Reviewerly/exHarmony huggingface dataset:
Confusion Matrix as Performance Measure for Corner Detectors

Nowadays, corner detection algorithms have been proposed by several researchers who described them contrarily, depending on their respective viewpoints to obtain the data and information as a human eye does. Basically, no researchers have come up with a technique to compare corner detectors with another’s. Thus, this study proposed to adapt the confusion matrix technique as a performance measure for corner detectors. The judgement accuracy of every corner detector will only be pleased if the actual corner points are already known. Therefore, this study is attracted to explore the accuracy of corner detectors, namely the Global and Local Curvature Scale space (GLCSS), Affine Resilient Curvature Scale Space (ARCSS), and Harris. These corner detectors were analysed using the nine characters selected from Jawi, Chinese, and Tamil characters, three characters each, respectively. This study specifically detected the true corners for these characters using the determined corner detectors. The actual corner of all these characters was confirmed through a survey of twenty respondents. The majority of marked corners by respondents were considered actual corner points. Then, the input image for all characters was converted into a grayscale image. Every image will undergo pre-processing step, the process of boundary extraction using Canny edge detector. Thus, the edge image was extracted to get the corner point by applying the corner detectors, and the corner point detected was marked on that image. Above and beyond, the study aims to introduce a confusion matrix approach as a performance measure to carry out the most outstanding algorithm in detecting the true corner points for all the tested characters. From the evaluation, GLCSS and Harris algorithms have shown good accuracy. Henceforth, the study is not trying to judge the goodness of each corner detector but only to introduce confusion matrix as a tool that can be considered to measure the performance of the corner detector.
Potential sections for the development of solar energy using remote sensing data and GIS in Dak Nong Province, Viet Nam

For promoting the development of renewable energy in Vietnam, in 2015 the Government approved the "Strategy for the development of renewable energy in Vietnam to 2030, with a vision to 2050" intending to gradually increase the proportion of renewable energy in national energy production and reduce dependence on fossil energy sources, contribute to ensuring energy security, mitigating climate change, and sustainable socioeconomic development.In recent decades, remote sensing and Geography information of system (GIS) have been able to build thematic maps with high accuracy for managing and monitoring natural resources and the environment, including the solar radiation potential.Establishing solar potential maps from satellite data combined with natural conditions, topography, and land cover will effectively assist in planning solar energy development while helping to identify the appropriate technology and lowest cost.Therefore, this paper presents the zonalization of the solar energy potential based on its calculations from Himawari-8 satellite and elevation from The Shuttle Radar Topography Mission data applied for Dak Nong province.The results show that only about 18% of land in Dak Nong province is suitable for solar energy development.The appropiate sites for the development of solar energy are distributed in different regions of the Dak Nong province.
Data Storage, Data Forwarding, Data Retrieval With Big Data Deepfakes in Secure Cloud Storage

Deduplication technologies are extensively used in distributed storage management to lower the space and transfer rate needs of services by eliminating redundant information and storing only its single replica. Many clients redistributing comparable data to a shared repository is ideal for deduplication; however, this practise raises concerns about data ownership and confidentiality. Each data owner can confidently show a distributed storage server that they are the rightful owner of a piece of data via confirmation of holding plans. In contrast to state, many clients would encrypt data before re-appropriating it to distributed storage for added safety. Due to the unpredictable nature of encryption, this prevents data duplication. Some recent deduplication systems have been proposed to solve this issue by allowing multiple owners to share the same encryption key for identical data.
Top-down Determination of Fluctuations in Topographic Measurements

A top-down method is presented and studied for quantifying topographic map height (z) fluctuations directly from measurements on surfaces of interest. Contrary to bottom-up methods used in dimensional metrology, this method does not require knowledge of transfer functions and fluctuations of an instrument. Fluctuations are considered here to be indicative of some kinds of uncertainties. Multiple (n), successive topographic measurements (z = z(x,y)) are made at one location without moving the measurand relative to the measurement instrument. The measured heights (z) at each position (x,y) are analyzed statistically. Fluctuation maps are generated from the calculated variances. Three surfaces were measured with two interferometric measuring microscopes (Bruker ContourGT™ and Zygo NewView™ 7300). These surfaces included an anisotropic, turned surface; an isotropic, sandblasted surface; and an abraded, heterogeneous, multilayer surface having different, complex, multiscale morphologies. In demonstrating the method, it was found that few non-measured points persisted for all 100 measurements at any location. The distributions of uncertainties are similar to those of certain features on topographic maps at the same locations, suggesting that topographic features can augment measurement fluctuations. This was especially observed on the abraded ophthalmic lens; a scratch divides the topographic map into two zones with different uncertainty values. The distributions of fluctuations can be non-Gaussian. Additionally, they can vary between regions within some measurements.
Developing a Dayak Folk Storybook as Supplementary Material for Teaching Narrative Text

This study aimed to develop a Dayak Folk Storybook that can be used as supplementary material to teach the narrative text. This research is Design and Development Research. The respondents of this research were year-8 students of SMP Pelita Harapan Pontianak. This research was done by conducting three phases of the ADDIE approach: Analyze, Design, and Develop. The researchers analyzed the needs focusing on identifying the materials and tasks that should be created based on the result of students’ questionnaires, teacher’s interviews, and syllabus reviews in the analysis phase. Then, the phase continued to design the materials, creating the draft of the materials and tasks. Finally, it was finalized in the development phase of the product. The result of this research is a supplementary folk storybook for learning narrative text, namely Collection of Kalimantan Dayaknese Legends for Learning English Reading. From the result of internal evaluation of the product, it was indicated that the product was appropriate and relevant with the teacher’s and students’ background, needs, and preferences. Therefore, the supplementary storybook is recommended to be used by teachers to teach narrative text to 8th Grade students of junior high school.
Developing Picture Storybook in English with Wetlands Theme for Young Learners

Stories encourage young learners to understand concepts and vocabulary while expanding their background knowledge about their surroundings. Therefore, providing young learners with a variety of storybooks to develop their understanding of their environment is important to be done by considering their interests and the content of the books. Based on this view, this research aimed to design a picture storybook written in English and with a wetlands environment. Simultaneously this research and development (R&amp;D) referred to development procedures by Borg and Gall (2007) that simplified the process to only seven procedures: prelim approach methods data collection, research planning, early product development, expert validation, revision, field testing, and the last is disseminating and implementing the final product. The instruments used to collect data were a questionnaire, an interview with 15 kindergarten teachers on the need analysis stage and field testing stage, and two validation sheets validating the content and design stage. The results from data analysis showed that it is important to name some animals and plants that live specifically in South Kalimantan wetlands as the main characters of the storybook, such as timpakul, haruan, papuyu, bekantan, mangrove trees, and kasturi. Next, the developed picture storybook was feasible to be used as media for introducing English and wetlands since it was proven to be valid, practical, and effective.
The Implementation of Flipped Classroom to Develop Students' Reading Skill

The objective of this study was to investigate how the implementation of the flipped classroom in improving students' reading skills. This research was conducted using the Classroom Action Research (CAR) method that uses four phases according to the Kemmis and McTaggart model which includes planning, action, observation, and reflection. The subjects of this study were 18 students of class 10 science 1 SMAN 19 Kabupaten Tangerang. The data gathered in this study through observation sheet, interviews, and tests. The findings of the study found that the implementation of the flipped classroom succeeded in improving students' reading skills in which the average score of students who reached the minimum completeness criteria that students obtained an average score of 77.33 in the post-test cycle 2 compared to the pre-test score in which most of the students did not meet the minimum completeness criteria. This study also found that the implementation of the flipped classroom had a positive impact on student learning participation.&#x0D; Keywords: Classroom Action Research; Flipped Classroom; Reading Skills
Factors Affecting Labor Productivity on Construction in Kurdistan of Iraq: Web Survey

This study was set out to investigate factors affecting labor productivity on construction in the north of Iraq (Kurdistan) and to rank all the factors based on engineers, contractors, and designer’s opinions. 76 factors were analyzed based on previous literature and a pilot study. Next, by using online Google Form, a questionnaire form was created and sent to people who have experience in the construction industry. Afterward, the questionnaire form was sent to targeted people by email and social media apps. Factors were divided into nine groups “Management, Technical and Technology, Human and Workforce, Leadership, Motivation, Safety, Time, Material and Equipment, and External”. However, 202 respondents participated in this study, and they were asked to give weight to the factors using the Likert scale from 1 to 5. Finally, the Relative Importance Index RII was used to determine the factors statically with MS Excel 2015. In brief, all the respondents agreed upon “Economic condition in the country” is the first ranking factor. While “Site complication” was the last factor that affect labor productivity in construction. Last but not least, the “Motivation” group was the first ranked group. Apart from the factors, respondents agreed that Site Engineers have more effect on construction projects than Contractors and Designers.
Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records

In recent years, there has been a huge amount of data collected in hospitals in terms of electronic medical records (EMR), continuity of care documents (CCD), electronic healthcare records (EHR), and many more. These records contain information about patients such as their location, symptoms, diagnosed disease, blood test results, scan images and reports, recommended medications, and doctor’s notes. The aforementioned details can assist physicians and care providers in providing better healthcare solutions for the end-user. On the other hand, the amount of data available has increased tremendously, necessitating the use of analytical, clinical, and business intelligence tools to transform it into useful information. Researchers are attempting to integrate the latest breakthroughs in deep learning technologies to extract clinical information from data traditionally used for local billing and archiving purposes, which is advantageous to people. Because these records were created for the sole purpose of local administration and hospital management, they were unstructured, necessitating deep learning research, which has been shown effective in a number of other domains because of its 134efficacy in capturing data dependencies. Clinical risk prediction based on such big data analysis of healthcare records will undoubtedly aid in the prediction of undesirable occurrences such as cardiac arrest, lowering patient mortality rates. Several researchers are working on this topic in order to provide better patient care and gain more insights into how to enhance healthcare in the future. The most difficult aspect of dealing with these records is the disparity in how patient data is kept, such as weight as numbers, admission data as dates and times, categorical values for diagnosis, natural language for doctor’s remarks, and so on. Other issues, such as sparsity, missing data, and large data dimensionality, require specific research attention. As a result, more research is needed regarding mining these massive amounts of data; at present, research has shortcomings in model design, deep learning technique deployment, and the lack of globally acknowledged assessment standards that are highly needed. The present work examines the current state of the art, existing deep learning approaches used, the research gap, and recommendations for improved deep-learning deployment in EHR research.
A Speech Act Analysis of a Pre-Service Teacher in an EFL Classroom

This research aims to find out and analyze the classification of speech acts and the function performed by a pre-service English teacher at SMAN 7 Malang. This implies a qualitative descriptive study, looking at the pre-service English teacher’s talk in an EFL classroom. The present study focuses on the classification of speech acts employed by Searle. The finding of this study indicates four speech act types uttered by the teacher. It consists of 41 representatives out of 185 utterances, 120 utterances of directive type, 7 utterances of commissives, and 17 utterances of expressive. To conclude, the results of this study show that directive type dominates the data with 64.9% out of the utterances, followed by representatives at 22.2%, expressive at 9.2%, and commissives at 3.8%.
The Cultural Values as Represented in the Indonesian Autism EFL Textbook: Critial Discourse Analysis

Culture is an inseparable aspect when an individual learns a target language. English textbook, one of the teaching-learning media, keeps a lot of hidden cultural value inside. There is a limited study investigating the representation of cultural values in an English textbook for autism students. Thus, this study is addressed to investigate both cultural representation and cultural values inside the Indonesian autism EFL textbook published by the Ministry of Education and Culture. The analysis adopted the four dimensions of culture or 4Ps as the framework. The 4Ps refer to Product, Practice, Perspective, and People. The findings of this study underscore the multicultural values represented in the autism English textbook. Those cultural values are as follows: (1) respecting the diversity of a religious group; (2) respecting the Indonesian national cultural traits; (3) respecting the diversity of ethnic groups; (4) respecting democracy; (5) having good manners; and (6) having gratitude. Recommendation for teacher or professional researcher of research and development closes the section of this study and deepened on the semiotics contained in the English textbook for children with autism and the potential theoretical which can be obtained through the analysis of the intercultural language used in the English textbook.
Students' Perceptions on Vlog as Speaking Assessment Technique at Senior High School

Vlogs among students are highly viral and beneficial for developing their language skills as a learning technique. In this study, the researcher were interested in describing how to assess students' speaking performance through fluency and pronunciation using Vlogs and how students perceived it as an assessment. The subjects of the study were students from grade tenth science three from Yos Sudarso School. Since the phenomena of pandemic is happening, hence a backup plan is required for study specifically at home. This research aims to explain how students’ speaking performance also finding out students’ first impression on the use of Vlog as a speaking assessment at SMA Yos Sudarso. The method to collect the data of this research is sequential design which is both qualitative and quantitative. This first phase was accompanied by the compilation and analysis of qualitative data in order to discuss or build on the first phase of quantitative results. The subsequent qualitative phase of the analysis was structured to be focused on the outcomes of the quantitative phase. The data of the students' speaking performance through Vlog show that most of the students have a good result in speaking through Vlog but some of the students are having difficult experience to speak in front of the camera. The research results are expected to be useful and give relevant data on how students of English, as a foreign language, use vlog as learning tool to assess speaking English performance and provide teachers innovations in utilizing the latest digital technology and trends in their teaching. As an evaluation, it is also expected that the result of this study can be meaningful feedback for the study program and the lecturer of the Speaking course.
Breast Cancer Classification using Improved Hybrid Model by Leveraging Slime Mould Algorithm and Whale Optimization Algorithm

Abstract Breast Cancer Classification is important in the medical field for disease diagnosis and assists in decisions in treatment. Poor convergence and local optima are common limitations in the existing feature selection techniques. Overfitting and imbalance data problems are common limitations in existing classifiers. The hybrid method of Whale Optimization Algorithm (WOA) – Slime Mould Algorithm (SMA) is proposed for relevant feature selection and an Auto stacked encoder is applied for classification. The WOA technique performs exploration in the first half of the iteration and the SMA method performs exploitation in the second half of iterations for relevant feature selection. The WOA-SMA technique is applied in BreakHis and IDC datasets to evaluate breast cancer classification. The ResNet18 model uses the convolutional layer, pooling layer, and fully connected layer for feature extraction process. The stacked autoencoder technique helps to map relevant features to increase learning rate of the model. The feature selection method of WOA-SMA selects unique features to improve learning efficiency of the model.
Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation

Modern software development has moved toward agile growth and rapid delivery, where developers must meet the changing needs of users instantaneously. In such a situation, plug-and-play Third-Party Libraries (TPLs) introduce a considerable amount of convenience to developers. However, selecting the exact candidate that meets the project requirements from the countless TPLs is challenging for developers. Previous works have considered setting up a personalized recommender system to suggest TPLs for developers. Unfortunately, these approaches rarely consider the complex relationships between applications and TPLs, and are unsatisfactory in accuracy, training speed, and convergence speed. In this paper, we propose a new end-to-end recommendation model called Neighbor Library-Aware Graph Neural Network (NLA-GNN). Unlike previous works, we only initialize one type of node embedding, and construct and update all types of node representations using Graph Neural Networks (GNN). We use a simplified graph convolution operation to alternate the information propagation process to increase the training efficiency and eliminate the heterogeneity of the app-library bipartite graph, thus efficiently modeling the complex high-order relationships between the app and the library. Extensive experiments on large-scale real-world datasets demonstrate that NLA-GNN achieves consistent and remarkable improvements over state-of-the-art baselines for TPL recommendation tasks.
Cost Effective Cloud Storage Interoperability Between Public Cloud Platforms

With recent advancement in technology, cloud storage became cheaper enabling organizations around the world to store more data on the cloud (texts, images, videos, databases etc.), whereas it’s for a backup, archiving or just storing data streams. New digital laws and regulation (eg. General Data Protection Regulation) require these organizations to change their way of processing or handling data, which results usually in a change of cloud providers or adoption of hybrid architecture or multi-cloud one. With the amount of data stored increasing year after year, it becomes difficult for these organizations to change cloud platforms or cloud provider and migrate their data without thinking about the technical complexity, the time and the huge cost it may incur. This article discusses the data migration and interoperability issues between cloud platforms; the proposed approach provides a simple cost-effective migration that would help organizations save time and money in this process based on a hybrid ontology approach for the brokerage of data transfers.. Keywords-Cloud Computing; Storage; Security; data; migration; cost optimization;
HOW GENSHIN IMPACT ONLINE GAME IMPACTS PLAYER’S ENGLISH SKILL

This study is qualitative research that aims to determine the effect of the game Genshin Impact on the English skills of the players. The ability referred to in this study is limited to the ability to write and read. Correspondents in this study were 20 players of the Genshin Impact game between the ages of 16 – 36 years consisting of 19 male players and 1 female player. The method of data collection was done by using a closed interview method. Then, the information collected will then be analyzed in depth and detail using a grounded theory approach to see the relationship between playing Genshin Impact and the players' English (reading and writing) abilities. The results of this study are true that Genshin Impact has an influence on the English skills of the players although it is not significant
Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images

Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We present a general approach for training semantic segmentation neural networks on much smaller input sizes called Segment-then-Segment. To reduce the input size, we use image crops instead of downscaling. One neural network performs the initial segmentation on a downscaled image. This segmentation is then used to take the most salient crops of the full-resolution image with the surrounding context. Each crop is segmented using a second specially trained neural network. The segmentation masks of each crop are joined to form the final output image. We evaluate our approach on multiple medical image modalities (microscopy, colonoscopy, and CT) and show that this approach greatly improves segmentation performance with small network input sizes when compared to baseline models trained on downscaled images, especially in terms of pixel-wise recall.