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Milman_Interview_47_20191120.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 346.4 KB - SHA512: 6228ec31cd270c0b145a7273e1dd9666f1de68e540419468b602c05c9e22fb6a7de15a6f228cbced46fc31d468c88244ff97ce31d6ad30d2e23736d7ed9a5063
Data
Milman_Interview_48_20191120.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 336.4 KB - SHA512: 27a79a2e34ebed343ee82313e34e1699d6ac2a68754df550408ad8d3e877d8e667f12c77d08ae3dd105a0f23e6550bf01388f953abca31681295437aaae5e622
Data
Milman_Interview_49_20200203.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 170.7 KB - SHA512: b525d1a90fac3ac612210b4b07b754ad2f71a56d8a964ef38110839651fbc26dc4bebd6c3455cabbe94ffe0a44dd0654b5afe1ee3a29c7cc6eef40cff474455e
Data
Milman_Interview_50_20200312.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 256.5 KB - SHA512: 18e1d207596cd177a486bcb088a4e71c3f5087ce89a3d8bf1c861bca0b2ba8b9caf519097b3940d8fb7dd02b53e7c8d8f3162843740b0044d45c8caf06c825ec
Data
Milman_Interview_51_20200910.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 234.9 KB - SHA512: a700b880a726cc04c237a0adda646944cfb3e34e6640f1983a7b96ae3926e5ef5c414d3992ab6424d3f6aef84d582c0c2fd3031edb16557bc169df8e89783f29
Data
Milman_Interview_52_20200920.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 171.8 KB - SHA512: 7a491ebb140c0c6add23653071f5656f87cde378020275871607148890034f9bc3234a5af06acbcd04a57d68ac8a6e48345b0534fcc9734d7ab5af3c14769579
Data
Milman_RedactionProtocol.pdf
Milman_RedactionProtocol.pdf
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Adobe PDF - 79.5 KB - SHA512: ae4f907f8f98eb3bd60bd000e39db9d4743a2be2bd1a0d965d4ee857a5879868b767ba673a915b079012747f0f588bc0f81674705380da73ee6eae548de740c5
Documentation
README_Milman.txt
Oct 3, 2023 - Ascertaining Intergovernmental Coordination Mechanisms
Plain Text - 8.7 KB - SHA512: 83a5ddbd1cc48213c9d73e50242b4a938ee63d32d5a43327216b9870f77f4366fb0fe8e1a906ac0a647a0487d231b80fce93e30c104d9ec8ab0621b2d1c91f29
Documentation
Data for: The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI
Data for: The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI
Sep 25, 2023
Bremers, Alexandra; Fang, Xuanyu; Friedman, Natalie; Ju, Wendy. 2023. "Data for: The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI". Qualitative Data Repository. https://doi.org/10.5064/F6TAWBGS. QDR Main Collection. V1
Project Overview For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders – their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed...
Bremers-et-al_1001.zip
Sep 25, 2023 - Data for: The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI
ZIP Archive - 36.3 MB - SHA512: f59aa8a3a939297db9e3f0b1abb8dc5075838a9988858aa3acef7cb9db0e0577f0fa47be7af5aa2a0ca24010bc9db5ef5dbdee9cbd48bcaf9e23860b26a399b2
Data
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