Reviewerly/exHarmony huggingface dataset:
A Study on Korean Learners’ Motivation to Learn Chinese Language - Based on the Correlation between Learners’ General Characteristics and Their Academic Achievement

The purpose of this study was to examine the correlation between Korean learners’ motivation to learn the Chinese language and their general characteristics (gender, HSK rating, the number of acquired Chinese characters, period of studying in China, and period of learning the Chinese language) and the correlation between their motivation to learn Chinese and their academic achievement. For this study, with 25 survey questions regarding the motivation to learn the Chinese language based on Gardner’s (1985) “The Attitude and Motivation Test Battery” (AMTB) and precedent research, a survey targeting Korean undergraduate students was carried out and the results were analyzed. The correlation between Korean learners’ motivation to learn Chinese and their general characteristics was examined using ANOVA, Bonferroni, Linear Trend and Structural Equation Modeling (SEM) as the statistical analysis methods. Then, we further examined what factors continuously increased their motivation to learn by investigating whether a change in their general characteristics enhanced their motivation to learn Chinese and whether there was a linear relationship of proportional increase between such factors and their motivation to learn the Chinese language. In addition, direct and indirect effects of motivation to learn on Korean students’ academic achievement regarding the Chinese language were considered. Currently, there is a significant lack of investigation and studies on the intrinsic problems of learners who are the most important subject of Chinese education. However, identifying factors that are correlated with Korean learners’ motivation to learn Chinese and which motivation to learn has direct and indirect effects on their academic achievement regarding the Chinese language is very important and should be prioritized. The result of this study will help Korean learners successfully learn Chinese by using the correlation between Korean learners’ motivation to learn the Chinese language and their academic achievement more actively and efficiently in the future.
THE INTERNET IMPACT ON THE FUNCTIONING OF THE EDUCATIONAL ENVIRONMENT IN RUSSIA: THE EXPERIENCE OF THE 2020 PANDEMIC

A. Skuratov, A. LanskikhUral Federal University named after the First President of Russia B.N. Yeltsin (RUSSIAN FEDERATION)
<i>CAR</i> Ad Hoc Reviewers 2021 / <i>RCC</i> Réviseurs ad hoc 2021

Contemporary Accounting ResearchVolume 39, Issue 1 p. E1-E9 Miscellaneous CAR Ad Hoc Reviewers 2021 / RCC Réviseurs ad hoc 2021 First published: 07 March 2022 https://doi.org/10.1111/1911-3846.12762Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume39, Issue1Spring 2022Pages E1-E9 RelatedInformation
Lightweight Wavelet-Based Network for JPEG Artifacts Removal

In recent years, deep learning-based methods have made remarkable progress in removing blocking artifacts caused by JPEG compression. However, most of these methods directly learn the mapping between compressed images and corresponding clear versions in pixel domain by designing complex network structures and consuming huge computing power, which limits the application on mobile devices. To address this issue, we propose a lightweight wavelet-based network for JPEG compression artifact removal. Specifically, we observe that the signal removal operation in JPEG will introduce diverse distortion for different sub-frequency spectrums. Based on this, we propose a divide-and-conquer strategy to learn the mapping on each sub-frequency spectrum with a lightweight sub-network to reduce network parameters and save computation power. Furthermore, we speed up the inference time by integrating reparameterization technology. The comparison results with the state-of-the-art methods on mobile device demonstrate that our method achieves comparable deblocking performance with $${\times }$$ 100 less computationally complex and $${\times }$$ 50 faster inference time.
Analysis of Steering Using Davis Mechanism

The paper focuses on the analysis of Davis Steering Mechanism. The motive of the research was to study and later obtain the better results of using Davis Steering Mechanism in the vehicles by considering the standard factors. In this, after studying about Davis steering mechanism, its calculations, the variables, its functioning we analysed it on a software known as mechAnalyzer software by applying different values of the variables such as lengths, angles, joints. After using the appropriate values and formulating the results, we obtained various graphs of joints and links for displacement, velocity and acceleration with respect to linear as well as angular and they have been analysed accordingly.
Ipotesti comici: fedelta e variazioni metrico-ritmiche nelle citazioni della poesia lirica in Aristofane

No AccessIpotesti comici: fedelta e variazioni metrico-ritmiche nelle citazioni della poesia lirica in AristofaneLoredana Di VirgilioLoredana Di VirgilioSearch for more papers by this authorhttps://doi.org/10.13109/9783949189326.219SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverStudia ComicaVolume 14 1st editionISBN: 978-3-949189-30-2 eISBN: 978-3-949189-32-6HistoryPublished online:March 2022 PDF download
Water Wave Optimization with Distributed-Learning Refraction

Water wave optimization (WWO) is a nature-inspired metaheuristic that simulates propagation, refraction and breaking of shallow water waves to search the global optimal solution in the search space. To suppress premature convergence and further accelerate the speed of the basic WWO, we propose an improved WWO with a new distributed-learning refraction operator (called DLWWO), which makes stationary waves learn from more better waves to increase the solution diversity. DLWWO also adopts a nonlinear dimension reduction strategy in the propagation operator to accelerate the search process. We test the DLWWO algorithm on 15 function optimization problems from the CEC2015 single-objective optimization test suite. Experimental results show that DLWWO exhibits very competitive performance compared to the original WWO and other comparative algorithms, which validates the effectiveness and efficiency of the two new proposed strategies.
Coherent Ising machines: models for architectures and applications

Coherent Ising machines (CIMs) are an experimentally promising class of physics-based computational architectures that embed hard combinatorial optimization problems into systems of coupled nonlinear optical oscillators. The solution-finding mechanisms employed by CIMs feature complicated dynamical bifurcations occurring on a network scale, posing significant challenges to the development of theory and models for their underlying principles of operation. These difficulties are especially pronounced in the ultra-low-power or quantum regimes where the benefits in computational efficiency over conventional optimization algorithms are expected to be largest. We discuss some of our recent approaches and results at this intersection of dynamical systems theory and quantum model reduction, which have highlighted some potentially useful architectures and applications on the horizon for CIMs.
Application of Big Data Encryption Algorithm in Financial Data Security Protection

In the big data technology environment, all fields and industries of society have obtained new development opportunities and technical support. At present, the banking industry is highly dependent on information technology, which makes the security, reliability and effectiveness of information technology system directly related to the security of the whole banking industry and the stability of the financial system. With the rapid development of science and technology in China’s banking industry, especially in the past two years, the concentration of information and data logic in banking industry has been continuously improved, and information technology risk has become another important hidden danger in the steady operation of banking financial institutions. This paper introduces the privacy protection encryption algorithm, DES encryption protection algorithm, MD5 encryption protection algorithm of the financial industry in detail, and introduces the specific application of privacy protection encryption algorithm in detail.
Automatic text searching for information retrieval

The capabilities of automatic text search systems are reviewed and evaluation results are cited comparing the performance of automatic text searching systems with the effectiveness of manual controlled-term indexing methods. Some possibilities arethen examined for improving automatic text search systems, including the use of complex content identifiers instead of single words, and the introduction of user-system interaction. A likely scenario is given for the operations of future automatic text search systems.
Minority class augmentation using GANs to improve the detection of anomalies in critical operations

With the ever-increasing adoption of interconnected technologies and rapid digitization observed in modern-day life, many online networks and applications face constant threats to the security and integrity of their operations or services. For example, fraudsters and malicious entities are continuously evolving their techniques and approaches to bypass current measures in place to prevent financial fraud, vandalism in online knowledge bases and social networks like Wikipedia, and malicious cyber-attacks. As such, many of the supervised models proposed to detect these malicious actions face degradations in detection performance and are rendered obsolete over time. Furthermore, fraudulent or anomalous data representing these attacks are often scarce or very difficult to access, which further restricts the performance of supervised models. Generative adversarial networks (GANs) are a relatively new class of generative models that rely on unsupervised learning. Moreover, they have proven to effectively replicate the distributions of real data provided to them. These models can generate synthetic data with a degree of quality such that their resemblance to real data is almost indistinguishable, as demonstrated in image and video applications – like with the rise of DeepFakes. Based on the success of GANs in applications involving image-based data, this study examines the performance of several different GAN architectures as an oversampling technique to address the data imbalance issue in credit card fraud data. A comparative analysis is presented in this paper of different types of GANs used to fabricate training data for a classification model, and their impact on the performance of said classifier. Furthermore, we demonstrate that it is possible to achieve greater detection performance using GANs as an oversampling approach in imbalanced data problems.
An IoT-Enabled Healthcare System: Auto-predictive Colorectal Cancer with Colonoscopy Images Combined with the Convolutional Neural Network

Colonoscopy images play a vibrant role in predicting the intensity of the patient's colorectal cancer and there are many techniques for diagnosis and investigation. Colonoscopy image datasets and colorectal cancer analysis were used because of their generalized use. With the evolution of deep learning technologies, input datasets are automatically classified. Deep learning is the largest machine learning procedure widely utilized in a wide variety of applications, including image classification, image analysis, clinical records and object recognition. The classification and segmentation of diseases in intelligent systems is a significant challenge for these image analysis and computer vision systems. Convolutional Neural Network (CNN) is a class of deep neural networks that have spread in computer-assisted diagnosis systems. CNN is designed to learn unavoidably and flexibly the spatial classification of features by back propagation using several building blocks such as convolution layer(s), pooling layer(s) and fully connected layer(s). A method based on deep learning is proposed to automatically measure and predict cancer intensity accurately.
Injection-induced sequences give us insights about what is happening at depth during natural earthquake swarms

&amp;lt;p&amp;gt;Natural earthquake swarms occur in various geological contexts, and are usually interpreted as driven by fluid pressure diffusion. However, little is known about their fluid-driving processes, as no direct observations of either fluid and deformation are possible at such depths. To improve our understanding of the processes involved in swarms, we develop a quantitative comparison between natural and injection-induced swarms. Fluid injections in the crust, for instance geothermal reservoir development or wastewater storage, are accompanied by a prolific seismicity, that can be related to the fluid-pressure perturbation and potentially in association with aseismic slip at depth. It is well-accepted that the released seismic moment scales with injected fluid volume, but proposed relations usually not consider the contribution of aseismic deformation. Constraining such a relation might provide information on what happens at depth during natural earthquake swarms. Indeed, based on the numerous similarities observed between natural and injection-induced swarms, we confirm that both types of sequences seem to obey the same physics. In our work, we establish a framework to relate seismic observables to the fluid volume circulating at depth. This allows us to quantify aseismic slip for all types of swarms, but also to estimate the volume of fluids circulating at depth during natural earthquake swarms. By focusing on several natural swarms, this sheds a new light on the processes driving swarms of seismicity.&amp;lt;/p&amp;gt;
Threshold Logic Units

The description of biological neural networks in the preceding chapter makes it natural to model neurons as threshold logic units: if a neuron receives enough excitatory input that is not compensated by equally strong inhibitory input, it becomes active and sends a signal to other neurons.
UTILIZATION OF COMPUTER THREE-DIMENSIONAL GRAPHICS FOR THE DEVELOPMENT OF COGNITIVE SKILLS USING MICROLEARNING

The article focuses on the analysis of current software and hardware options for working with 3D computer graphics and their influence on students’ cognitive abilities. Thanks to the integration of ICT into teaching, nowadays at almost all levels of education, and together with technological development, this issue is becoming more and more relevant, in demand and yet not fully explored. As a part of the current research, tasks for working with 3D graphics have been created, the aim of which is to foster the development of students’ cognitive abilities. These tasks were created with regard to the wide range of programs and applications available at the current market. Working with a personal computer as well as, for example, a mobile device with the most used operating systems, is taken into account. In the theoretical part of this work, the issue is analyzed from the information-technological point of view, in which the basic concepts necessary for correct comprehension of the issue are stated.The methodological part presents a preliminary and pilot study for the main research. It was necessary to analyze in detail the current tools for working with 3D graphics and the initial assumptions of the students to work with them. Following the findings, we worked with the students during the main research, where the effectiveness of the tasks was analyzed.
Self-awareness for robust miner robot autonomy

&amp;lt;p&amp;gt;Robustness and resilience are crucial requirements for robots operating in unstructured and hazardous environments, such as the systems developed within the ROBOMINERS project. The miner robot shall handle, at least to some extent, the disturbances it may suffer; especially given the reduced possibility of human intervention in deep, small, and difficult-to-access deposits. In ROBOMINERS, we use self-awareness mechanisms to enhance robot miner autonomy. This capability enables the robot to be aware of the state of all its components (both hardware and software) and to what extent they are complying with their functions. Moreover, self-aware systems can reason about the run-time state and detect the causes of system failures. Depending on the specific characteristics of the affected robot, failure management mechanisms can be implemented at different levels. Robots can be designed to change their physical or software configuration, change the functions of some of their components, or adapt their behaviour to match mission needs. Our approach uses the knowledge of the systems engineer through machine-readable metamodels to provide the robot with information about the mission, the environment, and itself. These formal models allow the system to reason about its run-time situation. The ROBOMINERS resilience-augmenting solution is based on deep modeling of the functional architecture of the autonomous robot in combination with runtime reasoning. The reflective reasoning of the robot allows for both self-diagnosis and reconfiguration during mining operations. One of the main advantages of this knowledge-centric approach is the explicit definition, allocation, and linkage of system requirements, design decisions, system realization, and run-time information. This approach can transparently use robot structural and functional redundancy to ensure mission satisfaction, even in the presence of faults. Moreover, the use of several meta-models and ontologies allows the segmentation of information into different domains and levels of abstraction. These independent assets can then be re-targeted and adapted to a variety of systems, sub-systems, and contexts to improve asset reuse.&amp;lt;/p&amp;gt;
IMPROVING E-LEARNING WITH ARDUINO

A. Dairbayev1, B. Baikenov2, Y. Daineko1, B. Kozhakhmetova1 1International Information Technology University (KAZAKHSTAN) 2Almaty university of power engineering and telecommunications (KAZAKHSTAN)
Convolutional Neural Networks (CNN) Model for Mobile Brand Sentiment Analysis

Nowadays, the demand of sentiment analysis is increasing due to the requirement of analyzing and structuring hidden information from unstructured data. The sentiment on public views such as on products, services, and issues can be collected from social media platforms in text form. Convolutional neural network (CNN) is a class of deep neural networks method which can enhance the learning procedures by utilizing the layers with convolving filters that are updating to local features CNN models. This will achieve excellent results for Natural Language Processing (NLP) tasks especially as it can better reveal and obtain the internal semantic representation of text information. Due to this reason, this study attempts to apply this technique in mobile brand reviews sentiment analysis. There are four phases involved in this study which is knowledge acquisition and data preparation; CNN model development and enhancement; and model performance evaluation phases. As a result, the CNN model has been proposed by enhancing the model strategic mapping for optimal solution in producing high accuracy model. In future work, this study plans to explore other parameters such as in data pre-processing and network training to enhance the performance of CNN model. The proposed method can be used as sentiment analysis mechanism in many areas such as in review analytic, search query retrieval and sentence modelling.
Big data analysis of credit card default detection

The national economy has also brought about the change of the payment method. The credit card has changed from a convenient payment and settlement tool to a popular consumer credit tool. The consumption data of credit card is huge, it is difficult to identify whether it is in default by manual, and the traditional statistical analysis technology is also difficult to solve such a complex task. Therefore, this study uses data mining methods, for the data pre-processing, data statistics and analysis, data visualization and data analysis report writing as the core, and based on the specific analysis of the current situation of China's credit card business, carries out data cleaning from three aspects: Missing value processing, Abnormal value screening and dataset division. It also makes exploratory analysis combined with descriptive statistics and data visualization, so as to further establish the credit card default identification model. And put forward suggestions, improve customer information, strengthen information sharing within the industry, strengthen risk data analysis, rational formulation of risk prevention and control strategy, and future research operation skills. Finally, it draws a conclusion and puts forward the prospect of operation skills for future research.
Sentiment Analysis of COVID-19 Tweets Using Classification Algorithms

A large number of people have been affected by the Coronavirus pandemic. In addition, it is a tremendous influence on mental health for millions of people around the world. Nowadays, most people are expressing their opinions on several incidents or situations through social media platforms. There were many tweets, posts; comments, etc., are being posted across social media platforms. Therefore, content related to COVID-19 over social media can be considered to analyze and understand people’s opinions and situations. The model aims to collect the tweets with the hashtag COVID-19 from Twitter and analyze tweets’ sentiments. This model has utilized various libraries in Python and natural language processing (NLP). Therefore, with the help of this model, we can classify tweets into one of the following categories: positive, negative, extremely positive, extremely negative, and neutral.