NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Artificial intelligence

Search Publications

Search Title, Abstract, Conference, Citation, Keyword or Author
  • Published Date
Displaying 176 - 200 of 260

Trojan Detection Evaluation: Finding Hidden Behavior in AI Models

October 10, 2020
Author(s)
Michael Majurski, Derek Juba, Timothy Blattner, Peter Bajcsy, Walid Keyrouz
Neural Networks are trained on data, learn relationships in that data, and then are deployed to the world to operate on new data. For example, a traffic sign classification AI can differentiate stop signs and speed limit signs. One potential problem is

Adaptive Estimation of Near-Optimal Electrostatic Force in Micro Energy-Harvesters

September 28, 2020
Author(s)
Kamran Sayrafian, Masoud Roudneshin, Amir G. Aghdam
Recent advancements in micro-electronics have led to the development of miniature-sized wearable sensors that can be used for a variety of health monitoring applications. These sensors are typically powered by small batteries which could require frequent

On the Use of Machine Learning Models to Forecast Flashover Occurrence in a Compartment

September 15, 2020
Author(s)
Jun Wang, Andy Tam, Paul A. Reneke, Richard Peacock, Thomas Cleary, Eugene Yujun Fu, Grace Ngai, Hong Va Leong
This paper presents a study to examine the potential use of machine learning algorithms to build a model to forecast the likelihood of flashover occurrence for a single-floor multi-room compartment. Synthetic temperature data for heat detectors from

Time Series Feature Extraction and Selection Tool for Fire Data

September 15, 2020
Author(s)
Jun Wang, Youwei Jia, Eugene Yujun Fu, Jiajia Li, Andy Tam
This paper aims to facilitate the use of machine learning to carry out supervised classification/regression tasks for time series data in fire research. Specifically, a feature engineering tool, FAST (Feature extrAction and Selection for Time-series), is

A NEIGHBORHOOD-BASED NEURAL NETWORK FOR MELT POOL PREDICTION AND CONTROL

September 1, 2020
Author(s)
Paul Witherell, Vadim Shapiro, Yaqi Zhang
One of the most prevalent additive manufacturing processes, the powder bed fusion process, is driven by a moving heat source that melts metals to build a part. This moving heat source, and the subsequent formation and moving of a melt pool, plays an

Effectiveness of dataset reduction in testing machine learning algorithms

August 25, 2020
Author(s)
Raghu N. Kacker, David R. Kuhn
Abstract— Many machine learning algorithms examine large amounts of data to discover insights from hidden patterns. Testing these algorithms can be expensive and time-consuming. There is a need to speed up the testing process, especially in an agile

On Data Integrity Attacks against Industrial Internet of Things

August 24, 2020
Author(s)
Hansong Xu, Wei Yu, Xing Liu, David W. Griffith, Nada T. Golmie
Industrial Internet of Things (IIoT) is predicted to drive the fourth industrial revolution through massive interconnection of industrial devices, such as sensors, controllers and actuators, integrating advances in smart machinery and data analytics driven

Four Principles of Explainable Artificial Intelligence (Draft)

August 18, 2020
Author(s)
P Phillips, Carina Hahn, Peter Fontana, David A. Broniatowski, Mark A. Przybocki
We introduce four principles for explainable artificial intelligence (AI) that comprise the fundamental properties for explainable AI systems. They were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer

NIST 2020 CTS Speaker Recognition Challenge Evaluation Plan

July 29, 2020
Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Elliot Singer, Douglas A. Reynolds, Lisa Mason
Following the success of the 2019 Conversational Telephone Speech (CTS) Speaker Recognition Challenge, which received 1347 submissions from 67 academic and industrial organizations, the US National Institute of Standards and Technology (NIST) will be

Overview of the TREC 2019 Deep Learning Track

July 27, 2020
Author(s)
Ellen Voorhees, Nick Craswell, Bhaskar Mitra, Daniel Campos, Emine Yilmaz
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

July 14, 2020
Author(s)
Brian L. DeCost, Jason R. Hattrick-Simpers, Zachary T. Trautt, Aaron G. Kusne, Martin L. Green, Eva Campo
Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID-19

July 8, 2020
Author(s)
Ellen Voorhees, Ian Soboroff, Tasmeer Alam, Kirk Roberts, William Hersh, Dina Demner-Fushman, Steven Bedrick, Kyle Lo, Lucy L. Wang
TREC-COVID is an information retrieval (IR) shared task initiated to support clinicians and clinical research during the COVID-19 pandemic. IR for pandemics breaks many normal assumptions, which can be seen by examining nine important basic IR research

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

July 2, 2020
Author(s)
Werickson Fortunato de Carvalho Rocha, Charles Prado, Niksa Blonder
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such

Detection of Dense, Overlapping, Geometric Objects

July 1, 2020
Author(s)
Adele P. Peskin, Boris Wilthan, Michael P. Majurski
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting

MSEC: A QUANTITATIVE RETROSPECTIVE

June 25, 2020
Author(s)
Rachael Sexton, Michael Brundage, Alden A. Dima, Michael Sharp
The Manufacturing Science and Engineering Conference (MSEC) in 2020 is the 15th annual conference put on by the Manufacturing Engineering Division (MED) of ASME. MED and ASME MSEC focuses on manufacturing sciences, technology, and applications, including

NIST Pilot Too Close for Too Long (TC4TL) Challenge Evaluation Plan

June 18, 2020
Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Douglas A. Reynolds
One of the keys to managing the current (and future) epidemic is notifying people of possible virus exposure so they can isolate and seek treatment to limit further spread of the disease. While manual contact tracing is effective for notifying those who

{A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

June 4, 2020
Author(s)
Howard L. Joress, Brian L. DeCost, Suchismita Sarker, Trevor M. Braun, Logan T. Ward, Kevin Laws, Apurva Mehta, Jason R. Hattrick-Simpers
Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high- throughput structural and electrochemical
Was this page helpful?