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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.

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57,851 to 57,860 of 72,657 Results
Figure1_IV_61to80.xls
Aug 6, 2020 - Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing
MS Excel Spreadsheet - 1.4 MB - MD5: 8631478471413137b13f7cf0c37cf2e6
Endurance
Figure1_IV_81to100.xls
Aug 6, 2020 - Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing
MS Excel Spreadsheet - 1.4 MB - MD5: 6220cfb0df17e1d5cd8bdd99d99b67a8
Endurance
Figure3_Soft boundaries protocol.docx
Aug 6, 2020 - Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing
MS Word - 363.7 KB - MD5: 4f5f5ec3336ec982558813663fc7e0fb
Soft boundary analysis protocol
Figure4_STDP protocol.docx
Aug 6, 2020 - Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing
MS Word - 62.2 KB - MD5: 0fd3aff91cec53fb18d1d16e295f64f7
STDP measurement protocol
Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Aug 6, 2020 - Social and Affective Neuroscience
Esposito, Gianluca; Gabrieli, Giulio, 2019, "Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages", https://doi.org/10.21979/N9/YCDXNE, DR-NTU (Data), V2, UNF:6:OXjgBp2o1P5HHQM/zPdxYA== [fileUNF]
The aesthetic appearance of websites can influence the perception of their usability, reliability, and trustworthiness. Several studies investigated the relationship between single aesthetic features and explicit aesthetic judgments, demonstrating the existence of attribution bia...
ElasticNetExplicit.joblib
Aug 6, 2020 - Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Unknown - 25.5 KB - MD5: 803759dc074d93ef70466a79e932948c
Models
Trained model
ElasticNetImplicit.joblib
Aug 6, 2020 - Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Unknown - 25.6 KB - MD5: dba7459d30a53e3aeb8328ffd12953d3
Models
Trained model
Explicit.py
Aug 6, 2020 - Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Python Source Code - 9.1 KB - MD5: 79d24b130293f99dc99feafaf93dfb3b
HPC Script for training and testing of the Lasso and ElasticNet models
Implicit.py
Aug 6, 2020 - Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Python Source Code - 9.8 KB - MD5: ffc0bbac9b4fb1aaeb4ed43f5941ce15
HPC Script for training and testing of the Lasso and ElasticNet models
LassoExplicit.joblib
Aug 6, 2020 - Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages
Unknown - 57.7 KB - MD5: bef6c44a914a59cc6469c74ced5384ed
Models
Trained model
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