Reviewerly/exHarmony huggingface dataset:
SISTEM INFORMASI PELAYANAN PENDUDUK RW 009 BOJONG PONDOK TERONG CIPAYUNG DEPOK BERBASIS JAVA

The purpose of this research is to design a population service information system that currently running on the RW 009 Bojong Pondok Terong Village Cipayung District Depok, from manual to based computer. In addition, this research also aims to design a population service information system at the RW 009 Bojong Pondok Terong Village Cipayung District Depok to be able to overcome the problems in data storage, be more effective in making reports, and so that data is not easily lost. The time research was carried out starting from March until July 2022. This research uses a qualitative approach by developing a system using the waterfall method. The result of this research is the application that the researcher made can make it easier to manage data in the database RW 009 Bojong Pondok Terong Village Cipayung District Depok because it is computerized and easy to use. With the application that the researcher made, the data of the residents of RW 009 Bojong Pondok Terong Village Cipayung District Depok is safer because it no longer uses paper media so there is no more risk of paper loss or tearing.
The Effect of Peer Assisted Learning Strategies (PALS) Toward Students’ Reading Comprehension

The study's purpose was to clarify how the peer-assisted learning method (PALS) affected students' reading comprehension in order to increase their engagement in text discussion and increase their motivation to read. The result showe that s Students' knowledge of daily life and the environment was increased. The 253 students in the entire 10th grade courses at SMAN 14 Kota Tangerang made up the study's sample population. Then, using random sampling, 72 students were chosen as the research's sample. Peer Assisted Learning Strategy (PALS) as a variable X and short answer test students' reading comprehension as a variable Y were used to collect the data. Pre- and post-test data were collected. While the control class scored 28.64 with a minimum of 7 and a maximum of 47, the experimental class's PALS score was 38.44, with a minimum of 10 and a maximum of 57. The results of the Mann-Whitney U Test's calculation of the null hypothesis can be seen; the value in the control class was lower than the t-table with a value of 0.0650.05, while the value in the experiment class was higher with a value of 0.109&gt;0.05. Given that Ha was approved and Ho was refused, it can be said that the Peer Assisted Learning Strategy (PALS) has a considerable impact on students' reading comprehension in the 10th grade of SMAN 14 Kota Tangerang.
English Writing Performance of Students in Kurir Langit Barru Modern Mosque

This study aimed to determine students’ English writing performance in Kurir Langit Barru Modern Mosque. This study used a descriptive quantitative research design with a survey method. The purposive sampling technique was used to answer the research question with a total sample was 32 students. The data of the study was obtained using a writing test. The data analysis showed that the total mean score of students’ performances in writing the descriptive text was 69.68 and was categorized as good. This means that students in Kurir Langit Barru had good English writing performance, they do not have any difficulties in writing a descriptive text, although in some aspects their performance was fair.
REPORT ON POST-EDITING OF ABSTRACT OF AEROSPACE ARTICLES UNDER THE GUIDANCE OF FUNCTIONAL EQUIVALENCE THEORY

Background: According to the Language Service Development in China: 2020, Machine Translation(MT) is increasingly used in the language services industry. To analyze the problems of MT and find solutions, the author post-edited ten abstracts of aerospace papers from CNKI core journals by Google translate. Principal Findings: The fact is that MT is restricted to the original text features and confined to formal correspondence, failing to recognize the differences between English and Chinese, resulting in errors at all levels: lexical, syntactic, and contextual, which greatly affects the textual quality. Therefore, it is quite necessary to conduct post-editing to correct the mistakes. In order to make the MT version accurate and readable, the author conducts the post-editing under the guidance of Functional Equivalence Theory and summarizes PE techniques. For lexical errors, especially for terminology errors, the author must figure out word meanings by checking the authoritative references. At the sentence level, the author needs to change the word order according to the meaning so as to achieve semantic equivalence. At the discourse level, the author must pay attention to the coherence and make the logical relationship clear to reproduce the contextual meaning of the original texts. Conclusions: The results of the paper show that post-editing under the guidance of the Functional Equivalence Theory can correct MT errors, increase translation accuracy, and provide references for post-editing of MT.
An Efficient Data Protection for Cloud Storage Through Encryption

Encryption of data pose data confidentiality, availability and integrity by eliminating all data leakage possibilities which is done by converting data into a cipher text that cannot be understood by unauthorized users through the use of a mathematical function called key. This research developed a secure system used for encryption and decryption process on the data supplied before such data is stored on the cloud storage system. The model used in developing the secure system is 256-bit key encryption and adopts the Base64 character encoding scheme rather than the widely used ASCII standard. Variations of data supplied to the proposed secure system were from several multimedia data (such as video, audio, text, and image) and other types. The model performance was tested and evaluated by brute-force attack and program execution time. The implementation was done using Python programming language and Google Drive as the cloud storage and the encrypted data is supported by the cloud storage system. The adopted model creates a better and efficient mechanism for data supplied by encoding the key to be used for encryption in base64. It gives a better security technique with lesser computational time.
Effective and efficient active learning for deep learning-based tissue image analysis

Abstract Motivation Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface. Results We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our diversity-aware data acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy [Network Auto-Reduction (NAR)] that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a tumor-infiltrating lymphocytes classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different convolutional neural networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection. Availability and implementation Source code: https://github.com/alsmeirelles/DADA.
The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

The Holy Qur’an has recently gained recognition in the field of speech-processing research. It is the central book of Islam, from which Muslims derive their religious teachings. The Qur’an is the primary source and highest authority for all Islamic beliefs and legislation. It is also one of the most widely memorized and recited texts around the world. Listening to and reciting the Qur’an is one of the most important daily practices for Muslims. In this study, we propose a deep learning model using convolutional neural networks (CNNs) and a dataset consisting of seven well-known reciters. We utilize mel frequency cepstral coefficients (MFCCs) to extract and evaluate information from audio sources. We compare our proposed model to different deep learning and machine learning methodologies. Our proposed model outperformed the competing models with an accuracy of 99.66%, compared to the support vector machine’s accuracy of 99%.
Game-theoretical cyber deception strategy generation based on honeynet and intrusion prediction

Cyber deception is a critical component of proactive cyber defense. It releases false information about devices, applications and networks to attackers to mislead attackers, sabotage attack actions and mitigate impact of intrusion based on cyber deception strategy. There have been many researches about cyber deception strategy. Most of them consider the process of cyber attack and defense as non-cooperative game theory model and design algorithms to find optimal solutions. However, most existing researches abstract attackers and defenders into simple characters, having limitations in applicability. To supplement this part of research, we introduce intrusion prediction and honeynet to game-theoretical cyber deception and propose a novel cyber deception strategy model. We run some simulations to verify the effectiveness of our proposed model. Based on the simulation results, the rules of multi-stage cyber deception and model application are summarized, which can provide a novel and effective guidance of cyber deception.
Sentiment analysis-based social network rumor detection model with bi-directional graph convolutional networks

This paper proposes a model for social network rumor detection that combines sentiment analysis and bi-directional graph convolutional networks (Bi-GCN) to deeply mine the semantic, sentiment, and structural features of information propagation contained in social network texts in order to improve rumor identification’s effectiveness. In this model, a BERT model is used to extract the semantic feature vector from a text, a Bi-GRU+Attention model is used to extract the sentiment feature vector from the text’s comments, and the feature vector is propagated along with the information extracted by the Bi-GCN networks to enrich the rumor detection model’s input features. The experimental results indicate that the precision ratio, recall ratio, and accuracy ratio of the method proposed in this paper are 10%, 9%, and 7% higher than those of the best performing model in the comparison models, respectively, demonstrating the model’s effectiveness.
Evaluating Various Machine Learning Techniques in Credit Risk Area

Implementing machine learning techniques to credit scoring is a popular method, which is widely used by many financial institutions and banks at present. As the fast development of machine learning tools, these technologies could provide people more accurate predictions and help enterprises avoid future risk. A supervised machine learning technique is utilized in this research as the classification approach. In this experiment, several machine learning algorithms will be compared in order to present the performance by evaluating the type of credit risk. The data is about assessing customers of a German banking systems from the UCI Machine Learning Repository, which contains 5000 instances and 21 attributes. The final result of this research shows the comparison of 12 scenarios among different combinations of balancing methods, feature selection methods, and predictive algorithms, which finally presents that the collection of Adaptive Synthetic, Boruta and k-Nearest Neighbor receives the highest accuracy score.
Formation of Professional Competence of Future Primary School Teachers Against the Background of Large-Scale Military Aggression (a response to the challenges of the times)

In contemporary European literature, the problem of shaping future educators against the backdrop of crisis (military) transformations has not been explored. Ukrainian teachers demonstrate valuable experience in the teaching under warfare conditions, which should be the subject of a separate study. In addition, the problem of the professional formation of future teachers is also relevant, given the numerous terminological debates between scientists. The article aims to analyze the formation of professional competence of future elementary school teachers against the background of large-scale military aggression. The work is formed on the use of theoretical pedagogical research methods. Noticeable attention is paid to the methods of generalization, abstraction, analysis, synthesis. Among the empirical methods, the methods of experiment and SWOT-analysis were used. With the help of the latter, the strengths and weaknesses of the formation of professional competence in the system of distance learning of future elementary school teachers against the background of military aggression are reflected. The results considered terminological discussions regarding the concept of “professional competence”, highlighted the main features of professionalism of modern teachers, analyzed the use of game techniques and their impact on the formation of important skills for future teachers, developed a training model to ensure maximum exposure to professional competence in future teachers. In the conclusion, it was noted that the appeal to game techniques allows to deepen the assimilation of professional competence, moreover, conducting games during the war has an additional psychological effect. The best training model for future teachers is to conduct distance learning sessions according to the established program, which provides an increase in the amount of practice and actualization of the experience of independent work.
The Rick and Morty Series: Humor or Philosophy?

The article explores the philosophy and humour in the popular animated science-fiction sitcom Rick and Morty, which premiered in 2013. The series offers productive material through which important philosophical and cultural questions and challenges can be explored. The subject of the study is the explication of the problem of reality and opposition of determinism and freedom in the context of modern society, which is largely characterized by technological advances. The research aims to show how the creators of the series interpret these problems and what place they leave for a human in the world and Universe. The series has already become a target of academic research, and this article shows how researchers use the material of the series to study social theories, ontological and ethical issues, the boundaries of the human, etc. The author attempts to consider the series as an object of complex humanitarian research. The relevance of the study is due to the fact that the growing interaction between humans and technology makes one turn to philosophy and culture in order to preserve the humane and prevent the formation of a non-human-sized world. The author concludes that the main message of Rick and Morty is the rejection of anthropocentrism and human exceptionalism. However, the seemingly pessimistic viewpoint advocated by the creators of the series is justified by the growing complexity of the world. The series motivates productive reflection on the current state of our world and its future.
Awareness and Attitude of Social Studies Teachers on the Use of Social Studies Apps for Teaching

This study aimed at assessing the awareness and attitude of social studies teachers on the use of social studies apps for enhancing students learning. It is a descriptive survey with a population of fifty social studies teachers from twenty-two public secondary schools in Khana Local Government Area of Rivers State. The entire population was used as the sample for this study. A total of five objectives, five research questions and three hypotheses guided the study. The Instrument used for data collection was a 52-item questionnaire titled: “Teachers’ Awareness, Attitude and Perception Toward the Use of Social Studies App (TAAPTUSSA)”. The reliability of the questionnaire was estimated using the Cronbach Alpha method of internal consistency with an overall reliability coefficient of 0.83. Mean and standard deviation were used to answer the research questions while an independent sample t-test was used to analyze hypotheses one and two, and Pearson Product Moment correlation (PPMC) was used to analyze hypothesis three. Findings revealed that Social Studies teachers are not very conversant with social studies apps, but the teachers have a positive attitude towards utilizing social studies apps for teaching. A positive but weak correlation exists between social studies teachers’ awareness level and their attitude toward the use of social studies apps. Based on the findings, it was recommended that the Rivers State Government should organize seminars to raise the level of awareness of social studies teachers on the existence of apps that can be used for teaching the subject. Workshops should also be organized by the Government to assist these teachers to learn how to use these apps. Above all, social studies teachers should develop a positive attitude towards the use of social studies apps in the classroom. There seemed to be a scarcity of studies covering all the variables in the study area; this study could therefore be regarded as one of the first in the study area to demonstrate empirically that the adoption of social studies apps can be used to enhance students’ learning.
APPLYING ROUND ROBIN TECHNIQUE ON STUDENTS’ SPEAKING ABILITY

- The objective of this study was to investigate the impact that using the RoundRobin technique had on the student's level of oral communication. This investigation was carried out with the participation of seventy students at SMAS Budi Agung Medan. The pretest and posttest results of the students were used to compile the data. This research was conducted using quantitative methods. The research design that was used in this investigation consisted of simple experiments. In this study, seventy students from the twelfth grade participated. The test consisted of a speaking component. The tests consisted of a pretest and a posttest, both of which were administered. The test was administered, and the results were analysed using SPSS. These are the results. The results showed that the average number of points obtained on the posttest by students in the control group was 78.4, while the average number of points obtained by students in the experimental group was 87.6. If the value of the hypothesis test showed that sig, two-tailed (p) was less than the number alpha () 0.005, then Ho (Nil Hypothesis) was rejected, and Ha (Alternative Hypothesis) was accepted. This decision was based on the value of the hypothesis test. In this research, the researchers obtained sig. 2 tailed was 0.000, and the hypothesis was proven true. It demonstrated that the application of the RoundRobin technique significantly affected the student's ability to communicate verbally.Keywords: RoundRobin Technique, speaking ability.
Application of Naive Bayes Model, SVM and Deep Learning Predicting

The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (name, name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings.&#x0D; Keywords—Payments, Algorithm, Performance
Information Competencies of Information Technology Specialists in the Conditions of the Pandemic: The Algorithms of Formation and Features of Development

The research has examined scientific developments and systematized practical data in the field of formation and development of information competencies of digital technology specialists in the conditions of the pandemic. This has become the research purpose of the academic paper. Digital transformations in the sphere of economy, management, social relations and production have caused significant changes in the methods and forms of training of specialists in the IT industry, and the research is focused on changes in the methods of organizing the educational process. The specifics of the evolution of teaching philosophy and methodology in the conditions of the pandemic are not gradual transformations, but the urgent need for rapid changes caused by a crisis situation in the conditions of quarantine restrictions. Unexpected changes require quick but qualitative changes. Distance learning technologies turned out to be relevant during the pandemic not only in the educational space of Ukraine; such changes took place in educational systems around the world. In order to study the issues outlined, a complex of bibliosemantic and analytical methods was used in the research.