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Trojan Detection Software Challenge - image-classification-sep2022-train
Data provided by National Institute of Standards and Technology
Round 11 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. 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 dataset consists of 288 AI models using a small set of model architectures.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Optimal Bayesian Experimental Design Version 1.2.0
Data provided by National Institute of Standards and Technology
Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.
Tags: GitHub pages template,experimental design,Bayesian,optbayesexpt,python,adaptive measurement,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - object-detection-feb2023-train
Data provided by National Institute of Standards and Technology
Round 13 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained both on synthetic image data build from Cityscapes and the DOTA_v2 dataset. 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 dataset consists of 128 AI models using a small set of model architectures.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - cyber-pdf-dec2022-train
Data provided by National Institute of Standards and Technology
Round 12 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of pdf malware classification AIs trained Contaigio dataset feature vectors. 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 dataset consists of 120 AI models using a small set of model architectures.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Simantha: Simulation for Manufacturing
Data provided by National Institute of Standards and Technology
Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.
Tags: discrete-event simulation,manufacturing,production,maintenance,python,
Modified: 2024-02-22
Views: 0
Python tools for measuring filament defects in embedded 3D printing
Data provided by National Institute of Standards and Technology
In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.
Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,
Modified: 2024-02-22
Views: 0
Entropy Source Validation Client
Data provided by National Institute of Standards and Technology
This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.
Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,
Modified: 2024-02-22
Views: 0
Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making
Data provided by National Institute of Standards and Technology
Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.
Tags: discrete-event simulation,manufacturing,production,maintenance,python,
Modified: 2024-02-22
Views: 0
FCpy: Feldman-Cousins Confidence Interval Calculator
Data provided by National Institute of Standards and Technology
Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.
Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,
Modified: 2024-02-22
Views: 0
Software and Data associated with "Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA"
Data provided by National Institute of Standards and Technology
The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following referenceDeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.The software and data can also be found at https://github.com/usnistgov/dye_dna_plates.
Tags: fluorescence,numerical-optimization,mathematical-modeling,fluorescence-data,total-least-squares,python,intercalating dyes,noise-removal,
Modified: 2024-02-22
Views: 0