Reviewerly/exHarmony huggingface dataset:
An Exploratory Study on the Influence Factors on Major Satisfaction and Employment Preparation Behavior of College Students Majoring in Cartoon Animation: Focusing on the Motivation for Choosing a Major and Occupation Values

The purpose of this study is to explore factors that can promote employment preparation behavior of college students majoring in cartoon animation. To this end, this study collected data by distributing a questionnaire to 200 college students majoring in cartoon animation, and identified the causal relationship between variables. The main result of this study is that in the case of college students majoring in cartoon animation, the intrinsic job value had a negative effect on major satisfaction, and major satisfaction served as a factor promoting employment preparation behavior. Through this, this researcher emphasized the need to reorganize the curriculum that meets the needs of learners of domestic animation major university education institutions.
Analysis of College Student’s Perception of Liberal Arts Classes and Demands for Improvement of Liberal Arts Curriculum

The purpose of this study is to draw improvement plans by identifying the perceptions of good liberal arts classes of college students and the demand for organization and operation of liberal arts curriculum for those who have taken liberal arts classes. The FGI study was conducted as a research method, and 73 university students (30 in 2020, 43 in 2021) participated. Data collection was conducted for 1 hour 7 times in November 2020 and 12 times in November 2021. FGI data were transcribed and the content was analyzed and classified. The main research results are as follows. First, students perceived interactive classes, classes without grades, classes with easy explanations, classes with updated content, and classes that broaden their horizons as good liberal arts classes. Second, in relation to the organization and operation of the liberal arts curriculum, it was requested to reduce the number of credits required to complete the liberal arts, increase the number of classes, and improve the operation of language and expression/foreign language/liberal education fields. Third, based on the results of the analysis, measures were sought to improve the quality of the liberal arts curriculum.
A Case Study on the Cultural Experience of Art History Convergence/Complex Liberal Arts Curriculum

For the purpose of improving convergence knowledge and ability, this curriculum was selected and subject development was carried out by publicizing the convergence and complex liberal arts subject development project. The significance of this study through case analysis of convergence and complex subject development classes is to encourage creative and convergent thinking of college students and to encourage the activation of multidisciplinary academic exchanges. In the future, we would like to propose the development of an emotional education culture program that combines history and paintings for the cultural cooperation and revitalization of local communities through the linkage base related to community convergence projects.
A Study on the Methodology of Strengthening Competencies of Vietnamese Students Studying in Korea

The purpose of this study is to present convergence majors and programs, and a systematic methodology for strengthening the competency of Vietnamese students studying in Korea. By applying this methodology, if Vietnamese students succeed in finding jobs or starting businesses in Korea or in Vietnam, a virtuous cycle which creates demands for new Vietnamese students can be established. The specific methods are as follows; first, academic guidance and career coaching by Vietnamese, second, the education of languages for specific purposes, third, the major of Vietnamese interpretation and translation, fourth, reverse study abroad system in Vietnam and credit exchange system with Vietnamese universities based on distance learning system, fifth, convergence majors with Vietnamese characteristics such as Southeast Asian studies and multinational tax accounting major, sixth, joint operation of online courses between Korean and foreign universities and a credit linkage system between the universities in both countries.
Mediation Effect of the Organization Culture of the Institution in the Relationship between the Principal’s Professionalism and Teacher’s Play Efficacy in the Teacher-Child Interaction

This study was to examine the mediation effect of the organizational culture of the institution in the relationship between the principal's professionalism and the teacher’s play efficacy in the teacher-child interaction. The subjects of the study were 200 teachers working at kindergartens and daycare centers located in South Korea. Questionnaire study was carried out. For the data analysis, using SPSS, reliability analysis, frequency analysis, descriptive statistical analysis, correlation analysis, and multiple regression analysis. Finally, in order to examine the significance of the mediating effect of the organization culture the bootstrap method was conducted. As a results, first, there were positive correlations between main variables respectively. Second, there was a partial mediation effect of the organizational culture of the institution in the relationship between the principal's professionalism and the teacher’s play efficacy in the teacher-child interaction. In conclusion, the results of this study suggest the need for a multi-faceted approach to support teacher’s play efficacy in the field of teacher-child interaction.
Development of a Metaverse Course: ‘3D Time Machine’ Using Blender

As the metaverse is being discussed as the main educational environment, it is necessary to take this into consideration in the higher education curriculum to create specific subjects that can develop creativity. Accordingly, by producing a lecture that implements the metaverse not only in theory, but also in practice, the students' responses – the possibility of developing practical creativity in the educational field – were examined. This paper introduced a lecture using Blender(3D engine), as an elective course in the history department of the university, and shared insights found in this lecture (especially mini-projects performed with participating students). In addition, the correlation between constructivist activities that implement metaverse environments and creativity development classes is considered through educational theory, which provides an opportunity for peer researchers working in higher education to benchmark lectures that can effectively promote students' creativity.
A Study on the Introduction and Revitalization of Maker Space in University Library: Based on the National Changwon University Maker Agit(FabLab Changwon)

Universities are trying to change according to educational trends and are In order to foster creative and convergence human resources, we are forming space construction through the use of maker space. Maker spaces lead to changes in the spatial environment in university libraries and It plays an important role in creating an educational space for the needs of learners and future education. The researcher attempted to present the need for the construction and operation of a maker space that can be creative, experimented with collaboration and shared values, and fused. As a result, I would like to examine the cases of maker spaces in domestic and foreign university libraries. Through comparative analysis with the case of Changwon National University Maker agit an institution that selects best makerspace cases and We would like to propose a plan to introduce and revitalize the maker space.
GEMCash ― Towards a Diamond Redeemable Stable-Growth Coin

Blockchain-based cryptocurrencies have enabled secure and trustless transactions between parties. As a result, they have gained widespread adoption and popularity in recent years. As of December 2021, over 11,000 cryptocurrencies with a total market value exceeding USD 1.5 trillion, were in circulation. However, these traditional cryptocurrencies suffer from several issues i) price volatility, ii) no defined governance, iii) no physical attributes, iv) excess energy consumption to reach consensus v) no commercial use case, vi) no sustainability, vii) no loss protection, viii) no predictable transfer cost ix) no widely recognized value etc. With this research, we are proposing a diamond-redeemable/backed stable coin “GEMCash”. We have introduced physicality to the GEMCash currency with multiple tangible attributes. The GEMX point of sale (P.O.S.) system includes incorporating a novel debit card that can be used as a “cold” wallet. Further, this approach provides a way for users to acquire and spend their GEMCash Coins at local Retailers around the world. The proposed GEMCash currency is built on top of the GEMCash blockchain that is scalable, lightweight, distributed, as well as a permissioned blockchain system. The GEMCash crypto coins are stored as ERC20 tokens on the ledger. The GEMCash blockchain nodes run embedded-in specialized hardware devices. The blockchain supports sharding-based consensus, real-time transactions, and concurrent smart contracts. The control, of the blockchain nodes, is handled by an elected consortium of known parties authorized by GEMNation. With GEMCash, we designed a non-volatile, asset-backed, loss-protected, and environmentally friendly cryptocurrency that has real-world usage.
Fault Detection of Subway Bottom Bolts Based on Improved Faster-RCNN

With the acceleration of urbanization, the subway has become the first choice for urban residents to go out. Therefore, it becomes more and more important to guarantee the safety of the subway. Bolts are the most numerous part at the subway bottom. In daily operation, bolts falling off due to unexpected factors will bring great hidden dangers to the operation of the subway. This paper raises a new target detection algorithm based on Faster-RCNN. In view of the problem that the bolt size is small and difficult to detect, the FPN structure is introduced on the basis of the traditional Faster-RCNN, and then we improve the traditional FPN structure to enhance its robustness. Besides, we adjust the anchor size in the RPN structure according to the bolt size in the dataset. Finally, the whale optimization algorithm is introduced to ameliorate the loss function of RPN to make the accuracy of the algorithm better.
Optimal Network Selection Method Using Federated Learning to Achieve Large-Scale Learning While Preserving Privacy

These days, smartphones are equipped with multiple radio access technologies (RAT) such as wireless LAN (WLAN) and 4G/5G cellular with autonomous switching between them. However, it is often the case with RAT switching algorithms being based on availability but not on predicted quality of communications, which poses significant frustrations on the part of users, especially when their smartphones connect to poor-quality WLAN automatically. In order to provide uninterrupted communication to smartphone users, it is necessary to predict WLAN quality before switching networks and change the network based on the result. Although machine learning approaches are useful for predicting network quality, conventional machine learning is infeasible to train on large amounts of data collected from users. It is because WLAN data contain private information such as user location information, and it is possible to track user behavior from the training data. In addition, employing complex machine learning algorithms is not possible with the computational resources of a smartphone due to constraints on training and inference time. To the best of our knowledge, no paper implements and evaluates a solution to this problem. To solve this issue, this paper utilizes latent class regression model trained by Federated Learning. Our proposal achieves on-device training on a smartphone with limited computer resource and protects users’ private information. It predicts the quality of access points with various characteristics with high accuracy (95.0% precision, 23.7% recall). Moreover, by developing and installing the application on an Android smartphone, we show that the proposed method reduces the actual application communication disruption time to within 3 seconds at the 75th percentile (85% reduction compared to no functionality) and completes training within 1 second per session on smartphones with limited computational resources.
Research on Underwater Image Enhancement Algorithm Based on SRGAN

Due to the limitation of the special underwater imaging environment, underwater images usually have problems such as low contrast, blurred texture features, color distortion and so on. Based on the typical problem of underwater images, this paper improves the network structure and loss function on the basis of the original SRGAN network model, and achieves good results. The generative network reduces the convolutional layers and removes the normalization layer (BN layer), reducing resource consumption. The loss function introduces L1 content loss and VGG19 perceptual loss to improve the stability of training. The experimental results show that the improved SRGAN network model effectively solves the color distortion and blurring of underwater images, and has a good enhancement effect on underwater images.
Construction of a Model of a U-Shaped Section of a Cerebral Vessel Based on its Images

The paper proposes a coordinate system with a curvilinear generatrix, which allows modeling of cerebral vessels with a complex structure. This allows you to explore the movement of blood in vessels of complex shape, in this case, U-shaped.
Masked Face Recognition Using FaceNet

The global epidemic of COVID-19 has seriously affected people's life. To prevent and control the outbreak, people are required to wear masks, which poses a formidable challenge to the existing face recognition system. A masked face recognition method based on FaceNet is proposed to tackle the problems. In this paper, a smaller model based on the Inception-ResN et Vl model is proposed. The main idea is to reduce filter numbers in each inception block while maintaining the whole structure. The reduced version has much fewer parameters to compute and can recognize faces with and without masks. Comprehensive experiments on both masked and unmasked datasets have been conducted. With 99.79% test accuracy in the masked MS-Celeb-1M dataset, the model trained in this paper can be integrated into existing face recognition programs designed to recognize faces for verification purposes.
Urban Rail Train Number Recognition System Based on Image Processing

Aiming at solving problems such as easy to fall off damage, loss of vehicle number risk for RFID number recognition system, a type of number recognition system that integrates image acquisition and image processing is proposed. Firstly, the industrial camera is used to take pictures of the car number on the side of the urban rail train, then the techniques such as accelerated robust feature algorithm and neural network are used to locate, correct and segment the captured pictures, and finally the VGG-16 network model is used to recognize the segmented car number characteristics. The test results show that the system has good robustness, high recognition accuracy and fast speed, and can meet the requirements of urban rail train number acquisition.
Instance Segmentation of Ship Objects in Remote Sensing Images Based on Attention Mechanism

Detection and segmentation of ship targets in remote sensing images is a research hotspot in the field of computer vision. However, due to the large coverage area of sea surface remote sensing images, the complex and changeable environment of the ship target, such as cloud interference, coastal buildings, navigation ripples, the ship causes low detection and segmentation effect. In this paper, we propose an attention module-based method for background noise processing in remote sensing images. To solve the problem of complex background features and noise interference in remote sensing images, this paper introduces an attention module to suppress noise and other interfering features in the complex background by using channel attention mechanism and spatial attention mechanism, which can enhance the network's ability to extract object features, and improve the detection and segmentation effect of the network on remote sensing images. Firstly, we introduce Group Convolution into the original Residual Network to enhance the feature representation capability of the model. Secondly, Swish activation function with better performance in deep network is introduced to replace ReLU activation function in original Residual Network to improve the accuracy of ship detection and segmentation. Finally, in view of the complex environment of ships in remote sensing images and the problem of noise interference, we introduce attention mechanism to suppress the interference area and highlight the characteristics of ship areas. The experimental results show that with the improved method, the average accuracy (AP) of ship detection and segmentation has increased from 70.7% and 62.0% to 76.8% and 66.4%, respectively.
A One Stage Non-motorized Vehicle Detection Network

With the growth of non-motorized vehicles, low-carbon and environmentally friendly travel has gradually become the mainstream of people all over the world. The wide distribution and small proportion of non-motorized vehicles in the image make it difficult for traffic regulators to detection them, which increases the work of urban managers. Focusing on the task of non-motorized vehicle detection, we improve the YOLOv5s. The symmetric revolution module is used to construct the spatial attention mechanism, so as to improve the robustness of the network model. We design a spatial attention module to reduce the parameters. The proposed dimension decoupling module decouples the dimensions of the width and height of the feature map respectively, so that the network can adapt to the width and height of non-motorized vehicle targets. Through multi-scale receptive field fusion module, residual connection is used to improve the detection ability of the network to small targets. Finally, above modules are integrated into the YOLOv5s. Experiments on real road traffic images show that the proposed model has achieved good results in non-motorized vehicle detection.
Model Parameter Adaptive Approach of Extended Object Tracking Using Random Matrix and Identification

In many scenarios, the motion state and shape changes of the target need to be taken into account when tracking the target, as this allows for a better description of the target state. Many tracking methods based on random matrix (RM) theory tend to share a common drawback of inaccurate estimation of the extended state of the target when it undergoes high maneuvers. In this paper, an improved adaptive tracking method based on RM theory is proposed mainly for elliptical extended targets or group targets. The method uses convex packet algorithm to introduce the identification information, which successfully overcomes the drawback that the original method cannot achieve tracking due to random matrix divergence. The simulation results show that the improved adaptive method can effectively improve the tracking accuracy for elliptical extended targets.
Learning Neural Network for Multifactor Authentication Using Biometric Technologies

The article describes the development and integrated implementation of software modules of photo and video identification system, the system of user voice recognition by 12 parameters, neural network weights, Euclidean distance comparison of real numbers of arrays. The user's biometric data is encrypted and stored in the target folder. Based on the generated data set was developed and proposed a method for synthesizing the parameters of the mathematical model of convolutional neural network represented in the form of an array of real numbers, which are unique identifiers of the user of a personal computer. The training of the training model of multifactor authentication is implemented using categorical cross-entropy. The training sample is generated by adding distorted images by changing the receptive fields of the convolutional neural network. The authors have studied and applied features of simulation modeling of user authorization systems. The main goal of the study is to provide the necessary level of security of user accounts of personal devices. The task of this study is the software implementation of the synthesis of the mathematical model and the training neural network, necessary to provide the maximum level of protection of the user operating system of the device. The result of the research is the developed mathematical model of the software complex of multifactor authentication using biometric technologies, available for users of personal computers and automated workplaces of enterprises.
A Novel Epoch-Based Transaction Consistency Sorting Protocol for DAG Distributed Ledger

Because of the characteristics of decentralization, immutability, and transparency, blockchain has gradually become a new and revolutionary technology, which has far-reaching significance for the development of modern technology. However, the traditional Bitcoin blockchain that supports synchronous consensus suffers from the fatal flaw of low throughput. To improve throughput, a number of DAG distributed ledgers have been proposed that support asynchronous consensus, all of which allow multiple nodes to process concurrent transactions asynchronously. However, most DAG distributed ledgers do not implement consistent sorting of transactions, making it difficult to deploy smart contracts. To overcome this problem, in this paper, an epoch-based transaction consistency sorting protocol for DAG distributed ledger is proposed, which not only provides the possibility for the deployment of smart contracts but also can be used to resolve conflicting transactions in the ledger. Transaction consistency sorting protocol provides a more reasonably ordered list of all transactions by taking scalars, such as the set of their own past and future, parent block, and timestamp. In addition, through theoretical analysis, the stability and rationality of the transaction consistency sorting protocol are proved, and there is no Condorcet cycle. Finally, the simulation results demonstrate the protocol is efficient and achieve a throughput of at least 2000 transactions per second.