Software & Systems

Articles of journal № 4 at 2022 year.

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11. A GraphQL dynamic schema in integrated information system implementation [№4 за 2022 год]
Authors: Chernysh, B.A., Murygin, A.V.
Visitors: 1882
The paper discusses the possibility of using the GraphQL toolkit with a dynamically changing data schema. The standard way to define data types and operations in GraphQL is a static schema. When using it, the en-tire data structure is determined in advance and cannot be changed dynamically while the application serv-ing requests is running. This circumstance does not allow using GraphQL in applications where the data struc-ture can change dynamically. To solve this problem, there is an approach that consists in storing the data schema in the application memory, and regenerating this schema in case of metadata changes. This paper presents a method for implementing this approach using the SciCMS software platform de-veloped by the authors as an example. A feature of the system is its focus on the requirements for working with data on technically complex products. These requirements include data storage in a tree view with op-timizing retrieval mechanisms, product version control, the ability to use different language representations of products, product lifecycle management, and advanced integration with multiple data sources. The paper outlines the techniques and technologies involved in building the system, provides UML dia-grams and diagrams of the main structures and processes of the application core. It also describes the im-plementation details of individual platform subsystems. Experimental data sampling was carried out in order to evaluate the efficiency of executing queries using joins. The most effective tools for optimizing the selection of hierarchical data were selected based on the data obtained. The paper presents the possibilities of the platform for its integration with other systems with-in a single information space.

12. Implementing some of the Mantevo project applications on the OpenTS DMPI platform [№4 за 2022 год]
Authors: Osipov V.I., Matveev G.A., Roganov V.A.
Visitors: 1425
OpenTS is a system for parallel programming that supports dynamically loaded adapters for a communica-tion layer. The OpenTS system implements a T++ language for parallel computing, which is an extension of the C++ programming language. The syntax of the T++ language differs from the syntax of the C++ lan-guage by adding several keywords to it. The OpenTS system assimilates many other parallel programming technologies: a special shared memory model, a model of distributed threads and objects, distributed gar-bage collection, and, finally, a high-level language add-in, which is a technology unique in its characteristics to support maximum compatibility with traditional languages in syntax and semantics, but at the same time an effectively parallelizable computing model. The OpenTS system uses its own MPI implementation designed as the OpenTS DMPI dynamic library (Dynamic MPI). The OpenTS DMPI library provides a basic subset of functions from the MPI standard ei-ther by redirecting calls to the local MPI library installed on the target system, or on top of the TCP/IP proto-col. When initializing the OpenTS system, the DMPI subsystem is activated. This subsystem dynamically loads the local MPI library that is specified in the application environment variables. The developers of the OpenTS system implemented some of the MPI functions directly. The Mantevo project includes several parallel applications that implement algorithms for solving some partial differential equations: a molecular dynamics simulator, a simulator of linear electronic circuits and other applications. The paper briefly describes the implementation of the MiniAMR, MiniMD, MiniFE and MiniXyce appli-cations from the Mantevo project on the OpenTS DMPI library and compares the effectiveness of such im-plementation with the original MPI version of the application.

13. Algorithms for generating training sets in a system with case-based inference based on example situations [№4 за 2022 год]
Authors: Glukhikh I.N., Glukhikh D.I.
Visitors: 1220
The paper considers the issue of creating training sets and their scaling in machine learning problems. The subject of the study is the process of generating training sets based on examples in order to augment them. To implement the idea of expansion, it is proposed to use the transformation of existing examples of sit-uations. The transformation of examples is based on a well-known optimization method - the method of coordinate descent. The paper describes the statement of the problem of transformations of example situations in terms of the introduced representation model. There are proposed algorithms that make it possible to obtain an ex-tended set from the initial set of example situations specified using formal representations, which will include situations that meet the similarity criteria with these examples. The paper presents the testing of the proposed algorithms for expanding a set of example situations, car-ried out in order to form a data set for the studying artificial neural networks. The obtained results are of practical importance for training artificial neural networks used in intelligent decision support systems. The proposed algorithms make it possible to automate the formation of datasets using the available prepared and approved examples of typical situations and solving the transformation problem as the problem of finding the optimum of the similarity objective function.

14. Classification of common design patterns for multi-agent systems [№4 за 2022 год]
Author: S.A. Chernyshev
Visitors: 1829
Typically, developing multi-agent systems (MAS) involves using special frameworks or simulation model development environments. They provide the developer with the necessary functionality of an agent launch-ing environment, communication between agents, organization of access to resources and much more. However, there are cases when a stakeholder stipulates that it is necessary to avoid dependencies in the form of these toolkits. The lack of a unified database of MAS design patterns without their binding to specific domain in this case is a significant problem. Therefore, developers are coming up with solutions that have been already described earlier. The purpose of this work is to review and analyze the existing classifications of MAS design patterns, to identify common design patterns without their binding to specific domain, which can be used in the design of multi-agent systems and their classification. From more than 200 MAS design patterns in the public domain, the author has formed a base of 60 pat-terns that are not related to a specific domain. He also proposed the following classification of common MAS design patterns: structural, behavioral, migration, communication, architectural (system), protective and cognitive. Some of the classes of patterns allow introducing additional elements that extend functionali-ty of the system, while others aim to implement different aspects of both the agent and MAS. The most prominent class of all proposed patterns is architectural (systemic) patterns, as they specify different types of agent architectures, multi-agent systems or elements that lay down rigid software constraints on the functioning of the developed system or its parts.

15. Semiotic network editing software for robot control systems [№4 за 2022 год]
Authors: Sorokoumov P.S., M.A. Rovbo
Visitors: 1461
The problem of processing data structured as a graph is important for applications in many domains, includ-ing natural language text processing. For voice control of a robot, command interpretation is based on matching between semantic networks that describe a robot’s world model and a received instruction. The simplest and most understandable way for an expert to describe such matching process is a system of infer-ence rules that determine constructs recognizable for a robot in the results of the semantic analysis of text. Since the existing processing tools and methods are either overly complex or do not support the debugging of complex rule systems well enough, there is a need in a special solution. The proposed software tool for network data processing facilitates the development of such inference rules for modifying a world model. The joint representation of data and their processing methods is imple-mented within the framework of a semiotic approach, which has signs that combine information about the state of entities in the real world and possible changes in this state as the main entities of the model. Applica-tion of the semiotic approach to organizing a rule-based system allows linking data and rules within a single world model of an intelligent agent. Providing a user with a rationale for the decisions made in the form of easily interpretable lists of applied parameterized rules facilitates extension, debugging and maintenance of the system. The developed software can also be useful in other domains where it is convenient to describe the system state modification by logical inference.

16. Terms extraction from texts of scientific papers [№4 за 2022 год]
Authors: Dementeva Ya.Yu., E.P. Bruches , Batura T.V.
Visitors: 1789
The relevance of the task of extracting terms from the texts of scientific articles is due to the need for auto-matic annotation and extracting keywords in an ever-increasing flow of scientific and technical documents. This paper explores the influence of various language models on the quality of extracting scientific terms from Russian texts. We compare two models: the mBERT model that was pretrained on texts of different languages, and the ruBERT model pretrained only on Russian data. Two training sets of annotated texts were prepared. The au-thors carried out fine-tuning and further comparison of the performance indicators of the two models using these training sets. They also studied the influence of the choice of the language model on the quality of ex-tracting the terminology contained in the texts of scientific articles. The results have become the base for modernizing the algorithm for extracting terminology from texts applied by the Terminator tool, developed at the A.P. Ershov Institute of Informatics Systems. The obtained results showed that within the framework of the task of extracting terminology from the texts of Russian scientific articles, the ruBERT model, which gave the best performance in an ensemble with a dictionary and heuristics, can be considered as the most applicable model. In addition, the difference in the results of models on full and partial match can be stated due to the problem of defining the boundaries of terms in the texts described in the paper. The results obtained also allow concluding that the quality of the training set markup affects the quality of terminology extraction.

17. Aspect extraction from scientific paper texts [№4 за 2022 год]
Authors: Marshalova A.E., E.P. Bruches , Batura T.V.
Visitors: 1729
The paper focuses on the problem of automatic aspect extraction from the texts of Russian scientific pa-pers. This problem is relevant due to the increase in the number of scientific publications and the growing need for automated extraction and structuring of key information from them. The study involved the creation of a corpus consisting of 291 abstracts of Russian scientific papers an-notated with the following aspects: task, goal, contribution, method, tool, use, advantage, example, and conclusion. The paper provides descriptions and examples for each aspect. As a result of the corpus annota-tion, 1494 aspects were identified with 44 % of them were the contribution aspect. In addition, the paper proposes an algorithm for automatic aspect extraction. The paper considers the aspect extraction problem as a sequence-labeling problem. The BERT neural network is used to implement the algorithm. The authors have conducted a number of experiments related to the use of vectors obtained from various language models, as well as to freezing the weights of the model. A multilingual model fine-tuned on our data, that is, trained without freezing of the weights, has shown the best result. To improve the quality of aspect extraction, some heuristics, which are listed in the paper, have been developed, and the model has been further trained on the new data obtained from automatic labeling followed by manual edit-ing. The developed system can be useful to other researchers, as it simplifies selection of publications on a particular topic, review of methods for solving a particular problem, and analysis of results obtained in other works.

18. Designing a decarbonising closed-loop Nature–Technology control system [№4 за 2022 год]
Authors: R.I. Solnitsev , G.I. Korshunov , Lei Wang
Visitors: 1664
This paper considers carbon dioxide emissions from both energy and industrial enterprises in order to build a decarbonising closed-loop Nature–Technology control system based on the example of Beijing, PRC. Along with the known approaches to achieve atmospheric decarbonisation, the paper proposes an alter-native approach to solving the problem based on a closed-loop Nature–Technology control system. Math-ematical models and basic approaches to the analysis and synthesis of a closed-loop Nature–Technology control system are proposed for the combination of energy and industrial enterprises as a basis for develop-ing an appropriate automated process control system. The analysis and parametric synthesis of controls ac-cording to these models is based on mathematical modelling. In this case, the main criterion is to minimise CO2 emissions. The paper considers the construction of such a system as a part of energy and industrial en-terprises, which might be applied to different types of production. The paper gives the stages of designing the main subsystems and links of the closed-loop Nature–Technology control system, which generates control and is implemented in the form of automated process control system. Final control regulators - filters, chem-ical adsorbers, catalysts and others are applied depending on the facility. This paper considers the natural fuel sources of the energy enterprises causing the highest CO2 emissions (coal, natural gas, etc.). As every fuel source is different in metric units and carbon content, this paper con-verts each source to standard coal with conversion factors. There are results on the status and prospects of the environmental situation regarding CO2 emissions in Beijing, PRC, estimates of energy consumption limits from energy and industrial enterprises. Based on the modelling of the proposed multidimensional control sys-tem, the authors propose a solution to the problem of minimising CO2 emissions for a combination of indus-trial enterprises and energy source enterprises (Thermal Power Plant). Taking Beijing, PRC as the example, the paper shows the possibility of fundamentally solving the problem of minimising CO2 emissions by im-plementing the considered decarbonising control system based on modern hardware and software modules automated process control system and an appropriate knowledge base.

19. Informational and algorithmic support of an environmental air monitoring intelligent system based on neural networks [№4 за 2022 год]
Authors: Yarygin G.A., Bayukin M.V., Kornyushko V.F., Shmakova E.G., Sadekov L.V.
Visitors: 1987
The article discusses algorithmic and informational support of an intelligent control system for modern gas analyzers used in environmental air monitoring systems called the Electronic nose. Neural networks form the base of information support. The paper describes a modern automatic odor recognition system based on measurements using low-selective sensors in multi-sensor systems for detecting components of gas mixtures in ambient air. It also shows the advantage of the proposed system compared with traditional systems with highly selective sensing elements. There is a library of smell images based on a series of prerecorded respons-es from the sensor matrix. It is stored in the intelligent system database. Then the responses of an analyzed gas are compared with the responses of individual substances from the image library. The authors propose a two-stage data clustering method for information processing. First, observational data is normalized so that each input parameter equally affects the system. Then the data are assembled in-to clusters using self-organizing Kohonen maps and the k-means algorithm. Each cluster represents an odor with a similar smell. Specific assessments are based on experimental data collected in the environmental monitoring system in the area of the waste incineration plant in Kozhukhovo. The paper considers the choice of an odor identification criteria, which will be used by experts in deciding on odor identification. There is a substantiation of choosing the proximity metric of analytical samples as the norm of the distance between the odor vectors in each sample as a criterion. The authors have developed an algorithm for identifying a substance’s gas analytical sample using neu-ral networks and the selected criterion for decision-making support. There is also a developed (using R pro-gramming language) software product that allows assessing data membership obtained from a device to a certain smell followed by providing visual results of a odors’ spread dynamics in real-time. The paper pre-sents the application results of the developed algorithm in the eco-monitoring system of the incinerator plant in the Kosino-Ukhtomsky district of the Moscow region.

20. A precedent approach to evaluating a lightning impact on a street lighting system using the ontologies [№4 за 2022 год]
Authors: M.V. Chernovalova, Chernensky L.L., Makarova I.M.
Visitors: 1598
The paper considers the possibility of improving the energy efficiency and energy security of street lighting systems under conditions of direct and indirect electromagnetic effects of atmospheric electricity and light-ning discharges based on the development and use of an intelligent decision support system. A case-based approach is used as a research method with the representation of knowledge in the form of ontologies. This approach provides the possibility of forming decisions based on knowledge of similar situa-tions that have taken place before, which allows working in open, dynamic, poorly formalized subject areas, where uncertainty can be of an improbable nature. Ontological models provide the possibility of applying a case-based approach in the absence of the necessary amount of statistical information for decision making by representing knowledge in the form of a hierarchy of conceptual terms of a given subject area and a set of relationships. The paper proposes an ontology of the subject area that describes an artificial thundercloud. It is used to identify patterns in information arrays in the integrated management of the processes of energy supply and energy security of street lighting systems under conditions of a different spectrum of electromagnetic effects of atmospheric electricity and lightning. There is a developed algorithm for generating decisions on managing the elements of the specified system using a case-based approach and ontology: its distinctive feature is the possibility of forming quantitative solutions, adapting to the current situation the results of previously im-plemented and described in a linguistic form situations when managing elements of street lighting systems. The developed model and algorithm were implemented in the form of an intelligent decision support sys-tem focused on automating the process of managing these systems, the use of which in practice allows in-creasing the efficiency, validity and effectiveness of decisions made.

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