Digital Transformation and Global Society

Research has been proposed to determine an approach for studying cyber-social trust in different social spheres. A survey to better understand the trust Saint Petersburg citizens’ have in information technologies was conducted using a Social Construction of Technology (SCOT) approach. From the 600 respondents to this survey, the sampling error does not exceed 4% with a 95% level of reliability. The research demonstrates a new approach to a cybersocial trust construction. The questionnaire contained variables to evaluate the experience of use and the level of trust in new technologies in the areas of interaction with the government, the economy, healthcare, education, and interpersonal communication. According to the survey data, the category of cyber social trust was defined as the synergy of three components: institutional, transactional, and informational trust. According to the study, the experience of respondents strongly determines their willingness to use technology in various fields.

The article is devoted to the analysis of civic activity in modern Russia. The article presents the results of a longitudinal study of civic activity in Russia since 2014. The study is conducted by a survey of experts. Particular attention is paid to the analysis of the development of online and offline civic activity.Considerable attention is paid to the analysis of mobilization and demobilization in civic activity. It examines what forms of organizations are most significant in civic engagement, as well as how authorities react to their activities, what tools are used to demobilize citizens.The research show that the degree of development of civic activity has remained at approximately the same level for several years. At the same time, on-line activism is more developed than off-line. It seems that online activism is more massive and affordable, less labor-intensive for ordinary participants. At the same time, the Internet provides a fairly diverse set of tools, the application technologies of which are developing. Internet technologies are used as a mechanism by which political action can be seen by authorities and the public. At the same time, the state is forced to respond to such changes and is stepping up to regulate various forms of activity on the Internet.

The results of current authors’ research on the role of Internet discussions in processes of political e-participation are represented in the article. The investigators discover and evaluate fundamental parameters of socio-political discussions’ quality from their deliberative point of view on basis of profound content analysis of 11 online discussions on Russian pension reform. The study of such parameters allows to formulate essence of discourse participants’ opinions towards various social issues (for example, support or denial of pension reform). As a result, data sets combining original author’s text of posts and generalized opinion are formed. Henceforth, it lets predict a type of opinion in dependence of text content, find out plurality and ambiguity of opinions on up-to-date theme. Such parameters as argumentation and civility are extensively analyzed in the article. The research methodology, based on conception of J. Habermas, that can be used in further similar investigations is revealed in detail.

The impact of phygital reality on generation Z is the focus of this article. Phygital is understood as a system of interaction in the new world when digital space penetrates the physical and integrates with a person. For researchers, as well as for representatives of business, education, and media culture, the question of how phygital reality practices influence formation of the values of the most active digital audience - generation Z is becoming more and more relevant. Issues of value system are commonly seen among a typical representative of the generation Z that lives in St. Petersburg. The change in the value priorities of modern society is investigated. An analysis of the results made it possible to obtain the image of generation Z from generation Y viewpoint, to study the hierarchy of values of generation Z and to draw inter-generational analogies. It is shown that some features attributed to this generation are not supported by data. The results of the study of the value system of a typical representative of generation Z in St Petersburg are in some contradiction with the value system of generation Z in other countries. The authors believe that generation Z studies present valuable material for developing governing solutions in terms of desired goals and actions to achieve a developed digital society.

The digitalization of governance is meant to increase public satisfaction with state service and facilitate citizen involvement in safety provisions. Due to the digitalization trend, one can expect expansion of digital technologies in policing. Those technologies can be used for surveillance but they can also create a platform for cooperation between various actors in security production. Therefore, citizen’s perceptions of technologies are of critical importance. Russia cannot be considered a pioneer in the use of police technologies. However, the number of closed circuit television (CCTV) cameras in public places is steadily growing. Police surveillance extends to virtual spaces as well such as social networks and public messengers. Based on a 570 sample of university students St. Petersburg, Russia we explore views related to CCTV and online monitoring. Results show that young people are willing to accept surveillance in public places, but are unsupportive of online surveillance tools and regulations. Citizens views are ambivalent regarding effectiveness of police technologies in crime prevention, enhancing security, and increasing police accountability. However, they express concerns regarding possible infringement of their privacy due to the use of technology. The study was funded by the Russian Foundation for Basic Research (grant 18–011-00756 A “The study of citizens involvement in public governance under conditions of the formation of a digital government”).

Online protest activity has become a trend of the last few years, attracting the attention of both theorists and practitioners of protest movements and social and political campaigns. The events of the last few years allow us to say that social media play a significant role in protest activity around the world. In 2019, one of the most visible and appealing forms of protest activity was environmental activism, which, however, increasingly had politicized features. In this paper, the authors define online protest as well as the role of social media in this process. The perspectives on environmental protest discussed in this paper are used to analyze three cases. Based on them, the main topics and directions that are used by environmental protest initiators to mobilize social media users are identified. Each of the selected topics of protest actions is analyzed in terms of the impact on the protest in general, as well as on users’ activity and their desire to support a certain direction of protest.

The Internet forms not only new cyberspace but also has a significant impact on the perception of time and its organization. Focusing on the phenomenon of temporal behavior in social media, the current study aims to identify factors that can determine the dynamics of communication in the comments of the popular Russian social network Vkontakte. The research is based on data from six major online media: “Meduza,” “Lenta.ru,” “Rossiyskaya Gazeta,” “Novaya Gazeta,” “Mayak,” and “Russia Today.” We examine the frequency of publications, the dynamics of communication, the temporal distribution of comments, and the post response rate. The identified four temporal behavior models described as “Discussion media,” “Stimulus is a response,” “From call to call,” “Timeless or Silence is gold,” provoke assumptions about the possible causes of differences in the dynamics of communication between Russian Internet users.

Automated decision making is becoming more and more popular in various domains and demonstrates high performance capabilities. The growing model complexity has limited the opportunities for understanding and justifying the model behaviour. Explainable Artificial Intelligence (XAI) has emerged to make complex models more transparent and provide insights of model behaviour. There are numerous XAI tools for implementing different types of explanations, but the majority of these tools’ outputs are quite complex and can be misused. Therefore, this research aims to make explanations more comprehensible. We plan to review existing approaches to explanation, study user needs for interpretation tools and propose the design of the tool, selecting the appropriate approach and returning explanation in a simple form.

Player experience is often difficult to understand, and it is not always clear to game developers which game features should be eliminated or maintained. By analyzing reviews of particular games, it is possible to create high-quality games and understand player psychology and game preferences. Our pilot study aimed to find out how people evaluate their game experience based on reviews on the Steam platform and to extract its main dimensions. By utilizing the Structural Topic Model (STM), we extended previous research on main components of game experience and found topics which were not discussed in previous works. Using the community detection method, we divided our topics into seven main dimensions that connect with different strength. Our research contributes more knowledge about the processes of evaluation as a whole and game evaluation in particular.

Massive Open Online Courses (MOOCs) are a popular educational format. However, it is not clear how to assess students’ experience in MOOCs. This pilot study aims to determine how the discussion of essential aspects of online education differs for students taking online courses from four different categories of education. Moreover, this study is an attempt to explore the value dimensions of the students of MOOCs – the attributes of online education experience that online learners deem most important. Using texts of student feedback it is possible to identify underlying aspects of courses students value and the difference of features and their frequency of mentioned across four fields of education.We collected 8558 sets of feedback from students taking 37 courses on Coursera. Text mining methods – frequency analysis, keyword extraction, and dictionary construction – were used to identify the MOOCs features to which students pay the most attention.This study identifies the dimension values of MOOCs students and distinct trends in the discussion of online learning experiences that are strictly specific to different areas.Conducting a value analysis of MOOCs using feedback allows us to look at the features of the courses that are important for students.

The urgent scientific problem of multifactor clustering using various methods of normalization and averaging is investigated. Metric calculation values to improve the quality of clustering. A literary review of scientific publications on the topic of clustering social graphs and identifying communities has been carried out. The shortcomings of modern research in the field of analysis of social networks are identified. The list of network analysis metrics recommended as basic for data pre-processing is presented. The algorithm of the hybrid method of multifactorial clustering is presented, which allows reducing the computational costs of data clustering. An algorithm execution procedure is described for selecting several centrality metrics. Various methods of averaging centrality metrics are presented. This approach can significantly increase the assessment of the quality of clustering. The developed hybrid method based on averaging and the Louvain multi-factor clustering algorithm allows us to reduce computational resources. The clusterization application problem in the online community ITMO.EXPERT of the VKontakte social network is considered.

The research project we are conducting is devoted to text emotional analysis. In this paper, we report the preliminary results of the non-discrete data assessment method, which uses an original interface developed to annotate texts according to emotion model known as Lövheim Cube. Swedish neurophysiologist H. Lövheim put eight basic emotions in the cube vertices according to the particular combination of three monoamines triggers each of them. We took four supporting diagonals of the cube and mapped them onto assessment scales: Distress/Enjoyment, Rage/Disgust, Shame/Excitement, Fear/Surprise. 172 human assessors were asked to adjust the pointer of a slider between two opposite emotions on the scales after having been read each of 48 text fragments retrieved from Russian social network VKontakte. By converting labeled scalars into spatial coordinates in the cube space, we obtained a set of comparable evaluations. The effectiveness of the approach has been validated using the Intra-class correlation metric. The proposed method offers noticeable benefits when compared to the discrete assessment procedure, giving to each text a multidimensional evaluation, which is closer to the natural text perception while reading.

Automatic evaluation of public opinion is an actual problem in many areas, including both governmental and private sectors. There is number of scientific schools and corporations which work on to solve the problem of automatic evaluation of publications in media, social networks and other internet resources, in order to solve such problems as evaluating public image of a company, product or persona, evaluating work of PR departments and agencies, analyzing the most socially significant and resonant newsmakers and issues. The problems involve area of natural language processing and understanding, which is considered to be technologically and mathematically complex, and is nowadays being solved using deep learning models, which require a large marked dataset with texts of similar domain, which is hard and expensive to obtain. Another problem of such systems is performance issues. In this work an informational system is described, which attempts to solve the outlined problems. In the paper an approach is proposed, which allows to classify the most important/positive/negative/resonant topics and publications, and to analyze their dynamic characteristics. The proposed approach is not based on manual creation of keyword dictionary, or labelling of big amounts of documents and allows to evaluate documents according to arbitrary criterion. The approach was verified on one criterion by comparing it’s results to a dictionary-based system.

We present the polylingual problems encountered in the design of the educational ontology OntoMathEdu and describe the developed solutions. Some of these problems and their solutions became the basis for the development of new solutions in the design of the entire ontology. The content of mathematical education, approaches to the definition of a number of mathematical concepts both in general and at different levels of instruction are different. Therefore, there are differences between the sets of concepts of ontology as a whole and its educational projections for use in Russian-language, English-language, and Tatar-language learning environments. It is planned to use the OntoMathEdu ontology with projections for various language learning environments in teaching digital systems. In particular, for teaching mathematics in English to foreign students, helping Russian schoolchildren and students to learn English.

Data-based and data-driven decisions are at the core of digital government transformation. However, the more the data is to be used to guide policy development, the higher are the requirements to the data accuracy and readiness. Larger reliance on data to inform policy decisions should not lead to increased reporting requirements and hence excessive administrative burden on businesses. Therefore, identifying and reducing duplication in statistical data should be performed at the early stages of the government digital transformation. Given the constantly increasing number of strategic documents and continuous amendments to the list of statistic indicators measured, there is a need for an instrument allowing for timely identification and elimination of possible duplication in statistical and other indicators.In this paper we propose a methodic approach to identifying and evaluating possible duplication in statistical and other administrative indicators which is based on a partially automatable algorithm complemented by expert evaluation. The results of piloting this approach on a set of about 6,000 statistical indicators suggest that it could become a useful tool for data management that would allow to improve the quality of aggregated data, on the one hand, and reduce administrative reporting burden on businesses – on the other. The proposed approach could also be applied in a broader context, i.e., for the analysis of strategic planning documents, and may be of interest to practitioners from other countries where the quality of statistical data and duplication of administrative information is considered a barrier for further government digitalization.

In this paper we present a corpus of Russian strategic planning documents, RuREBus. This project is grounded both from language technology and e-government perspectives. Not only new language sources and tools are being developed, but also their applications to e-government research.We demonstrate the pipeline for creating a text corpus from scratch. First, the annotation schema is designed. Next texts are marked up using human-in-the-loop strategy, so that preliminary annotations are derived from a machine learning model and are manually corrected.The amount of annotated texts is large enough to showcase what insights can be gained from RuREBus.

The article analyzes the problems and prospects of using recognition technologies for human faces. The authors note that their development over recent years brings together the problems of the right to a personal image and the right to privacy, enshrined in the constitutions of most democratic countries. This is due to the fact that these technologies make it difficult, and, in some cases, impossible (or inappropriate) to use traditional legal mechanisms to protect these rights. In this regard, the authors propose to extend the concept of personal integrity to the “digital forms of existence” of an individual reflected in personal images, videos, virtual accounts, etc.The authors propose to put some approaches formulated in the article as the basis of the legal regulation of the use of facial processing technologies. In particular, there should be a legislative ban on the development and use of programs and systems that search and process photo and video images that are not publicly available, and legal liability measures should be established for its violation. On the contrary, a person’s posting of such information in the public domain should be interpreted as his consent to their search and comparison.Otherwise, issues should be resolved with the processing of photo and video images, as a result of which they are subjected to various kinds of distortions. Although the prohibition on creating such fakes is unreasonable, their publication and distribution may be restricted by law.

The participatory budgeting as a part of e-Government becomes powerful approach that helps citizens to solve issues in the local area with support from government. Implementation of the participatory budgeting in new areas as well as its development is existing ones requires analysis of successful cases integrated with survey of local citizens. The paper addresses to the development of an approach that can be used to conduct surveys, analyze results and get statistics in semi-automatic way. Starting with an overview of top analytical tools, this paper identifies requirements and presents data-analysis system prototype. The prototype is focused on providing information space to analyze data. Data is gathered from various public and government information systems including surveys on participatory budgeting conducted by the governments. The system introduces data processing mechanics decisive for deeper data understanding. The paper also demonstrates prototype microservice architecture, including reasoning for each chosen technology. Prototype was evaluated on the survey conducted in Saint Petersburg municipality.

The evolution of e-governance and the institutionalization of digital government is accompanied by contradictory trends. Expanding the technological capabilities of providing electronic public services stipulates a more convenient and effective way for citizens and governments to interact. On the other hand, the innovative nature of e-communication with the e-government creates citizens’ concerns about the security of personal data. Public sentiments associated with fears of increasing control over the population through the use of electronic services are becoming more pronounced. The increasing uncertainty and the lack of digital competencies actualizes the problem of trust in the e-government. This research examines Russian citizens’ perceptions and attitudes towards digital government in terms of assessing their activity and level of trust in electronic services. The factor analysis was performed on the basis of data collected in telephone surveys across ten Russian regions.

The paper presents main results of the intellectual classifier development, which is the one of approaches to optimize the process of message submitting to the “Our St. Petersburg” portal and its further verification by special moderation services. Based on training data sample and 200 categories, the algorithm of automatic classification was trained using such classical methods of machine learning as naive Bayes classifier, decision trees, support vector machine and artificial neural networks. Metrics of the effectiveness and relevance of the developed solution are presented, statistics, results and analysis of testing on real data are presented, and prospects for further work are determined.

Cities are different in their organization, but perform the same key functions everywhere in the World. However, cities sometimes provide their residents with radically different living conditions despite their apparent identity. Is it possible to compare different cities looking at their spatial structure? How to evaluate the relevant parameters? How to objectively describe living conditions, taking into account the existing nature of the use of the territory? The presence of a sufficient number of life support objects, their correct location and accessibility ultimately determine the environment quality for people life activity. The morphology of urban development is determined by the planning and zoning of the territory, urban planning standards, in which there are such indicators of regulation of development as the building coefficient, the density of the road network, the maximum and average number of buildings floors, etc. In this regard, it is proposed to introduce a systematic indicator of the quality of the urban environment, such as the area coherence, which will allow to assess the development conformity of provision infrastructure to meets the population needs. The paper discusses the analysis of urban space structure and suggests methods for measure metrics that will objectively assess the infrastructure provision of a particular section of the urban area, as well as obtain an integral indicator of urban environment quality. It is proposed to use the methods of geospatial data analysis and GIS technology to do this.

This paper presents the expert survey results on e-participation development at municipal level in Saint Petersburg. The research was based on the institutional approach. The institutional model of e-participation functioning showing 6 communication channels between government and citizens was used in the research design: portals of urban issues, e-receptions, participatory budgeting technologies, e-voting, crowdsourcing platforms, and personal visits. 50 experts from the city administration, municipalities, house and communal services companies, and subordinated institutions took part in the survey. The survey revealed the most frequently used channels for communicating with citizens: responses to citizens’ requests and applications at Governor’s e-reception and portal “Our Petersburg”. According to the survey, the most effective channels for applying for various problems and solving them are “Our Petersburg” portal, as well as personal appeals of citizens to government bodies. The research results could be of interest for the development of practical recommendations for the municipal administrations for e-participation activities organization.

Arctic cities exist in hash weather conditions and are particularly sensitive to climate change. To solve these problems the Arctic cities require “smart” solutions to become more sustainable and effective and be able to adapt to climate change influence. The aim of the research is to find out the main solutions for smart and sustainable strategies implemented in the Arctic cities based on the international experience. The case study was chosen as the main research method. Information was obtained from several Arctic cities as Reykjavik (Iceland), Umea (Sweden), Oulu (Finland) and Anchorage (USA) as well as several Russian cities included in the Arctic zone of the Russian Federation (AZRF). Along with temperature increase the main features of northern cities primarily include low temperatures, strong winds, heavy rainfall, insufficient solar radiation. All these require more amount of energy for heating of buildings, which leads to an increase in greenhouse gas emissions and, consequently, climate change influence. To combat with climate change some smart solutions for reduction of greenhouse gases were implemented by using alternative energy, electric vehicles, smart parking, energy-efficient buildings, intelligent building management, smart heating and lighting systems, heated sidewalks could be used for solving climate change problems. The results of the case-study show that smart solutions being implemented in Arctic cities mentioned above correlate with many UN SDGs, especially the goals 7 - «Affordable and Clean Energy», 12 - «Responsible Consumption and Production», 13 - «Climate Action» and others as 9, 11, 8, 3.

Illumination plays an important role in urban life. Recent technology developments make it possible to increase the quality of streetlight services. However, the cost of their implementation is high. The effectiveness of such improvements could be analyzed using calculated optimal time schedule for outdoor lighting which considers weather conditions and the possibility of light dimming. For this purpose, an estimation of illuminance during the twilight is needed. This study introduces new procedure for modelling twilight illuminance in urban area using machine learning algorithms. The model is estimated based on the illuminance records collected by the light sensor. The obtained results are consistent with the measurement values distribution and can be used as approximation of twilight illuminance specific for a certain location.

The paper deals with evaluation of the quality of public service infrastructure in the city of Kronstadt, the historical part of Saint-Petersburg agglomeration. Public services are considered as FMCG, cultural and recreational venues people use in everyday life. We consider the quality of public services through a set of objective (availability, accessibility, variability) and subjective (users perception) indicators. We measure the quality of public service infrastructure based on the data from open digital sources, such as Technical Passports of Houses from Open Data of Saint-Petersburg Platform, Google Maps, Google Places, and validate usability of services based on a sociological survey. We illustrate our analysis with maps which provide a detailed view on the localization, accessibility, variability of the service infrastructure. We conclude that public service infrastructure in Kronstadt does not address the needs of the dormitory areas which make up one third of all citizens of the city.

Sustainable development of megacities requires a transition to the new management methods and technologies, based on the wide use of a large amount of heterogeneous data. Managing the urban economy needs to consider environmental restrictions, environmental monitoring tasks, engineering facilities, and transport. Operational control over the urban environment and the surrounding area can be produced using unmanned aerial vehicles (UAVs), and the collected data can be processed using a wide range of software and hardware technologies related to the field of artificial intelligence. However, along with any fairly new technology, intelligent unmanned technologies have both advantages and disadvantages. Strengths are mobility and efficiency, relative cheapness, the possibility of a high degree of automation, whereas weaknesses are short flight time, dependence on weather conditions, the certain outstanding tasks of data management and processing. This paper considers the possibilities of using intelligent unmanned technologies based on UAVs for solving the problems of monitoring the urban environment of the Kazakhstan megalopolises. Consideration is also being given to the scope for extending possibilities of applying these technologies to the field of environmental monitoring, monitoring of hazardous geological processes, technical constructions and vehicles. Furthermore, technological and economic issues, as well as necessary data processing technologies, are discussed. The economic effect of the use of IUVAT is estimated at $ 70-200 million, but it requires solving a set of data processing, control and technical problems.

We investigate the characteristic of several intelligent algorithms for the program dialogue module of the support system of development personnel of high-tech service-oriented companies. Briefly describes the parametric model of workplace learning as base for personnel development and the most appropriate approaches to the formation of specific staff competencies. One of the elements of the proposed system is the means of answering personnel professional questions. In such applications, an important role is played by means of preliminary classification of queries that allow to narrow the search domain and increase the relevance. Three approaches to classifying of questions were compared: The Naive Bayes classifier, Random Forest Classifier and neural network. A comparative assessment of such approaches was carried out.

The study identifies the need to develop the entrepreneurial competencies of specialists in business and management in the digital economy age and reveals the understanding of opportunities and risks of the digital transformation of the economy. It provides a theoretical framework, based on a scientometric analysis of publications on digital economy with VOSviewer Software and an extensive literature review, emphasizing the necessity of entrepreneurial competencies in the digital economy. The results of the exploratory qualitative study show how undergraduate students in management and business explain what digital economy is and who is the manager in the digital age, and explain opportunities and threats that they associate with the digital transformation. The paper discusses the results and major problems concerning the students’ perception of the digital economy and a manager in the digital context. This study contributes to the research that focuses on the development of management and entrepreneurship in the digital economy.

Increased attention to the growth of collaborative consumption (CC) is primarily connected to rapid digital transformation and spread of information technology, in both developed and emerging markets. CC affects most areas of everyday life, changing transportation methods (Uber), short-term rent (Airbnb), ways of entertainment (Youtube), tasks (TaskRabbit), and financing (Kickstarter). As the younger generation representatives grew up in the era of social network and cyberspace, CC seems essential for them. The main objective of this study is to identify the typology of young customers based on their attitude to CC adoption factors in emerging markets, and specifically, the Russian market. The paper attempts to bridge the theoretical gaps by analyzing and systematizing different approaches to understanding the phenomenon of CC, considering the factors that affect CC of different generations. Based on the results of an online survey of 2038 Russian users, CC adoption factors for the younger generation have been identified. Based on the results of the online survey of 2038 Russian CC services users, seven factors of the CC adoption by the younger generation: difficulty of CC adoption, risk of fraud, economic and environmental benefits, hygienic issues, ownership as a status symbol, CC as a modern lifestyle, social norms, and approval of the reference group; and five clusters of the CC users have been identified.

Today online client reviews (OCR) are the crucial factor in choosing the best hotel to visit. However, as the number of reviews sites is growing, tourists tend not to write feedback about their stay, especially when everything was quite good. The aim of the present work is to determine the factors compelling the publication of online reviews by Russian consumers about hotels, they have visited recently, and to suggest methods of increasing their readiness to publish feedback. Quantitative approach (online questionnaire) was applied and based on principal component analysis 9 factors were revealed. People tend to write reviews more often because of desire to help others, satisfactory/unsatisfactory performance of hotel, positive or negative experience, company’s policy for reviews, whether it is easy to leave a review and some other incentives. At the end, we developed recommendations for hotels to achieve higher engagement rates for OCR publications from Russian hotel guests.

Today art museum is facing the challenge of adapting it’s mechanisms of keeping and presenting the works of art to spectators belonging to the communication society. Therefore, a museum gets more and more engaged in the process of digitalization using such newer technologies as internet of things, virtual reality, artificial intelligence, bid data design etc. The aims of a museum are currently shifting from traditional keeping the art pieces and studying them to—developing a scientific networks, announcing the highlights in social media and creating platforms which present digitalized pieces online allowing a viewer to collect the information through the web, moreover, an offline visit could be guided by a specified application customized to fit the necessitates of each user. An art institution today is supposed to be flexible and democratic enough to create an engaging, immersive area for a visitor to interact with, in other words, we argue that a museum armed with newer technologies is supposed not only a to secure and present the works of art but also to incorporate these pieces into the bigger flux of information, make them visible and important to viewers, to create the conditions for a lasting dialogue. We argue that this process involves not only the technical development of a museum, but also a new approach no narration of art history.

Previous research has found out that simulated, Virtual Reality (VR)-based forests can bring equal or even higher restorative effects than real forests. In this study, a Virtual Reality (VR)-based forest was created in order to compare whether VR-based forest can possess restorative effects in the context of primary school, and how the possible effects compare to restorative effects of a normal recess or to a situation when there is no recess at all. The effects were measured using Restorative Outcome Scale (ROS) and problem-solving tasks after the intervention. The results showed that VR-based forest had the highest restorative effect. There was a significant difference between VR-forest and no recess-groups, but no statistical difference between VR-forest and normal recess-groups. Future research avenues and implications of virtual forests are discussed.

The prevalence of technology and use thereof by part-time learners during lectures presents particular challenges to facilitators of learning. Devices used for learning can be misused for off-task activities, lowering engagement levels, and negatively impacting learning. This research investigated how learners use technology to contribute to learning, but also disengage from the learning process, and contrast it with their personal engagement to determine the potential impact. The quantitative data provides evidence of a relationship between on-task use of technology in the classroom and higher engagement levels in the learning process. Analysis reveals three insights about learning in the age of digital transformation. Firstly, the design of learning interventions should be as interactive as possible to ensure that learners do not disengage. Secondly, facilitators of learning need to ensure their learning design incorporates activities making use of the technology and thus create an environment of digital engagement and active learning. Finally, faculty that use technology to individualize learning should enable students that are working full time to become creators of media and not just consumers.

Automated translation from sign languages used by the hearing-impaired people worldwide is an important but so far unresolved task ensuring universal communication in the society. In the paper, we propose an original approach to recognizing gestures of the Russian Sign Language ​​based on the combined use of the linguistic Hamburg System of Notations (HamNoSys) and OpenPose library for tracking human movements. Our software based on the specially constructed and trained artificial neural network (ANN) model performs recognition of the two main components commonly identified in gestures: handshape and location (while the hand orientation, the movement and the non-manual component are so far not considered). The recognition accuracy obtained in the experimental validation with the standard Leap Motion SDK hand tracking algorithm was 100% for adult signers and about 76% for the children. Details of the software architecture and the image recognition process with skeletal data are provided.

This paper covers the results of a comparative analysis of the effectiveness of passive and active data collection methods for the purpose of extracting mentions of adverse drug reactions (ADRs) in Russian. In terms of their effectiveness, two systems of data collection were compared: a data mining system for gathering post and comment text content from social media, and an experimental chatbot conversational survey, integrated into a thematic community and targeted at collecting ADR reports. The study was conducted on VK, a Russian social network, on a community dedicated to the discussion of user experiences with taking drugs for treating mental illnesses. A comparative analysis of the comprehensiveness of data obtained by the passive method and the chatbot was carried out. The results show that an active information collection system allows subsequent information processing to be performed more effectively. Based on the results, areas for further development of conversational surveys for medical research were identified.

As the growth in the number of scientific publications keeps accelerating, exceeding 10% per year for certain booming fields, such as Neuroscience, employment of IT methods for secondary research becomes urgent. Our paper is dedicated to bibliometric text mining of PubMed databases that contain over 30 million publications, in search for the features reflecting cognitive reserves, which became an important topic particularly due to the ongoing ageing of the world population. For that end, we supplemented Entrez (EDirect) utility software tools with dedicated ontology implemented in OWL, integrating and extending several existing neuroscientific ontologies: BRCT, OntoNeuroLOG, etc. We extracted over 45 thousand publications related to cognitive functions and cognitive resources, and analyzed them per the keywords in such categories as brain structures, EEG oscillations, mental operations and activities. The results suggest that prefrontal cortex, beta range (13–30 Hz), inhibition and information load have been receiving the most attention from researchers in 1990–2019. Correspondingly, it would be practical to focus the subsequent quantitative analyses of psychometric and neurophysiological data on these factors, in order to find indicators of cognitive reserves.