{"id":"https://openalex.org/W4309617564","doi":"https://doi.org/10.1145/3555536","title":"CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing","display_name":"CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing","publication_year":2022,"publication_date":"2022-11-07","ids":{"openalex":"https://openalex.org/W4309617564","doi":"https://doi.org/10.1145/3555536"},"language":"en","primary_location":{"id":"doi:10.1145/3555536","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3555536","pdf_url":null,"source":{"id":"https://openalex.org/S4210183893","display_name":"Proceedings of the ACM on Human-Computer Interaction","issn_l":"2573-0142","issn":["2573-0142"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Human-Computer Interaction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100354592","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0001-8135-369X"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051511394","display_name":"Ruohan Zong","orcid":"https://orcid.org/0000-0002-6499-3406"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruohan Zong","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074191748","display_name":"Lanyu Shang","orcid":"https://orcid.org/0000-0002-7480-6889"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lanyu Shang","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045401193","display_name":"Ziyi Kou","orcid":"https://orcid.org/0000-0002-9916-0930"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyi Kou","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101960707","display_name":"Huimin Zeng","orcid":"https://orcid.org/0000-0003-0198-2352"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huimin Zeng","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100354592"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":0.9148,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.73267754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"6","issue":"CSCW2","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.9562630653381348},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.8139241337776184},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7506466507911682},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.707330048084259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6968870162963867},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6875802278518677},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.683964729309082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4859670400619507},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4463995099067688},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.42835575342178345}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9562630653381348},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.8139241337776184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7506466507911682},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.707330048084259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6968870162963867},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6875802278518677},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.683964729309082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4859670400619507},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4463995099067688},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.42835575342178345},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3555536","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3555536","pdf_url":null,"source":{"id":"https://openalex.org/S4210183893","display_name":"Proceedings of the ACM on Human-Computer Interaction","issn_l":"2573-0142","issn":["2573-0142"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Human-Computer Interaction","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5199999809265137,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G3525289959","display_name":null,"funder_award_id":"IIS-2202481, CHE-2105005, IIS-2008228, CNS-1845639, CNS-1831669","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W218142243","https://openalex.org/W1499150509","https://openalex.org/W1601808502","https://openalex.org/W1901492489","https://openalex.org/W1966129222","https://openalex.org/W1988301066","https://openalex.org/W2141766660","https://openalex.org/W2155189155","https://openalex.org/W2200000192","https://openalex.org/W2210969396","https://openalex.org/W2398018337","https://openalex.org/W2556522401","https://openalex.org/W2605093170","https://openalex.org/W2608596745","https://openalex.org/W2750040298","https://openalex.org/W2767655584","https://openalex.org/W2768447691","https://openalex.org/W2769948160","https://openalex.org/W2807323495","https://openalex.org/W2885371327","https://openalex.org/W2903302073","https://openalex.org/W2914427239","https://openalex.org/W2919501997","https://openalex.org/W2942250707","https://openalex.org/W2943102408","https://openalex.org/W2963136578","https://openalex.org/W2963240573","https://openalex.org/W2963446712","https://openalex.org/W2963815651","https://openalex.org/W2982492177","https://openalex.org/W2985970013","https://openalex.org/W2996801518","https://openalex.org/W3020992208","https://openalex.org/W3033780772","https://openalex.org/W3037833767","https://openalex.org/W3094217062","https://openalex.org/W3102253155","https://openalex.org/W3137899025","https://openalex.org/W3199878855","https://openalex.org/W3209828932","https://openalex.org/W3212301219","https://openalex.org/W4200559411","https://openalex.org/W4309618238","https://openalex.org/W4401075340","https://openalex.org/W6725739302","https://openalex.org/W6774926797"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4205712847","https://openalex.org/W1974336862","https://openalex.org/W3014750173","https://openalex.org/W4287818966","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"AI-based":[0],"smart":[1,15,219],"urban":[2,30,38,225],"sensing":[3,39],"(ASUS)":[4],"has":[5],"emerged":[6],"as":[7],"a":[8,47,137,183],"scalable":[9],"and":[10,18,36,109,196,224,241],"pervasive":[11],"application":[12,249],"paradigm":[13],"in":[14,55,116,134,161,207,245],"city":[16,220],"planning":[17],"management":[19],"that":[20,87,140,188,231],"aims":[21],"to":[22,61,69,105,124,136,155,199],"automatically":[23],"assess":[24],"the":[25,29,63,71,85,88,99,127,157,162,168,172,177,190,201,235,247],"physical":[26],"status":[27],"of":[28,90,171],"environments":[31],"by":[32,84,98,144],"leveraging":[33],"AI":[34,100,135,149,173],"techniques":[35,191],"massive":[37],"data.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,180],"focus":[45],"on":[46],"crowd-driven":[48,202],"neural":[49],"network":[50],"(NN)":[51],"hyperparameter":[52,74,131,159,164,194,204,242],"optimization":[53,132,205,243],"problem":[54,133,139,206],"ASUS":[56,78,92,208,216,248],"applications.":[57,209],"Our":[58,80],"goal":[59],"is":[60,82,103,122,153],"utilize":[62],"human":[64],"intelligence":[65],"from":[66,192,213],"crowdsourcing":[67],"systems":[68],"identify":[70,156],"optimal":[72,158],"NN":[73,130,203],"configuration":[75,160],"for":[76],"an":[77],"model.":[79,174],"work":[81],"motivated":[83],"observation":[86],"hyperparameters":[89],"current":[91],"models":[93],"are":[94],"often":[95],"manually":[96],"configured":[97],"specialists,":[101],"which":[102],"known":[104],"be":[106,142],"both":[107],"error-prone":[108],"suboptimal.":[110],"Two":[111],"key":[112],"technical":[113],"challenges":[114],"exist":[115],"solving":[117],"our":[118],"problem:":[119],"i)":[120],"it":[121,152],"challenging":[123],"effectively":[125],"translate":[126],"highly":[128],"complex":[129],"simplified":[138],"can":[141],"solved":[143],"crowd":[145],"workers":[146],"without":[147],"extensive":[148],"expertise;":[150],"ii)":[151],"difficult":[154],"large":[163],"search":[165],"space":[166],"given":[167],"blackbox":[169],"nature":[170],"To":[175],"address":[176,200],"above":[178],"challenges,":[179],"develop":[181],"CrowdOptim,":[182],"crowd-AI":[184],"collaborative":[185],"learning":[186],"framework":[187],"integrates":[189],"crowdsourcing,":[193],"optimization,":[195],"estimation":[197],"theory":[198],"The":[210],"evaluation":[211,253],"results":[212],"two":[214],"real-world":[215],"applications":[217],"(i.e.,":[218],"infrastructure":[221],"monitoring":[222],"(SCIM)":[223],"environment":[226],"cleanliness":[227],"assessment":[228],"(UECA))":[229],"show":[230],"CrowdOptim":[232],"consistently":[233],"outperforms":[234],"state-of-the-art":[236],"deep":[237],"convolutional":[238],"networks,":[239],"crowd-AI,":[240],"baselines":[244],"achieving":[246],"objectives":[250],"under":[251],"various":[252],"scenarios.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}