Reviewerly/exHarmony huggingface dataset:
Embracing the Challenge to Become a Teacher in the Digital Age

Isolated within the communist Eastern Block until 1989, Romania has been experiencing numerous educational reforms, initiated by the National Ministry of Education and generally aligned with the European Union goals through the Bologna Treaty. The socioeconomic macrosystem is an important source of influence and we are looking at its main directions. Scholarship on the topic of teaching and learning in Romania is emergent but Computer-Assisted-Teaching (CAT) education in Romania remains insufficiently explored, despite the ever-growing need. This chapter analyzes the state of CAT formative programs for the preservice and the in-service teachers at Lucian Blaga University in Sibiu, Romania. We are now using the lessons learned from the Covid-19 crisis to reflect on new needs for digital competencies, Professional Development (PD), and proposed systemic changes. Such professional training is offered first during the university years, during the BA and MA studies. At the university level, the teacher training curriculum includes CAT, while for the in-service teachers, the PD continues through a combination of self-pursued and mandatory teacher training sessions offered by the regional school districts, universities, and professional organizations. CAT PD is not usually the focus of teacher preparation degree programs; the computer-assisted teaching course during teachers' training program offers a general, introductory perspective on using technology in teaching. For in-service teachers, most specialized PD happens independently and/or through self-teaching. The Covid-19 crisis created not only the urgent need to learn how to teach online but also the awareness that computer-assisted teaching PD needs certain changes.
A Study on Language of The Third Medium–Messenger

본 연구의 목적은 구어•문어의 이분법을 넘어서 일상 언어에서 중요한 지위를 가지게 된 메신저 언어의 어휘적 특성을 고찰하는 것이다. 거시적 차원에서의 메신저 언어의 특성을 고찰하기 위해, 본 연구에서는 메신저 언어를 원시 말뭉치로 하여 구축된 140만 어절의 맞춤법 교정 병렬 말뭉치를 활용하기로 한다. 또한 맞춤법 교정 말뭉치의 원문을 교정문 및 세종 구어, 문어와 비교 분석함으로써 메신저 언어의 특수성을 분석하기로 한다. 본 연구는 다음과 같이 구성된다. 2장에서는 연구 대상 말뭉치의 특성과 구조, 연구 방법론을 살펴본다. 3장에서는 세종 문어 및 구어, 교정 전후의 어절의 비교를 통해 메신저 언어의 불일치 어형을 분석하고, 규범적 관점에서 불일치 어형의 유형을 분석한다. 이때 불일치 어형의 유형은 한글 맞춤법, 표준어 규정, 외래어 표기법, OoV, 이모티콘의 유형으로 분석된다. 4장에서는 불일치 어절의 유형이 메신저 언어의 특성인 구어성, 경제성, 정표성 등의 기능과 일정한 관계를 가지고 있으며, 특정한 경향성을 가지고 있음을 분석할 것이다. 메신저 언어의 특수 어형의 특성을 살펴보는 것은 새로운 기술과 매체의 등장으로 변화하고 있는 의사소통 양상을 살펴보는 기초적인 작업일 뿐만 아니라 형태소 분석기, 맞춤법 검사기 등 NLP 도구의 성능 향상에 대한 실제적인 자원을 제공한다는 의의가 있다. This study examines the lexical characteristics of messenger language, which holds an important position in everyday language beyond the dichotomy of spoken and written language. To investigate the macroscopic characteristics of messenger language, we utilize a customized parallel corpus of 1.4 million words of messenger raw text and its corrected text. In addition, the study analyzes the specificity of messenger language by comparing the raw text with the corrected text, Sejong spoken corpus, and Sejong written corpus. This study is structured as follows. First, in Chapter 2, we examine the characteristics and structure of the target corpus, as well as the research methodology. Then, in Chapter 3, we extract and analyze the messenger-specific forms by comparing the messenger raw text with the corrected corpus, the Sejong Written Corpus, and the Sejong Spoken Corpus. Furthermore, we categorize their messenger-specific forms based on Korean orthography, Korean standard language regulations, Korean Loanwords Notation, Out-of-Vocabulary words, and emoticons. Finally, in chapter 4, we will analyze how the types of messenger-specific forms are related to the functional characteristics of messenger language, such as orality, economy, and formality. Examining the characteristics of messenger-specific forms not only serves as a primary task to observe the changing communication patterns due to the emergence of new technologies and media but also provides practical resources for improving the performance of NLP tools such as morphological analyzers and spell checkers.
A Study on Subjectivity Expression Strategies in Korean Newspaper Editiorials

본 연구는 신문 사설을 대상으로 주관성 표현 전략으로의 언어 표현들을 밝히는 것을 목적으로 하였다. 이에 따라 우선, 주관성 표현의 전략을 발화 의도를 기준으로 분류하였는데 ‘판단적 태도의 언어 표현’, ‘감정적 태도의 언어 표현’, ‘관계적 태도의 언어 표현’이 그것이다. 다음으로 5개 신문사의 사설을 각각 20편씩 총 100편을 선정하여 수집하여 이를 면밀히 검토하여 주관성 표현의 전략으로 사용된 언어 표현들을 파악하였다. 연구의 결과 ‘판단적 태도의 언어 표현’들은 이성적으로 판단한 결과를 전달하고 있음을 확인하였고 ‘감정적 태도의 언어 표현’들은 매우 다양한 감정형용사가 사용됨을 확인하였다. 또한 ‘관계적 태도의 언어 표현’은 많지 않은 수의 표현들이 빈번하게 사용되고 있었으며 대부분 독자를 직접적으로 언급하며 어떠한 행위를 하도록 촉구하기 위하여 사용되고 있었다. 본 연구의 결과는 한국어교육 현장, 쓰기 교육 현장 등에서의 교육 자료로 활용될 수 있다는 점에서 의의가 있다고 할 수 있으며 지금까지 언어 수행 전략을 발화 의도에 따라 분류하여 이를 정리한 연구가 없었다는 점에서도 연구로서의 가치가 있을 것이다. The purpose of this study is to analyze language expressions as subjectivity expression strategies in newspaper editorials. For this purpose, first of all, the strategy of subjectivity expression was classified based on stance, including "language expression of judgmental attitude", "language expression of emotional attitude", and "language expression of relational attitude". Next, a total of 100 editorials from each of the five newspapers were selected and collected, and they were closely reviewed to identify language expressions used as strategies for subjectivity expression. As a result of the study, it is confirmed that "language expression of judgmental attitude" conveyed the results of rational judgment, and "language expression of emotional attitude" confirmed that a wide variety of emotional adjectives are used. In addition, "language expression of relational attitude" is frequently used in a small number of expressions, and most of them were used to directly refer to the reader and urge him to do something. The results of this study are meaningful in that it can be used as educational materials in the field of language education and it is also valuable as a study in that there have been no studies that categorize language performance strategies according to stance.
Estudio comparado de la educación virtual en Cuba y América Latina: dimensiones y profesionalización docente

Virtual education is a distance learning modality in which the educational space is found on the web, constituted by computer tools that enable didactic interaction, used exclusively through communication networks for the development of academic activities by materializing their dimensions. The objective of this article is to present a comparative study of virtual education in Cuba and Latin America in relation to the theory of its dimensions and teacher professionalization or technological pedagogical competencies as a process that allows for continuous and ascending professional development, in order to identify strengths and limitations. Related research was selected for the study. With this, it was possible to verify that, in general, Cuba presents similar characteristics in relation to virtual education as those of the Latin American region to which it belongs. Likewise, collaboration between Cuban universities and the Latin American region in this area of knowledge should continue to be strengthened. The combination of multidisciplinary groups and work objectives that allow for short and medium-term results would be other pending goals.
Analisis Kualitas Layanan Terhadap Penggunaan Aplikasi Blended Learning Menggunakan Model EESS

&#x0D; &#x0D; &#x0D; &#x0D; Research aim : This study aims to test the Quality of Moodle Borneo E-Learning (BEL) which includes Service Quality, Student Quality and Lecturer Quality on Perceived Satisfaction, Perceived Usefulness and Benefits based on the E-learning System Success Evaluating Model for active BEL users&#x0D; Design/Methode/Approach : This research uses a quantitative approach with non-probability sampling method and the technique used is Quota Sampling. To determine the number of samples, the hair formula was used which determined that the sample consisted of 280 University of Borneo students who had used BEL. The data analysis method used is SEM (Structural Equation Modeling) with the help of the SmartPLS program.&#x0D; Research Finding : The results showed that the variables of Service Quality, Student Quality and Lecturer Quality had a positive and significant effect on satisfaction and usability. And the Satisfaction and Usefulness Variables also have a positive and significant effect on benefits.&#x0D; Theoretical contribution/Originality : It is hoped that this research can provide insight and knowledge as well as provide information to researchers and academics regarding the analysis of the quality of BEL application service quality using the EESS model.&#x0D; Practitionel/Policy implication : The results of this study are used as input for the Borneo Tarakan University's LP3M, so that in the future it can improve the quality and quality of BEL so that in the future students will be more comfortable doing online learning with BEL.&#x0D; Research limitation : In this study it only focuses on evaluating the quality of using the BEL application but only looks at it from the perspective of students at the University of Borneo Tarakan. And also focuses on the EESS conceptual model which includes only social factors, namely Service Quality, Learner Quality, Instructor Quality.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; sitive and significant effect on benefits
Optimalisasi Pemanfaatan Media Pembelajaran Online Sebagai Upaya Meningkatkan Hasil Belajar Siswa di Sekolah Menengah Atas

This study aims to evaluate the utilization of online learning media as an effort to improve student learning outcomes in high schools. The method used was literature review by collecting information from relevant sources on the research topic. The results showed that the use of online learning media has advantages such as flexibility, interactivity, and easy accessibility. However, there are also disadvantages such as limited internet access, lack of social interaction, and increased workload for teachers. The use of online learning media has a positive effect on improving student learning outcomes in high schools. Factors such as the quality of learning content, availability of facilities and infrastructure, and teacher competence in managing online learning also influence the success of online learning media implementation. Nevertheless, there are also barriers that can hinder the improvement of student learning outcomes such as lack of support from parents and the environment, as well as students' and teachers' readiness to face changes in learning methods. In conclusion, the utilization of online learning media as an effort to improve student learning outcomes in high schools during the COVID-19 pandemic has great potential. However, successful implementation requires attention to the quality of content, facilities and infrastructure, as well as the competence of teachers and students in managing online learning
Quality Evaluation of C-E Translation of Legal Texts by Mainstream Machine Translation Systems—An Example of DeepL and Metasota

Despite significant progress made in machine translation technology and the ongoing efforts in practical and commercial application of neural machine translation systems, their performance in vertical fields remains unsatisfactory. To avoid misunderstandings and excessive expectations of a specific machine translation system, this research selected legal texts as its real data research object. The text translation tasks were accomplished using two popular neural machine translation systems, DeepL and Metasota, both domestically and internationally, and evaluated using internationally recognized BLEU algorithm to reflect their Chinese-to-English translation performance in legal fields. Based on the determined BLEU score, the study adopted an artificial analysis method to analyze the grammatical aspects of the machine translation output, including the accuracy of terminology usage, word order, subject-verb agreement, sentence structure, tense, and voice to enable readers to have a rational understanding of the gap between machine translation and human translation in legal text translation, and objectively assess the application and future development prospects of machine translation in legal text fields. The experimental results indicate that machine translation systems still face challenges in achieving high-quality legal text translations and meeting practical needs, and that further post-translation editing research is needed to improve the accuracy of legal text translation.
Analysis of Prospective Primary School Teachers' Attitudes towards Mobile Learning Tools and Acceptance of Mobile Learning

Although the use of mobile technologies in education is rapidly becoming widespread, there are limited findings about the attitudes, competencies and acceptance behaviors of prospective teachers who use and will use these technologies. However, prospective teachers will be the implementers of new technologies such as mobile learning in education when they start their teaching profession in the future. In this study, it was aimed to examine prospective teachers' attitudes towards mobile learning and their mobile learning acceptance behaviors in terms of some variables. The study was conducted by cross-sectional survey method with the participation of prospective teachers studying at two universities in Kazakhstan. The study was composed of 260 prospective teachers at different grade levels. According to the findings of the study, the acceptance behaviors of prospective teachers towards mobile technologies were found to be moderate, while their attitudes towards these technologies were found to be positive and high. In the study, it was found that prospective teachers' attitudes towards mobile learning and mobile learning acceptance behaviors showed significant differences according to gender and grade level. Finally, it was found that attitudes towards mobile learning predicted mobile learning acceptance behaviors significantly and positively in Kazakhstani prospective teachers
Image stitching based on a multistage keypoint matching and reconstruction network

Image stitching is one of the important tasks of computer vision, which is used in many fields such as autonomous navigation and autonomous driving. However, traditional stitching methods rely too much on the quality of feature detection and show poor performance for images with few features or low resolution. Although existing deep learning-based methods can make up for the shortcomings of traditional methods, they are only used on mobile robots with smallbaseline or fixed perspectives. To address the above limitations, we propose an image stitching network consisting of three modules: multistage keypoint matching module, DFAST module and multistage image reconstruction module. First, we use a multistage keypoint matching module to align the reference image and the target image, and obtain deep homography estimates between reference and target images at different scales of features. After that, the DFAST module is designed to stitch images of arbitrary views and generate stitched feature maps at different scales. Finally, the multistage reconstruction network is used to reconstruct and optimize the stitched feature maps from feature level to pixel level and fuse stitched images of different scales to generate finer texture details. Experiments results show that our method surpasses previous methods including state-of-the-art traditional and CNN-based methods.
TVN: Detect Deepfakes Images using Texture Variation Network

Face manipulation technology is rapidly developing, making it impossible for human eyes to recognize fake face photos. Convolutional Neural Network (CNN) discriminators, on the other hand, can fast achieve high accuracy in distinguishing fake/real face photos. In this paper, we look at how CNN models discern between fake and real faces. Face forgery detection relies heavily on Texture Variation Network (TVN) information, according to our findings. We propose a new model, TVN, for robust face fraud detection, based on Convolution and pyramid pooling (PP), as a result of the aforesaid discovery. To produce a stationary representation of composition difference information, Convolution combines pixel intensity and pixel gradient information. Simultaneously, multi-scale information fusion based on the PP can prevent the texture features from being destroyed. Our TVN beats previous techniques on numerous databases, including Faceforensics++, DeeperForensics-1.0, Celeb-DF, and DFDC. The TVN is more resistant to image distortion, such as JPEG compression and blur, which is critical in the wild.
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

Abstract This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.
Pengembangan dan Pelatihan Sistem PSB di SMK Ma’arif 1 Temon, Kulon Progo, Daerah Istimewa Yogyakarta

SMK Ma'arif 1 Temon is a private Vocational High School with 3 expertise programs namely Accounting and Institutional Finance, Clinical and Community Pharmacy, and Software Engineering which is located on Raya Wates Purworejo Street, RT/RW : 27/13, Kaliwangan Hamlet, Temon Wetan Village, Temon District, Kulon Progo Regency, Special Province of Yogyakarta. Even though it has resources and one of the expertise in Information Technology (IT), this school does not yet have a new student admissions system (PSB) and so far, it is still using Google forms. Service activities are carried out by creating an information system that can provide convenience for SMK Ma'arif 1 Temon, as well as making a dashboard to display the PSB recapitulation results. With this community service activity in the form of creating a PSB system, it is hoped that it will facilitate data collection and selection of new student admissions at SMK Ma'arif 1 Temon.
UAV small object tracking based on spatio-temporal continuity and feature consistency

Recently, UAVs have gradually been widely used in both military and civilian fields, and play an increasingly important role in various industries. The study of stable and reliable UAV visual target tracking is of great significance to the application of UAVs. Object tracking still faces many challenges, such as target occlusion, target deformation, and other situations. In order to solve the problem that targets are easily lost due to fast moving speed and occlusion, we propose an independent particle based continuous tracking algorithm for the ground object tracking by UAV. We use an iterative update method to get the optimized position estimation of the target at different moments. For accurate and efficient estimation, the features extracted by convolutional neural networks, as well as target trajectory features, are used in the updating process. Multi-scale features are extracted to ensure that the algorithm remains highly accurate for target scale changes. Experimental results show that our algorithm can effectively detect UAV moving targets and still have a good performance for target occlusion.
Cluster and denoise data with general ratio density

Density-based clustering is famous for its ability to extract clusters of arbitrary shapes and to detect noise samples, but many existing density-based clustering algorithms suffer from high dimensional or varying density data. To address these issues, we introduce a novel density-based clustering algorithm, general ratio density (GRD). Based on <i>k</i>-NN graph, it combines global density estimation and local density estimation to better detect noise points in varying density situations. During the process of shifting noise points and dividing clusters, our clustering algorithm can cluster and denoise at the same time. Experiment results on real life dataset and synthetic dataset demonstrates the state-of-the-art performance of our algorithm compared to other methods.
Design and application of stateful middleware management platform based on cloud native

Web service specifications of WSRF and WSN provide a new method to solve the problem of stateful middleware services. At present, cloud native technology has been widely used in the field of stateful middleware management platform. Based on stateful Web services, a middleware SOA framework for the control of physical examination population has been designed, and its stateful resource encapsulation method and state, message notification mechanism when resource changes and other key technologies have been described in detail. Finally, the middleware has been implemented through Globus Toolkit 4.0, and good application results have been achieved on the medical examination appointment platform.
Advances in federal learning technology research

Along with the boom in data rights and the increasing number of responses to protect data privacy, scholars have begun to study the "data isolation" phenomenon while ensuring data privacy and security. Users upload source data to a high computing power cloud server for centralised training required by traditional machine learning algorithms, which leads to uncontrolled data theft and sensitive data leakage problems. By removing data barriers and collaborating on modelling utilizing data from several clients, federated learning has become a successful remedy for this issue. In this presentation, the history of federated learning is discussed, federated learning’s precise defining categories is clarified, and its technological evolution is examined. How well training frameworks for federated learning, including the FedAvg, MOON, and Fedprox algorithms perform is assessed too. Finally, the current problems of federated learning algorithms and the future directions of development are also illustrated objectively.
Research on classroom behavior recognition based on convolutional neural network

In recent years, with the rapid development of computer technology, the application of artificial intelligence has gradually expanded to various fields, including: medical care, agriculture, logistics, education, transportation and other industries. Among them, the deep integration of information technology and education teaching has become a trend, which has made the application of smart campuses develop rapidly. In classroom teaching, students' behavior status can reflect students' classroom participation and teaching effect, which has reference significance for teaching quality evaluation and improving teaching methods. Therefore, this paper studies an algorithm for object detection and student behavior recognition based on convolutional neural network, which counts students' classroom participation and analyzes classroom evaluation by identifying students' behavioral states in the classroom. The main research contents of this paper are as follows: Firstly, the research background of the application of artificial intelligence in the field of education and teaching is expounded. Secondly, the working principle and basic algorithm of image prediction technology are summarized. At the same time, the classroom behavior detection and recognition algorithm based on convolutional neural network is introduced, and the performance of the algorithm is analyzed and optimized.