)
Anirudha Sahoo
In this paper, we present a machine learning (ML) based dynamic spectrum access (DSA) scheme which can be used in a system in which primary user (PU) spectrum occupancy can be represented as a sequence of busy (on) and idle (off) periods. We use real world

Intelligent Task Caching in Edge Cloud via Bandit Learning

March 19, 2021
Author(s)
Hamid Gharavi
Task caching, based on edge cloud, aims to meet the latency requirements of computation- intensive and data-intensive tasks (such as augmented reality). However, current task caching strategies are generally based on the unrealistic assumption of knowing

Sensors and Machine Learning Models to Prevent Cooktop Ignition and Ignore Normal Cooking

March 18, 2021
Author(s)
Amy Mensch, Anthony Hamins, Andy Tam, John Lu, Kathryn Markell, Christina You, Matthew Kupferschmid
Cooking equipment is involved in nearly half of home fires in the United States, with cooktop fires the leading cause of deaths and injuries in cooking-related fires. In this study, we evaluate 16 electrochemical, optical, temperature and humidity sensors

The Industrial Ontologies Foundry (IOF) perspectives

March 3, 2021
Author(s)
Mohamed H. Karray, Neil Otte, Rahul Rai, Farhad Ameri, Boonserm Kulvatunyou, Barry Smith, Dimitris Kiritsis, Chris Will, Rebecca Arista
In recent years there has been a number of promising technical and institutional developments regarding use of ontologies in industry. At the same time, however, most industrial ontology development work remains within the realm of academic research and is

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 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

Fan-out and Fan-in properties of superconducting neuromorphic circuits

December 4, 2020
Author(s)
Michael Schneider, Segall Kenneth
Neuromorphic computing has the potential to further the success of software-based artificial neural networks (ANNs) by designing hardware from a different perspective. Current research in neuromorphic hardware targets dramatic improvements to ANN
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