{"id":"W4412495092","doi":"10.54254/2755-2721/2025.po25263","title":"DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University","funders":"","keywords":"Dual (grammatical number); Computer science; Path (computing); Net (polyhedron); Computer network; Real-time computing; Artificial intelligence; Mathematics; Art","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":[0.0004439417,0.001105381,0.0007926014,0.001305934,0.0003101143,0.0005737853,0.001239351,0.0004919129,0.002923657],"category_scores_gemma":[0.001322987,0.0002786354,0.0004925196,0.0009073456,0.0002146656,0.0009538816,0.001697996,0.0006159014,0.001392159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000694885,"about_ca_system_score_gemma":0.0007003446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01073536,"about_ca_topic_score_gemma":0.02355293,"domain_scores_codex":[0.9996824,0.00004457296,0.00001444392,0.0001315647,0.00007505246,0.00005200428],"domain_scores_gemma":[0.9997385,0.00007806341,0.00002567983,0.00004371408,0.00008042444,0.00003357478],"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":[0.0006593073,0.0005084127,0.01202209,0.0002905107,0.0002902916,0.0002201273,0.0001109803,0.03582951,0.02487184,0.00282977,0.0400461,0.8823211],"study_design_scores_gemma":[0.00007328252,0.000310363,0.01000036,0.00002831842,0.000117295,0.0004731631,0.0001121365,0.944361,0.02078065,0.006436718,0.01725418,0.00005250058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1200606,0.002483205,0.8257586,0.0006378062,0.0006511441,0.000415224,0.008744027,0.03116259,0.01008662],"genre_scores_gemma":[0.7777894,0.0007812498,0.1934723,0.0006795774,0.0001967444,0.0004097157,0.01535925,0.0002829374,0.01102883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01073536,"threshold_uncertainty_score":0.02134573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008029380998463242,"score_gpt":0.2068887324099628,"score_spread":0.1988593514114995,"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."}}