Reviewerly/exHarmony huggingface dataset:
Factors leading to high levels of indiscipline cases among pupils: A case of selected secondary schools in Lusaka District, Zambia

Indiscipline among pupils refers to a situation in which pupils do not control their behavior or obey school rules. Indiscipline entails any act that diverges from acceptable societal norms and values. It is a violation of school rules and regulations which is capable of obstructing the smooth and orderly functioning of the school system. Discipline in a classroom situation, is the ability to guide and control the class towards the attainment of predetermined objectives of education, while Indiscipline on the other hand entails a situation whereby energy and impulses are uncontrolled by moral principles or external authority. To this end, indiscipline in a school may be defined as the unwillingness of pupils and staff to respect authority, obey school rules and regulations, and maintain a high standard of moral and attitudinal behavior conducive to the teaching-learning process as essential to the smooth running of the school. It was for this aim that the study was conducted to analyze factors that lead to a high level of indiscipline among pupils in secondary schools of Lusaka district. The study employed a mixed-method approach and a descriptive survey design that sampled head teachers, teachers, and pupils. Data was obtained from the respondents by means of interviews and questionnaires. The sample consisted of three hundred respondents. Percentages, tables, graphs, and pie charts were used to analyze the data with a combination of software MS Access and MS Excel (version 26). The results of the analysis showed that pupils and teachers agreed on the causes and effects of indiscipline cases in secondary schools which included among others; the Influence of peers, Negative attitudes towards education, Irresponsible parents, and Schools’ inability to enforce rules and regulations. The study therefore recommended that the Ministry of Education should intensify monitoring of schools to ensure that teachers and pupils are fully involved or committed to the teaching and learning process. Additionally, Rules and regulations should be realistic and stated clearly for the absorption of all pupils in all secondary schools.
Frequent Errors in Modeling by Machine Learning: A Prototype Case of Predicting the Timely Evolution of COVID-19 Pandemic

Background: The development and application of machine learning (ML) methods became so fast that almost nobody can follow their developments in every detail. There is no wonder that numerous errors and inconsistencies in their usage have also spread with a similar speed independently from the tasks: regression and classification.. This work summarizes frequent errors committed by certain authors with the aims of helping scientists to avoid them. Methods: The principle of parsimony governs the train of thought. Fair method comparison methods can be completed with multicriteria decision making techniques, preferably sum of ranking differences (SRD). Its coupling with analysis of variance (ANOVA) decomposes the effect of several factors. Earlier findings are summarized in a review-like manner: the abuse of the correlation coefficient and proper practices for model discrimination are also outlined. Results: Using an illustrative example, the correct practice and the methodology is summarized as guidelines for model discrimination, and for minimizing the prediction errors. The following factors are all prerequisites for successful modeling: proper data preprocessing, statistical tests, suitable performance parameters, appropriate degrees of freedom, fair comparison of models, outlier detection, just to name a few. A checklist is provided on how to present ML modeling properly. The advocated practices are reviewed in the discussion. Conclusions: Many of the errors can easily be filtered out with careful reviewing. The authors’ responsibility is to adhere to the rules of modeling and validation.
Transcendentalism

Abstract The chapter focuses on three women who played central roles in American transcendentalism. Mary Moody Emerson (1774–1863) inaugurated transcendentalism’s characteristic themes, and she was the dominant educational influence on a central figure in the movement, her nephew Ralph Waldo Emerson. Elizabeth Palmer Peabody (1804–1894), known today mainly as an educational reformer, was also a significant theologian and philosopher in her own right. The most prominent woman in the movement is Margaret Fuller (1810–1850), who edited The Dial and authored Woman in the Nineteenth Century. The dominant concern for all three writers is the development of the mind. They thematized the innocence of childhood and perfectibility of humans through education, the dialectic of mind and nature in the service of spiritual growth, and the idea that we achieve the millennium in reforming our social institutions.
Comparing Information Security Compliance Between New Zealand, USA, and Vietnam

The number of employees working remotely has grown significantly in recent years, creating new challenges for information security functions within organizations. The increasing threat surface and lack of control over personal employee networks and devices pose a significant risk, especially when it comes to information handling and incident reporting. We built upon prior work on the human aspects of information security and conducted a quantitative study to compare behaviors, attitudes, and knowledge of employees from New Zealand, the USA, and Vietnam. Our findings suggest that although information security best practices are universal, some of them are understood and applied differently across the globe. Our study offers valuable practical and theoretical implications and can serve as a foundation for organizations to revise their security education, awareness, and training (SETA) programs to reflect more accurately the nuanced nature of the diverse workforce of the 21st century.
INFORMATION AND ANALYTICAL ANALYSIS OF SOCIAL IMPACTS AND EFFECTS OF DIGITAL TRANSFORMATION OF EDUCATION

The article analyses scientific research on the digitalization of the educational space and reveals that digitalization has both positive and negative effects. The article notes that the key positive effects of digitalization are the modernization of teaching tools and technologies, the transition to a qualitatively new stage of education development, and the formation of a single digital educational space. At the same time, not enough attention is currently paid to the study of the impact of digitalization on personal development in terms of establishing social communications, preserving psychological health, and the humanization of education in general. In the process of analyzing publications on social impacts on society, and thus on people, four interrelated and complementary trends were identified: socio-pedagogical, socio-cultural, sociopsychological, and societal. On the one hand, the digitalization of education contributes to greater openness and accessibility, greater interest of students in the learning process, more flexible management of the educational process, and the development of network communications, and on the other hand, digitalization forms a completely new model of educational life, significantly changing the purpose of modern education. The article emphasizes that the relationship between all participants in the educational process is transforming, and the configuration of contacts between teachers and students is changing. The article substantiates the need to study and systematize the social impacts and effects of digitalization. It is noted that such a focus of information analysis opens up opportunities for research to study the demand and needs of teachers and students, including to find out the answer to the question: what models of social communication can reduce the negative effects of digitalization. The article was prepared to solve the planned tasks of the applied research of the Department of Scientific Information and Analytical Support of Education "Information and Analytical Support of the Digital Transformation of Education: Domestic and Foreign Experience" (2023-2025).
PROBLEMS OF DIGITAL COMPETENCE IN THE SCIENTIFIC PRODUCTION OF UKRAINIAN RESEARCHERS

In the context of the implementation of the scientific study "Information and analytical support of education and pedagogy: domestic and foreign experience" (2023–2025) by the department of scientific information and analytical support of education of V. O. Sukhomlynskyi State Scientific and Educational Library of Ukraine&nbsp; have been analyzed the scientific achievements of Ukrainian researchers regarding the issues of formation and development of digital competence of subjects of educational activity (scientific and pedagogical workers, students of education, etc.). The current trends in the development of the information society and the key problems caused by them in the introduction of information, communication and digital technologies in Ukrainian education, which require an urgent solution, are considered. The motivational, cognitive, activity and corrective aspects of the formation of digital competence of future teachers are considered. In particular, the indicators that testify to the formation of positive motives that ensure the activity of all subjects of the educational space regarding the acquisition of digital competence are their conscious understanding of the need to develop abilities related to digital competence, the desire to learn multimedia and ICT tools. The article talks about an important characteristic of the formation of a future specialist in the field of education - digital competence, which is part of the ten basic competencies prescribed in the Concept "New Ukrainian School" (2016). Digital competence is recognized as one of the key competences of a modern person and occupies a leading place in their list. The components of digital competence and a generalized vision of the necessary components of digital competence are presented. The framework of digital competence for pedagogical and research-pedagogical workers is considered, which has 5 areas of competence: Digital literacy, Professional involvement, Digital educational and scientific resources, Educational activities and Digital competence of education seekers.
COLLECTIONS OF ELECTRONIC RESOURCES AS THE BASIS OF A SCIENTIFIC AND EDUCATIONAL ENVIRONMENT

The article analyzes the concept of "scientific-educational" environment and examines the features of its components, determined by the trends of digitization of education and science. It was determined that a significant number of scientists who studied this concept assign a significant role to the resource filling of the environment. Researchers note the importance of presenting information in digital form and using modern methods and technologies for working with it. The proposed study reveals the need of consumers - subjects of the educational environment in ordering and structuring the information flow of various (scientific, methodical, managerial, etc.) documents and creating their electronic collections, as the basis of the information scientific and educational environment. Digital objects can be combined into a collection by content, types of documents (text, handwritten, cartographic, sheet music, etc.), chronology, purpose, types of sources, etc. The article notes that there is currently no centralized interaction and proper coordination of work on projects. Legal, methodical, technological, organizational and financial tasks have not been solved, the formation of digital scientific collections is largely spontaneous and depends primarily on the need to provide informational support for research or educational activities. This also fully applies to collections of scientific articles, as well as materials of scientific conferences. The creation of electronic collections covering various scientific areas can significantly help educators and scientists in their professional activities. The article provides examples of such collections, including the Electronic Library of the National Academy of Educational Sciences of Ukraine and the Virtual Reading Room of the State Scientific and Educational Library of Ukraine.
THE LIBRARY AS A PLATFORM FOR ADULT EDUCATION IN THE CONDITIONS OF DIGITAL TRANSFORMATIONS

The article analyzes the progressive experience of Ukrainian libraries in the study of consumer needs and the use of this research in the expansion of new library service offerings. The author based on the analysis of the progressive experience of libraries draws conclusions about the new status of public libraries in the life of the community, about the need to introduce marketing research and community needs research into library management. Analysis of the experience of libraries indicates the need to expand services focusing on the needs of the community in the conditions of digital transformations of society. The author emphasizes the study of vulnerable segments of the population (elderly people, people with special needs, low-income and large families, etc.) and the opportunities offered by working with these categories of clients in conditions of digitalization. The article reveals a new perspective on the functionality of libraries, in which the library is considered as a platform for lifelong education of adults. In particular, the article considers the prospects of creating university for adults on the platform of library. The article presents the intermediate results of the ascertainment stage of the scientific and applied research "Information and Analytical Support of the Digital Transformation of Education and Pedagogy: Domestic and Foreign Experience". The author draws conclusions regarding the directions of using the progressive Ukrainian experience in the dynamic development of the information and digital space of adult education. It is emphasized that the digital transformation of adult education is a promising direction for the development of modernization and reform of library centers. The role of the V. Sukhomlynskyi State Scientific and Educational Library of Ukraine in this context has been updated and a significant contribution to the scientific output of researchers in the system of innovation of information and analytical support for the digital transformation of the educational sector and pedagogical science has been noted.
An Improved Shared Encoder-Based Model for Fast Panoptic Segmentation

Panoptic segmentation aims to generate coherent scene segmentation by unifying semantic segmentation and instance segmentation, which is one of the important tasks in the scene perception and understanding for the unmanned autonomous system. Deep learning methods have been widely used in the panoptic segmentation task and achieved great research progress. However, how to balance the efficiency and accuracy of the deep learning-based model is still a challenging problem in the field of panoptic segmentation. To deal with this problem, an improved shared encoder-based model for fast panoptic segmentation is proposed in this paper. In the proposed model, a simple and effective shared encoder is proposed to provide semantically rich multi-scale features. In addition, a lightweight semantic branch is presented to fully exploit the multi-scale features to extract semantic information, and a dynamic kernel-based instance branch is proposed to extract instance information. At last, a panoptic fusion head is proposed to fuse the information of the two branches based on the occlusion-aware discrimination mechanism to generate the final panoptic segmentation output. Finally, the proposed model is tested on two public datasets Cityscapes and COCO. The results show that the proposed model can achieve a good balance between efficiency and accuracy.
AI-based Sensor Attack Detection and Classification for Autonomous Vehicles in 6G-V2X Environment

Autonomous Vehicles (AVs) mainly rely on sensor data and are anticipated to transform the transportation sector. The abnormal sensor readings generated by malicious cyberattacks or defective vehicle sensors can result in deadly crashes. This paper proposes a Sensor Attack Detection and Classification (SADC) framework in a 6G-V2X environment to examine the cybersecurity concern for AVs against sensor attacks. The SADC framework employs GPS and LiDAR sensor attack detectors and the Pattern-based Attack Classification (PAC) algorithm. It combats new-age cyberattacks and provides an accurate sensor attack detection and classification mechanism in AVs. A protocol-based attack detection scheme in SADC is developed to identify the abnormal source sensor based on the detector's results. The PAC algorithm classifies malicious sensors by analyzing different strategies: instant, constant, bias, and gradual drift. The results show that the SADC framework has a 0.98% higher accuracy than the existing counterparts in detecting attacks and classifying them efficiently.
A Reactive Approach to Handling Multirobot Collision Based on $p$-Norm Approximation

In this article, we propose a new method for multirobot systems to have reactive responses to various collision scenarios in real time. This method contains a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -norm approximation-based reactive approach, which allows multiple robots to avoid mutual collisions or to adapt to the collisions with intelligently regulated force. Compared with existing approaches, the implementation of the proposed method is simpler and more convenient since our reactive approach works as several linear quadratic programming constraints, allowing for flexible utilization by inverse kinematics solvers. In addition, it requires low computational complexity, achieves high accuracy, and does not need training. In the experiments, we employ a multirobot system to conduct comprehensive comparisons between the proposed method and state-of-the-art approaches, effectively showcasing the efficacy of our work.
Deep Unfolding-based Distributed MIMO Detection

This paper proposes a distributed multiple-input multiple-output signal detection algorithm based on DU (deep unfolding), which is known as the optimizing method of parameters involved in an iterative algorithm by using commonly used deep learning techniques such as backpropagation and stochastic gradient descent. In the distributed system, multiple distributed SPUs (sub processing units) having multiple antennas execute an iterative signal processing process to detect transmitted signals in cooperation with a CPU (central processing unit). In applications where SPUs are located distributedly, SPUs are placed under heterogeneous SNR (signal-to-noise ratio) environments depending on geographical location and distances from transmitters. We introduce weighting parameters corresponding to each SPU into the aggregation process of the proposed method to incorporate the heterogeneous SNR environments into the detection algorithm. In addtion, we apply DU to optimize the parameters and obtain improvement of the detection perfomance. Results of numerical experiments indicate that the proposed method with the parameters learned by DU improve detection performance by appropriately tuning the weighting parameters according to the heterogeneous SNR environments.
Multi-Scale Similarity Learning for Age Estimation Based on Facial Images

The facial age estimation is an important task for efficient human-robot interaction. A supervised similarity learning has not been widely adopted to a facial age estimation and is still a challenging task. In this paper, we propose a multi-scale similarity learning model which is able to jointly learn similarities between multi-level age features extracted from three different images: an input, a sample with same age named positive and a sample with contrary age named negative. The idea behind using the multi-level feature extraction is to make our feature similarity learning model robust to scale invariance of individual images. As is known, the quality of images in datasets for age estimation vary considerably and deep-learning based networks are often sensitive to image quality and resolution. On that account, we employ a multi-scale feature extraction structure to our model and prove its ability to extract reliable features for a similarity learning. Experimental results show that the proposed approach outperforms previous research and provides a new state-of-the-art age estimation accuracy of UTKFace and CACD benchmark datasets for age estimation.
Simulation of Physical Adversarial Attacks on Vehicle Detection Models

Physical adversarial attacks are a type of attack that aim to fool deep learning based object detectors by modifying the appearance of real-world objects or scenes. CG based simulation techniques are a type of method that use computer graphics to generate realistic adversarial examples that can be printed or projected onto physical objects or scenes. This technical research paper investigates physical adversarial attacks using synthetic image data and simulation based on computer graphics. Two application areas are explored, including object detection and physical adversarial attacks. The study presents successful attack results with an AP50 drop of 89%, demonstrating the vulnerability of object detection systems to physical adversarial attacks. The research utilizes synthetic image data and simulation to generate adversarial examples, providing a cost-effective and scalable method for testing the robustness of object detection systems. The findings of this study have important implications for improving the security and reliability of computer vision systems in real-world applications.
Blockchain-Enabled Federated Learning Approach for Vehicular Networks

&lt;p&gt;Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. &lt;/p&gt;
Dehazing of Satellite Images with Low Wavelength and High Distortion: A Comparative Analysis

Dehazing is a procedure to improve visibility of remote sensing (RS) satellite images that are affected negatively visually by meteorological conditions when the horizontal visibility on the ground level is more significant than 1 km. Remote sensing is the method of acquiring the material characteristics of an area by estimating the reflected and emitted radiation at a distance. The presence of low wavelengths, such as gamma waves, makes it difficult for the light to penetrate the atmosphere and thus makes the image more scattered by airborne particles. Therefore, it is necessary to use gamma-corrected remote sensing images to better the quality of dehazing. The proposed study compares various strategies used in dehazing remote sensing satellite images, and then specific quality standards are employed to judge their performance.