Reviewerly/exHarmony huggingface dataset:
Methodology for assessing the schoolchildren’s research competency level

The article presents the structure of research competency, we reveal the main criteria for its formation. The necessity of developing research competency in the period of personality formation is shown. The results of an ascertaining experiment conducted on a sample of 100 sixthformers using the following diagnostic tools are presented – the author's questionnaire aimed at identifying the level of research competency of schoolchildren, pedagogic observation, the method of peer review for pedagogues "Identification of research skills" by Aleksandr Savenkov, questioning of parents. Approbation of the results of the study was carried out on the basis of secondary school #30 in Kostroma. Identification of the specifics of the research competency of schoolchildren makes it possible to outline the prospects for pedagogic activity aimed at creating favourable socio-cultural conditions for the development of research skills and motivation for the participation of children in research activities. It is assumed that the targeted application of a set of activities that develop the research competencies of schoolchildren has a positive impact not only on the effectiveness of their cognitive activity, but also an important educational value, since it contributes to the development of self-control skills, responsibility, self-development, the ability to act in a team and solve complex problems together.
Educational potential of problem-based learning as a pedagogic technology in the structure of higher education

The article considers the method of creating a problem-oriented environment in foreign language classes at a higher education institution as topical technology that develops students’ universal and professional competences, contributes to a deeper understanding of the studied material and the application of the knowledge in practice than in the experience of classical education. The questions of technological principles of this pedagogic technology are studied and the advantages and disadvantages of its implementation are analysed. The article aims to explore the theoretical side of the issue and exhibit the possibilities of this concept of making higher education classes with the possibility to conceptually change the learning process, namely radical rethinking of the teacher – student roles and the overall academic content of their collective activities.
Pedagogic support for youth leadership development in the digital age

Youth education differs from similar processes aimed at both children and adults, so the postulates of both pedagogy and andragogy in this area do not always work. The article is devoted to the analysis of the process of pedagogic support of the leaders of future changes and the justification of its possibilities for the socialisation of the youth of the digital era. The authors attempt to resolve the contradictions between the new challenges of the post-globalisation era and the insufficient effectiveness of traditional methods, forms and technologies of raising leaders of future changes. Arguments are given in favour of proving the need to take into account the socio-cultural characteristics of the formation of modern youth simultaneously in two worlds (real and virtual) to build pedagogic strategies to support youth leadership. The data of an empirical study of the study of the socio-role identity of young leaders are presented and analysed. It is concluded that the pedagogic support of modern young leaders should be aimed not at preferences that produce their inflated self-esteem, but at eliminating the imbalance, including through a creative approach to promoting social ideas in global networks and making commitments to care not only about personal well-being, but also about the public good.
Prospects and risks of gamification implementation in modern education

The article analyzes the prospects and risks of introducing gamification in modern education. The interdisciplinary nature of gamification is emphasized, which makes it possible to consider it as a business technology, a management tool, and a pedagogical technology. The relevance of the introduction of gamification in the educational process of organizations of various types is substantiated. The authors consider gamification as a technology of using game techniques in non-game situations in the educational process. The possibilities of gamification as an innovative pedagogical technology in the digital educational environment are analyzed. Digital services, educational platforms and software tools based on the use of gamification technology are described. Gamification in the digital educational environment is considered as a tool to increase educational motivation and the quality of education. The psychological and pedagogical aspects of the use of gamification in the educational process, its difficulties and risks are analyzed. The necessity of taking into account the psychological and pedagogical laws of this process is emphasized. Among the possible reasons for failures in the implementation of gamification, the authors name: the lack of a systematic approach to the introduction of gamification, ignoring the characteristics of the team and players, the unprofessionalism of the teacher or the pedagogical community. Taking into account the results of the analysis of possible risk factors, ways to overcome difficulties in introducing gamification into the educational process are proposed.
Design of pre-professional pedagogical training of schoolchildren in an educational organization

The solution of the problem of teaching staff, the quality of their professional activity depends largely on the organization of pre-professional pedagogical training of schoolchildren who consciously choose the profession of a teacher and strive to achieve their goal. The article defines the concepts of "pre-professional pedagogical training", "design", examines the essence of design in an educational organization, determines its relationship with other similar concepts (modeling, design, programming, planning). The importance of pedagogical design is emphasized for the unification and development of cooperation of interested subjects (teachers, children, their parents, specialists, teachers of pedagogical universities and colleges) in the process of developing the concept and program, plans for pre-professional pedagogical training, projects providing continuous pedagogical education. The functions of design (analytical, prognostic, developing, integrating, etc.) are considered, the characteristics of projects (concept, model, program, plan, route) are proposed, the principles that must be followed in the design of pre-professional pedagogical training (forecasting, self-development, motivational support, interaction, the subjective position of adults and children in the design process are determined, technological efficiency, continuity, systematicity and cyclicity). Based on the analysis of the results of many years of experimental work, the stages of designing pre-professional training of schoolchildren focused on the teaching profession are proposed, a brief description of the actions of adults and children in the process of project development is given.
Practical insights can help college, university leaders to successfully manage rapid changes

Michael Horn is the Co‐Founder and Distinguished Fellow at the Clayton Christensen Institute for Disruptive Innovation, a nonprofit think tank based on the theories of the former Harvard professor. He's also the author of several books on education, including most recently 2022's From Reopen to Reinvent: (Re)creating School for Every Child .
Dais Layered Intrusion

The Dais Intrusion in upper Wright Valley is a spectacular layered intrusion connected to Bull Pass by the Basement Sill, the connection of which can be traced along the south wall of Wright Valley, with the layering becoming more distinct and cleaner with distance to the Dais. The Dais Intrusion is relatively small for a body as well layered as this: it is perhaps 1,000 m thick but only the upper half is visible. The lower half of the visible section is coarse grained ultramafic orthopyroxenite with 20 wt.% MgO, containing several periodic meter-thick anorthosite layers, which are lobate in nature, perhaps reflecting formation by avalanching slurries. The upper half of the Dais Intrusion is more tholeiitic in bulk composition, and the transition between top and bottom is dramatic, marking event horizons, much like the transition between mantle and oceanic crust for Earth as a planet. The style of layering becomes more delicate upwards in the section. Because the body cooled relatively quickly many annealing processes have been quenched prior to full completion. These are clearly textural processes that actively operate in many larger bodies but are never seen because of strong long term annealing effects.
Cooperative Eye Tracking and Localization Based on Corneal Imaging

Corneal imaging has been studied as an eye-tracking method, and the focused object and point-of-gaze are estimated by extracting scene information reflected on the corneal surface. However, when an eye tracker is regarded as a daily-use device, it is an essential function to estimate its self-position. In our previous research, we attempted self-position estimation using interior illumination patterns reflected on the cornea. However, the accuracy of localization was not sufficient; thus, we focused on convolutional neural network developed remarkably and the inertial measurement unit (IMU) as additional improvements. In this study, we propose to improve the accuracy of localization by integrating SE-ResNet and IMU with Monte Carlo localization. Data collection and annotation become a problem when deep neural network is employed. To address this problem, three-dimensional (3D) environmental map was reconstructed, and SE-ResNet was trained using virtual images generated using the 3D environmental map. We conducted comparative experiments and confirmed the feasibility of our proposed method.
Conjugate Gradient Method

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Convex Optimization

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Random Processes

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Vector Differentiation

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Subject Index

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Gradient-Descent Method

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Decentralized Optimization I: Primal Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Convergence Analysis II: Stochastic Subgradient Algorithms

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Gradient Noise

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Author Index

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Convergence Analysis I: Stochastic Gradient Algorithms

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Proximal and Mirror-Descent Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.