Reviewerly/exHarmony huggingface dataset:
The Protection for Personal Information Based on IoT Network Management and Data Sharing in Big Data Era

A few decades ago, the protection of personal information was basically in the state of none, with more and more problems due to personal information, such as the use of information to achieve fraud and the use of false information to publish bad information, causing great property losses to people’s lives. People only began to have awareness of the protection of personal information. After this, the civil law protection of personal information in IoT management has been developed. In this paper, we present a comparative analysis of the application of data sharing and protection of personal information based on the Internet of Things (IoT) management, as well as the sharing mechanisms used in data information, the protection of information security, and the drawbacks, which explains the safety information analysis of personal information in the case of data sharing and the calculation method used by the IoT in data sharing. A comparative study found that on the basis of IoT management, the security and concealment of personal information have been improved by about 20%. In practical application, IoT also brings great convenience in information data sharing. It increases the efficiency of operation, reduces losses, and to a certain extent guarantees the security of people’s individual information.
Examining the Effects of Scholarships on Student Persistence: A Case of Open University

This article examined the effects of scholarships on academic persistence in distance learning environments. Student persistence is one of the most important measurements for both institutional management and student success amidst the changing educational landscape in higher education. It is a particularly important issue for open and distance universities where the student retention rates are much lower than in conventional face-to-face education. To this end, this study aimed to understand how the provision of scholarships may contribute to students' persistence, especially focusing on distance adult learners.&#x0D; Data was collected from a Korean public open university regarding students' academic and scholarship information who were enrolled in the first and second semesters in the year of 2019. Logistic regression models were applied to identify the relationship between scholarship types and students' persistence. The finding suggested that students who received need-based scholarships were more likely to continue in their studies than those who received merit-based scholarships. Additional analyses estimating the impact of specific types of scholarships on persistence highlighted the following: in comparing specific types of need-based scholarships, financial scholarship recipients compared to social scholarship recipients may increase the probability of persistence. In terms of different merit-based scholarship types, non-academic scholarship than academic scholarship may positively contribute to students' persistence, controlling for students' individual attributes. Suggestions on scholarship policy concerning open and distance universities as well as implications for future research were also discussed.
Exploring the Influential Factors of Teacher-student Relationship based on Random Forest Regression and Interpretation using SHAP

The purpose of this study is to explore factors affecting the teacher-student relationship of middle and high school first grade students. It was intended to provide information for improving the teacher-student relationship through the analysis results. This study performed random forest regression analysis using GEPS data. The subjects of this study were 5,586 middle school students and 5,348 high school students. The explanatory variables used in the analysis were 478 items in middle school and 499 items in high school. 24 major factors were derived using SHAP value. The result is as follows: First, the main factors that influenced teacher-student relationships in the first grade of middle school were ‘school satisfaction’, ‘friendship relationship’, ‘teacher’s passion’, ‘teacher’s teaching ability’, ‘teacher’s teaching method’, ‘vacation life’, and ‘parent attachment-trust’. Second, the main factors that influenced teacher-student relationships in the first grade of high school were ‘school satisfaction’, ‘friendship relationship’, ‘teacher’s passion’, ‘teacher’s teaching method’, ‘self-esteem’, and ‘parent attachment-trust’. School satisfaction(8 items), peer relationship(6 items), teacher passion(1 item) and parent attachment-trust(1 item) were the same influencing factors, but the contribution of each question was different depending on the school level. Schools need to approach improving teacher- student relationships considering the results of the study.
Development of Android Based Educational Games to Enhance Elementary School Student Interests in Learning Mathematics

This research is an android-based educational game development research to increase interest in learning mathematics for elementary school students. This study aims to make the development of educational games able to be a solution in increasing students' interest in learning Mathematics at the initial level, namely the elementary school level. The method used in this research is the Research and Development model ADDIE (Analyse, Design, Development, Implement, Evaluation). In this study, it was limited to the implementation stage which was tested through experimental research. This research is included in the type of experimental research. The experimental design used is a simple experiment in the form of Posttest Only Control Group Design. The conclusion that can be drawn from this research is that Android-based educational games are proven to increase the interest in learning mathematics for elementary school students.
Chatbot System for Mental Health in Bahasa Malaysia

Chatbot has been the driving force of modern communication for business, customer service and even mental healthcare. At the same time, there are not many research and project regarding mental health chatbots in Bahasa Malaysia. This project focuses on developing a chatbot application for mental healthcare in Bahasa Malaysia. This chatbot system is integrated with artificial intelligence and natural language processing. This chatbot utilize the feedforward neural network model to train the datasets. Apart from the backend of the application, Kivy and KivyMD are used to create the app's graphical user interface.
Quantum coherence and parameter estimation for mixed entangled coherent states

We study the temporal evolution of quantum coherence and parameter estimation for two-mode coherent-state superposition by using the master equation. Analytic expressions are provided and numerically displayed, which can illustrate the reliance of the coherence and Fisher information during the dynamics. Generally, we find that the amount of the quantum measures has the same behavior during the time variation, and displays a different order that depends on the different strength regimes of the input radiation fields and then disappears at infinite times. We show that these measures start from maximal value at the initial instant and decrease the time goes on. Moreover, we can observe that these measures can be protected resisted from the environment effect during the dynamics, showing an important feature of coherence and Fisher information. The obtained results can be assumed more practical in the characterization of the behavior of the coherence and Fisher information under the effect of the environment. Our observations can provide significant implications in harnessing these phenomena in quantum optics and information.
AlphaDDA: strategies for adjusting the playing strength of a fully trained AlphaZero system to a suitable human training partner

Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, the AI system surpasses the level of a strong human expert player in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. AlphaDDA learns and plays a game the same way as AlphaZero, but can change its skills. AlphaDDA estimates the value of the game state from only the board state using the DNN. AlphaDDA changes a parameter dominantly controlling its skills according to the estimated value. Consequently, AlphaDDA adjusts its skills according to a game state. AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello with other AI agents. Other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. AlphaDDA can weaken itself according to the estimated value. However, AlphaDDA beats the random player because AlphaDDA is stronger than a random player even if AlphaDDA weakens itself to the limit. The DDA ability of AlphaDDA is based on an accurate estimation of the value from the state of a game. We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system.
The Mediating Effects of Self-Efficacy and Career Adaptability on the Relationship between Emotional Intelligence and College Adaptation of Undergraduate Students

Objectives The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them.&#x0D; Methods The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them.&#x0D; Results First, the degree of suitability of the causal structural model for college students' emotional intelligence, self-efficacy, career adaptability, and college adaptation was all appropriate, it was confirmed that the proposed model satisfies the goodness-of-fit criterion. Second, it was found that college students' emotional intelligence, self-efficacy, and career adaptability had a positive effect on college adaptation. Third, it was analyzed that self-efficacy and career adaptability had significant mediating effects indirectly in the relationship between emotional intelligence of college students and college adaptation.&#x0D; Conclusions The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them.
A deep learning model to discern indoor from outdoor environments based on data recorded by a tri-axial digital magnetic sensor

The increased use of wearable devices (WDs) for monitoring daily-life activities has led to the development of different location-driven applications. The first fundamental distinction is between indoor and outdoor environments. The most intuitive approach is the analysis of GPS coordinates or Wi-Fi signals [1] but both solutions are power consuming. In this study, we proposed and tested the use of deep learning techniques for indoor/outdoor discrimination based on local magnetic field properties of the specific environment during free-living activities. Eight participants were recruited in four different centres (Turin, Italy; Sheffield, UK; Newcastle upon Tyne, UK; Tel Aviv, Israel) and were equipped with the INDIP system [2] (including four magneto-inertial units attached to each foot, lower back, and non-dominant wrist), a smartphone (running the Aeqora mobile application) and were monitored during 2.5-hours of daily free-living activities. Magnetometer data was used to train a deep learning model, while indoor/outdoor probability based on GPS coordinates was provided by the Aeqora app and used as a reference. For each WD, the following features were extracted: x, y, z components and norm of the magnetometer and the 10-sample moving average (0.1 s window) of the latter features as a “contextual” rating. A bi-layer long short-term memory structure with a linear layer as a tail and with a gaussian error linear unit as activation unit has been implemented [3]. To achieve a lower-bias training and a more robust model, the network has been validated by exploiting a leave one subject out validation approach. In addition, the classification is based on two different observation timeframes: windows of length equal to the magnetometer data acquisition period (0.01 s) and 1 s windows. The average accuracy of the model, across participants, in the classification of indoor/outdoor environments while using as input one WD at a time and all WDs together is reported in Table 1. Based on this preliminary analysis, the model seems suitable for discerning indoor from outdoor environments with an average accuracy score higher than 88.3% in participants spread through three countries (different morphology of the territory, culture, lifestyle, etc.). With this respect, considering a longer observation time (1 s vs. 0.01 s) has resulted in increasing of the accuracy for all conditions. Overall, the best performances were obtained by using the whole INDIP system, with a 94.1% score. However, very similar performances were obtained when only one WD is considered. The effect of the experimental setup (e.g., the number and position of the WDs) and the input of the model (e.g., the length of the time-observation window) on the performance metrics require further investigations.
Influence Mechanism of Educational Leadership on Environmental Accounting Based on Big Data Algorithm

Over the last 20 years, big data techniques in teaching have been overgrown. Making educational decisions now includes information knowledge as a crucial component. This started a trend for using big data algorithms strategically. Technological advances have been used to analyze the enormous amount of information and develop strategic judgments. The current study aims to address issues with the conventional instructional, administrative management solution focused on manual rule formulation in big data storage and interpretation and has poor efficiency in analyzing big data and lacking value in developing education leadership qualities. The study suggests an educational leadership model based on big data algorithm (ELM-BDA) to explore the student leadership performance that relies on cooperative filtration and fuzzy C-means (FCM) and big data. The different influencing mechanisms and factors directly linked to educational leadership were also analyzed using a big data algorithm. To build an intelligent institutional administrative system, the research also exposes it to organizational studies. By evaluating the big data research already in existence, this study emphasizes the expanding significance of big data. Additionally, this study explores the effects of big data analytics on educational leadership qualities by utilizing an FCM. A scoring system is designed to predict the student’s leadership level, and using the big data algorithms, the students are motivated and trained to improve their skills. The education and learning method can be enhanced at educational institutions through better decision-making to use this big data for leadership development. Big data facilitates efficient educational decision-making by merging various data and telecommunications technologies. Using big data in schooling will increase—leadership quality among students. To effectively use big data for decision-making, academic leaders must create new types of learning and monitoring systems.
Development and Testing of Combined Global Monitoring Systems for Moving Objects

The article describes a combined system for global monitoring of moving objects. General approaches to solving the problem of tracking moving objects located at any point on the Earth’s surface are considered. The shortcomings and limitations of existing systems are noted and ways to eliminate them are proposed through the use of a combined system that combines the globality of satellite systems with the advantages of Internet of things systems. A variant of the practical implementation of the combined global monitoring system and the results of its testing in real operation conditions in various regions of Russia are described.
The Application of Artificial Intelligence in the Dissemination of Local Culture in Hainan Province: Taking the Dissemination of Bai Yuchan’s Thoughts as an Example

Bai Yuchan’s Taoist thought is an important part of Taoist health-preserving thought. Excavating, sorting out, and translating Bai Yuchan’s Taoist thought will not only help increase cultural self-confidence and protect traditional culture but also become an important medium for foreign exchanges. With the advent of the digital age, artificial intelligence has helped the dissemination of excellent traditional Chinese culture with its unique technological advantages, improving the effectiveness, intensity, and breadth of cultural dissemination. In domain machine translation, whether domain terms can be correctly translated plays a decisive role in the translation quality. It is of practical significance to effectively integrate domain terms into neural machine translation models and improve the translation quality of domain terms. This paper proposes a method of incorporating new Bai Yuchan’s thought term information as prior knowledge into neural machine translation. Using the term dictionary constructed from Bai Yuchan’s thought bilingual terminology knowledge base as a medium, two different knowledge integration methods are proposed and compared: (1) term replacement, which means using the target term to replace the source term on the source language side, and (2) term addition, which means splicing the source term and the target term on the source language side and both the source language side and the target language side. Use identifiers as special external knowledge to identify the beginning and end of the target term. Based on the Chinese-English bilingual alignment corpus of New Bai Yuchan’s thoughts and the constructed Chinese-English alignment termbase, the experiments are carried out. The results show that on the test set, the BLEU value of the proposed method is 6.38 and 6.38 higher than the baseline experiments, respectively, which proves that the proposed method can effectively incorporate domain terminology knowledge into the translation model and improve the translation quality of domain terminology.
Complicated Background Suppression of ViSAR Image for Moving Target Shadow Detection

The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR.
Analysis of Effect of Six Sigma Method Combined with CI Strategy on Improving of Nursing Quality in Outpatient Infusion Rooms

The infusion room is the last part of an outpatient visit, with high patient density, large staff mobility, and a wide variety of conditions. In addition, most patients are accompanied by their families during infusion, and nursing staff in infusion rooms have to face more trivial and miscellaneous tasks than nursing staff in other treatment departments, which are more complex. The purpose of this research is to explore the impact of the Six Sigma method and CI strategy on the quality of nursing management in infusion rooms, so as to provide reference for clinical research.A total of 2142 patients treated in our outpatient infusion rooms from June 2019 to June 2020 was included into this retrospective analysis. Of these, 1105 patients admitted before 2020 received routine care management services and were considered as the control group. Another 1037 patients were admitted after 2020 and received the Six Sigma method combined with CI strategic care management and were considered as the research group. The incidence of adverse events during treatment was counted in both groups, and patients' compliance behavior and psychology were investigated. After treatment, patients' evaluation of the quality of nursing and their satisfaction with the nursing were investigated.The incidence of adverse events during infusion in the research group was dramatically lower than that in the control group, while the compliance behavior scores were higher (P < 0.05). In addition, SAS and SDS in the research group were lower than those in the control group, while the quality of nursing were higher (P < 0.05). It was also clear that the research group had 93.39% satisfaction with nursing, which was also higher than the control group (P < 0.05).Implementation of infusion room nursing management according to the Six Sigma method with CI strategic plan can avoid adverse events and improve infusion nursing satisfaction. It also helps reduce the incidence of dispute events.
SoftSkip

Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual scales are not completely eliminated but approximated with minimal additional computation. Experimental evaluation, using 3 benchmark REC datasets and an embedded device implementation, shows that LGMDP can achieve 33% latency savings, with an accuracy loss 0.5% - 2%.