Reviewerly/exHarmony huggingface dataset:
Comparative analysis of affective achievement between innovative high school and general high school in Gyeonggi province

Using the data for 8th of the GEPS, this study analyzed and compared affective achievement between innovative and general high school students. To compare high school students’ self-esteem, self-efficacy, school satisfaction and career planning between 2 types of high schools, this study utilized t-test and multiple regression analysis. The results of this study show that firstly, innovative high school students’ school satisfaction was higher than that of general high school students. On the other hand, in terms of self-efficiency, general high school showed more positive result than innovative high school. Second, in the analysis with multiple regression model, innovative high school students’ school satisfaction and self-respect were significantly higher than those of general high school students. Based on the above results, this study suggested that more rigorous research design is needed through follow-up studies and we need to develop more effective innovative school programs to improve students’ self-efficacy and career competency.
The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims

Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.
Analysis of Subject Education Professors and Subjects in the Department of Specialized Subject Education in Colleges of Education and Non-Education Colleges

중등 직업교육을 통해서 산업 및 기술의 발달에 따라 필요한 핵심 기능 및 기술 인력을 양성하기 위해서는 직업교육에 필요한 양질의 교육과정을 전문교과 담당 교사가 구성하고 학생들을 지도할 수 있는 교과교육 전문성이 확보되어야 한다. 이에 전문교과 중등 교사를 양성하는 사범대 및 비사범대의 전문교과 교육과들(이하 전문교과 교육과, 총 9개 학과)의 교과교육 전공 교수와 담당 과목 실태를 분석하는 데 이 연구의 목적이 있었다. 연구 결과 첫째, 전국 사범대 및 비사범대 전문교과 교육과의 교과내용학 전공 교수와 교과교육학 전공 교수의 비율은 7:3이었다. 학과별 교과교육 전공 교수는 대다수 학과에서 1명에 그치거나 아예 전무한 경우도 있어 부족한 편인 것으로 드러났다. 둘째, 예비 전문교과 교사가 이수해야 하는 전공 교육과정에 편성된 교과내용학 과목의 비율은 최소 80%에서 최고 91%인 반면, 교과교육학 과목의 비율은 최소 9%에서 최고 20%로 상대적으로 낮은 비율을 보였다. 셋째, 각 학과에서 운영 중인 교과교육학 과목들은 교원자격검정령 기준에 따라 최소 이수 기준 3과목(8학점 이상)에 맞게 교과교육 과목으로 지정 및 학점 인정이 되고 있었다. 이 과목들은 교과교육 전공 교수들이 강의하며 최소 이수 기준을 초과하는 과목은 교과내용학 학점으로 인정하는 학과도 있었다. 이러한 결과는 중등 직업교육을 담당하는 우수한 전문교과 교사를 양성하기 위해서는 교사양성과정 내에서 교과교육을 전공한 교수들에 의해 지도될 필요가 있음을 보여준다. 아울러 보다 다양한 교과교육 과목들이 추가될 필요가 있고, 동시에 교과교육 이수학점 기준을 상향하거나 기준을 초과한 교과교육 이수학점은 전공학점으로 인정될 필요가 있음을 시사한다. The purpose of this study was to analyze subject education professors and subjects in specialized subject education departments (9) at colleges of education and non-education colleges that train secondary school specialized subject teachers. The ratio of professors who majored in subject matter to professors who majored in subject education was 7:3 in the departments. Most departments had at least one professor who majored in subject education, but 2 departments did not have a single professor of subject education. In the curriculum completed by prospective teachers, the proportion of subjects related to subject matter ranged from a minimum of 80% to a maximum of 91%, and the proportion of subjects related to subject education was relatively low, ranging from a minimum of 9% to a maximum of 20%. In addition, the subjects were designated as subject education credits according to the minimum standard of 3 subjects (8 credits or more) based on the teacher qualification examination standards and taught by a professor who majored in subject education. The remainder is recognized as subject matter credits. These results show that in order to cultivate excellent specialized subject teachers in charge of secondary vocational education, the departments need to increase the number of professors who majored in subject education. Also, the subjects related to subject education need to be more diverse and taught by them through pre-service teacher education. In addition, there is a need to raise the standard for subject education credits or to establish clearer guidelines in which subject education credits exceeding the standard are recognized as major credits
A Survey of Sign Language Recognition

Sign Language is mainly used by deaf (hard hearing) and dumb people to exchange information between their own community and with other people. It is a language where people use their hand gestures to communicate as they can't speak or hear. The goal of sign language recognition (SLR) is to identify acquired hand motions and to continue until related hand gestures are translated into text and speech. Here, static and dynamic hand gestures for sign language can be distinguished. The human community values both types of recognition, even if static hand gesture recognition is easier than dynamic hand gesture recognition. By creating Deep Neural Network designs (Convolution Neural Network designs), where the model will learn to detect the hand motions images throughout an epoch, we are using Deep Learning Computer Vision to recognize the hand gestures. After the model successfully recognizes the motion, an English text file is created that can subsequently be translated to speech. The user can choose from a variety of translations for this paragraph. This application can be used without an internet connection and is entirely offline. With this model's improved efficiency, communication will be easier for the deaf (hard of hearing) and disabled people. We shall discuss the use of deep learning for sign language recognition in this paper. Key Words: sign language, convolutional neural network, computer vision.
READING STRATEGIES EMPLOYED BY HUE UNIVERSITY NON-ENGLISH MAJORED STUDENTS IN READING ENGLISH LEVEL B1 TEXTS

The first half of the 20th century has witnessed a change in language teaching and learning, especially in reading skill. It is argued that, “although for beginner readers, reading seems to be a linear, predictable and clear-cut process, various reading comprehension models have been developed to prove the complexities of reading process” [4, p.173]. However, though students have to read a large volume of academic texts in English, many of them entering university education are unprepared for the reading demands placed on them [5]. As a result, the focus in reading research was shifted from the product of reading to the process of reading, with particular attention to strategies that readers use to assist comprehension in various reading contexts. This paper aims to find out the reading strategies that non-English majored students often used in their reading of English level B1 texts. A questionnaire was used before and after the reading strategy instruction to collect data. The findings of this research indicate that students use a wide variety of strategies in their reading English texts after receiving the intervention. Accordingly, reading strategy instruction should be taken into greater considerations by both EFL teachers and students so as to purposely activate and strengthen these strategic reading tools for students’ life-long learning.
On Categorical Approach to Reaction Systems

Abstract In every matured theory, there is a need to investigate possible relationships between considered objects. To address this issue, it is natural to relate a category with given model of computing. Thanks to such approach, many properties are unified and simplified. In this paper, we investigate how category theory can be used to give a faithful semantics for reaction systems. In particular, we propose and discuss possible approaches to the problem of defining morphisms between reaction systems. We provide the definition of morphism that keep the behaviour of the original reaction system. Especially, some equivalences of reaction systems are reflected in terms of morphisms. For this purpose we expressed isomorphisms and sections in term of transition systems. Moreover, accelerating morphism defined in the last section give a new approach for including time in reaction system.
Desktop as a Service Using Local Computers

In today's fast-paced digital landscape, where businesses face growing demands to optimize productivity, enhance flexibility, and safeguard their data against ever-evolving threats, companies are constantly seeking innovative solutions. Desktop as a Service (DaaS) emerges as a transformative paradigm in this context, offering organizations virtualized desktop environments that address the core challenges faced by businesses. Whether it's the ability to seamlessly integrate with existing on- premise or cloud-based infrastructures, providing a secure environment through robust encryption and authentication protocols, or delivering an intuitive user experience that empowers employees to be more productive, DaaS ensures that businesses achieve optimal operational efficiency. This project aims to delve deeply into the comprehensive implementation and analysis of DaaS, examining its seamless integration into existing infrastructures, robust security protocols that help in mitigating cybersecurity risks, the user-centric design that ensures a smooth and delightful experience, and the overall positive impact it has on operational efficiency across various industries and use. Key Words: Cloud-Based Technologies, Infrastructure Optimization, Remote Access, Virtual Desktop Environments, Cost Optimization.
Study on the Phenomenon of Mandatory Typing Styles in Modern Chinese: Focusing on the Semantic Generation Approach

生成词库理论的核心思想是, 通过丰富词项的词语表征和语义生成机制来解释词在上下文中的意义调整。基于生成词库理论和轻动词假设, 本文对‘动宾结构’、‘主谓结构’、‘时间名词+方位词’结构以及‘名词谓语结构’进行了全面地考察, 发现这些结构当中存在事件强迫现象。类型强迫本质上是一种释义机制, 生成一个词项在具体上下文中的意思。事件强迫形式本质上是语义压缩结构, 事件强迫形式的理解或释义的过程是解压缩的过程。事件强迫形式可以从形式和语义两个方面来判定：形式上, 表现为某个位置既可以出现名词性成分, 也可以出现动词性成分；语义上, 表现为释义时可以添加一个谓词。事件强迫与轻动词相关, 语义模式中的谓词其实是一个意义比较虚的轻动词, 这个轻动词就是隐含谓词的公共语义因子。不同的轻动词对应不同类型的动词：可控事件和不可控事件。强迫是一种普遍的语言学机制, 在汉语和英语中都有广泛的应用。相比之下, 宾语强迫现象在汉语中相对较少, 汉语谓词更倾向于出现在句子层面。汉语是一种动词型语言而英语是一种名词型语言。也体现了两种语言在分析度上的差异：汉语是一种高分析性的语言, 倾向于每一个语义单位都以独立的词性来表达, 较少采用压缩形式；相形之下, 英语的综合性比较强, 倾向于采用压缩形式。因此严格来说, 汉语缺乏典型的事件强迫。
Survey On “Deepvision’s Human Eye Blink Pattern Analysis for Deepfake Detection”

Deepfake technology has rapidly evolved, enabling the creation of highly convincing synthetic media, including images, videos, and audio recordings. These maliciously manipulated digital artifacts can have severe consequences for privacy, security, and trust in digital content. Therefore, the development of effective deepfake detection methods is of paramount importance. This paper proposes a novel approach for deepfake detection based on the analysis of human eye blinking patterns. The human eye blink is a unique and subtle behavioral trait that is challenging to replicate accurately in deepfake videos. We leverage this inherent biometric feature to identify anomalies in video content and distinguish between genuine and synthetic videos. Key Words: Eye Blink to Speech, Machine Learning.
TRAVEL AND TOURISM MANAGEMENT SYSTEM USING CHATBOT RECOMMENDER

Chatbots have gained increasing importance for research and practice with a lot of applications available today including Amazon’s Alexa or Apple’s Siri. In this paper, we present the underlying methods and technologies behind a Chatbot for e-tourism that allows people textually communicate with the purpose of booking hotels, planning trips, and asking for interesting sights worth being visit. In particular, we show how model- based reasoning can be used for enhancing user experience during a chat, e.g., in cases where too many possible selections are available or where user preferences are too restricted causing inconsistencies and as a consequence not possible answers to be provided. Besides the underlying foundations, we provide a use case from the intended tourism domain to show how such a model-based chatbot effectively can be used in practice. Key Words: travel recommendation, time sensitivity, recency effect, personalization, social media
Social Listening – A Review and its Use for Customer Engagement &amp; Customer Retention by Organizations

In today's digital age, social media has emerged as a vital platform for businesses to connect with their customers. As of July 2023, there are 5.19 billion internet users worldwide, which amounted to 64.6 percent of the global population. Of this total, 4.88 billion, or 59.9 percent of the world's population, were social media users (Source : Statista ). Tech and policy think tank Esya Centre has found that daily engagement of Indians in terms of time spent is the highest for social media at 194 minutes a day. This number for OTT and Online Gaming stands at 44 minutes and 46 minutes, respectively. Thus it has become important for organizations to be available on these social media platforms to connect with customers. Presence of organizations on Social media platforms has helped companies connect with common people , end users. Social listening, the practice of monitoring and analyzing online conversations and discussions, has gained significant attention as a strategic tool for understanding customer sentiments and preferences. This research article presents a comprehensive review of the concept of social listening and its pivotal role in enhancing customer engagement and retention strategies. To provide a comprehensive perspective, the article also mentions about various organizations that have successfully integrated social listening into their customer-centric strategies. In sum, this research article sheds light on the role of social listening in customer engagement and retention by making use of the insights derived from social listening. Keywords: Social Listening, Social Media, Customer Engagement, Ethics
Development of Real-Time Emotion Recognition System Using Facial Expressions

This research presents a real-time emotion recognition system that combines human-friendly machine interaction with picture processing. For many years, facial detection has been available. Moving further, it is possible to simulate the emotions that people express on their faces and experience in their brains through the use of video, electric signals, or image forms. Since it is hard for computers to detect emotions from images or videos and a difficult task for the human eye, machine emotion detection requires a variety of image processing approaches for feature extraction. The approach proposed in this paper consists of two primary processes: facial expression recognition (FER) and face detection. The experimental investigation of facial emotion recognition is the main topic of this study. An emotion detection system's workflow consists of face detection, feature extraction, pre-processing, classification, and image acquisition. The emotion identification system uses the Haar cascade algorithm, an object detection algorithm, to recognize faces in an image or a real-time video, and the KNN Classifier for image classification in order to identify such emotions. Using the webcam to capture real-time photos, this system operates. The goal of this research is to develop an automatic facial expression recognition system that can recognize various emotions. Based on these studies, the system may be able to distinguish between a number of people who are fearful, furious, shocked, sad, or pleased, among other emotions.
Health Care Chatbot

Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. This paper presents Healthcare Chatbot using Artificial Intelligence that can make a human-system interaction to resolve basic queries regarding health parameters before consulting a doctor. The actual purpose behind this work is to work on the user's symptoms and to provide medical suggestions according to it, to reduce the time and cost required for the process. The chatbot works on provided input by the user, It takes sentence keywords and makes decisions to solve the user's query and answers it accordingly. Given these effectual benefits, it is not surprising that chatbots have rapidly evolved over the past 2 decades and integrated themselves into numerous fields, such as entertainment, travel, gaming, robotics, and security. Chatbots have been proven to be particularly applicable in various health care components that usually involve face-to-face interactions. With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes. A user can be able to recognize the actual disease by providing symptoms of it. As if a person will also know about the solutions or we can say precautions and remedies that they should take accordingly.
Steganographic Techniques for High-Capacity Covert Communication in Images and Videos

Steganography is the art of concealing information within other data to obscure the transmission process. Digital photos are the preferred carrier files due to their ubiquity online. Various steganography techniques exist for hiding sensitive data in video frames, each with distinct strengths and weaknesses. Some applications require total invisibility for critical information, while others can accommodate larger secret messages. Our project conceals messages within visuals, enabling users to select replacement bits instead of employing less secure LSB substitution. Data transmission, the process of sending data from source to destination, often involves slower mediums like coaxial cables and twisted pair wires, which can result in data leaks or losses. To address these issues, fiber optics are employed. Fiber optics transmited data as light beams, minimizing data leakage and incorporating security measures. In our proposed system, we enhance fiber optic encryption through the use of images. Key Words: Steganography, data concealment, digital photos, steganography techniques, data invisibility, LSB substitution, data transmission, coaxial cables, twisted pair wires, fiber optics, data security, image encryption.
App Development in Virtual Reality

Our project aims to create an immersive application in the realm of virtual reality (VR) entitled as [APP DEVELOPMENT IN VIRTUAL REALITY]. The main goal is to provide users with an engaging and interactive experience by utilizing VR technologies and design principles. By carefully developing the application using a chosen VR development platform/engine, we aspire to create a seamless and captivating virtual environment that responds to user interactions and offers a unique combination of entertainment and utility. In summary, our project seeks to push the boundaries of VR application development, promising an exciting and innovative contribution to the VR landscape that will enhance user experiences in this dynamic digital realm. The primary goal of this project is to create a virtual reality application that immerses users in a simulated environment, where they can have interactive experiences. By utilizing advanced VR technology, the objective is to design an app that provides users with captivating and realistic experiences. Through the use of motion controllers and head tracking, the app will enable users to interact naturally and intuitively, thus enhancing their sense of presence in the virtual world. Moreover, the app will incorporate a variety of sensory inputs, including visual, auditory, and haptic feedback, in order to create an experience that truly engages the user's senses. Keywords: virtual reality, frames per second, android application package
Secure Land Registration

Land Registration is very long and troublesome process in many parts of the world. There are various intermediate agents or agencies involved in this process. This paper takes a good look to the possibilities and the problems that will be solved using Block-chain based solution. The system is aEthereum based Blockchain system that stores all the transactions made during this Land- Ownership Transfer . We will use the concept of Contracts in Blockchain . In this Development various events such as User Registration , Uploading documents and fund transfer from buyer to seller will occur . The Proposed system solves the problem faced by all the three parties i.e. Buyer , seller and Government Officers. The system will also reduce the Frauds and the transactions will be Immutable due to blockchain. Keywords—component, formatting, style, styling, insert.
Privacy-preserving recommendation system based on user classification

Recommender systems have become ubiquitous in many application domains such as e-commerce and entertainment to recommend items that are interesting to the users. Collaborative Filtering is one of the most widely known techniques for implementing a recommender system, it models user–item interactions using data such as ratings to predict user preferences, which could potentially violate user privacy and expose sensitive data. Although there exist solutions for protecting user data in recommender systems, such as utilising cryptography, they are less practical due to computational overhead. In this paper, we propose RSUC, a privacy-preserving Recommender System based on User Classification. RSUC incorporates homomorphic encryption for better data confidentiality. To mitigate performance issues, RSUC classifies similar users in groups and computes the recommendation in a group while retaining privacy and accuracy. Furthermore, an optimised approach is applied to RSUC to further reduce communication and computational costs using data packing. Security analysis indicates that RSUC is secure under the semi-honest adversary model. Experimental results show that RSUC achieves 4× performance improvement over the standard approach and offers 54× better overall performance over the existing solution.