{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003525882,0.0001803322,0.0001752766,0.0002429106,0.002175702,0.0003328101,0.003111263,0.00008980618,0.000007996971],"category_scores_gemma":[0.000024791,0.0002023514,0.00007553236,0.0002229958,0.0003193904,0.0004583168,0.0003350964,0.0004638894,0.00003852308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005325863,"about_ca_system_score_gemma":0.00002623415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002240688,"about_ca_topic_score_gemma":0.00006944557,"domain_scores_codex":[0.9986559,0.00008608856,0.0003514215,0.0004907718,0.0001676654,0.0002481934],"domain_scores_gemma":[0.9934424,0.0002947481,0.0002020159,0.005895769,0.00003908968,0.0001259963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005353257,0.0006529364,0.0006745277,0.00001345052,0.00002506901,8.244201e-7,0.00161388,0.0006406145,0.001425883,0.01274351,0.00001237935,0.9821916],"study_design_scores_gemma":[0.003601357,0.0001869444,0.06796876,0.0002774839,0.0001009289,0.0001196407,0.001193252,0.8226688,0.005221737,0.03840103,0.05861255,0.001647531],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01546314,0.0001730196,0.9789518,0.004005444,0.00007970975,0.0006014215,0.0000269651,0.0001681066,0.0005304259],"genre_scores_gemma":[0.6964543,0.0007362362,0.3025666,0.00003470939,0.00003055665,0.00014206,0.00001829957,0.0000091927,0.000007951837],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.980544,"threshold_uncertainty_score":0.9991233,"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."}}