Reviewerly/exHarmony huggingface dataset:
Innovative, Scientific and Technological Priorities of Railway Freight Transport

Seven main priorities for development of rail cargo transportation are emerging in the context of an active intensification of scientific and technological development cycles, widespread digitalisation of business processes and an increase in competition in the transport industry. A key element of increasing the attractiveness of rail cargo transportation is its innovative, scientific, and technological development, especially in terms of increasing customer focus. Today, the principles of customer focus in the field of railway transportation of goods are among core factors in competitiveness of railway carriers. This issue is particularly relevant for the Russian Federation that owns a high potential for development of transit routes thanks to its advantageous geographic location. This fact determines attractiveness of railway transportation for cargo haulage customers. The image of future scientific and technological development of railway cargo traffic is formed under the influence of such trends as development of heavy-haul rail traffic, international container transportation, proactive introduction of digital technologies for interacting with customers and monitoring the condition of rolling stock, the provision of services within a single transport and information space, increase in margins and environmental safety of transportation. Digital transformation of the transportation process is among high priorities, which is explained through the importance of maintaining safety of transportation, increasing the transit capacity of the railway network and development of an integrated system for ensuring openness of data to provide customers with all the necessary information. Another priority area serves to increase the economic efficiency of cargo traffic by achieving growth in the share of high-margin goods. Such goods may include pharmaceutical and perishable products, information and communication, electronic and radio-electronic products, etc. An increase in the share of such transportation with a decrease in the volume of coal/oil transportation also has a positive effect on external appearance of the railway company for external investors. The image of the future of rail cargo transportation is largely dependent on various external and internal factors having effect on the industry. Besides, it depends on the emerging macro-trends in the global economy, which determine a certain vector for development of the railway industry. The study, to achieve its purposes, has widely used qualitative research methods and bibliometric analysis of scientific literature, international strategic and program documents of the industry and its companies.
An Empirical Analysis of the Determinants of Freight Rates Using VECM: Based on Information of Cargo Freight Information Network

In the domestic transportation market, the transportation share of freight cars accounts for 92.6%. In the truck market, real freight rates decreased due to the increase in cost burden due to the increase in prices such as oil prices, and accordingly, overwork, overload, and speeding competition intensified. Accordingly, in 2018, the government implemented a safe fare system as a way to advance cargo transportation and applied it to import and export containers and cement (BCT). Therefore, this study attempted to provide implications for matters to be considered when expanding the safety fare system through time series analysis on the influence of truck fares and various exogenous variables. The research method analyzed the unit root, cointegration test, and VECM of freight rates by tonnage recorded in the actual cargo information network and time series indexes such as domestic economic indicators, industrial indexes, price indexes, and supply indexes. As a result of the analysis, it was confirmed that the exogenous variables affecting the freight rates of each ton class were different, with a positive(+) effect on the whole industry index and import index for trucks of less than 8 tons.
Preliminary research on calibration method of digital image correlation measurement system

Digital image correlation method, also known as digital speckle correlation method, is a non-contact optical measurement method based on modern computer technology and optical principle. The main purpose is to study the displacement and strain of the measured object. The digital image correlation method has the characteristics of full field, real-time, non-contact, high measurement accuracy and fast response speed, and has developed rapidly in recent years. At present, the composition and principle of typical digital image correlation systems have been basically agreed, but there is no more feasible and effective method for the calibration of displacement measurement systems based on digital image correlation technology. This paper first analyzed the factors affecting the accuracy of the system, among which the speckle image is an important information source affecting the calculation results of the digital image correlation system. Therefore, we generated the simulated speckle image by writing the program and simulated the displacement, and used the software of the measurement system to calculate the simulated displacement. Then, a verification experiment was designed to prove the effectiveness of using the speckle image to complete the calibration. We built a set of calibration experiment equipment, using the laser interferometer with the characteristics of high measurement accuracy and small numerical limit of the measurement displacement and the printed speckle target points to complete the calibration process. Finally, we completed the experiment and carried out the precision analysis, and realized the preliminary research on the calibration method of the digital image correlation measurement system.
Quantum Computing—An Emerging Computing Paradigm

The field of quantum computing is rapidly evolving as it is gradually shifting from curiosity driven research to engineering of Noisy Intermediate Scale Quantum (NISQ) computing devices (Preskill 2018). It is therefore difficult to cover all the developments in a chapter. The intent of this chapter is to introduce quantum computing as a complementary computing machine capable of solving computationally hard problem.
Sampling Techniques and Error Estimation for Linear Canonical S Transform Using MRA Approach

A linear canonical S transform (LCST) is considered a generalization of the Stockwell transform (ST). It analyzes signals and has multi-angle, multi-scale, multiresolution, and temporal localization abilities. The LCST is mostly suitable to deal with chirp-like signals. It aims to possess the characteristics lacking in a classical transform. Our aim in this paper was to derive the sampling theorem for the LCST with the help of a multiresolution analysis (MRA) approach. Moreover, we discuss the truncation and aliasing errors for the proposed sampling theory. These types of sampling results, as well as methodologies for solving them, have applications in a wide range of fields where symmetry is crucial.
Research of North Korea's Financial Management during Kim Jong-un's Period With a focus on the change in the National Budget of the Supreme People's Assembly

Capitalist countries try to solve the crisis by using their own finances when an economic crisis hits the country. In addition, when there is an industry that the state wants to invest in, it uses finances. And this is the same for the socialist states even North Korea. These characteristics add research value to finance.&#x0D; However, as with most data on North Korea, there is not enough data on North Korea's finances. The budget and settlement of the national budget are announced every year, but compared to other countries, it is quite insufficient. Due to these limitations, much research on North Korean financial management has not been conducted.&#x0D; Nevertheless, it is necessary to comprehensively understand and analyze the budget proposal announced by North Korea. This is because North Korea's limited but self-released materials contain many meanings.&#x0D; From the North's budget proposal, we can see that they increase the amount of budget revenue by changing or adding items to increase the amount of finance. Also, spending on national budgets shows that they try to invest in the industries they need, but sometimes these attempts do not pay off.
Analysis of Question Types and Their Effectiveness in Institutional Conversation : Focused on Confirmation Hearings for Prime Minister Nominees

The purpose of this study is to analyze confirmation hearings for Prime Minister Nominees from the perspective of institutional conversation analysis, to identify what types of questions politicians use and their effectiveness related to the institutional context. The data consist of confirmation hearings for the 45th former Prime Minister Lee Nak-yon and the 46th former Prime Minister Chung Sye-kyun, and were transcribed and analyzed based on the transcription system suggested by Gail Jefferson. The findings show that a confirmation hearing is a highly institutionalized conversation that has legal regulations on the turn-taking system and time limit for speaking with a clear purpose of verifying the qualification of bureaucrat nominees. Questioners set topical agenda and strategically used questions in the form of polar, alternative, and close-ended with prefaced statements implying specific presuppositions to limit the nominees' answers. Questioners recognized and performed their role under the institutional context, and at the same time played the role of an intermediary relaying the answer to the listener outside the conversation situation. Questions have significance in that they are being used as an effective means to achieve the institutional purpose of the confirmation hearing system.
Several improvements of traditional system calibration method for fringe projection profilometry

Fringe projection profilometry (FPP) is widely used because of its many advantages, such as high accuracy, no-scanning properties, and full-field measurement. When fringe projection profilometry is employed, system calibration is one of the vital procedures. The accuracy of system calibration directly affects the quality of measurement results. The traditional calibration methods of measurement system include several calibration contents, like verticality, parallelism and the calibration of system geometric parameters. Aiming at the difficulties of them and the complexity of operation and adjustment process, this paper puts forward corresponding improvements. The calibration of verticality and parallelism uses checkerboard, which eliminates the requirements of hardware preparation for calibration such as standard parts. In the calibration of system parameters, it is not necessary to know the specific value of each parameter, and the parameters can be divided into two parts for calibration. The validity and practicability of the proposed methods have been proved by experiments. We take the ceramic step shape standard part as the tested object. After calibration by the above methods, the maximum measurement error of the fringe projection profilometry measurement system is -0.1400mm and the maximum standard deviation is 0.7251mm.
(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

With the vigorous development of contemporary information technology, the field of computer technology is constantly innovating and progressing. Among them, the field of computer vision has achieved more prominent development. Computer vision is a discipline that uses cameras and computers to imitate the structure of human organs, processes and analyzes the collected video sequences, and then makes corresponding behavior judgments. Moving object detection and tracking is an important branch of computer vision. Moving target detection and tracking has broad application space and development scenarios. Here, we aim to study the detection and tracking method of moving targets in a martial arts arena video based on non-scene target features. Based on this research theme, we combine the particle filter moving target tracking algorithm based on non-scene target features to carry out the moving target detection and tracking experiments in the video of the martial arts arena. The experiment concluded: compared with the conventional methods, the particle filter moving target tracking algorithm based on the non-scene target features can perform the following tasks without affecting the detection accuracy. It reduces the time for detecting and tracking the same moving target in the video of the martial arts arena by 1.5 min. It shows that the algorithm can improve the efficiency of non-scene moving target detection and tracking.
A Corpus-based Study of Translation Universals in Korean-English Literary Translation

Translation universals hypothesizes that translational languages have conventional yet unique features of translations, distinct from those of non-translations, and these features are universally present in all translations regardless of language pairs. However, the verification of translation universals has been restricted mainly to genetically close language pairs, such as English and closely related European languages. To confirm its universality, this article investigated Korean-into-English translations in the literary genre, which have remained mostly unexplored in translation universals studies. By constructing an approximately 500,000-word comparable corpus the Comparable Corpus of American — and Korean Novels—this study tested four major sub-hypotheses: simplification, explicitation, normalization, and leveling-out. Setting aside the lexical density of simplification indices, the results from all the other indices—mean sentence length, proportions of high- and low-frequency words, normalized frequencies of connectives and lexical bundles, and standard deviations of lexical density and mean sentence length—supported the translation universals hypothesis, indicating its high generalizability.
Error-Tolerant Mapping for Quantum Computing

Quantum computers are built with fragile and noise/error-prone qubits. Some prominent errors include, decoherence/dephasing, gate error, readout error, leakage, and crosstalk. Furthermore, the qubits vary in terms of their quality. Some qubits are healthy whereas others prone to errors. This presents an opportunity to exploit good quality qubits to improve the computation outcome. This chapter reviews the state-of-the-art mapping techniques for error tolerance. We take quantum benchmarks as well as approximate algorithms for applications covering MaxCut, object detection and factorization to illustrate various optimization challenges and opportunities.
Corpus Stylistic Analysis of Virginia Woolf’s The Voyage Out : Modernist Revision of Literary Genre and Incomplete Bildung

This study analyzes Virginia Woolf’s 1915 novel The Voyage Out by adopting a corpus-based stylistic methodology. Woolf scholars have not reached a consensus on how to distinguish the novel in terms of literary genre an adventure or a female domestic novel tradition, and recently this text has increasingly been defined as a failed bildungsroman due to its slow narrative development and the tragic plot in which the female protagonist dies without achieving self-fulfillment. This study intends to investigate whether a quantitative methodology can supplement the current scholarly discussion of Woolf’s novel and see if this new methodology can help analyze lengthy modern texts more efficiently and objectively. First, using Stylo software through R, I will map out the genre of The Voyage Out among a large number of 18-19th century literary texts. After testing the legitimacy of corpus-based genre classification through a detailed reading of the novel’s lexical patterns sampled through AntConc tool, this study continues to explore the stylistic characteristics of this transitional modernist text as an incomplete coming-of-age novel.
Cybersecurity and Game Theory

Game theory is the mathematical study of interactions between players. It has been applied extensively to social sciences such as economics. Cybersecurity has a social component in the sense that players can have co-operative or adversarial relationships. We will frame cybersecurity in terms of game theory. Note that many concepts elsewhere in the book can be analyzed using game theory.
A Hybrid framework for mobile augmented reality

Augmented reality (AR) aims to combine real and virtual worlds, offering real-time 3D interactions between users and virtual information. Existed AR systems detect specific markers in environments and augmented them with virtual contents such as 3D objects and videos. With the explosive growth of powerful, less expensive mobile devices, AR systems are extended to mobile devices. However, they fail to achieve the accurate and real-time integration of the real world and virtual contents because of the insufficient detection accuracy to objects in real world and computational resource shortage. In this paper, we propose a hybrid AR framework that integrates cloud computing, 5G communication and deep learning technology to achieve accurate tracking and ultra-low latency mobile AR applications. Our AR framework contains three blocks, an image capturing block, a deep learning based objects tracking block and a WebGL based rendering block. We use cloud computing technology to deploy them on mobile devices and a cloud server separately according to calculation amounts and use 5G to achieve communication between the cloud server and mobiles devices. The experiment results show that the accuracy and running speed of our framework can meet the requirements of most mobile AR applications.
Automated inventory management system on Android using barcodes and QR-codes

A software is developed for inventory automation based on Android using the Firebase database in the Java programming language. The appendix uses a system of reading barcodes and QR-codes, which provide the addition of goods to the warehouse with all the details of the selected product and information on all stocks in the warehouse with their price, category, name and code goods. Implemented authorization of system users using the open FirebaseAuth standard. Creating a single service for authorization and registration allows to make the system scalable. The system implements the search for goods by code, as well as viewing stocks in the warehouse with automatic price calculation. The main service of the database is a cloud database of the NoSQL class, which allows storing and synchronizing data between several clients, there is an API for data encryption. In addition, the database remains available only in the application to avoid unauthorized access and editing. While development, it was decided to make an automated control system on the Android OS device, because industrial digitalization (precisely because of the mass introduction of smart devices) requires mobility and accelerated processing of stocks at various stages of turnover. Testing of the developed software is carried out.
Do Kernel and Neural Embeddings Help in Training and Generalization?

Abstract Recent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines the convergence of the optimization during training. Such analyses also provide upper bounds on the generalization error. We experimentally investigate the implications of these results to deeper networks via embeddings. We regard the layers preceding the final hidden layer as producing different representations of the input data which are then fed to the two-layer model. We show that these representations improve both optimization and generalization. In particular, we investigate three kernel representations when fed to the final hidden layer: the Gaussian kernel and its approximation by random Fourier features, kernels designed to imitate representations produced by neural networks and finally an optimal kernel designed to align the data with target labels. The approximated representations induced by these kernels are fed to the neural network and the optimization and generalization properties of the final model are evaluated and compared.
Federated learning algorithm based on matrix mapping for data privacy over edge computing

Purpose This paper aims to provide the security and privacy for Byzantine clients from different types of attacks. Design/methodology/approach In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing. Findings By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped. Originality/value By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.
Study on Various GAN models and Datasets for Deepfake Detection

Because generative adversarial networks are better at creating high-quality images and videos, deepfake images and videos have become a critical problem today. Many tech giants have tried to develop algorithms to spot deepfakes to solve this problem; however, they have focused on different artifacts and GAN models. They have also examined various experimental scenarios. It isn
Pool-Based Sequential Active Learning For Regression Based on Incremental Cluster Center Selection

Active learning uses a certain algorithm to select the most useful samples for labeling to obtain a model with better performance, thereby reducing labeling costs. Active learning can be used not only for classification, but also for regression, named as ALR. Pool-based sequential active learning refers to selecting one sample from a pool for labeling in each iteration. For pool-based sequential ALR, existing methods are guided by criteria such as informativeness, representativeness, or diversity singly, but rarely consider these three criteria synthetically. In this paper, we propose a pool-based sequential ALR method based on incremental cluster center selection (ICS), which takes into account the representativeness and diversity. Besides, we further improve ICS by combining it with informativeness-based methods, so that the combined method comprehensively considers the above three criteria. Experiments on multiple real world datasets in various fields, using ridge regression, proved the effectiveness of the proposed methods.
CREATIVITY OF EGOR ZAGREBIN IN MODERN LITERARY EDUCATION

In this article, the features of studying the creative path and the artistic world of the Udmurt writer E. Zagrebin at the school lessons of Udmurt literature are considered. Much attention is paid to the contextual analysis of his literary works, which takes into account the biography of the writer and his creative laboratory, as well as the intertextual connections of a literary work and its interaction with other types of art. In order to form a holistic, versatile view of the writer's work, it is advisable to familiarize schoolchildren with the critical works that develop their ability to analyze literary works independently and help to form critical thinking. In addition, the functional and content potential of electronic forms of textbooks on Udmurt literature in the study of writers' creativity is revealed. Multimedia and interactive opportunities of an electronic textbook can become factors contributing to increasing the motivation of students to study Udmurt literature. For example, electronic simulators and final tests allow exciting and effective monitoring and evaluation of educational achievements of schoolchildren. The analysis of program materials on the creativity of E. Zagrebin presented in the article can be productively used by teachers for designing modern lessons of Udmurt literature.