Reviewerly/exHarmony huggingface dataset:
Eternal Problems as a Factor in the Development of Philosophical Knowledge

Key words: eternal problems, philosophical knowledge, worldview, theory, unique and universal knowledge The eternal philosophical questions that determine the nature of scientific thought and the meaning of artistic imagination appear in the minds of people in the pattern of new (actual) problems within the formation of sometimes mystical hypotheses, but more often – rigorous scientific theories. The following philosophical problems can be regarded as the most interesting: the problem of the beginning, the problem of the material and the ideal, the problem of soul and body, the problem of freedom and creativity of the individual, the problem of the meaning of life, the problem of truth. For each of them there is a list of solutions proposed by philosophers of a particular era. Scientific research in the field of natural and social sciences involves a corresponding process in which the presented problem is subjected to a hypothetical solution and its theoretical base is formed. In the field of philosophy, this mechanism does not work. Philosophy is not only a science, but also a worldview. Worldview problems do not have an unambiguous and final settlement. The problems here are eternal. Each historical epoch creates a compact system corresponding to the level of knowledge and worldview development of its time. However, in the process of further development, the “reliable theory” faces a new problem, for the solution of which the study undergoes a new cycle. These transitional stages accumulate an undeniable system of knowledge; however, the main question always remains open and unresolved. Eternal problems feed science with new theoretical propositions and create new foundations and opportunities for its subsequent development. This is not only the logic of philosophy development, but also the logic of science development as a whole.
The Need for Effective Cooperation between Teaching and Administrative Staff in Public Educational Institutions

Key words: intra-school control, effectiveness, culture of cooperation, joint event, peer lesson observations, school-wide work The article touches upon the issue of effective cooperation between pedagogical and administrative staff in public educational institutions. An emphasis has been placed on the importance of effective human resource management, the increase of responsibility for teaching and training activities as well as the need to preserve democratic principles. The content of the legal requirements arising from the rights and obligations of teaching staff is emphasized in terms of improving the quality, efficiency and accountability of the educational process. It also highlights the introduction of a mechanism of mutual trust and the creation of a healthy working atmosphere. In addition, it emphasizes the need to acquire information, ensure its accuracy, and provide methodological and pedagogical support in the educational processes aimed at work planning, accountability implementation, and enhancement. The necessity of organizing joint seminars and trainings for administrative and pedagogical staff, as well as the implementation of intra-school supervision and development, the introduction of the necessary tools for these works – mastering ICT tools, conducting analysis in the process of accountability, storing of information and comparative analyzes are highlighted. A developed pedagogical team sets goals of three types: actual educational (current goals of education), goals for the development of educational activities, goals for the self-development of the team. The organization of the team is its ability to form a rational structure of joint activities and flexibly rebuild it under the changing conditions. The cohesion of the team reflects the ability to withstand internal and external influences that adversely affect the effectiveness of joint activities.
The practice of waste management in the RUDN University

Abstract The pandemic period has left a serious imprint on the development of universities: the formats and content of lectures, workshops, research directions and, in general, the organization of university life. However, the role of universities as centres for the development of a culture of sustainable development remains unchanged: development of new educational courses, demonstration of the university’s achievements in reducing the amount of waste generated, as well as interdisciplinary research on the climate neutrality of the waste management industry. The University implements educational activities for schoolchildren, students, and volunteers. Waste recycling programs have been implemented. A new direction of the university’s activity is the analysis of the carbon footprint of organizations within the framework of the work of the greenhouse gas validation and verification body, the first in Russia to be opened at the RUDN. Management of greenhouse gas flows in waste management is a very popular area of validation and verification. The involvement of university students in research programs involves the development of this topic. The University is a representative facility for estimating greenhouse gas flows caused by waste flows. Thus, the pandemic period has become a period of opportunities for RUDN to create a new scientific and educational direction.
The architecture of green space by medicinal plants in the university and its impact on people’s ealth, education and environmental in the Post-Pandemic Time

Abstract Medicinal plants have played an essential role in human healthcare as essential ingredients in traditional medicines and the combination of novel drugs. The architecture of the green space in the university is mainly aimed at the cultivation of plant species that have an eye-catching application. But the green space including medicinal plants in the university space has both effects on the physical and mental health of people and has a good view. It can also be used for teaching students. Therefore, the architectural style of successful herbal botanical gardens needs to reflect a broad understanding of botany and encourage creative thinking in garden design and interpretation. Medicinal Plants gardens are more than just a “pleasant place to visit” because of their unique combination of resources, and it offers a unique opportunity to use plants for human well-being. As a result, the architectural design of the garden may have an impact on its performance. The purpose of this article is to describe the effect of medicinal plants on people’s health and education, as well as their architectural design in universities.
Best Practice Towards Sustainability Development in Politeknik Sultan Idris Shah

Abstract Sustainability balances environmental, economic, and social issues. It’s the concept that humans can live and flourish in nature. To put it another way, it’s the idea that everything humans need to survive and thrive is provided by the natural world in some ways. To ensure peace and prosperity for people and the planet, now and into the future, the Sustainable Development Goals (SDGs) were created as a reference. Because of this, the Department of Polytechnic and Community College Education, Ministry of Higher Education, Malaysia has initiated to introduce the Blueprint SmartGreen Polytechnic &amp; Community College 2021-2026 (BSGPC), which is provides for a more all-encompassing coverage by placing special emphasis on the 17 SDGs. By encouraging eco-friendly practices at universities and colleges, it may help the country achieve its sustainable development goals. Towards the empowerment of green culture, Politeknik Sultan Idris Shah (PSIS) has embraced numerous best practices such as organizing the PSIS Green Campus Programme, Green Studio Project, and the latest Aquaponic with Solar System project. Therefore, the PSIS SmartGreen Committee leads to the involvement of PSIS in UI GreenMetric where there is a rise in success in the global university ranking.
Code classification with graph neural networks: Have you ever struggled to make it work?

Code classification is a meaningful task with plenty of practical applications. Combined with recently popular graph neural networks (GNNs), a body of research attempts to address the code classification problem with the help of fruitful achievements from the deep learning (DL) area. We systematically investigate the practices of existing works on the GNN-based code classification task and find out three important but often overlooked questions. (1) Existing works usually illustrate the effectiveness of GNNs in code classification tasks by contrasting them with non-graph baselines. But the contribution of the message-passing mechanism, which is at the core of GNNs, has not been shown on its own. So how does the message-passing mechanism help code classification? (2) Various problem formulations, model architectures, and learning objectives have been suggested in the literature to apply GNNs to code classification. In practice, how should we make choices among them? (3) One of the most prominent applications of code classification is automated vulnerability detection. However, learning-based vulnerability detection has not been widely accepted as a practical approach. How does GNN-based code classification perform in this task, especially in real-world scenarios? A comprehensive experimental study is conducted around these questions to evaluate the performance and feasibility of the GNN on different datasets and scenarios. Results suggest it is not easy to make the GNN perform well on the code classification task in the face of the intricate nature of programs. Interesting findings about the effectiveness of GNNs with different training objectives are reported for the first time in this paper, providing insights that can inform future research on code classification with GNNs.
A coarse-to-fine multi-scale feature hybrid low-dose CT denoising network

With the growing development and wide clinical application of CT technology, the potential radiation damage to patients has sparked public concern. However, reducing the radiation dose may cause large amounts of noise and artifacts in the reconstructed images, which may affect the accuracy of the clinical diagnosis. Therefore, improving the quality of low-dose CT scans has become a popular research topic. Generative adversarial networks (GAN) have provided new research ideas for low-dose CT (LDCT) denoising. However, utilizing only image decomposition or adding new functional subnetworks cannot effectively fuse the same type of features with different scales (or different types of features). Thus, most current GAN-based denoising networks often suffer from low feature utilization and increased network complexity. To address these problems, we propose a coarse-to-fine multiscale feature hybrid low-dose CT denoising network (CMFHGAN). The generator consists of a global denoising module, local texture feature enhancement module, and self-calibration feature fusion module. The three modules complement each other and guarantee overall denoising performance. In addition, to further improve the denoising performance, we propose a multi-resolution inception discriminator with multiscale feature extraction ability. Experiments were performed on the Mayo and Piglet datasets, and the results showed that the proposed method outperformed the state-of-the-art denoising algorithms.
A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.
Contrastive deep support vector data description

In comparison with support vector data description (SVDD), deep SVDD (DSVDD) is more suitable for dealing with large-scale data sets. DSVDD uses mapping network to replace the role of kernel mapping in SVDD. Moreover, the objective of DSVDD is to simultaneously learn the optimal connection weights of mapping network and the minimum volume of hypersphere. To further improve the performance of DSVDD for tackling large-scale data sets and obtain the discriminative features of the given samples in a self-supervised learning manner, contrastive DSVDD (CDSVDD) is proposed in this study. In the pre-training phase of CDSVDD, the contrastive loss and the rotation prediction loss are jointly minimized to achieve the optimal feature representations. Furthermore, the learned feature representations are utilized to determine the hypersphere center. In the training phase of CDSVDD, the distances between the obtained feature representations and the hypersphere center together with the contrastive loss are simultaneously minimized to derive the optimal network connection weights, the minimum volume of hypersphere and the optimal feature representations. In addition, CDSVDD can efficiently solve the hypersphere collapse problem of DSVDD. The ablation study on CDSVDD verifies that compared with the case of determining the hypersphere center by the feature representations of the original samples, the hypersphere center determined by the feature representations of the augmented samples makes CDSVDD achieve better hypersphere boundary and more compact feature representations. Experimental results on the four benchmark data sets demonstrate that the proposed CDSVDD acquires better detection performance in comparison with its six pertinent methods.
Kindergarten teachers’ experiences and difficulties in implementing moral education for young children in China

본 연구는 중국 유치원에서의 덕육교육 실행에 대한 유아교사의 경험과 덕육교육을 실행하는 과정에서 경험하는 어려움을 심층적으로 알아보는데 목적이 있다. 이를 위하여 중국 H시에 소재한 9개 유치원의유아교사 10명을 대상으로 2021년 12월부터 2022년 3월까지 개인별로 총 30회의 심층면담을 실시하였다. 면담의 내용은 녹음을 통해 기록하고 전사하였으며 질적 연구의 분석 과정을 거쳐 분석하였다. 연구의 결과는다음과 같다. 첫째, 중국 유치원에서 유아교사의 덕육교육 실행 경험을 살펴본 결과 교사들은 유아들의일일활동에서 문제가 발생했을 때, 교육과정에서 스포츠 게임, 특색 있는 주제와 연계하여, 가족과 유치원이협력하는 방식으로 덕육교육을 실행하고 있었다. 둘째, 중국 유치원에서 유아교사가 덕육교육을 실행하는과정에서 경험하는 어려움을 살펴본 결과 유아교사는 교사 내적으로, 유아와의 관계에서, 여러 주체 간의협력에서 어려움을 겪고 있었다. 본 연구의 결과를 바탕으로 유아교사의 덕육교육 능력과 소양 함양을 위한교육과 지원을 제공할 필요가 있으며, 학부모, 유치원, 사회 등과 협력하여 유아에게 건강한 덕육교육 환경을조성하기 위한 노력도 계속되어야 할 것이다.The purpose of this study was to gain insight into the experiences of kindergarten teachers in implementing moral education in Chinese kindergartens and the difficulties they experience in implementing it. For this, in-depth interviews per person were conducted with a total of 30 times from December 2021 to March 2022, targeting ten kindergarten teachers from nine kindergartens in H City, China. The researcher recorded and transcribed the interview via a tape recording, and analyzed through the process of qualitative research. Results of this study are as follows. Firstly, the experiences of moral education by kindergarten teachers in Chinese kindergartens showed that moral education for young children has been implemented in the daily activities of young children, were linked to sports games, distinct topics in the curriculum, and the collaborative efforts of families and kindergartens in promoting virtue education. Secondly, kindergarten teachers in China faced difficulties due to their personal capacity, in their interactions with young children, and in fostering cooperation among various subjects. It is necessary to provide education and support for the cultivation of moral education capabilities and literacy of kindergarten teachers, while to continue efforts to create a healthy moral education environment for young children in cooperation with parents, kindergartens and society.
Creating a chatbot using text information processing methods

The article considers the relevance and applied aspects of the use of chatbots. The structural and functional system of the chatbot is analyzed. A comparative description of the platforms for creating chatbots has been made. The functional blocks are based on the example of the Telegram social network, a prototype of the system. As part of the chatbot development, the necessary and sufficient condition for working with text is described - the use of various text analysis methods as an input element for interacting with the bot and its training. The article discusses a number of solutions used for text analysis and creation of text data analysis models: lemmatization methods, text vectorization, various machine learning models. The article focuses on the consideration of the methodology of text processing in different formats and using different technologies, which ensures the scalability and versatility of the proposed technology and the overall effectiveness of the future work of the chatbot.The article will be of interest to programmers, text analysts and anyone interested in working with text and creating systems for working with text information. A chatbot has been created in the Telegram messenger in the Python programming language that automates the work of a customer service specialist.
A Study of Trend of Family Business Research Using Text Mining Analysis

This study analyzes the trend of family business research in Korea using text mining methods. The analysis reveals that research on family businesses has predominantly focused on succession-related areas, as indicated by the frequency of studies. The temporal analysis of TF-IDF results demonstrates a gradual increase in the significance of succession, suggesting a growing research interest corresponding to the retirement period of incumbent CEOs. Moreover, topic modeling identifies five main themes, with the highest proportion dedicated to management strategy and governance. By examining these trends in family business research, it is anticipated that both the interest in and the breadth of research topics will expand.
A study on comparison of pseudorandom number generator

Abstract In this paper, we have collected some theoretical and practical pseudorandom number generators (PRNGs) with uniform distribution. We have also analyzed some of the most prevalent statistical tests and we present some observations and statistical tests of the sequences generated by the described generators.
Using Optimization Algorithms to Improve The Classification Of Diseases On Rice Leaf Of EfficientNet-B4 Model

Rice is an important food source, so increasing rice yields is essential to disease management. Much related research has performed classification and disease detection on rice leaf using machine learning models. However, this study aims to synthesize data to evaluate rice leaf diseases through collected data and contribute new data sets. This data set uses optimization algorithms (RMSprop and Adam) combined with the EfficientNet-B4 model with learning rates of 0.01 and 0.001. The research showed that the optimal algorithm combined with the EfficientNet-B4 model gave high results of 93% (F1-Score) and an accuracy of 89%. The research results show the influence of optimal parameters on the models and find the most optimal parameter results.
Unsupervised Anomaly Detection using Deep Autoencoding Mixture of Probabilistic Principal Component Analyzers

Although many unsupervised anomaly detection algorithms with outstanding performance have been proposed in last few decades, their performance for high-dimensional data is not guaranteed. Therefore, this paper proposes a new framework called deep autoencoding mixture of probabilistic principal component analyzers (DA-MPPCA). This framework uses deep autoencoder (DAE) as a compression network to prepare the low-dimensional representations for a subsequent estimation network NN-MPPCA. Different to conventional MPPCA that is trained by EM algorithm, NN-MPPCA is the neural network form of MPPCA which parameters can be updated via back-propagation algorithm. By jointly training DAE and NN-MPPCA in an end-to-end manner using a defined loss function, we force both dimensionality reduction and density estimation tasks into the unified framework. The experimental results on a variety of public datasets have demonstrated the superior performance of DA-MPPCA over both shallow and deep baseline models with an improvement on F1-score up to 7% over the best baseline.
An Autoencoder Based ASCII Art Generator

ASCII art is a way to represent an image with character shapes. It is common to carry ASCII art instead of displaying image files on Internet bulletin boards. Multibyte encodings contain various characters that are useful to shape an image. The essential idea required to get ASCII art is to approximate color distribution at the portion of a target image to a character shape. In this study, we make a machine learning model that learns the shapes of characters in a multibyte encoding to convert a partial image of a target image to a font image.
A Study on the Optimization of the CNNs for Adversarial Attacks

Convolutional Neural Networks (CNNs) have shown high accuracy in image classification tasks, including on popular datasets like MNIST and ImageNet. However, these models can be easily fooled by small perturbations added to the input image. To address this issue, the VOneBlock architecture was proposed, which can be added to the frontend of a CNN-based model to improve its robustness to adversarial attacks. In this paper, we compare the performance of CNN models with and without VOneBlock when fine-tuning on adversarial datasets, and show how the inclusion of VOneBlock affects the model's robustness. Additionally, we investigate how the number of Gabor filter kernels used in VOneBlock affects its performance. Through our experiments, we present an optimal way to enhance the robustness of CNN models to adversarial attacks using VOneBlock. Finally, we evaluate whether the classification model with VOneBlock performs well in classifying real-world attacked images, as well as adversarial attacked images. While VOneBlock was developed to improve robustness to small perturbations, we find that the neural network with VOneBlock performs slightly better in classifying real-world attacked images.