{"id":"W4387430347","doi":"10.1007/978-3-031-45275-8_45","title":"HEART: Heterogeneous Log Anomaly Detection Using Robust Transformers","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Anomaly detection; Computer science; Parsing; Transformer; Data mining; Artificial intelligence; Voltage; Engineering","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":[0.0009305195,0.001147101,0.0009481496,0.001859946,0.0003496014,0.001616878,0.00204999,0.0007275376,0.006541679],"category_scores_gemma":[0.003652009,0.0005527815,0.0005981199,0.001098745,0.0004787956,0.002592636,0.001883794,0.001196459,0.002949258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003686111,"about_ca_system_score_gemma":0.000636478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001445661,"about_ca_topic_score_gemma":0.001581735,"domain_scores_codex":[0.9991136,0.00008216641,0.00004869555,0.0002664239,0.0004118668,0.00007724881],"domain_scores_gemma":[0.9984998,0.0004833439,0.0001501575,0.0005261134,0.0002591487,0.00008150883],"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":[0.000972719,0.0002218861,0.005456902,0.0001737612,0.0001576081,0.0003728932,0.0001204695,0.02589777,0.04038716,0.009372992,0.02665746,0.8902085],"study_design_scores_gemma":[0.0001003483,0.000220129,0.002074355,0.0000265184,0.00008441476,0.0008044584,0.00006941094,0.9024966,0.05002189,0.03199296,0.01205271,0.00005631264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009399435,0.0001782437,0.9479708,0.000092555,0.0001122169,0.00006826791,0.0005021928,0.04030364,0.001372573],"genre_scores_gemma":[0.4261201,0.0002597095,0.5595043,0.0002077968,0.000192224,0.0001165126,0.002140846,0.003622535,0.007835952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006541679,"threshold_uncertainty_score":0.02188408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768107247194464,"score_gpt":0.2453704293377929,"score_spread":0.2176893568658482,"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."}}