{"id":"https://openalex.org/W4310882080","doi":"https://doi.org/10.1145/3563357.3567747","title":"Privacy-preserving data augmentation for thermal sensation dataset based on variational autoencoder","display_name":"Privacy-preserving data augmentation for thermal sensation dataset based on variational autoencoder","publication_year":2022,"publication_date":"2022-11-09","ids":{"openalex":"https://openalex.org/W4310882080","doi":"https://doi.org/10.1145/3563357.3567747"},"language":"en","primary_location":{"id":"doi:10.1145/3563357.3567747","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3563357.3567747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5039671304","display_name":"Hiroki Yoshikawa","orcid":"https://orcid.org/0000-0002-4929-2580"},"institutions":[{"id":"https://openalex.org/I52765264","display_name":"Kyoto Tachibana University","ror":"https://ror.org/02e2wvy23","country_code":"JP","type":"education","lineage":["https://openalex.org/I52765264"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Yoshikawa","raw_affiliation_strings":["Kyoto Tachibana University, Kyoto, Japan"],"raw_orcid":"https://orcid.org/0000-0002-4929-2580","affiliations":[{"raw_affiliation_string":"Kyoto Tachibana University, Kyoto, Japan","institution_ids":["https://openalex.org/I52765264"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001032272","display_name":"Akira Uchiyama","orcid":"https://orcid.org/0000-0001-7563-6191"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Uchiyama","raw_affiliation_strings":["Osaka University, Osaka, Japan"],"raw_orcid":"https://orcid.org/0000-0001-7563-6191","affiliations":[{"raw_affiliation_string":"Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0925,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38980491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"286","last_page":"287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8916507959365845},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6543565392494202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6217418909072876},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.600518524646759},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5755521059036255},{"id":"https://openalex.org/keywords/thermal-sensation","display_name":"Thermal sensation","score":0.5499951839447021},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4641292095184326},{"id":"https://openalex.org/keywords/sensation","display_name":"Sensation","score":0.44436272978782654},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.40178409218788147},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35586169362068176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3114890456199646},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18357530236244202},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17557987570762634},{"id":"https://openalex.org/keywords/thermal-comfort","display_name":"Thermal comfort","score":0.1292390525341034},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11271479725837708},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.08190387487411499}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8916507959365845},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543565392494202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6217418909072876},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.600518524646759},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5755521059036255},{"id":"https://openalex.org/C2989386686","wikidata":"https://www.wikidata.org/wiki/Q774514","display_name":"Thermal sensation","level":3,"score":0.5499951839447021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4641292095184326},{"id":"https://openalex.org/C130093455","wikidata":"https://www.wikidata.org/wiki/Q173253","display_name":"Sensation","level":2,"score":0.44436272978782654},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.40178409218788147},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35586169362068176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3114890456199646},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18357530236244202},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17557987570762634},{"id":"https://openalex.org/C133913538","wikidata":"https://www.wikidata.org/wiki/Q774514","display_name":"Thermal comfort","level":2,"score":0.1292390525341034},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11271479725837708},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.08190387487411499},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3563357.3567747","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3563357.3567747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2914794838","https://openalex.org/W3186092710","https://openalex.org/W3215634884","https://openalex.org/W4210314805","https://openalex.org/W4283454122","https://openalex.org/W4311415873"],"related_works":["https://openalex.org/W2101477348","https://openalex.org/W2372891741","https://openalex.org/W4256106804","https://openalex.org/W4236656556","https://openalex.org/W1016756656","https://openalex.org/W2479063354","https://openalex.org/W4365211920","https://openalex.org/W2223342440","https://openalex.org/W3014948380","https://openalex.org/W2622478039"],"abstract_inverted_index":{"Machine":[0],"learning-based":[1],"methods":[2,15],"show":[3],"high":[4],"performance":[5],"in":[6,87,97],"estimating":[7],"the":[8,29,32,65,70,88,98,117,122,125,151],"thermal":[9,66,83,103,126,145],"sensation":[10,67,84,104,127,146],"of":[11,22,31,124],"a":[12,19,82,112,144],"person.":[13],"These":[14],"are":[16],"based":[17],"on":[18],"huge":[20],"amount":[21],"personal":[23,35],"data.":[24],"The":[25,77,129],"dataset":[26,85,139],"used":[27],"for":[28,64,116],"training":[30],"estimator":[33,147],"includes":[34,39],"physiological":[36,72],"data,":[37,52],"which":[38],"people's":[40],"private":[41,54],"information.":[42,55],"Generative":[43],"models":[44],"have":[45],"received":[46],"significant":[47],"attention":[48],"to":[49,92,120,142],"anonymize":[50],"such":[51],"including":[53,69],"In":[56],"this":[57,108],"paper,":[58],"we":[59,110],"propose":[60],"privacy-preserving":[61],"data":[62,73],"augmentation":[63],"dataset,":[68],"subject's":[71],"using":[74],"Variational":[75],"Autoencoder.":[76],"generative":[78,118],"model":[79,119],"trained":[80],"with":[81],"collected":[86],"uncontrolled":[89],"environment":[90,99],"tends":[91],"be":[93],"biased":[94],"because":[95],"subjects":[96],"rarely":[100],"report":[101],"extreme":[102],"labels.":[105,128],"To":[106],"tackle":[107],"problem,":[109],"introduce":[111],"weighted":[113],"loss":[114],"function":[115],"mitigate":[121],"bias":[123],"evaluation":[130],"result":[131],"shows":[132],"that":[133,140],"our":[134],"method":[135],"generates":[136],"an":[137],"anonymized":[138],"works":[141],"train":[143],"as":[148,150],"well":[149],"original":[152],"dataset.":[153]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
