TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
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
Podcast Metadata and Content: Episode Relevance and Attractiveness in Ad Hoc Search
Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate
User Guide for NIST Media Forensic Challenge (MFC) Datasets
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
What Futuristic Technology Means for First Responders: Voices from the Field
The public safety communication technology landscape in the United States (U.S.) is evolving to supplement the use of land mobile radios with the use of a broader spectrum of communication technologies for use on the newly created Nationwide Public Safety
Organizations use phishing training exercises to help employees defend against the phishing threats that get through automatic email filters, reducing potential compromise of information security for both the individual and their organization. These
NIST Handbook 150-17 presents the technical requirements and guidance for the accreditation of laboratories under the National Voluntary Laboratory Accreditation Program (NVLAP) Cryptographic and Security Testing (CST) program. It is intended for
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks
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
The Common Weakness Enumeration (CWE) community publishes an aggregate metric to calculate the 'Most Dangerous Software Errors.' However, the used equation highly biases frequency and almost ignores exploitability and impact. We provide a metric to
Reinforcement Learning Based Optimal Tracking Control UnderUnmeasurable Disturbances with Application to HVAC Systems
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
In today's cloud data centers and edge computing, attack surfaces have significantly increased, hacking has become industrialized, and most security control implementations are not coherent or consistent. The foundation of any data center or edge computing
Ontology-driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing
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
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
A Pseudo Exhaustive Software Testing Framework for Embedded Digital Devices in Nuclear Power
The major challenge faced by the nuclear industry related to software testing of digital embedded devices is the identification of practical software (SW) testing solutions that provide a strong technical basis and is at the same time effective in
Fearless Steps Challenge Phase-3 (FSC P3): Advancing SLT for Unseen Channel and Mission Data across NASA Apollo Audio
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
Resource and performance dependent blocking mechanisms for entanglement routing in quantum networks are identified and characterized in simulations of a DLCZ architecture under different loss and resource availability conditions.
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging
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
Ray-based framework for state identification in quantum dot devices
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