Reviewerly/exHarmony huggingface dataset:
Pengelompokan Jenis Penerimaan Pajak di Kota Makassar Menggunakan Fuzzy Clustering

This research discusses grouping types of tax receipts in the City of Makassar with the method of Fuzzy C-Means (FCM). The data used are secondary data from the tax office in the form of the amount of the receipts of income tax, value-added tax, and land tax building each district in the year 2018. The discussion starts from the theory of fuzzy clustering, simulation, and the characteristics of the simulation results. In this study, the obtained number of clusters the optimum, namely the 7 clusters that have the potential for tax revenue is relatively high, namely at cluster seven consists only of the District Tamalanrea, and the lowest in the cluster a sixth consisting of the Kecamatan Tallo, Ujung Tanah, and Sangkarrang. After hashing on a cluster that is formed can be used to see the level of the welfare of the community in each district.
Research on Long Text Classification Model Based on Multi-Feature Weighted Fusion

Text classification in the long-text domain has become a development challenge due to the significant increase in text data, complexity enhancement, and feature extraction of long texts in various domains of the Internet. A long text classification model based on multi-feature weighted fusion is proposed for the problems of contextual semantic relations, long-distance global relations, and multi-sense words in long text classification tasks. The BERT model is used to obtain feature representations containing global semantic and contextual feature information of text, convolutional neural networks to obtain features at different levels and combine attention mechanisms to obtain weighted local features, fuse global contextual features with weighted local features, and obtain classification results by equal-length convolutional pooling. The experimental results show that the proposed model outperforms other models in terms of accuracy, precision, recall, F1 value, etc., under the same data set conditions compared with traditional deep learning classification models, and it can be seen that the model has more obvious advantages in long text classification.
Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter

In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.
Assistance in the preparation of financial reports and asset inventory at BUMDes Wringinputih

During the Covid-19 pandemic, financial reports were prepared and asset data was collected in an effort to improve accountability and professionalism in the management of BUMDes. The Balkondes Guyub Rukun, Bojong Hamlet, Wringinputih Village, Borobudur District, Magelang Regency, was the venue of this program. A BUMDes manager and Balkondes Guyub Rukun employee, was the target of this program. Field surveys, problem identification, needs analysis, training, and mentorship are just a few of the activities that have been undertaken. It is envisaged that hosting activities and training in the preparation of financial reports and asset data collecting will provide insight and experience, as well as boost the efficacy of financial report preparation that is currently done manually. This program produces digital financial reports based on pivotable Excel spreadsheets and well-documented BUMdes asset inventory data.
READING STRATEGIES EMPLOYED BY SENIOR HIGH SCHOOL ENGLISH TEACHERS IN THE PHILIPPINES

One of the most difficult aspects of pedagogy is teaching reading. This study looked into the pre-, during, and post-reading strategies used by English teachers in a public secondary school, as well as the obstacles they experienced when teaching reading. This study used a mixed-methods approach, with a survey-questionnaire to collect quantitative data and a semi-structured interview used to collect qualitative data. The research included eighteen (18) senior high school English teachers. Teachers utilize pre-, during-, and post-reading methods extensively, according to the findings. Teachers invite students to generate questions about the topic during pre-reading exercises. Teachers use during-reading strategies such as asking students to identify the main concept of the reading material and asking questions to evaluate their understanding. They ask students to complete a task (as a post-reading strategy), and the semi-structured interview reveals that the respondents' top challenges are students' vocabulary size and teachers' lack of training. Based on the findings of this study, supplementary reading material was developed to assist English language teachers in teaching reading in their respective classes.
Emerging Word Games to Improve Young Learners' Vocabulary Skills in Ambonese Islamic School

This study aims to determine the implementation of word games and their impact on student learning outcomes in learning new vocabulary in grade VII Mts Hasyim Asyari Ambon academic year 2020/2021. This research was conducted in three cycles. Using word games as a methodology in teaching English vocabulary starts with the preliminary activities of the teacher opening the class, preparing word game activities, and conveying the objectives of the material. Then in the core activity, the teacher applies the word games. The teacher gives instructions about the rules and activities of word games. In the closing activity, the teacher asks students to express their difficulties and problems when doing word games. The subsequent finding is that student learning outcomes increase each cycle, wherein only five students, or 20%, have completed the pre-cycle using conventional methods. During the first cycle, learning was carried out using word game methodologies. The number of students who achieved completeness threshold rose to 16 people or 64%. In the last cycle, all students can complete the post-test with the same methodology. It shows that using the word games methodology to deliver vocabulary learning materials has significantly improved student learning outcomes.
Enhancement of Very Fast Decision Tree for Data Stream Mining

Traditional machine learning (ML) algorithms use static datasets to model knowledge.Nowadays, there is an increasing demand for machine learning based solutions that can handle very huge amounts of data in the shape of streams that never stop.The Very Fast Decision Tree (VFDT) is one of the most widely utilized data stream mining algorithms (DSM), despite the fact that it wastes a huge amount of energy on trivial calculations.The machine learning community has come first in terms of accuracy and execution time while designing algorithms like this.When assessing data mining algorithms, numerous types of studies include energy usage as a crucial factor.The purpose of this research is to create a hyper model to optimize the VFDT algorithm, which reduces the waste of energy while maintaining accuracy.In the proposed method, some fixed algorithm parameters were changed to dynamic parameters after analyzing each of them separately and knowing the extent of their positive impact on reducing energy consumption in several cases in algorithm.The practical experiment was conducted on both the algorithm in its basic form and the algorithm in the proposed form on several different types of datasets in the same application environment The main advantage of the results of the proposed method compared to the results of the basic algorithm is that there was a noticeable development in the performance of the algorithm in terms of reducing its energy consumption and maintaining its accuracy levels.
Fused-Grain Feature Learning for Unsupervised Person Re-identification

Most supervised learning methods are currently used to solve the task of person re-identification (Re-ID) and yield excellent results.But these methods usually need manual annotation of training data.Especially for large data sets, they need too high cost of manual annotation and the data are difficult to obtain for fully pairwise labeling.So unsupervised learning becomes a necessarily trend for person Re-ID.This paper is trying to solve the problem by unsupervised learning method.Moreover, global features focus on spatial integrity of person features, and local ones help to highlight discriminative features of different patches.Therefore, fused-grained unsupervised (FGU) learning framework of global and local branches' feature learning is proposed to solve Re-ID task.Specifically, for the local branch, one extracts patches from a feature map which learned on a PatchNet network of images, and learns their fine-grained features to pull close the similar patches and push away the dissimilar ones.For the global branch, one maximizes the diversity between classes by repelled loss and similarity within classes through attracted loss, then similarity and diversity in the unlabeled data sets are used as information for unsupervised cluster merging and learning their coarse-grained features.The two branches are used to jointly achieve the effect of increasing inter-class differences and intra-class similarity.A large number of experiments verify the superiority of the proposed method for unsupervised person re-identification.
Effect of Rural Banditary on IFAD-CASP Participating Crop Producer

he research study analyzed the effect of rural banditry on IFAD-CASP participating crop farmers in Zamfara State, Nigeria. Interview schedule was used to collect data from 360 respondents that were selected for the study. A multi stage and simple random sampling technique was used in selecting the respondents. Descriptive statistical tools (mean, percentages, frequency distribution) and inferential statistics (logic regression analysis, paired sample T-test and Spearman`s correlation coefficient) were used for data analysis. The results of the study reveals that majority of the respondents were married with an average household size of 8 persons and income level of less than N 20,000/month. Most of the respondents (50%) indicate that the conflict is very severe resulting to great economic, physical and social effects on the respondents. The results of ranking of effect of rural banditry on IFAD-CASP in the study area, indicates poor participation, increased rural poverty, poor supervision of programme, poor economic growth and poor information dissemination were ranked 1st, 2nd, 3rd, and 4th respectively. It could be concluded that rural banditry poses poor economic growth, increased rural poverty, poor participation, and poor programme implementation and induces fear and uncertainty. The study therefore, recommend that urgent provision of adequate security personnel to Mann the porous borders, prompt enrollment of the vulnerable into government social investment programmes, organize training/seminars for peace building purpose and also government should register all mining site and their activities properly monitored.
The Influence of the Application of Game Based Learning (GBL) Models Assisted by the Math Games Platform on the Mathematics Learning Outcomes of Fifth Grade Elementary School Students

This study uses an experimental quantitative approach to determine whether or not there is a result of a treatment. This study aims to determine the effect of implementing the Game Based Learning (GBL) model with the help of the Math Games platform on the mathematics learning outcomes of fifth grade elementary school students. This study used the One Group Pretest-Posttest Design method, with a subject of 29 students in an elementary school in Kediri Regency which was taken through purposive sampling technique. Data collection techniques using observation and objective tests. The instrument used was a multiple choice test with 10 questions that had been tested to determine the validity and reliability of the instrument. The results were analyzed using descriptive statistics and inferential statistics with the help of SPSS for Windows version 21. Based on the results, the mean pretest score was 60.35 and the posttest average score was 85.52 with an N-gain percentage of 66%. Analysis of research data was carried out through 2 stages, namely prerequisite tests and hypothesis testing. Based on the results of the prerequisite test, it was found that the research data were normal and homogeneous (having the same variance). The results of the hypothesis analysis using the paired sample t-test show that the data significance value is 0.000, where 0.000 <0.05, which means Ho is rejected and Ha is accepted. So it can be concluded that there is a significant difference between learning outcomes before and after the implementation of the game based learning model with an average increase in learning outcomes in the medium category.
On the Way to Successful Learning and Teaching: Constructive Feedback

This research studies the perceptio7ns of feedback by University and school teachers, as well as the prevailing type of feedback in the participants’ teaching practice. Participants are 90 educators from Bogdan Khmelnystky Melitopol State Pedagogical University and teachers of primary and secondary schools of Melitopol and Melitopol region (Ukraine). Feedback is described as information a teacher or another learner gives to learners on how well they are doing. According to the research results, teachers mostly prefer to give oral feedback. The authors come to the conclusion that feedback should be constructive and should be focused on students’ achievements. Further perspectives of the research include raising teachers’ awareness of feedback through trainings and optional courses in pedagogical Universities, identification of strategies for providing online feedback. The research findings lay the foundation for further research of feedback as an inseparable part of learning and teaching.
Ideational Metaphor Analysis on EFL Students’ Academic Writing

Over the last decade, research on the quality of EFL students' English academic writing has increased. However, research on EFL students' academic writing that incorporates grammatical metaphors is regarded scarce. As such, this study seeks to elucidate the occurrences of ideational metaphors in students' academic writing essays. The qualitative descriptive method was used in this study. This research involved a total of 25 undergraduate students from the University of HKBP Nommensen Medan, Indonesia, English Department, Faculty of Language and Arts, who studied Academic Writing course in the third semester. Students were required to submit a 350-word essay on the subject "Learning Challenges during the Coronavirus Pandemic." The research's data corpus totals 10,252 words. The results indicate that the students' writings contain 281 clauses comprising the ideational metaphor. It is deemed little in comparison to the corpus's overall data set of 10,252 words. Material processes dominate the incidence of ideational metaphors, followed by mental processes, relational processes, behavioral processes, and existential processes. This finding implies that the essay structure of EFL students is unlikely to have a large proportion of solid grammatical metaphor clauses. The pedagogical implication of this research is that EFL students, lecturers, teachers, and other educational practitioners interested in teaching English academic writing should be aware of grammatical metaphors in writing texts as a proxy for the quality of scientifically academic writing.
PEMBUATAN MEDIA PEMBELAJARAN MENGGUNAKAN CANVA PADA PELATIHAN MEDIA PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI DAN KOMUNIKASI DI KABUPATEN MUBA

The purpose of this study was to find out the extent of the competence of trainees in making learning media using canva in MUBA Regency. The research method used is a quantitative descriptive method where the researcher makes an image or descriptive about a situation objectively using numbers, starting from data collection, interpretation of the data and appearance and results. This research was conducted at Madrasah Aliyah Negeri 1 MUBA with a sample of 35 participants in learning media training based on information and communication technology. Data collection techniques through pretests and post-tests as well as participant results or products. The result of this study is that with the training of learning media based on information and communication technology, there is an increase in knowledge and skills related to making learning media using Canva. This proves that training in information and communication technology-based learning media with Canva is very beneficial for teachers.
Integration Aspect of Training Teachers of Art Disciplines in Pedagogical Universities

The aim of this study is to collect the latest data on changes in the teaching of art disciplines under the influence of the spread of the COVID-19 pandemic, as well as to trace how this revolution affects the emergence of innovations in teaching. The ultimate goal is to find an approach that will help future teachers solve problems related to the technologization of education and new requirements of the labor market. A promising way to solve this problem is to implement the principles of STEAM education in the training of future specialists. This will bridge the gap between technical and creative disciplines, theory and practice, and make it possible to integrate individual knowledge into creative innovative projects.
A Case Study of a Student’s Reading Habits on Academic Texts

This research was aimed to investigate reading habits on academic texts. The investigation was covered reading habits and strategies on academic texts. The participant of this research was an eight-semester student of the English Language Education Study Program who is a good academic achiever, especially in reading subjects. The data were derived from a qualitative case study as the research methodology. Then, the researcher used data triangulation through a semi-structured interview, diaries, and direct observations. All of the data were analyzed by thematic analysis and the investigation answered two research questions such as the research participant’s academic reading habits and academic reading strategies. The first result revealed that; (i) the research participant usually read e-books, journal articles, and educational websites at Untan library, Home, and cafes with approximately 30 mins to 3 hours reading time; several academic reading purposes such as reading for assignments, reading for writing a research proposal, curiosity, self-development, creating ideas, getting knowledge, thinking specifically towards problematic things, recalling memory, spare-time fulfillment, and reading to get motivation; the academic reading factors including self-factor, academic environment factor, peer factor, and the availability of reading sources and access; the second result revealed that; (ii) the student employed four reading strategies namely cognitive strategies that involved self-questioning, note-taking, citing and quoting, outlining, and summarizing; metacognitive strategies, such as evaluating the reading process and realizing; compensation strategies that involved comparing the reading texts, finding the context clues, and reading other sources to improve vocabularies; and mind mapping as the memory strategy in academic reading activity.
Image De-Noising Using Convolutional Variational Autoencoders

Abstract: Typically, image noise is random colour information in picture pixels that serves as an unfavourable by-product of the image, obscuring the intended information. In most cases, noise is injected into photographs during the transfer or reception of the image, or during the capture of an image when an object is moving quickly. To improve the noisy picture predictions, autoencoders that denoise the input images are employed. Autoencoders are a sort of unsupervised machine learning that compresses the input and reconstructs an output that is very similar to the original input. The autoencoder tries to figure out non-linear correlations between data points. An encoder, a latent space, and a decoder all exist in autoencoders. The encoder reduces the dimensionality of an original picture to its latent space representation, which is then used by the decoder to reconstruct the reduced dimensional image back to its original image. Basic Autoencoder, Variational Autoencoder, and Convolutional Autoencoder are the three approaches that were employed to denoise the picture. In the basic and convolutional autoencoders, there is only one loss parameter, however in the variational autoencoder, there are two losses: generative loss and latent loss. TensorFlow as the frontend and Keras as the backend are used to implement autoencoders in this project. The noisy pictures are trained on every convolutional variational autoencoder techniques to produce a decent prediction of noisy test data.
KONTRIBUSI MINAT BACA DAN PENGUASAAN KOSAKATA TERHADAP KEMAMPUAN MEMBACA PEMAHAMAN MAHASISWA SEMSETER I STIFARM PADANG

This study aims to describe the following. First, how much is the contribution of reading interest to the reading ability of Semester I students of STIFARM Padang. Second, how much is the contribution of vocabulary mastery to the reading comprehension ability of Semester I Students of STIFARM Padang. Third, how much is the contribution of reading interest and vocabulary mastery together to the reading comprehension ability of Semester I students of STIFARM Padang. This correlational study took the population of Semester I STIFARM Padang students. Sampling was carried out using the purposive sampling technique so that 53 students were obtained as samples. The research instrument is in the form of questionnaires and tests that have been tested for validity and reliability. The results of this study are three, (1) Reading interest gives a significant contribution of 33.9% to the ability to read comprehension because tcount &gt; ttable or 5,110 &gt; 1,684. (2) Vocabulary mastery gave a significant contribution of 35.3% to reading comprehension ability because tcount &gt; ttable or 5.27 &gt; 1,684. (3) Reading interest and vocabulary mastery together gave a significant contribution of 51.8% to reading comprehension ability because tcount &gt; ttable or 26.87 &gt; 3.18.
Existentially positive Mustafin theories of S-acts over a group

The paper is connected with the study of Jonsson spectrum notion of the fixed class of models of Sacts signature, assuming a group as a monoid of S-acts. The Jonsson spectrum notion is effective when describing theoretical-model properties of algebras classes whose theories admit joint embedding and amalgam properties. It is usually sufficient to consider universal-existential sentences true on models of this class. Up to the present paper, the Jonsson spectrum has tended to deal only with Jonsson theories. The authors of this study define the positive Jonsson spectrum notion, the elements of which can be, non-Jonsson theories. This happens because in the definition of the existentially positive Mustafin theories considered in a given paper involve not only isomorphic embeddings, but also immersions. In this connection, immersions are considered in the definition of amalgam and joint embedding properties. The resulting theories do not necessarily have to be Jonsson. We can observe that the above approach to the Jonsson spectrum study proves to be justified because even in the case of a non-Jonsson theory there exists regular method for finding such Jonsson theory that satisfies previously known notions and results, but that will also be directly related to the existentially positive Mustafin theory in question.