{"id":"W4387303184","doi":"10.1109/tmc.2023.3321701","title":"Online Incentive Mechanisms for Socially-Aware and Socially-Unaware Mobile Crowdsensing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Innovation for Defence Excellence and Security","keywords":"Computer science; Incentive; Incentive compatibility; Reverse auction; Rationality; Mobile device; Exploit; Computer security; Selection (genetic algorithm); Bidding; Artificial intelligence; World Wide Web; Microeconomics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006490397,0.0004184722,0.0004939304,0.0003727534,0.001846178,0.0004113949,0.0004762635,0.000236,0.000006873505],"category_scores_gemma":[0.0000181696,0.0004643621,0.0002903797,0.0009936747,0.0001313101,0.0003137509,0.00003443521,0.0004640543,0.00002917228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001743525,"about_ca_system_score_gemma":0.0003248126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004324257,"about_ca_topic_score_gemma":0.00008563365,"domain_scores_codex":[0.997029,0.0001560322,0.0005477801,0.001003351,0.000416134,0.0008476971],"domain_scores_gemma":[0.9979396,0.0008189591,0.0002182717,0.0005325515,0.0002888101,0.0002018189],"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":[0.00003882521,0.0004480018,0.00001272483,0.0002069115,0.0001773686,0.00005475371,0.007184162,0.2188274,0.01088968,0.002901696,0.0005438757,0.7587146],"study_design_scores_gemma":[0.001355944,0.0005507844,0.000113889,0.0003434873,0.00007153812,0.00004041538,0.003528535,0.964575,0.02209217,0.005672513,0.0007755407,0.0008801908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142195,0.00004748768,0.8813142,0.0002577685,0.001777615,0.0009889129,0.00005308824,0.001304289,0.00003713776],"genre_scores_gemma":[0.9553325,0.00003959455,0.04361657,0.0002931004,0.0002299803,0.000104238,0.00001952467,0.00007488763,0.0002896432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.841113,"threshold_uncertainty_score":0.9997808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987343469896156,"score_gpt":0.2798639538124632,"score_spread":0.2599905191135016,"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."}}