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24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Laputenko, Andrey Evtushenko, Tatiana Klochkova, Elena Yevtushenko, Nina National Research Tomsk State University Department of Information Technologies Tomsk Russia Graduate School of Applied Linguistics and Translation Studies Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Ivannikov Institute for System Programming of the Russian Academy of Sciences Department of Software Engineering Moscow Russia

ISBN: (纸本)9798350336870

The paper addresses the problem of assessing media text readability depending on the correlation of objective and subjective text complexity. Objective text complexity refers to a set of measurable characteristics such as baseline, morphological, syntactic and lexical characteristics of a media text (a piece of news published on university websites), which is calculated automatically for each text. In total, 34 parameters are considered. Subjective text complexity score is assigned to each text by human experts who assess the clarity, structure, cohesion and coherence of the text. The correlation between subjective and objective text complexity is studied using machine learning models. The readability level is measured using machine learning methods and regression-correlation analysis. Namely, an artificial neural network and regression models are used. It is demonstrated that the use of polynomial features and lasso regularization allows to get a compact regression model of a high quality. The use of the chosen machine learning techniques made it possible to estimate the impact of distinct linguistic features on the readability level. © 2023 IEEE.

关键词： Machine learning

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Lemma Functions for Frama-C: C Programs as Proofs

Lemma Functions for Frama-C: C Programs as Proofs

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ivannikov ISPras Open Conference (ISPras)

作者： Grigoriy Volkov Mikhail Mandrykin Denis Efremov Faculty of Computer Science Higher School of Economics Moscow Russia Software Engineering Department Ivannikov Institute for System Programming of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781728112763;9781728112756

This paper describes the development of an auto-active verification technique in the Frama-C framework. We outline the lemma functions method and present the corresponding ACSL extension, its implementation in Frama-C, and evaluation on a set of string-manipulating functions from the Linux kernel. We illustrate the benefits our approach can bring concerning the effort required to prove lemmas, compared to the approach based on interactive provers such as Coq. Current limitations of the method and its implementation are discussed.

关键词： Tools Manuals Contracts Linux Kernel Economics

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Lemma functions for frama-C: C programs as proofs

arXiv

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arXiv 2018年

作者： Volkov, Grigoriy Mandrykin, Mikhail Efremov, Denis Faculty of Computer Science National Research University Higher School of Economics Moscow Russia Software Engineering Department Ivannikov Institute for System Programming Russian Academy of Sciences Moscow Russia

关键词： Formal verification

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Using Machine Learning Techniques for Assessing Media Text Readability

Using Machine Learning Techniques for Assessing Media Text R...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Andrey Laputenko Tatiana Evtushenko Elena Klochkova Nina Yevtushenko Department of Information Technologies National Research Tomsk State University Tomsk Russian Federation Graduate School of Applied Linguistics and Translation Studies Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russian Federation Department of Software Engineering Ivannikov Institute for System Programming of the Russian Academy of Sciences Moscow Russian Federation

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments 5

Scientific Data Lake for High Luminosity LHC project and oth...

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5th International Conference on Particle Physics and Astrophysics, ICPPA 2020

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Smirnov, A. Smirnov, S. Zarochentsev, A. Compiler Technology Department Ivannikov Institute for System Programming of the Ras Moscow109004 Russia Cloud Technologies and Big Data Analytics Laboratory Plekhanov Russian University of Economics Moscow117997 Russia Atlas Group Universidad Andres Bello Santiago7550196 Chile Department of Computing Resources and Technologies Nrc "kurchatov Institute"-PNPI Gatchina188300 Russia Physics Department Brookhaven National Laboratory UptonNY11973 United States Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg198504 Russia

The next phase of LHC Operations-High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-The-Art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Preface

引用

CEUR Workshop Proceedings 2017年 1989卷 5-7页

作者： Pozin, Boris A. Cavalli, Ana Rosa Petrenko, Alexander K. Department of Information and Analytical Systems EC-leasing Co. Russia Department of Network Software TELECOM and Management SudParis France Department of System Engineering Institute of System Programming of RAS Russia

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Preface

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CEUR Workshop Proceedings 2019年 2514卷 1-2页

作者： Avetisyan, Arutyun Ivar, Jacobson Pozin, Boris A. Petrenko, Alexander K. Pozin, Boris A. Petrenko, Alexander K. Cavalli, Ana Rosa Petrenko, Alexander K. Avetisyan, Arutyun Arlazarov, Vladimir Avdoshin, Sergey Batovrin, Victor K. Bershadsky, Alexander M. Boichenko, Alexander Juris, Borzovs Vasenin, Valery A. Schlingloff, Holger Kalyanov, Georgy Kantorovich, Georgy G. Korolev, Anton Kosolapov, Mikhail S. Kostogryzov, Andrey I. Koznov, Dmitry V. Kuznetsov, Sergey Prokhorov, Sergei Soyfer, Victor A. Starykh, Vladimir A. Stolyarov, Gennady K. Stupnikov, Sergey A. Telnov, Yury Ph. Shmid, Alexander Mira, Kajko-Mattssen Miroslawa Wentzl, Wilfried Mayer, Walter H. Yevtushenko, Nina Zakharov, Victor Zmeev, Oleg Cavalli, Ana Rosa Petrenko, Alexander K. Pozin, Boris A. RAS ISP Russia Ivar Jacobson International Switzerland NRU HSE «-leasing» Co Russia NRU HSE ISP RAS Russia Information and Analytical Systems «-leasing» Co Russia Department of Network Software TELECOM and Management SudParis France NRU HSE Russia Software Engineering of Ivannikov Institute for System Programming RAS Russia RAS System Analysis Institute of Federal Research Centre Informatics and Management Russia Russia Penza State University Russia Federal State Educational Centre of Higher Learning «Plekhanov Russian University of Economics» Russia CISA Academy of Sciences of Latvia State University of Latvia Latvia MSU Institut für Offene Kommunikationssysteme FOKUS Germany Verifikation und Testtheorie an der Humboldt-Universität zu Berlin Germany Institute of Control Sciences RAS Russia Institute of Sociology RAS NRU HSE Russia ISO IEEC Institute of Informatics Problems Federal Research Centre IM RAS Russia St. Petersburg State University Russia Institute for System Programming RAS Moscow State University Russia SRI Korolev Samara State Aerospace University Russia Belarus FRC IM RAS Russia FSEC «Plekhanov Russian University of Economics» Russia KTH School of Information and Communication Technology Sweden Governmental Agricultural Analyst Austria Austria Ivannikov Institute for System Programming RAS Russia Institute of Informatics Problems Federal Research Centre IM RAS Russia Tomsk State University Russia

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Simulation of transonic low-Reynolds jets using quasi-gas dynamics equations

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Journal of Physics: Conference Series 2019年第1期1382卷

作者： M V Kraposhin A S Epikhin T G Elizarova K A Vatutin Ivannikov Institute for System Programming of the RAS Russia Moscow Solzhenitsyna str. 25 The Department of Aeromechanics and Flight Engineering Moscow Institute of Physics and Technology Russia Moscow Region Jukovskiy Gagarin str. 16 Keldysh Institute of Applied Mathematics of the RAS Russia Moscow Miusskaya Sqr. 4 Bauman Moscow State Technical University Russia Moscow 2nd Baumanskay str. 5 building 1 Faculty of Mechanics and Mathematics Lomonosov Moscow State University Russia Moscow GSP-1 Leninskiye gory 1

In this paper application of the OpenFOAM solver QGDFoam for numerical simulation of transonic viscous flows is considered. The developed solver, which implements regularized or quasi- gas dynamics (QGD) algorithms, is validated using the transonic low-Re jet flow case (Ma=0.9, Re=3600). The conducted numerical simulations allow the assessing applicability of the solver for modelling hydrodynamic instabilities and their interaction with transonic flow. Results of the numerical simulations are compared with experimental observations and Navier-Stokes-based code simulation. Results of the present study formulate a guideline for choosing (values of) QGD-algorithm tuning parameters.

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

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Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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