Reviewerly/exHarmony huggingface dataset:
Interactive Learning Strategies and the Vocabulary Knowledge of Grade Seven Students

Vocabulary is an essential component that one should be mastered in language learning. A robust vocabulary matters because it improves all areas of communication-listening, speaking, reading and writing. The students’ vocabulary will be their tool towards achieving the concept of what they are trying to read or listen. It is a pre-requisite for grammar and language proficiency. In this study, the researcher employed the interactive learning strategies (ILS) such as the word wizard, word connect, syntonyms word game and day’s word in teaching English subject among sixty (60) Grade 7 students who are divided into two experimental groups to find out whether these strategies will help the students improve their vocabulary knowledge in terms of word meaning, word use and context clues. Based on the results of the study, the findings revealed that there is a significant difference between the pretest and posttest mean scores of experimental group 1 which indicates that the use of interactive learning strategies-word wizard and word connect helped students to improve their vocabulary knowledge. Likewise, similar findings for experimental group 2 was obtained due to a significant difference between their pretest and posttest mean scores which signifies that the use of interactive learning strategies-syntonyms word game and day’s word, improved their vocabulary knowledge. Thus, the null hypotheses are rejected.
A Research on the Predicament and Improving Strategies of China College Entrance Examination Thinking in IELTS Reading—Comparative Analysis of Chinese College Entrance Examination English Reading and IELTS Reading

Over recent years, the mainland examination cohort has revealed a gradual trend of getting younger. More and more high school students are attempting to apply for undergraduate degrees in foreign universities, leading to an increasing trend of taking the IELTS examination. This article examines the challenges of candidates tackling IELTS English reading within the context of college entrance exams. It explores the thought processes of Chinese candidates and discusses strategies for enhancing the accuracy of IELTS reading, providing valuable references for the advancement of English education in China, especially at secondary and tertiary levels, and thus fostering well-rounded talents in the country.
Blockchain-Based Smart Contracts in Insurance Service Delivery: A Conceptual Analysis

Blockchain is a revolutionary technology that offers a new kind of inventive service. It can handle a variety of sophisticated issues associated with the secrecy, integrity, and availability of fast and secure distributed systems. This concept paper begins by addressing the shift in people's attitudes towards the insurance industry, particularly in Malaysia, and then goes on to understand how the Unified Theory of Acceptance and Use of Technology (UTAUT), Task Technology Fit (TTF), and Initial Trust Model (ITM), influence behavioural intentions in using blockchain smart contracts. A digital insurance platform must be redefined after the increase in online insurance sales transactions prompted by COVID-19 to satisfy the market's expectations. Whereas traditional paper contracts rely on middlemen for execution, blockchain smart contracts are now based on blockchains, which include an immutable record of data and the ability to remove single points of failure. Despite the growing popularity of blockchain research in recent years, research on blockchain smart contract adoption behaviour at the individual level concerning insurance services remains limited. Hence, this study utilises the three models to characterise how performance expectancy, technological context, and initial trust interact to forecast behavioural intention. Furthermore, we stressed the need for additional research to demonstrate the intention to employ blockchain smart contracts is impacted by performance anticipation, technical environment, and personal initial trust. Based on the review, we will design realistic research that will incorporate prospects for theoretical progress as well as empirical discoveries in blockchain smart contract studies. The findings are intended to assist policymakers in developing suitable and improved strategies for capturing interest in blockchain smart contract insurance services in the Malaysian market. We also believe that the evolution of blockchain technology in tandem with smart contracts will enable the creation of new sorts of innovative services, such as insurance.
INDIVIDUAL EDUCATIONAL TRAJECTORY BUILDING AS A SUCCESSFUL TEACHER SKILL IN THE DIGITAL AGE

Young people often face the problem of a lack of formed skills in independent learning activities, setting achievable goals, and having the ability to choose methods, forms, and means of training. They often have insufficiently developed the ability to reflect on personal achievements and take responsibility for decisions made. This actualizes the need for individual training and development of their educational path. The article aims to analyze the construction of the IET to realize a particular educational trajectory as a skill of a modern teacher. To achieve the goal, theoretical methods of scientific knowledge were used: terminological analysis to clarify the research thesaurus, content analysis, and detailed comparison of the construction of an individual training plan for implementing a particular educational trajectory. The authors discuss the fragmented nature of scientific findings and their implications for developing a customized curriculum. They introduce the concept of an individual plan and highlight the importance of teachers establishing realistic objectives. The paper offers general recommendations for effectively setting goals when designing a personalized curriculum, suggesting creating a specific educational path. It comprehensively explains the process involved in developing a unique curriculum and implementing a personalized academic trajectory. The authors clearly explain SMART goals, which are defined as specific, measurable, attainable, relevant, and time-bound. They emphasize that SMART goals should include observable indicators, a well-balanced timeframe for achievement, and criteria for assessing student progress. The practical significance of the work is in justifying the recommendation to follow the idea of individualized learning, which will contribute to the formation of skills of independent activity, as well as awareness of learning goals, increasing responsibility for their learning. The article opens directions for further research. These are using digital technologies and tools for individual professional training; implementation of professional development courses for teachers, coaches, and tutors on the development of SMART goals when developing unique training programs or personal development.
Leveraging Machine Learning and Artificial Intelligence for Fraud Prevention

Fraud remains a pervasive global issue, affecting individuals and organizations alike.In the modern technologydriven landscape, the role of machine learning (ML) and artificial intelligence (AI) has become paramount in combating fraud across various sectors.This article critically examines traditional fraud prevention methods, highlighting their limitations in the face of ever-evolving fraudulent tactics.It further explores how ML and AI technologies revolutionise fraud prevention efforts by facilitating rapid digitalization.By harnessing the power of ML algorithms and AI techniques, organizations can effectively analyze massive volumes of data, uncover patterns, and identify abnormal behaviors that often signify fraudulent activities.This article delves into the invaluable role played by ML and AI in augmenting fraud prevention through advanced data analytics, anomaly detection, and predictive modeling.It emphasizes how these technologies enable organizations to detect and mitigate fraud risks proactively, thus safeguarding their operations and stakeholders.
An Event Based Digital Forensic Scheme for Vehicular Networks

The software in today's cars has become increasingly important in recent years. The development of high-tech driver assistance devices has helped fuel this movement. This tendency is anticipated to accelerate with the advent of completely autonomous vehicles. As more modern vehicles incorporate software and security-based solutions, "Event-Based digital forensics," the analysis of digital evidence of accidents and warranty claims, has become increasingly significant. The objective of this study is to ascertain, in a realistic setting, whether or not digital forensics can be successfully applied to a state-of-the-art automobile. We did this by dissecting the procedure of automotive forensics, which is used on in-car systems to track the mysterious activity by means of digital evidence. We did this by applying established methods of digital forensics to a state-of-the-art car.Our research employs specialized cameras installed in the study areas and a log of system activity that may be utilized as future digital proof to examine the effectiveness of security checkpoints and other similar technologies. The goal is to keep an eye on the vehicles entering the checkpoint, look into them if there is any reason to suspect anything, and then take the appropriate measures. The problem with analyzing this data is that it is becoming increasingly complex and time-consuming as the amount of data that has been collected keeps growing. In this paper, we outline a high-level methodology for automotive forensics to fill in the blanks, and we put it through its paces on a network simulator in a state-of-the-art vehicle to simulate a scenario in which devices are tampered with while the car is in motion. Here, we test how well the strategy functions. Diagnostics over IP (Diagnostics over IP), on-board diagnostics interface, and unified diagnostic services are all used during implementation. To work, our solution requires vehicles to be able to exchange diagnostic information wirelessly.These results show that it is possible to undertake automotive forensic analysis on state-of-the-art vehicles without using intrusion detection systems or event data recorders, and they lead the way towards a more fruitful future for automotive forensics. The results also show that modern autos are amenable to forensic automotive analysis.
ViolationTracker: Building Precise Histories for Static Analysis Violations

Automatic static analysis tools (ASATs) detect source code violations to static analysis rules and are usually used as a guard for source code quality. The adoption of ASATs, however, is often challenged because of several problems such as a large number of false alarms, invalid rule priorities, and inappropriate rule configurations. Research has shown that tracking the history of the violations is a promising way to solve the above problems because the facts of violation fixing may reflect the developers' subjective expectations on the violation detection results. Precisely identifying the revisions that induce or fix a violation is however challenging because of the imprecise matching of violations between code revisions and ignorance of merge commits in the maintenance history. In this paper, we propose ViolationTracker, an approach to precisely matching the violation instances between adjacent revisions and building the life cycle of violations with the identification of inducing, fixing, deleting, and reopening of each violation case. The approach employs code entity anchoring heuristics for violation matching and considers merge commits that used to be ignored in existing research. We evaluate ViolationTracker with a manually-validated dataset that consists of 500 violation instances and 158 threads of 30 violation cases with detailed evolution history from open-source projects. Violation Tracker achieves over 93 % precision and 98 % recall on violation matching, outperforming the state-of-the-art approach, and 99.4 % precision on rebuilding the histories of violation cases. We also show that ViolationTracker is useful to identify actionable violations. A preliminary empirical study reveals the possibility to prioritize static analysis rules according to further analysis on the actionable rates of the rules.
Pengaruh Metode Flash Card terhadap Kemampuan Membaca dan Kreativitas Verbal pada Anak Tunagrahita Ringan

This study aims to determine the effect of the flash card method on the reading ability of mild mentally retarded children at SLB Dharma Wanita Grogol Kediri, where the flash card method as a learning method can improve the reading and verbal abilities of mild mentally retarded children. In this study, the population was 85 students of SLB Dharma Wanita Grogol Kediri and the sample was 7 students with mild mental retardation. These 7 students became the research sample where the selection was based on the objectives of the research and the recommendations of the teacher and head of the SLB. This research is a quasi-experimental research with one group pretest-posttest design. The design of this study used a group of subjects who were subjected to treatment for a certain period of time, measurements were made before and after the treatment was given. The data analysis used in this study was the nonparametric Mann Whitney test. Based on this data analysis, it was concluded that there was an increase in the reading ability of mild mentally retarded children with Mean Rank pretest = 4.50 and posttest = 10.50 and there was no increase in the verbal creativity of mild mentally retarded children with Mean Rank pretest = 6.43 and posttest = 8 , 57. So it can be concluded that the flash card method affects the ability to read with Sig. 0.006 &lt;0.05 and the flash card method has no effect on verbal creativity with Sig. 0.336 &gt; 0.05.
ANALYSIS OF INFORMATION SYSTEM NEED DESIGN FOR RECORDING AND REPORTING INTEGRATED HEALTHCARE CENTER TODDLERS IN KASIN VILLAGE, WORKING AREA OF BARENG COMMUNITY HEALTH CENTER, MALANG CITY

Background: Integrated Healthcare Center is a form of Community Based Health Efforts (UKBM) that has an active role through health cadres in realizing the Community Health Efforts (UKM) to strengthen basic health services (Primary Health Care). Now, it starts to implement a health management information system (Health Management Information System) that is a form of e-health implementation as an effort to accelerate the digital transformation of health in Indonesia according to Regulation of The Minister of Health of The Republic of Indonesia Number 46 of 2017. The recording and reporting system of the integrated Healthcare Center in Kasin Village is still manual and performs the data input using microsoft excel and spreadsheet.&#x0D; &#x0D; Objectives: This research purpose is providing information system based on website for recording and repoting needed by Integrated Healthcare Center Toddlers in Kasin Village, working area of Bareng Community Health Center, Malang City.&#x0D; &#x0D; Methods: This research is descriptive qualitative to analyze user needs, system requirements, design system and interface design required for the information system for recording and reporting activities of the Integrated Healthcare Center.&#x0D; &#x0D; Results: User needs in the recording and reporting of the Integrated Healthcare Center Information System can be identified as the needs of health cadres users and the need of Community Health Center Officers. The design of the information system design for recording and reporting of the Integrated Healthcare Center for toddlers had been compiled using Unified Modeling Language (UML) diagrams using simple and easy to operate features and menus to meet the data needs of the Integrated Healthcare Center and Community Health Center.&#x0D; &#x0D; Conclusion: The design of the information system that facilities the performance of health cadres and reduce the chance of data errors.
Textural and Depositional Environment Analyses of Maastrichtian Ajali Sandstone Outcrops at Igbere-Abiriba Area Afikpo Basin South Eastern Nigeria

The study presents the Textural characteristics of the Ajali sandstone at Igbere-Abiriba area in Afikpo basin, southeastern Nigeria. The intent is to highlight possible constraints of the environment of deposition of the source material on one hand and to infer the provenance on the other hand. The investigation approach includes field studies involving grain size analysis. Field studies shows that the Ajali Sandstone is friable at all location and range in colour from white in freshly cut stone, to reddish brown on weathering. In addition, the Ajali Sandstone units are cross-bedded and show graded bedding, exemplified by fining upward sequence. Textural examination indicates that the sandstone ranges from fine to medium grain and few coarse grain sizes constituting about 76-99% sand fraction, with graphic mean grain size of 0.93-2.60. Standard deviation (sorting) ranges from 0.71-1.48 and implies poor – moderately sorted sediments, also symmetrical, mesokurtic to leptokurtic were observed in skewness and kurtosis. Bivariate plot from the grain size parameter combination gives a more satisfactory approach toward predicting the sedimentary environment. General studies inferred from the Textural index, the depo-environmental description of the Ajali Sandstone revealed a Fluvial-river system-dominated sedimentary process.
A New Class-Weighting Formulation for the Class Imbalance Problem: A Methodological Research

Objective: Many of the machine learning classification algorithms are not robust against unbalanced classes and result in poorly accurate and biased models.One way to address class imbalance is to assign weights to classes.This article proposes a new class-weighting approach to improve the classification problem when there is an imbalance between two class.Material and Methods: The performances of the new formulation were compared with the previously proposed Inverse of Square Root of Number of Samples, effective number of samples weighting formula and unweighted Random Forest solutions.A simulation study was performed using performances of 3 imbalance rates (0.10, 0.20, 0.30), 6 different sample sizes (250, 300, 350, 400, 450, 500) and 4 different methods with 1,000 repetitions.Additionally, the methods were analyzed on the lung cancer dataset with 39 samples in the minority group and with 270 samples in the majority group.Results: Experimental results demonstrated that our proposed weighting formula, least number of ratio and range multiplier, performed equal to or better solution than Inverse of Square Root of Number of Samples in both simulations and real data.Generally, minority class accuracy and balanced accuracy of our formulation were either very close to or higher than that of Inverse of Square Root of Number of Samples.Conclusion: The new formulation provided accuracy estimates of the 2 classes in a balanced way for each sample size and for each imbalance rate.Additionally, as the sample size increased from 250 to 500, stable decreasing weights could be obtained for the patient and control groups.
Pre Encryption Data hiding techniques using Reserving Room Approach

The data hiding may easily embed data along with a visible watermark in an encrypted picture thanks to a novel approach suggested in this research that involves room reservation prior to encryption using a typical RDH algorithm. People pay greater care these days while transferring secure data. Since it preserves the feature that the original picture is retrieved from an encrypted image and embedded data without investigation into it, the Reversible Data Hiding technique is often used in this situation. The current technique, however, embeds data by reversibly removing space from encrypted pictures, which further causes data loss at the receiving end. As a result, the authors provide a way that uses the Reversible Data Hiding (RDH) algorithm to make space for data transfer from sender to receiver. This approach makes it simple for data hider to embed data reversibly in an encrypted picture. As a result, the sections of the suggested approach for data extraction and picture recovery are error-free. Additionally, investigation demonstrates that the suggested technology may incorporate data 10 times larger payloads for the same quality picture as the existing method. Hidden data is extremely lossless and recovered from the encrypted picture and the visible watermark using pixel prediction. With this technique, it is possible to extract secret data, remove apparent watermarks, and restore images without making any mistakes. This modification of the encrypted data may be subject to some errors on data extraction and/or image restoration, but those errors can also be eliminated by using error-correcting codes. Because the PSNR of the restored picture vs the original image is high in comparison to the prior approaches, the suggested methodology in this case offers greater embedding bit-rates at lesser distortion.
Implementasi Model Pembelajaran Savi (Somatis-Auditori-Visual-Intelektual) Terhadap Hasil Belajar Siswa Pada Mata Pelajaran PAI di SMP N 6 Lubuklinggau

The purpose of this study was to determine the application of the SAVI (Somatic-Auditory-Visual-Intellectual) learning model to the PAI learning outcomes of students at SMP N 6 Lubuklinggau. The research method used is a quantitative research method with hypothesis testing using the SPSS 17.0 application. In this study, and conducted more than one test at the beginning (Pretest) and carried out at the end (Posttest). The test used is a paired sample statistical test with a significance level of 0.05. The results showed that the t count (8.787) was greater than the t table (2.228) and the value of Sig. (2-tailed) (0.00) is smaller than the probability (0.05) so it can be concluded that Ha is accepted and Ho is rejected. This means that there is a significant influence between the SAVI learning model on the learning outcomes of class VIII students of SMP N 6 Lubuklinggau. The results also showed that the increase in student learning outcomes after being given treatment using the SAVI learning model was in the medium category.
PENERAPAN DATA MINING PENGELOMPOKAN JAMINAN HARI TUA (JHT) BERDASARKAN WILAYAH DALAM MASA PANDEMI (STUDI KASUS : BPJS KETENAGAKERJAAN CABANG LANGKAT)

During the COVID-19 pandemic, many companies experienced a decline or went bankrupt, so they had to reduce the number of workers and even close the company. BPJS Ketenagakerjaan is a public legal entity that is responsible to the president and functions to administer four programs, namely Work Accident Insurance (JKK), Death Insurance (JKM), Old Age Security (JHT), with the addition of the Pension Guarantee program (JP). One of them is the submission of claims from too many participants of the Old Age Security program from various regions, especially the Langkat sub-district, so that it becomes a big problem to provide good service or information for the participants. For this reason, the author tries to create a system to support a computerized grouping process that can help automatically classify JHT claims by region, so there is an opportunity to design a grouping data mining system in it. Data mining is a process of mining data in very large amounts of data using statistical, mathematical methods, to utilizing the latest artificial intelligence technology. Clustering is a method that is applied in creating a grouping data mining system to make it easier for employees to group JHT by region. Based on the analysis that has been done in the grouping of old-age insurance data using the clustering method, it is necessary to do the cluster process several times to get the same results according to the process that was first carried out, namely in cluster 1 : 2 3 2 cluster 2 : 2 8 2, cluster 3 : 2 13 2 with 545 data in cluster 1, 308 data in cluster 2 and 421 data in cluster 3.