DR-NTU (Data)

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DR-NTU (Data) is for research data deposit. For research paper deposits, please use DR-NTU.

Mission: DR-NTU (Data) curates, stores, preserves, makes available and enables the download of digital data generated by the NTU research community. The repository develops and provides guidance for managing, sharing, and reusing research data to promote responsible data sharing in support of open science and research integrity.

Who can deposit? NTU faculty, research staff and students.

What can be deposited? Final, non-sensitive research data from projects carried out at NTU. The uploaded content must not infringe upon the copyrights or other intellectual property rights, and must be void of all identifiable information.

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Asian School of the Environment (ASE)

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68,641 to 68,650 of 72,651 Results

UPtry.tab Feb 28, 2020 - Replication Data for: A Low-power Reliability Enhanced Arbiter Physical Unclonable Function Based on Current Starved Multiplexers Tabular Data - 24.5 KB - 128 Variables, 49 Observations - UNF:6:McYqKK3mHv2Oi3aTM/IqfQ==
ZTC.tab Feb 28, 2020 - Replication Data for: A Low-power Reliability Enhanced Arbiter Physical Unclonable Function Based on Current Starved Multiplexers Tabular Data - 273 B - 8 Variables, 7 Observations - UNF:6:JhNNKeLXrjJsTHkRnMWmXg==
Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection Feb 28, 2020 - WANG Si Wang, Si, 2020, "Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection", https://doi.org/10.21979/N9/WCIL7X, DR-NTU (Data), V1 This dataset contains the program source code, model file and experimental data for analysis of the paper "Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection"
AsianHOSTdata.tar.gz Feb 28, 2020 - Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection Gzip Archive - 1022.9 MB - MD5: de6cf304780bdf5cd3f5e7744d2bd987
source code.tar.gz Feb 28, 2020 - Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection Gzip Archive - 13.2 KB - MD5: b0f06825beb719deb87714d1ff18d6d4
Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs Feb 28, 2020 - WANG Si Wang, Si, 2020, "Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs", https://doi.org/10.21979/N9/YPY0EB, DR-NTU (Data), V1 This dataset contains model file, program source code and the experimental data for the analysis of the paper " Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs".
DTmobilenet.ipynb Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs Jupyter Notebook - 22.5 KB - MD5: e80222aa040cbd0b9084dfb65d9f8109 source code to generate decision tree for mobilenet
DTvgg16.ipynb Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs Jupyter Notebook - 35.7 KB - MD5: 410ad43334e820a53dea170b0a697ac6 source code to generate decision tree for vgg16
ISCASdata.tar.gz Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs Gzip Archive - 492.6 MB - MD5: 1ef6e12ff2681bb29237cb3f419a89a7
Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure Feb 28, 2020 - LI Tanghua Li, Tanghua, 2020, "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure", https://doi.org/10.21979/N9/26AY8H, DR-NTU (Data), V1 The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America.

UPtry.tab

Feb 28, 2020 - Replication Data for: A Low-power Reliability Enhanced Arbiter Physical Unclonable Function Based on Current Starved Multiplexers

Tabular Data - 24.5 KB - 128 Variables, 49 Observations -

ZTC.tab

Feb 28, 2020 - Replication Data for: A Low-power Reliability Enhanced Arbiter Physical Unclonable Function Based on Current Starved Multiplexers

Tabular Data - 273 B - 8 Variables, 7 Observations -

Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection

Feb 28, 2020 - WANG Si

Wang, Si, 2020, "Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection", https://doi.org/10.21979/N9/WCIL7X, DR-NTU (Data), V1

This dataset contains the program source code, model file and experimental data for analysis of the paper "Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection"

AsianHOSTdata.tar.gz

Feb 28, 2020 - Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection

Gzip Archive - 1022.9 MB -

source code.tar.gz

Feb 28, 2020 - Replication Data for: Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection

Gzip Archive - 13.2 KB -

Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs

Feb 28, 2020 - WANG Si

Wang, Si, 2020, "Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs", https://doi.org/10.21979/N9/YPY0EB, DR-NTU (Data), V1

This dataset contains model file, program source code and the experimental data for the analysis of the paper " Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs".

DTmobilenet.ipynb

Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs

Jupyter Notebook - 22.5 KB -

source code to generate decision tree for mobilenet

DTvgg16.ipynb

Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs

Jupyter Notebook - 35.7 KB -

source code to generate decision tree for vgg16

ISCASdata.tar.gz

Feb 28, 2020 - Replication Data for: Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs

Gzip Archive - 492.6 MB -

Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure

Feb 28, 2020 - LI Tanghua

Li, Tanghua, 2020, "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure", https://doi.org/10.21979/N9/26AY8H, DR-NTU (Data), V1

The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America.

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