Software & Systems

Articles of journal № 1 at 2022 year.

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11. Software implementation of the algorithm for finding the optimal temperature condition of the catalytic process [№1 за 2022 год]
Authors: E.V. Antipina , S.A. Mustafina , A.F. Antipin
Visitors: 1355
The paper describes the software for solving problems of optimal control of the catalytic process in an ideal mixing reactor. The general form of the formulated optimal control problem is based on a mathe-matical model of the process in an ideal mixing reactor. The refrigerant temperature is considered a control parameter; its values are limited. In order to solve the problem numerically, the paper presents a step-by-step algorithm based on the evolutionary method of artificial immune systems. The method of artificial immune systems for solving optimal control problems makes it possible to obtain an ap-proximate solution to the problem in a time that is acceptable from a practical point of view. The formulated algorithm is a basis for the developed application for the catalytic reaction of α-methylstyrene dimerization in the presence of the NaHY zeolite catalyst, whose products (linear di-mers) are widely used in industrial production. To calculate the process in an ideal mixing reactor, the program implements a number of numerical methods. The software tool allows the user to customize both the catalytic process parameters and the parameters of the algorithm of the artificial immune system method. The user sets the optimization criterion during the program operation, which makes it possible to use it for various formulations of the tasks of the catalytic process of α-methylstyrene dimerization and to obtain a set of optimal concentrations of substances and optimal temperature conditions that correspond to the specified process indicators. The paper gives a solution to the problem of finding the optimal tem-perature condition for the considered catalytic process, the optimality criterion of which is the achievement of the maximum yield of linear dimers with a minimum yield of cyclic dimers and trimers.

12. Software for solving the precedence constrained generalized traveling salesman problem [№1 за 2022 год]
Authors: Petunin A.A., Ukolov S.S., Khachay M.Yu.
Visitors: 2577
The paper considers the generalized problem of the precedence constraint traveling salesman (PCGTSP). Like the classical traveling salesman problem (TSP), the authors search a minimum cost closed cycle in this problem, while the set of vertices is divided into nonempty pairwise disjoint sub-sets that are clusters; each feasible route must visit each cluster in a single vertex. In addition, the set of valid routes is constrained by an additional restriction on the order of visiting clusters, that is, some clusters must be visited earlier than others. In contrast to the TSP and the generalized traveling sales-man problem (GTSP), this problem is poorly studied both theoretically and from the point of view of algorithm design and implementation. The paper proposes the first specialized branch-and-bound algorithms using the solutions obtained using the recently developed PCGLNS heuristic as an initial guess. The original PCGTSP problem un-dergoes several relaxations, therefore there are several lower bounds for the original problem; the larg-est of them is used to cut off the branches of the search tree and thereby reduce the enumeration. The algorithms are implemented as open source software in the Python 3 programming language using the specialized NetworkX library. The performance of the proposed algorithms is evaluated on test exam-ples from the PCGTSPLIB public library in comparison with the state-of-the-art Gurobi solver using the MILP model recently proposed by the authors, and seems to be quite competitive even in the cur-rent implementation. The developed algorithms can be used in a wide class of practical problems, for example, for opti-mal tool routing for CNC sheet cutting machines, as well as for assessing the quality of solutions ob-tained using other methods.

13. A software package prototype for analyzing user accounts in social networks: Django web framework [№1 за 2022 год]
Authors: Oliseenko V.D., Abramov, M.V. , Tulupyev A.L. , Ivanov K.A.
Visitors: 2976
The paper considers the issues implementing a prototype of a research and practical complex to auto-mate the analysis of user accounts in social networks. Such prototype is used as a tool to indirectly as-sess users’ psychological features manifestation, their vulnerabilities to social engineering attacks as well as to develop recommendations for protection against these attacks. The prototype is developed in the Python 3.8 programming language using the Django 3.1 web framework and PostgreSQL 13.2, Boot-strap 4.6. This paper aims to increase the efficiency of extracting information from data posted by users in social networks, which allows indirect assessment of psychological, behavioral and other characteris-tics of users. The goal is achieved by automating data extraction and developing tools for their analy-sis. The subject of the study is the methods of automated extraction, pre-processing, unification, and presentation of data from users' accounts in social networks to protect them against social engineering attacks. A prototype application based on the Django web framework solves the problem of automated ex-traction, preprocessing, unification, and presentation of data from user accounts in social networks. The solution of this problem is one of the essential steps to build a system for analyzing the security of users from social engineering attacks. The theoretical significance of the work is in the combination and validation through the automation of previously developed methods and approaches to recover missing values of the attributes of the account, the comparison of online social networks users' ac-counts for their belonging to the same user. The practical significance comes from the development of an application tool located on the sub-domain sea.dscs.pro, which allows performing primary analysis of users' accounts in social networks.

14. Systems and approaches for processing information represented by large dynamic graphs [№1 за 2022 год]
Author: Gulyaevsky S.E.
Visitors: 2529
The paper performes an overview of the key features and advantages for the main existing approaches and systems for processing large graphs on a personal computer. The analysis involves single PC graph processing systems such as GraphChi, TurboGraph, GraphChi-DB and distributed systems like Apache GraphX. Special attention is paid to the problems that require significant changes in the graph structure during the commutation process and the details of implementing such algorithms in graph processing systems. The conducted experiments used a well-known algorithm for network inference based on the ob-served spread of infections among the population, or the spread of news and memes in social networks. The algorithm relies on a stochastic gradient to obtain estimates of the time-varying structure and tem-poral dynamics of the proposed network. The algorithm was implemented for GraphChi and Apache Spark computations models. The authors measured the performance for various real and synthetic da-tasets, described several limitations for these computation models discovered during experiments. Computations were performed on a single computer for GraphChi, and on a cluster of various sizes for the Apache Spark based implementation. According to the results of the review and the conducted experiments, the existing systems are di-vided into three classes: fast systems with static graph partition and expensive repartition with signifi-cant structure changes; on average, slower systems that are able to handle large amounts of changes ef-ficiently; even more slower but highly scalable systems that compensate low single node performance with the ability to scale computation to a large number of nodes. The conclusion drawn from the con-ducted review and experiments shows that the problem of dynamic graphs efficient storage and pro-cessing is still not solved and requires additional research.

15. A formal model of multiagent systems for federated learning [№1 за 2022 год]
Authors: Yuleisy G.P., I.I. Kholod
Visitors: 3231
Recently, the concept of federated learning has been actively developing. This is due to the tightening of legislation in the field of working with personal data. Federated learning involves performing data training directly on the nodes where the data is stored. As a result, there is no need to transfer data an-ywhere, and they remain with the owners. To generalize the trained models, they are sent to the server that performs the aggregation. The concept of federated learning is very close to a multi-agent system, since agents allow training machine learning models on local devices while maintaining confidential information. The ability of agents to interact with each other makes it possible to generalize (aggregate) such models and reuse them. Taking into account the tasks that are solved by the federated learning methods, there are several learning strategies. Learning be carried out as follows: sequentially when the model is trained in turn at each node; centrally when models are trained in parallel at each node and aggregated on a central serv-er; or decentralized where training and aggregation is performed on each of the nodes. Interaction and coordination of agents should be carried out taking into account these learning strategies. This article presents a formal model of multi-agent systems for federated learning. It highlights the main types of agents required to complete the full cycle of federated learning: an agent that accepts a task from a user; an agent that collects information about the environment; an agent performing train-ing planning; an agent performing training on a data node; an agent providing information and access to data; an agent performing model aggregation. For each of them, the paper defines the main actions and types of messages exchanged by such agents. It also analyzes and describes the configurations of agent placement for each of the federated learning strategies.

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