{"id":"https://openalex.org/W4385078976","doi":"https://doi.org/10.1109/iwcmc58020.2023.10182514","title":"Cyclic Code Word Embedding and Chi-Squared Test-Based Attention Mechanism for Deep Learning-Based Server Fault Detection*","display_name":"Cyclic Code Word Embedding and Chi-Squared Test-Based Attention Mechanism for Deep Learning-Based Server Fault Detection*","publication_year":2023,"publication_date":"2023-06-19","ids":{"openalex":"https://openalex.org/W4385078976","doi":"https://doi.org/10.1109/iwcmc58020.2023.10182514"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc58020.2023.10182514","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwcmc58020.2023.10182514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Wireless Communications and Mobile Computing (IWCMC)","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/A5111012668","display_name":"Yiyang Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyang Xiong","raw_affiliation_strings":["China Telecom Research Institute,Beijing,China","China Telecom Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom Research Institute,Beijing,China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]},{"raw_affiliation_string":"China Telecom Research Institute, Beijing, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029675011","display_name":"Shilei Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shilei Dong","raw_affiliation_strings":["China Telecom Research Institute,Beijing,China","China Telecom Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom Research Institute,Beijing,China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]},{"raw_affiliation_string":"China Telecom Research Institute, Beijing, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1897,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48920826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"959","last_page":"964"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9990000128746033,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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":0.9894999861717224,"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/word-embedding","display_name":"Word embedding","score":0.7701742649078369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7397724986076355},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5755977630615234},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5727659463882446},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5424339175224304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5362327098846436},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.529725968837738},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.5236327648162842},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49551165103912354},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.434085488319397},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3481173515319824},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3446861803531647},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.16527920961380005},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09342965483665466}],"concepts":[{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.7701742649078369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7397724986076355},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5755977630615234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5727659463882446},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5424339175224304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5362327098846436},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.529725968837738},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.5236327648162842},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49551165103912354},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.434085488319397},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3481173515319824},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3446861803531647},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.16527920961380005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09342965483665466},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc58020.2023.10182514","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwcmc58020.2023.10182514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5099999904632568,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1999721813","https://openalex.org/W2156826724","https://openalex.org/W2799900537","https://openalex.org/W2808420356","https://openalex.org/W2898760173","https://openalex.org/W2943160824","https://openalex.org/W2944851425","https://openalex.org/W2959159189","https://openalex.org/W3010215199","https://openalex.org/W3015773004","https://openalex.org/W3036700726","https://openalex.org/W3080359515","https://openalex.org/W3156333129","https://openalex.org/W3190730109"],"related_works":["https://openalex.org/W947140380","https://openalex.org/W4286432911","https://openalex.org/W4230884544","https://openalex.org/W4245453790","https://openalex.org/W3194985222","https://openalex.org/W3216571906","https://openalex.org/W4214830338","https://openalex.org/W2518587255","https://openalex.org/W4287599800","https://openalex.org/W4385432812"],"abstract_inverted_index":{"Server":[0],"log":[1,88],"data":[2,15,70,229],"offers":[3],"a":[4,22,85,118,132,144,201,242,248],"comprehensive":[5],"record":[6],"of":[7,13,31,121,135,151,216],"system":[8],"operations,":[9],"making":[10],"the":[11,78,122,149,156,175,181,186,196,214,221,232,253],"analysis":[12],"this":[14,139],"via":[16,163],"algorithms":[17,96,106,166],"for":[18],"autonomous":[19],"fault":[20,33,50,66],"diagnosis":[21,34,51,67],"critical":[23],"research":[24],"area.":[25],"At":[26],"present,":[27],"two":[28],"primary":[29],"methods":[30],"automatic":[32],"prevail:":[35],"traditional":[36],"machine":[37,94,164],"learning":[38,95,105,165],"algorithms,":[39,61],"such":[40],"as":[41,129],"random":[42],"forests,":[43],"which":[44,62],"can":[45,63,112],"efficiently":[46],"and":[47,57,72,167,246],"accurately":[48],"deliver":[49],"results":[52],"after":[53],"manual":[54],"feature":[55],"extraction;":[56],"deep":[58,104],"neural":[59,110,176,197,233],"network":[60],"execute":[64],"end-to-end":[65],"with":[68],"large":[69],"sets":[71],"yield":[73],"superior":[74],"outcomes":[75],"compared":[76],"to":[77,98,160,219],"former":[79],"method.":[80],"However,":[81],"both":[82,152],"techniques":[83],"exhibit":[84],"significant":[86],"shortcoming:":[87],"files":[89],"constitute":[90],"time-series":[91],"data,":[92,225],"but":[93,116],"struggle":[97],"effectively":[99],"capture":[100],"sequence":[101,114],"information.":[102],"Conversely,":[103],"based":[107],"on":[108],"recurrent":[109],"networks":[111],"grasp":[113],"information":[115],"lack":[117],"thorough":[119],"comprehension":[120,230],"system,":[123],"rendering":[124],"their":[125],"implementation":[126],"relatively":[127],"demanding,":[128],"it":[130],"necessitates":[131],"considerable":[133],"volume":[134],"labeled":[136],"data.":[137],"In":[138],"paper,":[140],"we":[141,191],"put":[142],"forth":[143],"groundbreaking":[145],"solution":[146],"that":[147,203],"amalgamates":[148],"strengths":[150],"algorithms.":[153],"We":[154,212],"employ":[155],"chi-square":[157,187],"test":[158,188],"algorithm":[159,237],"extract":[161],"features":[162],"subsequently":[168],"in-corporate":[169],"an":[170],"attention":[171,182],"mechanism":[172,183],"layer":[173,179],"into":[174,195],"network.":[177,234],"This":[178],"modifies":[180],"parameters":[184],"using":[185,241],"output.":[189],"Moreover,":[190],"integrate":[192],"cyclic":[193],"codes":[194],"network\u2019s":[198],"word":[199],"embedding,":[200],"technique":[202],"has":[204,238],"proven":[205],"highly":[206],"effective":[207],"in":[208,252],"communication":[209],"channel":[210],"coding.":[211],"introduce":[213],"utilization":[215],"Hamming":[217],"distance":[218],"quantify":[220],"disparities":[222],"among":[223],"various":[224],"thereby":[226],"facilitating":[227],"rapid":[228],"by":[231],"Our":[235],"proposed":[236],"been":[239],"corroborated":[240],"publicly":[243],"accessible":[244],"dataset":[245],"demonstrated":[247],"2":[249],"percent":[250],"enhancement":[251],"F1":[254],"score.":[255]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}