{"id":"https://openalex.org/W2597945040","doi":"https://doi.org/10.1109/taai.2016.7880160","title":"Exploring multi-view learning for activity inferences on smartphones","display_name":"Exploring multi-view learning for activity inferences on smartphones","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2597945040","doi":"https://doi.org/10.1109/taai.2016.7880160","mag":"2597945040"},"language":"en","primary_location":{"id":"doi:10.1109/taai.2016.7880160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","raw_type":"proceedings-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/A5036808772","display_name":"Gunarto Sindoro Njoo","orcid":"https://orcid.org/0000-0002-4991-8491"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Gunarto Sindoro Njoo","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058788784","display_name":"Chien-Hsiang Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chien-Hsiang Lai","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039657695","display_name":"Kuo-Wei Hsu","orcid":"https://orcid.org/0000-0002-3496-5439"},"institutions":[{"id":"https://openalex.org/I87354575","display_name":"National Chengchi University","ror":"https://ror.org/03rqk8h36","country_code":"TW","type":"education","lineage":["https://openalex.org/I87354575"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuo-Wei Hsu","raw_affiliation_strings":["National Chengchi University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chengchi University, Taipei, Taiwan","institution_ids":["https://openalex.org/I87354575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036808772"],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":0.501,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74823368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"212","last_page":"219"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8405251502990723},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7130943536758423},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6621921062469482},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6346762180328369},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5949804186820984},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5930553674697876},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.4766003489494324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46146824955940247},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4565929174423218},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3738349676132202},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3261423110961914}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8405251502990723},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7130943536758423},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6621921062469482},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6346762180328369},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5949804186820984},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5930553674697876},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4766003489494324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46146824955940247},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4565929174423218},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3738349676132202},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3261423110961914},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taai.2016.7880160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai.2016.7880160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1496115555","https://openalex.org/W1670132599","https://openalex.org/W1817561967","https://openalex.org/W1992905840","https://openalex.org/W2017634428","https://openalex.org/W2021717943","https://openalex.org/W2048679005","https://openalex.org/W2066078729","https://openalex.org/W2073021764","https://openalex.org/W2085789144","https://openalex.org/W2101535084","https://openalex.org/W2109743529","https://openalex.org/W2126511896","https://openalex.org/W2127069950","https://openalex.org/W2140076625","https://openalex.org/W2145295623","https://openalex.org/W2153635508","https://openalex.org/W2164535072","https://openalex.org/W2167686542","https://openalex.org/W2182955076","https://openalex.org/W2408569144","https://openalex.org/W4247105055","https://openalex.org/W6636883489","https://openalex.org/W6641446668","https://openalex.org/W6680860788","https://openalex.org/W6684067080"],"related_works":["https://openalex.org/W3195649134","https://openalex.org/W2281498195","https://openalex.org/W2017526120","https://openalex.org/W2610664080","https://openalex.org/W2188304107","https://openalex.org/W2055243143","https://openalex.org/W4231775656","https://openalex.org/W2761510556","https://openalex.org/W2046435967","https://openalex.org/W2892259437"],"abstract_inverted_index":{"Inferring":[0],"activities":[1],"on":[2,12,41,101,128],"smartphones":[3,21,42,96,102],"is":[4],"a":[5,129],"challenging":[6],"task.":[7],"Prior":[8],"works":[9],"have":[10],"elaborated":[11],"using":[13],"sensory":[14],"data":[15,40,80],"from":[16,52],"built-in":[17],"hardware":[18],"sensors":[19],"in":[20],"or":[22],"taking":[23],"advantage":[24],"of":[25,39,55,79,95,107,138],"location":[26],"information":[27],"to":[28,43,75,87,132],"understand":[29],"human":[30],"activities.":[31,69],"In":[32],"this":[33],"paper,":[34],"we":[35,119],"explore":[36],"two":[37],"types":[38,78],"conduct":[44],"activity":[45,99],"inference:":[46],"1)":[47],"Spatial-Temporal:":[48],"reflecting":[49],"daily":[50],"routines":[51],"the":[53,67,89,105,111,134,152],"combination":[54],"spatial":[56],"and":[57,81,115,136,158],"temporal":[58],"patterns,":[59],"2)":[60],"Application:":[61],"perceiving":[62],"specialized":[63],"apps":[64],"that":[65,92,145],"assist":[66],"user's":[68],"We":[70],"employ":[71],"multi-view":[72],"learning":[73],"model":[74,86],"accommodate":[76],"both":[77],"use":[82],"weighted":[83],"linear":[84],"kernel":[85],"aggregate":[88],"views.":[90],"Note":[91],"since":[93],"resources":[94],"are":[97],"limited,":[98],"inference":[100],"should":[103],"consider":[104],"constraints":[106],"resources,":[108],"such":[109],"as":[110],"storage,":[112],"energy":[113],"consumption,":[114],"computation":[116],"power.":[117],"Finally,":[118],"compare":[120],"our":[121,139,146],"proposed":[122],"method":[123],"with":[124],"several":[125],"classification":[126],"methods":[127,150],"real":[130],"dataset":[131],"evaluate":[133],"effectiveness":[135],"performance":[137],"method.":[140],"The":[141],"experimental":[142],"results":[143],"show":[144],"approach":[147],"outperforms":[148],"other":[149],"regarding":[151],"balance":[153],"between":[154],"accuracy,":[155],"running":[156],"time,":[157],"storage":[159],"efficiency.":[160]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
