"úÁÐÉÓËÉ ÎÁÕÞÎÙÈ ÓÅÍÉÎÁÒÏ× ðïíé"

• âÁÂÕÛËÉÎ í. ÷., óËÏÐÉÎÁ í. á. æÒÅÊÍÙ ×ÓÐÌÅÓËÏ× ÎÁ ÍÎÏÖÅÓÔ×Å $M$-ÐÏÌÏÖÉÔÅÌØÎÙÈ ×ÅËÔÏÒÏ× ....... 5 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• áÈÍÅÔÇÁÒÅÅ×Á á., áÂÒÁÍÏ× á., ëÕÌÅÛÏ× é., ìÅÝÕË ÷., æÅÎÏÇÅÎÏ×Á á. òÕÓÓËÏÑÚÙÞÎÏÅ Á×ÔÏÍÁÔÉÞÅÓËÏÅ ÒÅÆÅÒÉÒÏ×ÁÎÉÅ: ÍÏÖÎÏ ÌÉ ÒÅÛÉÔØ ÐÒÏÂÌÅÍÕ ÏÇÒÁÎÉÞÅÎÎÏÓÔÉ ÄÁÎÎÙÈ ÁÒÈÉÔÅËÔÕÒÏÊ? ....... 5 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• çÁÌÌÑÍÏ× ë., èÁÅÒÔÄÉÎÏ×Á ì., äÅÎÉÓÏ×Á ë. õÌÕÞÛÅÎÉÅ ÓÏ×ÍÅÓÔÎÙÈ ×ÌÏÖÅÎÉÊ ÔÅËÓÔÏ× É ËÏÄÁ ÄÌÑ ÚÁÄÁÞÉ ÐÏÉÓËÁ Ó ÜÆÆÅËÔÉ×ÎÙÍ ÐÏ ÐÁÒÁÍÅÔÒÁÍ ÄÏÏÂÕÞÅÎÉÅÍ ....... 27 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• öÁ×ÏÒÏÎËÉÎ á., ðÁÕÔÏ× í., ëÁÌÍÙËÏ× î., óÅ×ÒÀÇÏ× å., ëÏ×ÁÌÅ× ä., òÏÇÏ× ï. à., ïÓÅÌÅÄÅà é. UnGAN: ÍÅÔÏÄÙ ÍÁÛÉÎÎÏÇÏ ÒÁÚÕÞÉ×ÁÎÉÑ ÞÅÒÅÚ ÁÔÁËÕ ÎÁ ÎÁÌÉÞÉÅ × ÏÂÕÞÁÀÝÅÊ ×ÙÂÏÒËÅ ....... 46 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• úÁÊÃÅ× ë. üÆÆÅËÔÉ×ÎÏÓÔØ ÍÅÔÏÄÏ× ÉÚ×ÌÅÞÅÎÉÑ ÐÅÒÓÏÎ ....... 61 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• éÏ× é. ì., îÉËÉÔÉÎ î. ï. ïÐÔÉÍÉÚÁÃÉÑ ËÏÎ×ÅÊÅÒÁ ÒÁÚÒÁÂÏÔËÉ ÐÒÉÚÎÁËÏ× × AutoML Ó ÉÓÐÏÌØÚÏ×ÁÎÉÅÍ ËÒÕÐÎÙÈ ÑÚÙËÏ×ÙÈ ÍÏÄÅÌÅÊ ....... 82 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ìÅÏÎÅÎËÏ×Á ÷., ûÕÍÉÔÓËÁÑ å., áÎÃÉÆÅÒÏ×Á á., ÷ÁÔÏÌÉÎ ä. Ti-Patch: ÐÌÉÔÏÞÎÙÊ ÆÉÚÉÞÅÓËÉÊ ÁÄ×ÅÒÓÉÁÌØÎÙÊ ÐÁÔÞ ÄÌÑ ÍÅÔÒÉË ËÁÞÅÓÔ×Á ×ÉÄÅÏ ÂÅÚ ÜÔÁÌÏÎÁ ....... 113 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ìÏÂÁÎÏ× á., çÁÓÎÉËÏ× á. ïÃÅÎËÁ ÍÁËÓÉÍÁÌØÎÏÇÏ ÕÒÏ×ÎÑ ÛÕÍÁ × ÚÁÄÁÞÁÈ ÞÅÒÎÏÇÏ ÑÝÉËÁ ....... 132 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ìÕËØÑÎÏ× ë., äÒÏÂÙÛÅ×ÓËÉÊ í., ôÕÒÄÁËÏ× ä. ÷ÙÑ×ÌÅÎÉÅ É ÕÓÔÒÁÎÅÎÉÅ ËÏ×ÁÒÉÁÎÔÎÙÈ ÓÄ×ÉÇÏ× × ÄÁÎÎÙÈ ÄÌÑ ÂÏÌÅÅ ÎÁÄÅÖÎÏÇÏ ÐÒÏÇÎÏÚÉÒÏ×ÁÎÉÑ ÏÔËÁÚÏ× HDD ....... 148 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ðÁ×ÌÀËÅ×ÉÞ ÷., öÅÒÄÅ×Á á., íÁÈÎÙÔËÉÎÁ ï., äÙÒÍÏ×ÓËÉÊ ä. õÌÕÞÛÅÎÉÅ RAG Ó ÐÏÍÏÝØÀ ÄÏÏÂÕÞÅÎÉÑ LoRA ÄÌÑ ÇÅÎÅÒÁÃÉÉ ÔÅËÓÔÁ ÐÅÒÓÏÎÁÖÁ ....... 162 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• íÕÒÁ×Ø£× ó., ëÁÚÁËÏ×ÃÅ× ÷., õÓÏ× é., ûÐÉÎÅ×Á ð., íÕÒÁ×Ø£×Á ï., ûÁÌÙÔÏ á. ïÔËÒÙÔÁÑ ÂÉÂÌÉÏÔÅËÁ ÄÌÑ ÍÕÌØÔÉÍÏÄÁÌØÎÏÊ ËÌÁÓÔÅÒÉÚÁÃÉÉ ÍÅÔÏÄÁÍÉ AutoML ÎÁ Apache Spark ....... 178 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• óÁÂÉÅ×Á á., öÁÍÁÎÈÁÎ á., öÅÔÅÓÏ× î., ëÕÂÁÅ×Á á., áÈÍÅÔÏ× é., ðÁË á., áÈÍÅÔÏ×Á ä., öÁËÓÙÌÙËÏ×Á á., åÌÅÎÏ× á. ïÂÚÏÒ ÚÁÄÁÞÉ Á×ÔÏÍÁÔÉÞÅÓËÏÇÏ ÐÏÒÏÖÄÅÎÉÑ ÏÔ×ÅÔÏ× ÎÁ ÀÒÉÄÉÞÅÓËÉÅ ×ÏÐÒÏÓÙ ....... 194 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• óÕÈÁÎÏ×ÓËÉÊ î., òÙÎÄÉÎ í. MMA: ÂÏÒØÂÁ ÚÁ ÕÓËÏÒÅÎÉÅ ÍÎÏÇÏÑÚÙËÏ×ÙÈ ÍÏÄÅÌÅÊ ....... 214 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ûÁÊÈÅÌÉÓÌÁÍÏ× ä., äÒÏÂÙÛÅ×ÓËÉÊ í., âÅÌÅ×ÁÎÃÅ× á. ëÁË ÏÂÅÓÐÅÞÉÔØ ÎÁÄÅÖÎÙÊ ËÏÄ: ÉÓÐÏÌØÚÏ×ÁÎÉÅ ÓÔÁÔÉÞÅÓËÏÇÏ ÁÎÁÌÉÚÁÔÏÒÁ ÄÌÑ ×ÙÑ×ÌÅÎÉÑ É ÕÓÔÒÁÎÅÎÉÑ ÄÅÆÅËÔÏ× × Á×ÔÏÍÁÔÉÞÅÓËÉ ÐÏÒÏÖÄÅÎÎÏÍ ËÏÄÅ ....... 233 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• áÌÅËÓÅÅ× á., ôÉÌÌÁÂÁÅ×Á á., ëÁÂÁÅ×Á ç. äÖ., îÉËÏÌÅÎËÏ ó. é. ðÒÉÍÅÎÅÎÉÅ ÓÉÎÔÁËÓÉÞÅÓËÉÈ ÐÁÒÓÅÒÏ× ÄÌÑ ÔÕÒÅÃËÏÇÏ ÑÚÙËÁ × ÚÁÄÁÞÅ ÒÁÚÍÅÔËÉ ËÙÒÇÙÚÓËÉÈ ÓÉÎÔÁËÓÉÞÅÓËÉÈ ËÏÒÐÕÓÏ× ....... 252 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• ìÏÍÛÁËÏ× ÷. í., îÉËÏÌÅÎËÏ ó. é. ðÒÉÍÅÎÅÎÉÑ ÂÏÌØÛÉÈ ÑÚÙËÏ×ÙÈ ÍÏÄÅÌÅÊ ÄÌÑ ÚÁÄÁÞ ÐÏÒÏÖÄÅÎÉÑ É ÏÂÒÁÂÏÔËÉ ÐÒÏÇÒÁÍÍÎÏÇÏ ËÏÄÁ ....... 276 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• çÁÒÁÎÉÎ ÷. á., óÅÍÅÎÏ× ë. ë. îÅÐÁÒÁÍÅÔÒÉÞÅÓËÉÅ ÍÅÔÏÄÙ ÒÅÛÅÎÉÑ ÚÁÄÁÞÉ ÓÏÇÌÁÓÏ×ÁÎÉÑ ÄÁÎÎÙÈ ....... 351 áÎÎÏÔÁÃÉÑ ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)
• òÅÆÅÒÁÔÙ .......406 ðÏÌÎÙÊ ÔÅËÓÔ(.pdf)

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Contents 
• Akhmetgareeva A., Abramov A., Kuleshov I., Leschuk V., Fenogenova A. Towards Russian summarization: can architecture solve data limitations problems? ....... 5
• Galliamov K., Khaertdinova L., Denisova K. Refining joint text and source code embeddings for retrieval task with parameter-efficient fine-tuning ....... 27
• Zhavoronkin A., Pautov .M., Kalmykov N., Sevriugov E., Kovalev D., Rogov O. Y., Oseledets I. UnGAN: machine unlearning strategies through membership inference ....... 46
• Zaitsev K. Exploring the effectiveness of methods for persona extraction ....... 61
• Iov I. L., Nikitin N. O. Feature engineering pipeline optimisation in AutoML workflow using large language models ....... 82
• Leonenkova V., Shumitskaya E., Antsiferova A., Vatolin D. Tiled physical adversarial patch for no-reference video quality metrics ....... 113
• Lobanov A., Gasnikov A. Improved maximum noise level estimation in black-box optimization problems ....... 132
• Lukyanov K., Drobyshevskiy M., Turdakov D. Detecting and eliminating covariate shifts in data for a more robust HDD failure prediction ....... 148
• Pavliukevich V., Zherdeva A., Makhnytkina O., Dyrmovskiy D. Improving RAG with LoRA finetuning for persona text generation ....... 162
• Muravyov S., Kazakovtsev V., Usov I., Shpineva P., Muravyova O., Shalyto A. An opensource library for AutoML multimodal clustering on apache spark ....... 178
• Sabieva A., Zhamankhan A., Zhetessov N., Kubayeva A., Akhmetov I., Pak A., Akhmetova D., Zhaxylykova A., Yelenov A. Survey on the legal question answering problem ....... 194
• Sukhanovskii N., Ryndin M. MMA: A fight for multilingual models acceleration ....... 214
• Shaikhelislamov D., Drobyshevskiy M., Belevantsev A. Ensuring trustworthy code: leveraging a static analyzer to identify and mitigate defects in generated code ....... 233
• Alekseev A., Tillabaeva A., Kabaeva G. Dzh., Nikolenko S. I. Syntax transfer to Kyrgyz using the treebank translation method ....... 252
• Lomshakov V. M., Nikolenko S. I. Large language models for source code generation and editing ....... 276
• Garanin V. A., Semenov K. K. Nonparametric methods for solving the data reconciliation problem ....... 351
• Reviews ....... 406

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• Paging 415 pp.

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