{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007531152,0.0006657914,0.0005246011,0.0003179092,0.0003857382,0.0006578248,0.0008341455,0.0007522291,0.001808588],"category_scores_gemma":[0.00256343,0.0002549464,0.0005198068,0.0005412713,0.000694113,0.001694427,0.001248666,0.001839838,0.0004881874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009656014,"about_ca_system_score_gemma":0.001344598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006200754,"about_ca_topic_score_gemma":0.0087384,"domain_scores_codex":[0.9996079,0.00008962887,0.00002675929,0.00007744181,0.0001335026,0.0000648077],"domain_scores_gemma":[0.9994281,0.0001698627,0.00006069507,0.0001912866,0.0001115804,0.00003840064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004490985,0.0001729335,0.002732718,0.0001619132,0.0001121799,0.0002520038,0.0001771277,0.532155,0.01899463,0.05447715,0.01093012,0.379385],"study_design_scores_gemma":[0.00000518824,0.0000231038,0.0001451553,0.000005300573,0.000005550108,0.00002447445,0.000009438567,0.9865257,0.003165111,0.009108116,0.0009777852,0.000005061645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03215517,0.0005062816,0.9631882,0.0006648724,0.00008235002,0.00004235564,0.0002116109,0.001441958,0.001707252],"genre_scores_gemma":[0.7019477,0.0007967223,0.288403,0.0004615122,0.0001085602,0.0001336689,0.001207979,0.0001750098,0.006766023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006200754,"threshold_uncertainty_score":0.01232934,"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."}}