{"id":"W2980984693","doi":"10.1145/3350546.3352550","title":"Detecting Anomalous Behaviour from Textual Content in Financial Records","year":2019,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Anomaly detection; Malpractice; Deep learning; Anomaly (physics); Base (topology); Artificial intelligence; Content (measure theory); Finance; Scale (ratio); Work (physics); Machine learning; Information retrieval; Mathematics; Business; Engineering","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":[0.001184323,0.0006163125,0.000535464,0.006122993,0.0004277835,0.001351468,0.0007096275,0.0009308223,0.0007514099],"category_scores_gemma":[0.009962943,0.0001868328,0.0003660046,0.003250705,0.0003283754,0.002469718,0.0007605444,0.0006832113,0.0008254514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005891451,"about_ca_system_score_gemma":0.0005186902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002424619,"about_ca_topic_score_gemma":0.003609832,"domain_scores_codex":[0.9984806,0.0002838634,0.0002713481,0.0002705798,0.0005526929,0.0001408651],"domain_scores_gemma":[0.9892581,0.00492035,0.00293958,0.0008108724,0.001678314,0.0003927241],"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":[0.001302942,0.001014822,0.3288302,0.000743345,0.0001625107,0.002068596,0.001718129,0.01410922,0.04282656,0.002496662,0.01139211,0.5933349],"study_design_scores_gemma":[0.00004145763,0.0005448534,0.1986638,0.0002227494,0.0001788683,0.002173082,0.002389831,0.7100752,0.06219471,0.008824635,0.01457961,0.0001112784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988422,0.0009646271,0.08405513,0.001272773,0.0001413716,0.0002388007,0.006883631,0.004507426,0.003094112],"genre_scores_gemma":[0.9416209,0.0003453255,0.05159209,0.0001012793,0.0001212024,0.00005589666,0.005149652,0.00005574121,0.0009579859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006122993,"threshold_uncertainty_score":0.006263435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09267781734851123,"score_gpt":0.3137047356491844,"score_spread":0.2210269183006732,"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."}}