Legal Barriers and Risks of Regional Economy Digitalization

The scientific article is dedicated to the study of existing regulatory and legal barriers that prevent the digitalization of the regional economy. The relevance of this study is determined by the need to create a favorable legal environment for the digitalization project development. In addition, the significance of the research topic is determined by the possible occurrence of national state interests’ threats. The general scientific research methods were used when writing the article, such as the analysis of normative legal acts, the study and generalization of information, classification. The article contains a conclusion about the current normative project activity trends of regional economy digitalization in the Russian Federation. By the end of 2019, less than half of the Russian Federation’s regions have developed economy digitalization programs. This fact is based on the lack of understanding of economy transformation concept among the authorities. Moreover, this situation is caused by the imperfectness of the methodological base. The article also identifies the “weak spots” of the domestic industry legislation that hinder the “digital economy” development and lead to the occurrence of threat to national state interests. Particular importance in the context of ensuring the national interests of the Russian Federation have legislative gaps on the use of citizens’ personal data as well as the information constituting state, banking, and medical secrets. The conducted research will allow participants of strategic planning at all levels of administration and subjects of law-making in the Russian Federation to form draft normative legal acts containing necessary provisions for the effective economy transformation in the context of digitalization.

Research on information classification and storage in cloud computing data center based on group collaboration intelligent clustering

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%.

Universities as Centres of Attraction for Proactive Youth in the Ural Region

Universities, comprising a strategic resource in building up a region’s human capital, play a key role in ensuring sustainable economic growth. For proactive young people seeking opportunities to obtain higher education, develop professional trajectories and enhance their social connections, Russian regions lacking such facilities are seen as less attractive. This situation provokes an outflow of the most promising university candidates from the peripheral regions to the various centres of attraction. Thus, a relevant research question concerns the relationship between the quality of regional universities and the retention of young specialists, who may be expected to support the future development of industrial enterprises in the region. The assessment of interregional mobility carried out by the present study is based on an analysis of responses from applicant and graduate surveys supplemented with statistical data (Monitoring the Effectiveness of Russian Universities, Rosstat). In or der to process this information, classification and data comparison methods were used. The results of the study showed that the Sverdlovsk and Tyumen oblasts are the primary centres of attraction for university entrance candidates from other Russian regions to the Urals, with the inflow of such applicants to these oblasts comprising on average 4.1 % and 13.2 %, respectively, of 18 year olds enrolling in these institutions during the 2015– 2019 period. At the same time, the largest universities provide relevant training for the region’s industrial base (up to 87 % of employed Ural Federal University graduates work in the Ural Region). The research results can be used to enhance the activities of universities and employment services in terms of developing tools for attracting and retaining proactive youth, improving the mechanisms for studying postgraduate migration in order to increase the region’s attractiveness.

Security aware information classification in health care big data

These days e-medical services frameworks are getting famous for taking care of patients from far-off spots, so a lot of medical services information like the patient’s name, area, contact number, states of being are gathered distantly to treat the patients. A lot of information gathered from the different assets is named big data. The enormous sensitive information about the patient contains delicate data like systolic BP, pulse, temperature, the current state of being, and contact number of patients that should be recognized and sorted appropriately to shield it from abuse. This article presents a weightbased similarity (WBS) strategy to characterize the enormous information of health care data into two classifications like sensitive information and normal information. In the proposed method, the training dataset is utilized to sort information and it comprises of three fundamental advances like information extraction, mapping of information with the assistance of the training dataset, evaluation of the weight of input data with the threshold value to classify the data. The proposed strategy produces better outcomes with various assessment boundaries like precision, recall, F1 score, and accuracy value 92% to categorize the big data. Weka tool is utilized for examination among WBS and different existing order procedures.

An information classification model for public sector organizations in Sweden: a case study of a Swedish municipality

Purpose The purpose of this study is to create an information classification model that is tailored to suit the specific needs of public sector organizations in Sweden. Design/methodology/approach To address the purpose of this research, a case study in a Swedish municipality was conducted. Data was collected through a mixture of techniques such as literature, document and website review. Empirical data was collected through interviews with 11 employees working within 7 different sections of the municipality. Findings This study resulted in an information classification model that is tailored to the specific needs of Swedish municipalities. In addition, a set of steps for tailoring an information classification model to suit a specific public organization are recommended. The findings also indicate that for a successful information classification it is necessary to educate the employees about the basics of information security and classification and create an understandable and unified information security language. Practical implications This study also highlights that to have a tailored information classification model, it is imperative to understand the value of information and what kind of consequences a violation of established information security principles could have through the perspectives of the employees. Originality/value It is the first of its kind in tailoring an information classification model to the specific needs of a Swedish municipality. The model provided by this study can be used as a tool to facilitate a common ground for classifying information within all Swedish municipalities, thereby contributing the first step toward a Swedish municipal model for information classification.

Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy

Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.

Development of building information classifier elements to create and maintain information models of capital construction objects in terms of design processes, construction process management and construction information

Introduction. Integral approach to the application of construction information in the creation and maintenance of information models of capital construction objects is key in the constant development of construction activities. Besides, according to the global trends, the direct implementation of construction activity including construction of especially complicated and unique objects and typification of classical ones requires application of a unified system of building information classification to optimize duration, costs and improve the quality of the constructed object. Development of the Russian classifier of building information was the first step in this direction allowing to make a tool which is the unified system of building information classification generally available. The Russian building information classifier developed in 2020 contains a lot of elements among which we can distinguish those groups which allow to manage the cost, duration and quality of the future capital construction object both at the early stages of its life cycle and later: management processes, design processes and information. Materials and methods. International systems of classification of building information that have found wide practical application in the field of construction: OmniClass (USA), Uniclass 2015 (Great Britain), CCS (Denmark) and CoClass (Sweden) are considered. The analysis of the structures and composition of existing classification systems, as well as the analysis of the current regulatory and technical framework in the Russian Federation in the field of construction in areas related to the management of processes, design of capital construction object and its information entities. Results. Taking into account the analysis and generalization of world practice in the field of construction, and classification of building information, parts of the building information classifier adapted to the specifics of the national base of normative and technical documentation in construction, applicable to the design and management of capital construction object, as well as for its description, were developed. The structure recommended by the standard ISO 12006-2:2015 is adopted as the basis for such classification tables of the building information classifier. When developing the composition of the classifier, the requirements for unification and standardization of existing national classifiers and experience in the construction industry on domestic and foreign objects were taken into account. Classification tables of the building information classifier for the two areas of activity in construction (Management, Design) and a classification table describing the information entities of the capital construction object were developed. Conclusions. Classification tables “Process Management”, “Design Processes”, “Information” of the building information classifier in the developed structures and composition provide the formation of a unified structure of management and design of capital construction object, allowing to combine its parts for adaptation to the requirements of a particular object and organization. Thus, providing an opportunity to optimize its technical and economic indicators, including the duration of construction and the cost of the object in the extent of its life cycle, to develop a tool for typing design and management processes, including planning tools and quality and cost control. An additional tool for the interrelation of various activities in construction (e.g., design, operation, construction, etc.) is the developed classification table “Information”, which describes the information entities of the capital construction object.

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Traffic classification is very important field of computer science as it provides network management information. Classification of traffic become complicated due to emerging technologies and applications. It is used for Quality of Service (QoS) provisioning, security and detecting intrusion in a system. In the past used of port, inspecting packet, and machine learning algorithms have been used widely, but due to the sudden changes in the traffic, their accuracy was diminished. In this paper a Multi-Layer Perceptron model with 2 hidden layers is proposed for traffic classification and target traffic classify into different categories. The experimental results indicate that proposed classifier efficiently classifies traffic and achieves 99.28% accuracy without feature engineering.

Cipher-Policy Attribute-Based Encryption on Cloud Computing

Cloud computing has drawn expanding interests from both scholastic and industry in the recent years because of its productiveness and low-cost management. Since it offers different types of assistance in an open organization, it is critical for users to utilize secure information for stockpiling and sharing data to guarantee information classification and data users protection. To safely Protect information, the most broadly utilized technique is encryption. The dual-access control, with regards to cloud-based storage, is a control system over both the data owner and the user who can upload and download files without loss of safety, data[1] and effectiveness. The credulous arrangement is that the user can download the whole database[2]. The framework planned in this paper is when the A user logs in with his credentials and after successful acceptance from the two different administrators, the user will be able to download the file present over the cloud. When a malicious user requests, the administrators can find it because it will generate a null value and they can block the user from downloading the files.