{"id":"https://openalex.org/W2809764342","doi":"https://doi.org/10.1109/icin.2018.8401601","title":"Cloud based content classification with global-connected net (GC-Net)","display_name":"Cloud based content classification with global-connected net (GC-Net)","publication_year":2018,"publication_date":"2018-02-01","ids":{"openalex":"https://openalex.org/W2809764342","doi":"https://doi.org/10.1109/icin.2018.8401601","mag":"2809764342"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icin.2018.8401601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5057982353","display_name":"Zhi Chen","orcid":"https://orcid.org/0000-0002-1335-847X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zhi Chen","raw_affiliation_strings":["University of Waterloo"],"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089628133","display_name":"Pin\u2010Han Ho","orcid":"https://orcid.org/0000-0002-0717-1481"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Pin-Han Ho","raw_affiliation_strings":["University of Waterloo"],"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.292,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":3,"citation_normalized_percentile":{"value":0.562275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":74,"max":77},"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Memristive Devices for Neuromorphic Computing","score":0.9979,"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/T10502","display_name":"Memristive Devices for Neuromorphic Computing","score":0.9979,"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/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9956,"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/T11689","display_name":"Adversarial Robustness in Deep Learning Models","score":0.9932,"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/mnist-database","display_name":"MNIST database","score":0.6916096},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.59800684},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.55391604},{"id":"https://openalex.org/keywords/object-recognition","display_name":"Object Recognition","score":0.544018},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object Detection","score":0.541428},{"id":"https://openalex.org/keywords/brain-inspired-computing","display_name":"Brain-inspired Computing","score":0.516941},{"id":"https://openalex.org/keywords/interest-point-detectors","display_name":"Interest Point Detectors","score":0.516381}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8517102},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7530199},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6916096},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6593932},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.59800684},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.55391604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.50234175},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4770538},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4486721},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.42208517},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35830748},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.23620728},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21443668},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icin.2018.8401601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":31,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1686810756","https://openalex.org/W1694178301","https://openalex.org/W1836465849","https://openalex.org/W1907282891","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2123872146","https://openalex.org/W2156387975","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2169488311","https://openalex.org/W2194775991","https://openalex.org/W2201305792","https://openalex.org/W2284050935","https://openalex.org/W2331143823","https://openalex.org/W2335728318","https://openalex.org/W2406474429","https://openalex.org/W2949117887","https://openalex.org/W2949578333","https://openalex.org/W2950621961","https://openalex.org/W2962835968","https://openalex.org/W2963446712","https://openalex.org/W2963452022","https://openalex.org/W2963542991","https://openalex.org/W2963574257","https://openalex.org/W2963606038","https://openalex.org/W2963685250","https://openalex.org/W2963911037","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W4387163578","https://openalex.org/W4360783045","https://openalex.org/W4309224979","https://openalex.org/W4206451978","https://openalex.org/W4200096682","https://openalex.org/W3195622388","https://openalex.org/W3041443116","https://openalex.org/W3036048022","https://openalex.org/W3026879719","https://openalex.org/W2952813363"],"abstract_inverted_index":{"Real-time":[0],"classification":[1,18,27,101],"of":[2,35,54,129,165,178],"video":[3,17,26,31,82,99],"content":[4,59,83,100],"has":[5,143],"been":[6],"envisioned":[7],"to":[8,23,49,86,146,183,185],"revolutionize":[9],"human":[10],"lives.":[11],"The":[12,61],"paper":[13],"introduces":[14],"a":[15,52,105,122,163],"cloud-based":[16],"system":[19],"that":[20,142,153],"is":[21,44,76,84,116,119],"able":[22,182],"perform":[24],"lightweight":[25,96],"on":[28],"real-time":[29],"captured":[30],"content.":[32],"An":[33],"Internet":[34],"things":[36],"(IoT)":[37],"device,":[38],"called":[39],"Content":[40],"Classification":[41],"Box":[42],"(CCB),":[43],"defined":[45],"as":[46,74,136,138],"an":[47,139],"add-on":[48],"one":[50],"or":[51],"number":[53,164,177],"cameras":[55],"in":[56,78,162],"vicinity":[57],"for":[58,90,127],"classification.":[60],"CCB":[62],"will":[63,151],"communicate":[64],"with":[65],"the":[66,80,87,95,103,131,144,154],"cloud":[67,88],"server":[68,89],"once":[69],"any":[70],"interested":[71],"content/event":[72],"(such":[73],"abnormality)":[75],"identified,":[77],"which":[79],"corresponding":[81],"transported":[85],"further":[91],"inspection.":[92],"To":[93],"achieve":[94,159],"and":[97,172,187],"intelligent":[98],"at":[102],"CCB,":[104],"novel":[106,123],"convolutional":[107],"neural":[108],"network":[109],"(CNN)":[110],"framework,":[111],"namely":[112,170],"Global-Connected":[113],"Net":[114],"(GC-Net),":[115],"introduced.":[117],"GC-Net":[118],"featured":[120],"by":[121],"deep":[124],"learning":[125],"architecture":[126],"exploitation":[128],"all":[130],"earlier":[132],"hidden":[133],"layer":[134],"neurons,":[135],"well":[137],"activation":[140],"function":[141],"potential":[145],"approximate":[147],"complexity":[148],"functions.":[149],"We":[150],"show":[152],"proposed":[155],"CNN":[156],"framework":[157],"can":[158],"similar":[160],"performance":[161],"object":[166],"recognition":[167],"benchmark":[168],"tasks,":[169],"MNIST":[171],"CIFAR-10/100,":[173],"under":[174],"significantly":[175],"less":[176],"parameters,":[179],"thus":[180],"being":[181],"apply":[184],"low-computation":[186],"low-memory":[188],"scenarios.":[189]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2809764342","counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2024-11-22T20:57:43.782499","created_date":"2018-07-10"}