{"id":"https://openalex.org/W2951768055","doi":"https://doi.org/10.1145/3292500.3330688","title":"Active Deep Learning for Activity Recognition with Context Aware Annotator Selection","display_name":"Active Deep Learning for Activity Recognition with Context Aware Annotator Selection","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2951768055","doi":"https://doi.org/10.1145/3292500.3330688","mag":"2951768055"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330688","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330688","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330688","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/10.1145/3292500.3330688","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027555291","display_name":"H M Sajjad Hossain","orcid":"https://orcid.org/0000-0001-9847-6493"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"H M Sajjad Hossain","raw_affiliation_strings":["University of Maryland Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068320631","display_name":"Nirmalya Roy","orcid":"https://orcid.org/0000-0003-4827-3393"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nirmalya Roy","raw_affiliation_strings":["University of Maryland Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027555291"],"corresponding_institution_ids":["https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":2.0425,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.89788166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1862","last_page":"1870"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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":0.9998999834060669,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9915000200271606,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.8742834329605103},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7795345783233643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7602111101150513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7269890308380127},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.599219024181366},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5941953063011169},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5934960842132568},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5546374917030334},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5053701996803284},{"id":"https://openalex.org/keywords/credibility","display_name":"Credibility","score":0.46556687355041504},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.45142924785614014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8742834329605103},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7795345783233643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7602111101150513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7269890308380127},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.599219024181366},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5941953063011169},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5934960842132568},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5546374917030334},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5053701996803284},{"id":"https://openalex.org/C2780224610","wikidata":"https://www.wikidata.org/wiki/Q1530061","display_name":"Credibility","level":2,"score":0.46556687355041504},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.45142924785614014},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330688","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330688","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330688","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330688","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330688","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330688","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1676992889","display_name":null,"funder_award_id":"N00014-15-1-2229","funder_id":"https://openalex.org/F4320338298","funder_display_name":"Office of Naval Research Global"},{"id":"https://openalex.org/G2414283628","display_name":null,"funder_award_id":"AARG-17-533039","funder_id":"https://openalex.org/F4320322130","funder_display_name":"Association France Alzheimer"},{"id":"https://openalex.org/G3281463495","display_name":null,"funder_award_id":"N00014-15-1-2229","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5285322173","display_name":null,"funder_award_id":"N00014-15-1-222","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G6433383820","display_name":null,"funder_award_id":"N00014-15-1-2229","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7243731340","display_name":null,"funder_award_id":"-17-533039","funder_id":"https://openalex.org/F4320306219","funder_display_name":"Alzheimer's Association"},{"id":"https://openalex.org/G7248091257","display_name":null,"funder_award_id":"1750936","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7908106915","display_name":null,"funder_award_id":"AARG-17-533039","funder_id":"https://openalex.org/F4320306219","funder_display_name":"Alzheimer's Association"},{"id":"https://openalex.org/G7931865478","display_name":null,"funder_award_id":"4-15-1-","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306219","display_name":"Alzheimer's Association","ror":"https://ror.org/0375f4d26"},{"id":"https://openalex.org/F4320322130","display_name":"Association France Alzheimer","ror":"https://ror.org/015yand93"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320338298","display_name":"Office of Naval Research Global","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2951768055.pdf","grobid_xml":"https://content.openalex.org/works/W2951768055.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1541505110","https://openalex.org/W1757796397","https://openalex.org/W1970792136","https://openalex.org/W2002261403","https://openalex.org/W2059732136","https://openalex.org/W2067760738","https://openalex.org/W2165150801","https://openalex.org/W2173248099","https://openalex.org/W2233838193","https://openalex.org/W2270470215","https://openalex.org/W2312609093","https://openalex.org/W2400059445","https://openalex.org/W2497235688","https://openalex.org/W2507418274","https://openalex.org/W2591112160","https://openalex.org/W2604630936","https://openalex.org/W2610740816","https://openalex.org/W2748818549","https://openalex.org/W2751594996","https://openalex.org/W2777460464","https://openalex.org/W2784125077","https://openalex.org/W2785232201","https://openalex.org/W2794717185","https://openalex.org/W2795342689","https://openalex.org/W2798926091","https://openalex.org/W2851629429","https://openalex.org/W2869166307","https://openalex.org/W2885453802","https://openalex.org/W2888680699","https://openalex.org/W2888955000","https://openalex.org/W2962882167","https://openalex.org/W4230586052"],"related_works":["https://openalex.org/W2388687352","https://openalex.org/W1990162851","https://openalex.org/W2370187191","https://openalex.org/W2369854048","https://openalex.org/W2374291020","https://openalex.org/W2355907197","https://openalex.org/W3171042291","https://openalex.org/W2390961115","https://openalex.org/W2388138913","https://openalex.org/W4396769302"],"abstract_inverted_index":{"Machine":[0],"learning":[1,131,184],"models":[2,109],"are":[3,115],"bounded":[4],"by":[5,55,157],"the":[6,20,41,49,82,88,99,142,159,162,193],"credibility":[7],"of":[8,19,37,43,59,70,75,79,81,87,94,101,103,144],"ground":[9,24,95],"truth":[10,25,96],"data":[11,62],"used":[12],"for":[13,120],"both":[14],"training":[15],"and":[16,30,85,112,174,218],"testing.":[17],"Regardless":[18],"problem":[21],"domain,":[22],"this":[23,124],"annotation":[26],"is":[27],"objectively":[28],"manual":[29],"tedious":[31],"as":[32],"it":[33],"needs":[34],"considerable":[35],"amount":[36,93],"human":[38],"intervention.":[39],"With":[40],"advent":[42],"Active":[44],"Learning":[45],"with":[46,67,168,214],"multiple":[47,65,197],"annotators,":[48],"burden":[50],"can":[51],"be":[52],"somewhat":[53],"mitigated":[54],"actively":[56],"acquiring":[57],"labels":[58],"most":[60],"informative":[61],"instances.":[63],"However,":[64],"annotators":[66],"varying":[68],"degrees":[69],"expertise":[71],"poses":[72],"new":[73],"set":[74],"challenges":[76],"in":[77,185,201,222,224],"terms":[78],"quality":[80],"label":[83],"received":[84],"availability":[86],"annotator.":[89],"Due":[90],"to":[91,170,191,211],"limited":[92],"information":[97],"addressing":[98],"variabilities":[100],"Activity":[102],"Daily":[104],"Living":[105],"(ADLs),":[106],"activity":[107],"recognition":[108],"using":[110],"wearable":[111],"mobile":[113],"devices":[114],"still":[116],"not":[117],"robust":[118],"enough":[119],"real-world":[121,202],"deployment.":[122],"In":[123],"paper,":[125],"we":[126],"first":[127],"propose":[128,151],"an":[129],"active":[130,207],"combined":[132],"deep":[133,182,208],"model":[134,156,179,209],"which":[135],"updates":[136],"its":[137],"network":[138],"parameters":[139],"based":[140],"on":[141],"optimization":[143],"a":[145,152,186],"joint":[146],"loss":[147],"function.":[148],"We":[149],"then":[150],"novel":[153],"annotator":[154],"selection":[155],"exploiting":[158],"relationships":[160],"among":[161,196],"users":[163],"while":[164],"considering":[165],"their":[166,171],"heterogeneity":[167],"respect":[169],"expertise,":[172],"physical":[173],"spatial":[175],"context.":[176],"Our":[177,199],"proposed":[178],"leverages":[180],"model-free":[181],"reinforcement":[183],"partially":[187],"observable":[188],"environment":[189],"setting":[190],"capture":[192],"action-reward":[194],"interaction":[195],"annotators.":[198],"experiments":[200],"settings":[203],"exhibit":[204],"that":[205],"our":[206],"converges":[210],"optimal":[212],"accuracy":[213,223],"fewer":[215,225],"labeled":[216],"instances":[217],"achieves":[219],"~8%":[220],"improvement":[221],"iterations.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}