{"id":"W4226428024","doi":"10.1609/aaai.v36i11.21465","title":"Knowledge Sharing via Domain Adaptation in Customs Fraud Detection","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Institute for Basic Science; National Research Foundation","keywords":"Safeguarding; Business; Adaptation (eye); Revenue; Knowledge sharing; Tax revenue; Computer security; Knowledge management; Finance; Computer science; Public economics; Economics","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":[0.008751454,0.0007989842,0.001072727,0.003228187,0.00121878,0.002438307,0.002632629,0.001891931,0.00102628],"category_scores_gemma":[0.02045049,0.0005525787,0.0008755945,0.003603802,0.001506689,0.006741649,0.005234281,0.001907307,0.0005893116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047508,"about_ca_system_score_gemma":0.001667625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004159014,"about_ca_topic_score_gemma":0.002979049,"domain_scores_codex":[0.9942948,0.002484293,0.0003773267,0.001656192,0.0008137404,0.000373696],"domain_scores_gemma":[0.9859523,0.005996244,0.001305262,0.005024413,0.001193818,0.0005281175],"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":[0.0007247439,0.002686567,0.03188301,0.0002407388,0.0003711115,0.0007327445,0.002348632,0.1494928,0.01553416,0.007799505,0.006586609,0.7815995],"study_design_scores_gemma":[0.00006224629,0.000192598,0.008292142,0.00005137756,0.0001256267,0.0003883317,0.001073194,0.9379838,0.01415335,0.0288233,0.008784829,0.00006921707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3398902,0.000873851,0.6438034,0.001709153,0.0001729835,0.000843949,0.0005896818,0.005006868,0.007109913],"genre_scores_gemma":[0.8472931,0.0002104142,0.1492727,0.000367546,0.00006918346,0.0002346487,0.0007647646,0.00007267865,0.0017149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008751454,"threshold_uncertainty_score":0.04628265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08251334886681946,"score_gpt":0.3011480409752738,"score_spread":0.2186346921084543,"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."}}