Arutyun Avetisyan

Malignant neoplasms remain one of the leading causes of death worldwide, continuously driving the development and active implementation of new innovative treatments into clinical practice. This article provides a comprehensive review of the current status and prospects for the application of mRNA technologies in oncology, analyzing global clinical trial experience, the mechanism of action of therapeutic mRNA cancer vaccines, manufacturing processes, and the regulatory framework for the introduction of individual biotechnologically modified drugs (iBTMD) in the Russian Federation.

The article describes state of cybersecurity as a field of science. Cybersecurity is defined as a set of system programming technologies that aim at finding program errors and vulnerabilities, removing them, and neutralizing the effects of the ones that remain. A brief review of required scientific methods and their implementation in Russian products is provided. Special attention is paid to the changes caused by the expansion of artificial intelligence (AI). New types of errors in and threats from the artificial intelligence systems have primarily emerged due to AI’s datacentric nature. A model for long-term development of trusted technologies is proposed, based on ideas of collaborative economy. The model has been successfully applied in various Russian Academy of Sciences’ Institute for System Programming projects implemented in two distributed centers of excellence: one for analysis of system software and another for trusted AI.

In this paper we discuss the problems arising out of active introduction of artificial intelligence (AI) technologies to medicine and other humanities, as well as causes of these problems and steps that are taken worldwide to solve them. We focus on the methods and tools for developing trusted AI technologies, which are being created within the ISP RAS Trusted AI Research Center. We present the results of interdisciplinary projects executed in the Center and suggest a number of solutions to speed up the development of humanitarian AI technologies. The paper expands on the report given at the General Assembly of the RAS on March 12, 2024.

This article introduces a novel method for the precise and scalable detection of memory leaks comprising two primary stages. Initially, context-, flow-, and field-sensitive static analysis is used to identify potential memory leaks. This includes an annotation system that allows specifying key properties of functions. Therefore, they do not need to be reanalyzed every time they are called. It also allows manual annotation of important library or system functions, thus enhancing analysis quality. The static analysis is conducted in reverse topological order on the call graph, enabling the parallel processing of functions within the same level of hierarchy. Subsequently, directed symbolic execution provides path-sensitivity and effectively filters out false positives. This process is performed concurrently for each bug detected by static analysis. This two-stage approach aims to enhance the efficiency and precision of memory leak detection in industrial software. The proposed method was implemented in the MLH (Memory Leak Hunter) tool, which identified numerous bugs in the open-source software, including OpenSSL, FFmpeg , and Radare2 . These bugs were reported and confirmed by the community, thereby proving the effectiveness of the developed method.

«Россия – серьезный игрок в гонке ИИ»

«Россия – серьезный игрок в гонке ИИ»

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review that was administered by the volume editors

Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.