Emotions may be recognized through various sources, and while the outward expressions of emotion may be hidden, the physiological signs, such as electroencephalogram (EEG), will remain stable. One promising approach that could handle complex dependencies in EEG signals and low percentage of labeled data is self-supervised learning (SSL). However, existing approaches have complex architectures. This paper proposes a simple to understand and implement approach based on a convolutional encoder with SSL using 2 data preprocessing techniques. We also introduced a contrastive loss and batching function. The proposed approach has been evaluated on SEED and DEAP, widely used datasets for emotion classification. We achieved 87,9% and 85,8% on SEED and 93,3% and 63,1% on DEAP for subject dependent and independent splits respectively. The vector representations of the signal obtained by encoder after training on pre-text task without true labels was compressed into 2D space, and shown groups distributed on positive pairs. Another important outcome is that the encoder showed high performance during fine-tuning on a small percentage of the labeled data. In conclusion, the proposed approach reached both simplicity in architecture, effective vector representations of EEG signal and emotion classification accuracy compatible with SOTA.

The adoption of Knowledge Graphs (KGs) and especially ontologies by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs and facilitating human-centric data analysis, but are usually disregarded when designing Application Architecture: its Programming Interfaces (APIs), GUI, etc. In this paper we analyze existing specifications and tools, using during ontology-based application development and provide an approach to unify and reuse ontology-based KG data. The main feature of the approach proposed is the ability to automatically adopt Application Components based on KG data, without the need to redesign and recreate the Application Architecture in general. Our results also reveal several limitations on existing specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic GUI generation, ontology-based versioning and validation.

This paper summarizes the outcomes of applying the IT Crisisology approach for sustainable development of complex sociotechnical systems. The application framework comprised formal models, agile methods and supporting computer-aided tools. A set of the multi-agent simulation models included a family of scenarios that represented structure and behavior of the sociotechnical systems in crisis situations. The other models of the framework involved ontologies, categories, and domain-specific formalisms including functional programming languages. Additionally, agile patterns and practices were applied to harness the human factors which may help or hinder sustainable development in sociotechnical systems. The tools featured a collaborative decision support system to assist in team-based multi-criteria analysis of possible use cases. This system is on top of a set of enterprise-level systems intended for data acquisition, daily management, resource planning and business intelligence. The comprehensive approach resulted in more sustainable and better resilient development of the systems investigated in crisis conditions.

The tourism sector is quite complex from the standpoint of innovative technological development, since it is an example of a static competitive environment. Tourist sites are constant and the only thing that travel companies can influence is the image formed during the development of the tourist site. The article presents a developed and tested scheme for applied integrated marketing research, including Kelly’s repertory grid methodology for analyzing the motivation, values, perceptions and attitudes of visitors to tourist sites (cultural heritage sites). The results obtained can be applied in the context of developing requirements for information systems, avoiding “perception errors” and rejection by end users of new systems, since it is designed based on their psychocognitive and psychosemantic preferences.

A good pricing strategy is a key factor in the success of a company's products. However, fierce competition and an ever-increasing number of market players are forcing companies to use automated methods and systems to respond in real time to changes in customer purchasing power and competitors' marketing techniques. More importantly, multi-agent modeling tools are used to select the most successful pricing strategy in the first place and consider all factors influencing the final decision. This paper presents a possible variant of a multi-agent model for determining the optimal pricing policy of a company. The results show that the limited rationality and interaction of each agent determines the unique behavior of the system and that the pricing strategy affects the profit and market share of the firm.

The EOLANG programming language is a novel technology relying on formal phi-calculus similar to lambda-calculus for functional programming languages, and on design choices declared as mitigating most weak points of mainstream object-oriented programming languages. EO is under active development up to date and has some obvious development vectors. One of these vectors is development of compilation time type checker. With dynamic type checking provided by now, writing and testing programs in EO takes long and is vulnerable to bugs. In this paper the assessment of static type checking compliance of EOLANG is performed. The results reveal serious weak points in EOLANG implementation and theoretical basis making development of static type checker impossible.

Object-oriented programming (OOP) is one of the most popular paradigms used to build software systems. However, there is currently no object-oriented programming language using the pure idea of OOP. The EO programming language is an object-oriented language developed as a research solution, the purpose of which is to show that industrial programming in pure OOP ideas is practically possible. This work consists in analyzing the EO language, finding the optimally needed collection objects, as well as developing a library of collection objects. In the course of the research, the following tasks were formulated: domain analysis, overview of the basic concepts of the EO language, object analysis in Java, implementation of a library of collection objects, that characteristics objects should have and what tasks they should solve, prototyping in the EO language.

Class cohesion, understood as the degree of how tightly are bound or related its internal elements to one another, is often a critically important for quality assurance (QA) of object-oriented software. Typically, a low cohesive class contains disparate and/or isolated members; therefore, cohesion level is useful for detecting of poorly designed classes and ensuring faster and better system reconfiguration. There are over thirty different metrics to measure cohesion, based on class member analysis in terms of number and structure of attributes and methods. Utilizing class cohesion metrics can promote Java code static analysis quality, improve object-oriented programming practices, and suggest advanced and efficient ones. The jPeek tool with its library for inspecting objects in Java was developed by Huawei Technologies Co. Ltd. to achieve these goals [1]. The idea of code quality assessment is well known for a long time; class connectivity metrics were proposed by community several years ago and have not become generally applicable practice in industrial programming. The main goal of this study is to make a methodology for using cohesion metrics to analyze code that allows users to estimate their projects in the most appropriate way. During the study, each of the metrics was studied in detail, the influence of programming patterns on the result of the metrics was evaluated, and as a result a methodology for using the metrics to obtain the most reliable estimate was formulated.

In recent times, the subject of interoperability has become very popular. In large-scale software applications development, it is a common practice to combine multiple languages in solving peculiar problems and developing robust solutions. The ability to combine multiple languages allows an easy migration of an existing project from one language to another or use existing libraries in another language. This makes interoperability a force to be reckoned with when developing new programming languages. The Eolang programming language is a new research and development initiative aimed at achieving true Object-Oriented Programming by having all components of the program as objects. As such, the construct and syntax of Eolang is vastly different from that of Java. This makes integration and interoperability between these two languages a challenging issue related to method/object naming conventions, keywords and operators, etc. In this paper we explore the potential of Eolang interoperability with Java by looking at the interoperability mechanisms of some other languages with Java, describe ways to overcome these challenges with Eolang and develop the solution. Specifically, we focus on the possibility to call Java code from Eolang while the semantics of both languages remain preserved. Our solution allows Java code to be called in Eolang through wrappers that turn Java classes and methods into Eolang Objects.

According to the subjective opinions of many industry experts, object immutability is a virtue in object-oriented programming, since it leads to side-effect-free design, cleaner code, better concurrency, and many other factors. However, it has never been empirically demonstrated exactly how immutability affects quality metrics of object-oriented programs. In the following research, we analyzed 97508 classes from 240 public Java repositories to find out how immutability affects the size of the code.

The present work is devoted to the problems of Digital Twin development of industrial enterprises in the field of mining. The main goal of this article is to formulate the principles of designing platform solutions for the integration of the most important functional elements that ensure the implementation of technological processes of a full production cycle. Various classification schemes for heterogeneous, poorly structured information spaces that form the distributed digital environment of a mining enterprise are proposed. Based on the results of structural and functional modeling, a number of principles and requirements are formulated for the implementation of the Digital Twin technology of the transport and technological process in quarry. A conceptual diagram of the functional structure of the Digital Twin platform is proposed, taking into account the need to include Industry 4.0 technologies such as Industrial Internet of Things, Big Data (including Predictive Analytics and Machine Learning), Autonomous Haulage Systems and Dynamics 3D Optimization Modeling. Some aspects of the implementation and functioning of the prototype version of the Digital Twin platform are considered in terms of the use of instrumental solutions based on the Unity visual modeling environment.

The issue of accessibility of new drugs is always an acute one, as for patients and for doctors, as well as the healthcare system managers. The appro-priateness of speeding up market approval for new drugs was assessed using the example of the markets of the Russian Federation and the USA. The nomenclature of available antitumor drugs was compared and comparison of data on mortality caused by malignant neoplasms was performed. The results showed that with the availability of new drugs for the treatment of cancer, mortality did not show the expected decline, but increases in the United States by an average of 2.51% per year for the period from 2013 to 2017. Moreover, in the absence of 74% of new antitumor drugs on the market of the Russian Federation, mortality decreases by an average of 0.74% per year over the same period. The results retain their orientation when individually considering different age groups and localization of neoplasms, which indicates the general nature of the described phenomenon.

This paper discusses crises in software product development. It defines a development crisis and presents the new discipline of IT Crisisology, which systematically studies and addresses crises in software production. Essential ingredients of this IT Crisisology discipline include models, methods, tools, patterns and practices. The IT Crisisology framework developed addresses technology, business and human factors; for each kind of factors, it uses a dedicated set of the above-mentioned ingredients. These include tradeoff optimization, agility matrix, models for knowledge transfer and data lifecycle, to name a few. For enterprise scale development, these ingredients are further enhanced and improved. In case of systematic application, the suggested IT Crisisology framework allows for better disciplined, predictable, and manageable software product development, even in the event of a crisis.

This paper presents the architectural solution for a mission-critical, software intensive, large-scale software product intended for autonomous robotized open pit mining. The idea is to establish a stable architecture as early as possible. Therewith, the core concern is providing resilient communication between the client and the developer parties. The key issue is aligning functional requirements with the constraints of both technical and business nature. Although software engineering provides a number of frameworks, a few are really suitable for such an alignment and resilience. For this reason, we suggest a carefully selected blend of tradeoff-centered analysis and optimization methods, including ATAM and ACDM from CMU/SEI and our own enterprise architecture matrix-based approach. Based on the approach suggested, we identify and prioritize the mission-critical quality attributes required for the software product to be built, and describe the architecture in terms of components and connectors as prescribed by the SWEBOK software engineering standard. In addition, based on the product scale and scope, we recommend the high-level architecture patterns for the future system, such as layers, pipelines, and microservices.

In this article, the requirements for a prospective register of assisted reproductive technologies are considered. The basis of such a register is a specialized data store - the electronic passport of the woman’s reproductive health. This register will serve as a basis to analyse the effectiveness of the use of assisted reproductive technologies and for supporting the making of medical decisions about the likelihood of pregnancy.

This paper discusses experimental verification results of the tools for automatic determining optimal configuration of a virtual machine (VM) implemented based on previously developed models and methods (including the conditions of changing loads). For information process models, machine learning algorithms with reinforcement are applied. These models are constructed automatically in typed π-calculus taking into account the VM logs. In order to calculate the optimal configuration of the VM, a machine learning Q-algorithm has been implemented. It features reduction of terms correspondent to information processes on the basis of an abstract machine with states. The implemented method uses an applicative approach in the form of an abstract machine.

The present paper proposes an analysis of the experimental verification results of the means of automatic determining of the optimal configuration of a virtual machine (VM) implemented based on previously developed models and methods of automation for virtual machine configurations (including in the conditions of changing loads). For information process analytical models, machine learning algorithms with reinforcements are applied. All the while, models are constructed automatically in the language of the typed π-calculus taking into account the journal entries of the functions performed by the VM. In order to calculate the optimal configuration of the VM, a machine learning Q-algorithm has been implemented. Its special feature is the reduction of terms correspondent to information processes on the basis of an abstract machine with states. This being said, the implemented method for modeling information processes performed by the VM uses an applicative approach in the form of an abstract machine.

This paper presents tools aimed at automating the data integration process between different SaaS-applications. Data transformations creation and customization represents a major task in this area. We propose a solution based on type theory and describe an approach to automated transformation construction in form of typed functions.

The paper discusses application of the algorithm based information processes modeling and Q-machine learning to the task of virtual machines auto configuration. Usage of virtual machines is one the most common solution nowadays for almost every companies. Although using of virtual machines simplifies settings of hardware landscape and allows physical servers decentralization they may cause performance decrease. To overcome this issue, we propose using method of virtual machines auto configuration. The method is based on the following premises: tracking real business process held by virtual machines, using algorithms of machine learning to investigate optimal configuration, and virtual machine configuration by independent process.