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Part 1: Document Description
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Citation |
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Title: |
WISDM Dataset Processed |
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Identification Number: |
doi:10.21979/N9/KJWE5B |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2022-05-27 |
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Version: |
1 |
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Bibliographic Citation: |
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "WISDM Dataset Processed", https://doi.org/10.21979/N9/KJWE5B, DR-NTU (Data), V1, UNF:6:UNCqR5C0NzMZQm8xrjVq0Q== [fileUNF] |
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Citation |
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Title: |
WISDM Dataset Processed |
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Identification Number: |
doi:10.21979/N9/KJWE5B |
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Authoring Entity: |
Ragab, Mohamed (Nanyang Technological University) |
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Eldele, Emadeldeen (Nanyang Technological University) |
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Software used in Production: |
Python |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Ragab, Mohamed |
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Depositor: |
Ragab, Mohamed |
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Date of Deposit: |
2022-03-06 |
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Holdings Information: |
https://doi.org/10.21979/N9/KJWE5B |
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Study Scope |
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Keywords: |
Computer and Information Science, Computer and Information Science, Human Activity Recognition, Domain Adaptation |
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Abstract: |
WISDM is another popular activity recognition dataset for the evaluation of time series domain adaptation. In this dataset, accelerometer sensors were applied to collect data from 36 subjects. This data can be more challenging because of the class imbalance issue among different subjects. Particularly, some subjects may contain only samples from a subset of the overall classes. We consider each subject as a separate domain and we randomly select five cross-domain scenarios. |
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Kind of Data: |
Time series |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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File Description--f85782 |
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File: Wisdom_ar_all_column.tab |
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Notes: |
UNF:6:Y3nzRauIwyzGCGoG0U2bVA== |
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File Description--f85784 |
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File: Wisdom_ar.tab |
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Notes: |
UNF:6:5/GCtB+gN+IMxeUvQcw2Rg== |
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List of Variables: | |
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Variables |
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f85782 Location: |
Summary Statistics: Min. 1.0; StDev 10.214225107129932; Valid 1098209.0; Max. 36.0; Mean 18.860667687094033 Variable Format: numeric Notes: UNF:6:cTdWOwu5aiclJa7f/eFOCQ== |
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f85782 Location: |
Variable Format: character Notes: UNF:6:kXVz4UW52Gx0e730UdtWjw== |
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f85782 Location: |
Summary Statistics: StDev 4.94496079194138E13; Mean 3.3408924094799953E13; Valid 1098209.0; Min. 0.0; Max. 2.09397361451E14 Variable Format: numeric Notes: UNF:6:jt8nHwBDvRU8gX7mXSKP9A== |
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f85782 Location: |
Summary Statistics: Min. -19.61; Max. 19.95; Mean 0.6628694278924379; Valid 1098209.0; StDev 6.849044005904711; Variable Format: numeric Notes: UNF:6:lRSHmhT0BtPpkyUJiY7NcA== |
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f85782 Location: |
Summary Statistics: Min. -19.61; Max. 20.04; Valid 1098209.0; Mean 7.255623560097563; StDev 6.746213846399087; Variable Format: numeric Notes: UNF:6:4d156jbr+3CCC86ZrarFHw== |
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f85782 Location: |
Variable Format: character Notes: UNF:6:BsofKGi3vGHD2d2UUkdVJg== |
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f85784 Location: |
Summary Statistics: Min. 1.0; Valid 1098209.0; Mean 18.860667687094033; Max. 36.0; StDev 10.214225107129932 Variable Format: numeric Notes: UNF:6:cTdWOwu5aiclJa7f/eFOCQ== |
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f85784 Location: |
Summary Statistics: StDev 6.849044005904711; Min. -19.61; Mean 0.6628694278924379; Max. 19.95; Valid 1098209.0; Variable Format: numeric Notes: UNF:6:ztH37Qi7WEHg8KgRUH10eQ== |
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f85784 Location: |
Summary Statistics: StDev 6.746213846399087; Max. 20.04; Valid 1098209.0; Mean 7.255623560097563; Min. -19.61; Variable Format: numeric Notes: UNF:6:iQnNTqDIV2uRsDIQu+JY+A== |
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f85784 Location: |
Summary Statistics: Mean 0.4110773332782123; Max. 19.61; Valid 1098207.0; Min. -19.8; StDev 4.754121837139125; Variable Format: numeric Notes: UNF:6:1pOCU3RLTrPLygD5k6Ltog== |
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f85784 Location: |
Summary Statistics: Mean 2.932700424053438; Valid 1098209.0; Max. 5.0; Min. 0.0; StDev 1.9434649094486438 Variable Format: numeric Notes: UNF:6:B8UVMif+HrasLGGPOnDEkg== |
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