Reviewerly/exHarmony huggingface dataset:
Information Technologies for Business Management: From Big Data to Blockchain, a Literature Overview

Big data and blockchain technologies have become very popular, and they are deeply changing the way businesses are run. This chapter dwells on the effects that these technologies can have on several managerial practices, with a particular focus on accounting and control. Management accounting and control can potentially benefit from the information provided by big data since the latter can speed up the decision-making process, improve the quality of accounting data and increase the effectiveness of planning practices. Nevertheless, some critical aspects emerge since these technologies can entail difficulties in data management, high investment costs and wrong decisions. In the wake of this conflicting background, the literature is rich in theoretical insights on how big data can affect management accounting and control practices, but it still lacks empirical evidence. Accordingly, finding empirical evidence on the above issues is highly topical and extremely important as well.
Unboxing the Network: The Empirical Case Study

This last chapter highlights the empirical evidence emerging from the case study analysis. Investigating the network creation path related to blockchain adoption within the chicken supply chain, the author found that the traditional accounting and control practices allowed the correct implementation of the technology. Accounting provides the key information on which the blockchain is customized and built while management control activities remedy some of the technology’s intrinsic defects. The blockchain, for its part, proves to be a “superstructure” of the traditional accounting system that, by expanding the visual space for calculations, extends the monitoring activities within the supply chain, allowing the action at distance, improving inter-organizational controls and putting order within the network. Considering the case study evidence, the chapter offers several managerial hints useful for the effective implementation of the blockchain and overcoming the barriers to its adoption.
Genetic Algorithm for Searching Bipolar Single-Flux-Quantum Pulse Sequences for Qubit Control

Nowadays most of superconducting quantum processors use charge qubits of a transmon type. They require implementation of energy efficient qubit state control scheme. A promising approach is the use of superconducting digital circuits operating with single-flux-quantum (SFQ) pulses. The duration of SFQ pulse control sequence is typically larger than that of conventional microwave drive pulses but its length can be optimized for the system with known parameters. Here we introduce a genetic algorithm for unipolar or bipolar SFQ control sequence search that minimize qubit state leakage from the computational subspace. The algorithm is also able to find a solution in the form of a repeating subsequence in order to save memory on the control chip. Its parallel implementation can find the appropriate sequence for arbitrary system parameters from a practical range in a reasonable time. The algorithm is illustrated by the example of the rotation gate around the Y-axis by an angle $$\pi/2$$ with fidelity over 99.99 $$\%$$ . In this paper, we present the results for a single-qubit system, but in the future we will apply the developed approach to study a system of two qubits.
Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia

Corpora that contain tabular data such as WebTables are a vital resource for the academic community. Essentially, they are the backbone of any modern research in information management. They are used for various tasks of data extraction, knowledge base construction, question answering, column semantic type detection and many other. Such corpora are useful not only as a source of data, but also as a base for building test datasets. So far, there were no such corpora for the Russian language and this seriously hindered research in the aforementioned areas. In this paper, we present the first corpus of Web tables created specifically out of Russian language material. It was built via a special toolkit we have developed to crawl the Russian Wikipedia. Both the corpus and the toolkit are open-source and publicly available. Finally, we present a short study that describes Russian Wikipedia tables and their statistics.
THE METHOD OF BUILDING THE OPTIMAL EDUCATIONAL TRAJECTORY OF HIGHER EDUCATION ACQUISITIONERS

With today’s lectures and lessons, rules and bells, we continue to use an industrial model of education designed to produce factory workers. Students learn to agree with the masses, but they do not learn to think for themselves. Subjects are taught in isolation, as a result of which students do not understand how all knowledge is interconnected. Our current system undermines the ability of parents to guide the moral formation of their children. Fathers and mothers are denied a voice in the values ​​taught in our schools. Many school districts believe that once a student enters the school’s doors, parents no longer have jurisdiction. A careful examination of modern education reveals that it is not education at all. Our current system has at least three fundamental flaws: a misplaced focus on right knowing instead of right being, a lack of instruction on how to recognize truth and communicate it profitably, and a disintegration of subjects without teaching how things fit together. One of the solutions to a number of modern educational problems can be to study according to the optimal educational trajectory, which forms a large baggage of knowledge for the student for his future professional activity. This also means that a highly qualified specialist will easily enter the labor market. It is possible to enumerate the strengths of quality education for quite a long period of time, but the question arises: how exactly to form this education? How to make the learning process as interesting and productive as possible for the student? At the moment, there are quite a lot of training programs, and it is hardly possible to create something completely new. But no one canceled the option with the modification of the existing program. Therefore, this scientific work proposes a method of building an educational trajectory, which uses to build a list of mandatory and optional disciplines that cover a set of necessary competencies.
HQNet: An Efficient Convolutional Neural Network for Cervical Cancer Classification

With the rapid development of artificial intelligence, cancer cell classification recognition is also gradually intelligent. Although the classification and recognition methods for some common cancers are mature, Cervical Cancer, as a common cancer among middle- aged women, is often neglected in diagnosis because of the low incidence of malignant tumors. In recent years, the incidence of Cervical Cancer has been increasing year by year and has also gained attention. In this paper, an efficient deformable convolu- tional neural network system(HQNet) was constructed to identify Cervical Cancer cells with different degrees of development.
Low-Sample Image Classification Based on Intrinsic Consistency Loss and Uncertainty Weighting Method

As is well known, the classification performance of large deep neural networks is closely related to the amount of annotated data. However, in practical applications, the quantity of annotated data is minimal for many computer vision tasks, which poses a considerable challenge for deep convolutional neural networks that aim to achieve ideal classification performance. This paper proposes a new, fully supervised low-sample image classification model to alleviate the problem of limited marked sample quantity in real life. Specifically, this paper presents a new sample intrinsic consistency loss, which can more effectively update model parameters from a “fundamental” perspective by exploring the difference between intrinsic sample features and semantic information contained in sample labels. Secondly, a new uncertainty weighting method is proposed to weigh the original supervised loss. It can more effectively learn sample features by weighting sample losses one by one based on their classification status and help the model autonomously understand the importance of different local information. Finally, a sample generation model generates some artificial samples to supplement the limited quantity of actual training samples. The model adjusts parameters through the combined effect of sample intrinsic consistency loss and weighted supervised loss. This paper uses <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25 \%$ </tex-math></inline-formula> of the SVHN dataset and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30 \%$ </tex-math></inline-formula> of the CIFAR-10 dataset as training samples to simulate scenarios with limited sample quantities in real life, achieving accuracies of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$94.59 \%$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$91.27 \%$ </tex-math></inline-formula> respectively, demonstrating the effectiveness of our method on small real datasets.
ChildNet: Structural Kinship Face Synthesis Model With Appearance Control Mechanisms

Kinship face synthesis is an increasingly popular topic within the computer vision community, particularly the task of predicting the child appearance using parental images. Previous work has been limited in terms of model capacity and inadequate training data, which comprised of low-resolution and tightly cropped images, leading to lower synthesis quality. In this paper, we propose ChildNet, a method for kinship face synthesis that leverages the facial image generation capabilities of a state-of-the-art Generative Adversarial Network (GAN), and resolves the aforementioned problems. ChildNet is designed within the GAN latent space and is able to predict a child appearance that bears high resemblance to real parents’ children. To ensure fine-grained control, we propose an age and gender manipulation module that allows precise manipulation of the child synthesis result. ChildNet is capable of generating multiple child images per parent pair input, while providing a way to control the image generation variability. Additionally, we introduce a mechanism to control the dominant parent image. Finally, to facilitate the task of kinship face synthesis, we introduce a new kinship dataset, called Next of Kin. This dataset contains 3690 high-resolution face images with a diverse range of ethnicities and ages. We evaluate ChildNet in comprehensive experiments against three competing kinship face synthesis models, using two kinship datasets. The experiments demonstrate the superior performance of ChildNet in terms of identity similarity, while exhibiting high perceptual image quality. The source code for the model is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MartinPernus/ChildNet</uri> .
Data Generation With Filtered <i>β</i>-VAE for the Preoperative Prediction of Adverse Events

Adverse events after surgery not only affect the patient’s recovery but also increase the burden on doctors and patients due to prolonged hospitalization. Predicting adverse events from patient data before surgery with a machine learning method is highly expected. It is difficult to collect a large amount of patient data since the number of surgeries in a year is limited and predict the occurrence of adverse events accurately since patient data are imbalanced data. To improve the accuracy of adverse event prediction, this paper proposes data generation with Filtered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -VAE for the preoperative prediction of adverse events. Filtered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -VAE has filters by the reconstruction error and by a machine learning method. After <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -VAE generates minority class data, the two layers of filtering are used to remove low-quality minority class data that have little contribution to the adverse event prediction. In the evaluations, patient data obtained from Tokyo Dental University Ichikawa General Hospital were used. The proposed method can predict adverse events with a recall of 0.848, which is 5.6% more accurate than existing methods. The effects of filtering in Filtered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -VAE are visualized, and the reasons for the improvement in prediction accuracy are clarified. Furthermore, this paper shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -VAE can generate arbitrary patient data even in table data, corresponding to the distribution of the original patient data.
Synthesis of Tax Return Datasets for Development of Tax Evasion Detection

Datasets are an essential part of data science processes. However, retrieving a dataset, especially a tax return dataset, is challenging as privacy becomes more evident in our daily lives. Thus, data synthesis is an approach selected for our work by utilizing publicly available data and augmenting it using Generative Adversarial Network (GAN) and Synthetic Minority Oversampling TEchnique (SMOTE). The evaluation is performed using a Correlation Matrix, Principal Component Analysis (PCA), and Quality Score. In addition, fundamental machine learning models are utilized to detect tax evasion based on a literature review. The data are gathered from the financial statements of companies registered within the Stock Exchange of Thailand (SET). Our results indicate that synthetic datasets with 0.87 average Quality Score can train models that yield approximately 0.95 Accuracy and 0.91 F1-Score. Additionally, by increasing more instances, the effect of class imbalance and high variance can be mitigated. The expected benefits include the use of open data for analysis and application of synthetic datasets. Forthcoming research could consider the statistical behavior of different business sectors, multiclass labeling for advanced recommendations, and implementation of unsupervised models.
Development of expert thinking of future teachers

The paper is devoted to the problem of the development of expert thinking of students. The urgency of this issues is in the changes in the functional duties of teachers of secondary general educational organizations and the need to develop expert competence. The axiological aspect of professional consciousness is common in expert and pedagogical activity. It is expressed in the adoption of professional and moral values and the definition of professional actions that ensure the quality of the learning and upbringing process. In a practical aspect, the expert thinking of a teacher reflects a complex of intellectual processes: analysis, synthesis, criticality and consistency. Intellectual process in expert activity of a teacher is aimed to solve problems formulated as tasks. The peculiarities of the solution of expert problems consist in the choice of methods and technologies of expert activity. Expert thinking is closely related to research activities. In the course of an expert study, the elements are identified that affect the quality of educational systems and problematic ones that require development. Expert thinking manifests itself at the methodological, tactical and operational levels. Like pedagogical thinking, expert thinking approaches scientific thinking and includes a systematic understanding of theories, laws, principles of education development, as well as knowledge of scientific methods and possession of the logic of thinking, scientific evidence in the determination of practical approaches to the solution of problems. The tactical level of expert pedagogical thinking is expressed in the assessment of pedagogical educational practice, its contradictions and the formulation of problems as pedagogical tasks. Expert thinking approaches diagnostic and includes the process of choosing methods and making diagnostic decisions. Operational level includes the manifestation of professional self-awareness, when personal results are evaluated and the connection with critical thinking is clearly traced and the ability to creatively generate innovative ideas. In particular, the paper reveals the general and special components in the manifestation of professional pedagogical and expert thinking. The main manifestations of the expert thinking of a teacher are shown as the ability to analyze, synthesize, identify cause-and-effect relationships, draw conclusions and highlight contradictions in the activities of an educational organization. The paper substantiates the need for a new theoretical vision and practical rethinking of the educational process of the development of expert thinking of students in a university.
Commercialization of Qalifa Islamic Disruptive School Based on Digital Islamic Boarding School with Personalized Learning Technology and B2E Profit Sharing

This online media penetration can provide flexibility for the public, especially teenagers, to obtain various information related to religious information needs (religious need).The Pew Internet and American Life Project notes that about 28 million people worldwide use the internet for the benefit of Religious Surfers.However, the dichotomy and dualism that separates world education from religion is still visible.Whereas soft skill and hard skill training can be integrated with the delivery of collaborative religious content.In this context, this innovation is present as one of the innovative solutions that provide services to the community to learn the Qur'an and Islam more easily.This activity is packaged through a virtual bootcamp by raising the latest topics.The purpose of this research is to develop innovations that combine learning the Qur'an and Islam with soft skill development training.In the implementation of the learning media business process, the researcher uses a method, namely startup development phases.At the pre-production stage using the design thinking process method, and market segmentation.Stages of production using the AIDAS method.The development stage uses the Nine Core Of Marketing and establishing method to proceed to the next business class on a broader scale.This research is expected to contribute to a more interactive Al-Qur'an learning service and support character learning in society.
Analysis of software products for the development of electronic textbooks in the information and educational environment of the university

At the current stage of development of the educational space, one of the ways to enhance the learning activities of students is the use of information technology. The use of electronic information and educational resources in the educational process (e.g., electronic textbooks and teaching aids) can contribute to the development of independent research activities of students, increase their cognitive and professional interest. The article analyzes requirements for existing educational materials that can be used to improve the information and educational environment of the university. Specialized software tools for creating electronic textbooks are considered, the advantages and disadvantages of electronic textbooks are investigated, and the priority functions of software tools used in universities are described.
On the dualistic unity of natural science and the humanities

This paper is an analysis of the effectiveness of the Russian learners from the 2019 TIMSS International Monitoring Study report. While analyzing the document, the initial conclusion drawn by the author is that the mathematical and natural science literacy of Russian schoolchildren is quite high and that the educational system in Russia is stable. At the same time, the author suggests that rethinking the directions of educational development is impossible without taking into account the underlying processes occurring in the depths of basic general education. In the author's opinion, the lack of significant gains in the scores of Russian students over the last 2 phases of the TIMSS studies is an indicator of certain stagnation in primary mathematics and science education. The author is of the belief that a statement of this fact should contribute to the fundamentalism of education. Assessing the structural correlation between natural science and humanities knowledge by quantitative volume of academic load of mathematics and natural science disciplines, the author points to the dualistic unity of natural science and humanities knowledge in high school, which should contribute to the development of creative independent thinking of students. The author's understanding of the results of the context survey of Russian schoolchildren indicates the need to improve the professional expertise of the science teachers, which will improve the outcome of the educational process. The author suggests adopting the experience of other countries in implementing the interdisciplinary approach to the educational process. The author emphasizes that STEM education imposes special requirements on the teacher, who must have integrated knowledge of mathematics, science and technology, be fluent in computer programs, be motivated to work with children and be skilled in didactics.
Methodical practice model of future teachers of mathematics

The professional training of subject teachers should be aimed at developing professional competencies that will allow them to be successful in their field, to be professionals, ready for change, and be able to adapt to various situations. A significant element of professional training at a university is pedagogical practice, during which the student performs the subject teacher functions. However, students in practice often face such difficulties as organizing their activities and the activities of students in the lesson, allocating time to the lesson stages and observing the time schedule, selecting didactic materials, evaluating outcomes of students’ activities, solving unexpected pedagogical situations, and others. Particular attention should be paid to the psychological readiness of the future mathematics teacher to work at school. Therefore, it is essential to prepare students methodically and psychologically for the implementation of primary professional activities in teaching practice. This can make it feasible to carry out methodological practice, which is a transitional stage between educational and industrial practice. The issue of organizing methodological practice is currently new. Based on the literature analysis and the experience of organizing and managing practices, the paper proposes a model of methodological practice, which is aimed at applying active teaching methods to prepare future mathematics teachers for the main activity in teaching practice, namely, preparing and conducting lessons. The model includes seven stages with tasks, describing active learning methods and student activities.