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Artificial intelligence

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Displaying 176 - 200 of 267

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020
Author(s)
Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques

Prognostics and Health Management to Improve Resilient Manufacturing

October 23, 2020
Author(s)
Michael P. Brundage, Brian A. Weiss
Manufacturers need to be resilient to effectively mitigate substantial disruptions to manufacturing operations so they may remain competitive. Disruptions resulting from the COVID-19 global pandemic have caused manufacturers to experience new challenges

An Empirical Study on Flow-based Botnet Attacks Prediction

October 22, 2020
Author(s)
Mitsuhiro Hatada, Matthew A. Scholl
In the era of the Internet of Things, botnet threats are rising, which has prompted many studies on botnet detection and measurement. In contrast, this study aims to predict botnet attacks, such as massive spam emails and distributed denial-of-service

International Workshop on Deep Video Understanding

October 21, 2020
Author(s)
Keith Curtis, George Awad, Shahzad K. Rajput, Ian Soboroff
This is the introduction paper to the International Workshop on Deep Video Understanding. In recent years, a growing trend towards working on understanding videos (in particular movies) in a more deeper level started to motivate researchers working in

Augmenting Deep Learning Models for Speech Emotion Recognition

October 19, 2020
Author(s)
Ram Sriram, Dinesh Manocha, Sarala Padi
We present a Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition. MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method and building the deep learning model to recognize the

Cognitive Automation and its Impact on Additive Manufacturing

October 15, 2020
Author(s)
Albert T. Jones, Zhuo Yang, Yan Lu
The English word manufacturing firstly appeared in 1683 and it was derived from Latin manu factus, meaning making by hand. For more than thousands of years now, and four Industrial Revolutions, the physical, and mostly mechanical processes, associated with

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
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