Compliance of the Directions and Programs of Training in Postgraduate Studies: The Transition Period

The new classification of scientific majors for which academic degrees are awarded and the amendments to the Federal Law “On Education in the Russian Federation”, which came into force on September 1, 2021, have significantly changed the list of scientific majors and the status of postgraduate studies. There is a need to establish the correspondence between scientific and pedagogical personnel training directions in postgraduate studies and scientific majors of the Classification 2021, in which academic degrees are awarded. The article presents an analysis of the adapting document “Fields of training in the postgraduate studies of OKSO 2016 – Scientific majors/branches of science of the Classification 2021” and considers measures on the way to reorganize the system of training and certification of academic degree holders

Analysis of Artificial Intelligence Training Indicators According to the Results of Russian Universities Monitoring

Artificial intelligence (hereinafter referred to as AI) is currently an area of strategic importance and a key technology ensuring a new digital economy development in Russia. Qualified AI specialist's training plays an important role in achieving ambitious AI-related goals as stated in government documents. The article presents survey results of more than 200 Russian universities, which enabled to create indicators characterizing both current and planned training volumes of AI specialists.According to the research results, Russian universities have responded quickly to the AI market development. Since 2019, they have been enrolling students at AI learning programs by intensifying training volumes annually. More than half of all AI learning programs are implemented within the «09.00.00 Informatics and Computer Science» and «01.00.00 Mathematics and Mechanics» majors/ specialties. AI specialist training in Russian universities is largely carried out at the expense of budgetary funds. The number of students enrolled at the AI learning programs is much higher for the bachelor programs.The specialists’ graduation in AI-related education programs was evaluated until the year 2025. The authors have also analyzed the best foreign practice in AI specialists training and proposed some measures to increase training volumes of AI specialists at Russian universities, for example, re-orienting higher education programs in the IT field at AI-related technologies. It is important that AI learning programs take into account recruitment needs projection in terms of training volumes and skills profiles

Training of Highly Qualified Scientific Personnel in Pedagogical Sciences: Retrospective Analysis (2011–2020)

Pedagogical sciences occupy a significant place in the system of training and certification of highly qualified scientific personnel. During the period from 2011 to 2020, about 10 thousand dissertations were defended in pedagogical sciences, which is 6.5% of the total number of defenses within the entire certification system. The subject of dissertations should correspond to the challenges of modern society and be aimed at the topical problems of youth education. The purpose of the study is a general assessment of the system of training and certification of candidates and doctors of pedagogical sciences, the development and testing of a method for identifying the mainstream in the subject of dissertation research and quantifying the implementation of priority pedagogical research directions in dissertation works for a ten-year retrospective period.The results of the analysis revealed a reduction in the number of dissertation defenses in pedagogical sciences, which is in line with the general trend of reducing defenses, aging of dissertation councils members, a small number of young doctors of sciences. Thematic analysis of dissertations has shown that research meets the challenges of modern society, while there is a slight thematic delay associated with the time duration of the procedure for preparing and defending a dissertation. The largest number of dissertations was devoted to various issues of higher education in comparison with the other levels of education. Among the academic disciplines, foreign language, mathematics, Russian language and computer science have gained the greatest popularity in the subject of dissertation research. The thematic mainstream includes the formation of professional and communication competencies

Harmonization of Postgraduate Training System with the Certification of Candidates of Sciences

Harmonization of Postgraduate Training System with the Certification of Candidates of Sciences The activities of the postgraduate school and the network of dissertation councils represent the stages of the trajectory of the formation of a researcher with an academic degree. This means that their activities should be coordinated. The article discusses several approaches to the harmonization: at the level of each organization – by the presence or absence of one of the participants in the trajectory, and at the thematic level. For each approach, statistical estimates of consistency are calculated, and cartographic representations of indicators in the context of the subjects of the federation are given. The analysis showed that, in general, there is a territorial alignment of the organizations for postgraduate student training with the organizations in which a candidate’s thesis can be defended

Introduction of a new nomenclature of academic specialities in Russia: Continuity and innovations

Introduction. The nomenclature of academic specialities to be awarded academic degrees is a system-forming element of academic degree holders’ certification system in any country of the world. Therefore, there is a need to revise and update the relevant structure of academic specialities in order to meet the prospective demand from research and development sector of high-tech industries. Apparently, the current nomenclature of 2017, as the instrument of realisation of public policy in the sphere of the state certification of academic and teaching staff in Russia, needed updating. The aim of the present research was to analyse a new version of the nomenclature of academic specialities in Russia to be awarded the degree of Doctor or Candidate of Science, and to make conceptual proposals on the implementation of new nomenclature provisions taking into account possible transformation of scientific and educational environment, including the emergence of new complex areas of academic research. Methodology and research methods. The research object is a system of certification of academic and teaching staff from the position of a three-level structure of the nomenclature of academic specialities, according to which academic degrees are awarded. The methods of comparison and data statistical analysis were applied to assess structural changes in the nomenclature. Results and scientific novelty. The authors considered the prerequisites for update of the nomenclature of academic specialities approved in 2017. The features of a new edition of the nomenclature of academic specialities were highlighted. The analysis of its new structure and content changes was carried out. It is demonstrated that current network of dissertation councils (1696 councils) can be divided into 4 groups according to the degree of compliance of academic specialities and fields of science with the previous and recent versions of nomenclature. It is necessary to create new dissertation councils for 20 new academic specialities. Practical significance. The authors revealed the features of the new nomenclature, requiring the reorganisation of councils network for the defense of doctoral and candidate dissertations. On this basis, to provide promising areas of academic research, the proposals on the implementation of new nomenclature provisions are outlined. The stages of re-opening of dissertation councils network are described.Введение. Номенклатура научных специальностей, по которым присуждаются ученые степени, является системообразующим элементом для аттестации кадров высшей научной квалификации в любой стране мира. Этим обусловлена необходимость поддержания актуальной структуры научных специальностей, отвечающей перспективному спросу со стороны сектора исследований и разработок высокотехнологичных отраслей экономики. В связи с этим действующая номенклатура 2017 года как инструмент реализации государственной политики в сфере государственной научной аттестации научных и научно-педагогических кадров в России нуждалась в обновлении. Целью работы являются анализ новой редакции номенклатуры научных специальностей в России, по которым присуждаются ученые степени, и концептуальные предложения по путям реализации положений новой номенклатуры с учетом возможных траекторий трансформации научно-образовательной среды, включающих прогнозную оценку появления новых комплексных областей научных исследований. Методология и методы. Объект исследования – система аттестации научных и научно-педагогических работников с позиции трехуровневой структуры номенклатуры научных специальностей, по которым присуждаются ученые степени кандидата и доктора наук. Для оценки структурных изменений номенклатуры использовались методы сравнения и статистического анализа данных. Результаты и научная новизна. Рассмотрены предпосылки необходимости обновления номенклатуры специальностей, утвержденной в 2017 году. Выделены особенности новой редакции номенклатуры научных специальностей, по которым присуждаются ученые степени. Проведен анализ ее структурных и содержательных изменений. Показано, что действующую сеть диссертационных советов (1696 советов) можно разделить на 4 группы по степени соответствия научных специальностей и отраслей науки предыдущей и новой номенклатурам. Для 20 новых научных специальностей необходимо открывать новые диссертационные советы. Практическая значимость. Выявлены особенности новой номенклатуры, обуславливающие необходимость реорганизации сети советов по защите докторских и кандидатских диссертаций. На основе этого сформированы предложения по путям реализации положений новой номенклатуры, обеспечивающих перспективные направления научных исследований. Описаны этапы перерегистрации сети диссертационных советов

Staffing the Sphere of Artificial Intelligence with Higher-Educated Personnel

Развитие сферы искусственного интеллекта (ИИ) и внедрение ИИ-технологий в различных отраслях российской экономики является одной из приоритетных задач. Любое развитие связано с ресурсами; в случае с экономикой, основанной на знаниях, таким ресурсом выступают высококвалифицированные кадры. В статье исследуются источники обеспечения кадровой потребности сферы искусственного интеллекта, основными из которых являются выпуск системы высшего образования по профильным образовательным программам в сфере ИИ, самообразование работников с высшим образованием, профессиональная переподготовка. Методологической основой исследования стал балансовый метод, реализованный на опросных и статистических данных. Определено, что потребность сферы искусственного интеллекта в кадрах с высшим образованием за счет выпускников вузов на краткосрочном горизонте планирования обеспечивается на уровне 35%, что ниже среднего по российской экономике. Суммарный вклад всех рассмотренных источников позволит обеспечить только 70% потребности сферы ИИ в кадрах с высшим образованием. Качественный анализ обеспечения потребности позволил выделить дефицитные группы образовательных специальностей, а также сформировать перечень вузов-лидеров по подготовке кадров с компетенциями в сфере ИИ. Научная новизна исследования заключается в том, что количественный и качественный анализ источников покрытия кадровой потребности для российской сферы ИИ проведен впервые. Практическая значимость работы отражается в конкретизации объемов подготовки кадров в сфере ИИ, определении обеспеченности кадровой потребности по отдельным группам специальностей/направлений подготовки, а также выявлении центров подготовки таких кадров. Эта информация служит ориентиром при формировании системных управленческих решений о корректировке контрольных цифр приема и разработке образовательных программ и профессиональных стандартов в сфере ИИ. Статья будет полезна руководителям и сотрудникам профильных ведомств, принимающих участие в развитии сферы ИИ, а также представителям научно-образовательного сообщества из этой профессиональной области.The development in the sphere of artificial intelligence and the introduction of its technologies in the sectors of the Russian economy is a priority task. Within knowledge-based economy, the resource to provide this development is highly qualified personnel. Our article examines the sources of supplying the staff demand in the sphere of artificial intelligence. The main sources are as follows: higher-educated graduates of the corresponding educational programs, self-educated and/ or professionally re-trained workers with higher education. The methodological basis of the study is the balance method, applied to poll and statistical data. It is found out that the demand for personnel with higher education in the sphere of artificial intelligence is going to be supplied by university graduates only at the level of 35 % in the nearest future, which is below the average for the Russian economy. The total contribution of all the sources considered will provide only 70 % of the demanded higher-educated staff. A qualitative analysis of meeting the demand made it possible to identify deficient groups of educational specialties, as well as to form a list of leading universities in training personnel with necessary competencies. This study is the first attempt to quantitatively and qualitatively analyze the sources of covering the staff demands in the Russian sphere of artificial intelligence. The work practically specifies the volume of necessary training, determines the provision with staff according to different groups of specialties / areas of training, and identifies training centers for such personnel. When making system management decisions on adjusting admission quotas, when developing educational programs and professional standards in the sphere of artificial intelligence, this article might be of use for directors and employees of relevant departments, as well as for representatives of the corresponding scientific and educational communities

Postgraduate Training and Academic Degree Certification for Foreigners

Исследовательская статья. Целью написания статьи является анализ подготовки и аттестации кадров высшей научной квалификации из числа граждан иностранных государств в российских университетах за последние 5 лет. Поставленная цель достигнута путем использования обширного статистического материала по системе высшего образования, включая уровень аспирантской подготовки; по сети диссертационных советов. Предметная область охвата включает все уровни высшего образования; систему аттестации кадров высшей научной квалификации; территориальное распределение по странам студентов, аспирантов и соискателей ученой степени; российские университеты, обеспечивающие подготовку и аттестацию иностранных граждан. Результатом проведенного исследования является выявление Топ-10 стран, граждане которых в большей степени используют российскую систему присвоения ученой степени кандидата и доктора наук. В ходе исследования выявлено, что 41 % иностранных граждан закончил аспирантуру с защитой кандидатской диссертации, что в два раза выше общероссийского показателя (21 %). Как следствие, этот факт может служить обоснованием для увеличения в среднесрочной перспективе объемов подготовки аспирантов, а также кандидатов наук в российских университетах. Проведенное исследование послужит базисом для выработки управленческих решений на федеральном уровне по обоснованию целевых индикаторов программы «Развитие экспортного потенциала российской системы образования». Оригинальность и ценность статьи заключается во введении в научный оборот новых фактических сведений о деятельности системы подготовки и аттестации кадров высшей научной квалификации для граждан иностранных государств.The purpose of the article is to analyze training foreigners in Russian universities and conferring academic degrees to them during the latest five years. There has been used extensive statistical material on the system of higher education, including the level of postgraduate training and data on the network of dissertation councils. The subjects of the article include all levels of higher education, i. e. the system of certifying highly qualified scientific personnel, geographical distribution of students and postgraduates, Russian universities providing foreigners’ training and certification. The result of this research is the identification of top 10 countries using the Russian system of conferring academic candidate and doctor degrees. The study showed that 41 % of foreigners completed postgraduate studies with the defense of PhD thesis, which is twice as high as the average level of defenses in Russia (21 %). This fact can prove the necessity to increase in the medium term the number of PhD students and candidates of science in Russian universities. The study can also be a basis for developing managerial decisions at the federal level to work out the target indicators of the program «Development of the Export Potential of the Russian Education System». The article is approvingly supposed to introduce into the scientific circulation certain new factual data about the system of training and certifying higher scientific qualification staff, as provided for the foreigners.Статья подготовлена при выполнении проекта в рамках государственного задания Министерства образования и науки Российской Федерации № 2.13261.2018/12.1

An adaptive delayed acknowledgment strategy to improve TCP performance in multi-hop wireless networks.

In multi-hop wireless networks, transmission control protocol (TCP) suffers from performance deterioration due to poor wireless channel characteristics. Earlier studies have shown that the small TCP acknowledgments consume as much wireless resources as the long TCP data packets. Moreover, generating an acknowledgment (ACK) for each incoming data packet reduces the performance of TCP. The main factor affecting TCP performance in multi-hop wireless networks is the contention and collision between ACK and data packets that share the same path. Thus, lowering the number of ACKs using the delayed acknowledgment option defined in IETF RFC 1122 will improve TCP performance. However, large cumulative ACKs will induce packet loss due to retransmission time-out at the sender side of TCP. Motivated by this understanding, we propose a new TCP receiver with an adaptive delayed ACK strategy to improve TCP performance in multi-hop wireless networks. Extensive simulations have been done to prove and evaluate our strategy over different topologies. The simulation results demonstrate that our strategy can improve TCP performance significantly