Reviewerly/exHarmony huggingface dataset:
Adaptive Fast XGBoost for Multiclass Classification

The popularization of sensoring and connectivity technologies like 5G and IoT are boosting the generation of data streams. Such kinds of data are one of the last frontiers of data mining applications. However, data streams are massive and unbounded sequences of non-stationary data objects that are continuously generated at rapid rates. To deal with these challenges, the learning algorithms should analyze the data just once and update their classifiers to handle the concept drifts. The literature presents some algorithms to deal with the classification of multiclass data streams. However, most of them have high processing time. Therefore, this work proposes a XGBoost-based classifier called AFXGB-MC to fast classify non-stationary data streams with multiple classes. We compared it with the six state-of-the-art algorithms for multiclass classification found in the literature. The results pointed out that AFXGB-MC presents similar accuracy performance, but with faster processing time, being twice faster than the second fastest algorithm from the literature, and having fast drift recovery time.
Learning English At Workplace From E-Module To Improve English Proficiency Of Employees At Besilam Village

This research purposed to design and develop an E-Module for English at workplace at Administrative Office in Besilam village. This research used Research and Development (R&amp;D) method with the ADDIE model with five stages; Firstly was analyzing problems, potentials, and needs. Secondly was designing to determine Canva application used, the design/theme of the e-module, and the format. Thirdly is developing the design that has been previously made by using Canva application. In addition, at this stage, a validation test was conducted for the validators, namely material experts, linguists, and media experts to determine the feasibility of the E-Module that has been designed and developed. Fourthly is the implementation. In this stage, a practicality test and effectiveness test were administered to the staff and the instructor. Lastly was the evaluation. At this evaluation stage, the improvement of E-Module that has been given input by material experts, linguists, and media experts. Quantitative and qualitative data were used to collect the information. The research findings proved that the average score for eligibility assessment was 91.34%, the average score for practicality assessment was 95.01%, and the average score for effectiveness was 85. In conclusion, the eligibility assessment was classified as "Very Eligible", the practicality assessment was classified as "Very Practical", and the effectiveness assessment was classified as "Very Effective". E-module of English at workplace can be used as an alternative method to improve English proficiency to be better.
THE EFFECT OF FISHBOWL STRATEGY IN SPEAKING ABILITY FOR THE SECOND YEAR STUDENTS OF SMPN 1 TERARA IN THE SCHOOL YEAR 2022-2023

Fishbowl strategy is extremely significant strategy in teaching speaking skill especially English for the language learners. This study focus on finding the effect of fishbowl strategy in teaching speaking ability for the eighth grade students of SMPN 1 Terara in the school year 2023. This study was an experimental research. The population of this study was all the the eighth grade students of SMPN 1 Terara in the school year 2020. The sample of this study was 32 students. In collecting the data, this study used speaking test instrument. Then, to analyze the data this study used descriptive statistic and testing hypothesis. The result of this study found that the mean score of experimental group was 41.2 and for the control group was 40.5. In testing the hypothesis was found that t-test was 2.948 and t-table in significant rank 5% was 2.10 (2.948 &gt; 2.10), those founding means that the hypothesis was accepted. It means that there is an effect of fishbowl strategy toward speaking ability and the students.
ANALISIS PENGELOLAAN ARSIP DINAMIS AKTIF LAPORAN HASIL PEMERIKSAAN PADA INSPEKTORAT PROVINSI JAWA TENGAH

&lt;p&gt;There is an intensity to the observation of this research to understand the management of the active dynamic archive of the Examination Results Report (LHP) at the Central Java Provincial Inspectorate and also the obstacles and solutions to such obstacles. As for the observations, the research is qualitatively descriptive and uses a method of data collection, documentation, and interviews. Observations show that LHP archive processing includes creation, maintenance, storage, and shrinkage. Creation processes include creation in the processing unit, recording in the archive unit, and scanning. LHP archives are stored in the archive unit room and also in the record center using a number storage system. The process of LHP shrinkage has not been completed. The obstacles faced are the lack of SDM, lack of storage space, insufficient security aspects of the record center, and also poor archival systems, according to Agus Sugiarto and Teguh Wahyono. The advice that can be given is to: 1) make the recruitment of archive experts; 2) repair the corrupted record center; 3) make the procurement of archive equipment; 4) improve the security aspects of the record center; and 5) make the process of narrowing strictly.&lt;/p&gt;
Using Machine Learning for Non-Functional Requirements Classification: A Practical Study

Non-Functional Requirements (NFR) are used to describe a set of software quality attributes such as reliability, maintainability, and performance. Since the functional and non-functional requirements are mixed together in software documentation, it requires a lot of effort to distinguish them. This study proposed automatic NFR classification by using machine learning classification techniques. An empirical study with three machine learning algorithms was applied to classify NFR automatically. Precision, recall, F1-score, and accuracy were calculated for the classification results through all techniques. The results showed that the SGD SVM classifier achieves the best results where precision, recall, F1-score, and accuracy reported were 0.66, 0.61, and 0.61.
Realizing Refactoring Prediction through Deep Learning

Refactoring is the process of changing the internal structure of a software in order to improve its quality, without modifying its behavior. Recent studies have shown that the act of refactoring brings positive results for maintaining and understanding the code and the system as a whole. It turns out that, currently, this method is still little used, with expertise and intuition being the main factors that determine the need for software refactoring. Before starting the refactoring process, an analysis is essential to check whether refactoring is really necessary. Therefore, the present study analyzes artificial intelligence techniques, such as Deep Learning, to predict when software refactoring is essential. Deep Learning models like CNN, RNN, LSTM and DenseLayer were analyzed and compared using precision, recall and accuracy metrics. The results demonstrated that Machine Learning models performed better than Deep Learning algorithms using the same data set, however, the good performance of Deep Learning models stands out in scenarios where the data is very unbalanced.
Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner

The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively
Pelatihan Penggunaan Media Pembelajaran Tentang Budaya Keris Berbasis Virtual Reality di Sekolah Dasar

The utilization of Virtual Reality (VR) technology for educational purposes, namely in instructing Indonesian culture, particularly the Keris culture, holds significant importance. This is due mainly to the limited dissemination of knowledge on Keris and traditional weaponry inside educational institutions, as they are not currently incorporated into the curriculum. The Universitas Negeri Semarang and Politeknik Negeri Jakarta teams have developed interactive learning media focusing on virtual reality (VR) technology. The primary objective of the virtual reality (VR) application is to provide a comprehensive exploration of Keris culture, intending to foster a deeper understanding and appreciation of Indonesia's rich cultural legacy among the younger generation, mainly elementary school pupils. The approach involves offering instructional sessions to students and educators, focusing on utilizing virtual reality (VR) technology as an educational instrument. Training activities were provided at Barusari 02 Elementary School in South Semarang. Considering technological accessibility is crucial to ensuring that all students can access educational materials without obstacles. The findings derived from administering pre- and post-assessments to primary school children indicate that Virtual Reality Technology has the potential to engender greater interest among pupils in acquainting themselves with Indonesian culture. Most participants, above 80%, expressed a strong positive inclination towards utilizing Virtual Reality (VR) Technology to acquire knowledge about various cultural aspects. By providing appropriate support and adopting a comprehensive approach, virtual reality (VR) has the potential to serve as an efficacious instrument for enhancing students' comprehension and admiration of Indonesian culture.
Improvement of Social Inclusion-Based Rural Library Management and Services to Support Sustainable Development Goals (SDGs)

&lt;span class="ShortAbstract"&gt;&lt;em&gt;&lt;span&gt;The rural library is one of the existing facilities in the village and has an integral role as a center for community empowerment activities. In the management of the village library, there are several problems faced, such as many people who do not know the rural library, lack of competence for library managers, non-standard library management, lack of public interest in reading, and low community participation in the rural library program. This service aims to promote social inclusion-based village library programs that can increase community participation and even improve welfare through programs provided to the community. The service was carried out at the Rural Library of Giripurwo Village, Girimulyo District, Kulon Progo Regency, Special Region of Yogyakarta. The target group for community service is the entire rural community, including village officials and village library managers. The results of the service program are that the management of the rural library has become national standard and the programs owned by the village library also accommodate and increase community participation. So that community skills can increase and increase in social inclusion-based rural library programs.&lt;/span&gt;&lt;/em&gt;&lt;/span&gt;
METHODOLOGICAL BASIS OF TEACHING AND SYSTEMATIZATION OF CHOREOGRAPHIC EDUCATION

The article deals with the problem of forming future teachers of choreography in the modern concept of education. There are considered traditional and modern approaches to pedagogicalchoreographic education to achieve the versatile development of the subject. As well as, there is proposed a framework on training future teachers of choreography in the systematic - activity approach.
THE IMPACTS OF SOCIAL MEDIA ON EMPLOYEES’ PRODUCTIVITY AMONGST ADMINISTRATIVE EMPLOYEES AT UITM PAHANG, CAMPUS JENGKA

This study focused on the impacts of social media on the employees’ productivity at Universiti Teknologi MARA Pahang, Campus Jengka. A total of 113 questionnaires were distributed to the administrative employees by using simple random sampling, out of which 108 (98.58%) questionnaires were returned. A statistical analysis was conducted by using SPSS 23.0 to investigate the impacts of four factors of social media that consists of communication channels, knowledge sharing, intention to use and intrinsic motivation on employees’ productivity. The findings showed that all four factors had mean scores within the range of high and very high. In addition, the results also revealed that the four hypotheses constructed showed positive relationships between the factors of social media on employees’ productivity. Therefore, all the four research hypotheses were accepted. These findings are useful in assisting the employers in Malaysia to increase employees’ productivity with the use of social media as one of the advanced information technologies.
Objects Detection in an Image by Color Features

The present article introduces an algorithm for object detection in video material based on color characteristics. This algorithm demonstrates efficiency in conditions of varying illumination, low video resolution, as well as when dealing with a substantial number of objects of the same type, but with distinct color attributes. Its potential applications encompass tasks related to object retrieval through real-time video surveillance using stationary and mobile cameras, both in real-time scenarios and video recordings.
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer

By hiding the front-facing camera below the display panel, Under-Display Camera (UDC) provides users with a full-screen experience. However, due to the characteristics of the display, images taken by UDC suffer from significant quality degradation. Methods have been proposed to tackle UDC image restoration and advances have been achieved. There are still no specialized methods and datasets for restoring UDC face images, which may be the most common problem in the UDC scene. To this end, considering color filtering, brightness attenuation, and diffraction in the imaging process of UDC, we propose a two-stage network UDC Degradation Model Network named UDC-DMNet to synthesize UDC images by modeling the processes of UDC imaging. Then we use UDC-DMNet and high-quality face images from FFHQ and CelebA-Test to create UDC face training datasets FFHQ-P/T and testing datasets CelebA-Test-P/T for UDC face restoration. We propose a novel dictionary-guided transformer network named DGFormer. Introducing the facial component dictionary and the characteristics of the UDC image in the restoration makes DGFormer capable of addressing blind face restoration in UDC scenarios. Experiments show that our DGFormer and UDC-DMNet achieve state-of-the-art performance.
A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning

Vehicle damage localization and severity estimation is essential to post-accident assessments, with a traditional process taking an average of seven days and requiring substantial work from both customers and dealers. Towards improving this process, we propose an end-to-end system which inputs a set of user-acquired photographs of a vehicle after an accident and outputs a damage assessment report including the set of damaged parts and the type and size of the damage for each part. The system is composed of three deep learning modules: a model to identify whether a vehicle is present in the image, a model to localize the vehicle parts in the image, and a model to localize the damage in the image. We demonstrate the effectiveness of each module by evaluating them on labeled datasets containing images of vehicles after an accident, some collected by the OE (Original Equipment) Insured Fleet and some acquired by users of the OEM (Original Equipment Manufacturer) mobile application. We also describe how the modules fit together with a post-processing step to aggregate outputs between the different modules across multiple user-acquired views of the accident. Our approach demonstrates the potential for an accurate and automated vehicle damage estimation system to support a substantially more efficient vehicle damage assessment process.
End-to-End Transformer for Compressed Video Quality Enhancement

Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatio-temporal information of adjacent frames mainly by deformable convolution. However, the CNN-based methods can only exploit local information, thus lacking the exploration of global information. Moreover, current methods enhance the video quality at a single scale, ignoring the multi-scale information, which corresponds to information at different receptive fields and is crucial for correlation modeling. Therefore, in this work, we propose a Transformer-based compressed video quality enhancement ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVQE</i> ) method, consisting of Transformer based Spatio-Temporal feature Fusion ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSTF</i> ) module and Multi-scale Channel-wise Attention based Quality Enhancement ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCQE</i> ) module. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSTF</i> module learns both local and global features for correlation modeling, in which window-based Transformer and the encoder-decoder structure greatly improve the execution efficiency. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCQE</i> module calculates the multi-scale channel attention, which aggregates the temporal information between channels in the feature map at multiple scales, achieving efficient fusion of inter-frame information. Extensive experiments on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JCT-VT</i> test sequences show that the proposed method increases PSNR by up to 0.98 dB when QP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 37. Meanwhile, the inference speed is improved by up to 9.4%, and the number of Flops is reduced by up to 84.4% compared to competing methods at 720p resolution. Moreover, the proposed method achieves the BD-rate reduction up to 23.04%.
Pedagogical views of K.D. Ushinsky and the McGuffey brothers: a comparative analysis of conceptual approaches to teaching and upbringing

The article examines the legacy of the founder of Russian pedagogical science K.D. Ushinsky, his influence on domestic pedagogy, characteristics of ideas and main fields ofactivity. A comparative analysis of K.D. Ushinsky's readers "Children's World", "Native Word" and a series of readers by the McGuffey brothers was carried out. The background of their creation was identified, the presence of similar ideas was determined, the features of their implementation were highlighted; approaches to the selection of material were studied; the influence on modern pedagogy was noted; the author's concepts, their content and methodological components are characterized.
Improving the methodology for teaching Geography based on the analysis of the results of the Unified State Examination

The article presents an analysis of the results of the tasks of the Unified State Examination in Geography in the Kabardino-Balkarian Republic in 2023, conducted in order to identify the main problems and objectives. During the analysis typical errors and its causes were identified, specific methodological recommendations were given to Geography teachers, heads of municipal and school methodological associations aimed at improving the quality of geography teaching in general education institutions. Recommendations were made taking into account the identified deficits among the Unified State Examination participants.
Study of project and research activities for schoolchildren in Biology and Chemistry

This review presents an analysis of the collective monograph "Project and research activities of schoolchildren in Biology and Chemistry", that is devoted to the issue of organizing project and research activities for schoolchildren in general education organizations in Biology and Chemistry. The main sections of the work and the issues concerning the importance and prospects of project and research activities for improving the system of general education are considered in the analysis. The authors of the monograph considered a wide range of difficult issues that teachers may face while organizing project and research activities for students. Recommendations on the project and research activities organization in Biology and Chemistry are given. Among the advantages of the monograph it is noted that all sections have a highly concentrated material, logical structure, systematic presentation of information, consistency and purposefulness. Based on the analysis, it is concluded that the materials presented in the monograph can be used in the educational process and are a valuable resource for Chemistry and Biology teachers.
Protection and development of native languages as a strategic direction of educational policy in the Kabardino-Balkarian Republic

The article is concentrated on the protection and development of the native languages of the nationalities of the Kabardino-Balkarian Republic as a strategic direction for the education system. The modern regulatory and legal framework, that determines the strategic need to study native languages, is characterized. The current statistical data on the terms of studying the native languages of the nationalities of the Kabardino-Balkarian Republic are analyzed. The data on the staffing in native languages in educational organizations are given. The experience of implementing republican ethno-cultural activities is described. The authors of the article suggest possible ways to protect and further develop native languages. Fundamentally important recommendations are proposed for the development of native languages and ethnocultural education. The materials presented in the article can be used in the development of regional strategic documents in the field of education and in preparation of analytical materials