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4,161 to 4,170 of 4,319 Results
Replication Data for: GenSlice: Generalized Semantic History Slicing
Aug 21, 2020 - Software Reliability and Security Lab
Zhu, Chenguang; Li, Yi; Rubin, Julia; Chechik, Marsha, 2020, "Replication Data for: GenSlice: Generalized Semantic History Slicing", https://doi.org/10.21979/N9/LPHCUS, DR-NTU (Data), V3
This dataset contains the raw experiment data and replication package for the ICSME'20 paper: "GenSlice: Generalized Semantic History Slicing". The replication package is also available at: https://github.com/Chenguang-Zhu/icsme20-artifact (2020-08-06)
replication-package.tar.gz
Aug 21, 2020 - Replication Data for: GenSlice: Generalized Semantic History Slicing
Gzip Archive - 22.1 MB - MD5: bad1a6b6e9e480248a69937f75e3c8ff
Replication package for GenSlice experiments
raw-results.tar
Aug 7, 2020 - Replication Data for: GenSlice: Generalized Semantic History Slicing
TAR Archive - 165.3 MB - MD5: 7daf37461207cb15da0d96121e626e0c
Raw data obtained in the GenSlice experiments
Replication Data for: Towards Automated Verification of Smart Contract Fairness
May 27, 2020 - Software Reliability and Security Lab
Liu, Ye; Li, Yi; Lin, Shang-Wei, 2020, "Replication Data for: Towards Automated Verification of Smart Contract Fairness", https://doi.org/10.21979/N9/0BEVRT, DR-NTU (Data), V1
Software artifacts created for the paper titled "Towards Automated Verification of Smart Contract Fairness" accepted at FSE'20.
faircon_data.tar
May 27, 2020 - Replication Data for: Towards Automated Verification of Smart Contract Fairness
TAR Archive - 4.8 MB - MD5: 9c829d07a4d2a0208aa442b159087a67
Tarball containing contract code and raw console logs from the experiments.
Obtain_FairCon.md
May 27, 2020 - Replication Data for: Towards Automated Verification of Smart Contract Fairness
Markdown Text - 11.5 KB - MD5: bc233c7fde39d83eccec490a7de3ba14
Information on how to obtain FairCon.
README.md
May 27, 2020 - Replication Data for: Towards Automated Verification of Smart Contract Fairness
Markdown Text - 8.2 KB - MD5: c0a424f42840678f2362a5cf23ae6513
Information about the dataset usage.
Related data for: Heterogeneous Transfer Learning for Thermal Comfort Modeling
May 12, 2020 - Wen Yonggang
Hu, Weizheng; Li, Jie; Zhu, Chenxiao; Zhang, Wei; Wen, Yonggang, 2020, "Related data for: Heterogeneous Transfer Learning for Thermal Comfort Modeling", https://doi.org/10.21979/N9/56MTEH, DR-NTU (Data), V2, UNF:6:PfZTy8hHFxpEL725OduxrA== [fileUNF]
These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and h...
itcm_dataset_6689.tab
May 12, 2020 - Related data for: Heterogeneous Transfer Learning for Thermal Comfort Modeling
Tabular Data - 694.7 KB - 15 Variables, 6689 Observations - UNF:6:PoNs4FzlIgmgecCNzhvvuw==
itcm_dataset_person1_346.tab
May 12, 2020 - Related data for: Heterogeneous Transfer Learning for Thermal Comfort Modeling
Tabular Data - 35.6 KB - 15 Variables, 346 Observations - UNF:6:KF2TNS3vYMelgCoIWfVCZQ==
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