Reviewerly/exHarmony huggingface dataset:
Topic Classification Based on Scientific Article Structure: A Case Study at Can Tho University Journal of Science

With a massive amount of stored articles, text-based topic classification plays a vital role in enhancing the document management efficiency of scientific journals. The articles can be found faster by filtering out the appropriate topic and speeding up to determine appropriate reviewers for the review phase. In addition, it can be beneficial to recommend related articles for the considered manuscript. However, fetching entire documents for the process can consume much time. Especially, Can Tho University Journal of Science (CTUJS) is a multidisciplinary journal with many topics. Therefore, it is necessary to evaluate various common structures in an article. Extracted sections can be short but efficient in determining the article's topic. In this study, we explore and analyze the paper structure of articles obtained from CTUJS for topic classification using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The results show that Random Forest outperforms Naïve Bayes and SVM regarding performance and training time. As shown, the topic classification performance based on the section of "Method" can reach 0.53 compared to the whole content of the paper with 0.61 in accuracy.
Optimization Strategies of Virtual Presence on Fashion User Behavior in Live Streaming E-Commerce

The fashion industry has undergone significant changes with the advent of live-streaming technology and virtual platforms. Live streaming of fashion events and live streaming of fashion products has become a popular mediums for attracting fashion enthusiasts, showcasing collections, and increasing consumer interest. Virtual presence allows fashion users to immerse themselves in real-time fashion experiences through live streaming platforms and has become a key focus for optimizing live fashion e-commerce. This study uses systematic literature analysis and participant observation research methods to explore the impact of virtual presence on fashion users and delve into optimization strategies of virtual presence on live fashion user behavior to enhance the shopping experience and ultimately increase conversion rates.
Deplatforming sex education on Meta: sex, power, and content moderation

Public health research has heralded social media as a space where sex education can be delivered to young people. However, sexual health organisations are increasingly concerned that restrictive content moderation practices impede their ability to distribute sex education content on social media platforms. To better understand these experiences, this article uses an autoethnographic case study of my experience navigating Meta's content moderation policies and practices when I promoted the Bits and Bods sex education web series. Using conjunctural analysis, I contextualise Bits and Bods's two experiences of content moderation (when our account was deleted from Instagram and advertising was rejected by Facebook) through policy analysis of Meta's content moderation policies. I then conclude by questioning whether public health practitioners should still be conceiving Meta's platforms as a space where they can deliver sex education to young people.
Exam Cheating Detection Based on Action Recognition Using Vision Transformer

Cheating is the use of prohibited actions to illegally gain in the process of taking tests and exams. These actions cause negative consequences, making learners less capable of learning and creating, leading to unqualified individuals in the social workforce. In this study, we propose an action recognition method based on LSTM, VGG-19, Faster R-CNN, Mask R-CNN, and Vision Transformer architectures to classify fraudulent actions such as copying or pulling up to copy other students' works, communicating, or transferring test material. Experimental results show that Vision Transformer identifies fraudulent actions with an accuracy of above 98%. This contributes to supporting teachers in managing candidates in exams, creating fairness and transparency in education.
Application of 3-gram to English Statements Posted at Q&amp;A Sites to Obtain Factor Scores

The widespread diffusion of the Internet has enabled us to communicate on Question and Answer (Q&A) sites. While there have been increasing numbers of registered users and posted Q&A statements, the issues of mismatches between the intentions of questioners and respondents are becoming conspicuous. In order to solve these problems, nine factors for Japanese Q&A statements were obtained through impression evaluation experiments. Syntactic information was then extracted through morphological analysis (MA) and employed as the feature values in performing multiple regression analysis to obtain factor scores for any Q&A statements. Factor scores can be calculated by utilizing N-gram applied to Part-of-Speech extracted through MA. Since most of the methodology so far have mainly depended on Japanese, the validity of the method applied to other languages must also be inspected. So far, in a similar fashion as in Japanese, the nine factors were experimentally obtained from English Q&A statements. The factor scores were then calculated utilizing feature values of English statements extracted through MA. It has also been confirmed that N-gram could be applicable to calculate factor scores instead of syntactic information using mere MA. Therefore, in this paper, 3-gram, as well as MA, was utilized as the feature values of English Q&A statements to obtain factor scores. Through multiple regression analysis, it has been shown that applying 3- gram would lead to as good accuracy as mere MA for all nine factors. Hence it could be concluded that 3-gram would also be applicable to the methodology when applied to English materials.
Bangla News Classification Employing Deep Learning

In this paper, we have introduced a deep learning model for the classification of Bangla news articles as it is an important task for maintaining and managing Bangla news articles. The proposed model combines a hybrid Recurrent Neural Network (RNN) comprising Bi-directional Long-Short Term Memory (LSTM) and Bi-directional Gated Recurrent Unit (GRU) algorithms to improve classification accuracy. We tested the performance of proposed classifier with traditional machine learning techniques e.g. naïve Bayes and decision tree induction. The experimental results indicate that the deep learning models achieved almost 90% accuracy. This study provides valuable insights into the performance of various machine learning and deep learning techniques for Bangla news classification and highlights the potential of deep learning for this task.
Is this an ad? How influencers disclose paid content after a change in the law

As of July 2022, the Spanish government explicitly requires influencers to disclose commercial content. However, there is no standardised model for this. This study, therefore, analyses how influencers disclose paid content and whether any significant changes came into being after the law came into force. For this purpose, a content analysis was conducted on 903 Instagram publications with brand mentions from 40 Spanish influencers. The results demonstrate that posts without any disclosure tag decreased by 25.12% following the law's implementation. Within this context, there is an observed 23.04% increase in the use of adwords (ADW) within the text and a slight increase (2.08%) in the exclusive use of Instagram's integrated paid partnership label (PPL). Despite previous studies having demonstrated that PPL is the most effective means of identifying persuasive communication, it is confirmed that its usage remains in the minority (7.19%). Likewise, most ADWs are located at the end of the caption (86.86%), remaining hidden at first sight for mobile users. Furthermore, it is observed that clear identification of commercial content by influencers is rewarded with higher organic engagement.
YOUNG ENGLISH LEARNERS’ ANXIETY: THE CASE IN INDONESIAN ELEMENTARY CLASSROOM

It is a common phenomenon that English language learners suffer from anxiety when they step into a language class, especially in the oral-focused lesson. This situation is experienced not only by adult English learners but also by the young ones who are learning in English-speaking classes at the elementary school level. Language anxiety they have is triggered by or originated from numerous factors, including the teacher’s identity factor. A teacher who is a ‘native’ speaker of the language is believed to gear greater anxiety among English as a foreign language learners resulted in passiveness and withdrawal from oral activities in the class. Referring to the circumstance, this study, was conducted to see the level of anxiety in English speaking classes which were taught by a native English speaker teacher (NEST) as well as to see the possible aspects evoking foreign language anxiety among young English learners. This present research, particularly, involved 69 active students of Tarakanita 1 elementary school in Jakarta. The research data was collected using a qualitative method with a set of questionnaires as a major data collection instrument, embracing three key indicators namely fear of communicating, fear of negative evaluations, and testing anxiety. To identify similarities and differences among students of differing anxiety levels, five categories: very low, low, moderate, high, and very high anxiety are applied. Quantitative analysis of the data revealed that the participants exhibit different levels of anxiousness in speaking English with a native English speaker. The highest percentage fell at high level category which was applied for the three aforementioned indicators. Pertaining to the factors influence might arouse anxiety in speaking class fear of communicating, contribute as much as the other two: fear of negative evaluations, and testing anxiety. The findings of this study are expected to benefit both teachers and students. It is hoped that students will develop more awareness towards factors, as anxiety, that may hinder them from mastering English-speaking skill and find solutions to overcome anxiety in speaking. Moreover, teachers are hoped to be more understanding and concerned with the anxiety young English learners experience and help them reduce their anxiety during the classroom performance through implementing various strategies.
A SURVEY OF FULL-TIME TEACHING INTERNSHIP FOR ENGLISH EDUCATION MAJORS AT LOCAL UNIVERSITIES: A CASE STUDY OF ZHAOQING UNIVERSITY

Teaching internship is a necessary part of teacher training. Zhaoqing University has jointly established Teacher Education Reform and Innovation Pilot Zones or Collaborative Education Cooperation Zones with seven counties. Some normal university students complete important pre-job training by interning at full-time teaching positions in these pilot zones. Through journal reviews, on-site visits, and focus group interviews, this study explores the problems encountered by English education majors during their full-time teaching internship in these zones and strategies to address them to provide references for the continuous improvement of full-time teaching internship. KEYWORDS: English education majors, full-time teaching internship, teacher education, promoting learning through competitions, Teacher Education Reform and Innovation Pilot Zones.
SDCANet: Enhancing Symptoms-Driven Disease Prediction with CNN-Attention Networks

Deep learning algorithms have revolutionized healthcare by improving patient outcomes, enhancing diagnostic accuracy, and advancing medical knowledge. In this paper, we propose an approach for symptom-based disease prediction based on understanding the intricate connections between symptoms and diseases by accurately representing symptom sets, considering the varying importance of individual symptoms. This framework enables precise and reliable disease prediction, transforming healthcare diagnosis and improving patient care. By incorporating advanced techniques such as a one-dimensional convolutional neural network (1DCNN) and attention mechanisms, our model captures the unique characteristics of each patient, facilitating personalized and accurate predictions. Our model outperforms baseline methods through comprehensive evaluation, demonstrating its effectiveness in disease prediction.
Effortless Attendance Recording with MyOnTime: A Modern Approach for Educational Institutions

This study introduces MyOnTime, an innovative application designed to revolutionize student attendance management at Sekolah Indonesia Kota Kinabalu (SIKK) in response to the evolving educational landscape in the aftermath of the COVID-19 pandemic. The primary objective of this research was to develop an efficient and user-friendly solution that enhances attendance recording, promotes administrative efficiency, and aligns with current health and environmental guidelines. To achieve this goal, we employed the Extreme Programming (XP) and harnessed the AppSheet platform, a low-code application development tool. MyOnTime offers a streamlined approach to attendance recording through barcode scanning, minimizing manual data entry and reducing reliance on paper-based methods. We conducted a comprehensive user survey to evaluate the application's functionality, responsiveness, and reliability. The results indicate overwhelmingly positive user feedback, highlighting MyOnTime's effectiveness and its ability to meet the specific needs of students, teachers, and administrative staff. MyOnTime empowers educational institutions by providing real-time attendance data, comprehensive reporting capabilities, and a user-friendly interface. It supports sustainable practices by reducing paper waste and contributes to a more organized and disciplined learning environment. MyOnTime represents a promising tool for enhancing the educational experience in the post-pandemic era, promoting efficiency, convenience, and environmental friendliness in attendance management.
Implementation QR-Code for Introducing Optical Instruments

The laboratory is one of the important facilities and supports the implementation of learning in tertiary institutions. However, in carrying out practicum activities, students are often constrained by information regarding names, functions, and steps for using instruments. Therefore, the need for information about laboratory equipment is quite important for students. This study aims to produce a website based on a QR code to provide information about the instrument to users and the steps to use it. This research uses research and development methods with the ADDIE model (Analysis, Design, Develop, Implementation, and Evaluation). The questionnaire is used to measure user responses regarding the QR code and its application. Based on the analysis of the problems found, the QR code was successfully created. This QR code was validated by media experts and instrument content experts and obtained valid results. Users show a positive response regarding the use of the QR code for introducing of Refraction Laboratory instruments. Thus, a website-based QR code can be used to provide optical instrument information to users. However, socialization about its use is very necessary for laboratory users.
Analysis of Teachers' and Students' Perception of the Use of Metaphors in Biology

This study aims to investigate teachers' and students' perceptions of the use of metaphor in learning biology including the effectiveness of and the interest in using metaphor in learning biology. The investigation builds on assumption that metaphor is a potential method to alter the way teachers and students respond to abstract and complex concepts. The data in this study was obtained qualitatively using the triangulation method involving Focus Group Discussion (FGD) with an expert group and individual interviews. The instrument utilized in this study is a discussion guide and an interview guide containing open questions which later was transcribed and analyzed. The respondents of this study are 30 biology teachers and 71 senior high school students from three districts in West Java, Indonesia. The result shows that teachers and students respond positively to the use of metaphors in learning biology concepts. In addition, teachers express that metaphor helps in delivering abstract and complex biology concepts as well as developing creativity in teaching. Meanwhile, students express that metaphor has the potential to help them understand biology concepts, increase interest in learning biology, and build relevance to the concept.
Analisis Control Access Dalam Strategi Pencegahan Tindakan Tawuran Di Jakarta International Stadium

This study aims to analyze the use of access control strategies as part of efforts to prevent acts of violencein Jakarta International Stadium. The study utilizes the CPTED (Crime Prevention ThroughEnvironmental Design) theory, with a focus on access control as a key component of the preventionstrategy. The research methodology involves analyzing the existing access control system in JakartaInternational Stadium, including evaluating entrances, parking areas, and the surrounding vicinity. Datais collected through direct observations, interviews with security personnel, and analysis of relevantdocuments. The findings of the study indicate that the implementation of access control strategies basedon the CPTED theory can contribute to the prevention of violence in Jakarta International Stadium.Effective access control measures, such as enhanced entrance monitoring, well-regulated traffic inparking areas, and adequate barrier installations, can reduce the potential for conflicts among spectators.In conclusion, the implementation of access control strategies based on the CPTED theory is an effectivemeasure in preventing acts of violence in stadiums.
Face manipulated deepfake generation and recognition approaches: a survey

With the progression of deep-learning techniques, digital media recording and synthesis media generation have become exceptionally easy. Due to open access of user-friendly deepfaking applications generated using Artificial Intelligence methods especially Generative Adversarial Networks (GANs) and Variational Auto-encoders (VAEs), synthesis of recorded media has been made more effortless. Digital media synthesized using such applications is termed as deepfake. Generation of realistic fake audio/video content poses critical threats to an individual and society at large. To curb the threat of deepfaking, numerous deepfake detection algorithms have been proposed. This paper presents a survey on state-of-the-art deepfake generation techniques, categorized into face swap, attribute manipulation, and lip-sync manipulation. An analysis of some recently developed techniques that can generate images from text is also presented in the proposed paper. In addition, state-of-art image and video datasets released in the domain of deepfake detection are also discussed. The analysis provided in this survey reveals that every new deepfake generation technique calls for the development of novel deepfake detection techniques. Among deepfake detection techniques proposed so far, MobileNet-CNN model, multi-scale temporal CNN model, and GAN-based technique outperform good for face swap, lip-syncing, and attribute manipulation detection with an accuracy of 99.28%, 97.1% ,and 100%, respectively. This survey would help researchers to understand the literature on deepfake generation and detection which is required for future development in this field.
The Research on the Role of Abstract Culture in the Composition of Chinese Virtual Streamer Fans Culture --Taking A-SOUL as an Example

The rapid development of the virtual streamer industry in China in recent years has caused extensive discussions in Chinese Mainstream media. As a corporate virtual streamer group jointly launched by two entertainment giants called ByteDance and YH ENTERTAINMENT, A-SOUL has received widespread concern from many fields in Chinese society. This paper starts from the perspective of Social Identity theory and analyzes the frequent words on the bullet-screen sent by A-SOUL fans in live-streamings as well as the popular comments of A-SOUL related videos, which in order to explore the role of the emerging online language abstract words and the abstract culture behind them in the fan culture of Chinese virtual steamer. The study found that A-SOULs fans has used abstract words to increase exposure for the virtual streamer group, to vent their emotions and to satisfy their own sense of in-group identity. Abstract culture plays an important role in the cultural composition of virtual streamer fans, which has practical significance for the study of Chinese Internet subcultures and emerging online language trends.
An Investigation of a Touch-Based Eye-Tracking System with Smart Feedback and Its Influences on Learning - Simulation of Covid-19 Rapid Test System

This study investigates the impact of a touch-based simulation system (TBSS) with smart feedback on learners' eye movements, behaviors, and performance in Covid-19 rapid testing. The participants included sixty graduate students divided into experimental (TBSS with smart feedback) and control (TBSS with basic feedback) groups. Results revealed that smart feedback improved learning outcomes, leading to higher performance and more efficient task completion compared to the control group. Participants showed positive attitudes toward the usefulness and ease of use of the Covid-19 simulation system, expressing high satisfaction with its functionality. The smart feedback mechanism was found to facilitate efficient decision-making by guiding attention and providing immediate, detailed feedback. This research provides valuable insights into the influence of feedback mechanisms on user behavior and performance in simulation systems, particularly in the context of Covid-19 training and testing procedures.
Big Data Tools, Deep Learning &amp; 3D Objects in the Metaverse

Metaverse is a virtual space to explore, for those who are not physically available in context. We are in the early stages of the development of the metaverse and it promises a rapid phase shift as well as proliferation in every aspect of technology. The metaverse comprises of eight key technologies, such as Extended Reality, Customer (user) Interactivity, Artificial intelligence, Block Chain Technology, Computer Vision, Internet of Things, Robotics, Fog/ Edge/ Cloud Computing, additionally, 5G/ 6G (Future) Mobile networks that are relevant to the metaverse. Big data is a significant source that complements each of these techniques. In this paper, we will discuss how different big data techniques help with the growth of the metaverse. The comparison of 3rd Dimensional objects in terms of Spark architecture with HDFS is carried out. We utilized large-scale 3D data to implement and evaluate various machine learning and deep learning models, facilitating a thorough comparison of their performance. Both of the Models have their own advantages and drawbacks, Random Forest model stands out in terms of processing speed, though SimpleNet requires more time for the validation accuracy improvement.
Sentiment Analysis of Twitter Comments Using Naive Bayes Classifier

Social media has a significant role in how people express their emotions and elaborate on their opinions in today's culture. There are many new forms of social media, and Twitter is one of them. In this experiment, the sentiment of pre-processed Twitter comment data was examined using naive Bayes and logistic regression techniques. In order to categorise the emotional tendency of text for Twitter comments, a naive Bayesian classifier is created. In processing this material, the Naive Bayes and logistic regression models' benefits and drawbacks are compared and summarised. Naive Bayes can achieve good accuracy with binary emotion analysis. The accuracy of the naive Bayes model is 0.06 points higher than that of logic training under identical processing settings, and the recall rate is 0.05 points higher.