{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002250597,0.0002972561,0.0002882465,0.0001625907,0.0003222745,0.0006293879,0.0008884419,0.0004628907,0.00006961302],"category_scores_gemma":[0.000008847931,0.0003107984,0.000273253,0.0005450734,0.00003786129,0.0003018398,0.001235366,0.0007666577,0.00001021853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001593037,"about_ca_system_score_gemma":0.0001460577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000119263,"about_ca_topic_score_gemma":0.0001133636,"domain_scores_codex":[0.9981025,0.00007754613,0.0004027871,0.0008397385,0.0002354932,0.0003419333],"domain_scores_gemma":[0.9982898,0.00003169481,0.0001916243,0.001184302,0.0001736805,0.0001289506],"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":[0.000002237798,0.0000697381,0.00001362963,0.00002513952,0.00003484854,0.00001229752,0.0001168768,0.6729056,0.0003448035,0.0005081799,0.00008602835,0.3258806],"study_design_scores_gemma":[0.00006210405,0.00002217811,0.0001260623,0.00003299662,0.00001772625,0.00006846173,0.00002731391,0.9949974,0.00341787,0.0002404279,0.0006402978,0.0003472054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05080904,0.0002027351,0.945919,0.0001107071,0.0006952545,0.0003545704,0.000001422611,0.001268965,0.0006383462],"genre_scores_gemma":[0.7035166,0.00007243969,0.2957295,0.0001511514,0.0002398407,0.0001021558,0.00000969532,0.00002004458,0.0001586083],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6527076,"threshold_uncertainty_score":0.9999344,"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."}}