{"id":"W2913694129","doi":"10.1145/3279952","title":"Deep Learning–Based Multimedia Analytics","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Laboratory of Pattern Recognition; National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Analytics; Closed captioning; Multimedia; Milestone; Domain (mathematical analysis); Visual analytics; Learning analytics; Data science; Artificial intelligence; Visualization; Image (mathematics)","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":[0.0006936312,0.001128823,0.0006079635,0.001789241,0.0002254279,0.001860599,0.001259269,0.0007585829,0.003916964],"category_scores_gemma":[0.002842797,0.0002995464,0.0005802626,0.001578994,0.0005013723,0.002954497,0.001321071,0.001740888,0.001934314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008466492,"about_ca_system_score_gemma":0.0006856142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00275211,"about_ca_topic_score_gemma":0.003626789,"domain_scores_codex":[0.9995115,0.00008413391,0.00003193606,0.0001016702,0.0002133419,0.00005741873],"domain_scores_gemma":[0.9993626,0.0002875786,0.00004740775,0.00007417047,0.0001961078,0.00003212343],"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":[0.0002467072,0.0001433353,0.001888886,0.0007160963,0.000139648,0.0001496799,0.0000996989,0.08825563,0.01351655,0.03210711,0.02974492,0.8329918],"study_design_scores_gemma":[0.00001884173,0.00008424756,0.001119457,0.0001985857,0.00005584315,0.0001239129,0.00007308827,0.8821883,0.01873621,0.06299064,0.03437229,0.00003859909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02122949,0.02459758,0.9232965,0.002917834,0.0004625617,0.0001468029,0.002747739,0.006433748,0.01816772],"genre_scores_gemma":[0.5087914,0.04678361,0.4137431,0.001543025,0.001071423,0.0002763747,0.00893914,0.0006525815,0.01819938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003916964,"threshold_uncertainty_score":0.0131036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748353478677071,"score_gpt":0.2872168292985619,"score_spread":0.2697332945117912,"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."}}