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Artificial intelligence

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Displaying 126 - 150 of 260

TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime

July 11, 2021
Author(s)
Ellen M. Voorhees, Ian Soboroff, Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that the best models with large data are likely deep neural networks. This paper

User Guide for NIST Media Forensic Challenge (MFC) Datasets

July 6, 2021
Author(s)
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
Author(s)
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

Securing AI Testbed (Dioptra) Documentation

June 14, 2021
Author(s)
Harold Booth, James Glasbrenner, Howard Huang, Cory Miniter, Julian Sexton
The NCCoE has built an experimentation testbed to begin to address the broader challenge of evaluation for attacks and defenses. The testbed aims to facilitate security evaluations of ML algorithms under a diverse set of conditions. To that end, it has a

Ray-based framework for state identification in quantum dot devices

June 7, 2021
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement

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

June 3, 2021
Author(s)
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
Author(s)
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

Evaluating Multimedia and Language Tasks

May 24, 2021
Author(s)
Ian Soboroff, George Awad, Asad Butt, Keith Curtis
Evaluating information access tasks, including textual and multimedia search, question answering, and understanding has been the core mission of NIST's Retrieval Group since 1989. The TRECVID Evaluations of Multimedia Access began in 2001 with a goal of

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
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