Reviewerly/exHarmony huggingface dataset:
A Study on Korean Second Language Education for Marriage Immigrants: Focusing on Heo Hwang-ok's story

The purpose of this paper is to present the story of Heo Hwang-ok, a character in the Gaya Founding Mythology, recorded in the History of the Three Kingdoms, to the Korean Second language education for marriage immigrants. To this end, we first examine the current status of old stories covered in Korean textbooks for marriage immigrants and present features that can be used through Heo Hwang-ok's story in Korean Second language education for marriage immigrants. In Korean language education, old stories have been used as a way to learn Korean values and lifestyles or to compare them with old stories in the learner's motherland. Accordingly, this paper presents the story of Heo Hwang-ok, a character in the Gaya founding myth of the Three Kingdoms recorded in the History of the Three Kingdoms, as a material for Korean Second language education for marriage immigrants. There are a total of three materials presented in this paper: narrative cultural elements, character cultural elements, and usable content materials. The narrative cultural element is about the cultural element that existed in ancient Korea and is about the faith of ancient Korea. The character culture elements are about the characteristics of Heo Hwang-ok and King Suro, the main characters, and are about cultural phenomena dealt with in modern Korea through their characteristics. Content materials are content projects conducted by Gimhae-si and Internet materials that can be used for learning. Based on the data presented later, the educational plan is presented in a general manner.
A Qualitative Study on Teachers' Perception of Changes in Education Policy and the Activation of Youth Volunteer Activities

This study attempted to explore ways to revitalize youth volunteer activities for middle and high school teachers who operate volunteer activities in order to revitalize youth volunteer activities that are shrinking due to changes in education policy. Six teachers with experience in volunteer activity in middle and high schools were selected and a focus group interview was conducted on September 21, 2022. A semi-structured questionnaire derived through discussion among the researchers was used. From the analysis results, three major themes, nine sub-topics, and nine semantic units were derived. First, 'teachers' thoughts on system change' revealed concerns and regrets about the reduction and contraction of volunteer activity, the loss of educational opportunities and difficulty in incorporating new systems, changes in the quality of volunteer work and the possibility of incorporation into the curriculum. Second, 'the realistic challenges of volunteer activity' suggested the lack of specificity and capacity to realize the curriculum, the lack of teacher responsibility and recording methods for realizing small group volunteer activities, and the reality and feasibility of establishing educational plans. Third, 'the proposal for the operation of volunteer activities' suggested the introduction of volunteer subjects to have learning effects and internal stability and linkage the free semester system, linking with community-related organizations and experts for a greater leaps, and easing restrictions to recognize meaningful volunteer activities. Based on these findings, the development of volunteer subjects, preparation of specific measures to promote volunteer activities in clubs and career activities, meetings with community-related institutions and teachers, and the need for a qualitative approach to volunteer activities in terms of finding social problems and social participation were discussed.
Development and Development Strategies of Bilingual Talent Training Curriculum for 'Korean + English' : Focusing on the Korean language department in Heilongjiang Province, China

As an important component of the global economy, China inherits the consciousness and responsibility of a large country and actively develops economic exchanges and cooperation with the surrounding countries and other countries in the world in line with the trend of economic globalization. In recent years, as economic globalization intensifies, political, economic and cultural exchanges between countries around the world are getting closer, and China is implementing policies of One Belt, One Road and Heilongjiang Economic Belt in response to the trend of global development. The Heilongjiang region is located in an important position on the “One Belt, One Road” and the “Heilongjiang Economic Belt” line, so exchanges with the Korean Peninsula are frequent due to its superior geographical location, and Korean has become an important tool for communication and exchange between the two countries. Therefore, the establishment of a foreign language curriculum among “Korean + English” has become an important part of fostering talent in the Heilongjiang region in the Korean language department belonging to Heilongjiang Province. This paper aims to introduce the background of the development of existing 'Korean + English' dual foreign languages in the Korean language department of Heilongjiang University, analyze the status of 'Korean + English' dual foreign language talent training, and present a series of innovative countermeasures to existing problems. This is meaningful in that the Korean language department can present a positive and meaningful argument for establishing a dual foreign language talent.
Threat detection based on multi-scale spatiotemporal feature fusion of user behavior

With the great changes brought about by digitization, traditional security threat detection capabilities have been greatly challenged. Traditional threat detection technologies are based on signatures, rules and manual analysis, and there are serious lags and blind spots in security visibility. Unknown attacks cannot be detected and are easily bypassed. Multi-scale user behavior fusion analysis uses artificial intelligence methods and spatiotemporal feature engineering to associate multisource heterogeneous user behavior feature data to realize threat detection of multi-modal and multi-scale data.
An intelligent optimization algorithm of steel sheet pile foundation pit concrete thin seal technology

In recent years, with the rapid development of foundation pit engineering, many technically complex foundation pit engineering projects have emerged. The scale and depth of foundation pit excavation are getting bigger and bigger, and the requirements are getting stricter. The safety and stability of the foundation pit will determine the success or failure of the foundation pit project, and it is very important for the smooth development of the entire foundation pit project. The purpose of this paper is to study an intelligent optimization algorithm of thin-seal concrete layer technology for sheet pile foundation pits. Taking the support project as an example, the steel sheet pile support is determined as the foundation pit support scheme. The finite element software is used for simulation analysis, and the construction conditions are simulated for the scheme at the same time, the construction scheme is formulated, and the simulation results are compared and analyzed with the on-site monitoring results. The results show that when the construction of the sealing layer concrete is completed, the displacement monitoring value of the steel sheet pile is not much different from the simulated value, and does not exceed the early warning value, which is within the error range.
Principles of Customization for Models of Complexly Organized Intelligent Systems

The article presents classification and formats developed by the author for descriptions of operations for expanding and detailing formal models of intelligent systems. Operations are proposed that allow automating the processes of creating such models adapted to the properties of subject areas. Model descriptions are based on the formalization of universal parameters and properties associated with intelligent systems. The basis for building various models is the universal model. It presents the concepts and properties of knowledge representation formalisms, descriptions formats of knowledge flows and processes and control elements of intelligent systems. When creating a universal model, the principles of hierarchy and plurality of descriptions are taken into account. This provides a high initial level of the de­sign process based on generalizing unified designs of the universal model. Descriptions of additional common model elements adapt the initial universal model. Such an approach allows overcoming the problem of multiple properties of intelligent systems, reflecting ideas about them that formed in different fields of knowledge. Transformations of the universal model into a model with given properties are implemented as transformations of formalized descriptions of parameters and properties. Transformations adjust the universal model, from which general and applied models of intelligent systems are constructed. Model execution algorithms allow you to create specific intelligent systems. Such systems exist as software implementations of algorithms in the digital spaces of these areas.
The landscape of cyber security education and training programmes: The case of England (United Kingdom)

This chapter looks at the provision of cyber security education and training programmes in England (United Kingdom). It zooms in on the characteristics of the education and training programmes leading to entry-level jobs in the cyber security field, the current profile of learners, and their education and labour market outcomes. Particular attention is paid to strategies and initiatives to diversify enrolment, especially among young people and adults from disadvantaged backgrounds, boost employers’ participation in the design and delivery of cyber security learning opportunities, and ensure the quality of cyber security training.
An analysis of informal language learning using topic modeling: A case study of the Korean learning community of Reddit. Foreign Languages Education

The forms and demands of language learning are changing in the pandemic era. Learners no longer rely solely on a formal language curriculum. Instead, they are using various informal language learning (ILL) channels. Although informal language learning has been in the spotlight and is growing, more research on ILL is needed. In this study, ILL taking place through an online community was analyzed. Articles from the Korean learning community (r/Korean in Reddit) were collected, and the topic modeling technique, Latent Dirichlet Allocation, was conducted on the collected data. As a result, seven major topics were selected. The most common topics in all posts were issues faced by beginner learners, followed by vocabulary and sentence meanings, interest in Chinese characters and applications of Korean language skills, culture and daily life, translation, online learning materials, and Korean phonics. Through this, the interests of ILL learners and the characteristics of learners could be identified. Due to the nature of ILL, in which a formal curriculum does not exist, it was found that questions about general strategies for learning and questions that could not be solved in formal language education were most prominent. In addition, the characteristics of ILL learners who actively sought learning content and materials were also found.
The Effect of Chi Square Feature Selection on the Naïve Bayes Algorithm on the Analysis of Indonesian Society's Sentiment About Face-to-Face Learning During the Covid-19 Pandemic

Criticisms and comments submitted to the Indonesian people regarding government policies regarding face-to-face learning during the Covid-19 pandemic have pros and cons. Not a few parents are worried about this policy because parents are still afraid of the spread of the new cluster of Covid-19 in Indonesia which is growing while on the other hand many people who think this is good to apply considering that online learning is considered less effective because many students are difficult to receive material delivered by teachers online and many students who do not have adequate tools. The number of opinions written on Twitter requires classifying according to the sentiments they must get the tendency of these opinions whether they tend to have neutral opinions, positive or negative. Sentiment analysis in this study was conducted using the Naïve Bayes method and chi square's characteristic selection in conducting classification. The results of the analysis of the Naïve Bayes method with chi square characteristic selection have an accuracy of 93% and without chi square characteristic selection has an accuracy of 92%, so it can be concluded the Naïve Bayes Method with chi square characteristic selection has a better accuracy compared without using chi square characteristic selection.
Executive summary

New technologies, such as Cloud Computing, Artificial Intelligence (AI) or the Internet of Things (IoT), offer great opportunities for societies to enjoy the benefits of an increasingly interconnected world. However, the use of digital technologies also implies greater exposure to the risks of cyber attacks. The number of cyber attacks is outstripping defence capabilities, and one reason for this is the lack of an adequately skilled cyber security workforce. Enterprises around the world are facing shortages of skilled professionals in this area, limiting their ability to contain threats.
EYE DISEASES CLASSIFICATION USING DEEP LEARNING

This paper proposes a deep learning-based approach for the automatic classification of eye diseases.The eyes are one of the most important organs in the human body, responsible for vision and many critical functions.Therefore, it is essential to diagnose and treat eye diseases promptly to prevent vision loss and related complications.However, traditional methods of eye disease detection are time-consuming, costly, and often require trained professionals, making them inaccessible to many people.The proposed approach utilizes deep learning techniques to classify eye diseases accurately and efficiently.Specifically, the deep learning model is trained on a large dataset of images of healthy and diseased eyes to learn features and patterns that distinguish different eye diseases.The trained model can then classify new images of eyes into one of the pre-defined categories of diseases, allowing for timely and accurate diagnosis.The importance of eye disease detection cannot be overstated, as it can prevent or reduce the risk of vision loss and related complications.Early detection can also lead to more effective treatment and management of eye diseases.Additionally, color blindness is a common condition that affects many people worldwide.Therefore, the proposed approach includes a color blindness test as part of the eye disease classification system to identify individuals with this condition.Overall, the proposed deep learning-based approach has the potential to revolutionize eye disease detection and improve the quality of eye care globally.
Towards tiny object tracking for low-illumination wide-field

Night is an inevitable scene for surveillance video. Due to the high image resolution, complex background, uneven illumination, and similarity between the target and the background of hawk-eye surveillance video, it is difficult for previous trackers to apply the tracking of a tiny object in such scenes. In this regard, this paper proposes to combine an online automatically and adaptively learning spatio-temporal regularized tracking algorithm with an efficient and effective low-light image enhancement algorithm to improve tracker performance. We constructed a new benchmark that includes 41 night surveillance sequences captured by Hawk-Eye cameras at night. Exhausted experiments have been conducted on this dataset, and the results show that by combining the two methods, the original algorithm can obtain better results in this dataset, and can meet the real-time object tracking, which contributes to the application of tiny object tracking in eagle-eye surveillance video at night.
A novel secret sharing scheme based on Shamir’s secret sharing scheme over field extensions

In 1979, secret sharing came into picture for the first time by Shamir [11] and Blakley [3]. To keep passwords, important formulas, etc. as secret, this technique was introduced. The key idea behind it is that a dealer distributes a secret among n shares and t from n can come together and recreate the secret. This approach has lots of applications in secure multiparty communication, key distribution, oblivious transfer,visual cryptography, etc. Here we mention a novel approach based on finite field extension to construct a new secret sharing technique using Shamir’s method as a base. In this scheme, a secret is an element of field extension, and few specified subsets of participants, called an access structure, recover the secret using the Lagrange interpolation. The recovery of a secret can be done by some uniquely determined elements of field extension. It is not easy to break a secret. This proves that the proposed scheme has very reliable and strong access structure collection of authorized subsets.
Automated Collection of Redirect Chains from Search Results Pages to Prize Scam Pages

Currently, there is an increasing number of prize scams in which users are tricked into entering their personal information to claim that they have won a smartphone or other device. In addition, the scams are not only led by email or SMS but also by Google searches in some cases. Users who access a linked page (Entrance page) from a search result are redirected to a scam page through several intermediate pages. In SCIS2021 and SCIS2022, we collected and analyzed redirection chain logs to reveal the reality of such a prize scam. First, in SCIS2021, we surveyed to gather records of redirect chains. However, we could conduct only 99 small surveys due to manual collection. Next, in SCIS2022, we obtained 496 redirect chain logs through an automatic display of redirect chain logs but could not implement automatic log determination. Therefore, this paper proposes a tool to automatically collect and determine the records of redirection chains using Power Automate Desktop. Furthermore, we will disclose the scam methods and sound a warning by highlighting such scam campaigns.
Compact Copyright: Quick Answers to Common Questions

"Compact Copyright: Quick Answers to Common Questions." Journal of Electronic Resources Librarianship, 35(1), pp. 80–81
Knowledge Graph Fusion for Language Model Fine-Tuning

Language Models such as BERT (Bidirectional Encoder Representations from Transformers) have grown in popularity due to their ability to be pre-trained and perform robustly on a wide range of Natural Language Processing tasks. Often seen as an evolution over traditional word embedding techniques, they can produce semantic representations of text, useful for tasks such as semantic similarity. However, state-of-the-art models often have high computational requirements and lack global context or domain knowledge which is required for complete language understanding. To address these limitations, we investigate the benefits of knowledge incorporation into the fine-tuning stages of BERT. An existing K-BERT model, which enriches sentences with triplets from a Knowledge Graph, is adapted for the English language and extended to inject contextually relevant information into sentences. As a side-effect, changes made to K-BERT for accommodating the English language also extend to other word-based languages. Experiments conducted indicate that injected knowledge introduces noise. We see statistically significant improvements for knowledge-driven tasks when this noise is minimised. We show evidence that, given the appropriate task, modest injection with relevant, high-quality knowledge is most performant.
Accuracy Improvement of Complex Sensor-based Activity Recognition Using Hybrid CNN

Applying smartphones and smartwatches for Human ActivitiesRecognition (HAR) is beneficial for analyzing the participant's dailybehavior and identifying mishaps, such as fall detection, enabling early healthcare monitoring and fall detection. Numerous HAR studies have investigated different recognition models based on sensor data. Nonetheless, this research mainly focused on basic motions such as sitting, standing and walking. In actual settings, human actions are often accomplished in complex ways. This research will examine the recognition of complicated activities using CNN and LSTM networks as deep learning classifiers. Furthermore, this study presented hybrid CNN for enhancing the identification of complicated activities. We evaluated these deep learning classifiers using WISDM-HARB, a publically available benchmark HAR. This dataset used accelerometer and gyroscope data from smartphones and smartwatches to collect sensor data of complicated activities. The experiment demonstrates that all deep learning classifiers recognize basic actions more accurately than complicated ones. Also, it was observed that the hybrid CNN classifier presented had the most fantastic accuracy with 96.25%. According to the research, these classifiers are effective at recognizing basic activities, but their performance decreases as the sophistication of the actions increases. Nonetheless, hybrid CNN models are competent for handling complicated tasks.
Lightweight Automatic Business Card Generator Utilizing the Swift Language

Business cards have always been a traditional item for any professional’s repertoire; it allows the receiver to get a basic idea of who the sender is and what organization they belong to. However, this requires said professionals to design and create their cards if they are not affiliated with any business entity. This process can take up a large sum of time and skills that may not be in the scope of the person in question. This paper aims to develop an application that automatically generates a usable digital business card simulated in a three-dimensional plane. The application will also act as an exploration of the Swift Language, developed by Apple Inc. Swift and its frameworks are relatively new to the programming scene; therefore, this paper will test their capabilities and potential to be implemented in other firms like other programming languages.
YOLO KeepSafe: Utilization of YOLOv4-Based Algorithm to Employ a Real-time People Detection and Social Distance Monitoring System

This paper outlines the process used to create the social distance detection system - YOLO KeepSafe. The researchers discuss several calibration procedures, investigate the difficulties that arise when used in real-time situations, and offer potential solutions.
A Self-Augmentation Transfer Learning Network for Image Deraining

This paper proposes a self-augmentation transfer learning network, a new framework for removing rain from images that includes autoencoder, improve block, and transfer learning as key components. The first step is to remove rain using both non-block-based autoencoder and block-based autoencoder. Following that is the improve block step, which was designed to improve the efficiency of rain removal. The image will then be analyzed to identify rain streaks for additional training in the transfer learning autoencoder step. The results will then be run through the improve block again. In the experiment, we used PSNR and SSIM to measure the efficacy of rain removal using 150 test images. On average, our method yielded a PSNR of 32.43 and a SSIM of 0.935.