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Sarala Padi, Omid Sadjadi, Ram D. Sriram, Dinesh Manocha
Automatic speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity, i.e., insufficient amounts of carefully labeled data to build and fully
Hyunseop Park, Hyunwoong Ko, Yung-Tsun Lee, Shaw C. Feng, Paul Witherell, Hyunbo Cho
Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM
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
Physicians are increasingly using clinical sequencing tests to establish diagnoses of patients who might have genetic disorders, which means that accuracy of sequencing and interpretation are important elements in ensuring the benefits of genetic testing
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
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
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
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
This work presents the design of an optimal disturbance rejecting tracking controller based on reinforcement learning. The problem involves finding the optimal control parameters that yield asymptotic output tracking in the presence of unmeasurable
Ruimin Chen, Yan Lu, Paul Witherell, Timothy Simpson, Soundar Kumara, Hui Yang
Additive manufacturing (AM) enables the creation of complex geometries that are difficult to realize using conventional manufacturing techniques. Advanced sensing is increasingly being used to improve AM processes, and installing different sensors onto AM
Aditya Joglekar, Omid Sadjadi, Meena Chandra-Shekar, Christopher Cieri, John Hansen
The Fearless Steps Challenge (FSC) initiative was designed to host a series of progressively complex tasks to promote advanced speech research across naturalistic "Big Data" corpora. The Center for Robuts Speech Systems at UT-Dallas in collaboration with
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
We present a new collection of processing techniques, collectively "factorized Kramers-Kroenig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale- error correction in coherent anti-Stokes Raman
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
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
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
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
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
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
Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted
Laser Powder Bed Fusion (LPBF) is an additive manufacturing process where laser power is applied to fuse the spread powder and fabricate industrial parts in a layer by layer fashion. Despite its great promise in fabrication flexibility, print quality has
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
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
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