Reviewerly/exHarmony huggingface dataset:
ADOPTING AUDIO-BASED LANGUAGE LEARNING IN ELEVATING THE READING SKILLS OF GRADE 9 STUDENTS

The purpose of this study was to elevate the reading skills of struggling readers in grade 9 students through the use of Audio-Based Language Learning.This study aimed to answer the following questions.First, what is the level of features of Audio-based language learning in terms of; relevance, comprehensibility, accuracy, catchiness and motivation.Second, what is the level of pre-reading and post-reading skills of the students in terms of; Reading comprehension and Reading Speed?Third, are there Significant Differences between the Reading Skills in terms of Reading Comprehension with regards to Pre Reading and Post Reading under: Control and Experimental group?And lastly are there Significant Differences between the Reading Skills in terms of Reading Speed with regards to Pre Reading and Post Reading under: Control and Experimental group?Only chosen students from the grade 9 level at Pacita Complex National High School would be the subjects of the study.In this study, the researcher would use a technique called purposive sampling.Relatively speaking, in this study, purposively selected grade 9 students who would be identified to have low scores in the pretest on reading skills would be the respondents of the study.Moreover, it also sought to test the null hypotheses; 1) There is no significant relationship between the features of audio-visual language learning and the students' reading skills in terms of Reading comprehension and Reading speed; There are no significant differences between the level of reading skills in terms of reading comprehension of experimental groups and control group; and, There are no significant differences between the level of reading skills in terms of reading speed of experimental groups; and control group.Hence, the following results were gathered after the conduct and analysis of data.The audio-based language learning has the condition or quality of being closely related or suitable in elevating the reading skills of students in terms of comprehension and speed.It also means that the two groups see that the audio-based language learning are all applicable in elevating the said skills.The level of features of Audio-based language learning in terms of Relevance, Comprehensibility, Accuracy, Catchiness,; and Motivation were all seen since the responses of the students fell under strongly agree; the level of pre-reading and post-reading skills of the students in terms of Reading comprehension, and Reading speed were lower in the pre-assessments rather than post-assessment; there was no significant relationship between the features of audio-based language learning and the students' reading skills in terms of Reading comprehension, and Reading speed; There were Significant Differences between the Reading Skills in terms of Reading Comprehension with regards to Pre Reading and Post Reading under experimental groups, and control group; and, There were Significant Differences between the Reading Skills in terms of Reading Speed with regards to Pre Reading and Post Reading under experimental groups, and control group.
DETECTING RANKING FRAUD FOR MOBILE APPLICATIONS USING AGGREGATED EVIDENCES

Ranking fraud in the mobile application industry refers to dishonest or deceptive activities performed with the intention of increasing the ranking of mobile Apps.Mobile application makers are indeed increasingly using dubious methods, such as inflating their apps' revenues or publishing fake app ratings, to conduct ranking fraud.Although the significance of preventing ranking fraud has long been understood, little knowledge and research have been initiated in this field.To present a comprehensive analysis of ranking fraud and to suggest a technique for detecting it in mobile applications.That, to suggest precisely locating the ranking fraud by mining the active times, namely the most activity sessions, of mobile apps.Such leading sessions can be used to identify local anomaly in app rankings as instead of global anomalies.Furthermore, Furthermore, by simulating app ranking, rating, and review behaviors through statistical hypotheses tests, three types of evidence-namely, evidence based on ranking, rating, and review will be investigated.A technique of optimization-based aggregation may also be recommended in order to include all the data for fraud detection.Finally, evaluate the proposed approach using actual app data that has been for a long time collected from the Google Play and iOS app stores.Along with the detection algorithm's scalability and some regularity in the ranking of fraud activities, this study.
THE EFFECT OF E-BOOK USE, DIGITAL LITERACY AND AVAILABILITY OF LIBRARY BOOK COLLECTIONS ON LEARNING ACHIEVEMENT

Digitalization changes the world of education starting from teaching methods that follow trends, subject matter that follows world developments and ultimately being able to produce output that is in accordance with the situation and conditions of the employment field. The level of success can be seen from the student achievement. Learning achievement is influenced by two factors, both internal and external factors. This study aims to analyze (1) the effect of using e-books on learning achievement (2) the effect of digital literacy on learning achievement (3) the effect of book collection availability on learning achievement. This research is a quantitative study using a survey method. The population in this study were 141 students of Economic Education class 2016-2018. This sampling technique uses probability sampling. The data analysis technique used is multiple linear regression analysis and the result showed that the use of e-books, digital literacy, and availability of book collections in the library partially has a positive and significant effect on learning achievement.
Video Game Audio in the Metaverse and Beyond

Rapidly advancing technologies and the increasing role of computers give the computer game boundless potential for the future. Among the various game industries, the console game industry in particular has attracted significant attention, bringing the ...
Implementasi Business Intelligence untuk Analisa dan Visualisasi Data Penyebab Kematian Di Indonesia Menggunakan Platform Tableau

Factors causing death in Indonesia are increasing, and many of them cannot identified with certainty. This study aims to identify the most common causes of death in Indonesia. such as causes of death due to social disasters, causes of death due to natural disasters, and causes of death due to non-natural disasters and diseases. This study employs a dataset method from www.kaggle.com with a time span of 2012-2021. The results of this study are reports in the form of a visualization dashboard using Tableau that contains information on the most common causes that occur in Indonesia with a range from 2012 to 2021 so that it can be used as a reference to support decision-making. In conclusion, the number of deaths in 2012–2021 was caused by the highest number of deaths due to natural disasters caused by earthquakes and tsunamis, which accounted for 2,615 deaths. Furthermore, the most cases of death due to non-natural disasters and diseases were caused by COVID-19, namely 144,094 deaths. And the most cases of death due to social disasters were caused by social conflict or social unrest, with 69 deaths.
Rancangan Sistem Klasifikasi Kekurangan Gizi Balita Dengan Metode K-Nearest Neighbor

Malnutrition in toddlers is a serious problem faced by developing countries like Indonesia, and the resulting long-term effects can reduce the intelligence of toddlers. The classification of the nutritional status of children under five is still carried out conventionally in community health centers. The K-Nearest Neighbor algorithm is included in a machine learning algorithm that can be used to classify one of the nutritional status classification problems. K-NN is used as a class determination algorithm for new data to be input according to the format. This research begins with a literature study, then identifies needs, followed by data collection that is planned to be used in the system to be built as well as a reference for making the design and the final stage of system design. This research succeeded in creating a system design using the Unified Model Language (UML), one use case that contains four functional systems, including uploading dataset files, displaying datasets, testing the accuracy of datasets, predicting new data, and designing system interfaces that will make system development easier..
Capturing populist elements in mediated discourse

The article measures the degree of populist elements present in party leaders’ discourses delivered as a part of their appearances in TV debates during the run up to the 2020 Slovak parliamentary elections. With the use of the ‘holistic grading’ approach, we empirically capture the presence and prominence of populist elements in textual transcripts of speeches delivered by 4 opposition party leaders. We start by defining populism as a discourse which can express a set of unique ideas and then follow up with arguments for the use of party leaders’ communication as the object of analysis. We then proceed to operationalize the exact coding process of the ‘holistic grading’ method which is used in our analysis. The measurement results quantitatively represent the degree of populist elements in the communication delivered during the electoral campaign. We note that the ‘holistic grading’ method was successfully able to capture populist elements in the performances of actors who have been previously labelled populist. We find consistent use of populist discourse in the performances of Boris Kollár, Igor Matovič, and Marián Kotleba, with the latter two producing several extremely populist performances. We then discuss our results and further implications derived from the collected data.
Improving lecturers' competence in writing nationally accredited scientific papers

Lecturers are required to produce scientific papers for improving their careers. However, there are many lecturers who do not understand how to write scientific papers and to publish it. This community service aims to improve the competence of lecturers in writing scientific papers so that they can make good scientific papers and can publish it. The program is carried out in two stages: 1) preparation stage, and 2) implementation through training on writing scientific paper. The results showed that the participants were able to understand 1) the basics of scientific writing, 2) the parts of scientific articles, 3) the scientific publication policies, 4) the criteria and requirements for publishing scientific papers in accredited journals, 5) the paraphrase techniques, 6) the citation using Mendeley, and 7) the technique of compiling according to the standard template of accredited journals.
Management of emotional intelligence and administration of non-communicable disease screening for posyandu cadres

Gamping Kidul Hamlet is growing fast and is an education center area. This condition brings social, economic and unhealthy lifestyle changes so that there is concern that there will be an increase in non-communicable diseases (NCDs). This community service is a continuation of the previous program which has formed groups concerned with healthy lifestyles and training in screening and prevention of NCDs. The training aims to enable health cadres to be able to understand and apply management of emotional intelligence for themselves and others as well as be able to apply the administration of NCDs screening in their respective posyandu. The training was attended by 14 participants. The activity was carried out using the discussion method for emotional intelligence management and practice for screening administrative management. At the end of the training, an assessment of the level of emotional intelligence was carried out and the provision of equipment grants for program implementation. The results of the assessment showed that 57% of cadres had high emotional intelligence, 29% are moderate and 14% are low. Emotional intelligence of non-cadres is 84.62% high and 7.69% moderate and low, respectively. The Mann Whitney test proves that the emotional intelligence of cadres is lower than that of non-cadres.
Artificial Bee Colony Optimization with Hybrid Image Encryption with Random Key Driven Share Creation Scheme for Blockchain-Assisted Question Paper Sharing

Educational tests and examinations cover massive data sharing to distribute question papers, aptitude tests, answer sheets, and quizzes for new admissions.Question Paper Leaking (QPL) can cause an unreasonable issue during exams.Today, QPL was a severe problem worldwide, from university entrance exams to public exams, and the situation is getting worse in emerging countries.QPL can cause specific rigorous results such as erosion of ethical standards and the education quality being compromised.There is a need for the security of e-learning mechanisms through cryptographic techniques.Blockchain (BC) fulfilled educational valuations and personalized curricula with smart contracts over a public permission BC.It exhibits the potential of this developing technology to enhance various core learning experiences, such as learner privacy, assessments, and curriculum personalization.This study presents an Artificial Bee Colony Optimization with Hybrid Image Encryption with Random Key Driven Share Creation Scheme for Blockchain Assisted Question Paper Sharing (ABCHIE-RKDSC) technique.In the presented ABCHIE-RKDSC model, three main procedures are involved, called share creation, share encryption, and BC-enabled transmission.During the process of share creation, a set of four shares is created for every input image and the random key generation method is performed by a user input equation instead of a conventional random seed function.Next, the generated shares undergo encryption by hybrid Advanced Encryption Standard with the Elliptic Curve Cryptography (AES-ECC) method.Moreover, the key generation process of the AES-ECC method is performed by the use of the ABC algorithm.The proposed ABCHIE-RKDSC technique involves BC technology that enables secure transmission of encrypted QPs.Finally, the share reconstruction and image decryption process take place to retrieve the original QPs.The experimental validation of the ABCHIE-RKDSC algorithm is investigated, and the outputs are evaluated under varied measures.A comprehensive result investigation highlighted the advancement of the ABCHIE-RKDSC method over other methods.
Improved Random Forest Algorithm using Chicken Swarm Optimization for Phishing Website Classification Model

Phishing is a type of online fraud which enables attackers to trick individuals into giving away confidential data like login credentials or financial data.A phishing website utilizes a URL that is comparable to a reasonable website to trick users into thinking it is legitimate, or it may comprise suspicious links or forms which has been developed for collecting sensitive data from users.Machine learning (ML) can be utilized to categorise websites as phishing or legitimate to protect users from falling victim to these attacks.There are several approaches to using machine learning for phishing website classification.This article focuses on the design of Chicken Swarm Optimization with Improved Random Forest for Phishing Website Classification (CSOIRF-PWC) technique.The CSOIRF-PWC technique aims to discriminate the legitimate and phishing websites accurately.To execute this, the presented CSOIRF-PWC approach initially performs the data normalization process.Next, the classification of phishing websites takes place using the IRF classifier.For improving the classification performance of the RF classifier, the parameter tuning process is performed through the CSO algorithm, which supports attaining improved classification performance.The simulation values of the CSOIRF-PWC methodology are investigated on two datasets, and the outputs are investigated under diverse measures.The comprehensive comparative outcomes emphasized the enhanced performance of the CSOIRF-PWC system over other methodologies in terms of several measures.
Design and Study of a Reflector-Separated Light Dispersion-Compensated 3D Microscopy System

The secondary-phase grating-based tomographic microscopy system, which is widely used in the biological and life sciences, can observe all the sample multilayer image information simultaneously because it has multifocal points. However, chromatic aberration exists in the grating diffraction, which seriously affects the observation of the image. To correct the chromatic aberration of the tomographic microscope system, this paper proposes a system that adopts blazed gratings and angle-variable reflectors as chromatic aberration correction devices according to the principle of dispersion compensation and Fourier phase-shift theory. A reflector-separated light dispersion-compensated 3D microscopy system is presented to achieve chromatic aberration correction while solving the problem of multilayer image overlap. The theoretical verification and optical design of the system were completed using ZEMAX software. The results show that the proposed system reduced the chromatic aberration of ordinary tomographic microscopy systems by more than 90%, retaining more wavelengths of light information. In addition, the system had a relatively wide range in the color difference compensation element installation position, reducing the difficulty of dispersion compensation element installation. Overall, the results indicate that the proposed system is effective in reducing chromatic aberration in grating diffraction.
An Opinion-based research on Cryptocurrency and it's functioning in India.

This paper aims to analyse the crypto market in India from the viewpoint of consumers who could potentially trade in these markets and extend the extent of their behaviour to the importance of a regulatory body within the economy. The central idea of this paper is to estimate the importance of a central bank in the minds of people and how it affects their trust on a currency which is unregulated. This forms the first tier of the paper In order to get insight into the more technical dynamics of cryptocurrencies, the paper then studies cryptocurrency from the point of view of experts who explain the different dimensions of the crypto markets. This forms the second tier of the paper. The paper aims to analyse from the surveys conducted and the available literature, the nature of the crypto market in India with a more reporting approach than a problem-solving model. The research is based on primary data collected by means of surveys and structured interviews. These are termed as the two tiers in the paper. Using quantitative statistical analysis to find the common trend of the sample and a descriptive approach for the analysis of the interviews, the paper examines how the two tiers behave single-handedly and their dynamic when they interact.
Domain-Specific Contextualized Embedding : A Systematic Literature Review

Word embedding has successfully resolved various Natural Language Processing (NLP) problems. Unfortunately, the method has a weakness in detecting polysemy and homonym. Those issues led to the emergence of a new approach, which is named contextualized embedding. Many researchers examined such embedding to resolve problems in various particular areas. However, the studies are published disparate and complex. To provide a more comprehensive overview of contextualized embedding research in specific domains, a Systematic Literature Review (SLR) was conducted. The SLR results show that research on domain-specific contextualized embedding pays more attention to solving NLP problems in the Healthcare domain, by the percentage of more than 65%, followed by the Academic &amp;amp; Research field and other areas. The popularity of the Healthcare domain is associated with the availability of abundant datasets, mostly in English. BERT is the most contextualized embedding models used for domain-specific tasks, followed by ELMo, and finally GPT-1, as well as XLNET. Almost all reviewed papers reported performance improvements by using domain-specific contextualized embedding in their proposed model. Contextualized embedding can resolve polysemy problems and reduces overfitting. Besides, many downstream tasks have proved the ease implementation of the embedding. The shortcomings of this embedding are the high requirements of computation resources, the long execution time, and the computation complexity. Domain-specific contextualized embedding has resolved many problems, mostly classification tasks (e.g., Question and Answering) and tagging tasks (e.g., Named Entity Recognition). The two evaluation methods for measuring the performance of domain-specific contextualized embedding are Intrinsic evaluation and Extrinsic evaluation.
A comprehensive review of techniques used for Sentence Embedding

Sentence Embedding is a fascinating and continuously evolving research field within the realm of Natural Language Processing (NLP). Vectors play a ubiquitous role in NLP studies, and while there has been extensive research on word embeddings, sentence embedding is a topic that is still actively being explored for its diverse characteristics. The aim of sentence embedding is to identify text with similar or identical meaning and/or context by finding semantic similarities. Various methods have been proposed and implemented in recent times to encode sentences into definite length word vectors, known as Sentence Embedding, which can be used in a variety of applications such as text generation, sentiment analysis, text classification, and more. There are several aspects of sentence embedding that are yet to be optimized and explored. In this paper, our objective is to explore the properties of various sentence embedding techniques by conducting a comprehensive study.
Benefits of using convolutional neural networks for seismic data quality analysis

Abstract. Seismic data represent an excellent source of information and can be used to investigate several phenomena such as earthquake nature, faults geometry, tomography etc. These data are affected by several types of noise that are often grouped into two main classes: anthropogenic and environmental ones. Nevertheless instrumental noise or malfunctioning stations detection is also a relevant step in terms of data quality control and in the efficiency of the seismic network. As we will show, visual inspection of seismic spectral diagrams allows us to detect problems that can compromise data quality, for example invalidating subsequent calculations, such as Magnitude or Peak Ground Acceleration (PGA). However, such visual inspection requires human experience (due to the complexity of the diagrams), time demanding and effort as there are too many stations to be checked. That’s why, in this paper, we have explored the possibility of “transferring” such human experience into an artificial intelligence system in order to automatically and quickly perform such detection. The results have been very encouraging as the automatic system we have set up shows a detection accuracy of over 90 % on a set of 840 noise spectral diagrams obtained from seismic station records.