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Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege.

, , и . CoRR, (2020)

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Ahmed Sedek Mahmoud Sayed

El-Sayed Mahmoud Zanoun

Khalid Ahmed Mahmoud Sayed Ahmed

Ahmed Ibrahim Mahmoud Sayed Labena

Ahmed Ibrahim Mahmoud Sayed Labena

 

Другие публикации лиц с тем же именем

The University of Maryland at the TREC 2020 Fair Ranking Track., и . TREC, том 1266 из NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval., , , , , , , , , и 22 other автор(ы). SIGIR Forum, 53 (2): 20-43 (2019)A Test Collection for Relevance and Sensitivity., , , , , , и . SIGIR, стр. 1605-1608. ACM, (2020)Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege., , и . CoRR, (2020)Providing More Efficient Access to Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege., , и . ACM Journal on Computing and Cultural Heritage, 15 (1): 5:1-5:19 (2022)Search with Discretion: Value Sensitive Design of Training Data for Information Retrieval., , , , , и . Proc. ACM Hum. Comput. Interact., 5 (CSCW1): 133:1-133:20 (2021)TRUESET: Nearly Practical Verifiable Set Computations., , , , , и . IACR Cryptol. ePrint Arch., (2014)TRUESET: Faster Verifiable Set Computations., , , , , и . USENIX Security Symposium, стр. 765-780. USENIX Association, (2014)Comparing Intrinsic and Extrinsic Evaluation of Sensitivity Classification., , и . ECIR (2), том 13186 из Lecture Notes in Computer Science, стр. 215-222. Springer, (2022)Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content., и . SIGIR, стр. 615-624. ACM, (2019)