Reviewerly/exHarmony huggingface dataset:
QUANTUM CRYPTOGRAPHY AND U.S. DIGITAL SECURITY: A COMPREHENSIVE REVIEW: INVESTIGATING THE POTENTIAL OF QUANTUM TECHNOLOGIES IN CREATING UNBREAKABLE ENCRYPTION AND THEIR FUTURE IN NATIONAL SECURITY

This study provides a comprehensive review of quantum cryptography and its implications for U.S. national security in the face of emerging quantum technologies. The primary objective is to investigate the potential of quantum cryptographic methods in creating unbreakable encryption and their future role in enhancing digital security. Employing a systematic literature review and content analysis, the study draws on recent peer-reviewed articles, institutional reports, and academic journals from 2013 to 2023. The methodology focuses on evaluating the evolution, current state, and challenges of quantum cryptography, along with its integration into existing security frameworks. Key findings reveal that Quantum Key Distribution (QKD) and post-quantum cryptography (PQC) offer promising solutions against the threats posed by quantum computing to classical encryption methods. However, the practical implementation of these technologies faces significant challenges, including technological limitations and the need for global standardization. The study underscores the urgency for U.S. national security policy to prioritize the development and integration of quantum-resistant cryptographic technologies and to foster international collaboration for standardization. Finally, the study highlights the transformative potential of quantum cryptography in digital security, emphasizing the need for continued research and collaboration to overcome implementation challenges. Future research directions include the development of efficient quantum cryptographic protocols and ethical considerations surrounding the deployment of quantum technologies. This study contributes to the discourse on securing national interests in the face of advancing quantum computing capabilities.&#x0D; Keywords: Quantum Cryptography, Digital Security, Post-Quantum Cryptography, Quantum Key Distribution.
Benefits, Challenges, and Methods of Artificial Intelligence (AI) Chatbots in Education: A Systematic Literature Review

In many fields, AI chatbots continue to be popular with new tools and attract the attention of universities, K12 schools, educational organizations, and researchers. The aim of this research is to review the research on AI chatbots by restricting it to the category of education and to examine this research from a methodological point of view. Therefore, we performed a systematic literature review with a sample of 37 SSCI articles published in the educational context. Within the scope of the selected studies, the advantages and disadvantages of AI chatbots in education for students and educators, as well as the types of chatbots used, year, keywords, and method were analyzed. According to the research results, increased motivation to learn and language skill development are advantages for students, while cost-effectiveness and reduced workload are advantages for educators. Limited interaction, misleading answers for learners, originality, and plagiarism are the most common disadvantages for educators. The study also includes research results and recommendations related to the methodological review.
Development of a Digital Twin Prototype for Industrial Manufacturing Monitoring System Using IoT and Augmented Reality

The world is currently abuzz with the rapid development of technology in the era of Industrial Revolution 4.0. Various technological advancements are facilitating progress and accelerating the development of industrial technology. This evolution has led to automation in production processes, transitioning toward digitalization. With the implementation of sensors that provide real-time data, production processes can now be monitored remotely. However, direct monitoring is still necessary at times to periodically check the condition of each operating machine. Therefore, there is a need for technology that can monitor production processes and reduce high maintenance costs. Currently, numerous new technologies are emerging to enhance the performance and efficiency of production processes in various industries. One such technology is the digital twin. A digital twin is a visual representation that offers insights into the continuous operations of a system. This research focuses on an industrial manufacturing monitoring system that integrates the Internet of Things (IoT) and augmented reality (AR) technologies. The system is composed of an application and a prototype machine in the form of a conveyor, which can simulate a digital twin of the prototype machine. It also transmits sensor data and error notifications to the application in real time. The designed system can serve as a prototype for implementing digital twin technology, combining IoT and AR. This makes it possible to apply the technology to machinery and production tools in various industrial sectors.
Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.
Unsupervised clustering of catalog-driven features for characterizing temporal evolution of labquake stress

Summary Earthquake forecasting poses significant challenges, especially due to the elusive nature of stress states in fault systems. To tackle this problem, we employ features derived from seismic catalogs obtained from acoustic emission (AE) signals recorded during triaxial stick-slip experiments on natural fractures in three Westerly granite samples. We extracted 47 physically explainable features from AE data that described spatio-temporal evolution of stress and damage in the vicinity of the fault surface. These features are then subjected to unsupervised clustering using the K-means method, revealing three distinct stages with a proper agreement with the temporal evolution of stress. The recovered stages correspond to the mechanical behavior of the rock, characterized as initial stable (elastic) deformation, followed by a transitional stage leading to an unstable deformation prior to failure. Notably, AE rate, clustering-localization features, fractal dimension, b-value, interevent time distribution, and correlation integral are identified as significant features for the unsupervised clustering. The systematically evolving stages can provide valuable insights for characterizing preparatory processes preceding earthquake events associated with geothermal activities and waste-water injections. In order to address the upscaling issue, we propose to use the most important features and, in case of normalization challenge, removing non-universal features, such as AE rate. Our findings hold promise for advancing earthquake prediction methodologies based on laboratory experiments and catalog-driven features.
How Should We Slice Up the Executive Function Pie? Striving Toward an Ontology of Cognitive Control Processes

Abstract There is a vast literature on executive functioning , or the control of one's thoughts and actions in pursuit of a goal. This broad construct is conceptualized as comprising multiple executive functions (EFs) that support academic achievement and other important life outcomes. However, there is a lack of consensus regarding what, exactly, these core EFs are . This commentary briefly reviews a few dominant models of EFs in developmental and cognitive psychology and outlines issues to be addressed; it then illustrates how insights from neuroscience could help us eliminate redundant terminology and identify EFs that have been largely overlooked. Reaching a consensus regarding the core set of EFs will inform efforts to scaffold EF development and improve educational outcomes.
On Faster Convergence of Scaled Sign Gradient Descent

Communication has been seen as a significant bottleneck in industrial applications over large-scale networks. To alleviate the communication burden, sign-based optimization algorithms have gained popularity recently in both industrial and academic communities, which is shown to be closely related to adaptive gradient methods, such as Adam. Along this line, this paper investigates faster convergence for a variant of sign-based gradient descent, called scaled SIGNGD, in three cases: 1) the objective function is strongly convex; 2) the objective function is nonconvex but satisfies the Polyak-Łojasiewicz (PL) inequality; 3) the gradient is stochastic, called scaled SIGNSGD in this case. For the first two cases, it can be shown that the scaled SIGNGD converges at a linear rate. For case 3), the algorithm is shown to converge linearly to a neighborhood of the optimal value when a constant learning rate is employed, and the algorithm converges at a rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(1/k+1/k^{2}+1/k^{3})$</tex-math></inline-formula> when using a diminishing learning rate, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the iteration number. The results are also extended to the distributed setting by majority vote in a parameter-server framework. Finally, numerical experiments are performed to corroborate the theoretical findings.
A Study on the Dong-Ju Yoon's Lyrical World and the Ideal of Childhood Innocence

The world of Dong-Ju Yoon’s poetry has a source of extreme and pure childhood innocence, like the innocent gaze of an unattached child, which enjoys the phenomenon itself with an intuition that can get close to the essence of things and accepts the world beyond its horizon. The consciousness and orientation of childhood innocence revealed in Dong-Ju Yoon’s poetry are not merely the result of conforming to the purposefulness of the times. However, they are inherited and deepened in his later works, constituting a consistent poetic sensibility that underlies the world of Dong-Ju Yoon’s poetry. Since there are many identical symbols, identifying consciousness of childhood innocence is necessary to understand the totality of the poet. Dong-Ju Yoon’s poetry coexists with childhood memories, reproduces them in epic paintings by dressing them in childhood innocence, and constructs the appearance of an archetypal world. The world of childhood innocence formed through the young narrator is expressed as a dynamic channel of reflection that seeks solutions to problems and overcomes the depression and lethargy of the poet's time. By symbolically depicting the lyrical world of the child, he vividly depicts the contradictions of the times and expresses the fundamental human emotions in his poems, forming the poet's authenticity. It is a search to find an outlet for hope through the style of children's poetry with clear and bright innocence and dynamic vitality without caving to the hardships of reality suffering from the dictatorship of a vast and oppressed world.
A Study on the Effect of Director's Authentic Leadership on Early Childhood Teacher's Professionalism: Centered on Mediating Effect of Organizational Culture of Early Childhood Educational Institute

The purpose of this study is to investigate the relationship between variables affecting the early childhood teacher's professionalism after setting the director's authentic leadership as an independent variable, the organizational culture of early childhood educational institute as a mediating variable, and the early childhood teacher's professionalism as dependent variable, considering that the director's authentic leadership was a motivational factor affecting the organizational culture of early childhood educational institute. The research questions established to achieve such research objective are as follows. First, what is the relationship among the director's authentic leadership, organizational culture of early childhood educational institute, and early childhood teacher's professionalism? Second, do the director's authentic leadership and organizational culture of early childhood educational institute affect the early childhood teacher's professionalism? Third, does the director's authentic leadership affect the early childhood teacher's professionalism through a medium of organizational culture of early childhood educational institute? The research object in this study was final 225 early childhood teachers except the unreliable respondents out of 250 teachers who worked in 'D' city. From the result of this research above, it may be summed up as follows. First, this study has shown that it had statistically significant positive relationship among the director's authentic leadership, organizational culture of early childhood educational institute, and early childhood teacher's professionalism. Second, the internalized moral viewpoint, relational transparency, and balanced processing of information among the subfields of director's authentic leadership had positively significant direct effect on the early childhood teacher's professionalism. In addition, the rational culture, relationship culture, hierarchy culture among the subfields of organizational culture of early childhood educational institute had positively significant direct effect on the early childhood teacher's professionalism. Third, the director's authentic leadership affected the early childhood teacher's professionalism, mediated by the organizational culture of early childhood educational institute. This researcher considers it as the director's authentic leadership and organizational culture of early childhood educational institute were the related variables that affected the early childhood teacher's professionalism.
The Analysis of Educational Needs for Secondary School Teacher’s Emotional Competencies: Focusing on General Education Teachers

The purpose of this study was to examine the gap between the importance level and their current performance level of secondary school teachers' emotional competencies and to identify the priority of education to improve emotional competencies. Using the emotional competency scale for secondary school teacher, a survey was conducted involving 250 teachers. The collected data were examined using a t-test, Borich's need analysis and Hershkowitz criticality function to determine the priorities of the educational needs regarding emotional competencies. The results of the study are as follows. First, the performance levels of secondary school teachers were lower than the importance levels in all of competencies, with statistically significant differences in all areas. Second, the highest priority educational competencies include emotional regulation, emotional understanding and appraisal, communication, and positivity, primarily focusing on individual emotional competencies. The results of this study indicate that, in the development of emotional competency enhancement programs for secondary school teachers, it is important to prioritize the emotional competencies that enable teachers to regulate, manage, and accurately understand their own emotions.
Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning

Multiagent reinforcement learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multiagent setting can be very difficult as the number of agents increases. Recent solutions such as value decomposition networks (VDNs), QMIX, QTRAN, and QPLEX adhere to the centralized training and decentralized execution (CTDE) scheme and perform factorization of the joint action-value functions. However, these methods still suffer from increased environmental complexity, and at times fail to converge in a stable manner. We propose a novel concept of residual Q-networks (RQNs) for MARL, which learns to transform the individual Q -value trajectories in a way that preserves the individual-global-max (IGM) criteria, but is more robust in factorizing action-value functions. The RQN acts as an auxiliary network that accelerates convergence and will become obsolete as the agents reach the training objectives. The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN, and VDN, in a range of multiagent cooperative tasks. The results illustrate that the proposed method, in general, converges faster, with increased stability, and shows robust performance in a wider family of environments. The improvements in results are more prominent in environments with severe punishments for noncooperative behaviors and especially in the absence of complete state information during training time.
Transforming Education

Generative AI systems are increasingly present in our daily lives, helping us make crucial decisions. They use machine learning algorithms and tools, fed with millions of data collected from the web, producing entirely new information and generating variations. And this is not just limited to texts — it can produce images, audio, videos, even code, or new programming languages. There are several fields where generative AI can have a considerable impact in the coming years. In this context, the issues proposed in this chapter are: What is generative AI? What is prompt engineering? How to transform education using generative AI and prompt engineering in creating synthetic content? To respond to the research problem, the following objective will be achieved: Investigate how to transform education using generative AI and prompt engineering in the creation of synthetic content. It is concluded that generative AI tools can also help create more efficient exercises. Teachers and educators can use technology to create instructional materials and present summaries of concepts.
A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.
OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation

Light field disparity estimation is an essential task in computer vision. Currently, supervised learning-based methods have achieved better performance than both unsupervised and optimization-based methods. However, the generalization capacity of supervised methods on real-world data, where no ground truth is available for training, remains limited. In this paper, we argue that unsupervised methods can achieve not only much stronger generalization capacity on real-world data but also more accurate disparity estimation results on synthetic datasets. To fulfill this goal, we present the Occlusion Pattern Aware Loss, named OPAL, which successfully extracts and encodes general occlusion patterns inherent in the light field for calculating the disparity loss. OPAL enables: i) accurate and robust disparity estimation by teaching the network how to handle occlusions effectively and ii) significantly reduced network parameters required for accurate and efficient estimation. We further propose an EPI transformer and a gradient-based refinement module for achieving more accurate and pixel-aligned disparity estimation results. Extensive experiments demonstrate our method not only significantly improves the accuracy compared with SOTA unsupervised methods, but also possesses stronger generalization capacity on real-world data compared with SOTA supervised methods. Last but not least, the network training and inference efficiency are much higher than existing learning-based methods. Our code will be made publicly available.