{"id":"W4416961905","doi":"10.1109/pst65910.2025.11268837","title":"Identifying and Addressing User-level Security Concerns in Smart Homes Using “Smaller” LLMs","year":2025,"lang":"","type":"article","venue":"","topic":"Spreadsheets and End-User Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Home automation; Software deployment; Work (physics); Smart city; Threat model; Security information and event management; Internet of Things; Smart grid","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":[0.002290593,0.0008745944,0.0005607805,0.001886835,0.0007040576,0.001200515,0.001050945,0.00160022,0.003382205],"category_scores_gemma":[0.009657118,0.000267872,0.001370237,0.001411681,0.0007061616,0.00302148,0.001819095,0.001424453,0.001898187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423506,"about_ca_system_score_gemma":0.000874279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009893368,"about_ca_topic_score_gemma":0.0202781,"domain_scores_codex":[0.998244,0.0008013736,0.0001164848,0.0004783907,0.000223069,0.0001365769],"domain_scores_gemma":[0.9962581,0.002194467,0.000264833,0.0007204415,0.0004037705,0.0001582802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.002438595,0.002301069,0.2091002,0.002526259,0.000443438,0.001405593,0.006085265,0.09173398,0.02867289,0.01408337,0.1394467,0.5017626],"study_design_scores_gemma":[0.0001966464,0.0006917156,0.07201109,0.0002750337,0.0001959956,0.0009208155,0.004241663,0.7754251,0.01621749,0.03177723,0.09787607,0.0001710776],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6406621,0.003794185,0.2639442,0.005568011,0.0005780102,0.00113542,0.05392904,0.01770894,0.01268006],"genre_scores_gemma":[0.8291788,0.0006955359,0.1065178,0.001160674,0.0001554572,0.0007414066,0.05530114,0.0002647588,0.005984414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009893368,"threshold_uncertainty_score":0.01967156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1472548306086126,"score_gpt":0.3540508870878329,"score_spread":0.2067960564792203,"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."}}