{"id":"W3197309071","doi":"10.1109/isc253183.2021.9562915","title":"DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamic time warping; Computer science; Anomaly detection; Limiting; Traffic flow (computer networking); Process (computing); Trajectory; Artificial neural network; Real-time computing; Data mining; Artificial intelligence; Machine learning; Computer network; Engineering","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":[0.00105127,0.001012888,0.0005330345,0.001636929,0.0003418522,0.0008323067,0.001097214,0.0007403176,0.001889273],"category_scores_gemma":[0.002261952,0.0004039218,0.0005056161,0.0008406843,0.0004424753,0.001745813,0.0008655442,0.001023196,0.0004578267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008613252,"about_ca_system_score_gemma":0.001101697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01399932,"about_ca_topic_score_gemma":0.01135999,"domain_scores_codex":[0.9996837,0.00005794261,0.00002016617,0.0001113151,0.00008317408,0.00004370677],"domain_scores_gemma":[0.9993075,0.0002627441,0.00008833165,0.0001007834,0.0001847892,0.00005591574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000346226,0.0004219246,0.01230458,0.0001100455,0.0002052382,0.0002374797,0.000123369,0.4872302,0.01459747,0.008996583,0.01331343,0.4621134],"study_design_scores_gemma":[0.000004865886,0.00001089115,0.0004234533,0.000001583133,0.000002942987,0.00001578397,0.000003306293,0.9957113,0.001491755,0.001910264,0.000419586,0.000004285828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07165521,0.000259801,0.9166086,0.0003433116,0.00007940267,0.00008855263,0.001199056,0.008582665,0.001183274],"genre_scores_gemma":[0.6058914,0.0003827686,0.3835309,0.0002129636,0.0001011099,0.0001683034,0.005092649,0.0003131087,0.004306839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01399932,"threshold_uncertainty_score":0.02783567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766094621403856,"score_gpt":0.2513909948002839,"score_spread":0.2337300485862453,"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."}}