Reviewerly/exHarmony huggingface dataset:
Macroeconomic stability of the country: the nexus of institutional and behavioural dimensions

The ongoing globalisation, recessions, and fluctuations in world development require the acceptance of timely government interventions and decisions to save the country's macroeconomic stability. The literature analysis has confirmed a gap in research on assessing and comparing macroeconomic stability at different levels of development, especially in terms of studies that consider the behavioural (culture and trust) dimensions and the quality of institutions. This paper aimed to empirically assess how the achievement of macroeconomic stability depends on the quality of institutions, increasing society's trust, and cultural diversity. This study investigated post-communist economies among EU countries and Ukraine (as the candidate for EU) over 2005-2020. Pearson correlation, OLS and FGLS, robustness test output, GEE, and stochastic frontier model were applied to achieve the paper's aims. The findings confirmed that countries with high-quality of institutions had a higher value of macroeconomic stability. In addition, social trust positively affected the relationship between the quality of institutions and macroeconomic stability. At the same time, various national cultural dimensions had different effects on macroeconomic stability. The four indicators of Hofstede Insights (power distance, masculinity, uncertainty avoidance, and indulgence) had a statistically significant impact on macroeconomic stability.
Quantum zero-knowledge protocol for identity authentication

This work presents a new scheme based on a quantum zero-knowledge proof for identity authentication. The novelty of this research is the migration of the classical concept Zero-knowledge into the quantum cryptographic framework that, to the best of our knowledge, has never been explored. This approach allows us to take advantage of the principles of quantum mechanics to build a protocol, which is secure against quantum computer attacks, for authenticating several users having access to the same network node. The protocol has been designed, its security analysed and implemented in modified Quantum Key Distribution devices. Two scenarios have been analysed experimentally, the first being both the prover and the verifier honest players, and the second case being the prover a malicious player, the latter demonstrating a notable increase in the quantum bit error rate that prevents a fraudulent authentication.
A Cross-domain Color Mapping from Exemplar Anime Image Colorization Networks

Image colorization is a hot topic in the research of image processing and generation techniques. It plays an important role in restoring old photos, colorization medical images, and colorization anime manuscripts. In recent years, with the rapid development of deep learning, some colorization methods based on deep learning have been proposed, and satisfactory colorization results have been achieved. However, these colorization methods generally use unguided feature learning to achieve color prediction, it cannot predict, select, and intervene in image colors based on the subjective ideas of the creator. At the same time, the generated colorization results may have unreasonable phenomena such as color concentration, saturation and brightness distribution. Therefore, we propose a cross-domain color mapping from exemplar anime image colorization method, which is used for coloring a given anime image with a reference style. This method involves a supervised colorization network, which is divided into two structures: a domain semantic matching sub-network and a colorization sub-network. The domain semantic matching sub-network maps the pixels of the input grayscale image domain and the reference color image domain to an intermediate domain by calculating the similarity of the correlation matrix, thereby establishing a corresponding semantic relation and obtaining a coarse colorization map. We also employed an end-to-end CNN encoder and decoder network to further extract the feature information of the matched image colors, and used the Lab color space to select, propagate, and predict the color distribution features of the image. Experimental results have demonstrated that the anime coloring images generated by our network have high restoration similarity compared to the original reference images, reasonable coloration in the coloring area, and this colorization method solves the problem of prior color style transfer in anime images.
A Study on Legal Issues and Legislative Directions for the Stability of the Utilization about Health and Medical Data

Efforts are underway to establish a legal foundation for the use of data in the healthcare field through the revision of the Personal Information Protection Act and the issuance of guidelines for the utilization of health care data. However, the current legislative framework for the utilization of health and medical data faces limitations, including conflicts with existing laws, challenges in ensuring the stability and reliability of data utilization, and competition between the necessity for industrial development. The regulatory framework regarding the utilization of health care data, despite having the format of guidelines, is being implemented as if it were actual legislation.&#x0D; In this study, four major legal issues related to the utilization of health and medical data are identified through a comprehensive examination. To promote the activation of health and medical data, it is crucial to ensure the stability of health and medical data utilization. Therefore, the establishment of an independent law is necessary, with a definition of the concept of health and medical data tailored to the practical characteristics of the health and medical field. Regulations should be put in place in the law regarding pseudonymization and pseudonymization procedures to ensure legal stability. The Data Review Committee should be regulated in a direction that secures a legal basis and expertise. Strengthening the legal status of data utilization agencies through permits and approvals, along with enhanced regulations on responsibilities for violations, is essential. In summary, through swift legislative responses and the establishment of a regulatory system, the foundation for the utilization and responsibility of health and medical data should be secured, ensuring successful integration into our society through effective regulation.
Penerapan Teams Game Tournament untuk Meningkatkan Motivasi Belajar pada Submateri Struktur dan Fungsi Reproduksi Manusia

This research aims to increase students' learning motivation on the sub-material of the structure and function of the human reproductive system, using the Teams Game Tournament learning model. This research is collaborative action research carried out at SMP Negeri 6 Pontianak from July to August 2023. The research was carried out in two learning cycles. Each cycle consists of four stages, namely planning, implementation, observation and reflection. The research subjects came from class IX F students with a total of 28 students consisting of 12 men and 16 women. The instruments used were learning implementation observation sheets and student learning motivation questionnaires. Data analysis uses descriptive statistical techniques. The learning process in cycle 1 was carried out with a percentage of 86% and increased in cycle 2 to 100% with very good criteria. The percentage value of student learning motivation in cycle 1 was 72.73%, and increased in cycle 2 to 80.20% with high criteria. Students with high learning motivation in cycle 1 were 55.85%, and increased in cycle 2 to 83.57%. It can be concluded that the application of the Teams Game Tournament model can increase student learning motivation in the sub-material of the structure and function of the human reproductive system.
An innovative method to assess the development of competencies of students when writing a graduate qualification paper

A topical issue in the modern education system is the objective control of a student’s independent work when writing a graduate qualification paper (Graduation Thesis). Currently, only the procedure for defending a Graduation Thesis is being assessed, which does not reflect the entire set of skills of independent research work. To solve this problem, within the more general task of creating a criteria-based assessment system, the authors developed a method for assessing the formedness of competencies by assessing the development degree of professional skills constituting competency indicators. The method is based on recording a wide range of objective indicators in the process of independent work using an electronic educational environment and on algorithms for calculating “Proficient” indicators for target professional competencies. The purpose of the study is to develop and test a method for assessing the development of students’ competencies when writing a Graduation Thesis. The developed method was tested on a group of graduate biology students specializing at the Chair of Human and Animal Physiology. Based on the results of the Graduation Thesis defense procedure, all students specializing at the Chair received an “excellent” rating, which implies an equally high level of development of competencies. As part of the testing, the authors assessed such activities as writing a literature review, mastering experimental equipment and collecting experimental material, statistical data processing, and preparing publications. It was found that the examined group of students is significantly heterogeneous in terms of some indicators. The authors identified skills where the majority of those surveyed showed a proficiency level below the required minimum. The testing results showed that, compared to the final assessment based on the thesis defense results, the developed method is more objective and informative for assessing the development of competencies.
Cybersecurity in centralized healthcare systems: overview, design framework, challenges, and research trends

Cybersecurity is an essential element of any healthcare system to protect patient data and ensure that information is used only for its intended purpose. Centralized healthcare systems are becoming increasingly interconnected and reliance on technology is increasing. Therefore, it is essential to have a comprehensive and robust cybersecurity framework in place to protect the system and its users. This chapter presents a summary of the present situation of cybersecurity in centralized healthcare systems, a framework for the design of such systems, and the challenges and research trends in this area. Using real-world examples, the chapter gives an in-depth evaluation of the role of cybersecurity in modern healthcare systems. It highlights the importance of designing and implementing effective cybersecurity measures, as well as the need for ongoing research to address the ever-evolving threats posed by malicious actors. Cybersecurity is an essential element of any healthcare system to protect patient data and ensure that information is used only for its intended purpose. This chapter primary focus is on a framework for designing such systems, as well as the difficulties and current research directions in this field. Using real-world examples, it highlights the importance of designing and implementing effective cybersecurity measures.
Design and Implementation Using Certificate Pinning for Smart Home Power Switch System

With the diffusion of IoT devices and applications, IoT security has been receiving attention. IoT applications are reshaping the way of life, including Smart Homes, Intelligent Driving, Smart Healthcare, and more. These IoT applications expose private and personal information to the architecture of the internet unintentionally. Given this, preventing the coveting, accessing, controlling, and paralyzing attacks of malicious actors is crucial for the Internet of Things (IoT). Currently, the approach involving the concept of machine learning is used to learn, classify, and provide defense against latent network attack characteristics. However, the behaviors and connection methods on the network are largely variable and not easily predictable. Relying solely on classification methods to thwart network attacks still raises concerns about erroneous judgments. Therefore, we eliminated Man-in-the-Middle attacks referred to as certificate pinning in the IoT-based Smart Home application paper using a Smart Home application power switch based on a mechanism. The developed method in this study is user-friendly, secure, and trustworthy.
Digital Literacy toward Historical Knowledge: Implementation of the Bukittinggi City History Website as an Educational Technology

This research aims to identify the challenges and opportunities in integrating digital literacy skills into history education. This research focuses on understanding educators' difficulties in integrating digital technologies into the traditional history curriculum. Educational technology as a means and facility to support education and learning is no exception for historical knowledge through access to historical websites. This study analyses digital literacy toward historical knowledge using the Bukittinggi City history website. This research is quantitative research with a survey approach with closed-ended questions. The research population is the millennial generation in Indonesia. Samples were taken with a non-probability sampling approach with purposive sampling. This study involved 831 respondents spread throughout Indonesia. The data analysis technique is partial Least Square Structural Equation Modelling (PLS-SEM). The results showed no difference in historical knowledge scores between males and girls. With a value of 0.697 and a 69.7% variance, the coefficient of determination (R2) result demonstrates significant volatility in historical knowledge. Additionally, Q2's value serves as a gauge for the model's predictive usefulness. The predictive relevance of the model's independent variables was assessed using the predictive relevance test (Q2). Men might be more adept at using online resources to broaden their knowledge of the city's past. Understanding the disparities in digital literacy between men and women will significantly impact the design of educational and literacy programs in Bukittinggi. Enhancing digital literacy can promote access to and understanding of the city's history, especially among women
ENHANCING IMAGE RETRIEVAL EFFICIENCY WITH SPATIAL DEPENDENCE MATRIX AND TRANSLATION INVARIANT DISCRETE WAVELET TRANSFORM

The present study describes the implementation of a highly effective content-based image retrieval (CBIR) system. This system utilizes an integrated approach for feature extraction, incorporating the use of a spatial dependence matrix (SDM) to extract texture features from the provided images. Additionally, a translation invariant discrete wavelet transform (TIDWT) is employed for low-level feature extraction. Moreover, the effectiveness of the proposed hybrid Content-Based Image Retrieval (CBIR) system was evaluated using the Tanimoto distance. The results of a comprehensive experimental investigation reveal that the suggested hybrid Content-Based Image Retrieval (CBIR) system exhibits a significant enhancement in efficiency when compared to standard CBIR systems.
Validity and Practicality of the Ethnoscience-Based Design Thinking E-book

Design thinking is an innovation methodology that combines skills, processes and mindset. Design thinking is becoming increasingly popular in education as a way to encourage students to think critically, collaborate effectively, and develop innovative solutions to complex problems. Teachers and students can solve wicked problems creatively using design thinking. This research aims to determine the validity and practicality of an ethnoscience-based design thinking e-book. This research was conducted using walkthroughs and questionnaires. Validation data was analyzed using Cohen's Kappa while practicality data was analyzed using percentages. The results show that the ethnoscience-based design thinking textbook that was developed was said to be valid in aspects of design, systematics, substance and writing style. The textbooks being developed are also said to be very practical in terms of design, language, material and interest aspects.
Theoretical perspectives on self-supervised learning: A survey of methods and applications

Self-supervised learning (SSL) has emerged as a prominent paradigm within the field of machine learning, presenting a unique approach to training models in the absence of labeled data. This review paper delves into the theoretical underpinnings of self-supervised learning, offering a comprehensive survey of diverse methods and their applications. The primary objective is to provide a holistic understanding of the evolving landscape of SSL, exploring the theoretical frameworks that drive its effectiveness and versatility.The survey begins by elucidating the fundamental principles that distinguish SSL from traditional supervised learning paradigms. It discusses the key concepts of pretext tasks, where the model is trained to predict certain aspects of the input data, and subsequent downstream tasks, which leverage the learned representations for specific applications. Various theoretical perspectives are explored, ranging from information theory to cognitive science, shedding light on the underlying mechanisms that enable self-supervised models to learn meaningful representations. A critical analysis of prominent SSL methods follows, categorizing them based on the nature of pretext tasks, such as contrastive learning, generative modeling, and predictive learning. Each category is scrutinized in terms of theoretical motivations, algorithmic implementations, and empirical successes. The survey extends its focus to real-world applications across domains such as computer vision, natural language processing, and audio signal processing, illustrating the adaptability and efficacy of SSL methodologies. Furthermore, the paper addresses challenges and open questions within the realm of SSL, paving the way for future research directions. It emphasizes the need for a unified theoretical framework to guide the development of novel SSL methods and foster a deeper understanding of the learning dynamics involved.
Ethical considerations in the application of machine learning for infectious disease surveillance: A theoretical overview

The rapid advancements in machine learning (ML) technologies have propelled their integration into various domains, including public health. In recent years, the application of ML in infectious disease surveillance has garnered significant attention due to its potential to enhance early detection, monitoring, and response to emerging health threats. However, the ethical implications associated with the use of ML in this context warrant thorough examination. This review paper provides a comprehensive theoretical overview of the ethical considerations inherent in deploying ML for infectious disease surveillance. It delves into the intersection of technology and public health, emphasizing the need for a balanced approach that maximizes the benefits of ML while minimizing potential risks and ethical pitfalls. The ethical considerations addressed encompass several key dimensions. First and foremost is the issue of data privacy and security. As ML relies heavily on vast datasets for training and analysis, ensuring the confidentiality of sensitive health information is paramount. The paper explores strategies such as anonymization, encryption, and secure data sharing protocols to mitigate privacy concerns. Another critical dimension is transparency and interpretability in ML algorithms. The opaque nature of some machine learning models poses challenges in understanding their decision-making processes, which may impact public trust. The review discusses the importance of developing interpretable models and implementing transparent practices to foster accountability and acceptance within the public health community and among the general population. Additionally, the paper addresses the potential biases embedded in ML algorithms, which may exacerbate existing health disparities. It highlights the importance of designing and validating models with diverse and representative datasets to prevent unintended consequences and ensure equitable outcomes in infectious disease surveillance. Furthermore, the ethical implications of decision-making autonomy granted to ML systems are explored. Striking a balance between human oversight and automated decision-making is crucial to avoid undue reliance on algorithms and to maintain accountability in public health interventions.
An Approach to the Utilization of Design Thinking in Artificial Intelligence Education

As artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future.
Meta-ZSDETR: Zero-shot DETR with Meta-learning

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.