Reviewerly/exHarmony huggingface dataset:
Tanoto Foundation Partner School Literacy Exploration : Best Practice Literacy of Elementary Education Students

This research aims to explore the good practices of student literacy activities that have been done by Tanoto Foundation partner schools. This method of research used a descriptive with qualitative approach. The study involved informants who were at Tanoto Foudation partner schools, including principals, teachers and students. Data were collected with interviews, observations as well as documentation. Data analysis is carried out with descriptive qualitative including data collection, data presentation, data reduction and drawing conclusions. The results showed best practice that has been done by regional Tanoto partner schools in driving literacy, among others, providing literacy corners, scheduling literacy activities, reading books for 10-15 minutes, making wall magazines, bartering movements and donating books, holding and participating in literacy competitions and posting literacy activities outside. These findings offer good practice that can be an example in increasing literacy in schools.
Habituation of the Character of Enjoying Reading Through the School Literacy Movement Program in the Lower Grades of Elementary Schools

This study aims to describe the implementation of character education fond of reading through the school literacy program for students at SDIT Muhammadiyah Al Kautsar, Kartasura. This study used a descriptive qualitative method with a phenomenological approach. The research subjects were the grade II teacher, the Library Division, and the Vice Principal for Student Affairs. This research instrument used Human Instrument, where this qualitative research determined the focus of research, selected information as data sources, conducts data collection, assessed qualitative data, analyzes data, interpreted data, and made conclusion based on its findings. In qualitative research, besides acting as the main instrument, researchers also used data collection techniques, including observation, interviews, and documentation. The results of this study showed that the application of the character of fond of reading in the School Literacy Movement program was carried out every day during school hours by reading books for 10-15 minutes of reading, then making a core of the task read after that telling or presenting it back. To support school literacy activities, facilities, and infrastructure in the form of reading corners located in classrooms, as well as literacy cards to record the results of students' independent reading, were provided. Meanwhile, students' reading interest at SDIT Muhammadiyah Al Kautsar Kartasura was known through the Literacy Card for student visits to the library. The implementation of literacy classes was related to increasing students' interest in reading because students can take the time to read a little to fulfill literacy cards. If the application of this love of reading can run well and regularly, the student's interest in reading will also increase.
Efektivitas E-Modul Dilan Berbasis Android (DIDROID) Terhadap Hasil Belajar IPA Siswa Sekolah Dasar

This research aims to analyze the effectiveness of the E-Module Dilan based on Android (DIDROID) on science learning outcomes on heat material on the student's learning outcomes of class V at SD Inpres Bumi Sagu. This study used experimental research conducted in the experimental and control groups. The research design used was a pretest-posttest control group design. Subjects in this study amounted to 37 students. The instruments used were tests, that was, multiple-choice questions that had been tested for validity and reliability. Data analysis used the prerequisite test, namely the normality and homogeneity tests, and hypothesis testing using the paired sample t-test analyzed using SPSS For Windows 25. Based on the results of this study shows a significance value of 0.000 &lt;0.05 then the hypothesis Ha is accepted, and Ho is rejected, so it can be concluded that the use of the Dilan E-module, based on android (DIDROID) on science learning outcomes on heat material is effectively used on the students’ learning outcomes of class V SD Inpres Bumi Sagu.
Development of Science Learning E-Modules Using the Flip PDF Application

This study aims to analyze the results of the validity and the practicality of science e-modules using the flip pdf application at SMP Muhammadiyah Al-Kautsar PK. Development research was conducted using the ADDIE model. Interviews and questionnaires were used as data collection techniques. The data analysis technique used descriptive qualitative and quantitative analysis. The e-module product validation was carried out by three experts, namely subject content experts, design experts, and media experts, with an average score of 93.3%, indicating that the E-Module was in very good qualification. The practicality test was carried out using a student response questionnaire after using the e-module; an average of 93.7% was obtained with very practical criteria. The conclusion is that the science e-module developed using the flip pdf application is valid and practical.
Improving Chest X-ray Report Generation by Leveraging Text of Similar Images

Abstract Automatic medical report generation is the production of reports from radiology images that are grammatically correct and coherent. Encoder-decoder is the most common architecture for report generation, which has not achieved to a satisfactory performance because of the complexity of this task. This paper presents an approach to improve the performance of report generation that can be easily added to any encoder-decoder architecture. In this approach, in addition to the features extracted from the image, the text related to the most similar image in the training data set is also provided as the input to the decoder. So, the decoder acquires additional knowledge for text production which helps to improve the performance and produce better reports. To demonstrate the efficiency of the proposed method, this technique was added to several different models for producing text from chest images. The results of evaluation demonstrated that the performance of all models improved. Also, different approaches for word embedding, including BioBert, and GloVe, were evaluated. Our result showed that BioBert, which is a language model based on the transformer, is a better approach for this task.
Pengembangan Video Pembelajaran Desain Grafis untuk Buku Digital Interaktif bagi Siswa Tunarungu

This research aims to develop graphic design learning videos for interactive digital books for deaf students. This development is intended to complement visual material in digital books as teaching materials specifically designed according to student characteristics. The subject being developed was graphic design at the high school level in special education schools. The method used in this study was research and development with SAM (Successive Approximation Model). The validation included media, material (content), learning, and language. This data analysis technique used quantitative descriptive research. This research data analysis technique used quantitative descriptive. The study results showed that the learning videos that had been developed could aid in the understanding of theory as an abstract for the deaf with explanations of text, infographics, and sign language in the video. Sign language expressions made students interested and motivated to learn. The results of the validity evaluation by experts from the media aspect were 76%, Very Feasible, no need for revision and content 76%, Very Feasible, no need for revision; learning aspects 79%, Very Feasible, no need for revision; language 82%, Very feasible, no need revision. The summary score of experts and students was 78%, which was Very feasible, and did not need to be revised.
The Dynamics of Economic Globalization and Income Inequality in Sub-Saharan African Countries: An Empirical Insight from Two Stage Least Square and Fixed Effect Approach

Many empirical studies have examined the nexus between economic globalization and income inequality. However, findings are controversial, especially for developing countries, which require further investigation. This study, therefore, aims to examine the effect of economic globalization on income inequality using unbalance panel data in 19 Sub Saharan African countries (SSA) from 2000 – 2015. Due to endogeneity problem and cross sectional dependence, the model was analyzed using a two-stage least square (2SLS) regression and the fixed effect model with Driscoll-Kraay standard errors regression respectively. The findings reveal that FDI, Trade openness, and ICT development are positive and significantly link to income inequality while remittances have a negative and significant effect. This is an indication that economic globalization indicators (FDI, Trade openness, ICT) have failed to bring a fairer income distribution in these countries while remittances have contributed to income distribution. On basis of this conclusion, policymakers should pursue policies, which will enhance a balanced development and improvement in the socioeconomic welfare of the population by improving measures of income distribution.
Premenstrual Syndrome and Mindfulness: the mediating effect of Perceived Social Support and health-related quality of life among deaf/hard of hearing women

The purpose is to explore the relationship between Premenstrual Syndrome (PMS) and Mindfulness as well as the mediating role of Perceived Social Support (PSS) and health-related quality of Life (HRQoL) among deaf/hard-of-hearing women. This is a cross-sectional study. An overall sample of 202 participants was selected. Path analysis was used. As indicated by the results, PMS correlated significantly with Mindfulness, PSS and HRQoL. Moreover, Mindfulness correlated significantly and positively with PSS and HRQoL. Besides, there is a significant negative relationship between PSS and HRQoL. PMS has direct effects on PSS and HRQoL. In addition, both PSS and HRQoL have immediate impacts on Mindfulness. To conclude, this study examined and confirmed the relationship between PMS and Mindfulness as mediated by PSS and HRQoL among deaf/hard-of-hearing women.
Treatment Missing Data of Daily and Monthly Air Temperature in Iraq by Using Mean Method

Frequent data in weather records is essential for forecasting, numerical model development, and research, but data recording interruptions may occur for various reasons. So, this study aims to find a way to treat these missing data and know their accuracy by comparing them with the original data values. The mean method was used to treat daily and monthly missing temperature data. The results show that treating the monthly temperature data for the stations (Baghdad, Hilla, Basra, Nasiriya, and Samawa) in Iraq for all periods (1980-2020), the percentage for matching between the original and the treating values did not exceed (80%). So, the period was divided into four periods. It was noted that most of the congruence values increased, reached in summer (70%-100%), and decreased somewhat in winter. While the daily treatment using the mean method for the stations Baghdad and Basra (2010-2020), it turns out that most of the congruence values in the summer ranged (70%-100%), but in winter, the congruence is often decreased. Therefore, this method gives high accuracy when treating monthly and daily temperatures in summer and less in winter.
Constructing a Digital Certificate Authentication System for Classified Documents

In this paper, we introduce a new digital authentication certification system to keep the classified documents' information safe. The proposed system is a steganography system divided into two subsystems, the first subsystem is responsible for embedding the information about the person, and it works in the main foundation that issues the documents, while the second subsystem is found in the beneficiary directorates to extract the true information of the person.
Facial Expression Recognition Based on Deep Learning: An Overview

Recognizing facial expressions and emotions is a basic skill that is learned at an early age and it is important for human social interaction. Facial expressions are one of the most powerful natural and immediate means that humans use to express their feelings and intentions. Therefore, automatic emotion recognition based on facial expressions become an interesting area in research, which had been introduced and applied in many areas such as security, safety health, and human machine interface (HMI). Facial expression recognition transition from controlled environmental conditions and their improvement and succession of recent deep learning approaches from different areas made facial expression representation mostly based on using a deep neural network that is generally divided into two critical issues. These are a variation of expression and overfitting. Expression variations such as identity bias, head pose, illumination, and overfitting formed as a result of a lack of training data. This paper firstly discussed the general background and terminology utilized in facial expression recognition in field of computer vision and image processing. Secondly, we discussed general pipeline of deep learning. After that, for facial expression recognition to classify emotion there should be datasets in order to compare the image with the datasets for classifying the emotion. Besides that we summarized, discussed, and compared illustrated various recent approaches of researchers that have used deep techniques as a base for facial expression recognition, then we briefly presented and highlighted the classification of the deep feature. Finally, we summarized the most critical challenges and issues that are widely present for overcoming, improving, and designing an efficient deep facial expression recognition system.
Offensive Language Detection in Social Media Using Transformers and Importance of Pre-training

Being exposed to offensive language on social media platforms is relatively higher because of anonymity and distant self-expression compared to real communication.Billions of contents are shared daily on these platforms, making it impossible to detect offensive posts with manual editorial processes.This situation arises the need for automatic detection of offensive language in social media posts to provide users' online safety.In this paper, we applied different Machine Learning (ML) models on over manually annotated 36,000 Turkish tweets to detect the use of offensive language messages automatically.According to the results, the most successful model for predicting offensive language is pretrained transformer-based ELECTRA model with 0.8216 F-1 score.We also obtained the highest F-1 score with 0.8342 in this dataset up to now by combining transformer-based ELECTRA and BERT models in an ensemble model.
Performance and Daily Bread: An Action Research

Agricultural education teaches students about agriculture skills, food and natural resources. While feeding program is always been important for providing of nutrients and enhancement of academic performance. This study investigated the outcomes of daily bread intervention to learners’ performance in agricultural subject. The study is action research utilize the framework of pre-experimental approach, particularly the pre-test and post- test design. The respondent of the study is the Grade 7 Learners of Daanbantayan National High school. The data were obtained from the learner’s performance in agriculture subject and their body composition. Through presentation, Analysis and interpretation of the data it was determined that the average performance of the learners in agriculture subject increases. While Learners body composition changes from severely wasted to normal body composition. The association between the two variables were analyzed through the body composition specifically the Body mass index (BMI) of the learners. Result showed that the daily bread intervention increases learners’ performance in agriculture subject. It also means that School based feeding program contributed to learners’ performance. Learners are more motivated to be on School daily by the help of the feeding program.
The Impact of Cybercrime on the Learning Outcomes of Social Studies Undergraduates in Delta State University.

The study examined the impact of cybercrime on the learning outcomes of Social Studies undergraduates. Correlational research design was the study design. Eighty (80) Social Studies students were utilised for the study. Two (2) research instruments (Students academic and (questionnaire) were employed to generate the study data. The data gathered were analysed and presented in tables. The Pearson Products Moment Correlation (PPMC) was used to answer the research questions, while regression statistics was utilised for the hypotheses testing. The study established that there is a significant relationship between cybercrime and students' learning outcomes of Social Studies undergraduates; there is a significant association between cyber-crime and unemployment. It was recommended that the government should provide youth empowerment as a solution to unemployment, government should implement cyber security best practices, especially in the financial sector, security software should be installed on personal devices and universities should introduce cyber security as a compulsory course.
Blockchain: Research and Application

Blockchain has been invented for years and it gradually become the most important technique in nowadays’ internet era. Blockchain technology has been widely used in various fields, such as finance, trading, logistics, currency, etc. This paper explains and demonstrates the main algorithm and application of the blockchain. Until now, the primary applications of blockchain are mainly focus on the digital currency such as ETH. The value of blockchain, however, is not limited by only the digital currency but all the aspect within the internet. For instance, based on these years’ social concern, the pandemic of COVID-19 is the most worried problem. The application of this paper concentrates on the connection between COVID-19 and blockchain. By relating the technique of blockchain, the security and data tracing of medical supplies have been insured. Moreover, the personal information security is maintained during the process of doing the covid-test and the trajectory of activity.
Petrology and Geochemical Features of Crystalline Rocks in Ora-Ekiti, Southwestern Nigeria

This research investigates and reports on the petrology and geochemical characteristics of crystalline basement rocks in Ora-Ekiti, Southwestern Nigeria. Exhaustive geological investigation reveals migmatite, banded gneiss, granite gneiss and biotite gneiss underlie the area. In reducing order of abundance, petrographic examination reveals that migmatite contains quartz, muscovite and opaque minerals. Banded geniuses contain quartz, biotite, plagioclase, and opaque minerals. Granite geniuses contain quartz, plagioclase, biotite, microcline and opaque; while biotite geniuses contain biotite, plagioclase, opaque minerals, and quartz. Silica contents in migmatite (69.50%-72.66%; ca. 71.23%), banded gneiss (71.66%-77.1%; ca. 75.23%), biotite gneiss (72.32%-76.18%; ca. 73.83%) and granite gneiss (69.82%-73.15%; ca. 71.95%) indicate the rocks are siliceous. High alumina contents in migmatite (12.18%), banded gneiss (10.28%), biotite gneiss (11.46%) and granite gneiss (9.97%) are comparable to similar rocks in the basement complex. All the rocks show Ba, Sr and Rb enrichment. Harker diagrams of Al2O3 versus SiO2 and CaO versus SiO2 show negative trends while Na2O versus SiO2, K2O versus SiO2 and TiO2 versus SiO2 plots showed positive trends. This variation probably depicts extensive crystal fractionation in the magmatic systems that produced the rocks prior to metamorphism or partial melting of the precursor rock. SiO2 versus (Na2O + K2O) classifies the rocks as granite to granodiorite. The rocks are high K-calc-alkaline and calc-alkalic on SiO2-K2O plot. This shows the rocks are potassic meaningthat they are formed from a potassium-rich source. The plot of Al2O3/(Na2O + K2O) versus Al2O3/(CaO +Na2O + K2O) reveals the crystalline rocks are orogenic and originated from granitoid with meta luminous affinity. The rocks consist of gneisses of no economic minerals, but the petrology reveals them as common rocks typical of metamorphic terrains and geochemical features of the rocks reveal they are felsic and of granitic composition.
THE DEGREE OF ELECTRONIC MANAGEMENT APPLICATION IN PUBLIC EDUCATION SCHOOLS IN HAIL IN LIGHT OF THE KINGDOM OF SAUDI ARABIA'S VISION 2030

The study aimed to identify the electronic management degree of application in public schools in Hail in light of Saudi Arabia's 2030 vision. The researcher developed a questionnaire consisting of three dimensions: (Management Operations, Human Resources, and Electronic Organization) which was distributed to a random sample of (65) female educational supervisors in public education schools in Hail city. The descriptive analysis method was used in the study. The study concluded that the three dimensions came with a medium mean. The results also showed no variations according to the educational qualification variable. Furthermore, there were significant differences according to the work experience variable, and the differences were for those with more experience.
ENHANCING THE PERFORMANCE OF SIGN LANGUAGE RECOGNITION USING CNN AND RNN CLASSIFIER

Sign language gave hope to deaf and dumb people to communicate with others. Although there is a significant development in sign language, Sign recognition systems are created to increase efficiency in sign recognition. Our sign language recognition technology was created to aid in communication for individuals suffering from hearing and visibility issues. We have created it using CNN (Convolutional neural network) and RNN (Recurrent neural network). After pre-processing the images, the images are used in the dataset containing the 25 English alphabets. We built CNN and trained it with the dataset. After training the model we have predicted the accuracy. To increase the accuracy we have enhanced the CNN model with RNN. The best accuracy obtained is 88.8% for CNN with RNN. Keywords- Convolutional neural network, Recurrent neural network
A Survey on Blockchain-Based Supply Chain Management in Agriculture

India is one of the fifth largest economies in the world by nominal GDP with 70 per cent of the rural households still depending on agriculture as their primary source of livelihood. India is responsible for 7.39 per cent of total world agricultural production. But the agricultural supply chain used in India is inefficient and caters less to the producer. Farmers who are the producers get very less profit from selling their produce. It is estimated that about 80 percent of the profits are secured by the middlemen who have significant control over the agriculture trade in the country. The lack of communication between different stages in the chain and data transparency is also one of the major reasons why the current supply chain in India is flawed. Proper communication is essential in any system to avoid the spread of any misinformation and at each stage in the supply chain sensors are required to share data with the users so that information is collected and made available to everyone concerned with the particular transaction of goods. The combination of blockchain and IoT in the agricultural sector can provide a better supply chain system from the ground up which addresses communication and transparency issues suffered by the latter systems.