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Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-test
Data provided by National Institute of Standards and Technology
Round 6 Test DatasetThis is the test 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 text sentiment classification 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train part2
Data provided by National Institute of Standards and Technology
Round 6 Train Dataset part2This 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 text sentiment classification 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-holdout
Data provided by National Institute of Standards and Technology
Round 6 Holdout DatasetThis is the holdout 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 text sentiment classification 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-train
Data provided by National Institute of Standards and Technology
Round 7 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 named entity recognition (NER) 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
SRM 2969 Vitamin D Metabolites in Frozen Human Serum (Total 25-Hydroxyvitamin D Low Level) Data
Data provided by National Institute of Standards and Technology
SRM 2969 Electronic files containing certified values and their uncertainties, and the data used to assign those values
Tags: vitamin D,25-hydroxyvitamin D,serum,mass spectrometry,
Modified: 2024-02-22
Views: 0
SRM 2970 Vitamin D Metabolites in Frozen Human Serum (25-Hydroxyvitamin D2 High Level) Data
Data provided by National Institute of Standards and Technology
Electronic files containing certified values and their uncertainties, and the data used to assign those values.
Tags: vitamin D,25-hydroxyvitamin D,serum,mass spectrometry,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-test
Data provided by National Institute of Standards and Technology
Round 7 Test DatasetThis is the test 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 named entity recognition (NER) 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-holdout
Data provided by National Institute of Standards and Technology
Round 7 Holdout DatasetThis is the holdout 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 named entity recognition (NER) 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Trojan Detection Software Challenge - nlp-question-answering-sep2021-train
Data provided by National Institute of Standards and Technology
Round 8 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 extractive question answering (QA 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.
Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;,
Modified: 2024-02-22
Views: 0
Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion
Data provided by National Institute of Standards and Technology
These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number.
Tags: Biosciences and Health,particle,protein particle,sub-micrometer particle,subvisible particle,particle tracking analysis,resonant mass measurement,electrical sensing zone,holographic particle characterization,flow imaging,laser diffraction,flow cytometry,
Modified: 2024-02-22
Views: 0