{"id":"W4415189903","doi":"10.1007/978-981-95-3453-1_9","title":"Federated Spatio-Temporal Attention for Time Series Anomaly Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Anomaly detection; Leverage (statistics); Discriminative model; Graph; Time series; Anomaly (physics); Architecture","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":[0.0009108867,0.0008997027,0.001220414,0.001439112,0.000449033,0.0009905777,0.001504863,0.00097635,0.004201182],"category_scores_gemma":[0.002105235,0.0002578146,0.0008337366,0.001740227,0.0003126468,0.001405659,0.00155978,0.001165509,0.001201093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006489237,"about_ca_system_score_gemma":0.0007442853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007147989,"about_ca_topic_score_gemma":0.007967243,"domain_scores_codex":[0.9994839,0.00006897993,0.00003257191,0.0001721492,0.0001490654,0.00009328232],"domain_scores_gemma":[0.9991825,0.0003294924,0.00005501142,0.0001657152,0.0002119946,0.00005523484],"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":[0.0003549171,0.0002314833,0.001463291,0.00009802507,0.0001175808,0.0001683227,0.00006644813,0.03253955,0.02470039,0.004120802,0.007296512,0.9288426],"study_design_scores_gemma":[0.000008898369,0.00007703632,0.001081448,0.00001174631,0.00005122347,0.000151046,0.00002585221,0.9793797,0.008602626,0.007972085,0.002626661,0.0000116854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03299278,0.001857454,0.9575585,0.0002548905,0.0003690366,0.00005675623,0.0004325651,0.004045766,0.002432303],"genre_scores_gemma":[0.6646766,0.001377818,0.3210464,0.000394302,0.0005950839,0.0001176422,0.001662604,0.0003043607,0.009825283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007147989,"threshold_uncertainty_score":0.01421279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009936095344302365,"score_gpt":0.2378768450473981,"score_spread":0.2279407497030957,"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."}}