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This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system's ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights
Mary Frances Theofanos, Yee-Yin Choong, Theodore Jensen
As artificial intelligence (AI) systems continue to be developed, humans will increasingly participate in human-AI interactions. Humans interact with AI systems to achieve particular goals. To ensure that AI systems contribute positively to human-AI
Zongxia Li, Andrew Mao, Jordan Boyd-Graber, Daniel Stephens, Emily Walpole, Alden A. Dima, Juan Fung
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often used, however, their validity has been questioned for neural topic models
Training in machine learning necessarily involves more operations than inference only, with higher precision, more memory, and added computational complexity. In hardware, many implementations side-step this issue by designing "inference-only" hardware
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML
Bruce D. Ravel, Phillip Michael Maffettone, Daniel Allan, Andi Barbour, Thomas Caswell, Dmitri Gavrilov, Marcus Hanwell, Thomas Morris, Daniel Olds, Maksim Rakitin, Stuart Campbell
Simon Taylor, Charles Macal, Andrea Matta, Markus Rabe, Susan Sanchez, Guodong Shao
Simulations are used to investigate physical systems. A digital twin goes beyond this by connecting a simulation with the physical system with the purpose of analyzing and controlling that system in real-time. In the past five years, there has been a
Edward Griffor, Matthew Bundas, Chasity Nadeau, Jeannine Shantz, Thanh Nguyen, Marcello Balduccini, Tran Son
Artificial Intelligence (AI) is often critical to the success of modern business processes. Leveraging it, however, is non-trivial. A major hurdle is communication: discussing system requirements among stakeholders with different backgrounds and goals
Yooyoung Lee, Jonathan Fiscus, Lukas Diduch, Jeffery Byrne
This document describes an evaluation of the 2022 Open Fine-grained Activity Detection (OpenFAD) challenge. The evaluation plan covers resources, task definitions, task conditions, file formats for system inputs and outputs, evaluation metrics, scoring
Labeling software security vulnerabilities would benefit greatly modern artificial intelligence cybersecurity research. The National Vulnerability Database (NVD) partially achieves this via assignment of Common Weakness Enumeration (CWE) entries to Common
Matthew Daniels, William Borders, Nitin Prasad, Advait Madhavan, Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Lei Wan, Michael Tran, Jordan Katine, Daniel Lathrop, Brian Hoskins, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this
Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There has been a range
Data collected from the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) are used to develop a physics-based and four machine learning models of ice-on-coil thermal energy storage (TES): linear
Superconducting Optoelectronic Networks (SOENs) combine pho- tonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges
We present the development of a bias compensating reinforcement learning (RL) algorithm that optimizes thermal comfort (by minimizing tracking error) and control utilization (by penalizing setpoint deviations) in a multi-zone heating, ventilation, and air
ryan muddiman, Kevin O' Dwyer, Charles Camp, Bryan Hennelly
Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400–4000 cm−1, with millisecond acquisition times. Raw BCARS spectra, however, are a coherent combination of vibrationally
Milos Drobnjakovic, Boonserm Kulvatunyou, Simon P. Frechette, Vijay Srinivasan
ISO and IEC have jointly initiated, and recently issued, a series of standards (the ISO/IEC 21838 series) for top-level ontologies. These standards have been used by industrial consortia to develop and disseminate standards for mid-level ontologies to ease
Wesley Garey, Richard A. Rouil, Evan Black, Tanguy Ropitault, Weichao Gao
The Open Radio Access Network (O-RAN) Alliance is the industry led standardization effort, with the sole purpose of evolving the Radio Access Network (RAN) to be more open, intelligent, interoperable, and autonomous to support the ever growing need of
Adam McCaughan, Bakhrom Oripov, Natesh Ganesh, Sae Woo Nam, Andrew Dienstfrey, Sonia Buckley
We show that model-free perturbative methods can be used to efficiently train modern neural network architectures in a way that can be directly applied to emerging neuromorphic hardware. These methods were investigated for training VLSI neural networks
Patrick D. O'Reilly, Kristina Rigopoulos, Larry Feldman, Greg Witte
During Fiscal Year 2022 (FY 2022) – from October 1, 2021, through September 30, 2022 –the NIST Information Technology Laboratory (ITL) Cybersecurity and Privacy Program successfully responded to numerous challenges and opportunities in security and privacy