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NIST Authors in Bold

Displaying 1 - 25 of 115

NVLAP Federal Warfare System(s)

July 21, 2021
Bradley Moore, John Matyjas, Raymond Tierney, Jesse Angle, Jeannine Abiva, Jeff Hanes, David Dobosh, John Avera
NIST Handbook 150-872 presents the technical requirements and guidance for the accreditation of laboratories under the National Voluntary Laboratory Accreditation Program (NVLAP) Federal Warfare System(s) (FWS) program. It is intended for information and

NIST 2021 Speaker Recognition Evaluation Plan

July 12, 2021
Omid Sadjadi, Craig Greenberg, Elliot Singer, Lisa Mason, Douglas Reynolds
The 2021 Speaker Recognition Evaluation (SRE21) is the next in an ongoing series of speaker recognition evaluations conducted by the US National Institute of Standards and Technology (NIST) since 1996. The objectives of the evaluation series are (1) to

User Guide for NIST Media Forensic Challenge (MFC) Datasets

July 6, 2021
Haiying Guan, Andrew Delgado, Yooyoung Lee, Amy Yates, Daniel Zhou, Timothee N. Kheyrkhah, Jonathan G. Fiscus
NIST released a set of Media Forensic Challenge (MFC) datasets developed in DARPA MediFor (Media Forensics) project to the public in the past 5 years. More than 300 individuals, 150 organizations, from 26 countries and regions worldwide use our datasets

DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks

June 24, 2021
Hamid Gharavi
Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the

Ray-based framework for state identification in quantum dot devices

June 17, 2021
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are one of the leading candidates for scaling up the number of qubits in quantum computing implementations. However, with increasing qubit number, the complexity of the control parameter space also grows

Exact Tile-Based Segmentation Inference for Images Larger than GPU Memory

June 3, 2021
Michael P. Majurski, Peter Bajcsy
We address the problem of performing exact (tiling-error free) out-of-core semantic segmentation inference of arbitrarily large images using fully convolutional neural networks (FCN). FCN models have the property that once a model is trained, it can be

Deep reinforcement learning assisted energy harvesting wireless networks

May 24, 2021
Junliang Ye, Hamid Gharavi
Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a promising approach to deal with the ever-growing traffic that can severely impact the power consumption of small-cell networks. Unfortunately, the amount of harvested energy

Optoelectronic Intelligence

May 7, 2021
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
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
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
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
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
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
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
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

March 2, 2021
Brian Stanton, Theodore Jensen
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 measure system trustworthiness

Towards Deep Transfer Learning in Industrial Internet of Things

February 26, 2021
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
Displaying 1 - 25 of 115