{"id":"W4401871638","doi":"10.1016/j.aej.2024.08.048","title":"Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Graph; Data mining; GRASP; Convolutional neural network; Artificial intelligence; Theoretical computer science","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":[0.0004815463,0.0001062635,0.0001341899,0.0002846586,0.00006026343,0.0001576609,0.0001249488,0.000115514,0.000005497242],"category_scores_gemma":[0.00002486464,0.0001087834,0.00006872521,0.0005124867,0.00001339751,0.0005796052,0.0000380655,0.0002926735,0.000001398864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005966424,"about_ca_system_score_gemma":0.00002279603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003373335,"about_ca_topic_score_gemma":0.000002110813,"domain_scores_codex":[0.9991967,0.00002253465,0.0003336178,0.0001853758,0.0001046058,0.0001571756],"domain_scores_gemma":[0.9996077,0.00007990247,0.0001174093,0.00009400571,0.0000511905,0.00004981195],"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":[0.000276097,0.0001844327,0.01511257,0.0004421504,0.000405069,0.00004597824,0.002467872,0.1090689,0.2272339,0.09543562,0.003549788,0.5457776],"study_design_scores_gemma":[0.0002804575,0.0001354704,0.002253562,0.0002131663,0.00001425915,0.0001202536,0.000004811029,0.9905672,0.003901702,0.001439352,0.0009576369,0.0001121347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08967239,0.0001937791,0.9091889,0.00009523945,0.0004723351,0.0001978205,6.031793e-7,0.0001437647,0.00003517901],"genre_scores_gemma":[0.9626409,0.00002776452,0.03705835,0.000005640899,0.000170624,0.00001342938,0.000002941684,0.00001292593,0.00006740091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8814983,"threshold_uncertainty_score":0.443606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069456445950054,"score_gpt":0.2134684213358179,"score_spread":0.2027738568763173,"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."}}