{"id":"W4402406946","doi":"10.1145/3643659.3648560","title":"AmbieGenVAE at the SBFT 2024 Tool Competition - Cyber-Physical Systems Track","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Track (disk drive); Competition (biology); Cyber-physical system; Operating system","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":[0.008305577,0.003644329,0.001471601,0.001646637,0.0009112014,0.002560314,0.003424204,0.003972674,0.02275716],"category_scores_gemma":[0.01625891,0.0006434234,0.001448969,0.0005762478,0.001391505,0.002336305,0.004119113,0.002885729,0.01534072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008905517,"about_ca_system_score_gemma":0.001799492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003781601,"about_ca_topic_score_gemma":0.006665107,"domain_scores_codex":[0.9927085,0.002477557,0.0002869113,0.001365216,0.002387325,0.0007746716],"domain_scores_gemma":[0.9899482,0.003996796,0.0003100536,0.001992662,0.002356021,0.001396331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003033353,0.00258617,0.01030662,0.001132611,0.0007612765,0.001966726,0.0006349324,0.07663119,0.03266544,0.01150372,0.5471431,0.3116349],"study_design_scores_gemma":[0.002073483,0.005671338,0.01551887,0.0003879221,0.0001874256,0.002542231,0.0005006053,0.4655508,0.0923294,0.02964801,0.3852187,0.000371314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.229393,0.002798614,0.459187,0.004329562,0.007690374,0.002798526,0.03925686,0.1763265,0.07821966],"genre_scores_gemma":[0.5969687,0.0003955092,0.2184474,0.001536974,0.0004482446,0.001652964,0.1132296,0.01252073,0.0547999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02275716,"threshold_uncertainty_score":0.07613033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113904323420167,"score_gpt":0.2602914419415798,"score_spread":0.2489010095995631,"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."}}