Abstract: The subject of the research in this article are the features and regularities of the functioning of systems based on ChatGPT technologies, the knowledge of which makes it possible to formulate appropriate modifications of the Turing test, as well as the features and regularities of the formulation and use of the Turing test for systems based on ChatGPT technologies. The purpose of the study is to identify the features and patterns of functioning of systems based on the technologies of ChatGPT, as well as the features and patterns of formulation and use of the Turing test for systems based on the technologies of Chat GPT. As research methods, the method of social experiment was used, when during the study of a system based on Chat GPT technologies, certain questions were asked, answers were received, the analysis of which allowed us to conclude about the features of the "thinking" of systems based on ChatGPT technologies. In the course of the study, the following was found. Unlike human thinking, which is based on certain facts, the "thinking" of systems based on ChatGPT technologies, in some cases is not based on facts that take place in reality, often the user is given deliberately false information about facts and circumstances that take place in reality.
In contrast to human thinking, which is usually systemic in nature, the "thinking" of systems based on ChatGPT technologies is disorderly and fragmentary. Systems based on ChatGPT technologies cannot admit their mistakes, and attempts to force systems based on ChatGPT technologies to critically comprehend their answers lead to a malfunction of these systems. The article also provides a Turing test developed by the author for ChatGPT, which made it possible to identify the features of the "thinking" of systems based on ChatGPT technologies.

Keywords: patterns, reflection, critical analysis, Chat GPT, artificial intelligence, technology, systems thinking, human thinking, Turing Test, computer science

References:
Alekseev, A. Yu. (2022). Computer facilities for the complex Turing test. Artificial intelligence in solving pressing social and economic problems of the 21st century: a collection of articles based on the materials of the Eighth All-Russian Scientific and Practical Conference with international participation, Perm, October 25–26, 2022. (pp. 23-28.). Perm: Perm State National Research University.
Gorbacheva, A. G. (2015). The Turing test: diagnosing humanity in the human-machine interface. Man.RU, 10, 99-106.
Danilina, E. V., & Selyukova G. P. (2020.). Alan Turing and artificial intelligence. Current issues of science and economy: new challenges and solutions: Collection of materials of the LIV Student Scientific and Practical Conference dedicated to the 75th anniversary of Victory in the Great Patriotic War, Tyumen, March 19–20, 2020. Part 2., pp. 42-345. Tyumen: State Agrarian University of the Northern Trans-Urals.
Efimov, A. R. (2020). Post-Turing methodology: breaking

Abstract: The subject of this study are investment design management tools that allow evaluating the effectiveness of investment project options. The object of the study is digital products (software solutions) designed for automated efficiency assessment and selection of attractive projects for investment. The author has determined that the choice of the most profitable project for investment is the key task of the pre-investment stage of the investment process, while the presence of a large amount of information, the influence of external and internal weakly controlled factors, the state of uncertainty accompanying the investment process actualize the use of software products. Particular attention is paid to identifying and formalizing requirements for software products for risk analysis and evaluation of the effectiveness of investment projects, which will save time and financial resources and eliminate the influence of the human factor on the choice of a project for investment.   The research methodology includes the use of a systematic approach to identifying tools and indicators for evaluating the effectiveness of investment decisions. The author conducted a comparative analysis of software products that act as tools for assessing the attractiveness of investment projects when choosing the most acceptable for the development of commercial activities of industrial enterprises. The results of the comparative analysis of domestic and foreign investment project management software solutions presented on the technology market made it possible to systematize programs, identify their strengths and weaknesses and formulate requirements for an optimal software package for analyzing and evaluating the effectiveness of investment projects of an industrial enterprise. The digital solution developed by the author for risk analysis and evaluation of the effectiveness of investment projects should have the following characteristics: functionality, reliability and stability, interface and usability, compatibility, price and licensing conditions, technical support. Based on the results of the study, the scope of application of the results of the comparative analysis of software products is determined – the further development of digital solutions for evaluating the effectiveness of investment projects, ensuring the effectiveness of the management process of investment design of enterprises.

Keywords: disadvantages of the software product, advantages of the software product, program characteristics, selection of investment projects, software product, efficiency evaluation methods, performance evaluation indicators, comparative analysis, system approach, Investment project

References:
Akinfina, M. A. (2014). Cost management for investment project activities using SAP ERP. Economic growth of the Republic of Belarus: globalization, innovation, sustainability: materials of the VII International Scientific and Practical Conference, Minsk, September 25-26, 2014 (vol. 2, pp. 261-262). Minsk: BSEU.
Asenova, Z. T. (2018). Application of foreign experience of project management in Russian practice. Economic sciences, 1(46), 37-41.
Belyaev, S. N. (2011). Investment analysis using software products. Almanac of modern science and education, 5(48), 142-143.
Burlin, E. B. (2023). Assessment of the investment attractiveness of the corporation. Bulletin of Eurasian Science, 15, S1. Retrieved from https://esj.today/PDF/33FAVN123.pdf
Bychkova, A. N. (2019) Key categories of economic analysis: economic indicator. Bulletin of Omsk University. The series "Economics", 17, 3, 17-28. doi:10.25513/1812-3988.2019.17(3).17-28.
Voevodina, O. V

Abstract: One of the main tasks in the management of technological processes is to reduce emergencies and failures of existing equipment. The statistical data obtained during the operation of machines and mechanisms require appropriate mathematical processing to analyze the dynamics of technological processes and establish relationships between deviations, influencing factors and failures. Regression and cluster analyses are convenient tools for processing these data. The failures of cavitation systems are an essential, and at the same time poorly illuminated topic in scientific periodicals. Cavitators are relatively common technical devices that allow maintaining the technological parameters of fuel oil in tank farms at the required level (viscosity, water content, adhesive properties). The practice of using cavitators on fuel oil farms of thermal power plants in the Kaliningrad region shows that these technical devices can fail relatively often.   So, in case of disconnection or restriction of the supply of the required volumes of gas to the thermal power plant, reserves of fuel oil from the fuel park can be used. In turn, the failure of the cavitation system may lead to the impossibility of entering reserve fuel and, as a consequence, to the shutdown of power generation. Thus, the problem of ensuring energy security and the reliability of cavitation systems are closely interrelated. In this study, an array of accumulated statistical information on the parameters of the functioning of cavitators in fuel oil farms and the moments of failure is analyzed. Regression and cluster analyses were used to process the data array, which made it possible to determine the relationship between the types of failures and the influencing factors and to rank the weight of factors according to the degree of their impact on cavitation equipment. Based on the results of mathematical processing and data analysis, proposals have been developed to ensure greater technical reliability of cavitators, reorganize their maintenance system and reduce the number of failures.

Keywords: equipment reliability, equipment failures, analysis of statistical data, cavitation equipment, oil and gas equipment, k-clustering method, least squares method, cluster analysis, regression analysis, wear and tear

References:
Mutugullina, I. A. Primenenie podogrevatelej pri ispol'zovanii mazuta [Application of heaters in fuel oil usage] / I. A. Mutugullina // Mezhdunarodnyj nauchnyj zhurnal. – 2017 – ¹ 03-2. – S. 92-95.
Saltanaeva, E. A. Opredelenie effektivnosti primeneniya kavitatorov pri podgotovke k szhiganiyu mazuta s vysokim soderzhaniem vody [Determination of the effectiveness of cavitators usage in preparation for the combustion of fuel oil with a high water content] / E. A. Saltanaeva // Tinchurinskie chteniya : Materialy XIV Mezhdunarodnoj molodezhnoj nauchnoj konferencii. V 3-h tomah, Kazan', 23 aprelya – 26 2019 goda / Pod obshchej redakciej E.YU. Abdullazyanova. – Kazan': Kazanskij gosudarstvennyj energeticheskij universitet, 2019. – S. 249-251. – EDN SEXETF.
Esayan, V. N. Specifika ispol'zovaniya kavitatorov dlya bor'by s zastyvaniem rezervnogo topliva (mazuta) v stal'nyh rezervuarah [The specifics of cavitators usage for combat the solidification of reserve fuel (fuel oil) in steel tanks] / V. N. Esayan // M

Abstract: Monitoring and analysis of consumption of energy resources in various contexts, as well as measuring of parameters (indicators) in time are of utmost importance for the modern economy. This work is dedicated to examination and interpretation of the anomalies of collecting data on consumption of energy resources (on the example of gas consumption) in the municipal formation. Gas consumption is important for the socioeconomic sphere of cities. Unauthorized connections are the key reason for non-technological waste of the resource. The traditional methods of detection of stealing of gas are ineffective and time-consuming. The modern technologies of data analysis would allow detecting and interpreting the anomalies of consumption, as well as forming the lists for checking the objects for unauthorized connections. The author’s special contribution lies in application of the set of statistical methods aimed at processing and identification of anomalies in energy consumption of a municipal formation. It is worth noting that the use of such technologies requires the development of effective algorithms and implementation of automation and machine learning algorithms. The new perspective upon time-series data facilitates identification of anomalies, optimization of decision-making, etc. These processes can be automated. The presented methodology tested on time-series data that describes the consumption of gas can be used for a broader range of tasks. The research can be combined with the methods of knowledge discovery and deep learning algorithms.

Keywords: average, smoothing, municipality, gas consumption, energy consumption, search for anomalies, statistical analysis, unauthorized consumption, gas accounting, optimization

References:
Prikaz Regional'noy energeticheskoy komissii – departamenta tsen i tarifov Krasnodarskogo kraya ot 31.08.2012 ¹2/2012-np «Ob utverzhdenii normativov potrebleniya kommunal'nykh uslug v Krasnodarskom krae (pri otsutstvii priborov ucheta)»
Chen H., Liu H. A remote electrocardiogram monitoring system with good swiftness and high reliablility //Computers & Electrical Engineering. – 2016. – T. 53. – S. 191-202.
Prikaz Minenergo Rossii ot 30.12.2013 N 961 (red. ot 26.12.2014) "Ob utverzhdenii Pravil ucheta gaza" // www.consultant.ru
Postanovlenie Pravitel'stva RF ot 06.05.2011 N 354 (red. ot 31.07.2021) "O predostavlenii kommunal'nykh uslug sobstvennikam i pol'zovatelyam pomeshcheniy v mnogokvartirnykh domakh i zhilykh domov" // www.consultant.ru
Ignatenko A.M., Makarova I.L., Kopyrin A.S. — Metody podgotovki dannykh k analizu slabostrukturirovannykh vremennykh ryadov // Programmnye sistemy i vychislitel'nye metody. – 2019. – ¹ 4. – S. 87 - 94. DOI: 10.7256/2454-0714.2019.4.31797
Kozłowski A., Sosnowski J. Energy effic

Abstract: In recent decades, the potential of analytics and data mining – the methodologies that extract valuable information from big data, transformed multiple fields of scientific research. Analytics has become a trend. With regards to education, these methodologies are called the learning analytics (LA) and educational data mining (EDM). Latterly, the use of learning analytics has proliferated due to four main factors: a significant increase in data quantity, improved data formats, achievements in the area of computer science, and higher complexity of available analytical tools. This article is dedicated to the description of building the model of decision support system (DSS) of a university based on educational data acquired from digital information and educational environment. The subject of this research is the development of DSS with application of learning analytics methods. The article provides a conceptual model of decision-making system in the educational process, as well as a conceptual model of the components of DSS component – forecasting subsystem. The peculiarity of forecasting subsystem model implies usage of learning analytics methods with regards to data sets of a higher educational institution, which contain the results of work of the digital information and educational environment, and include the characteristics of student activity. The main results of the conducted research is the examined and selected methods of clusterization and classification (KNN), the testing of which demonstrated palatable results. The author examined various methods of clusterization, among which k-prototypes method showed best results. The conclusion is made on favorable potential of application of the methods of learning analytics in Russian universities.

Keywords: risk group, forecasting methods, progress forecasting, electronic information and educational environment, educational analytics, classification, clustering, decision support, student progress, artificial intelligence

References:
Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., Vuorikari, R. (2016). Research Evidence on the Use of Learning Analytics — Implications for Education Policy. R. Vuorikari, J. Castaño Muñoz (Eds.). Joint Research Centre Science for Policy Report; EUR 28294 EN; doi:10.2791/955210, 8-22.
Belonozhko P.P., Karpenko A.P., Khramov D.A. Analiz obrazovatel'nykh dannykh: napravleniya i perspektivy primeneniya [Elektronnyy resurs] // Internet-zhurnal «Naukovedenie». — 2017. — T. 9. — ¹ 4. — URL: http://naukovedenie.ru/PDF/15TVN417.pdf (data obrashcheniya: 09.11.2020), 21 s.
Corbett, A. T. and Anderson, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4, 253—278.
Pavlik, P. I., Cen, H. & Koedinger, K. (2009) Performance Factors Analysis — A New A Pavlik, P. I., Cen, H. & Koedinger, K. (2009) Performance Factors Analysis — A New Alternative to Knowledge. Proceeding

Abstract: The author considers a problem of automatic synthesis (induction) of the rules for transforming the natural language formulation of the problem into a semantic model of the problem. According to this model a program that solves this problem can be generated. The  problem is considered in relation to the system of generation, recognition and transformation of programs PGEN ++. Based on the analysis of literary sources, a combined approach was chosen to solve this problem, within which the rules for transforming the natural language formulation into a semantic model of the problem are generated automatically, and the specifications of the generating classes and the rules for generating a program from the model are written manually by a specialist in a specific subject area. Within the framework of object-event models, for the first time, a mechanism for the automatic generation of recognizing scripts and related entities (CSV tables, XPath functions) was proposed. Generation is based on the analysis of the training sample, which includes sentences describing objects in the subject area, in combination with instances of such objects. The analysis is performed by searching for unique keywords and characteristic grammatical relationships, followed by the application of simple eliminative-inducing schemes. A mechanism for the automatic generation of rules for replenishing / completing the primary recognized models to full meaning ones is also proposed. Such generation is performed by analyzing the relations between the objects of the training sample, taking into account information from the specifications of the classes of the subject area. The proposed schemes have been tested on the subject area "Simple vector data processing", the successful transformation of natural language statements (both included in the training set and modified) into semantic models with the subsequent generation of programs solving the assigned tasks is shown.

Keywords: XPath-functions, domain-specific language, grammar parsing, training sample, eliminative induction, rules induction, code synthesis, program generation, natural-language processing, regular expressions

References:
Link Grammar Parser. URL: https://www.abisource.com/projects/link-grammar/ (data obrashcheniya: 14.09.2020).
Clark S., Curran J.R. Widecoverage efficient statistical parsing with CCG and log-linear models // Computational Linguistics 33(4), 2007. P.493–552.
Oda Y., Fudaba H., Neubig G. et al. Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation // 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2015. P.574-584.
Mandal S., Naskar S. Natural Language Programing with Automatic Code Generation towards Solving Addition-Subtraction Word Problems // Proceedings of 14th International Conference on Natural Language Processing (December, 2017). Jadavpur University, 2017. P.146-154.
Gvero T., Kuncak V. Interactive Synthesis Using Free-Form Queries // 37th IEEE International Conference on Software Engineering (ICSE). Florence: IEEE, 2015. P.689-692. DOI: 10.1109/ICSE.2015.224.
Wong E., Yang J., and Tan L., Autocomment: Mining question and answe

Abstract: The subject of the research is the process of collecting and preliminary preparation of data from heterogeneous sources. Economic information is heterogeneous and semi-structured or unstructured in nature. Due to the heterogeneity of the primary documents, as well as the human factor, the initial statistical data may contain a large amount of noise, as well as records, the automatic processing of which may be very difficult. This makes preprocessing dynamic input data an important precondition for discovering meaningful patterns and domain knowledge, and making the research topic relevant.Data preprocessing is a series of unique tasks that have led to the emergence of various algorithms and heuristic methods for solving preprocessing tasks such as merge and cleanup, identification of variablesIn this work, a preprocessing algorithm is formulated that allows you to bring together into a single database and structure information on time series from different sources. The key modification of the preprocessing method proposed by the authors is the technology of automated data integration.The technology proposed by the authors involves the combined use of methods for constructing a fuzzy time series and machine lexical comparison on the thesaurus network, as well as the use of a universal database built using the MIVAR concept.The preprocessing algorithm forms a single data model with the ability to transform the periodicity and semantics of the data set and integrate data that can come from various sources into a single information bank.

Keywords: time series, MIVAR, fuzzy time series, forecasting, clearing data, data unification, preprocessing, machine learning, data mining, knowledge base

References:
Anand S., Aggarwal R. R. An efficient algorithm for data cleaning of log file using file extensions // International Journal of Computer Applications. 2012. ¹ 8 (48).
Bose M., Mali K. Designing fuzzy time series forecasting models: A survey // International Journal of Approximate Reasoning. 2019.
Bzdok D., Altman N., Krzywinski M. Points of Significance: Statistics versus machine learning // Nature Methods. 2018. ¹ 4 (15). C. 233–234.
Gama J. [i dr.]. Knowledge discovery from data streams // Intelligent Data Analysis. 2009.
Han Q., Gao X., Wu W. Study on Web Mining Algorithm based on usage mining 2008.
Handelman G. S. [i dr.]. eDoctor: machine learning and the future of medicine // Journal of Internal Medicine. 2018. ¹ 6 (284). C. 603–619.
Ivanchenko N. O. MIVAR technologies modelling of enterprise’s technical and technological potential // Actual Problems of Economics. 2014.
Khedri R., Chiang F., Sabri K. E. An algebraic approach towards data cleaning 2013.C. 50–59.
Kim K.-J., Tagkopo

Abstract: The paper presents the results of evaluating the informative value of quantitative and binary signs to solve the problem of finding semantically close sentences (paraphrases). Three types of signs are considered in the article: those built on vector representations of words (according to the Word2Vec model), based on the extraction of numbers and structured information and reflecting the quantitative characteristics of the text. As indicators of information content, the percentage of paraphrases among examples with a characteristic, and the percentage of paraphrases with a attribute (for binary characteristics), as well as estimates using the accumulated frequency method (for quantitative indicators) are used. The assessment was conducted on the Russian paraphrase corps. The set of features considered in the work was tested as input for two machine learning models for defining semantically close sentences: reference vector machines (SVMs) and a recurrent neural network model. The first model accepts only the considered set of signs as input parameters, the second - the text in the form of sequences and the set of signs as an additional input. The quality of the models was 67.06% (F-measure) and 69.49% (accuracy) and 79.85% (F-measure) and 74.16% (accuracy), respectively. The result obtained in the work is comparable with the best results of the systems presented in 2017 at the competition for the definition of paraphrase for the Russian language (the second result for the F-measure, the third result for accuracy). The results proposed in the work can be used both in the implementation of search models for semantically close fragments of texts in natural language, and for the analysis of Russian-language paraphrases from the point of view of computer linguistics.

Keywords: accumulated frequencies, feature informativeness, support vector machine, neural network, paraphrase detection, text classification, semantic similarity, statistical evaluation, feature selection, machine learning

References:
El Desouki M. I., Gomaa W. H. Exploring the Recent Trends of Paraphrase Detection //International Journal of Computer Applications. – 2019. – T. 975. – S. 8887. DOI: https://doi.org/10.5120/ijca2019918317.
Smerdov A. N., Bakhteev O. Y., Strijov V. V. Optimal recurrent neural network model in paraphrase detection⇤ //Informatika i Ee Primeneniya [Informatics and its Applications]. – 2018. – T. 12. – ¹. 4. – S. 63-69. DOI: https://doi.org/10.14357/19922264180409.
Yin W., Schütze H. Convolutional neural network for paraphrase identification //Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. – 2015. – S. 901-911. DOI: https://doi.org/10.3115/v1/n15-1091.
Neculoiu P., Versteegh M., Rotaru M. Learning text similarity with siamese recurrent networks //Proceedings of the 1st Workshop on Representation Learning for NLP. – 2016. – S. 148-157. DOI: https://doi.org/10.18653/v1/w16-1617.
Dien D. et al. Viet

Abstract: The aim of the study is to prepare for the analysis of poorly structured source data, their analysis, the study of the influence of data "pollution" on the results of regression analysis. The task of structuring data, preparing them for a qualitative analysis is a unique task for each specific set of source data and cannot be solved using a general algorithm, it will always have its own characteristics. The problems that may cause difficulties when working (analysis, processing, search) with poorly structured data are considered. Examples of poorly structured data and structured data that are used in the preparation of data for analysis are given. These algorithms for preparing weakly structured data for analysis are considered and described. The cleaning and analysis procedures on the data set were carried out. Four regression models were constructed and compared. As a result, the following conclusions were formulated: Exclusion from the analysis of various kinds of suspicious observations can drastically reduce the size of the population and lead to an unreasonable decrease in variation. At the same time, such an approach would be completely unacceptable if, as a result, important objects of observation are excluded from the analysis and the integrity of the population is violated. The quality of the constructed model may deteriorate in the presence of abnormal values, but may also improve due to them.

Keywords: statistics, big data, linear model, anomaly detection, regression analysis, data cleaning, semi-structured data, modelling, noise removal, econometrics

References:
GOST R ISO 16269-4-2017 Statisticheskie metody. Statisticheskoe predstavlenie dannykh. Chast' 4. Vyyavlenie i obrabotka vybrosov.
Arustamov A. Predobrabotka i ochistka dannykh pered zagruzkoy v khranilishche. URL: https://basegroup.ru/users/aleksey-arustamov (data obrashcheniya: 16.07.2019)
Osipova, Yu.G. Obzor intellektual'nykh metodov ochistki dannykh / Yu.G. Osipova, A.M. Fonotov // Informatsionnye upravlyayushchie sistemy i komp'yuternyy monitoring.-Donetsk: DonNTU, 2012.-S. 121-125.
Vidishcheva E. V., Kopyrin A. S., Vasilenko M. S. Analiz i utochnenie klassifikatsii anomaliy i vybrosov na ekonomicheskikh dannykh // Vestnik Altayskoy akademii ekonomiki i prava. 2019. ¹ 6-1. S. 41–46.
Poruchikov M.A. Analiz dannykh: ucheb. posobie / M.A. Poruchikov. – Samara: Izd-vo Samarskogo universiteta, 2016. – 88 s.
Ratnikova T. A., Furmanov K. K. Analiz panel'nykh dannykh i dannykh o dlitel'nosti sostoyaniy : ucheb. posobie. M. : Izd. dom Vysshey shkoly ekonomiki, 2014. 373 s.
Osnovy strukturirovannykh i nestrukturirovannykh dannykh v intellektual'

Abstract: With the proliferation of online forms design services for online surveys, the number of researchers using questionnaires in research practice has increased significantly. One of the problems that was present in the traditional form of the survey on paper and was transferred to online surveys is the problem of data reliability. Most researchers using online surveys have higher queries to automate research. They are not ready to make significant efforts to increase the reliability of the data. In this paper, we propose to consider an algorithm for automating the process of analyzing the reliability of questionnaire data. The proposed algorithm is based on the use of a sliding exam procedure for testing individual multidimensional observations obtained during an online survey. The main hypothesis underlying the developed method consists in the fact that the subordination of the questionnaire questions to some general topic leads to some latent connections between answers that are violated by random answers. A multi-dimensional statistical criterion was developed for testing personal data. The method is very simple to use and is available even for not sophisticated researchers.

Keywords: Internet service, nominal features, computer technology, sliding exam, criterion fquality of data, latent connections, multivariate statistical methods, online survey, data quality, questionnaire

References:
Martyshenko S.N., Martyshenko N.S. Metody obrabotki nechislovykh dannykh v sotsial'no-ekonomicheskikh issledovaniyakh // Vestnik Tikhookeanskogo gosudarstvennogo ekonomicheskogo universiteta. – 2006. – ¹ 4 (40). – S. 48-57.
Martyshenko S.N. Metodicheskoe obespechenie analiza dannykh monitoringa sotsial'no-ekonomicheskikh protsessov v munitsipal'nykh obrazovaniyakh // Ekonomika i menedzhment sistem upravleniya. – 2012. – T. 6. – ¹ 4-2. – S. 259-267.
Fedorovskiy A.M. Kachestvo onlayn-oprosov. Metody proverok // Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny. – 2015. – ¹ 3 (127). – S. 28-35.
Moiseev S.P., Savinkova Yu.K. Vyborka, napravlyaemaya respondentom, v onlayn-oprose: k voprosu o dinamike i kachestve // Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny. – 2014. – ¹ 6 (124). – S. 43-50.
Maloshonok N.G., Terent'ev E.A. Vliyanie dizayna na kachestvo dannykh v onlayn-oprosakh studentov // Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny. – 2014. – ¹ 6 (124). – S. 15-27.