Raqamli texnologiyalar va iqtisodiyot: zamonaviy muammolari va istiqbollari mavzusida Respublika ilmiy-amaliy

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Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil )WTMRSWE'EWXVS.*7SJX[EVI%TPMGEGMSRIWHIIGYEGMSRIWHMJIVIRGMEPIWHI TVMQIVSVHIR-RZIWXMKEGMʭR]4VE\MWIRPE)RWIʫER^EHIPEW1EXIQʜXMGEWIH7EPE^EV8SVVIW . 4 'SRXVIVEW 7ERXERHIV = 0 )WTMRSWE'EWXVS . * &EVVERUYMPPE )HMGMSRIW 9RMZIVWMHEH 7MQʭR&SPʨZEV ,EUYI 1 9 (LEVQEHEWE - 7[SVRE > 8 6ENETEOWI 6 2 %LQEH , - XLMRO XLMW MW XLI QSWX HMWVYTXMZI XIGLRSPSK] )\TPSVMRK 7IRXMQIRXW SJ 'LEX+48 )EVP]%HSTXIVWYWMRK8[MXXIV(EXEEV<MZTVITVMRXEV<MZ Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., & Metaxas, D. “Stackgan: 8I\X XS TLSXSrealistic image synthesis with stacked generative adversarial networks”. In -'': 7GSXX 6IIH >I]RIT%OEXE <MRGLIR =ER 0ENERYKIR0SKIW[EVER &IVRX 7GLMIPI and Honglak Lee. “Generative adversarial text to image synthesis”. In CVPR, 2016 +SSHJIPPS[-4SYKIX%FEHMI.1MV^E1<Y&;EVHI*EVPI](3^EMV7 Bengio, Y. “Generative adversarial nets”. In NIPS, 2014. D. P. Kingma and M. Welling, ‘‘Autoencoding variational Bayes,’’ 2013, EV<MZ H. Dong, J. Zhang, D. McIlwraith, and Y. Guo. “I2t2i: Learning text to image W]RXLIWMW[MXLXI\XYEPHEXEEYKQIntation”. In ICIP, 2017. E. Mansimov, E. Parisotto, L. J. Ba, and R. Salakhutdinov. “Generating images from captions with attention”. In ICLR, 2016 A. Dash, J. C. B. Gamboa, S. Ahmed, M. Z. Afzal, and M. Liwicki. “TAC+%28I\X 'SRHMXMSRIH %Y\MPMEV] 'PEWWMJier Generative Adversarial Network”. arXiv preprint EV<MZ D. J. Rezende, S. Mohamed, and D. Wierstra. “Stochastic backpropagation and approximate inference in deep generative models”. In ICML,2014. 1MV^E1 3WMRHIVS7 'SRHMXMSREPKIRIVEXMZIEHZIVWEVMEPRIXWEV<MZ 47

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Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil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–I 52

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil (YREM (YREM 0 'LMPPEVʭR 4ʤVI^ 1 4IVMW*ENEVRʤW + 0IRKYE 0IRKYE - )YVS FERORSXIVIGSKRMXMSRW]WXIQJSVFPMRHTISTPI7IRWSVW 4EXV]GNE &A.; Osiński, D.; Wierzchoń, M.; Konieczny, J. Visual Echolocation 'SRGITX JSV XLI 'SPSVSTLSRI 7IRWSV] 7YFWXMXYXMSR (IZMGI 9WMRK :MVXYEP 6IEPMX]7IRWSVW =Y70II,/MQ.7XVIIXGVSWWMRKEMHYWMRKPMKLX[IMKLX'22WJSVXLIZMWYEPP] MQTEMVIH -R 4VSGIIHMRKW SJ XLI -)))':* -RXIVREXMSREP 'SRJIVIRGI SR 'SQTYXIV :MWMSR ;SVOWLSTW7ISYP/SVIE–3GXSFIV 7TEKRSP7,SJJQERR61EVXʨRI^1,9RRXLSVWWSR6&PMRH[E]JMRHMRK[MXL TL]WMGEPP]FEWIHPMUYMHWSYRHW-RX.,YQ'SQTYX7XYH– 7OYPMQS[WOM43[G^EVIO16EHIGOM%&YNEG^16^IW^SXEVWOM(7XVYQMPPS 4-RXIVEGXMZIWSRMJMGEXMSRSJ9HITXLMQEKIWMREREZMKEXMSREMHJSVXLIZMWYEPP]MQTEMVIH. 1YPXMQSHEP9WIV-RXIVJEGIW– (EIWGY3,YERK,;IMR^MIVP1(IITPIEVRMRKFEWIHJEGIVIGSKRMXMSRW]WXIQ [MXLWQEVXKPEWWIW-R4VSGIIHMRKWSJXLIXL%'1-RXIVREXMSREP'SRJIVIRGISR4)VZEWMZI 8IGLRSPSKMIW6IPEXIHXS%WWMWXMZI)RZMVSRQIRXW6LSHIW+VIIGI–.YRI I7MKLX%ZEMPEFPISRPMRILXXTWIWMKLXI]I[IEVGSQ EGGIWWIHSR3GXSFIV 2Y)]IW4VS%ZEMPEFPISRPMRILXXTW[[[RYI]IWGSQ EGGIWWIHSR 3GXSFIV 3V'EQ1])]I%ZEMPEFPI SRPMRILXXTW[[[SVGEQGSQIRQ]I]I EGGIWWIHSR3GXSFIV 3\WMKLX%ZEMPEFPISRPMRILXXTWS\WMKLXKPSFEPGSQ EGGIWWIHSR3GXSFIV 3XSR+PEWW%ZEMPEFPISRPMRILXXTW[[[NEQIWH]WSRE[EVHSVKIR +&TVSNIGXSXSRKPEWW EGGIWWIHSR3GXSFIV %RKPI)]I%ZEMPEFPISRPMRILXXTW[[[GPSWMRKXLIKETGSQERKIPI]IWIVMIW ERKPII]IWQEVXVIEHIVERHERKIPI]IWQEVXKPEWWIW EGGIWWIHSR3GXSFIV 53

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil 2)=6328%61358)<2303+-=%0%6-(%2*3=(%0%2-&8%7:-60%6(%+- OBYEKTLARNING STATISTIK MA’LUMOTLARINI TO‘PLASH UCHUN WEB ILOVA ISHLAB ',-5-7, 1YLVMHHMR9QEVSZ4SOM^E.YQEFS]IZE7MVSNMHHMR)VKEWLIZ “University of Management and Future Technologies” universiteti dotsenti; QYLVMHHMRYQEVSZ$KQEMPGSQ “University of Management and Future Technologies” universiteti magistranti %RRSXEXWM]E >Emonaviy axborot texnologiyalari (AT) bozorida sun’iy intellekt (SI) bo‘yicha vazifalar doirasi juda keng bo‘lib, ishlab chiqarishda sifat nazorati masalalarini hal UMPMWL ZE SF]IOXPEVHE \EZJWM^PMORM XE QMRPEWL \SHMQPEVRMRK GLEVGLSUPEVM ZE I XMFSVPMPMKMRM kuzatish, aniqlash uchun katta hajmdagi statistik ma’lumotlarni to‘plash va qayta ishlashni o‘z ichiga oladi. Texnik vositalardan foydalanishdagi xatolar va hodisalarning oldini olish. Bunday muammolarni turli xil usullar, jumladan, mashinali o‘qitish va nI]VSR XEVQSUPEVM yordamida hal qilish mumkin. Avtomatlashtirilgan tizimlarni joriy etish bo‘yicha joriy so‘rovlar ko‘pincha tasvir yoki video fayllarni tahlil qilish bilan bog‘liq. Bu nafaqat dastlabki ma’lumotlarni olish qulayligi bilan bog‘liq, balki bunHE] JSVQEX QEOWMQEP QMUHSVHE ma’lumot to‘plash imkonini beradi va vizual vositalar ko‘proq narsani aks ettiradi. /SRZSP]YXWMSR RI]VSR XEVQSUPEVHER JS]HEPERMF ZMHISHE XYVPM \MP SF]IOXPEVRM XSTMWL YPEVRM XERMFSPMWLZEXEWRMJPEWLWLYRMRKHIOYPEVLEUMHEUMziqarli ma’lumotlarni to‘plash mumkin. Kalit so‘zlar: konvolyutsion neyron tarmoq, sun’iy intellekt, statistik ma’lumotlarni to‘plash, obyektlarni tanib olish. /MVMWL /SRZSP]YXWMSR RI]VSR XEVQSUPEV XEWZMVPEV FMPER WEQEVEPM MWLPE] SPEHMKER RI]VSR XEVQSUPEVRMRKFMVXYVMHMV?A&YRHE]XEVQSUPEVXEWZMVPEVHER\YWYWM]EX\EVMXEPEVMRMGLMUEVMWLKE USHMV YPEV OI]MRGLEPMO XEWZMVPERKER SF]IOXPEV LEUMHE XEVQSU XE\QMRPEVMRM ]EVEXMWL YGLYR MWLPEXMPEHM <YWYWM]EXPEV \EVMXEWM XEWZMVHEKM JEUEX RI]VSR XEVQSU YGLYR XYWLYREVPi bo‘lgan maydonlar to‘plamini yoki tasvirdagi obyektga xos bo‘lgan juda mazmunli xususiyatlarni ko‘rsatishi mumkin (1VEWQ VEWQ:M^YEPPEWLXMVMPKER\YWYWM]EX\EVMXEPEVMKEQMWSP 54

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil <YWYWM]EX \EVMXEPEVMRM SPMWL OSRZSP]YXWMSRUEXPEQPEV SVUEPM EQEPKE SWLMVMPEHM %]RER OSRZSP]YXWMSR UEXPEQPEV FY XYVHEKM RI]VSR XEVQSUPEVRM XEWZMVRM UE]XE MWLPEWLHE WEQEVEPM Bunday qatlam kirish tasvirining barcha piksellari ustidan o‘tadi va ularga konvolyutsiya yadrosi orqali matritsalarni ko‘paytirishni qo‘llaydi. Konvolyutsiya yadrosi odatda o‘lchamlari 3x3 dan 7x7 gacha bo‘lgan og‘irlik koeffitsientlari matritsasi sifatida ifodalanadi. O‘quv NEVE]SRMHE OSIJJMXWMIRXPEV XEOSQMPPEWLXMVMPEHM FY IWE \YWYWM]EX \EVMXEPEVMRM ]EREHE ERMUVSU XY^MWLMQOSRMRMFIVEHM?A 7LYRMRKHIOOSRZSP]YXWMSRRI]VSRXEVQSUPEVMEV\MXIOXYVEWMRMRKQYLMQUEXPEQPEVMHER biri bu Pooling qatlamidir. Bu qatlam 2x2 guruhdan ma’lum bir pikselni tanlab, xususiyat xaritasida faqat uni qoldirib, xususiyat xaritasini chiziqli bo‘lmagan siqishni amalga oshirEHM 1YE]]ERTMOWIPRMXERPEWLEPKSVMXQMJEVUUMPMWLMQYQOMRPIOMRSHEXHEQEOWMQEPJYROWM]EHER foydalaniladi. Bu xususiyat xaritasining fazoviy o‘lchamini sezilarli darajada kamaytiradi, bu esa o’qitish davomida hisoblash resurslarini tejash imkonini beradM?A Konvolyutsion neyron tarmog‘ida konvolyutsion va namuna olish qatlamlari ko‘p QEVXE XEOVSVPERMWLM QYQOMR %KEV RI]VSR XEVQSUHER SPMRKER XEWZMVHER SF]IOXPEVRM LEQ tasniflashi kerak bo‘lsa, topologiyaning oxiriga 23 ta to‘liq bog‘langan qatlam qo‘shiPEHM 1IXSHSPSKM]E %*S]HEPERMWLHMEKVEQQEWM rasmda ilovadan foydalanish diagrammasi ko‘rsatilgan. Asosiy sahifada JS]HEPERYZGLMXERMFSPMWLYGLYRZMHIS]YOPEWLMQYQOMR8ERMFSPMWLYGLYRZMHISRMXERPEWLM OIVEO&YRMXEOPMJUMPMRKERHIQSvideolardan tanlash yoki o‘zingiznikini yuklash orqali amalga oshirish mumkin. Keyinchalik, foydalanuvchi kerakli chiqish ma’lumotlar formatini tanlashi ZERI]VSRXEVQSUXSQSRMHERXERMFSPMRMWLMYGLYRWMRJPEVRMXERPEWLMOIVEO:MHISRMXERMFSPMWL YGLYR]YForgandan so‘ng, foydalanuvchi tanib olish tugashini kutishi kerak, shundan so‘ng dastur natijalari oldindan tanlangan chiqish ma’lumotlar formati asosida unga ko‘rsatiladi. Agar foydalanuvchi ma’lumotlarni qayta ishlash uchun yuklab olgan bo‘lsa, uni ko‘riWL ZE qayta yuklab olish uchun natijalar sahifasiga o‘tishi mumkin. Agar ilovada xatoliklar yuzaga kelsa, foydalanuvchiga tegishli xato ma’lumotlari so‘raladi. 55

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil VEWQ*S]HEPERMWLHMEKVEQQEWM &&ENEVMPMWLHMEKVEQQEWM VEWQHEREXMNEPEVRMXERMFolish va ko‘rsatish uchun video yuborish uchun bajarilish diagrammasi ko‘rsatilgan. Foydalanuvchi chiqish formatini tanib olish va aniqlash uchun videoni tanlash orqali dastur bilan o‘zaro ishlaydi. Shundan so‘ng, dastur barcha ma’lumotlar FMPERWIVZIVKELttp so‘rovini yuboradi. VEWQ&ENEVMPMWLHMEKVEQQEWM 7IVZIVJS]HEPERYZGLMRMRKZMHISWMRMHMWOOEWEUPE]HMRI]VSRXEVQSURMMWLKEXYWLMVMWL uchun foydalanuvchidan olingan videolarni ma’lumotlarga aylantiradi, so‘ngra olingan 56

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil ZMHISRMXERMFSPMWLYGLYRRI]VSRXEVQSUFMPEROMGLMONEVE]SRRMMWLKEXYWLMVEHM8ERMFSPMWL jarayoni tugallangandan so‘ng, neyron tarmoq foydalanuvchi tomonidan ko‘rsatilgan WS^PEQEPEVKE UEVEF ZMHIS ]SOM .732 JE]PRM WEUPE]HM 2I]VSR XEVQSU UY]M NEVE]SRM tugallangandan so‘ng, server naXMNEPEVXE]]SVPMKMRMFMPHMVYZGLMHEWXYVKELXXTNEZSFMRM]YFSVEHM Ilova tanib olish natijalariga ega sahifani ko‘rsatadi va bir vaqtning o‘zida serverdan video va JSON faylini so‘raydi. Barcha ma’lumotlar olingandan so‘ng, natijalar sahifasi olingan ma’lumotlarni ko‘rsatish uchun yangilanadi. 2EXMNEPEVZEXELPMP Neyron tarmog‘ini o‘qitish uchun 2333 ta tasvirdan iborat butun ma’lumotlar to‘plami 70/20/10 nisbatda o’qitish (1633 tasvir), tekshirish (467 tasvir) va test (233 tasvir) REQYREPEVMKEbo‘lingan. Keyinchalik, o‘qitish jarayonini tezlashtirish uchun barcha tasvirlar 640x640 piksel o‘lchamiga qisqartirildi. NEHZEP Ma’lumotlar to‘plami bo‘yicha o’qitish natijalari Ma’lumotlar to‘plami hajmi 4VIGMWMSR 6IGEPP Q%4 Q%4 O’qitish Google Colaboratory muhitida Nvidia Tesla T4 GPU bilan olib borildi. O’qitish 100 epoch uchun tasodifiy ishga tushirilgan vaznlar bo‘yicha o‘tkazildi. O’qitish 3,5 soat HEZSQIXHM Tarmoq olingan ma’lumotlar to‘plamining har biri bo‘yicha 100 epoch uchun o‘qitildi. O’qitishdan so‘ng neyron tarmoq parametrlari 1jadvalda ko‘rsatilgan. O’qitish davomida Recall va mAP 0,5 parametrlaridagi o‘zgarishlar grafiklari 4VEWQHEOIPXMVMPKER VEWQmAP 0,5 parametridagi o‘zgarishlar grafigi Ushbu ma’lumotlarga asoslanib, ma’lumotlar to‘plamining yanada ko‘payishi neyron 57

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil XEVQSU QSHIPMRMRK ]E\WLMPERMWLMKE SPMF OIPQE]HM HIKER \YPSWEKE OIPMWLMQM^ QYQOMR Shuning uchun to‘plangan ma’lumotlar to‘plamining hajmini optimal deb taxmin qilishimiz QYQOMR :IFQMNS^8]TI7GVMTXHE%RKYPEV*VEQI[SVOZE1EXIVMEP(IWMKRWXERHEVXMKEQYZSJMU stilize qilingan komponentlar yordamida amalga oshirildi. Foydalanuvchida ko‘rsatish uchun MOOMXEWELMJEQEZNYHZMHISRM]YOPEWLYGLYRJSVQEKEIKEWELMJEZEXERMFSPMWLREXMNEPEVMKE IKE WELMJE :MHISKE MWLPSZ FIVMPE]SXKERHE JS]HEPERYZGLMKE ]YOPEWL FIPKMWM ZE HEZSQ etayotgan jarayonni tushuntiruvchi yozuv ko‘rsatiladi. <YPSWE 9WLFYMWLHSMVEWMHERI]VSRXEVQSUXI\RSPSKM]EPEVMHERJS]HEPERKERLSPHEZMHISHEKM SF]IOXPEVLEUMHEstatistik ma’lumotlarni to‘plash uchun vebMPSZEMWLPEFGLMUMPHM8M^MQRMRK ishlashini yaxshilash uchun ma’lumotlar to‘plamini kengaytirishingiz va tanlangan neyron tarmoqni qayta o‘qitishingiz mumkin. Shuningdek, siz neyron tarmoq uchun maxsus ZE^RPEVRM ]uklashni qo‘shishingiz mumkin, bu foydalanuvchiga videodagi oldindan tayyorlangan modelda taqdim etilmagan obyektlar haqida ma’lumot olish imkonini beradi. %HEFM]SXPEV P. Shneha, P. Reddy, and V. M. Megala, “Artificial intelligence for vision impaired TISTle,” International Journal of Latest Trends in Engineering and Technology, pp. 031– Y. Qawqzeh, M. T. Alharbi, A. Jaradat, and K. N. A. Sattar, “A review of swarm MRXIPPMKIRGI EPKSVMXLQW HITPS]QIRX JSV WGLIHYPMRK ERH STXMQM^EXMSR MR GPSYH GSQTYXMRK environments,” PeerJ Computer Science, vol. 7, no. 6, p. e696, 2021. L. Deng, “Artificial intelligence in the rising wave of deep learning: the historical path and future outlook,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 1– 58

Respublika ilmiy-amaliy konferensiyasi 24-25-aprel, 2024-yil NEYRON TARMOQLAR VA ULARNING QO’LLANILISHI 1YLVMHHMR5YPQEXSZ2EWMFE<EPMOSZE, O‘rinboy Naziraliyev UMFT universiteti “Kommunikatsiya va raqamli texnologiyalar” kafedrasi assistent o‘qituvchilari RE^MVEPM]IZYVMRFS]$KQEMPGSQ %RRSXEXWM]E 9WLFY QEUSPEHE FM^ RI]VSR XEVQSUPEV RMQE IOERPMKMRM YPEV UERHE] MWLPEWLMRM ZE WYR M] MRXIPPIOXRM ]EVEXMWLHEKM VSPMRM ERMUPEWLKE LEVEOEX UMPEQM^ 7LY FMPER FMVKEPMOHE RI]VSR XEVQSUPEV RE^EVM]EWM EWSWPEVM QYLSOEQE UMPMREHM FY IWE OSQT]YXIV HEWXYVPEVMHE XEVQSUPEVHER EQEPM] JS]HEPERMWL YGLYR ERMU XY^MPQEPEV EPKSVMXQPEV ZE QEJOYVEPEVKEUS WLMQGLEQYVSNEEXUMPMWLMQOSRMRMFIVEHM /EPMX WS ^PEV Sun’iy neyron tarmoqlar, bog’lamlar backbox, TIVWITXVSR RI]VSR XEVQSK M+MTIVFSPMOXERKIRWJYROWM]EKVEJMKM'%48',% /MVMWL 2I]VSRXEVQSUPEV&YMFSVERMHI]EVPMLEQQENS]HEIWLMXEQM^2I]VSRXEVQSUPEVHER QEWLMRERMS VKERMWLEPKSVMXQPEVMOIRKUS PPERMPEHM%PKSVMXQRMRKMRWSRQM]EWMKES \WLEWLPMKM RI]VSRXEVQSUPEVRMRKRSQPERMWLMKEXYVXOMFS PHM-RWSRQM]EWMHEQE PYQSXRMUEFYPUMPYZGLM HIRHVMXPEV FMPER S ^EVS FSK PERKER RI]VSRPEV QEZNYH 9WLFY OMVMWLPEVHER RI]VSR EOWSRHER GLMUEHMKERIPIOXVWMKREPMRMMWLPEFGLMUEVEHMZEOI]MRFYWMKREPPEVRMEOWSRXIVQMREPPEVMSVUEPM FSWLUE RI]VSRPEVKE GLMUEVEHM -RWSR QM]EWMHEKM RI]VSRPEVKE S \WLEF WYR M] RI]VSR XEVQSUPEVM S ^EVS FSK PERKER RI]VSRPEV XSQSRMHER LSWMP FS PEHM YPEV XYKYRPEV HIF LEQ EXEPEHMYPEVFMVbiri bilan bog’lam deb ataladigan aksonlar orqali o'zaroXE WMVUMPEHM2I]VSR XEVQSK MHE XYKYRPEV UEXPEQPEVKE ]MK MPEHM ZE SHEXHE OIRK FE^EHER FSWLPEREHM &MVMRGLM UEXPEQ \SQ EWL]SHER MFSVEX XYKYRPEVKE FS PMRKER VEUEQPM UM]QEXPEV QEXR XEWZMV ]SOM tovush kabi ma'lumotlar. Keyin har bir tugun tarmoqning bog’laQPEVMSVUEPMOI]MRKMUEXPEQMKE QE PYQSX]YFSVEHM Har bir bog’lam o'zgartirilishi mumkin bo'lgan raqamli vaznga (algoritm) ega bo’lib tajriba asosida tuzilgan. Agar ulangan bog’lamlarning yig'indisi faollashtirish funktsiyasi deb RSQPERYZGLMFIPKMPERKERGLIKEVERMUSRHMVWEYOI]MRKMUEXPEQHEKMRI]VSRRMJESPPEWLXMVEHM &MVSU EKEV YPERKER UMVVEPEVRMRK ]MK MRHMWM FIPKMPERKER GLIKEVEKE XS K VM OIPQEWE JESPPEWLXMVMWLMWLKEXYWLQE]HM&YLEQQEWM]SOMLIGLREVWEXEVXMFMKESPMFOIPEHM7LYRMLEQ ]SHHE XYXMWL OIVEkki, har bir bog’lam bo'ylab og'irliklar tugunlarning har xil yo’nalishini ta'minlash uchun o’ziga xos bo’lishi lozim, ularning barchasi bir xil natijani qaytarmasligi ^EVYVLMWSFPEREHM 2I]VSRXEVQSUPEVRMRKOEQGLMPMOPEVMHERFMVMWLYRHEOMYPEVUSVEUYXMWMJEXMHEMWLPE]HM ]E RM XEVQSU ERMU REXMNEPEVRM XE\QMR UMPMWLM QYQOMR FS PWEHE YRMRK XY^MPMWLMRM OY^EXMWL REXMNEKE XE WMV UMPYZGLM S ^KEVYZGLMPEV LEUMHE GLIOPERKER ]SOM YQYQM] XEWEZZYVRM SGLMF FIVEHM 1EWEPER /MGOWXEVXIV MNSHM] PS]MLEPEV YGLYR HYR]SHEKM IRK ]MVMO QSPM]EPEWL TPEXJSVQEWM OEQTERM]EWMRMRKILXMQSPM]REXMNEPEVMRMFEWLSVEXUMPMWLYGLYRRI]VSRXEVQSUHER JS]HEPERKERHEXEVQSUOEQTERM]EXSMJEWMZEP]YXEQYHHEXZEQMRMQEPKEVSZQMUHSVMOEFM FMV UEXSV S ^KEVYZGLMPEVRM XELPMP UMPEHM EQQS FY YPEVRMRK ]EOYRM] REXMNEKE FS PKER QYRSWEFEXPEVMRMERMUPE]SPQE]HM &YRHERXEWLUEVMXYVPM\MPXSTSPSKM]EPEVZEXYVPMSK MVPMOPEVKEIKEFS PKERMOOMXERI]VSR 59

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