Applying the Douglas–Peucker Algorithm in Online Authentication of Remote Work Tools for Specialist Training in 10.00.00 “Information Security” Integrated Group of Specialties

— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection a...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Automatic documentation and mathematical linguistics Jg. 58; H. Suppl 4; S. S265 - S268
Hauptverfasser:	Uymin, A. G., Grekov, V. S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.12.2024 Springer Nature B.V
Schlagworte:	Accuracy Acknowledgment Algorithms Authentication Authenticity Biometric identification Biometrics Computer Science Data processing Distance learning Educational systems Educational technology Efficiency Gesture recognition Gestures Human-computer interaction Information Storage and Retrieval Interfaces Machine learning Neural networks Optimization Personal computers Recognition Security systems Simplification Training User interfaces Work Work at home data preprocessing HID mouse gesture trajectories biometric identification remote work Douglas–Peucker algorithm neural network data optimization authentication distance learning
ISSN:	0005-1055, 1934-8371
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection and usability, making their development and optimization extremely important. The issue is that accuracy and efficiency of mouse gesture recognition systems need to be improved without any specialized devices used and in the shortest possible time. This requires efficient preprocessing of such gestures to simplify their trajectories while preserving their key features. The Douglas–Peucker algorithm is proposed to be used for preliminary processing of mouse gesture trajectory data. This algorithm allows significantly reducing the number of points in the trajectories, simplifying them while preserving the principal shape of the gestures. The data with simplified trajectories are then used to train neural networks. The experimental part of the work showed that, when applied, the Douglas–Peucker algorithm allows for a 60% reduction in the number of points on the trajectories, increasing the gesture recognition accuracy from 70 to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency. The study confirmed the efficiency of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition problems. The results can be applied to develop more intuitive and adaptive user interfaces. In addition, directions for further research, including optimization of the algorithm’s parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods, are proposed.
AbstractList	— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection and usability, making their development and optimization extremely important. The issue is that accuracy and efficiency of mouse gesture recognition systems need to be improved without any specialized devices used and in the shortest possible time. This requires efficient preprocessing of such gestures to simplify their trajectories while preserving their key features. The Douglas–Peucker algorithm is proposed to be used for preliminary processing of mouse gesture trajectory data. This algorithm allows significantly reducing the number of points in the trajectories, simplifying them while preserving the principal shape of the gestures. The data with simplified trajectories are then used to train neural networks. The experimental part of the work showed that, when applied, the Douglas–Peucker algorithm allows for a 60% reduction in the number of points on the trajectories, increasing the gesture recognition accuracy from 70 to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency. The study confirmed the efficiency of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition problems. The results can be applied to develop more intuitive and adaptive user interfaces. In addition, directions for further research, including optimization of the algorithm’s parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods, are proposed. Abstract—With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric identification and authentication technologies to verify employees working remotely. Such technologies can provide a high degree of protection and usability, making their development and optimization extremely important. The issue is that accuracy and efficiency of mouse gesture recognition systems need to be improved without any specialized devices used and in the shortest possible time. This requires efficient preprocessing of such gestures to simplify their trajectories while preserving their key features. The Douglas–Peucker algorithm is proposed to be used for preliminary processing of mouse gesture trajectory data. This algorithm allows significantly reducing the number of points in the trajectories, simplifying them while preserving the principal shape of the gestures. The data with simplified trajectories are then used to train neural networks. The experimental part of the work showed that, when applied, the Douglas–Peucker algorithm allows for a 60% reduction in the number of points on the trajectories, increasing the gesture recognition accuracy from 70 to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency. The study confirmed the efficiency of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition problems. The results can be applied to develop more intuitive and adaptive user interfaces. In addition, directions for further research, including optimization of the algorithm’s parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods, are proposed.
Author	Grekov, V. S. Uymin, A. G.
Author_xml	– sequence: 1 givenname: A. G. orcidid: 0000-0003-1572-5488 surname: Uymin fullname: Uymin, A. G. email: au-mail@ya.ru organization: National University of Oil and Gas “Gubkin University – sequence: 2 givenname: V. S. orcidid: 0009-0006-4067-4976 surname: Grekov fullname: Grekov, V. S. email: grekov.vs.work@gmail.com organization: National University of Oil and Gas “Gubkin University
BookMark	eNp1kc9K7DAUxoMoOI4-gLuA617zp-m0y0G9cwcExRlxWWJ6WqOdpCbpYnbzDnerj-ELzZOYMoILEQ4cDuf3fV_IOUL7xhpA6JSSP5wSfr4ghAhKhGBiQghnfA-NaMHTJOcTuo9GwzoZ9ofoyPvnOIm8SEfoY9p17VqbBocnwJe2b1rpt5v_t9CrF3B42jbW6fC0wtrgG9NqA3jaR9YErWTQ1mBb4ztY2QD4wboXvLS29bi2Di86UFq22ge8dFKbISW6xCeTofB28zY3EVztfBag-hi13m7e8dwEaJwMUOGZs303hHzZBQ3-GB3UsvVw8tXH6P7v1fLiX3J9M5tfTK8TRYs8JHnFMjopuKg5VLUSecZiMWCZEsAkK6pcVvKRVZCpR6poRSOQEp5VgqiCAR-js51v5-xrDz6Uz7Z3JkaWnIk0Tydp_PwxojtKOeu9g7rsnF5Jty4pKYfrlD-uEzVsp_GRNQ24b-ffRZ8IO5dp
Cites_doi	10.1080/0144929x.2024.2321933 10.1109/soli.2015.7367596 10.1007/s10207-023-00711-0 10.1523/eneuro.0127-22.2023 10.1007/978-3-030-24900-7_6 10.1007/s12652-021-03301-x 10.48550/arXiv.1912.04786 10.3390/app11136083 10.1109/ccem50674.2020.00027 10.5281/zenodo.6594842 10.1145/1966913.1966983 10.1002/widm.1365 10.19101/ijacr.2019.940152 10.1109/jsyst.2012.2221932 10.1007/s11042-020-09197-7 10.1109/access.2021.3061589 10.1016/j.patrec.2017.03.027 10.3390/make4020023 10.5281/zenodo.7327249
ContentType	Journal Article
Copyright	Allerton Press, Inc. 2024 ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2024, Vol. 58, Suppl. 4, pp. S265–S268. © Allerton Press, Inc., 2024.Russian Text © The Author(s), 2024, published in Elektronnye Biblioteki, 2024, Vol. 27, No. 4, pp. 679–694. Allerton Press, Inc. 2024.
Copyright_xml	– notice: Allerton Press, Inc. 2024 ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2024, Vol. 58, Suppl. 4, pp. S265–S268. © Allerton Press, Inc., 2024.Russian Text © The Author(s), 2024, published in Elektronnye Biblioteki, 2024, Vol. 27, No. 4, pp. 679–694. – notice: Allerton Press, Inc. 2024.
DBID	AAYXX CITATION 7T9 8FE 8FG ABJCF ABUWG AFKRA ALSLI ARAPS AZQEC BENPR BGLVJ CCPQU CPGLG CRLPW DWQXO GNUQQ HCIFZ JQ2 K7- L6V M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PRQQA PTHSS
DOI	10.3103/S0005105525700323
DatabaseName	CrossRef Linguistics and Language Behavior Abstracts (LLBA) ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Social Science Premium Collection Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College Linguistics Collection Linguistics Database ProQuest Central ProQuest Central Student ProQuest SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Engineering Collection Engineering Database (subscription) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Social Sciences Engineering Collection (ProQuest)
DatabaseTitle	CrossRef Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Linguistics Collection ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Social Science Premium Collection Engineering Database ProQuest One Social Sciences ProQuest One Academic Eastern Edition Linguistics and Language Behavior Abstracts (LLBA) ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Linguistics Database Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Computer Science Database
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Library & Information Science Computer Science
EISSN	1934-8371
EndPage	S268
ExternalDocumentID	10_3103_S0005105525700323
GroupedDBID	-Y2 .86 .VR 06D 0R~ 0VY 1N0 23N 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 4.4 408 40D 40E 5GY 5VS 6NX 8TC 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABDBE ABDZT ABECU ABFTD ABFTV ABHQN ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACCUX ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADKNI ADRFC ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEMSY AEOHA AEPYU AETLH AEVLU AEXYK AFBBN AFFNX AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ AXYYD B-. BA0 BDATZ BGNMA CAG COF CS3 DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 H13 HF~ HG6 HLICF HMJXF HRMNR HZ~ H~9 IJ- IKXTQ IWAJR I~X I~Z J-C JBSCW JZLTJ KOV LLZTM M4Y MA- NPVJJ NQJWS NU0 O9- O93 O9J P9O PF- PT4 QOS R89 R9I RNS ROL RPX RSV S16 S1Z S27 S3B SAP SCO SDH SHX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW VH1 W48 WK8 YLTOR ZMTXR ~A9 AAYXX ABFSG ABJCF ABRTQ ABUWG ACSTC AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AHPBZ AHWEU AIXLP ALSLI ARAPS ATHPR BENPR BGLVJ CCPQU CITATION CPGLG CRLPW DWQXO HCIFZ K7- M7S PHGZM PHGZT PQGLB PRQQA PTHSS 7T9 8FE 8FG AZQEC GNUQQ JQ2 L6V P62 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c198t-8d2617935f3edfc58628622e26c5e2a29d8adab2de6cb1c1d16284036d50c92e3
IEDL.DBID	BENPR
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001453642700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0005-1055
IngestDate	Tue Dec 02 05:40:48 EST 2025 Sat Nov 29 08:08:13 EST 2025 Thu Mar 27 04:14:36 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	Suppl 4
Keywords	data preprocessing HID mouse gesture trajectories biometric identification remote work Douglas–Peucker algorithm neural network data optimization authentication distance learning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c198t-8d2617935f3edfc58628622e26c5e2a29d8adab2de6cb1c1d16284036d50c92e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-1572-5488 0009-0006-4067-4976
PQID	3254847410
PQPubID	2043878
ParticipantIDs	proquest_journals_3254847410 crossref_primary_10_3103_S0005105525700323 springer_journals_10_3103_S0005105525700323
PublicationCentury	2000
PublicationDate	20241200 2024-12-00 20241201
PublicationDateYYYYMMDD	2024-12-01
PublicationDate_xml	– month: 12 year: 2024 text: 20241200
PublicationDecade	2020
PublicationPlace	Moscow
PublicationPlace_xml	– name: Moscow – name: New York
PublicationTitle	Automatic documentation and mathematical linguistics
PublicationTitleAbbrev	Autom. Doc. Math. Linguist
PublicationYear	2024
Publisher	Pleiades Publishing Springer Nature B.V
Publisher_xml	– name: Pleiades Publishing – name: Springer Nature B.V
References	Ch. López (7245_CR4) 2023; 22 7245_CR18 G. Dahia (7245_CR6) 2020; 10 7245_CR17 N. Yusuf (7245_CR1) 2020; 10 7245_CR19 7245_CR3 7245_CR16 7245_CR9 7245_CR10 Q. Gao (7245_CR15) 2012; 9 7245_CR20 Yi. Zhang (7245_CR13) 2024; 44 S. Almalki (7245_CR8) 2021; 11 P.A. Thomas (7245_CR5) 2023; 14 N. Siddiqui (7245_CR12) 2022; 4 R. Ryu (7245_CR7) 2021; 9 G. Fenu (7245_CR14) 2018; 113 B. Sayed (7245_CR11) 2013; 7 A. Sarkar (7245_CR2) 2020; 79
References_xml	– volume: 44 start-page: 428 year: 2024 ident: 7245_CR13 publication-title: Behav. Inf. Technol. doi: 10.1080/0144929x.2024.2321933 – ident: 7245_CR17 doi: 10.1109/soli.2015.7367596 – volume: 22 start-page: 1665 year: 2023 ident: 7245_CR4 publication-title: Int. J. Inf. Secur. doi: 10.1007/s10207-023-00711-0 – volume: 9 start-page: 3 year: 2012 ident: 7245_CR15 publication-title: International Journal of Instructional Technology and Distance Learning – ident: 7245_CR18 doi: 10.1523/eneuro.0127-22.2023 – ident: 7245_CR9 doi: 10.1007/978-3-030-24900-7_6 – volume: 14 start-page: 339 year: 2023 ident: 7245_CR5 publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-021-03301-x – ident: 7245_CR16 doi: 10.48550/arXiv.1912.04786 – volume: 11 start-page: 6083 year: 2021 ident: 7245_CR8 publication-title: Appl. Sci. doi: 10.3390/app11136083 – ident: 7245_CR3 doi: 10.1109/ccem50674.2020.00027 – ident: 7245_CR20 doi: 10.5281/zenodo.6594842 – ident: 7245_CR10 doi: 10.1145/1966913.1966983 – volume: 10 start-page: e1365 year: 2020 ident: 7245_CR6 publication-title: Data Min. Knowl. Discovery doi: 10.1002/widm.1365 – volume: 10 start-page: 96 year: 2020 ident: 7245_CR1 publication-title: International Journal of Advanced Computer Research doi: 10.19101/ijacr.2019.940152 – volume: 7 start-page: 262 year: 2013 ident: 7245_CR11 publication-title: IEEE Syst. J. doi: 10.1109/jsyst.2012.2221932 – volume: 79 start-page: 27721 year: 2020 ident: 7245_CR2 publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-09197-7 – volume: 9 start-page: 34541 year: 2021 ident: 7245_CR7 publication-title: IEEE Access doi: 10.1109/access.2021.3061589 – volume: 113 start-page: 83 year: 2018 ident: 7245_CR14 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2017.03.027 – volume: 4 start-page: 502 year: 2022 ident: 7245_CR12 publication-title: Mach. Learn. Knowl. Extr. doi: 10.3390/make4020023 – ident: 7245_CR19 doi: 10.5281/zenodo.7327249
SSID	ssj0055894
Score	2.2756755
Snippet	— With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable biometric... Abstract—With educational systems shifting to distance learning and the trend towards remote work growing, an urgent need has arisen to develop reliable...
SourceID	proquest crossref springer
SourceType	Aggregation Database Index Database Publisher
StartPage	S265
SubjectTerms	Accuracy Acknowledgment Algorithms Authentication Authenticity Biometric identification Biometrics Computer Science Data processing Distance learning Educational systems Educational technology Efficiency Gesture recognition Gestures Human-computer interaction Information Storage and Retrieval Interfaces Machine learning Neural networks Optimization Personal computers Recognition Security systems Simplification Training User interfaces Work Work at home
SummonAdditionalLinks	– databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSyQxEC5c9bAX34vjizosHpSG7vSkTY4iil5EdFa8NT157A7otMyMnuc_eNWf4R-aX2IqnSC-Div0qRMqgaqkHqn6CuA3E85nKIRK0j1rqYWZTmReiKSQVaEsIbizyjeb2Ds9FVdX8izUcQ9jtnt8kvQ3tfcr05xqekmAuO-7luYs_wEznMBmyEW_uIzXL-dCNtDLHmKT8-Yp83MSb5XRq4X57lHU65qj-W_tcgHmgmmJ-40sLMKU6S_BfGzbgOEUL8FmqFXAbQzFSMScOL4Mz2SZUvUTOuMQg4U9GT-cGZ-DgfvXf-tBb_TvBnt9bKBKkSJtlHfUBACxtnhunBAYpGA8dur6eohuKQzt7p1sYSc0pyAqPvPNfTgZP77ZU-iuNxk_4UkEttDoA2a0SCBHoLAr8OfosHNwnITuDonKpBglQhMYvMy5zY22igsqkmXMsEJxwyomtah01WXaFKqbqUxnbkLbKVzNUyWZyX_BdL_um1VAWwmbZt3M_beUL1txLbSjInVVWCeILdiJbC5vGxCP0jk_xLDyA8NasBEFoQzneVjmzo92erydpS3YjYx_Hf6S2Np_zV6Hn8zZTE22zAZMjwZ3ZhNm1f2oNxxseTF_AdIL-ck priority: 102 providerName: Springer Nature
Title	Applying the Douglas–Peucker Algorithm in Online Authentication of Remote Work Tools for Specialist Training in 10.00.00 “Information Security” Integrated Group of Specialties
URI	https://link.springer.com/article/10.3103/S0005105525700323 https://www.proquest.com/docview/3254847410
Volume	58
WOSCitedRecordID	wos001453642700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1934-8371 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: P5Z dateStart: 20230201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1934-8371 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: K7- dateStart: 20230201 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database (subscription) customDbUrl: eissn: 1934-8371 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: M7S dateStart: 20230201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Linguistics Database customDbUrl: eissn: 1934-8371 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: CRLPW dateStart: 20230201 isFulltext: true titleUrlDefault: https://search.proquest.com/linguistics providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1934-8371 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: BENPR dateStart: 20230201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1934-8371 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055894 issn: 0005-1055 databaseCode: RSV dateStart: 20070201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTttAEB7x00MvUKBVQyGaA-IAsrDXrLN7QrQCtaoURSEg1Ivl7E-LRGMgoee8A1f6GH2hPEln7LUiWtFLJcsH25pdaT_vzs7OfB_AjlC0Z8iUieKO9yxhZiOdZirKdJEZzwzuoqjEJjrdrrq81L0QcBuHtMpmTqwmalsajpEfpLSToZn0MImPbm4jVo3i09UgobEIy8xURjhffn_S7fWbuVhKpWse5opvU8r6XJO1tbhGmAEpKx23OBXp05Vp7m7-cUJaLTynq__b5VewElxOPK4xsgYLbrQO26FgAXcxVCTxCGH41TfgF7unXAKF5CFicLNn04eeqxIx8Pj6KzU1-fYdr0ZY85Uih9s4-aiOAmLpse8ICQ45Io-DsrweIzWFQfOeAIaDoFDBVqr0N7pwNn180qcgsTeb_sRPDbuFxSpqxo0Ec8wM-xrOT08GHz5GQeIhMolWk0hZZoTXqfSps95IxZWyQjiRGelEIbRVhS2GwrrMDBOT2IQ-OKRV18rYaOHSN7A0KkfuLaAvlI-TYULPPSfNFtIqS1a0LTJPaGzBXjO8-U3N5JHTDoixkP-FhRZsNUOah596nM_HswX7DSjmr581tvlvY-_gpSBPqc6R2YKlyd2924YX5sfkanzXDohuw-LnTtTm1NQzuvfkF7r3zy5-A1JfBDk
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEB6VFgku5V-EtjAH6AFk1V53nd0Dh6pQNWqIohJEb8bZH6hU4tCkRdzyDlzhFbjxQnkSZuy1ooLg1gOST7Y1a9nfjGd2fj6Ax0JRzJApE8Vt75nCzEY6zVSU6SIznie4i6Iim2j3euroSPeX4EfTC8NllY1NrAy1LQ3vkW-lFMmQJd1O4lBBeeC-fKb4bPK884I-5hMh9l4OdvejQCEQGYqmp5GyPHFcp9KnznojFXdiCuFEZqQThdBWFbYYCusyM0xMYhO6YZusupWx0cKlJHdz_ClilirO5gbKjiuwojLdJkOwsnvY7b9tbL-UStdzn6v5nlLWeVTm8uKeZFYAWfHGxalIL_4JF-7tbxnZ6ke3d-N_e0U3YTW41LhT68AtWHKj27ARGjJwE0PHFSMQgym7Az_Z_eYWLyQPGEMYMZ997buq0AR3Tt7TUtMPH_F4hPU8VuTtRC6uqnc5sfR46AjpDjnjgIOyPJkgLYWvx44TEaRAOAgMHCylKu-jA-ezbxeeKVAIzmffsdNM77BY7QryIkEcT769C28u5eXeg-VROXL3AX2hfJwMEzrvuSi4kFZZkqJtkXnSthY8beCUj-tJJTlFeIy9_A_stWC9gVAejNYkX-CnBc8aEC4u_1XYg38LewTX9gevunm30ztYg-uCvMK6HmgdlqenZ24Drprz6fHk9GHQJoR3lw3YXyFAXS4
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwEB6VFqFeaCkglv4wB8QBFDVx6tQ-VsCqFWi1ahfUW5T1T1mpJKvdlPO-A1d4jL7QPgkex1ZVoAeElFNijS3N53hmPPMNwEsmnM9QCJWkh9ZSCzOdyLwQSSGrQllicGeVbzZxOBiI83M5DH1O5zHbPV5JdjUNxNJUt_tTbfe9j5nmVN9LYOK-B1uas_werB04R4Zyuk7PPsdfMedCdjTMnm6T8-5a8-8ibh9MN9bmbxek_tzpb_z3ijfhYTA58ajDyCNYMfUWbMR2Dhh29xbshhoGfIWhSImUFr8_hmuyWKkqCp3RiMHyXi6-D43PzcCjy4tmNmm_fMVJjR2FKVIEjvKRusAgNhZPjQOHQQrS46hpLufopsKzqaHYvcMcjkLTCpLiM-Lcg8vFj1trCl33loufeBIJLzT6QBpNEsQRWewT-NR_P3p7nISuD4nKpGgToYkkXubc5kZbxQUVzzJmWKG4YRWTWlS6GjNtCjXOVKYzN-DAHcSap0oykz-F1bqpzTNAWwmbZuPMvbeUR1txLbSTInVVWAfQHryOKi-nHblH6ZwiUlj5h8J6sBNBUYZ9Pi9z51-7891hrwdvIghuPt8p7Pk_jX4BD4bv-uXHk8GHbVhnzqzqEmp2YLWdXZlduK--tZP5bM-j_xeEKQWg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Applying+the+Douglas%E2%80%93Peucker+Algorithm+in+Online+Authentication+of+Remote+Work+Tools+for+Specialist+Training+in+10.00.00+%E2%80%9CInformation+Security%E2%80%9D+Integrated+Group+of+Specialties&rft.jtitle=Automatic+documentation+and+mathematical+linguistics&rft.au=Uymin%2C+A.+G&rft.au=Grekov%2C+V.+S&rft.date=2024-12-01&rft.pub=Springer+Nature+B.V&rft.issn=0005-1055&rft.eissn=1934-8371&rft.volume=58&rft.spage=S265&rft.epage=S268&rft_id=info:doi/10.3103%2FS0005105525700323
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0005-1055&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0005-1055&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0005-1055&client=summon