{"id":"https://openalex.org/W2899387030","doi":"https://doi.org/10.1145/3212725.3212729","title":"QualityDeepSense","display_name":"QualityDeepSense","publication_year":2018,"publication_date":"2018-06-15","ids":{"openalex":"https://openalex.org/W2899387030","doi":"https://doi.org/10.1145/3212725.3212729","mag":"2899387030"},"language":"en","primary_location":{"id":"doi:10.1145/3212725.3212729","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3212725.3212729","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3212725.3212729","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3212725.3212729","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005026261","display_name":"Shuochao Yao","orcid":"https://orcid.org/0000-0002-4070-6345"},"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":"Shuochao Yao","raw_affiliation_strings":["University of Illinois Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028772518","display_name":"Yiran Zhao","orcid":"https://orcid.org/0000-0001-7047-4146"},"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":"Yiran Zhao","raw_affiliation_strings":["University of Illinois Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032992976","display_name":"Shaohan Hu","orcid":"https://orcid.org/0000-0002-2877-2665"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaohan Hu","raw_affiliation_strings":["IBM"],"affiliations":[{"raw_affiliation_string":"IBM","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087114395","display_name":"Tarek Abdelzaher","orcid":"https://orcid.org/0000-0003-3883-7220"},"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":"Tarek Abdelzaher","raw_affiliation_strings":["University of Illinois Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5005026261"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.3811,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.86411112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"42","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9995999932289124,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9995999932289124,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8194525241851807},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6258948445320129},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.6217187643051147},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5973491668701172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5815987586975098},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5709584951400757},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5302495360374451},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5056817531585693},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4507472813129425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42230522632598877},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3949846625328064},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08294609189033508}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8194525241851807},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6258948445320129},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.6217187643051147},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5973491668701172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5815987586975098},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5709584951400757},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5302495360374451},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5056817531585693},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4507472813129425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42230522632598877},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3949846625328064},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08294609189033508},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3212725.3212729","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3212725.3212729","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3212725.3212729","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3212725.3212729","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3212725.3212729","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3212725.3212729","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5}],"awards":[{"id":"https://openalex.org/G1034401897","display_name":null,"funder_award_id":"W911NF-17-2-0196","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1198325749","display_name":null,"funder_award_id":"W911NF-17-2-0196.","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G1637379582","display_name":null,"funder_award_id":"W911NF-17-2-0196","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G5139883043","display_name":null,"funder_award_id":"CNS 13-20209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5259331294","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G5365978556","display_name":null,"funder_award_id":"CNS 16-18627","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5969598952","display_name":null,"funder_award_id":"W911NF-09-2-0053, W911NF-17-2-0196","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"},{"id":"https://openalex.org/G6152603095","display_name":null,"funder_award_id":"CNS 16-18627, CNS 13-20209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7561134949","display_name":null,"funder_award_id":"W911NF-09-2-0053","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G948678646","display_name":null,"funder_award_id":"W911NF-09-2-0053","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2899387030.pdf","grobid_xml":"https://content.openalex.org/works/W2899387030.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1943579973","https://openalex.org/W1991468035","https://openalex.org/W1997430507","https://openalex.org/W2057907879","https://openalex.org/W2090890268","https://openalex.org/W2097575504","https://openalex.org/W2100045669","https://openalex.org/W2546536770","https://openalex.org/W2553915786","https://openalex.org/W2612690371","https://openalex.org/W2626778328","https://openalex.org/W2804686008","https://openalex.org/W2963521844","https://openalex.org/W2963728985","https://openalex.org/W2964059111","https://openalex.org/W2964308564","https://openalex.org/W3137248573","https://openalex.org/W4236536015","https://openalex.org/W6751472381"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2347897961","https://openalex.org/W2979236518","https://openalex.org/W2358318464","https://openalex.org/W2340870721","https://openalex.org/W3091955004"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"are":[3,44,118],"becoming":[4],"increasingly":[5],"popular":[6],"in":[7,27],"mobile":[8,52],"sensing":[9,99],"and":[10,20,46,69,124],"computing":[11],"applications.":[12,32],"Their":[13],"capability":[14],"of":[15,31,67,73,92,128,146],"fusing":[16],"multiple":[17],"sensor":[18,59,74,93,112,129,138],"inputs":[19,94,113,139],"extracting":[21],"temporal":[22],"relationships":[23],"can":[24,87,132],"enhance":[25],"intelligence":[26],"a":[28,82,103,162],"wide":[29],"range":[30],"One":[33],"key":[34],"problem":[35],"however":[36],"is":[37],"the":[38,65,71,90,109,122,126,144,149,158],"noisy":[39],"on-device":[40],"sensors,":[41],"whose":[42],"characters":[43],"heterogeneous":[45,151],"varying":[47],"over":[48,62,95,114],"time.":[49,115],"The":[50],"existing":[51],"deep":[53,83],"learning":[54,84],"frameworks":[55],"usually":[56],"treat":[57],"every":[58],"input":[60],"equally":[61],"time,":[63],"lacking":[64],"ability":[66],"identifying":[68],"exploiting":[70],"heterogeneity":[72],"noise.":[75],"In":[76,165],"this":[77],"work,":[78],"we":[79,167],"propose":[80,102],"QualityDeepSense,":[81],"framework":[85],"that":[86],"automatically":[88],"balance":[89],"contribution":[91,127],"time":[96],"by":[97,161],"their":[98],"qualities.":[100],"We":[101,142],"sensor-temporal":[104],"attention":[105],"mechanism":[106],"to":[107,120],"learn":[108],"dependencies":[110],"among":[111],"These":[116],"correlations":[117],"used":[119],"infer":[121],"qualities":[123],"reassign":[125],"inputs.":[130],"QualityDeepSense":[131,147,156,169],"thus":[133],"focus":[134],"on":[135,175],"more":[136],"informative":[137],"for":[140],"prediction.":[141],"demonstrate":[143],"effectiveness":[145],"using":[148],"noise-augmented":[150],"human":[152],"activity":[153],"recognition":[154],"task.":[155],"outperforms":[157],"state-of-the-art":[159],"methods":[160],"clear":[163],"margin.":[164],"addition,":[166],"show":[168],"only":[170],"impose":[171],"limited":[172],"resource-consumption":[173],"burden":[174],"embedded":[176],"devices.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2018-11-09T00:00:00"}
