Reviewerly/exHarmony huggingface dataset:
A Study on Changam Park Man-hwan's Interpretation of 『Yeosaseo』

The purpose of this article is to examine the patterns and significance of the interpretation of 『Yeosaseo』, a Chinese teaching book for women, written by Changam Park Man-hwan during the Enlightenment period. 『Yusisaseo』 is a book of instruction for women edited and annotated by Wang Sang during the Qing Dynasty in China. In 1907, Park Man-hwan translated and published it at Yeongjujeongsa. This paper studied the translation of Park Man-hwan's 『Yeosaseo』 and examined the publication process, the interpretation pattern, and its characteristics. We also looked at the meaning within the context of women's instruction books at the time. In 1907, Park Man-hwan interpreted 『Yeosaseo』 and received verification from Jeun woo. He published it with Yeosaseo with a preface written by Song Byeong-soon and an afterword written by Jeun woo. If you look at the system, the original Chinese text and the Korean translation of the language are placed separately. This method is different from the one that was translated into Korean during the Yeongjo period of the Joseon Dynasty and wrote the sound of Chinese characters. And Park Man-hwan selected only the exemplary deeds of women from the contents of the commentary and translated them into Korean. In particular, the annotations of 『Yeogye』『Naehun』『Yeo Analects』, which contain a lot of didactic content about women and behavioral norms that women should follow, were not explained at all. On the other hand, 『Yeobeomcheoprok』 explained the exemplary behavior of women in detail in the annotations, and Park Man-hwan faithfully explained this. In this way, Park Man-hwan tried to interpret 『Yeosaseo』 focusing on the exemplary examples of women and encourage women to use it as an example and put it into practice. During the Enlightenment period, the need for women's education was emphasized, and many textbooks teaching women were produced, and Park Man-hwan's interpretation of 『Yeosaseo』 was in line with this trend. In addition, women's loyalty and attitude toward the state and wages were emphasized as they recognized women as members of the state, and this trend is reflected in the women's guidebook, and Park Man-hwan's interpretation of the 『Yeosaseo』 focused on responding to social demands and expanding women's consciousness.
Online Hierarchical Multi–label Classification

Existing approaches for multi–label classification are trained offline, missing the opportunity to adapt to new data instances as they become available. To address this gap, an online multi–label classification method was proposed recently, to learn from data instances sequentially. In this work, we focus on multi–label classification tasks, in which the labels are organized in a hierarchy. We formulate online hierarchical multi–labeled classification as an online optimization task that jointly learns individual label predictors and a label threshold, and propose a novel hierarchy constraint to penalize predictions that are inconsistent with the label hierarchy structure. Experimental results on three benchmark datasets show that the proposed approach outperforms online multi–label classification methods, and achieves comparable to, or even better performance than offline hierarchical classification frameworks with respect to hierarchical evaluation metrics.
Deep Learning for Sentiment Analysis: Exploring the Power of Deep Learning Techniques in Opinion Mining

Sentiment analysis (SA) is a popular method for collecting relevant and arbitrary information from text-based data. To locate, examine, extract reactions, and emotions from the data or states, it applies computational linguistics, biometrics, text analysis, and Natural Language Processing (NLP). A SA model can be developed and improved with the use of the features analysis method. However, it can be difficult to find the best classification methods for this type of data. When compared to current feature-based methods, deep learning models for the analysis of sentiment, are capable of providing precise representation and improved performance. In this paper, the deep learning method was proposed for improving sentiment classification performance with a sophisticated embedding word method and develop a network of Convolutional Neural Network and Long short-term memory (CNN-LSTM). For the purpose of predicting the sentiment of tweets for classification, a large amount of Amazon data is analysed. This study, concentrate on improving sentiment analysis performance by building a CNN-LSTM network and particularized model of deep learning with novel word embedding techniques. The CNN-LSTM model has achieved 98.20% accuracy, 99.86% precision, 99.50% recall, and 98.78% recall in sentiment analysis.
Combining Multiple Feature Extraction and Classification Methods to Study Performance of Handwritten Sanskrit Character Recognition

The recognition of Sanskrit handwriting has been found to be one of the most challenging research topics. The Sanskrit language is written using the Devanagari Script. In this paper, we implemented novel algorithm to recognize handwritten characters using four different feature extraction methods, resizing the image, Canny Edge detection, Hybrid Discrete Wavelet-Discrete Cosine Transform (DWT-DCT), Histogram of oriented Gradients (HOG). Classification is done by using support vector machine with cubic kenrel and neural network with ReLU activation function. When features are extracted by HOG, the SVM provides classification accuracy of 97.10% while the neural network provides 91.40%. SVM has been found to provide superior classification than neural networks for all feature extraction strategies.
Handwritten Digit Recognition using Fine-Tuned Convolutional Neural Network Model

Traditional handwriting recognition algorithms were reliant heavily on existing knowledge and personalized features. It is more difficult to train a computerized optical character recognition (OCR) system using these criteria. Recent advances in the identification of handwriting research have been made possible by using deep learning techniques as their main mechanism. But when the volume of written content quickly increased and incredibly powerful computing resources became available, digit recognition accuracy had to have deteriorated, mandating more applicants’ implementation in order to yield accurate and precise results. To develop an effective approach for resolving handwriting detection challenges, this work uses the convolutional neural network (CNN) model which demonstrated the outperforming results in interpreting the structure of handwritten characters as well as phrases. This model is also capable of reducing the operational complexity and cost by implementing this model on different optimizers namely, Adam and SGD. The results have been evaluated at different numbers of epochs which identified that the proposed model outperforms with Adam optimizer by showing the highest accuracy of 98% at epoch 50. This model can be further implemented by varying the learning rate values to achieve improved accuracy and reducing the loss.
TripleLP: Privacy-Preserving Log Parsing Based on Blockchain

The amount of computer log data in today’s world is exploding, and the huge amount of data is affecting data management, storage, and analysis. However, large-scale log analysis is a time-consuming and inefficient process, and the large amount of data makes data privacy protection extremely difficult. The TripleLP proposed in this article is an automated log parsing tool based on blockchain, which stores and manages logs in a structured and unified format within the blockchain. It provides a well-organized database for comprehensive log auditing of computing systems, which can efficiently parse large-scale log data while protecting data privacy. In this article, we validate TripleLP on a large-scale public log dataset (i.e. log hub). Experimental data shows that TripleLP can efficiently parse logs while protecting data privacy.
NeuEmot: Mitigating Neutral Label and Reclassifying False Neutrals in the 2022 FIFA World Cup via Low-Level Emotion

Sports have been extensively studied for their impact on people’s emotional well-being, with research revealing that they have the ability to reduce anxiety and unhappiness while boosting positive emotions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Among all sports, soccer stands out as the most popular and controversial <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , eliciting a wide range of emotional reactions from fans, players, officials, and spectators, particularly on social media. While sentiment classifications such as positive, negative, and neutral have been extensively studied, low-level emotions, which refer to more specific and granular emotional states beyond the three basic categories, have yet to be given much attention. This study scraped over 300,000 tweets during the 2022 FIFA World Cup in Qatar to gain a more in-depth understanding of human emotions. We use the dataset to finetune transformer models such as pre-trained BERT and RoBERTa to detect high and low-level emotions in the collected tweets. Pre-trained $\mathrm{RoBERTa}_{\mathrm{Sent3}}$ was initially used to predict three high-level classes. Three models were then trained using one model with 28 and two models with 27 GoEmotions labels from Reddit comments to break the high-level sentiment into low-level emotions. Impressively, these models outperformed GoEmotions paper classification research in terms of precision and F1-score for BER$\mathrm{T}_{\mathrm{Emot}28}$, and they predicted less neutral labels compared to $\mathrm{RoBERTa}_{\mathrm{Sent3}}$. To further improve the classification accuracy, BER$\mathrm{T}_{\mathrm{Emot}27}$ and RoBERT$\mathrm{a}_{\mathrm{Emot}27}$ were used to reclassify BER$\mathrm{T}_{\mathrm{Emot}28}$, and these models achieved better performance in terms of precision, recall, and F1-score compared to other models. BER$\mathrm{T}_{\mathrm{Emot}27}$ was particularly effective in eliminating false neutral classifications, reclassifying the predicted neutral label class from BER$\mathrm{T}_{\mathrm{Emot}28}$ into other classes and achieving retrieval counts of 50%, 51%, 53%, and 53% for weeks 1, 2, 3, and 4 respectively. This study stresses the importance of using a nuanced approach to emotion classification and provides valuable insights into the emotional landscape of a major sporting event. It demonstrates that tweets initially perceived as neutral contained other emotional content, highlighting the need for further research. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Mental Health Benefits of Sports <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> The Most Popular Sports In The World
Design of Japanese Translation Online Assistant System Based on Neural Network

In order to facilitate communication among people from various countries, this paper will develop an online Japanese translation assistant system using neural network technology. On this basis, a Japanese translation mechanism based on neural networks was proposed, which combined the editing and modification after Japanese translation with the design concept of knowledge base. A system structure consisting of a transceiver module, an automatic translation module, a manual error correction module, a task management module, and a storage module were established, and the performance of each module was analyzed in detail. Then, this article provides a detailed workflow design for the system, with a focus on combining neural networks with the business process of Japanese translation, thus achieving online assistance for Japanese translation based on neural networks. After testing, all functional modules of this system have achieved a 100% pass rate and achieved fast online translation, improving the success rate of pronunciation and multilingual translation.
Effect of a Self-growth Reading Program on Well-being in College Students

The purpose of this study was to analyze the impact of a reading program utilizing self-growth books on the well-being and emotions of college students. The participants in the study were college students from a university located in Seoul. Participants who were absent from the program more than three times were excluded, and the data of 59 participants in the experimental group and 26 participants in the control group were analyzed. The experimental group consisted of students who voluntarily participated in a long-term program of six weeks or more, while the control group consisted of students who only wanted to participate in surveys. To facilitate the program, a pre-workshop on reading therapy was conducted with nine graduate students majoring in counseling psychology, who were then organized into a total of eight groups and operated as leaders. The program consisted of sessions lasting 90 minutes each, conducted once a week for a total of six sessions. Subjective well-being scales, the Korean version of positive and negative affect schedule, were used to measure changes in the experimental and control groups. The results of the study showed a significant increase in subjective well-being in the experimental group, with sub-factors of life expectations and life satisfaction also significantly increasing. In terms of emotions, positive affect significantly increased, while there was no significant change in negative affect. In contrast, the control group showed no significant changes in any variables. This study partially confirms that a self-growth reading program has a positive effect on the happiness of college students. As college students are key players in future society, it is expected that the happiness of college students will contribute to the formation of a healthy and happy society. This research is meaningful in providing some ways to diversify and activate self-growth programs in the future to enhance the happiness of college students.
The Identity of a Christian University through the Academic Tradition and History of Yoenhee College

The purpose of this article is to examine the historical mission of Christian universities in Korea's modern and contemporary history, focusing on the history and academic traditions of Yeonhee College, the predecessor of Yonsei University. In addition, through this, it is intended to macroscopically reflect on the meaning and content of Christian education that Korean Christian universities facing various crises should have. Horace Grant Underwood (1859-1916) and Oliver R. Avison (1860-1956), during the patriotic enlightenment period at the end of the Chosun Dynasty, established modern higher education that could replace the crumbling feudal order through university education based on the Christian spirit. They established an interdenominational Christian university based on the ecumenical spirit, and wanted to provide equal educational opportunities to Chosun, which was captivated by the feudal status order. During this period, the person who established Christian liberal arts classes and chapels in Yeonhee College was missionary Harry A. Rhodes (1875-1933), who studied theology at Princeton University in the United States. He made Bible education and chapels possible, which were impossible to operate as regular subjects under the Private School Law of Japanese Government-General of Korea. Since then, Yeonhee's nationalistic academic style based on the Christian spirit was naturally formed around Jeong In-bo, Choi Hyeon-bae, Baek Nak-jun, and Baek Nam-un. This anti-Japanese nationalistic academic tradition had a profound influence on Yoon Dong-ju, who entered Yeonhee college in 1938. Yoon Dong-ju can be said to be a representative figure that symbolizes the Christian spirit of Yeonhee College. The history and academic tradition of Yeonhee College, which began as a Chosun Christian University, can provide a valuable precedent for how Christian higher education can merge with the history of the nation to Christian universities in Korea.
Quantum Computing: The latest development in computing

Quantum computing leverages the principles of quantum mechanics to address intricate problems that traditional computers find challenging. Operating on qubits, quantum computers execute multidimensional quantum algorithms, offering a unique perspective compared to traditional computing concepts. Positioned as the future of computing technology, quantum computing finds applications in diverse fields, including cryptography, material science, agriculture, and computer science. Despite promising progress, the field faces challenges in achieving quantum supremacy over conventional computers. This paper explores key quantum computing concepts, such as qubits and their characteristics, along with applications, while also delving into future advancements and the expansive scope of quantum technology. In essence, quantum computing represents the future of technology, harnessing quantum phenomena like superposition and entanglement for computational tasks.
Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems

This research paper aims to systematically review the literature published recently on the topic of parallel processing algorithms for sorting and searching methods. The paper emphasizesfinding the major gaps in the existing methods of using parallel sorting and searching methods for graph analytics problems and their use in scalable data science. Many peer-reviewed research papers and articles from international and national journals, conferences and other sources are searched using appropriate keywords and they are analyzed to understand the concept, current trend in the area and to find the research gaps, by employing a systematic review method of literature. The outcomes of this literature review haveresulted in the need to undertakea research work on "Design and Implementation of Parallel Sorting and Searching Algorithms for Graph Analytics Problems in Scalable Data Science". Also, we have identified the required research objectives and its plan of execution for the research proposal. Systematic literature review, Topic of research proposal along with research objectives, Listing of SWOT analysis of the research agendas, Listing of ABCD analysis of the research proposal
Information integration from human and sensing data for cognitive radar

This chapter discusses mathematical aspects of human-machine networks under cognitive biases. Since the human behavior in decision making is quite complex and uncertain, the cognitive radar sense-learn-adapt cycle is affected by the involvement of human input. The analysis of human-machine collaborative decision making is an important cognitive radar research area, where the goal is to optimize the system performance based on appropriate modeling of the human behavior.
Phenomenological analysis the aesthetic experience of the artist

Based on the aesthetic-phenomenological analysis of the artist’s creativity, the article shows how intentional experiences ‘work’, enriching the aesthetic skills of the artist. They produce new meanings, building new horizons of intersubjective relations with the world and oneself. The intentional structure of creative acts with certain activity-psychological attitudes makes it possible to reveal the process of life-functioning of the author’s consciousness as a non-linear system of correlation of the interpenetrating layers of intentional experiences. A special role is given to existential experiences as a special way of being, prerequisites and conditions for the artist’s creativity.
A Dynamic Codec with Adaptive Quantization for Convolution Neural Network

Current convolutional neural networks (CNN) have achieved a high inference accuracy by deeper architecture, generating a large amount of interlayer data. Limited to on-chip memory, massive feature maps significantly impact the performance of hardware platforms. In this paper, we propose a dynamic codec with adaptive quantization for compressing inlayer feature maps which efficiently reduces the memory storage. Discrete cosine transform (DCT) is utilized to concentrate essential information in the frequency domain while high-frequency components are compressed by adaptive quantization. The quantization tables are dynamically adjusted by the storage of the previous map and the number of the current layer to maintain a relatively high compression ratio and quality for each layer. In addition, Huffman coding and run length encoding (RLE) are used to code compressed data streams from 2-D to 1-D for storage. Furthermore, the codec is implemented on FPGA and synthesized in TSMC 28nm technology. It yields a reduction of on-chip memory by 44.17% and achieves an average compression ratio of 69.18% for feature maps at diverse depths in ResNet-50.
THE RELATIONSHIP BETWEEN THE DESIGN AND IMPLEMENTATION OF EDUCATIONAL PROGRAMS

The development and implementation of educational programs based on competence is currently one of the most complex and multifaceted tasks of modern education. The article notes that the successful implementation of educational programs depends on its purposeful design. In particular, the importance of proper registration of the learning outcomes of the main educational programs was emphasized. The study used methods of observation, analysis and generalization of the main educational programs and curricula in specific disciplines within the curriculum, assessment tools. Examples were given of how the implementation of the master's program «physics and mathematics education» can be achieved when teaching subjects. It has been shown that the acquisition of learning outcomes can be verified using appropriate level assessment tools. The formation of a scheme for assessing the level of achievability of learning outcomes of an educational program through one discipline in the curriculum was based on the level of a specific topic, interdisciplinary, interdisciplinary and interdisciplinary levels. Using examples, it is confirmed that the design of the educational program will be directed from the top of the pyramid to the base, and the implementation- mainly to the top.
Node Density Adjustment Method using Volume Estimation in Growing Neural Gas based 3D Perception System

With the recent development of robot technology, there are high expectations for autonomous robots. Specifically, the ability to perceive the 3D spatial structure in real-time is essential for autonomous robots. In this research, we propose a method for spatial recognition based on Growing Neural Gas (GNG), which learns the spatial structure of 3D point cloud data, according to the granularity of tasks given to the robots. Our aim is to establish an efficient methodology for perceiving three-dimensional space. In this paper, we estimate the node density by using Principal Component Analysis (PCA) for each cluster and estimating the volume of each cluster from the eigenvalues. We then propose a method to adjust the node density of each cluster based on the node density calculated by the estimated volume and the attenuation rate of the accumulation error, which is a reference for adding nodes in GNG. Finally, we verify the effectiveness of the proposed method through numerical experiments using simulation data and 3D point cloud data measured by an RGB-D camera.
Report of IHI Manufacturing Workshop 2022

The results of questionnaires for participants and internal staff of the IHI Manufacturing Workshop, regional events held at IHI's Yokohama Office and Toyosu Head Office, were analyzed in terms of IHI’s name making better known in the community and IHI’s employee engagement.