{"id":"https://openalex.org/W7125827097","doi":"https://doi.org/10.3233/faia251659","title":"Online Network Traffic Recovery Based on Probabilistic Sparse Self-Attention for Transfer Learning: A Lightweight and Efficient Tensor Completion Method","display_name":"Online Network Traffic Recovery Based on Probabilistic Sparse Self-Attention for Transfer Learning: A Lightweight and Efficient Tensor Completion Method","publication_year":2026,"publication_date":"2026-01-27","ids":{"openalex":"https://openalex.org/W7125827097","doi":"https://doi.org/10.3233/faia251659"},"language":null,"primary_location":{"id":"doi:10.3233/faia251659","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251659","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251659","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123939614","display_name":"Hongkai Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongkai Chen","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China"],"raw_orcid":"https://orcid.org/0009-0000-4083-3873","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123917775","display_name":"Miao Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Jiang","raw_affiliation_strings":["School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China"],"raw_orcid":"https://orcid.org/0000-0001-7128-8943","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123997482","display_name":"Yiqing Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqing Li","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China"],"raw_orcid":"https://orcid.org/0000-0001-7021-0992","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5123939614"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30053908,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8108000159263611,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8108000159263611,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.035599999129772186,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.01549999974668026,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6690000295639038},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5030999779701233},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.47760000824928284},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4194999933242798},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3952000141143799},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3840999901294708},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.3675999939441681},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.3596999943256378},{"id":"https://openalex.org/keywords/network-monitoring","display_name":"Network monitoring","score":0.3443000018596649}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7088000178337097},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6690000295639038},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5734000205993652},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3944000005722046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38909998536109924},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3474000096321106},{"id":"https://openalex.org/C81877898","wikidata":"https://www.wikidata.org/wiki/Q1965787","display_name":"Network monitoring","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2987015589","wikidata":"https://www.wikidata.org/wiki/Q1040098","display_name":"Learning network","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C2988166257","wikidata":"https://www.wikidata.org/wiki/Q924286","display_name":"Traffic network","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251659","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251659","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251659","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251659","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"contemporary":[1],"network":[2,11,20,33,57],"traffic":[3,12,21,28,51,71],"engineering":[4],"and":[5,24,32,53,151],"anomaly":[6],"detection,":[7,31],"the":[8,86,141,144,153],"measurement":[9,46],"of":[10,19,143,155],"is":[13,118],"critically":[14],"important.":[15],"A":[16],"comprehensive":[17],"dataset":[18],"provides":[22],"rapid":[23],"valuable":[25],"assistance":[26],"for":[27,115],"scheduling,":[29],"fault":[30],"monitoring.":[34],"However,":[35],"inadequate":[36],"monitoring":[37,58],"facilities":[38],"at":[39],"certain":[40],"observation":[41],"points,":[42],"along":[43],"with":[44],"high":[45],"costs,":[47],"result":[48],"in":[49],"incomplete":[50],"data":[52,72],"significant":[54],"sparsity":[55],"within":[56],"systems.":[59],"Recent":[60],"studies":[61],"have":[62],"demonstrated":[63],"that":[64,140],"tensor":[65,81,156],"completion":[66,82],"can":[67],"effectively":[68,122],"recover":[69],"missing":[70],"from":[73],"partial":[74],"measurements.":[75],"Despite":[76],"its":[77],"promising":[78],"potential,":[79],"existing":[80],"methods":[83],"often":[84],"lack":[85],"ability":[87],"to":[88,95,126,130],"retain":[89],"historical":[90,128],"information,":[91],"making":[92],"it":[93],"challenging":[94],"efficiently":[96],"capture":[97],"long-term":[98],"dependencies":[99],"among":[100],"tasks.":[101,132],"To":[102],"address":[103],"this":[104],"challenge,":[105],"an":[106],"online":[107],"approach":[108],"based":[109],"on":[110,135],"probabilistic":[111],"sparse":[112],"self-attention":[113],"(PSSA)":[114],"Transfer":[116],"Learning":[117],"proposed.":[119],"This":[120],"method":[121],"leverages":[123],"transfer":[124],"learning":[125],"transmit":[127],"information":[129],"new":[131],"Experiments":[133],"conducted":[134],"two":[136],"real-world":[137],"datasets":[138],"demonstrate":[139],"integration":[142],"PSSA":[145],"module":[146],"accelerates":[147],"model":[148],"training":[149],"speed":[150],"improves":[152],"accuracy":[154],"recovery.":[157]},"counts_by_year":[],"updated_date":"2026-01-28T23:18:48.515280","created_date":"2026-01-28T00:00:00"}
