Reviewerly/exHarmony huggingface dataset:
Features of modern university teacher training: The problem of unity and variability of the educational space

The relevance of the analysis of the current state of university teacher training is determined by the necessity to generalize the practical experience that has been developed as a result of the permanent reforming of the higher pedagogical education in the Russian Federation. The study purpose is to identify the features of the system of higher pedagogical education in Russia. This system has developed as a result of the implementation of various modernization projects in the 21st century. The context of the analysis includes the practice developed at the initiative of the Ministry of Education in order to ensure the unity of the educational space for the pedagogical teacher training (“The Core of Higher Pedagogical Education”). The study shows that the modern system of higher pedagogical education, which has a single institutional basis, is built variably from the point of view of the content in universities of different departmental subordination. This, thereby, creates real difficulties in the development of a single teacher-training educational space. The study sample includes experts who are university teachers (n = 55) and school teachers of different subject areas (n = 802) of different ages from different regions of Russia. The study methods (tools) are the methods of qualitative and quantitative analysis of primary data, case analysis of the largest teacher-training universities. Sources of analysis: 1) the data of monitoring universities (2022), which is presented on the open website “Higher Education Monitoring”, 2) cases of the largest teacher-training universities of various departmental subordination, 3) expert assessments by university and school teachers. The results. It has been found out that a distributed model of teacher training in universities of various departmental subordination has developed in Russia as a result of the permanent reforming of pedagogical education throughout the 21st century. This, in turn, contributes to the development of the environment of variability in the search for concepts and technologies in providing pedagogical education. The main conclusions. The study has revealed that there is a contradiction between the objective necessity to preserve the autonomy of universities in terms of conceptualization, design, realization of scientific and educational activities and the idea of developing unified approaches to the structure and content of teacher training (“The Core of Higher Pedagogical Education”). The results obtained are relevant for the coordination of methodological guidelines for further development of the system of higher pedagogical education by the scientific and professional community. This can serve as the basis for developing common approaches to ensuring the unity of teacher training in universities of various departmental subordination.
Users authentication issues and challenges in electronic commerce applications

Modern business models that use the cloud are electronic commerce and mobile commerce. A brand-new innovation called cloud computing makes use of the Internet to process and store data from a network of remote machines that are spread out all over the world. Security is a crucial element of every online transaction and it is a key problem with the cloud. Users may lose faith in electronic commerce if its security is compromised. Authentication is the process of validating a user&apos;s identification in electronic commerce systems. Personal information about a user shouldn&apos;t be accessible to or susceptible to interception by unauthorized parties while it&apos;s being transferred. It is a crucial step in the security of systems, as it ensures that only authorized users are able to access sensitive information or perform certain actions. The specific authentication method used will depend on the security requirements of the system, the level of trust required for access, and the level of user inconvenience that is acceptable. Strong authentication is essential for protecting sensitive information and ensuring the integrity of transactions in electronic commerce applications. By verifying the identity of users, authentication helps to prevent unauthorized access and protect against identity theft and fraud. The primary objective of this study is to develop a system for user authentication. Too much security can make it difficult for users to access their accounts, while too little security can make the accounts vulnerable to attack. The proposed system using the PKI based approach underwhich user&apos;s authentication is checked and verified by using the Digital Certificate. The empirical results shown that ECC is superior to RSA for generation of Digital Certificate.
Attendance management system using Python (Haar cascade and Open-CV)

The Attendance Management System using Haar cascade and OpenCV is a Python-based project that aims to automate the process of attendance management in various settings such as schools, colleges, or workplaces. The system utilizes computer vision techniques, specifically Haar cascade and OpenCV, to detect and recognize faces from images or video streams, enabling accurate and efficient attendance tracking. Traditional methods of attendance management involve manual processes, such as taking roll calls or signing attendance sheets, which can be time-consuming, prone to errors, and inefficient. Automating the attendance management process using computer vision techniques can significantly streamline the process, eliminate manual errors, and save time and resources. The conventional methods of attendance management often suffer from challenges such as inaccuracies, buddy punching (where one person marks attendance for another), and time-consuming administrative tasks. These issues can lead to discrepancies in attendance records and create difficulties in maintaining accurate attendance data.
Smart Learning in Virtual Worlds: A Systematic Literature Review on the Usage and Applications of Metaverse in Education

Interactive digital applications have made their way into the educational field. Since the COVID-19 pandemic, numerous countries have resorted to various platforms to reinvent teaching methodology in the digital era. One of these platforms is the metaverse, which presents itself as a resourceful way to teach in the future, with potential applications such as digital interaction among peers. It further offers more dynamic and immersive learning, as students can engage in virtual simulations, role-playing activities, and collaborative projects with other students globally. In an era of constant technological advancement, studying the metaverse and its applications in the educational field seems like a valid starting point to enrich the current teaching methodologies and those of the future. It is an acknowledgement of its impact on education, its usage, challenges and advancements, as well as its influence on the future of education. Therefore, this paper’s objective is to gather and analyse the current literature available relating to the application of the metaverse in the field of education. A systematic literature review (SLR) was conducted across the journal databases of IEEE Xplore, Scopus, ProQuest, ScienceDirect and ACM Digital Library. A total of 31 articles were reviewed according to criteria. The results of this SLR determined that, while research shows the significant potential regarding the usage of the metaverse in education, a set of concrete applications has yet to be investigated thoroughly in order to exploit this technology’s immense potential fully in the field of education.
TypeScript Basics

This chapter starts your TypeScript journey. Here, you will learn about different types and ways to use them in your projects. You will start with the number type, followed by the string and Boolean types. You will also learn about many more types and end by learning about interfaces.
Quantum Routing for Emerging Quantum Networks

Quantum routing, the entanglement of an input quantum signal over multiple output paths, will be an important aspect of future quantum networks. Implementation of such routing in near-term quantum networks via the noisy quantum devices currently under development is a distinct possibility. Quantum error correction, suitable for the arbitrary noisy quantum channels experienced in the routing process, will be required. In this work, we design a combined circuit for quantum routing and quantum error correction, and carry out an implementation of such a circuit on a noisy real-world quantum device. Under the assumption of statistical knowledge on the channel, we experimentally verify the quantum nature of the error-corrected quantum routing by determining the path-entanglement through quantum state tomography, measuring also its probability of success. The quantum error correction deployed is identified as successful in terms of improving the routing. Our experiments validate, for the first time, that error-corrected quantum routing in near-term quantum networks is feasible, provided enough statistical information on the noise intrinsic to the routing device is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> known.
Weighted Order-<i>p</i> Tensor Nuclear Norm Minimization and Its Application to Hyperspectral Image Mixed Denoising

Recently, tensor singular value decomposition (t-SVD) has demonstrated excellent performance in various high-dimensional information processing applications. However, in adapting t-SVD to handle the typical tensor data restoration tasks, such as hyperspectral image (HSI) denoising, the following questions remain inadequately addressed: 1) The existing tensor nuclear norm minimization (TNN) regime treats all tensor singular values alike; thus, it lacks flexibility and dominance in dealing with the sophisticated HSI tensor. 2) The existing t-SVD-based denoising methods can not directly process order- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> > 3) tensors; thus, they fail to comprehensively exploit the high-dimensional structural correlation of the HSI tensor along different modes. To address the above challenges, in this study, we first generalize a novel weighted order- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> TNN minimization regime, which integrates the adaptively reweighting strategy for matrix, third-order, and order- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> tensors in a unified architecture. Subsequently, an efficient subspace low-rank learning model is established, using HSI denoising tasks as an application example to corroborate the superiority of the proposed regime in approximating the high-dimensional low-rank structure of natural tensor data. Extensive experimental results substantiate that our effort surpasses existing state-of-the-art low-rank tensor recovery methods in both restoration accuracy and efficiency. The source code is available at https://github.com/CX-He/WTNN.git.
The role of bodily self-consciousness in episodic memory of naturalistic events: an immersive virtual reality study

Abstract Recent studies suggest that the human body plays a critical role in episodic memory. Still, the precise relationship between bodily self-consciousness (BSC) and memory formation of specific events, especially in real-life contexts, remains a topic of ongoing research. The present study investigated the relationship between BSC and episodic memory (EM) using immersive virtual reality (VR) technology. Participants were immersed in an urban environment with naturalistic events, while their visuomotor feedback was manipulated in three within-subjects conditions: Synchronous, Asynchronous, and No-body. Our results show that asynchronous visuomotor feedback and not seeing one’s body, compared to synchronous feedback, decrease the sense of self-identification, self-location and agency, and sense of presence. Moreover, navigating in the Asynchronous condition had a detrimental impact on incidental event memory, perceptual details, contextual association, subjective sense of remembering, and memory consolidation. In contrast, participants in the No-Body condition were only impaired in egocentric spatial memory and the sense of remembering at ten-day delay. We discuss these findings in relation to the role of bodily self-representation in space during event memory encoding. This study sheds light on the complex interplay between BSC, sense of presence, and episodic memory processes, and strengthens the potential of embodiment and VR technology in studying and enhancing human cognition.
Enhancing Practicality of Web-Based Mobile Learning in Operating System Course: A Developmental Study

Web-based Mobile Learning can enhance the learning experience in various educational contexts. However, in operating system courses, practical challenges arise when implementing a web-based mobile learning platform, which impacts the effectiveness and accessibility of learning materials for students. To overcome these challenges, this research and development (R&amp;D) aims to improve the practicality of web-based mobile learning in operating system courses. The research adopts a systematic 4D (Define, Design, Develop, Disseminate) model to identify and explore strategies to optimize the practicality of the platform. Data collected from lecturers and students showed a high average value of practicality, 88.33% and 88.35%, respectively. This research contributes to improving the practical aspects of web-based mobile learning, thereby enhancing students’ learning experience and outcomes in the context of operating system courses.
Optimized Dropkey-Based Grad-CAM: Toward Accurate Image Feature Localization

Regarding the interpretable techniques in the field of image recognition, Grad-CAM is widely used for feature localization in images to reflect the logical decision-making information behind the neural network due to its high applicability. However, extensive experimentation on a customized dataset revealed that the deep convolutional neural network (CNN) model based on Gradient-weighted Class Activation Mapping (Grad-CAM) technology cannot effectively resist the interference of large-scale noise. In this article, an optimization of the deep CNN model was proposed by incorporating the Dropkey and Dropout (as a comparison) algorithm. Compared with Grad-CAM, the improved Grad-CAM based on Dropkey applies an attention mechanism to the feature map before calculating the gradient, which can introduce randomness and eliminate some areas by applying a mask to the attention score. Experimental results show that the optimized Grad-CAM deep CNN model based on the Dropkey algorithm can effectively resist large-scale noise interference and achieve accurate localization of image features. For instance, under the interference of a noise variance of 0.6, the Dropkey-enhanced ResNet50 model achieves a confidence level of 0.878 in predicting results, while the other two models exhibit confidence levels of 0.766 and 0.481, respectively. Moreover, it exhibits excellent performance in visualizing tasks related to image features such as distortion, low contrast, and small object characteristics. Furthermore, it has promising prospects in practical computer vision applications. For instance, in the field of autonomous driving, it can assist in verifying whether deep learning models accurately understand and process crucial objects, road signs, pedestrians, or other elements in the environment.
Enhanced BaTiO<sub>3</sub>/Si<sub>3</sub>N<sub>4</sub> integrated photonic platform with VO<sub>2</sub> technology for large-scale neuromorphic computing [Invited]

The hybrid barium titanate (BaTiO 3 or BTO) – silicon nitride (Si 3 N 4 or SiN) platform integrated on silicon has been established as a promising candidate for implementing photonic integrated circuits with unique features in terms of high modulation speeds and low transmission loss. However, despite the high Pockels effect in BTO, switching device footprints are relatively large, which could compromise their use in applications with large scalability requirements, such as neuromorphic computing hardware. To address this limitation, we propose the integration of vanadium dioxide (VO 2 ) in the SiN/BTO platform to enable ultra-compact amplitude switching devices offering a scalar multiplication functionality with multilevel operation. More concretely, an electronically reprogrammable switching device with a 5-bit amplitude encoding capability and an insertion loss of only 0.5 dB is developed. The proposed device is built with a 9-µm-long VO 2 /SiN/BTO waveguide structure integrated with an efficient microheater using a transparent conducting oxide. Such a vital building block would offer significant potential for developing more complex photonic integrated circuits, including dot-product or matrix-vector multiplication engines. The combination of high speed, low loss, and reduced footprint makes the proposed enhanced platform an attractive solution for application in scalable and energy-efficient neuromorphic computing hardware.
Recommendation Item Selection Algorithm Considering the Recommendation Region in Embedding Space and New Evaluation Metric

In recent years, recommender systems based on machine learning have become common tools on various web services. Among recommendation models, embedding methods such as Item2Vec that utilize embedded representations of items are widely used in actual applications due to their effectiveness and ease of use. By utilizing embedded representations acquired through learning the interaction between users and items, it is easy to discover similar items from the viewpoint of the user’s purchasing tendencies. In contrast, with this method, only biased items are recommended, making it difficult to ensure a wide variety of recommended items. However, there is a trade-off between the diversity of recommended items and accuracy and providing diversity in recommended items while maintaining accuracy is a challenging problem. Therefore, in this study, we propose a method to expand the new evaluation metric
Relationship Between Human Rights Sensitivity and Rights-Respecting Childcare Practices of Childcare Teachers'

The purpose of this study is to examine the relationship between childcare teachers' human rights sensitivity and rights-respecting childcare practices. To this end, data were collected and analyzed for 322 teachers who participated in education for childcare teachers in Chungbuk in 2023. First, the level of human rights sensitivity of childcare teachers was not high, and the perception of results was the highest by sub-factors. In terms of human rights-related items by episode, the right to freedom and safety of the body was the highest, and the right to education of the disabled was the lowest. Second, the overall level of childcare teachers' respect for infant rights was relatively high. By sub-area, toilet visits (basic needs) were the highest in daily work and respect, and special activities were the lowest. Among the best interests of children, respect for intention was the highest and information provision was the lowest. Third, the higher the human rights sensitivity of childcare teachers, the higher the level of child care that respects rights. By sub-area, going to the bathroom (basic desire) showed the highest correlation. Through these research results, it is necessary to prepare and practice measures to improve the human rights sensitivity of childcare teachers in order to have a high level of respect for rights and continuously implement them in the childcare field. This study is meaningful in providing implications for preparing various support measures to improve human rights sensitivity in order to implement childcare with respect for rights.
Dynamics of educational programs in the context of the formation of professional competencies of a library specialist

The system of library and information education in modern conditions makes it possible to significantly improve the quality and efficiency of students’ education at the university. In the context, a model of factors influencing the development of library and information education is presented the article highlights the experience of designing educational programs of higher education in accordance with the requirements of the Federal State Educational Standard 3++ and professional standards. The example of the development of an educational program in the field of training 51.03.06 «Library and information activities for bachelors» at the Samara State Institute of Culture is considered. The main directions of improving the educational program in the direction of training 51.04.06 Library and information activities for masters are outlined. It is concluded that currently there is a rethinking of educational programs of the library industry within the framework of the Federal State Educational Standard, as well as factors affecting their further modernization. The models of the main educational programs that contribute to the establishment of closer cooperation between the university and the labor market, the promotion and development of professional competencies in the library institutions of the region are proposed.
An Analysis of Students’ English Oral Communication of English Educational Department, PGRI University of Banyuwangi

The objective of this research is to analyze the students’ oral communication in English of the sixth semester students of English Educational Department PGRI University of Banyuwangi in the Academic Year 2018/2019. This research is a descriptive quantitative. The respondents were the sixth semester students of English Educational Department PGRI University of Banyuwangi. The data were taken by using speaking test. The data of speaking test were analyzed by using percentage formula. The result of the data analysis was students’ problem based on grammar, pronunciation and fluency. Based on the data analyzed by using percentage formula, the first indicator is grammar. The students got the percentage scores about 33,78%. Based on the interval range scores, the percentage of students’ grammar scores was categorized as good. The second indicator is pronunciation. The students got the percentage scores about 33,43%. Based onthe interval range scores, the percentage of students’ pronunciationwas categorized as good. Then, the last indicator is fluency. The students got the percentage scores about 32,78%. From the interval range scores, the percentage of students’ fluency wascategorizedas good.