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Guest Editorial Special Issue "Recent Trends on Advanced Computing: The Converging Technologies"

programming AND COMPUTER SOFTWARE 2023年第8期49卷 597-598页

作者： Tchernykh, Andrei Juarez Ramirez, Reyes Mocskos, Esteban Nesmachnow, Sergio CICESE Res Ctr Carretera Tijuana Ensenada 3918 Ensenada 22860 BC Mexico RAS Ivannikov Inst Syst Programming Aleksandr Solzhenitsyn Ul 25 Moscow 109004 Russia Univ Autonoma Baja Calif Ave Univ 14418Parque Ind Int Tijuana Tijuana 22390 Baja California Mexico Univ Buenos Aires Pabellon 0 Infinito C1428EGA Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Ctr Simulac Computac P Aplic Tecnol CSC Godoy Cruz 2398C1425FQD Buenos Aires DF Argentina Univ Republ Montevideo Montevideo 11300 Uruguay

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Labeled property graphs: SQL or NoSQL?

Labeled property graphs: SQL or NoSQL?

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Ivannikov Memorial Workshop (IVMEM)

作者： Anikin, Dmitry Borisenko, Oleg Nedumov, Yaroslav RAS Higher Sch Econ Ivannikov Inst Syst Programming Moscow Russia Plekhanov Russian Univ Econ RAS Ivannikov Inst Syst Programming Moscow Russia RAS Ivannikov Inst Syst Programming Moscow Russia

ISBN: (纸本)9781728146232

There are two main approaches to graph databases: based on RDF model and based on labeled property graph model. RDF is well known and studied, but modern graph dataabases with labeled property graph model are studied much lesser. In this paper we evaluated several possible solutions for storing and querying graph data using Gremlin - general purpose graph query language from Apache TinkerPop. We used LDBC Graphalytics framework and compared NoSQL-based setups with SQL-based setups. We evaluated JanusGraph on HBase both on single machine and cluster and SQLG on top of PostgreSQL and H2. We used datasets from the different domains and of different sizes up to tens of millions vertices and edges. Evaluation results show that for the used workload SQLG with PostgreSQL is about ten times faster than JanusGraph on HBase and SQLG with H2 performance is in between.

关键词： graph databases labeled property graph post-gresql hbase TinkerPop

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Incremental and Heuristic Approaches for Deriving Adaptive Distinguishing Test Cases for Non-deterministic Finite-State Machines

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COMPUTER JOURNAL 2019年第5期62卷 757-768页

作者： El-Fakih, Khaled Yevtushenko, Nina Saleh, Ayat Amer Univ Sharjah Sharjah U Arab Emirates RAS Inst Syst Programming Moscow Russia

An incremental approach is proposed for deriving an adaptive Distinguishing Test Case (DTC) for a subset of states of an observable non-deterministic Finite-State Machine (FSM). The approach considers the states of the subset incrementally while checking the existence of a DTC. Experiments were conducted to assess and compare various versions of the incremental approach with respect to a non-incremental counterpart. In addition, two implementations of an efficient heuristic approach for the considered problem are proposed. The implementations are based on a special traversal of a successor tree up to certain height using some established construction rules. Experiments were conducted to assess the execution time and quality of obtained solutions for large FSMs. Moreover, we determine how often a DTC exists while varying the number of states, outputs and non-determinism of a given FSM. A complete summary of the obtained results is included.

关键词： adaptive distinguishing experiments non-deterministic finite-state machines finite-state machine-based testing

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Analysis of consistency for In Memory Data Grid Apache Ignite

Analysis of consistency for In Memory Data Grid Apache Ignit...

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Ivannikov Memorial Workshop (IVMEM)

作者： Tapekhin, Andrey Bogomolov, Igor Velikanov, Oleg RAS Ivannikov Inst Syst Programming Moscow Russia Plekhanov Russian Univ Econ RAS Ivannikov Inst Syst Programming Moscow Russia

ISBN: (纸本)9781728146232

The requirements for speed and capacity of data storage systems permanently increase. As a result, NoSQL and NewSQL database management systems became more popular nowadays. The CAP theorem implies that for a distributed data storage, one usually has to choose a tradeoff between its availability and consistency. However, the developers of distributed data storages could use these terms not in their original meanings, making the end users misunderstand the limitations of the systems. Moreover, even if the limitations are described in detail in the documentation, the software could have errors. Therefore a need for testing of data storage systems, not only from the performance point of view, but also for consistency and other properties. In this paper we present our results for consistency analysis for Apache Ignite using Jepsen framework.

关键词： data storage systems dynamic testing software testing

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Text recognition on images from social media

Text recognition on images from social media

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Ivannikov Memorial Workshop (IVMEM)

作者： Akopyan, M. S. Belyaeva, O. V. Plechov, T. P. Turdakov, D. Y. RAS Ivannikov Inst Syst Programming Moscow Russia

ISBN: (纸本)9781728146232

Text recognition problem has been studied many years. A few OCR engines exist, which successfully solve the problem for many languages. But these engines work well only with high quality scanned images. Social networks nowadays contain large number of images that need to analyze and recognize the text contained in them, but they have different quality: mixed text with images, poor quality images taken from camera of smartphone, etc. In this paper a text extraction pipeline is provided to address text extraction from various quality images collected form social media. Input images are categorized into different classes and then class specific preprocessing is applied to them (illumination improvement, text localization etc.). Then OCR engine used to recognize text. In the paper we present results of our experiments on dataset collected from social media.

关键词： text recognition social networks image processing deep neural networks

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Collaborative Development and Use of Scientific Applications in Orlando Tools: Integration, Delivery, and Deployment 6th

Collaborative Development and Use of Scientific Applications...

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6th Latin American Conference on High Performance Computing (CARLA)

作者： Feoktistov, Alexander Gorsky, Sergei Sidorov, Ivan Bychkov, Igor Tchernykh, Andrei Edelev, Alexei Matrosov Inst Syst Dynam & Control Theory SB RAS Irkutsk Russia CICESE Res Ctr Carretera Ensenada Tijuana 3918Post Box 360 Ensenada 22860 Baja California Mexico Ivannikov Inst Syst Programming RAS Moscow Russia South Ural State Univ Chelyabinsk Russia Melentiev Energy Syst Inst SB RAS Irkutsk Russia

ISBN: (纸本)9783030410056;9783030410049

The paper addresses practical challenges related to the development and application of distributed software packages of the Orlando Tools framework to solve real problems. Such packages include a special class of scientific applications characterized by a wide class of problem solvers, modular structure of software, algorithmic knowledge implemented by modules, computations scalability, execution in heterogeneous resources, etc. It is adapted for various categories of users: developers, administrators, and end-users. Unlike other tools for developing scientific applications, Orlando Tools provides supports for the intensive evolution of algorithmic knowledge, adaptation of existed and designing new ones. It has the capability to extend the class of solved problems. We implement and automate the non-trivial technological sequence of the collaborative development and use of packages including the continuous integration, delivery, deployment, and execution of package modules in a heterogeneous distributed environment that integrates grid and cloud computing. This approach reduces the complexity of the collaborative development and use of packages, and increases software operation predictability through the preliminary detecting and eliminating errors with significant reduction of the correcting cost.

关键词： Grid Cloud Applied software packages Continuous integration Delivery Deployment Collaborative computing

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LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments 21

LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression A...

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21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Cortes-Mendoza, Jorge M. Radchenko, Gleb Tchernykh, Andrei Pulido-Gaytan, Bernardo Babenko, Mikhail Avetisyan, Arutyun Bouvry, Pascal Zomaya, Albert South Ural State Univ Chelyabinsk Russia CICESE Res Ctr Ensenada Baja California Mexico North Caucasus Fed Univ Stavropol Russia RAS Ivannikov Inst Syst Programming Moscow Russia Univ Luxembourg Esch Sur Alzette Luxembourg Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781728195865

The protection of data processing is emerging as an essential aspect of data analytics, machine learning, delegation of computation, Internet of Things, medical and financial analysis, smart cities, genomics, non-disclosure searching, among others. Often, they use sensitive information that cannot be protected by traditional cryptosystems. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered suitable solutions for privacy protection. In this paper, we propose and analyze the performance of three homomorphic Logistic Regression (LR) models with Gradient Descent (GD) algorithms based on the Residue Number system (RNS). We compare their performance with four traditional non-homomorphic versions, one homomorphic algorithm based on RNS with Batch GD, and two state-of-the-art homomorphic algorithms. To validate our approach, we consider six public datasets of different medicine domains (diabetes, cancer, drugs, etc.) and genomics. We use a 5-fold cross-validation technique for a fair comparison in terms of the solution quality and training time. The results show that propose homomorphic solutions have similar accuracy with non-homomorphic algorithms, increased classification performance, and decreased training time compared with the state-of-the-art HE algorithms.

关键词： logistic regression residue number system homomorphic encryption cloud security

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Data Handling Optimization in Russian Data Lake Prototype 20

Data Handling Optimization in Russian Data Lake Prototype

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20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT)

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Petrosyan, A. Smirnov, S. Zarochentsev, A. RAS Ivannikov Inst Syst Programming Compiler Technol Dept Moscow 109004 Russia Plekhanov Russian Univ Econ Cloud Technol & Big Data Analyt Lab Moscow 117997 Russia Univ Andres Bello ATLAS Grp Santiago 7550196 Chile NRC Kurchatov Inst PNPI Dept Comp Resources & Technol Gatchina 188300 Russia Brookhaven Natl Lab Phys Dept Upton NY 11973 USA Joint Inst Nucl Res Lab Informat Technol Dubna 141980 Russia Natl Res Nucl Univ MEPhI Inst Nucl Phys & Engn Moscow 115409 Russia St Petersburg State Univ Lab Ultra High Energy Phys St Petersburg 198504 Russia

CERN experiments are preparing for the HL-LHC era, which will bring an unprecedented volume of scientific data. These data will need to be stored and processed by thousands of physicists, but expected resource growth is nowhere near the extrapolated requirements of existing models, in terms of both storage volume and compute power. Opportunistic CPU resources such as HPCs and university clusters can provide extra CPU cycles, but there is no opportunistic storage. In this article, we will present the main architectural ideas, deployment details, and test results, with emphasis on our research to build a prototype of a distributed data processing and storage system with a focus on optimizing the efficiency of resources by reducing overhead costs for accessing the data. The described prototype was built using the geographically distributed WLCG sites and university clusters in Russia.

关键词： Data handling

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GUEST EDITORIAL :Special Issue on Recent Trends in Advanced Computing, Engineering and Technology: Transformation and Innovation

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programming AND COMPUTER SOFTWARE 2022年第8期48卷 485-488页

作者： Tchernykh, Andrei Ramirez, Reyes Juarez Guarda, Teresa Portela, Filipe CICESE Res Ctr Carretera Tijuana Ensenada 3918 Ensenada 22860 BC Mexico RAS Moscow Ivannikov Inst Syst Programming Alexander Solzhenitsyn St 25 Moscow 109004 Russia South Ural State Univ Prospekt Lenina 76 Chelyabinsk 454080 Russia Univ Autonoma Baja California Calzada Univ 14418Parque Ind Int Tijuana Tijuana 22425 Baja California Mexico Univ Estatal Peninsula St Elena Ave principal Libertad St Elena 7047 Ecuador Univ Minho Campus Azurem P-4800058 Guimaraes Portugal

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ISP-Fuzzer: extendable fuzzing framework

ISP-Fuzzer: extendable fuzzing framework

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Ivannikov Memorial Workshop (IVMEM)

作者： Sargsyan, Sevak Hakobyan, Jivan Mehrabyan, Matevos Mishechkin, Maxim Akozin, Vitaliy Kurmangaleev, Shamil RAS Ivannikov Inst Syst Programming Compiler Technol Moscow Russia

ISBN: (纸本)9781728146232

In this paper we introduce ISP-Fuzzer, an extendable fuzzing framework. The framework supports plugins which makes possible to tune it for any fuzzing task. ISP-Fuzzer capable of performing fuzzing for: files, standard input, network, network protocols. As well it can generate BNF structured data for compilers and interpreters fuzzing. The framework supports number of plugins for performing: code static analysis, dynamic symbolic execution, directed fuzzing etc. ISP-Fuzzer designed to run on multiprocessor and distributed systems. During experimental setup the tool has detected number of defects in binary files from different Linux distributions.

关键词： ISP-Fuzzer fuzzing BNF structured data

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