{"id":"W2745390760","doi":"10.1145/3105970","title":"Securing Speech Noise Reduction in Outsourced Environment","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"University at Albany","keywords":"Computer science; Encryption; Plaintext; Noise reduction; Cryptosystem; Speech recognition; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007784706,0.000544598,0.0007043181,0.0003673447,0.0004690017,0.0007668545,0.0006556095,0.000590707,0.001052315],"category_scores_gemma":[0.002507923,0.0001743662,0.0005183126,0.00036557,0.0007417939,0.001262973,0.0008836258,0.0005311225,0.0005419031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004192529,"about_ca_system_score_gemma":0.0006724477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001018099,"about_ca_topic_score_gemma":0.0007730397,"domain_scores_codex":[0.9986002,0.0002350716,0.00008027589,0.0002216049,0.000716918,0.0001460715],"domain_scores_gemma":[0.9978819,0.0005655472,0.0002750609,0.0007460234,0.0004671506,0.00006424855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001463013,0.0002813662,0.004019495,0.0003829358,0.0001713026,0.001605835,0.000886743,0.1264447,0.4176944,0.03197492,0.002585944,0.4124893],"study_design_scores_gemma":[0.00004856576,0.0003086317,0.001423997,0.00002849393,0.0000864422,0.001019142,0.0003130072,0.6729585,0.3101358,0.009412426,0.004218121,0.00004692016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2177851,0.0004702566,0.7777427,0.0002343387,0.00007830161,0.0000642388,0.0000822482,0.0006761622,0.002866645],"genre_scores_gemma":[0.9158757,0.000371987,0.08075504,0.00009621975,0.00005702707,0.0000289459,0.0000859967,0.00004135574,0.002687817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001052315,"threshold_uncertainty_score":0.004116952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02678791002241084,"score_gpt":0.2839370993190619,"score_spread":0.257149189296651,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}