Reviewerly/exHarmony huggingface dataset:
Prediction Of New Students Using The Exponential Smoothing Method (Case Study: STMIK Kaputama)

With this prediction system for the number of new students, it functions to determine the priority of how many new students will be accepted in the following year. For each new teaching according to the factual and quite accurate new student data reports by implementing a computerized system, the data processing will be more precise and reduce errors in predicting it. This prediction will provide an overview based on the trend of the number of new students at STMIK KAPUTAMA. The method used is the exponential smoothing method by looking for how big the error is with different alpha values, namely 0.1 to 0.9. Each alpha tested will give different results. The purpose of the above calculation is to find out (a) alpha which produces the smallest forecast error. By taking data on the number of new students in the previous period, forecasting by determining the value of weight (a) alpha. The value of weight (a) alpha depends on the number of new students, where the nature of this forecasting determination of the value that is closest to the actual conditions. The forecasting results above are the closest to the overall number of new students alpha 0.9 is 1,207.9 and forecast error is 15.75.
A Credit Evaluation Structure for Voluntary Emission Reduction Mechanism Based on Blockchain Technology

Voluntary Emission Reduction Projects (VERs) play an important role in helping to achieve climate goals by reducing greenhouse gas emissions beyond what is required by law or regulation. The objective of these projects is to address climate change by mitigating greenhouse gas emissions in a cost-effective and efficient manner, while also promoting sustainable development. However, voluntary emission reduction projects, including China’s CCER (Certified Carbon Emission Reduction) program, have experienced the issue of double counting in relation to carbon credits. Double counting occurs when a carbon credit is counted more than once, meaning that the same reduction in emissions is being claimed by multiple parties. This can happen when a project generates a carbon credit that is sold to one buyer, but the project owner also claims the credit towards their own carbon reduction targets. To address the issue of double counting, this paper proposed a credit evaluation structure based on consortium blockchains to regulate the voluntary emission reduction market.
Report on China Smart Education Development Index 2022 (Composite Index)

AbstractIn order to gather consensus, evaluate progress, and effectively promote the vivid practice of smart education, CNAES have explored and constructed China Smart Education Development Index (CSEDI) and CSEDI for three sub-sectors (basic education, vocational education, and higher education), named as the “1+3” China Smart Education Development Index. The current chapter intend to present the results of the measurement, analyze the main achievements and deficiencies of China’s smart education development, and look forward to the important trend of China’s smart education development in the future.KeywordsSmart education in ChinaAssessment of smart educationSmart Education Development Index (SEDI)
Transformation and Practices of Smart Education in China

AbstractChina has been keeping its finger on the pulse of the times and its national realities, attaching great importance to the role of information technology in education reform. Through the digital transformation of education, China has been practicing smart education with an innovative approach. This Chapter reviews policies and practices China has adopted to promote education digitalization, and puts an emphasis on overall introduction to Smart Education of China platform.KeywordsSmart education in ChinaEducation digitalizationEducation policies of ChinaSmart Education of China platform
Report on China Smart Education Development Index 2022 (Higher Education)

AbstractIn order to gather consensus, evaluate progress, and effectively promote the vivid practice of smart education, CNAES have explored and constructed China Smart Education Development Index (CSEDI) and CSEDI for three sub-sectors (basic education, vocational education, and higher education). The current chapter intend to present the results of the measurement in higher education, analyze the main achievements and deficiencies of China’s smart higher education development, and look forward to the important trend of China’s smart higher education development in the future.KeywordSmart higher education in ChinaAssessment of smart higher educationSmart Education Development Index (SEDI)
Outlooks on Smart Education in China

AbstractSmart education in China faces challenges that are both global and unique. In this light, and based on extensive stakeholder consultation, this chapter presents issues that require attention and need to be addressed in the development of smart education. Toward the future development of smart education, the author suggest improving the strategic planning of smart education, facilitating reforms in the digital education model, building the smart lifelong learning system, carrying out data-empowered innovations in education, and deepening international communication and cooperation.KeywordsSmart education in ChinaProspects of smart educationEducation governanceTeaching and learningDigital education environmentData-driven innovation
Enhancing Academic Spoken English Skills Through an Immersive Virtual World: A Pilot Study Focusing on Motivation, Confidence, and Teacher Presence

In today's interconnected world, spoken English is a required skill on the academic level. Students are required to reach the B2 level of English according to the Common European Framework (CEFR) as a prerequisite for graduating and entrance to vocations. Students need to dedicate hours of practice in order to reach this level. To address this challenge, this study examines an attempt to teach advanced language skills using an innovative virtual world platform (Englehaven Island built on the OpenSim platform) to create an immersive, authentic and engaging setting. The purpose of this pilot study is to find out whether immersive and experiential learning, along with teacher presence, can enhance students' confidence and motivation to engage in English and ultimately improve their level of aural and oral skills. This study employs a mixed-methods design based on an end-of-course questionnaire encompassing both open and closed questions (N=41), comprehensive course data for all participants, pre and post-oral and aural assessment scores from an external assessment company (N=28), participant observation throughout the course (N=116) and seven in-depth interviews. Participants reported that their speaking skills (fluency, pronunciation, vocabulary, and grammar), as well as their listening skills, had improved significantly, and this was corroborated by the quantitative data. The findings reveal that participants demonstrated heightened motivation and confidence to speak English through an avatar in a gamified environment. This study suggests that language immersion facilitated by teacher presence and virtual world technology has tremendous potential for students, in particular for those who lack confidence in speaking. This innovative approach can serve as a transformative method to enhance academic speaking skills, providing a more enjoyable and effective learning experience. Its implications for language learning are discussed.
DEVELOPMENT OF SELF-DETERMINATION AS THE BASIS OF THE FUTURE TEACHER PROFESSIONAL SUCCESS CULTURE

In the conditions of modern realities, the profession of a teacher is characterized by high emotional costs, high responsibility for the effectiveness of subject-subject relations, the quality and responsibility of educational results. Performing professional tasks, the teacher most often does not think about his culture of professional success, its role in life, career. This key problem must be addressed purposefully and continuously in higher education. A program has been developed to activate the process of developing a culture of professional success for a future teacher, implemented for pedagogical areas. The theoretical part of the program introduces students to the concept of “culture of professional success” as a systemic concept, taking into account two significant phenomena. The phenomenon of “culture” is the basic personal basis in which the result of upbringing and education of students is concentrated, manifested in the harmony of the physical, mental, spiritual, moral development of each student. The concept of “professional success” is an important supra-professional (meta) quality that orients the subject to a high level of professional self-determination, striving for professional growth. The practical part of the program is based on the development of competencies aimed at self-determination and a culture of professional success. The experimental work showed a high level of readiness of the future teacher to develop a culture of professional success at the university, in the network interaction “mentor — student employer” under a number of conditions: creation of a holistic cultural and educational environment of the environment of success; taking into account the dialectic of the relationship between self-determination and the culture of professional success; development of a system of theoretical and practical knowledge; study of indicators of the culture of professional success; acquaintance with the issues of intensification of pedagogical work, a comprehensive assessment of the professional and functional state — as one of the components of the culture of professional success and self-determination.
Measurement of IT Security Governance Capabilities Using COBIT 2019 at Indonesian Business Sector

IT governance implementation in companies has occurred in the enterprise sector up to the BUMN scale. As stated in the Regulation of the Minister of Foreign Affairs Number 2 of 2013, Article 2(1) binds the business world. In the corporate sector, there are IT security issues. The measurement process uses the COBIT 2019 framework. The measurements taken will provide an analysis of the capacity level and gaps. Data was collected using quantitative (questionnaire) and qualitative (documentary studies and interviews) methods. The three relevant subdomains to measure are APO12 – Risk Management, APO13 – Managed Security, and DSS05 – Managed Security Services. The results of the capacity measurement show that the APO12 subdomain is at level 2, APO13 is at level 2, and DSS05 is stopped at level 2. These results indicate that there is a gap in the DSS05 subdomain. The results obtained show recommendations for improvement and level increase, especially in the DSS05 subdomain. The enterprise sector needs improvements in endpoint security policies, access policies, and event logs in IT incidents.
Detection of Time Series Anomalies Based on Data Mining and Neural Network Technologies

The article touches upon the problem of discovering subsequence anomalies in time series, which is currently in demand in a wide range of subject domains. We propose a new semi-supervised method to detect subsequence anomalies in time series. The method is based on the concepts of discord and snippet, which formalize, respectively, the concepts of anomalous and typical time series subsequences. The proposed method includes a neural network model that calculates the anomaly score of the input subsequence and an algorithm to automatically construct the model’s training set. The model is implemented as a Siamese neural network, where we employ a modification of ResNet as a subnet. To train the model, we proposed a modified contrast loss function. The training set is formed as a representative fragment of the time series from which discords, low-fraction snippets with their nearest neighbors, and outliers within each snippet are removed since they are interpreted as abnormal, atypical activity of the subject, and noise, respectively. Computational experiments over time series from various subject domains showed that the proposed model, compared with analogues, has on average the highest accuracy of anomaly detection with respect to the standard VUS-PR metric. The downside of the high accuracy of the method is the longer time spent on model training and anomaly detection compared to analogues. Nevertheless, in applications of intelligent building heating control, the method provides a speed sufficient to detect subsequence anomalies in real time.
Comparison of SVM and Naïve Bayes Algorithms with InNER enriched to Predict Hate Speech

Hate speech is one of the negative sides of social media abuse. Hate speech can be classified into insults, defamation, unpleasant acts, provoking, inciting, and spreading fake news (hoax). The purpose of this study is to compare the SVM and Naïve Bayes methods with feature extraction in the form of Indonesian NER (InNER) for detecting hate speech. To obtain the best model, this study applies five steps: a) data collection; b) data preprocessing; c) feature engineering; d) model development; and e) evaluating and comparing models. In this study, we have collected 7100 tweets as an initial dataset. After manual annotation, this study produced 1681 tweets: 548 insult tweets, 288 blasphemy tweets, 272 provocative tweets, and 573 neutral tweets. This study use two Python libraries that accommodate NER in Indonesian, namely the NLTK library and the Polyglot library. Based on the results of the evaluation of the proposed model, model 5, which develops the SVM algorithm with the NLTK library, is the best model proposed. This model shows an accuracy score of 92.88% with a precision of 0.93, a recall of 0.93, and an F-1 score of 0.92.
Framing a human rights approach to communication surveillance laws through the African human rights system in Nigeria, South Africa and Uganda

Today, in any society where crime is possible, communication surveillance is a necessary evil. This is because technologies now offer faster means of preventing crime while they are also capable of undermining the right to privacy. However, protecting privacy should not be mutually exclusive of ensuring public safety. This article argues that while communication surveillance may be permissible under narrow and limited circumstances, the laws made to regulate it in Nigeria, South Africa andUganda do not comply with international human rights standards. In demonstrating this, this article analyses the major laws in these countries alongside the various international human rights principles that must be complied with in framing a rights-respecting law on communication surveillance. The major contribution of this article is that communicationsurveillance laws can be designed in compliance with international human rights standards in the countries under focus. These include Nigeria, South Africa and Uganda carrying out specific legal reforms targeted at problematic laws on communication surveillance in order to bring them in line with international human rights standards. This can also be supportedby developing a more robust set of comprehensive guidelines through the African Commission and Human and Peoples’ Rights and ensuring that Nigeria, South Africa and Uganda embark on critical and strategic training for stakeholders involved in the enforcement and implementation of communication surveillance laws in these countries.
Application Prototype for Inclusive Literacy for People with Reading Disabilities

This article details the process of creating a prototype mobile application that aims to promote inclusive literacy for people with reading disabilities. The goal of this application is to help people with reading difficulties to become more independent so that they can participate in society and take advantage of educational and employment opportunities that were previously unavailable to them. The methodology used in this work is Design Thinking as it is a user-centered creative approach to solving difficult challenges and addresses creativity, design and problem solving. The results obtained from the expert judgment based on Atlas TI 22 provide a valuable perspective on the viability and potential of these technological tools. The analysis of the results of the application prototype designs gives an encouraging picture of 85%. Similarly, 75% confirm that the app effectively complements inclusive literacy efforts, a significant achievement in line with the objective, and 70% appreciate the app's interaction with people with reading disabilities. Finally, a staggering 87% would gladly recommend the app, underscoring its valuable impact. In conclusion, the article discusses how mobile applications can help people with reading difficulties become more literate. The good reception of the prototype confirms the importance of technology in inclusive education and the value of this approach to improving the lives and education of this demographic.
Comparative Study of Machine Learning Algorithms for Phishing Website Detection

Phishing, a prevalent online threat where attackers impersonate legitimate organizations to obtain sensitive information from victims, poses a significant cybersecurity challenge. Recent advancements in phishing detection, particularly machine learning-based methods, have shown promising results in countering these malicious attacks. In this study, we developed and compared seven machine learning models, namely Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Gradient Boosting, to assess their efficiency in detecting phishing domains. Employing the UCI phishing domains dataset as a benchmark, we rigorously evaluated the performance of these models. Our findings indicate that the Gradient Boosting-based model, in conjunction with the Random Forest, exhibits superior performance compared to the other techniques and aligns with existing solutions in the literature. Consequently, it emerges as the most accurate and effective approach for detecting phishing domains.
Feline Wolf Net: A Hybrid Lion-Grey Wolf Optimization Deep Learning Model for Ovarian Cancer Detection

Ovarian cancer is a major cause of mortality among gynecological malignancies, emphasizing the critical role of early detection in improving patient outcomes. This paper presents an automated computer-aided design system that combines deep learning techniques with an optimization mechanism for accurate ovarian cancer detection that utilizes pelvic CT images dataset. The key contribution of this work is the development of an optimized Bi-directional Long Short-Term Memory (Bi-LSTM) model which is introduced in the layers of CNN (Convolutional Neural Network), enhancing the learning process. Additionally, a feature selection method based on Lion with Grey Wolf Optimization (LGWO) is employed to enhance classifier efficiency and accuracy. The proposed approach classifies ovarian tumors as benign or malignant using the Bi-LSTM model, evaluated on the Ovarian Cancer University of Kaggle dataset. Results showcase the effectiveness of the method, achieving remarkable performance metrics, including 98% accuracy, 99.7% recall, 93% precision, and an impressive F1 score of 98%. The proposed method's efficiency is validated through comparison with validating data, demonstrating consistent and reliable results. The study's significance lies in its potential to provide an accurate and efficient solution for early ovarian cancer detection. By leveraging deep learning and optimization, the proposed method outperforms existing approaches, highlighting the promise of advanced computational techniques in improving healthcare outcomes. The findings contribute to the field of ovarian cancer detection, emphasizing the value of integrating cutting-edge technologies for effective medical diagnosis.
Segmentation of Motion Objects in Video Frames using Deep Learning

The segmentation of the moving objects in the video sequences is one of the most usable series in the machine vision field, which has absorbed the consideration of researchers in the latter decades. It is a challenging task, especially when there are several motion objects in the video, and then the system needs to discover the objects that should be segmented among the trail. Therefore, in this article, we present a new method to segment several motion objects at the same time. In this work, the propagation of the credence of the confidently-estimated frames by fine-tuning the DCNN model with the other frames is the main idea. We exert a DCNN model (which is pre-trained) for the frames to estimate the class of the object; then, we gather the frames where the approximation is locally or globally reliable. In the following, we apply a collection of the frames of CE as the training set to fine-tune the pre-trained network with the existing examples in a video. Our proposed model provides acceptable results, which are better than the results of similar models. These comparisons are made in the dataset of YouTube-VOS. Also, our presented approach is applied in the dataset of DAVIS-2017 and the obtained results are better than the results of the similar works.
Enhancing Breast Cancer Diagnosis using a Modified Elman Neural Network with Optimized Algorithm Integration

Breast cancer is a class of cancer that starts in the cells of the breast. It happens once the cells of the breast divide and amplify abnormally and uncontrollably. Other parts of the body, including lymph nodes, bones, lungs, and liver, can be affected by breast cancer. Early diagnosis and treatment are critical in helping to lessen the risk of death from breast cancer. Machine learning is a type of artificial intelligence that can be used to diagnose breast cancer. It uses algorithms to analyze data and assess patterns associated with breast cancer. Machine learning models can help improve diagnostic accuracy, reduce false-positive results, and improve the efficiency of diagnosis. Elman Neural Networks (ENNs) are machine learning algorithms that can be used to diagnose breast cancer. ENNs use medical data to detect patterns that are associated with the presence of cancer. The accuracy of ENNs in diagnosing breast cancer is still being researched, but they have the potential to help improve diagnostic accuracy and reduce false-positive results. In the existing study, a new modified version of ENN founded on a combination of an upgraded version of the imperialist competitive algorithm is proposed for this objective. Likewise, the results of the model compared with some other methods illustrated the proposed method's higher efficiency.
Student Preferences for Reference Services at a Remote Biological Station Library

abstract: During the 2020 and 2021 summer semesters, the University of Michigan Biological Station (UMBS) transitioned to hybrid classes that were primarily distance learning with two-week inperson sections. The library offered both synchronous and asynchronous reference assistance over the summer term. An analysis showed that students favored using the UMBS LibGuide over synchronous virtual reference help via Zoom. Students further preferred face-to-face interactions over virtual formats, and their preference for LibGuide assistance may carry into the post- COVID-19 classroom. This finding suggests that students prioritize convenience and immediacy over personalized assistance in the Zoom platform. Thus, in providing reference assistance to student populations in the field sciences, balancing face-to-face interactions with convenience and immediacy should be a priority. Recommendations based on the success of the 2020 and 2021 field seasons were suggested for reference interactions in future field courses.