Reviewerly/exHarmony huggingface dataset:
Acoustic Design of Elliptical Bells for Chime Sound Simulation

This project seeks to unravel the opulent acoustical characteristics and sonic effects of ancient Chinese chime bells. Initially, distinctive features of the chime bells are encapsulated, highlighting the bifonic phenomenon and elucidating the role of fundamental and harmonic frequencies in chime bell sound production, while emphasizing the timbral diversity attributable to its multifrequency sound source. An exploration into the impacts of various factors on chime bell sound, such striking position, is conducted through experimentation with a bronze chime bell model, underscoring their significant influence. Employing experimental modal analysis methods, vibrational modes of the chime bell surface at disparate frequencies are revealed, laying a foundation for understanding the sound production principles. Furthermore, image processing technologies are utilized to construct a 3D model of the chime bell, and finite element modal analysis methods are applied to visualize its modes. The feasibility of using a circular ring to simulate chime bell sound is explored, identifying the elliptical ring as a more promising option and undergoing numerous rounds of iterative optimization. The theoretical research is brought to fruition by manufacturing a cast iron elliptical ring model for striking experiments, thereby validating the theoretical insights. In summation, this research provides a comprehensive and systematic exploration into the acoustical mechanisms and design principles of chime bells, offering invaluable insights into ancient Chinese music and cultural heritage. The multi-faceted and rich nature of chime bells as musical instruments is unveiled through the integrated application of various methods, deepening the understanding of their acoustical characteristics.
Research on Mathematical Measurement Model of Fair Value Based on Big Data and Cloud Computing

New generation information technologies such as big data are very important for the development and application of fair value in the measurement model of financial instruments. This paper discusses the relevant theories of measurement of fair value, and analyzes the application scope of fair value in the measurement of financial instruments and the impact of the revision of international standards of financial instrument on the convergence of similar standards in China. Finally, based on the technical support of big data, this paper expounds the fair value in the measurement model of financial instruments, and puts forward the relevant improvement suggestions.
Prediction of Wordle based on ARIMA and PLS model

Nowadays, the New York Times Wordle game gains much popularity in the United States. In this paper, PLS regression analysis is applied to find 5 potential factors to interpret the independent variable information and obtain the prediction distribution result. Extra features such as time series have stationarity and nouns make up the vast majority of the total are investigated through data mining. It is also found that the fluctuating trend of the number of players is consistent with the infectious disease model. The paper gives a prediction of the population of players that is worth reading for reference and a simple classification method of difficulty of words. The model inferred in the paper is expected to help puzzle editors to give a word of proper difficulty.
Efficient Context and Saliency Aware Transformer Network for No-Reference Image Quality Assessment

No-Reference Image Quality Assessment (NR-IQA) aims to estimate the perceptual image quality without access to reference images. To deal with it effectively and efficiently, in this work we propose a Context and Saliency aware Transformer Network (CSTNet), which is built based on a lightweight pyramid Vision Transformer (ViT). Specifically, a Multi-scale Context Aware Refinement (MCAR) block is devised to fully leverage hierarchical context features extracted by the ViT backbone. Further, saliency map prediction is incorporated as a sub-task to simulate the human attention on salient regions when perceiving images. Extensive experiments on public image quality datasets demonstrate its efficiency and superiority compared to the state-of-the-art models.
Decision Support System for Single Tuition Scholarship Awardees in Higher Education Using Mamdani Fuzzy Inference

Universitas Madura implements the Single Tuition Fee (UKT) scholarship program in the Informatics department. The current UKT scholarship selection system uses a traditional model that is still not effective, causing obstacles such as inflexibility in registration time. The print-out documents are vulnerable to damage or loss and difficulty searching when it required. The criteria in the current system consisted of a minimum Grade Point Average (GPA) of 3, a letter of family condition, and also student status in semesters 3 to 7. The three criteria are not enough to determine a scholarship candidate. The recruitment process involves only the Head of the Study Program (Kaprodi). The Informatics study program still has many candidates applying for scholarships reaching around 280%. This research proposes a Decision Support System (DSS) using Fuzzy Mamdani with six criteria, including GPA, Achievement, Parents' Income, Parents' Dependents, Semester, and History of not receiving scholarships with the aim of overcoming these problems. The results show that the performance of the proposed SPK is very good, it is shown by the MAPE value of less than 10% and more efficient time than the current system. This system has also been in accordance with the required functions through the black test.
Feasibility of automatic evaluation metrics as quantitative assessment tools for Korean translation texts: focused on SacreBLEU

In this study, I conducted an examination of the effectiveness of automated evaluation metrics, primarily designed for machine translation, in the realm of human translation assessment. The research involved a thorough comparison of these automated metrics, such as BLEU and METEOR, with conventional manual evaluation techniques. Central to this investigation was the application of SacreBLEU, a refined metric with standardized preprocessing, to evaluate human-generated texts. The findings revealed a notable correlation, exceeding 0.8, between the outcomes of automated evaluations and human judgments. This result highlights the potential of automated metrics to enhance the accuracy and efficiency of human translation assessment, and it is easy to use, further emphasizing its practical utility. The study thus advocates for the integration of these metrics into academic and professional translation evaluation practices, offering a more streamlined, consistent, and objective approach to translation quality assessment.
How Gamification-Based Artificial Intelligence Educational Programs Affect Ethical Awareness of Artificial Intelligence among Elementary School Students

This study investigated the effects of a gamification-based education program centered on Artificial Intelligence (AI) ethical standards on elementary school students’ ethical awareness of AI. Therefore, a 16-session AI ethics educational program was developed based on the ADDIE model according to previous studies on AI ethics education and “human-centered AI ethical standards.” To verify the program’s effectiveness, the program extracted from the “AI ethics (Elementary School) Learning with AI Principles” (Gyeonggi Provincial Office of Education, 2022) was applied to compare between groups and used a student ethics consciousness test tool we developed.&#x0D; The study results obtained through the application and analysis of this program are as follows. We found statistically significant differences in all areas of interest and the need for AI ethics and ethics education. The results of the pre-and post-tests for each AI ethics element showed statistically significant changes in all areas, except for the “responsibility” element. The AI ethics education program developed in this study is expected to contribute to the formation of a proper AI ethics consciousness among elementary school students.
A Study on College Students' Perceptions of ChatGPT

At a time when interest in the educational use of ChatGPT is increasing, it is necessary to investigate the perception of ChatGPT among college students. A survey was conducted to compare the current status of internet and interactive artificial intelligence use and perceptions of ChatGPT after using it in the following courses in Spring 2023; ‘Family Life and Culture’, ‘Fashion and Museums’, and ‘Fashion in Movies’ in the first semester of 2023. We also looked at comparative analysis reports and reflection diaries. Information for coursework was mainly obtained through internet searches and articles, but only 9.84% used interactive AI, showing that its application to learning is still insufficient. ChatGPT was first used in the Spring semester of 2023, and ChatGPT was mainly used among conversational AI. ChatGPT is a bit lacking in terms of information accuracy and reliability, but it is convenient because it allows students to find information while interacting easily and quickly, and the satisfaction level was high, so there was a willingness to use ChatGPT more actively in the future. Regarding the impact of ChatGPT on education, students said that it was positive that they were self-directed and that they set up a cooperative class process to verify information through group discussions and problem-solving attitudes through questions. However, problems were recognized that lowered trust, such as plagiarism, copyright, data bias, lack of up-to-date data learning, and generation of inaccurate or incorrect information, which need to be improved.
Development Direction &amp; Strategy for the 2022 Revised National Level Home Economics Curriculum and Unfinished Tasks: Focusing on Reflective Observation of Curriculum Development Experience

This study was approached as a method of reflective observation to build practical knowledge about home economics by reevaluating the experience of participating in the national development of the 2022 revised home economics curriculum through self-questioning, review, and reflection. The results are as follows: First, “all learners will still live in an unpredictable future society and choose the best actions to better themselves, their surroundings, and the world.” Second, the value of home and education in the overall curriculum structure is “home economics education are life lessons that help future generations develop the competence necessary to lead a better life.” Third, “in order to ensure the sustainability of all humanity and the world in the future society, the area of intentional learning that helps young people choose and lead better lives should be the core, and home economics education should focus on ‘Good life Literacy’.
Practical Approaches to Cultivating the Value of Sustainable Developmental Education in Korean Music Education

This study presented various practical approaches to cultivating the values ultimately aimed by sustainable development education in Korean music education based on the 2022 revised music curriculum.&#x0D; To this end, first, literature research was reviewed to identify the meaning and value of sustainable developmental education and the connection between the 2022 revised music curriculum and sustainable developmental education. Second, on the basis of its characteristics, the relationship between Korean music education and sustainable developmental education was examined. Third, practical approaches to cultivating the value of education for sustainable development in Korean music education were presented.&#x0D; Four practical approaches reflecting the value of education for sustainable development in Korean music education were presented. The first is to communicate with generations through Korean music. The value of respecting human rights and future generations was cultivated through learning traditional children’s songs and creating Korean children’s songs. The second is examining the relationship of Korean music with nature and life. It is a way to cultivate the values of respecting human rights, future generations, and ecological diversity. Third, it is to value various local music cultures by appreciating Korean music and cultural heritage, talking about how to protect and preserve Korean music and cultural heritage for future generations, and cultivating the values of respecting human rights, future generations, and cultural diversity. Fourth, what should be done to promote sustainable Korean music in the world? The value of respecting human rights, future generations, and cultural diversity was cultivated by finding ways to improve and solve Korean music and cultural heritage registered in UNESCO to continue to maintain its status in the world. This study focused on cultivating the value of sustainable developmental education by linking it with the value of Korean music, not the content approach of Korean music education. Therefore, it is very important to highlight values and ask appropriate questions from teachers so that learners can cultivate them. Therefore, starting with this study, I hope that inquiry research, teaching and learning methods, and data-related studies will continue to cultivate the value of teachers.
Pemanfaatan Aplikasi GoodNovel dalam Peningkatan Apresiasi Sastra dan Literasi Digital Siswa SMK Paskita Global

Digital literacy writing literary novels through the GoodNovel application. GoodNovel is a global original fiction platform dedicated to producing the latest generation of "classic" novels and providing a free zone of imagination for users by harnessing the power of technology. The results of interviews and questionnaires conducted by the team regarding interest in writing literary works showed that 68% of students did not like writing literary works. Several problems are factors that cause students not to be interested in writing literary works, namely lack of ideas in developing stories, lack of skill in writing novels, and not liking to write, this happens because students are less interested in reading and writing novels. The methods used in this activity are socialization, education, training and use of digital applications. The results of this activity were reviewed based on the output of the activity, namely an increase in interest in writing and digital literacy of Paskita Global Vocational School students. Thus, it can be concluded that this program has achieved its objectives.
Efficient Record Management System for Higher Educational Institutions using TOE and UTAUT Model

Higher Educational Institutions maintain Academic Policy, Non Academic Policy, Students and Staff members’ details and financial records. Information retrieval from the existing records, Records maintaining process and decision making using previous history are crucial tasks in the Higher Educational Institutions. Even though, these process occupies many storage area, man power and Processing time etc., The objective of the proposed research is Academic and non academic activities record management, Academic Syllabus Generation like Outcome Based Education, Handling the students records, marks and attendance, Inventory system for the Institution and Laboratory, Efficient Library Management System, Maintaining the financial records like fees Collection, Financial Statements, Employee Payroll etc., The paper deals with the Academic and non-academic activities record management system by using the UTAUT and TOE Model
Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification

Computer technology has employed Machine Learning models in a variety of applications to improve performance. The hyperparameter of a machine learning model must be adapted to overcome learning limitations and increase its performance. In this research, the hyperparameters of machine learning classifiers are tuned to identify cases of benign or malignant breast abnormalities. An experimental investigation was conducted using the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. A fusion model, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB) is employed, which is combined with conventional classification approaches like Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). The proposed methods are compared to cutting-edge models like SVM, NB, LR, K-Nearest Neighbour (KNN), Random Forest, and Decision Tree using a wide range of parametric measures, such as Precision, Recall, Specificity, F-measure, Accuracy, True Positivity Rate (TPR), and False Positivity Rate (FPR). The results show that the proposed methods outperform the leading models.
Advancement in Denoising MRI Images via 3D-GAN Model with Direction Coupled Magnitude Histogram Consistency Loss

The diagnostics of medical pictures are essential for recognizing and comprehending a wide range of medical problems. This work introduces the Direction Coupled Magnitude Histogram (DCMH) as a novel structure picture descriptor to improve diagnostic accuracy. One of DCMH's unique selling points is its ability to include the edge oriented information that are oriented in any way inside a frame, enabling the expression of delicate nuances using various gradient features. The proposed method applies cartoon texture based textural loss and DCMH based structural loss to identify and analyse structural and textural information during the denoising time. A major contribution that improves the interpretability of images by emphasizing structural aspects that is inherent to the image. The proposed DCMH_3D_GANaverage results show exceptional performance, with an SSIM of 0.972995 and PSNR of 48.74, highlighting the effectiveness of the DCMH-based method in enhancing medical picture diagnosis. The capacity of Structured Loss to improve picture interpretability and lead to a more precise diagnosis is unquestionably advantageous. The newly developed DCMH-based approach, which includes texture loss and structured components, is a promising development in healthcare image processing that will enable better patient care through enhanced diagnostic abilities.
An Efficient Encryption System on 2D Sine Logistic Map based Diffusion

An optimal cryptographic model is proposed, enabling the feature of 2D sine logistic map-based diffusion algorithm. The 2D sine logistic map process is merged with the algorithm as it has the ability to provide random number generator as well as to overcome blank. The previous existing models based on image encryption use to work on raw images but without alteration for the process of confusion and diffusion. The main disadvantage as the nearby pixel values for an image always remains similar. This issue is resolved by a Pseudo random generator process which is based on key stream that alters pixel value. Furthermore 2D sine logistic map based diffusion process has shown an improvement in the key sensitivity and the complex relationships that use to get developed between cipher and test image.2D sine logistic map with diffusion method used to keep pixels intact with each other to such an extent as even a single bit modification in the intensity value of an original image pixel will lead to a huge change in most of the pixels of the cipher and thus makes the model very sensitive to make any changes in the pixel value or secret key for an image. As seen and analyzed with a variety of test results that strategic model used for encryption can easily encrypt the plain image into a cipher of a random binary sequence.
Precision in Classification: A Comparative Study of Logistic Regression, Naive Bayes, LSTM, and CNN for Spam Email Detection

This research presents a comprehensive comparative study on the precision of classification algorithms applied to spam email detection. The study focuses on four distinct algorithms: Logistic Regression, Naive Bayes, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). The evaluation encompasses a common dataset to ensure a fair and rigorous assessment of each algorithm’s performance. We investigate and analyze precision metrics, considering the trade- offs between true positives and false positives. The comparative study provides insights into the strengths and limitations of each algorithm in effectively identifying spam emails. The results contribute valuable knowledge to the field of email security and classification, guiding practitioners and researchers towards informed algorithm selection based on precision considerations.
Recognizing Criminal Intent through Facial Expressions

Artificial intelligence is developing rapidly as a result of recent advances in the identification of image and emotion patterns in human facial features. On the other hand, the rise in crime and wrongful imprisonment are causing society to disintegrate. Artificial intelligence has greatly aided in the development of our contemporary society. Knowing the issue and the appropriate set of instruments to address it is an essential skill, and having the ability to apply those tools effectively elevates one to the rank of supreme being in the cosmos. False imprisonment is an issue that requires attention. Recognizing the facts,1 in 20 criminal prosecutions in the US alone end in an incorrect conviction. Innocent persons who have been unfairly convicted make up 1% of US jail populations, or about 20,000 people, according to the Innocent Project group. Facial emotion detection and datasets gathered based on the study "Criminality in the face" can be used by artificial intelligence to help lessen the problem of false convictions. According to a Kinesics survey, specific body motions and movements can be used as a kind of non-verbal communication. By Ray Birdwhistell in 1952, the phrase was first used. As the proverb "Face is the index of mind" states, facial expressions are an important aspect of non-verbal communication. Analyzing a person's face might provide insight into the circumstances around a criminal suspect.
Effective Policies to Protect Information Resources: Identity Management and Organizational Access Studies X

Information resources are an important asset for organizations, both government and private. Information resources can be data, systems, or applications used to support organizational activities. Therefore, it is important to protect information resources from various threats, such as cyberattacks, misuse, or loss. Identity and access management (IAM) is one of the efforts to protect information resources. IAM is the process of managing the identity of users and their access to information resources. Effective IAM can help organizations ensure that only authorized users can access information resources, detect and prevent unauthorized access to information resources, and facilitate user identity management. This study aims to examine effective IAM policies to protect the information resources of Organization X. The results show that an effective IAM policy must meet the following criteria: comprehensive; IAM policies should cover all aspects of identity and access management, from user identity creation to access review, risk-oriented; IAM policies should be designed to minimize risks to information resources, Flexible; IAM policies must be adaptable to the changing needs of the organization, Management support; IAM policies must be supported by the organization's management to be implemented effectively. Based on the results of this study, it is advisable to ensure that information security policies remain effective over time, so organizations should carry out regular evaluations to protect their information resources.