Reviewerly/exHarmony huggingface dataset:
A Case Study of a Constructivist Class for the Development of Multicultural Education Competency of Pre-service Early Childhood Teachers

Objectives The purposes of this case study were to qualitatively examine the class process and results applied&#x0D; with the constructivist teaching and learning method to foster multicultural education competency of pre-service&#x0D; early childhood teachers and to provide basic data for exploring multicultural education curricula in the teacher&#x0D; training institution.&#x0D; Methods The participants were 24 sophomores who took the multicultural education class held in the first semester&#x0D; of 2022 at the department of early childhood education at a four-year university in the Chungcheon region.&#x0D; Research data were collected through e-campus postings, discussion activity reports, reflection journals, multicultural&#x0D; education contents, peer- and self-evaluation reports, and group interview. The data were analyzed as&#x0D; suggested by Miles and Huberman(1984).&#x0D; Results The multicultural education competencies cultivated by pre-service early childhood teachers though a&#x0D; class applied with the constructivist approach were divided into knowledge, skills, values and attitudes as follows.&#x0D; First, pre-service early childhood teachers acquired knowledge about individual and social prejudices and the&#x0D; concept of multiculturalism. Second, they acquired skills to collect and produce and to evaluate multicultural educational&#x0D; materials. Lastly, they cultivated an attitude of respecting and accepting various viewpoints and a sense&#x0D; of multicultural teaching efficacy.&#x0D; Conclusions These results suggest that the constructivist class emphasizing learner’s autonomy and initiative&#x0D; was effective to foster pre-service early childhood teachers' multicultural education competencies and that various&#x0D; opportunities are needed from the teacher training process to practice desirable multicultural education in the&#x0D; field. Based on this case, the direction of multicultural teacher education was discussed in the teacher training&#x0D; institution.
Kindergarten teachers' perception of educational contents and subject matters in implementing the curriculum

Objectives The purpose of this study is to investigate kindergarten teachers' perception of curriculum contents and subject matter in implementing the curriculum.&#x0D; Methods 8 teachers from public kindergartens located in Seoul, Busan, and Gyeongnam participated in semi-structured, in-person, or online interviews individually in January 2023. The data collected to underwent a comprehensive analysis based on the “Inclusive Analytic Procedures Based on the Pragmatic Eclecticism,” which involved processes such as “iterative reading/management of qualitative data, writing analytic memo, 1st/2nd/ 3rd coding, representation of results.” However, in practice, each stage overlapped with others, and the analysis process occurred in a cyclical and iterative manner, transcending the boundaries of all stages.&#x0D; Results First, the teachers had different perceptions of the curriculum, such as recognizing the five content areas of the Nuri curriculum as developmental/life/educational content and the educational contents as changed/not changed according to the curriculum revision. The educational contents were also recognized by reflecting the distinctiveness of the early childhood curriculum such as play, teacher autonomy, and integrated curriculum. Second, the teachers showed a low level of acceptance towards the term “subject matter,” but they did not consider the contents of subject matters as unnecessary. The contents of subject matters were already being covered indirectly in implementing the curriculum. Third, the teachers were understanding and implementing the curriculum based on their past experiences. They faced various challenges during the implementation process of the curriculum.&#x0D; Conclusions Therefore, various supports are needed to help early childhood teachers understand the distinctiveness of the curriculum and to develop a curriculum pedagogy that takes into account their own educational beliefs and the interests and developmental levels of young children, while co-creating the educational contents with them. There is also a need to create novel meanings for subject matters in early childhood education that is different with other school levels, in order to overcome resistance to terminology and to enable teachers to borrow the contents of subject matters as pedagogical knowledge that they can recognize and use. Finally, it suggests that there is a need to understand that teachers do not simply understand and implement the curriculum as a text, and to be more concerned with and supportive of the lives and experiences of teachers who are the agents of curriculum implementation.
Analysis of Tuberculosis Disease Case Growth From Medical Record Data, Viewed Through Clustering Algorithms (Case Study: Islamic Hospital Bogor)

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis infection. Tuberculosis can spread from one person to another through airborne transmission. This disease is most commonly found in the Asian region. Currently, Indonesia ranks second after India in terms of tuberculosis cases. The discovery of tuberculosis cases by province in Indonesia reveals that West Java Province is one of the contributors to the highest tuberculosis cases. It is known that the tuberculosis case rate in Bogor Regency is one of the highest in West Java. This serves as the foundation for the focus of this research, which will be conducted at Islamic Hospital Bogor, to determine the average age and gender of patients who are more susceptible to tuberculosis. One way to understand the growth of tuberculosis cases is through clustering using Data Mining Techniques, specifically several clustering algorithms such as k-means clustering, fuzzy c-means, and Gaussian mixture. These techniques aim to identify the growth of tuberculosis cases based on age range and gender. Therefore, the research results are expected to provide new insights, which could be valuable for decision-makers in various capacities, such as preventive measures, healthcare facility provision, and medication considerations.
Enabling Efficient Multidimensional Encrypted Data Aggregation for Fog-Cloud-Based Smart Grid

Smart grid is widely deployed in the modern smart city. However, the frequent network attacks on smart grid today have raised concerns about the privacy and security of individual fine-grained information collection. Moreover, once the secret key held by the data control center is disclosed, any adversary could easily break the indistinguishability of ciphertexts, thereby revealing users privacy. To this end, we propose an enabling efficient multidimensional encrypted data aggregation scheme for fog-cloud-based smart grid by using the improved Boneh-Goh-Nissim cryptosystem and the super-increasing vector technique, which can realize efficient multi-source multidimensional encrypted data aggregation in the fog-cloud architecture. The security analysis demonstrates that our scheme can satisfy security requirements of smart grid, i.e. it achieves the usage data privacy protection and confidentiality, multidimensional aggregation data integrity, and key-disclosure resistance. The performance evaluations show that our scheme is more efficient than related data aggregation schemes.
A Simple Model for Dirt Deposition Classification in Insulators Based on Visible Spectrum Images

In electrical plants, insulators suffer natural dirt deposition, which may eventually lead to electrical discharges, which can be avoided by the (costly) washing of insulators. Visual monitoring is a pragmatic approach to deciding when insulator cleaning is necessary. This study proposes a unique parameter based on the grayscale cumulative distribution for the classification of dirt deposition in four levels. This simple phenomenological model achieved similar results when compared to a more complex feature extraction approach for the same dataset of images. Furthermore, a simple affine formula was adjusted for a coarse Non-Soluble Surface Deposits estimation.
Federated Learning and Genetic Mutation for Multi-Resident Activity Recognition

Multi-Resident activity recognition refers to the task of recognizing activities performed by multiple individuals living in the same residence. It involves using sensors or other monitoring devices to capture data about the activities taking place in the living space, and then using Machine Learning (ML) or Deep Learning (DL) algorithms to analyze and classify these activities. Federated Learning (FL) is a technique that enables multiple devices to collaboratively train a model without sharing their data with each other, while Genetic Mutation (GM) is a technique used in evolutionary algorithms to introduce random changes to the genetic code of individuals in a population. Our proposed framework involves the use FL and GM for Human Activity Recognition (HAR). The approach was evaluated on the ARAS dataset, collected from two houses with different activity patterns. Two Recurrent Neural Network (RNN) models, Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), were employed for the activity classification task and a genetic mutation operator was applied to the weights of the models before federated averaging. The results indicate that FL is suitable for privacy preserving activity recognition, it can help with early deployment and even improve the performance of the models in some cases.
Approximate Automatic Verification and Formal Language Processing

In this paper, Formal Language Processing (FLP) is explicitly defined as processing generalized formal language problems via Machine Learning (ML) including Deep Learning (DL). Temporal Logic (TL) Model Checking (MC) and TL Satisfiability Checking (SC) can be considered as the two formal language problems. How to deal with them via ML? First, an online way for approximate Linear Temporal Logic (LTL) MC using ML is pioneered. Second, an algorithmic framework for approximate LTL-MC based on Graph Neural Network (GNN) and Recursive Neural Network (RecNN) is designed. Third, the approximate LTL-SC technique is analyzed and discussed. On the basis of it, an early exploration aiming to FLP automatic verification of temporal logic occurs.
Low-Computational-Complexity Zeroing Neural Network Model for Solving Systems of Dynamic Nonlinear Equations

Nonlinear equation systems are ubiquitous in a variety of fields, and how to tackle them has drawn much attention, especially dynamic ones. As a particular class of recurrent neural network, zeroing neural network (ZNN) takes time-derivative information into consideration, and thus, is a competent approach to dealing with dynamic problems. Hitherto, two kinds of ZNN models have been developed for solving systems of dynamic nonlinear equations. One of them is explicit, involving the computation of a pseudoinverse matrix, and the other is of implicit dynamics essentially. To address these two issues at once, a low-computational-complexity ZNN (LCCZNN) model is proposed. It does not need to compute any pseudoinverse matrix, and is in the form of explicit dynamics. Additionally, a novel activation function is presented to endow the LCCZNN model with finite-time convergence and certain robustness, which is proved rigorously by Lyapunov theory. Numerical experiments are conducted to validate the results of theoretical analyses, including the competence and robustness of the LCCZNN model. Finally, a pseudoinverse-free controller derived from the LCCZNN model is designed for a UR5 manipulator to online accomplish a trajectory-following task.
<i>iCAT+</i>: An <i>Interactive</i> <i>Customizable</i> <i>Anonymization</i> <i>Tool</i> using Automated Translation through Deep Learning

Data anonymization is a viable solution for data owners to mitigate their privacy concerns. However, existing data anonymization tools are inflexible to support various privacy and utility requirements of both data owners and data users. In most cases, this limitation is due to a lack of understanding of those requirements as well as the non-customizability of the existing tools. To address this limitation, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iCAT+</i> , which is an interactive and customizable anonymization approach. More specifically, we first automate the interpretation of data owners' and data users' textual requirements by deploying a Convolutional Neural Network (CNN) model for Natural Language Processing (NLP). Second, we introduce the concept of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anonymization space</i> to model possible combinations of per-attribute anonymization primitives based on the level of privacy and utility that each primitive provides. Third, we design an ontology model that maps the translated requirements into their appropriate anonymization primitives in the defined anonymization space corresponding to the plain data. Fourth, we evaluate the efficiency and effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iCAT+</i> based on both real and synthetic network data. Finally, we assess its usability through a real user study involving participants from industry and research laboratories. Our experiments show the effectiveness and efficiency of our solution (e.g., requirement translation accuracy of 99% at the data owner side and 98% at the data user side, with a computational time of around one minute for the Google cluster dataset).
Digital Economy: a Foreign Textbook in the Russian Educational Environment. Review of the Book by H. Overby and I. Odestad “Digital Economy”

The article discusses the possibilities of using the textbook written by HaraldOverby and Jan A. Audestad ‘Digital Economy’ in the context of educational and methodological support of relevant disciplines taught in Russian universities. Some Russian textbooks on digital economy is briefly overviewed. The advantages of the reviewed textbook are revealed, consisting in its conciseness, structuring, a high level of schematization of the material, and in terms of content – in supporting an optimal balance in the interpretation of the two basic components of the central concept: ‘digital (one)’ and ‘economy’. It is discussed that the definitions of key concepts are not sufficiently elaborated, as well as that when presenting the material, the digital economy is not embedded in the broader context of the development of the economy and society as a whole. There is also a lack of linking the author's narrative with the issues of self-determination of young people in the digital economy. The conclusion is made about the possible place and role of this textbook in the educational process.
Future Learning in the Metaverse—An Exploration of Virtual Architectural Design Studios

This study investigated seven virtual architectural design studios (VADS) at an Australian university, incorporating survey results returned from 47 students and one in-depth interview with the course coordinator. VADS have been attracting increasing interest particularly since the 2020 pandemic. VADS have the potential to be beneficial for students’ learning experiences by better facilitating students’ ideation process, providing greater accessibility, and enhancing global collaboration among architecture schools. Preliminary results from this study suggest that architectural students have a generally positive experience within VADS, especially in relation to the flexibility offered. These results also identify a need for further development and enhancement of related technologies, to better support the future of architectural design and learning. The results of this study provide an understanding of student and staff teaching and learning experiences of current VADS, which set a foundation for planning and designing of future Metaverse-based architectural education.
Hyper-parameter optimization in neural-based translation systems: A case study

Abstract Machine translation (MT) is an important use case in natural language processing (NLP) that converts a source language to a target language automatically. Modern intelligent system or artificial intelligence (AI) uses a machine learning approach and the machine has acquired learning ability using datasets. Nowadays, in the MT domain, the neural machine translation (NMT) system has almost replaced the statistical machine translation (SMT) system. The NMT systems use a deep learning framework in their implementation. To achieve higher accuracy during the training of the NMT model, extensive hyper-parameter tuning is required. The paper highlights the significance of hyper-parameter tuning in various machine learning algorithms. And as a case study, in-house experimentation was conducted on a low-resource English–Bangla language pair by designing an NMT system and the significance of various hyper-parameter optimizations was analyzed while evaluating its performance with an automatic metric BLEU. The BLEU scores obtained for the first, second, and third randomly picked test sentences are 4.1, 3.2, and 3.01, respectively.
Preface

The International Conference Research Collaboration 2023 (ICRC) aimed to promote cooperation and collaboration between The University of Kitakyushu and other universities, specifically in Indonesia and Malaysia. This conference marked the third event in a series, with the first held at Airlangga University on March 5, 2018, and the second conducted online on April 25-27, 2021. During the conference, the consortium presented and published the research findings of 147 articles in the areas of environmental management, education, and technology. Moreover, it provided an opportunity for The University of Kitakyushu to engage with community members and academics interested in research within these fields. ICRC 2023 extended an invitation to scholars and researchers to actively participate and contribute their knowledge and experiences to promote understanding and progress in environmental and sustainability matters within their respective countries. The conference provided a hybrid format, allowing participants to choose between attending in person or joining online. It aimed to foster innovation and exploration within the overarching theme of “Development of Science and Technology for Solving Environmental Problems.” The conference focused on several specific subtopics, which include: 1. Water and Wastewater Treatment and Recycling 2. Environmental Education 3. Energy Management and Air Pollution Control 4. Environmental Culture and Conservation 5. Environmental Health and Sciences 6. Waste Management and Treatment 7. Soil and Urban Environment This year, The University of Kitakyushu, Japan, hosted the third ICRC from June 2-3, 2023, featuring a hybrid conference format, followed by a workshop and training session in Kitakyushu, Japan, from June 4-6, 2023. There were 46 participants joining the on-site conference during the first and second day while the rest, around 265 participants from 78 universities and institutions around the world joining the conference virtually. Almost all the plenary speakers were attending the virtual session on the first day, while half of the invited speakers, which also coming from the Japanese industries and practitioners were joining onsite. Zoom was used as the meeting platform for the online participants. We have chosen 40 excellent manuscripts to be published in the Earth and Environmental Science IOP Proceedings. We would like to extend our sincere appreciation to the conference chairwoman, esteemed keynote speaker, reviewers, parallel session moderators, and all the participants. We would also like to express our gratitude to IOP for publishing our conference proceedings. It is our hope that readers will find valuable information and knowledge within our proceedings. We apologize for any errors identified during the conference or within the published papers. Scientific Steering Committee of ICRC 2023 List of Organizing Committee, Scientific Committee are available in this pdf.
Teamwork and its impact on employee performance mediated by job satisfaction

The purpose of this research is to study the effect of teamwork on employee performance in the services sector of Pakistan. The study has also investigated the mediating role of job satisfaction in the relationship between teamwork and employee performance. A total of 180 employees participated in this study from different service organizations in Pakistan through convenience sampling. The data was collected through a self-administered questionnaire. The collected data was analyzed using regression, correlation, and mediation analysis. The findings of the study contribute to the existing body of knowledge on teamwork and employee performance. The results indicated that there is a positive relationship between teamwork and employee performance. Moreover, the mediating role of job satisfaction was found to be significant in the relationship between teamwork and employee performance as well. Job satisfaction offers employees with a sense of belongingness to their organization and helps them develop trust in their team members. Thus, it can help improve teamwork effectiveness. The findings of this study should be useful for practitioners who are interested in promoting teamwork within their organizations as well as improving employee performance.&#x0D; Keywords: Teamwork, employee performance, job satisfaction, Pakistan.
ASSESSMENT OF THE IMPLEMENTATION OF #BECYBERSAFE PROJECT OF THE DEPARTMENT OF EDUCATION IN SECOND CONGRESSIONAL DISTRICT IN THE PROVINCE OF TARLAC

The study assessed the implementation of #BeCyberSafe Project in the Second Congressional District in the Province of Tarlac. It included 219 teachers, 86 non-teaching staff, 240 parents, and 370 students as the study’s respondents. The study used a quantitative research approach. It was revealed that the four groups of respondents perceived the overall implementation of #BeCyberSafe Project as highly implemented, but, there are no statistically significant differences on the perceived level of implementation of the respondents when they are grouped accordingly. Moreover, it was also revealed that the #BeCyberSafe project for the respondents is highly impactful for their cognitive, psychomotor, and affective development. Consequently, the results showed that there are no statistically significant differences on the perceived level of the impact of the implementation of the respondents when they are grouped accordingly. As a result, it is recommended that more respondents (e.g., body of Local Government Unit personnel, etc.) should be included in the study so their perceptions on the implementation of the #BeCyberSafe Project can be added to the bases of the overall assessment of the success of the said project. KEYWORDS: Assessment, #BeCyberSafe Project, Implementation, Project Management, Attainment of Objectives, Delivery of Content, Provision of Support Materials, and Project Management Team
Wave-Particle Duality via Quantum Fisher Information

Quantum Fisher information (QFI) plays an important role in quantum metrology, placing the ultimate limit to how precise we can estimate some unknown parameter and thus quantifying how much information we can extract. We observe that both the wave and particle properties within a Mach–Zehnder interferometer can naturally be quantified by QFI. Firstly, the particle property can be quantified by how well one can estimate the a priori probability of the path taken by the particle within the interferometer. Secondly, as the interference pattern is always related to some phase difference, the wave property can be quantified by how well one can estimate the phase parameter of the original state. With QFI as the unified figure of merit for both properties, we propose a more general and stronger wave-particle duality relation than the original one derived by Englert.