{"id":"W3117754898","doi":"10.1145/3421276","title":"Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; The Internet; Tensor (intrinsic definition); Computation; Big data; Autoencoder; Multimedia; Artificial intelligence; Key (lock); Deep learning; Data mining; World Wide Web; Computer security; Algorithm","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002872293,0.0002583929,0.0003431828,0.0002049205,0.0004776215,0.0001243478,0.01477398,0.0001510146,0.000005349346],"category_scores_gemma":[0.001547572,0.0002706599,0.0001268375,0.0006835076,0.0003804696,0.000366577,0.003785949,0.0004569611,0.00001274829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004087126,"about_ca_system_score_gemma":0.00004660556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003239893,"about_ca_topic_score_gemma":0.000008699927,"domain_scores_codex":[0.9980879,0.0001152063,0.0006630942,0.0006188283,0.0002227014,0.0002923027],"domain_scores_gemma":[0.9914522,0.002254492,0.0003534234,0.005547228,0.000231303,0.0001613344],"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":[0.00001544024,0.0003911672,0.00007831882,0.00008094175,0.00009952018,1.821486e-7,0.001653592,0.0003199029,0.003266345,0.004206949,0.0004780109,0.9894096],"study_design_scores_gemma":[0.0007632034,0.0001293337,0.0004385811,0.00004765838,0.00004490464,0.000003425248,0.0001151135,0.9792826,0.002393041,0.01378028,0.002751186,0.0002507175],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001532998,0.0001449266,0.9640049,0.03221434,0.00006272348,0.001243758,0.00007975587,0.0006736715,0.00004293952],"genre_scores_gemma":[0.4203665,0.0001207395,0.5789132,0.0002662309,0.00001879429,0.0002103737,0.00008344774,0.00001754055,0.000003205131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9891589,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04717957808957588,"score_gpt":0.3005218673537129,"score_spread":0.253342289264137,"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."}}