{"id":"W4324125491","doi":"10.1109/tnnls.2023.3252175","title":"Adversarial Danger Identification on Temporally Dynamic Graphs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Adversarial system; Artificial intelligence; Multivariate statistics; Identification (biology); Machine learning; Generalization; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001272476,0.0008605251,0.000638007,0.0007558485,0.000445529,0.0006494033,0.0008591339,0.0009677897,0.0009401454],"category_scores_gemma":[0.005943638,0.0002733748,0.0006154182,0.0004266103,0.001025633,0.001738421,0.001332907,0.001674115,0.0001737831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009591267,"about_ca_system_score_gemma":0.0006186061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003775487,"about_ca_topic_score_gemma":0.002667689,"domain_scores_codex":[0.9993603,0.0001797423,0.00002855797,0.0001697432,0.000160855,0.0001007953],"domain_scores_gemma":[0.9966169,0.002257213,0.0004319698,0.0002749279,0.0002905762,0.0001284224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009101941,0.00001609643,0.001070592,0.00002522711,0.00002214332,0.000124485,0.00005328786,0.9564441,0.002142134,0.01254753,0.0007654722,0.02669786],"study_design_scores_gemma":[0.000001117563,0.00000759681,0.000121853,0.000002082263,0.000002547254,0.00001651333,0.000005615197,0.9946648,0.0004363848,0.004611884,0.0001266129,0.00000300022],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1036444,0.0003015438,0.8926534,0.000497981,0.00007600946,0.0000365107,0.0001007494,0.0004716482,0.002217705],"genre_scores_gemma":[0.9671178,0.0002072453,0.03033426,0.0001529441,0.00003625758,0.00002789584,0.0001592483,0.00004686757,0.001917526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003775487,"threshold_uncertainty_score":0.007507026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214025023442221,"score_gpt":0.2493797509602252,"score_spread":0.237239500725803,"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."}}