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Information Technology

Artificial intelligence

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Displaying 276 - 300 of 433

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 M. 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 M. 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