Reviewerly/exHarmony huggingface dataset:
The Influence of Clutching Mechanism on Object Manipulation Tasks in Virtual Environments

Object manipulation is one of the most fundamental tasks in 3D user interfaces. Compared with interacting with objects within reach, remote object manipulation is often more challenging, which is often an iterative process that need clutching. However, while there are many interaction techniques designed for manipulating remote objects, it is not well studied the effects of clutching mechanism on remote object manipulation tasks. In this paper, we present a controlled experiment to evaluate the effects of two clutching mechanisms (Normal Clutching vs. Easy Clutching) on 3-DOF translation, 3-DOF rotation and 6-DOF docking tasks under various tasks settings. The results show that easy clutching can significantly save more completion time and clutching time compared with Normal Clutching. In addition, the results also reveal that easy clutching can be more effective especially when dealing with hard tasks and long user-target distance. We also find that users prefer easy clutching over Normal Clutching, indicating that well designed clutching mechanism can effectively improve user experience in manipulation tasks.
Analysis of Research Trends in Cosmetology Education using Text Mining

Identifying academic research trends is an essential task to understand the development patterns of how the discipline has changed in the times and social trends based on the accumulated research results so far, and further establish academic identity. This study was conducted according to the following procedure to analyze the trend of Cosmetology Education Research using text mining. The research procedure proceeded to the stages of data collection, data cleaning, text mining, network analysis, and CONCOR analysis. This study conducted a trend analysis of Cosmetology Education Research using text mining, one of the big data analysis methods, to suggest that the beauty field also needs to respond to changes in the times. Through this, it was confirmed that research on the curriculum and learning satisfaction is most actively conducted, and there is a research variations in the school system and the major. However, as this study has been limited to the field of cosmetology education, it is believed that it can be meaningful as more valuable basic data if big data analysis is conducted by expanding the scope of research.
Multimedia Authentication Through Digital Tattooing Scheme at IoT Perception Layer for Smart City

The move to a digital environment, where consumers may utilise, process, store, and transfer their data, has occurred in recent years. Furthermore, the quick and ongoing evolution of the Internet of Things (IoT) has resulted in a massive increase in the utilisation of digital signals and images. The Internet of Things (IoT) is a huge and varied network of connected devices, everyday objects, and gadgets that can communicate with one another over the Internet. The Internet of Things has a lot of potential to become a part of everyday life and improve people’s lives. In recent decades, digital tattooing has been offered as a great way to handle numerous elements of IoT security. This technology works by embedding a digital signal called a digital tattoo into another digital signal called a host signal, which can be text, image, audio, or video. The principle of this approach is to detect changed pixels using the Cyclic Redundancy Check (CRC) code. The CRC is a data integrity check that is both powerful and simple to use. It is the most used method of mistake detection in telecommunications. For these reasons, it has been used in the context of the fragile tattoo to determine whether or not the picture has been altered. The experimental results show that the proposed schemes perform well in contrast to the existing scheme and use traditional evaluation criteria, proving that the new scheme is distinctive.
Federated Learning for Clinical Event Classification Using Vital Signs Data

Accurate and timely diagnosis is a pillar of effective healthcare. However, the challenge lies in gathering extensive training data while maintaining patient privacy. This study introduces a novel approach using federated learning (FL) and a cross-device multimodal model for clinical event classification based on vital signs data. Our architecture employs FL to train several machine learning models including random forest, AdaBoost, and SGD ensemble models on vital signs data. The data were sourced from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure trains directly on each client’s device, ensuring no transfer of sensitive data and preserving patient privacy. The study demonstrates that FL offers a powerful tool for privacy-preserving clinical event classification, with our approach achieving an impressive accuracy of 98.9%. These findings highlight the significant potential of FL and cross-device ensemble technology in healthcare applications, especially in the context of handling large volumes of sensitive patient data.
Hesitant adaptive search with estimation and quantile adaptive search for global optimization with noise

Adaptive random search approaches have been shown to be effective for global optimization problems, where under certain conditions, the expected performance time increases only linearly with dimension. However, previous analyses assume that the objective function can be observed directly. We consider the case where the objective function must be estimated, often using a noisy function, as in simulation. We present a finite-time analysis of algorithm performance that combines estimation with a sampling distribution. We present a framework called Hesitant Adaptive Search with Estimation, and derive an upper bound on function evaluations that is cubic in dimension, under certain conditions. We extend the framework to Quantile Adaptive Search with Estimation, which focuses sampling points from a series of nested quantile level sets. The analyses suggest that computational effort is better expended on sampling improving points than refining estimates of objective function values during the progress of an adaptive search algorithm.
Goodness-of-fit testing for normal mixture densities

A novel goodness-of-fit test for assessing the validity of maximum likelihood estimates of normal mixture densities with known number of components is introduced. The theoretical contributions include analytic quantification of the test statistic's size and power functions under fixed and local alternatives. These are used to derive a closed-form bandwidth rule which optimizes the test's power while keeping its size constant at a given significance level, and a cut-off point suitable for finite sample implementations of the test. An extensive simulation study compares the performance of the new test to well-established tests in the literature and demonstrates the superiority of the former in all examples considered. Finally, its practical usefulness is demonstrated in the analysis of two real world datasets.
INNOVATIVE TECHNOLOGIES IN THE MODERN EDUCATION SYSTEM IN THE CONTEXT OF DIGITAL TRANSFORMATION

This study examines the features of the modern education system of the Russian Federation, current methods for implementing the educational process using innovative technologies. The mass introduction of information technologies into the work of educational institutions is analyzed, the influence of digitalization on the implementation of the educational process in higher educational institutions is studied, the features, positive and negative aspects of the digitalization of the educational process are noted. The distribution of the number of students of organizations engaged in educational activities in educational programs of higher education (bachelor’s, specialist’s, master’s) is given in the context of the constituent entities of the Russian Federation. The problems of modifying educational processes in modern universities and the development of a system of continuous education and the transformation of the educational environment in the context of the development of the digital economy in the Russian Federation are considered.
The Storytelling Method in Javanese Assisted by Loose Parts Media for Kindergarten Children

The ability to communicate in children is still low and has yet to develop according to expectations and age stages. Children can interact less and communicate properly and correctly with their environment. This research aims to create a storytelling method assisted by loose parts media to improve Javanese language skills in kindergarten. This type of research is development research, with this research method referring to the theory of Borg and Gall. Data collection techniques used were interviews, observations, and questionnaires. The analysis used in this study was quantitative and qualitative. The subjects of this study were kindergarten teachers and students. The material expert validation process assessment obtained an average result of 3.83 with a percentage of 95%, including the very good category. Linguists get an average result of 3.67 with a percentage of 92%, and the category is very good. The teacher's response obtained an average of 3.63, scoring 30 in the very good category. The child's response obtained an average result of 3.68 with a total score of 138 in the very good category. The results of calculating N-Gain in the field test as an experimental class obtained post-test results of 80.67 and 80.33, respectively, while for the control class, the post-test obtained results of 56.00. The results show that developing Javanese storytelling methods with the help of loose parts media for kindergarten children is feasible and effective in improving children's Javanese language skills.
Analysis of language errors in narrative essays of fifth grade elementary school students

This study describes the errors in capital letters, words, and punctuation marks. This research uses a descriptive qualitative research method. The subjects of this study were fifth-grade students of SDN Karet 1. Data collection techniques using tests, interviews, and documentation. The results of this study, namely (1) errors in writing capital letters in the first letter of the title name amounted to 21 errors, the first letter of the name of the nation totaled 11 errors, the first letter of the name of the month amounted to 24 errors, and the first letter of the name of geography amounted to 29 errors. (2) the error of writing the preposition at- totaled 28 errors, and the error of writing the preposition to- amounted to 4 errors. (3) writing errors of period punctuation totaling 37 and comma punctuation totaling 15 errors.
A RIO-Based Cybersecurity System Construction Method for Campus Network-Using Economic Concepts to Build a Network Security Protection System

By introducing RIO into the construction of campus cybersecurity, quantitative analysis of input and output is carried out to solve the problem of “bottomless investment and invisible results” of cybersecurity. This paper innovatively adopts RIO in the PPDRRF (Policy, Protection, Detection, Response, Recovery and Forensic) model, and quantitatively analyzes some key technologies of campus cybersecurity system construction, proposes AMTC Cybersecurity System Architecture (ACSA) and ensures the smooth progress of cybersecurity construction under resource constraints.
Oversampling Methods for Handling Imbalance Data in Binary Classification

Data preparation occupies the majority of data science, about 60–80%. The process of data preparation can produce an accurate output of information to be used in decision making. That is why, in the context of data science, it is so critical. However, in reality, data does not always come in a predefined distribution with parameters, and it can even arrive with an imbalance. Imbalanced data generates a lot of problems, especially in classification. This study employs several oversampling methods in machine learning, i.e., Random Oversampling (ROS), Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-sampling Technique (SMOTE), and Borderline-SMOTE (B-SMOTE), to handle imbalanced data in binary classification with Naïve Bayes and Support Vector Machine (SVM). The five methods will be run in the same experimental design and discussed in search of the best and most accurate model for the datasets. The evaluation was assessed based on the confusion matrices with precision, recall, and F1-score calculated for comparison. The AUC and ROC curve is also provided to evaluate the performance of each method via figures. The proposed work reveals that SVM with B-SMOTE has better classification performance, especially in datasets with high similarity characteristics between the minority and majority classes.
Investigation and Research on the Deep Integration of New Information Technology and Situational Teaching Method—a Case Study of Statistics Teaching in Primary Schools

With the promotion and application of new information technology in subject teaching, traditional teaching methods are constantly being challenged. This study takes the teaching of mathematics statistics in primary schools as an example to investigate the integration of new information technology and situational teaching methods. Based on the analysis of the obtained data, the existing problems are discussed and suggestions are put forward.
Intelligent Classroom Information Technology to Use English Reading to Improve Students’ Oral English Ability and Literacy

Nowadays, the development of science and technology has promoted the innovation and optimization of China’s education. In recent years, the development of smart classroom has been the attention of all walks of life. This teaching mode can create a good learning environment, but also improve the quality of education. Improving students’ reading ability can help students open their thinking, improve their language comprehension ability, and enrich their learning world. Improving students’ reading ability can further enhance their oral English ability, thus improving the quality of English teaching. However, at present, the teaching form of English teachers is still relatively rigid, leading to low students’ oral English literacy. Therefore, intelligent classroom should be actively adopted to promote the development of English education.
Research on Network Psychological Education Model Based on Cloud Computing

In order to construct the effective network psychological education model, the cloud computing is applied for it. Firstly, current situation of mental health education is summarized. Secondly, development goal of constructing network mental health education system is analyzed. Thirdly, application of cloud computing technology in college students’ network mental health education is studied. The cloud computing model is established. Finally, the network mental education system is established based on cloud computing, and the performance of the system is analyzed. Results show that the proposed system has better performance, which can provide the stability of network mental education system.
Study on Learning Method of Logistic Regression Classification for Class Imbalance Problem

In the innovation and development of information technology, the growth rate of data information in various fields is getting faster and faster. How to realize intelligent data processing and extract important information resources from large quantities of data information are the main problems discussed in the field of data mining at present. Data classification is the focus of technical research in the field of data mining. Although the traditional classification method has achieved excellent results in the processing of balanced data sets, in practice, the traditional algorithm does not have application advantages because most of the data combination is unbalanced. Therefore, this article studies in the class imbalance problem and based on the concept of logistic regression, based on the class imbalance problem put forward the corresponding logistic regression learning algorithm, and using the way of data preprocessing, comparison and analysis the RBLR, GBLR, FBLR with traditional logistic regression, owe sample logistic regression, logistic regression index of these three methods. The final results show that the new logistic regression algorithm effectively improves the performance of each index while maintaining the high accuracy of logistic regression.
New Blueprint of Labor Education: Intelligent Labor Education Based on the Concept of STEAM Education

Under the background of the change of labor form in the new era and the rapid development of new technology, we thoroughly analyze the value implication and existing predicament of labor education. The concept of smart labor education based on the concept of STEAM education is proposed for the first time, and the conceptual framework and curriculum system of smart labor education are constructed. In addition, the implementation path is proposed. The concept of intelligent labor education provides a new direction and draws a new blueprint for the future development of labor education.
Evaluation Method of Children's Quality and Ability Development Based on Cloud Platform

In view of the children's quality and ability development evaluation analysis from multiple perspectives, because individual score will be influenced by subjective factors, so the combination of cloud computing platform is critical to build a comprehensive evaluation system, help each evaluation index is objective, will score vector as input, and effective processing algorithm through the network. Therefore, based on the systematic understanding traditional preschool education during the development of ability quality assessment measure, according to the existing technical framework and project reflect theory to build the perfect evaluation system, and carried out in accordance with the function module partition, children's ability quality is estimated based on the Bayesian posterior expect quality assessment report, build rich information surrounding the core development to design more auxiliary functions. The final system test proved that the system interface screenshots and core codes not only standardized the development of children's ability and quality, but also improved the overall work efficiency of the kindergarten while realizing the system functions.
Visualization Analysis of Blockchain Technology in Education Arena Based on Citespace

Blockchain has brought tremendous value to all fields of today’s society, and it has also brought enormous challenges, which have attracted great attention from all walks of life. Blockchain also has emerged as an important concept at the interface of ICT and education. The article uses the information visualization software Citespace to study Blockchain in education in the Web of Science and CNKI database from 2016 to 2022, from macro to micro to the representative countries of the literature, keywords, and co-cited documents. Through visualization analysis, the article clarifies the key research directions, key documents, and hot spot frontiers in the field of blockchain in education research, forecasts the future development trends in this field, and compares the research situation at home and abroad, to provide readers and other researchers with certain reference and help.
The Application of OLAP and Web Technology in the Evaluation of Higher Educational Quality and the Design of Management System

In recent years, with the rapid progress of computer science and the rapid development of higher education, the level of informatization of education management has improved significantly. The education quality evaluation system is a comprehensive management of education quality and an important tool for evaluating teacher education quality. Therefore, this article aims to study the application of OLAP and web technology in college education quality evaluation and the design of management system. On the basis of analyzing the main characteristics of OLAP, OLAP multi-dimensional data analysis methods and the performance requirements of the education quality evaluation system, the education quality assessment system was designed, and finally the performance of the system was tested. The test results show that when the system is operated by 500 people concurrently, it still maintains a system response time of less than 1 s and a CPU response rate of 17.5%, which is normal.
Anonymized Data Assessment via Analysis of Variance: An Application to Higher Education Evaluation

The assessment of the utility of an anonymized data set can be operationalized by the determination of the amount of information loss. To investigate the possible degradation of the relationship between variables after anonymization, hence measuring the loss, we perform an a posteriori analysis of variance. Several anonymized scenarios are compared with the original data. Differential privacy is applied as data anonymization process. We assess data utility based on the agreement between the original data structure and the anonymized structures. Data quality and utility are quantified by standard metrics, characteristics of the groups obtained. In addition, we use analysis of variance to show how estimates change. For illustration, we apply this approach to Brazilian Higher Education data with focus on the main effects of interaction terms involving gender differentiation. The findings indicate that blindly using anonymized data for scientific purposes could potentially undermine the validity of the conclusions.
College English Blended Teaching Model Based on Mobile Learning Platform

The paper explores the construction of blended teaching model of college English based on the Super Star mobile learning platform. This study comprises six parts, introduction, related literature and studies, Super Star learning platform, methodology, experimental results and conclusion. Form the experiment, the conclusion has been reached that college English blended teaching model based on mobile learning platform not only can improve students’ English ability more effectively but can arouse students’ English learning interests.
ROSERS—A Deep Learning Framework for Earthquake Early Warning and Its Interpretation

Earthquake early warning (EEW) systems are swiftly evolving from standalone physics-inferred methods (requiring computationally expensive inversions) to data-driven strategies for efficient earthquake hazard mitigation in real time. Besides being speedy in prediction, data-driven approaches such as artificial neural networks usually require minimal assumptions in the training and execution processes. This study discusses and attempts to interpret the data-driven EEW framework: ROSERS (Real-Time On-Site Estimation of Response Spectra) proposed by Fayaz and Galasso (2022). ROSERS aims to utilize the early non-damaging p-waves and the recording-site characteristics to predict the acceleration response spectrum ( $$S_{a} \left( T \right)$$ ) of the anticipated on-site ground motion waveform. The framework's efficacy is analyzed using an extensive database of ground motions, and it is observed that ROSERS leads to exceptional prediction power when implemented in a real-time backdrop. To provide a better interpretation of the framework, this study utilizes the concepts of explainable artificial intelligence (i.e., Shapley additive explanation, SHAP) to obtain insights into the decision-making process of the trained neural networks. Particularly, the cause-effect relationship of the computed latent variables and $$S_{a} \left( T \right)$$ is explored. The analyses showcase that the two latent variables of the framework complement each other in capturing stiff short-period and flexible long-period $$S_{a} \left( T \right)$$ thereby leading to excellent reconstruction power.