Reviewerly/exHarmony huggingface dataset:
Penggunaan Media Internet Sebagai Sumber Belajar di Kalangan Peserta Didik Kelas XII IPS di SMA Pertiwi 1 Padang Pada Masa Pandemi Covid-19

This research was motivated by the use of internet media as a learning resource among students of class XII IPS at SMA Pertiwi 1 Padang during the Covid-19 pandemic. This study aims to explain the use of internet media as a learning resource among students of class XII IPS at SMA Pertiwi 1 Padang. This study uses an approach approach, with the type of phenomenological research, the selection of informants is done by purposive sampling with 18 informants, data collection by observation, in-depth interviews, and documentation studies. Data Reduction (Data Reduction), Data Display (Data Presentation), and Conclusion Drawing/Verification (Conclusion Drawing). The research location is in SMA Pertiwi1 Padang. The results of this study show that there are six motives that cause students to use the internet as a learning resource in the midst of the Covid-19 pandemic. 1) Efforts to fulfill school assignments. 2) Fast pace in information retrieval. 3) Information on learning resources on the internet is more complete. 4) Up-to-date notifications. 5) Make the task easier. 6) Cheaper rates.
INVESTIGATING SPHERICAL EPIPOLAR RECTIFICATION FOR MULTI-VIEW STEREO 3D RECONSTRUCTION

Abstract. Multi-view stereo (MVS) reconstruction is essential for creating 3D models. The approach involves applying epipolar rectification followed by dense matching for disparity estimation. However, existing approaches face challenges in applying dense matching for images with different viewpoints primarily due to large differences in object scale. In this paper, we propose a spherical model for epipolar rectification to minimize distortions caused by differences in principal rays. We evaluate the proposed approach using two aerial-based datasets consisting of multi-camera head systems. We show through qualitative and quantitative evaluation that the proposed approach performs better than frame-based epipolar correction by enhancing the completeness of point clouds by up to 4.05% while improving the accuracy by up to 10.23% using LiDAR data as ground truth.
Flood Prediction Using ML Classification Methods on Rainfall Data

Abstract: Floods are among the most destructive, most complex natural disasters to mimic. Research on the development of flood forecast models has contributed to risk reduction, a policy proposal, reduction of human lives, and mitigation of flood-related property damage. To mimic the complex statistical manifestations of natural flood processes, over the past two decades, neural network approaches have contributed significantly to the development of predictive systems that provide better performance and cost-effective solutions. To prevent this problem predict the occurrence of floods or not with a rain database by investigating neural network-based strategies. Database analysis by Multi-Layer Perceptron Classifier (MLP) to capture a number of details such as dynamic identification, deficit treatment, data validation, and data cleaning/preparation will be done across the given database. To apply flood forecasts for or without accurate calculation in the class division report, find the confusion matrix and the result shows the efficiency of the python frame-based flask based on the given attributes. Keywords: Flood forecast, decision tree, Random Forest, Descent, SVM, Flasks.
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification

Text classification struggles to generalize to unseen classes with very few labeled text instances per class.In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results.Previous studies mainly aim to derive a prototype representation for each class.However, they neglect that it is challenging-yet-unnecessary to construct a compact representation which expresses the entire meaning for each class.They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification.To deal with these issues, we propose a metalearning based method MGIMN which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning.The key of instance-wise comparison is the interactive matching within the class-specific context and episode-specific context.Extensive experiments demonstrate that the proposed method significantly outperforms the existing SOTA approaches, under both the standard FSL and generalized FSL settings.
DEVELOPMENT OF TEST CORPUS WITH LARGE VOCABULARY FOR TURKISH SPEECH RECOGNITION SYSTEM AND A NEW TEST PROCEDURE

The most fundamental problem in the automatic speech recognition systems is not the development of a domainspecific automatic speech recognition system, but the development of an automatic speech recognition system with a large vocabulary. Developed automatic speech recognition systems should be tested with a large vocabulary test dataset. For this reason, an automatic speech recognition test corpus was prepared within the scope of the study. Prepared automatic speech recognition test corpus includes conversations from 20 different areas and text files of these conversations. The test procedure presented in the study was also tested on Turkish automatic speech recognition systems with a large vocabulary. It has been observed that the word error rate results ranged between 14-21%. The test corpus and test procedure with a large vocabulary prepared are guiding for the success of automatic speech recognition systems in future studies to be revealed more clearly.
Explanation of Womens’ Ideological Linguistic Aesthetics in Poetry by Indonesian and Malaysia Women Poets

Linguistic aesthetics is seen from the linguistic side with a critical view. The text is built from several linguistic tools in which there are ideology and power. This study aims to find and describe (1) vocabulary, (2) the beauty of metaphor, (3) grammar in Indonesian and Malaysian poetry. The literary approach used is a comparative literature approach. This research data is in the form of a poetry text consisting of vocabulary, phrases, and lines with certain ideological values. Data collection activities were carried out using documentation techniques with knowledge and insight into the intrinsic and extrinsic elements of poetry, and critical discourse analysis of poetry, particularly concerning the ideological insights of women in Indonesian and Malaysian poetry, and interviews were conducted with the poets. Data analysis procedure using diagrams of the stages of understanding women’s ideology based on the elaboration of Ricoeur’s hermeneutics: explanation-analysis and interpretation. Triangulation was carried out to test theories, data, and research methods to avoid research bias. The findings of this study are the linguistic aesthetics of the poems by the Indonesian and Malaysian female poets in the form of (1) vocabulary, (2) the beauty of metaphor, and (3) grammatical. This study strengthens the theory of critical discourse analysis because the findings of this study indicate that linguistic aesthetics describes three stages: description, interpretation, and explanation. Practically, these findings are useful for language and literature researchers to enrich the application of critical poetry to poetry.
MENGUKUR TINGKAT KEPUASAN PENGGUNA APLIKASI KEARSIPAN MENGGUNAKAN SYSTEM USABILITY SCALE DAN PIECES FRAMEWORK

The Archives Application is an application owned by the Information System Student Association (HIMASI) of the National University which is used to manage various important documents and manage the data of the association administrators.The archival application was made by the research and development division.For the System Usability Scale (SUS) method and the PIECES Framework are two methods used to measure the level of satisfaction of the users of the HIMASI archive application.By knowing the level of user satisfaction, we can also find out whether the application is feasible to use and running well. in the System Usability Scale method, a score of 79 is included in the good category and grade B. Then the pieces framework method gets an assessment of 3,996, therefore it is in the satisfied category.With the results of the two methods, the level of satisfaction of the users of the archive application is stated to be satisfied and good by the users.Then proceed with testing the system using PageSpeed Insgihts.This tool is used to see the performance of an application.The average score given by PageSpeed Insight to archival applications is 78 with the score stating that the pages in the archiving application have had good performance
Opinion Piece: Can we Fix the Scope for Coreference?

Current work on automatic coreference resolution has focused on the OntoNotes benchmark dataset, due to both its size and consistency. However many aspects of the OntoNotes annotation scheme are not well understood by NLP practitioners, including the treatment of generic NPs, noun modifiers, indefinite anaphora, predication and more. These often lead to counterintuitive claims, results and system behaviors. This opinion piece aims to highlight some of the problems with the OntoNotes rendition of coreference, and to propose a way forward relying on three principles: 1. a focus on semantics, not morphosyntax; 2. cross-linguistic generalizability; and 3. a separation of identity and scope, which can resolve old problems involving temporal and modal domain consistency.
THE PROBLEM OF LANGUAGE TEACHERS IN ONLINE LEARNING DURING THE PANDEMIC AN ANALYSIS OF TEACHING DISCOURSE IN SCHOOLS

The problems in the research are how problematic language teachers are in online learning during the pandemic, and how to solve problems for Indonesian language teachers in online learning during the pandemic. The purpose of this study is to describe the problems of Indonesian language teachers in online learning during the pandemic and to describe problem solving solutions for Indonesian language teachers in online learning during the pandemic. This research is a qualitative descriptive research. This type of research does not have a comparison group. The subjects in this study were Indonesian language teachers at SMP and SMA Negeri 1 Ampibabo in Ampibabo village. Data collection in this study were questionnaires, interviews and documentation. The data from the evaluation were analyzed using qualitative data analysis methods. The results of this study are (1) Inadequate internet network access during the online learning process. (2) The teacher is a "debt collector" in carrying out the online teaching and learning process, the teacher only gives assignments continuously and collects assignments at a predetermined time. (3) Teachers still stutter in mastering technology so that in the implementation of online learning it is still difficult to choose relevant applications and teaching methods are still not varied. (4) The implementation of online learning greatly affects the psychological condition of students where in online learning these students experience boredom and are lazy to participate in learning. The teacher's practical solution in implementing online learning is by applying offline learning by visiting from home to home where in the offline learning process students are very active in the learning process compared to online learning which cannot be carried out effectively
THE INTERPLAY OF PERSONALITY FACTORS AND LANGUAGE LEARNING STRATEGIES IN DEVELOPING SPEAKING SKILL

Speaking skill was one of essential parts in language learning and needed to be mastered by every student for a better foreign language communication. The aims of this research were (1) to find out the language learning strategies used by the students based on their personality factors (2) to observe the extent to which the personality factors and language learning strategies correlate with speaking skill, (3) to elaborate the interplay of personality factors and language learning strategies in developing students’ speaking skill. 25 students in the fourth semester of 2015/2016 class of English Education Department at Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Muhammadiyah Bone were selected by purposive sampling. The research employed mixed method of descriptive quantitative (correlation) and descriptive qualitative analysis. Quantitative data were collected first from questionnaires and speaking test and qualitative data from interview. The results of the research indicated that most of extrovert students prefer to use indirect learning strategies (69%) while introvert students prefer to use direct learning strategies (89%). The correlation of personality factors and speaking skills was low (r=0.08), whereas the correlation of language learning strategies and speaking skill was strong (r=0.6). Therefore, the interplay of personality factors and language learning strategies showed that personality factors contribute to important roles in choosing the appropriate language learning strategies in developing speaking skill.
Improvement of Eye Tracking Based on Deep Learning Model for General Purpose Applications

The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.
Analisis Strategi Pemasaran Dalam Upaya Meningkatkan Jumlah Penerimaan Mahasiswa Baru

The research was conducted with the aim of knowing and analyzing marketing strategies in an effort to increase the number of new student admissions. The research was conducted at the Yadika Bangil School of Economics (STIE). Data were collected through observation, document analysis, and interviews. The data analysis technique was carried out using the SWOT analysis method (Strengths, Weaknesses, Opportunities and Threats). The results showed that the more programs there were, the more alternatives students had to choose from. In addition, the programs offered will increase student interest in registering. As well as large financial support for promotion and supported by good quality lecturers, various study programs and affordable prices can increase the number of students.&#x0D; Keyword: Increasing Number of Student, Marketing Strategy, SWOT
Information Separation Network for Domain Adaptation Learning

The Bai People have left behind a wealth of ancient texts that record their splendid civilization, unfortunately fewer and fewer people can read these texts in the present time. Therefore, it is of great practical value to design a model that can automatically recognize the Bai ancient (offset) texts. However, due to the expert knowledge involved in the annotation of ancient (offset) texts, and its limited scale, we consider that using handwritten Bai texts to help identify ancient (offset) Bai texts for handwritten Bai texts can be easily obtained and annotated. Essentially, this is a problem of domain adaptation, and some of the domain adaptation methods were transplanted to handle ancient (offset) Bai text recognition. Unfortunately, none of them succeeded in obtaining a high performance due to the fact that they do not solve the problem of how to separate the style and content information of an image. To address this, an information separation network (ISN) that can effectively separate content and style information and eventually classify with content features only, is proposed. Specifically, our network first divides the visual features into a style feature and a content feature by a separator, and ensures that the style feature contains only style and the content feature contains only content by cross-domain cross-reconstruction; thus, achieving the separation of style and content, and finally using only the content feature for classification. This greatly reduces the impact brought by cross-domain. The proposed method achieves leading results on five public datasets and a private one.
Automated speech tools for helping communities process restricted-access corpora for language revival efforts

Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs.One bottleneck is the time-intensive nature of annotation.An even narrower bottleneck occurs for recordings with access constraints, such as language that must be vetted or filtered by authorised community members before annotation can begin.We propose a privacy-preserving workflow to widen both bottlenecks for recordings where speech in the endangered language is intermixed with a more widely-used language such as English for meta-linguistic commentary and questions (e.g.What is the word for 'tree'?).We integrate voice activity detection (VAD), spoken language identification (SLI), and automatic speech recognition (ASR) to transcribe the metalinguistic content, which an authorised person can quickly scan to triage recordings that can be annotated by people with lower levels of access.We report workin-progress processing 136 hours archival audio containing a mix of English and Muruwari.Our collaborative work with the Muruwari custodian of the archival materials show that this workflow reduces metalanguage transcription time by 20% even with minimal amounts of annotated training data: 10 utterances per language for SLI and for ASR at most 39 minutes, and possibly as little as 39 seconds.
Do Institutions Cause Growth? Evidence from Asian Countries

A well-maintained political stability and economic freedom can encourage economic growth through investment, human capital, and technological developments. Adverse phenomena such as the Asia Rohingya conflict and uprisings in the Middle East conflict create an unstable political and economic environment, requiring institutions to develop an ideal environment for investors. This paper aims to identify the effect of political and economic institutions on economic growth. This paper uses panel data from developing Asian countries in 2009-2018 using the system GMM model. The results indicate that economic institutions have a positive and significant effect on economic growth. However, political institutions have no significant effect on economic growth. These results indicate that economic institutions have an essential role in maintaining and controlling the activities of emerging markets in Asia. Good institutions have to be in place to prevent fraud in market activities. In addition, economic freedom is one of the critical factors in attracting investment into the country to have a spillover effect on technological development.
Landscape Linguistic of Sumenep Tourism Destination in the New Normal Era

This study examines linguistic landscape perspective of language used in labeling historical objects at the Keraton Sumenep Museum. Keraton Sumenep Museum is a place to collect, treat, present and preserve the historical objects of the Sumenep Palace and one of the tourism destination in Sumenep. It employed a qualitative method to describe and explain the languages displayed, the sign meterials, and the sign categories in the museum following the taxonomy signs by Spolsky and Cooper. The applied method has assisted the researchers to reach the objectives of this study by getting 124 pictures of landscape linguistic signs. The result of this research shows that linguistic landscape in the museum buildings consist of monolingual and bilingual in Indonesian and English language. The Indonesian language is being chosen because most of the visitors are from locals who can communicate in Indonesian. Moreover, English language is displayed in LL because there are a number of international tourists who come to visit the museum.
Application of Decision Tree Algorithm in Teaching Quality Analysis of Physical Education

Physical education is not only an important part of national education, but also one of the important means to improve the physical quality of students and citizens. With the rapid innovation of information technology in China, the decision tree algorithm is introduced into the teaching quality analysis of physical education, which becomes the new reform thinking direction of PE teaching, and promotes the teaching method and the training way of coaches. Under the background of new era, in this paper, the basic principle and realization process of decision tree algorithm is analyzed, applying it to the data analysis of the teaching quality of physical education. The computer is used to excavate the factors that influence the teaching of physical education. And the results show that the method can classify the data well, and the acquired knowledge is of great significance to the teaching reform of the physical education class in the future. Through the rational evaluation of teachers’ overall teaching quality, teachers can optimize the teaching process according to the evaluation results, find the weak links of students’ learning in time, remind students to review and consolidate relevant knowledge, help students improve their study and examination scores, and further improve their impact on physical education teaching behavior.
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding

Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks.This study explores distilling visual information from pretrained multimodal transformers to pretrained language encoders.Our framework is inspired by cross-modal encoders' success in visual-language tasks while we alter the learning objective to cater to the language-heavy characteristics of NLU.After training with a small number of extra adapting steps and finetuned, the proposed XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG) benchmarks, and readability benchmarks.We analyze the performance of XDBERT on GLUE to show that the improvement is likely visually grounded.