Reviewerly/exHarmony huggingface dataset:
Application Of Adaptive Neuro Fuzzy Inference System (ANFIS) In Predicting Thermal and Electrical Efficiency of a Photovoltaic Thermal (PvT) System

The use of photovoltaic (PV/T) system that converts solar radiation to electricity and provide thermal needs concurrently stands as one of the most effective means of utilizing renewable energy. In recent times, machine-learning techniques have been extensively used in solar system applications due to their high accuracy in predicting the performances without necessarily going through physical modelling. The main objective of this study is to implement an intelligent algorithm adaptive neuro-fuzzy inference system (ANFIS) model to simulate and predict the thermal and electrical efficiencies of a water-based photovoltaic-thermal (PV/T) system. Thorough experimentation for the fabricated set-ups (conventional PV and a water-based PV/T system) was carried out. The real experimental results was validated using ANFIS model. Base on the results obtained it was confirmed that there was an excellent agreement between the predicted model outputs and the actual experimental data. However, the ANFIS model gave the highest prediction accuracy with the lowest error margin of 0.00021, 0.0089 and 0.0459 for MSE, RMSE and MAE with strong correlation (R2) of 0.9998. Base on the results obtained it was concluded that this intelligent algorithm is a reliable tool in predicting the PV/T system performances.
Debate: Will AI affect the transparency and accountability of public sector accounting?

"Debate: Will AI affect the transparency and accountability of public sector accounting?." Public Money & Management, ahead-of-print(ahead-of-print), pp. 1–2 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsJavier Cifuentes-FauraJavier Cifuentes-Faura is a member of the Department of Financial Economics and Accounting at the University of Murcia, Spain. His lines of research include, among others, those related to public administration and economics, governance and sustainability.
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.
Measuring the Psychometric Properties of the Customers' Experience with Banking Electronic Services During the COVID-19 Pandemic

The use of banking electronic services (e-services) increased dramatically during the coronavirus disease (COVID‐19) pandemic. This increase has been associated with failures, defects, and shortcomings in such services. Research on this topic is increasingly using different methodologies. The aim of this study was to examine the psychometric properties (i.e., reliability and validity) of a recently developed scale designed to measure customers’ experiences with banking e-services in Saudi Arabia during the COVID‐19 pandemic. This scale has been reported to have acceptable face and content validity. The quantitative research approach is applied and data is collected through a research survey. The scale’s questionnaire has two parts: one part consists of items on demographic variables (e.g., age, gender, and experience of service failure) and the other of 75 items on topics such as perceived information quality, digital commitment, service satisfaction, and cultural impact on choice of e-banking services. A descriptive-explorative, cross-sectional research design was used in this study, which focused on Saudi banks and utilized a non-probability sample of 555 customers. The scale was subjected to normality testing and did not violate normal distribution assumptions. The scale also showed acceptable levels of sphericity, and the Q–Q plot showed a significant relationship among items, reflecting normal distribution of items. The scale’s internal consistency value was 0.986. The results of a principal component analysis showed that all 75 items loaded above the cutoff point of 0.50 and could be subsumed under four factors: information processing and quality of services, perceived justice, security and safety, and e-services recovery and banking choices. The newly developed scale is valid and reliable for the target population and can be used to measure customers’ experiences with banking e-services. Further testing is warranted to ensure the scale’s suitability and stability over time and for other populations.
FINANCIAL FRAUD DETECTION IN FINANCIAL INSTITUTIONS USING TWO-LAYER-DEEP LEARNING AND SELF-IMPROVED HONEY BADGER ALGORITHM

ABSTRACT The financial industry, dealing with extensive data on individuals' health and finances, is particularly vulnerable to credit card theft and online financial fraud. To mitigate financial losses resulting from fraudulent activity, it is crucial to continuously enhance fraud detection systems as electronic payments become more prevalent. In this study, we propose a novel hybrid deep learning-based model for detecting online financial fraud in financial institutions. The proposed model consists of three main steps: pre-processing, feature extraction and selection, and a deep learning-based online financial fraud detection model. In the pre-processing stage, a data cleaning approach is employed to rectify or remove inaccurate, corrupted, improperly formatted, duplicate, or incomplete data from the collected raw dataset. During the feature extraction stage, various features are extracted from the pre-processed data, including measures of central tendency, degree of dispersion, Principal Component Analysis (PCA), and Mutual Information-based features. A score-level fusion strategy is utilized to combine these features into a comprehensive feature set for the fraud detection model. To select the set of optimal features, an optimized Self-Improved Honey Badger Algorithm (SI-HBA) is employed. Subsequently, the optimal feature set is used to train the Two-Layer Deep Learning Technique-based online financial fraud detection model, which learns patterns and traits of fraudulent behaviour. The hybrid classifier incorporates an optimized Convolutional Neural Network (CNN) and Radial Basis Function Networks (RBFNs) to identify probable frauds in the financial industry. The proposed method yields promising results, performing better than most current models used, and has the potential to enhance financial fraud detection systems. By leveraging a hybrid deep learning model and incorporating feature selection techniques, our approach offers an effective solution for detecting online financial fraud in financial institutions, contributing to the mitigation of financial losses and ensuring greater security in electronic payment systems. Keywords Financial fraud, Financial Institutions, SI-HBA, two-layer deep learning, RBFN, Central tendency.
Sistem Monitoring dan Evaluasi Kinerja Dosen Menggunakan Metode Multi Factor Evaluation Process

Monitoring and evaluating the performance of lecturers at the Faculty of Engineering, Tamansiswa Bachelor Wiyata University, the system that has been running has experienced many problems with the increasing number of active students and limited human resources. The evaluation of the learning questionnaire is filled out through the Google Form and the lecturer's data performance is processed using software such as Microsoft Office Excel or the like. In this study the aim was to produce a system that could support learning monitoring and evaluation activities in measuring the level of lecturer performance. The system is needed to process data as a tool to input and report on monitoring and evaluating lecturer performance. The method applied is the Multi Factor Evaluation Process (MFEP) using four criteria, namely pedagogic, professional, personality and social competence. Based on the results of the tests that have been carried out, the lecturer performance monitoring and evaluation system can provide decisions in the form of lecturer performance levels. The system can be used at several user levels, namely faculty academics, heads of study programs and Gugus Mutu Prodi (GMP) in assessing or measuring lecturer performance.
A Study on Automated Issue Detection in Presidential Records through Topic Modeling

Presidential records, which capture the essence of social and political transformations, are closely related to understanding of important societal issues. Therefore, any endeavor to preserve and harness presidential records must also encompass concurrent consideration of related social issues. This study aims to propose a method that can periodically and automatically identify key issues, marking a departure from the one-off issue detection approaches previously employed. In view of this, we introduced an automated issue detection model specifically designed for presidential records, and verified its efficacy through an extensive collection of news media data from the past five years. The results were derived in several phases: acquisition of new data, preprocessing, optimal condition analysis for topic modeling, and topic modeling analysis. The newfound issues pertinent to presidential records were validated through visualization and case studies. Consequently, the study validated the usefulness of this approach by effectively identifying major issues concerning the presidential records.
Trends in Intercultural Research in French Studies

The purpose of this study is to analyse the current state of intercultural research in the field of French Studies in Korea. It also aims to suggest directions for future research development. To achieve this, the study expanded the scope of intercultural-related keywords through a review of previous studies and conducted a literature review focusing on research trends, topics, and methodologies by examining journal articles in the field of French studies. The analysis revealed that intercultural research in the field of French studies has gradually increased since 2010, and has expanded to different academic fields since 2017. In particular, it was found that in the field of pedagogy, studies using qualitative research methods have been conducted on topics such as intercultural approaches and the development of teaching materials. Based on these findings, the following recommendations are made: First, intercultural research within the field of French studies in Korea is still lacking in both quantitative and qualitative research, so further research on this topic is needed. Second, intercultural research in the field of French studies in Korea is limited to pedagogical perspectives and qualitative research methods. Therefore, more diverse research methodologies should be considered and applied, with particular emphasis on the analysis and development of indicators for quantitative validation to ensure validity. In addition, although research topics in the field of pedagogy are shifting from recent literature to those using digital media, they have not been sufficiently applied and validated, so further research is needed. This study is significant in that it suggests future research directions and practical issues based on a review of previous studies on interculturalism within the field of French studies in Korea.
Designing an online discussion and debate class model for Korean languege learners for academic purposes

Objectives This study is to propose an online discussion and debate teaching learning models for academic purposes. To this end, review models used in existing discussions and debates that are used in existing discussions and debates suitable for learners in digital era and discussion classes suitable for learners.&#x0D; Methods In this study, the preceding studies on teaching learning about teaching learning, discussions on teaching learning, discuss what area using discussions and debates, discuss what areas such as learning, discussion and debate.&#x0D; Results The existing academic purpose discussion class model analyze the validity and suitable model, analyze what suitable model can be used in online, and establish a prerequisite for application.&#x0D; Conclusions In this study, it was designated based on online discussion and debate, based on online discussion and soil based on the proposed model based on the proposed model. Next, I would like to propose a class model can be used among real-time and discussion areas.
A Study on Synonyms ‘붓다’ (to pour) and ‘쏟다’ (to spill) using Corpus Analysis: Focusing on the co-occurrence relationship

Objectives This study aims to provide foundational insights for effective instruction and learning by examining the synonymous verbs ‘붓다 (to pour)’ and ‘쏟다 (to spill)’. The research focuses on analyzing the co-occurring words and semantic relationships within a corpus and visualizing the results through language networks.&#x0D; Methods Based on the examination of vocabulary usage in everyday conversation and spoken corpora of native Korean speakers, as well as the investigation of co-occuring words found in Korean language learner corpora, the semantic relationship and specific patterns of usage for ‘붓다’ (to pour) and ‘쏟다’ (to spill) were explored and visualized through language network analysis.&#x0D; Results The results of this study are as follows: while native Korean speakers use various extended meanings of ‘붓다’ (to pour) and ‘쏟다’ (to spill), Korean language learners tend to rely on a limited set of co-occuring words provided in Korean textbooks. It indicates an insufficient understanding of the expanded meanings of these synonyms among learners. Additionally, even intermediate and advanced Korean learners have been found to lack a comprehensive understanding of the co-occuring words of ‘붓다 (to pour)’ and ‘쏟다 (to spill)’, leading them to mistakenly interchange the two terms.&#x0D; Conclusions This study employs componential analysis to examine the distinctive characteristics of semantic relationships through the co-occurring words of ‘붓다 (to pour)’ and ‘쏟다 (to spill)’. Furthermore, we propose a suggested order of presenting meanings and representative co-occurring words for Korean language learners at different proficiency levels.
Exploration of Pre-Service Early Childhood Teachers’ Learning through Kindergarten Field Practice Experiences

Objectives The purpose of this study is to explore how pre-service early childhood teachers' learning through kindergarten field practice experiences is to prepare the basis for semester system practice.&#x0D; Methods This study was carried out using a qualitative research method, and the research participants were 24 pre-service early childhood teachers in the 3rd year of the Department of Early Childhood Education at K University. The research data were collected observation records and reflection papers written by pre-service early childhood teachers during the kindergarten field practice. Data analysis was qualitatively open-coded, coded as keywords according to classification criteria, and categorized into similar contents.&#x0D; Results As a result of the study, the experience through field practice was, first, an understanding of the early childhood curriculum, an experience of integrated education linked to the Nuri curriculum, a change in the perspective of looking at play, and learning how to record one's own observations. Second, the understanding of the learner understands the age and developmental characteristics of young children, the characteristics of children's play, and the characteristics of cooperation and consideration between peers. Third, as a teacher's role experience, I realized that the environment for child-centered play, activity planning linked to play context, and interactive support for young children.&#x0D; Conclusions The direction of field-linked semester practice for pre-service early childhood teachers was sought and the establishment of a cooperative system was discussed.
A Critical Review of the ‘Key Ideas’ in the 2022 Revised National Curriculum

Objectives The purpose of this study was to critically review the ‘key ideas’ introduced as the ‘big ideas’ by subject area in the 2022 Revised National Curriculum.&#x0D; Methods In this study, the background and meaning of big ideas, their nature and characteristics, and the connection between big ideas and in-depth learning were theoretically explored, and the design and composition principles, presentation format, and the validity of the statements of the key ideas were reviewed.&#x0D; Results As a result, it was difficult to understand the purpose, content, and design plan of the key ideas with the guidelines for the statement of key ideas, and it contained room for misunderstanding or confusion about the key ideas introduced as big ideas. In addition, through the key ideas design principle for each subject presented in the subject curriculum document, it was found that the understanding and design direction of the subject for the key ideas appeared in various ways. In terms of presentation format and content composition of key ideas, it was identified that difficulties in application caused by key ideas encompassing common curriculum, errors in statements brought about by an approach that covers three categories of content elements, and difficulties in designing classes based on big ideas due to concept obscurity and the problem of stating key ideas that did not fit as big ideas.&#x0D; Conclusions Finally, for the development of future curriculum, it was proposed to conduct research to explore big ideas and set big ideas for each subject and set key concepts at the curriculum level or at the cross-subject level in the national curriculum. And to implement the 2022 revised curriculum, preparing plans for designing units based on big ideas, strengthening expertise and competence of teachers in curriculum, instruction and assessment based on big ideas were suggested.
Development of Music Content Elements and Performance Expectations to Cultivate AI Literacy

Objectives The purpose of this study was to develop content elements and performance expectations for integrating AI literacy with music, based on the emphasis on artificial intelligence utilization education in the revised 2022 curriculum.&#x0D; Methods Based on a literature review of AI literacy, this study extracted AI-related components from the “Elementary and Middle School Artificial Intelligence Education Content Standards” developed by the Ministry of Education. Subsequently, AI content elements relevant to music were identified and selected, and corresponding performance expectations for elementary and middle school levels were formulated. These content elements and performance expectations were categorized into principles and applications, aligned with the three domains of AI literacy: understanding, applying, and critiquing.&#x0D; Results In the “Understanding” domain, participants grasped principles related to ‘Collection and Exploration of Music Data’ and ‘Visualization and Prediction of Music Data,’ which were then applied to activities fostering an understanding of ‘Music Elements and Characteristics through AI Utilization.’ Within the “Applying” domain, principles grounded in ‘AI Advancements and Diverse Musical Applications’ were employed for activities involving ‘Performance, Creation, and Appreciation through AI-Driven Music Engagement.’ In the “Critiquing” domain, principles concerning ‘Understanding Bias and Promoting Diversity in Music Data’ and ‘Ethics and Responsible Usage of AI in Music’ were identified. This section further encapsulates content elements and performance expectations that can be utilized to comprehend broader aspects of life and careers coexisting with AI in music, extending to the ‘Copyright and Artistic Value of AI-Generated Music Works.’&#x0D; Conclusions The AI literacy-enriched music content elements and performance expectations developed in this study are expected to offer insights into the future direction of curriculum development and instructional strategies within the context of AI education. These contributions are anticipated to guide the implementation of music curricula that can effectively integrate with the evolving AI education landscape, shaping the trajectory of teaching and learning.
The moderating effect of teaching creativity on the relationship between Early Childhood teachers classroom management strategies and teaching competence

Objectives This study aimed to test the moderating effect of teaching creativity on the relationship between infant and toddler teachers' classroom environment control strategies and teaching competence.&#x0D; Methods This study was conducted with 230 teachers working in daycare centers and kindergartens located in Seoul and Gyeonggi. Data were collected through a web-based survey from March 15 to March 30, 2021, and descriptive statistics were presented for each variable using the SPSS v25.0 program. Third, it was confirmed that teaching creativity moderates the effect of classroom environment control strategies on teaching competence.&#x0D; Results First, it was confirmed that infant and toddler teachers' strategies for controlling the classroom environment affected their teaching competence. Second, it was found that teaching creativity had a statistically significant effect on teaching competence. Third, it was found that teaching creativity has a positive moderating effect on teaching competence when teaching environment control strategies have a positive effect on teaching competence.&#x0D; Conclusions This study examined the moderating effect of teaching creativity on the relationship between classroom management strategies and teaching competence of infant and toddler teachers. The results showed that classroom management strategies had a significant effect on teaching competence, and the effect of teaching creativity on teaching competence was also confirmed.
Human Activity Recognition Using Smartphone Sensor Data Based on Hybrid Model

Accelerometers, gyroscopes, GPS, and various sensors have become widespread in smartphones. In accordance with this trend, many studies are actively conducting research on detecting and recognizing human activities using data acquired from smartphone sensors without separate attachments. Human activity recognition technology is gaining attention not only in specific fields such as security facilities and hospitals but also in everyday life and entertainment. In previous studies, researchers manually extracted effective features for activity recognition from raw signals acquired by sensors or utilized artificial neural networks to automatically extract features. However, no method showed significantly superior recognition performance compared to others. In this study, a hybrid CNN model that uses both handcrafted features and automatically extracted features using CNN is proposed. Experimental results on the UCI-HAR dataset representing six types of activities showed an impressive accuracy of 97.33%. It shows that the proposed approach is effective in recognizing human activity.