Programming and Computer Software最新文献

RuGECToR: Rule-Based Neural Network Model for Russian Language Grammatical Error Correction RuGECToR：基于规则的俄语语法纠错神经网络模型

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-07-30 DOI: 10.1134/s0361768824700129

I. A. Khabutdinov, A. V. Chashchin, A. V. Grabovoy, A. S. Kildyakov, U. V. Chekhovich

{"title":"RuGECToR: Rule-Based Neural Network Model for Russian Language Grammatical Error Correction","authors":"I. A. Khabutdinov, A. V. Chashchin, A. V. Grabovoy, A. S. Kildyakov, U. V. Chekhovich","doi":"10.1134/s0361768824700129","DOIUrl":"https://doi.org/10.1134/s0361768824700129","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Grammatical error correction is one of the core natural language processing tasks. Presently, the open-source state-of-the-art sequence tagging for English is the GECToR model. For Russian, this problem does not have equally effective solutions due to the lack of annotated datasets, which motivated the current research. In this paper, we describe the process of creating a synthetic dataset and training the model on it. The GECToR architecture is adapted for the Russian language, and it is called RuGECToR. This architecture is chosen because, unlike the sequence-to-sequence approach, it is easy to interpret and does not require a lot of training data. The aim is to train the model in such a way that it generalizes the morphological properties of the language rather than adapts to a specific training sample. The presented model achieves the quality of <b>82.5</b> in the metric <span>({{{mathbf{F}}}_{{{mathbf{0}}{mathbf{.5}}}}})</span> on synthetic data and <b>22.2</b> on the RULEC dataset, which was not used at the training stage.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constructing the Internal Voronoi Diagram of Polygonal Figure Using the Sweepline Method 使用扫线法构建多边形图形的内部沃罗诺伊图

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-07-30 DOI: 10.1134/s0361768824700105

L. M. Mestetskiy, D. A. Koptelov

引用次数: 0

Comparative Efficiency Analysis of Hashing Algorithms for Use in zk-SNARK Circuits in Distributed Ledgers 用于分布式账本中 zk-SNARK 电路的哈希算法效率比较分析

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-07-30 DOI: 10.1134/s0361768824700099

D. O. Kondyrev

引用次数: 0

Secure Messaging Application Development: Based on Post-Quantum Algorithms CSIDH, Falcon, and AES Symmetric Key Cryptosystem 安全信息传输应用开发：基于后量子算法 CSIDH、Falcon 和 AES 对称密钥密码系统

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-07-30 DOI: 10.1134/s0361768824700130

Nhu-Quynh Luc, Tat-Thang Nguyen, Chi-Hung Vu, Duc-Huy Quach, Thanh-Toan Dao

{"title":"Secure Messaging Application Development: Based on Post-Quantum Algorithms CSIDH, Falcon, and AES Symmetric Key Cryptosystem","authors":"Nhu-Quynh Luc, Tat-Thang Nguyen, Chi-Hung Vu, Duc-Huy Quach, Thanh-Toan Dao","doi":"10.1134/s0361768824700130","DOIUrl":"https://doi.org/10.1134/s0361768824700130","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper, the authors present a technique for developing a secure messaging service called “CryptoMess” which utilizes the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) algorithm for secure key exchange and the Advanced Encryption Standard (AES) to protect message content in communication. In addition, the authors have incorporated the Falcon post-quantum digital signature technology to ensure the integrity and authenticity of communications between the sender and the recipient. The novel post-quantum cryptographic algorithms utilized by the authors are still under consideration in the competition to select cryptosystems for use in quantum computing systems organized by the NIST Standards Institute. As a result, the messaging program “CryptoMess” is able to exchange messages between users, providing safety, security, integrity, and authenticity. The authors have included modern post-quantum cryptography techniques, such as the Falcon digital signature system, to guarantee that the product operates safely. The program has a transmitting rate of approximately 209–261 ms and a receiving rate of approximately 168–206 ms. The message signing time is about 260 ms, and the message verification speed is approximately 185 ms. Key generation time is about 741 ms, key encapsulation time is about 1.454 ms, and key decapsulation time is about 1.921 ms. The source code of the “CryptoMess” communications program has been analyzed and tested by the authors using the Fortify Static Code Analyzer tool to ensure that the product has been created safely and securely.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing 机密人工智能的分析评论：在云计算中部署的方法和算法

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-07-30 DOI: 10.1134/s0361768824700117

E. M. Shiriaev, A. S. Nazarov, N. N. Kucherov, M. G. Babenko

{"title":"Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing","authors":"E. M. Shiriaev, A. S. Nazarov, N. N. Kucherov, M. G. Babenko","doi":"10.1134/s0361768824700117","DOIUrl":"https://doi.org/10.1134/s0361768824700117","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The technologies of artificial intelligence and cloud computing systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Image Style Transfer using an Augmented Style Set 使用增强样式集自动传输图像样式

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-06-01 DOI: 10.1134/s0361768824700038

V. Ponamaryov, V. Kitov

引用次数: 0

Neural Network Method for Detecting Blur in Histological Images 检测组织学图像模糊的神经网络方法

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-06-01 DOI: 10.1134/s0361768824700026

G. Nazarenko, A. Krylov

引用次数: 0

Conversion of Point Cloud Data to 3D Models Using PointNet++ and Transformer 使用 PointNet++ 和 Transformer 将点云数据转换为 3D 模型

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-06-01 DOI: 10.1134/s0361768824700051

M. Sorokin, D. D. Zhdanov, A. Zhdanov

引用次数: 0

Specifics of the Development of an On-Board Visualization System for Civil Aircrafts 开发民用飞机机载可视化系统的具体细节

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-06-01 DOI: 10.1134/s0361768824700014

B. Barladian, N. B. Deryabin, A. Voloboy, V. Galaktionov, L. Shapiro, I. Valiev, Y. Solodelov

引用次数: 0

Joint Super-resolution and Tissue Patch Classification for Whole Slide Histological Images 整张切片组织学图像的联合超分辨率和组织斑块分类

IF 0.7 4区计算机科学

Programming and Computer Software Pub Date : 2024-06-01 DOI: 10.1134/s0361768824700063

Zh. Sun, A. Khvostikov, A. Krylov, A. Sethi, I. Mikhailov, P. Malkov

引用次数: 0