A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

This library implements a secure device onboarding protocol for Named Data Networking using SPAKE2 and NDNCERT. It contains three components corresponding to the roles defined in the protocol: device, authenticator, certificate authority.

The ASAPS Dataset includes eight continuous hours of data from across a fictitious ASAPS City representative of a normal day in a small city. The dataset includes multiple data types - video, audio, text, sensor, social media. The video data was created from a series of staged events, and then synchronized and augmented with recorded audio, simulated data for sensor, text, and social media related to the variety of emergency events across the city. The dataset represents a geographic area of approximately 68 square blocks.

Internet of Things (IoT) is comprised of interacting networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in their environment. The development of these systems spans all industrial sectors and demands collaborative effort between research and development teams from multiple institutions. Realizing the full potential of IoT requires interoperability between heterogeneous systems and development processes supported by robust platforms for experimentation and testing across domains.

This data set contains the data/information used to plot the graphs published in the 2022 Nat. Comm. article (titled "Amontons-Coulomb-like slip dynamics in acousto-microfluidics") of authors A. Quelennec, J.J. Gorman and D.R. Reyes.

This dataset contains CSV files for the figures in the paper titled "Restricted Domain Compressive Sensing for Antenna Metrology".

Round 9 Test DatasetThis is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round 9 Holdout DatasetThis is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round 9 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at https://doi.org/10.18434/mds2-2428)