{"id":"W2901801304","doi":"10.1016/j.knosys.2018.11.011","title":"A hierarchical memory network-based approach to uncertain streaming data","year":2018,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Changjiang Scholar Program of Chinese Ministry of Education; China Scholarship Council; Science and Technology Commission of Shanghai Municipality; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Forgetting; Computer science; Outlier; Artificial intelligence; Streaming data; Term (time); Memory model; Data mining; Machine learning; Shared memory","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":[],"consensus_categories":[],"category_scores_codex":[0.001033564,0.0002520792,0.0003024506,0.0002098598,0.0004694277,0.0002914289,0.00260135,0.000140699,0.000008277182],"category_scores_gemma":[0.00005404109,0.0002315148,0.00007614608,0.001388616,0.0001282789,0.0001921638,0.0004842571,0.0002148902,0.0002829107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001294377,"about_ca_system_score_gemma":0.0003921844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001429212,"about_ca_topic_score_gemma":0.0000393261,"domain_scores_codex":[0.9974951,0.000263312,0.0004024954,0.001020978,0.0002977366,0.0005203758],"domain_scores_gemma":[0.9963137,0.0002023221,0.000132459,0.002800535,0.000251031,0.0002999903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001498714,0.003567667,0.001320689,0.0008840289,0.0001791276,0.00002253462,0.001588318,0.05137462,0.003030503,0.2130362,0.4037865,0.3210599],"study_design_scores_gemma":[0.0002696478,0.000154566,0.00009482022,0.00009543691,0.000009600211,0.000006726392,0.00002615818,0.8934549,0.0009219135,0.0001157095,0.1045536,0.0002968499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007659699,0.0001700648,0.9771162,0.0003084862,0.0004942496,0.0008612878,0.00002567516,0.0008561374,0.01940192],"genre_scores_gemma":[0.8710635,4.879563e-7,0.1261181,0.0002667505,0.001157943,0.0004313331,0.00005624834,0.00002946932,0.0008761977],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8702976,"threshold_uncertainty_score":0.94409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05703154327201754,"score_gpt":0.3035101139668496,"score_spread":0.246478570694832,"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."}}