Reviewerly/exHarmony huggingface dataset:
PEDAGOGICAL ASSESSMENT OF DIFFICULTIES IN MATHEMATICS AMONG PRIMARY SCHOOL PUPILS

This paper focuses on the pedagogical assessment of primary school pupils' mathematical abilities and skills. The present study includes pupils taught by conventional methods and pupils taught by the so-called Hejny method based on the constructivist approach. Using standardized diagnostic methods, children nominated by teachers as having difficulties in mathematics were compared in terms of their performance in mathematics and non-verbal intellectual abilities with a control group of pupils without identified difficulties. The results showed significant differences in favor of the control group, especially for children taught within a constructivist approach.
THE USE OF PERSONALLY ORIENTED TECHNOLOGIES IN THE PROFESSIONAL TRAINING OF FUTURE SPECIALISTS

In the article, the scientific and theoretical analysis of personally oriented learning technologies showed that the essential element of their application in higher education practice is the transformation of the student from an object to a subject of the educational process. The condition for such a transformation is certain internal positions of teachers and students, namely: genuine interest and respect for the personality of the student as a specific individual; use of modern forms and methods of professional training of future specialists; positive-emotional perception of the pedagogical process; transformation of the student from a passive observer who acquires knowledge and experience to an active interlocutor; encouragement to study on the basis of cooperation and self-management, etc.
DIGITALIZING EDUCATION IN THE AGE OF PANDEMICS:

The paper investigates the impact of the COVID-19 pandemic on the acceleration of digitalization processes in the field of education. Specifically, the study examines the development and improvement of online learning formats during this period, highlighting both the advantages and disadvantages, as well as identifying the bottlenecks and challenges that emerged. The paper presents the results of a survey conducted among Bachelor's and Master's degree students at the Faculty of Distance Learning at Plekhanov Russian University of Economics, categorized according to their areas of study, to determine the efficacy of remote learning processes and methods. Additionally, in-depth interviews were conducted to provide a more detailed analysis of the benefits and drawbacks of online learning, as well as potential solutions to improve remote learning processes. Overall, this paper offers valuable insights into the role of the pandemic as a catalyst for digital transformation in education, and provides recommendations for enhancing the effectiveness and efficiency of online learning.
SOCIOLOGICAL AND PEDAGOGICAL PERSPECTIVES IN HIGHER EDUCATION:

The paper explores the relationship between the educational environment and pedagogical approaches in contemporary universities. The study emphasizes the need for further research on the contribution of each of these aspects to the dynamism and flexibility of the educational environment, as well as the professionalism of educators. Particular attention is paid to the input of teachers in all areas of education both in the theoretical and empirical sense. The article also highlights the changes that stimulate the interrelation of all spheres of education and focuses on the inner differentiation of the vocational and upbringing aspects. This approach supports the unity of vocational and pedagogical culture in higher education. In addition, the study addresses the sociological perspectives in higher education in the context of its interaction with pedagogy and sociology, emphasizing the role of the latter in personality development. Particular attention is paid to the meaning of upbringing and scientific education in contemporary universities as active instruments affecting conviction in the necessity of education, especially in the light of recent research.
Looking back at COPIM... and on to new adventures, with Open Book Futures!

With the COPIM project having finished at the end of 04/2023, we are looking back on what COPIM has been able to achieve over the last 3.5 years, before also casting a look ahead to the next project phase of Open Book Futures (05/2023-04/2026).
3D convolution-based video compression model without motion estimation

In recent years, there are more and more researches in the field of video compression based on deep learning. Most of these researches maintain the same framework, including motion estimation and compensation modules. We propose a different method, which does not need an explicit motion estimation module. Instead, video compression is achieved by extracting and compressing the residuals between two adjacent frames. The residual is extracted by a 3D pyramid network. Based on the robust representation ability of deep features in various applications, the whole coding and compression process is transferred from pixel space to feature space to improve compression efficiency. The transformation process is realized by two networks: feature extraction and frame reconstruction. Our video compression framework has a simple structure and a smaller number of parameters than most subsequent proposed models. The experiment result shows that the performance of our PSNR model is better than H.264, and is comparable to H.265 in some cases, while our MS-SSIM model is better than H.265 in all cases.
An improved transformer model based on global and local features for few-shot learning with noise samples

It is a challenging problem to identify new categories with few support samples. In the current few-shot learning tasks, it is usually assumed that the support set is accurate, but in real scenarios, the support set may still have noisy samples. Therefore, how to learn a reliable model on the noisy support set is the direction of current research. To address the situation in few-shot learning task where the support set contains noisy samples, we propose a Transformer model for noisy few-shot learning that considers both global and local features. Specifically, we consider the difference between local features and global features between noise samples and support set samples, then align the local features of the samples, and use the attention mechanism of the transformer model to reduce the interference of noise samples to the model. To evaluate the effectiveness of our proposed models, we extensively test these methods on noisy versions of MiniImageNet and TieredImageNet. Our results show that the model performs competitively in the presence of label noise.
Design of gesture recognition mobile car based on YOLOv5

In response to the less referees in non -professional basketball events, this article will design a multi -functional basketball technology station based on deep learning gesture recognition and image -based car -based cars. While tracking the referee position, the corresponding gesture can reduce the labor costs in basketball games and improve the smoothness of the game. This article uses the YOLOv5 algorithm, which can reaches more than 90%of gesture recognition. Using the OpenCV image processing module can better capture the position of the referee and make gesture recognition more accurate and fast.
An online modeling approach based upon improved self-organizing radial basis function neural network

In this paper, we propose an improved online self-organizing radial basis function neural network (IOS-RBF) modeling method which can dynamically add or merge hidden neuron while tuning the parameters. First, the initial center of the network is determined using the sample output clustering method, which is able to utilize the prior information contained in the output data. Second, a new neuron self-organization adjustment strategy is proposed, which is able to dynamically optimize the network structure according to the generalization ability of the network and the correlation between the nodes, and then, the network parameters are trained using the sliding window Levenberg-Marquardt (LM) algorithm to accelerate the network convergence speed. Finally, the effectiveness of IOS-RBF is demonstrated by two benchmark simulation experiments.
A Neig-DoubleRipple recommendation model based on fused neighbourhood attention and aggregation

In recommendation systems, the use of information from knowledge graphs to enhance the effectiveness of recommendations has become a hot research topic. Currently, the RippleNet model has become a relatively effective knowledge graph-based recommendation model. However, the RippleNet model only uses the information between the user to be predicted and the item, and does not take into account the information contained in the neighbouring entities around the item to be predicted. To solve this problem, this paper proposes a new model based on RippleNet, Neig-DoubleRipple, which augments the original model by adding a neighbourhood attention mechanism and a neighbourhood entity aggregation method similar to the KGAT model. The experimental results show that the new model achieves 92.52% in auc and 85.11% in acc, indicating that the method proposed in this paper can significantly improve the performance of the RippleNet model.
LiDAR-camera fusion for multi-modal 3D object detection

With the continuous development of autonomous driving technology, more and more vehicles are equipped with different kinds of sensors for environmental sensing. Among these sensors, LiDAR and camera are the most visible, but there are great data structure differences between the point cloud data of LiDAR and RGB image data of camera, which bring some difficulties to the fusion. To address the above issues, this paper proposes a multimodal fusion framework CAD-Net for LiDAR-Camera fusion for 3D object detection. CAD-Net demonstrates the significant performance improvement of the proposed method by conducting comparative experiments and correlation analysis on KITTI public dataset.
Analysis of Fishery and Forestry Ventures and Poverty Reduction in Nigeria

Fishery and forestry ventures are sub-sectors of agriculture with the potential for poverty reduction through employment creation, value addition, and improvement in export earnings. Nevertheless, they have received relatively less attention from all levels of government in Nigeria. This paper examined the effect of fishery and forestry ventures on poverty reduction in Nigeria between 1976 and 2022. Ex-post facto research design was adopted. Data were sourced from CBN annual statistical bulletin, the National Bureau of Statistics, publications of the World Bank, as the Federal Ministry of Agriculture and Rural Development. NARDL was adopted for data analysis. Phillip-Peron Unit root tests revealed a uniform order of integration, while Co-integration Bounds test revealed no existence of long-run relationship between the fishery and forestry ventures and poverty reduction. Findings revealed that agricultural production in fishery and forestry has no significant long-run nexus with poverty reduction in Nigeria. The paper recommended that the Federal Department of Fisheries should engage reputable research institutes and colleges in the fishery industry in Nigeria to train and retrain participants in the sub-sectors to provide skilled workforce in the development of sustainable fishery production towards poverty reduction in Nigeria. The paper also recommended that regulatory agencies in the forestry sub-sector in Nigeria appoint more supervisors and enforcement officers to ensure adequate regulation of the industry towards adding maximum economic value to the exploration and exploitation of the forestry resources in Nigeria by reducing leakages and ensuring that adequate taxes and levies are paid including collection of relevant penalties from defaulters in order to attain sustainable income generation and poverty reduction among the accredited participants.
English Language Teaching (ELT) Appraisal in the Trifocal System of the Philippine Education - Basis for Policy and Advancement Program

This study explored the English Language Teaching appraisal of teachers in the trifocal Philippine education system in the Bicol Region as a basis for policy and advancement programs. This study used the descriptive-comparative-associational method using a content-validated and reliability-tested questionnaire. The frequency and percentage technique, weighted mean, Chi-Square, and Anova Test of Variance were used in the data treatment. Findings revealed that ELT teachers were predominantly females with 73.97%, CHED, 80.21%, DepEd, and 71.43% TESDA along age groups of early and mid-30s and 46 years above. They served the agencies for less than 10 years, mostly married. Professionally, these teachers were still pursuing post-graduate education and attending training. Along with appraisals on their professional and work responsibility, ELT Trifocal teachers very evidently performed their professional and work responsibilities; on the delivery system, respondents conveyed the mastery and application of theoretical and pedagogical knowledge; on their communicative competencies, they were classified as experts. Meanwhile, there was no significant difference among ELT teachers’ appraisal in the three systems (F–value 2.21&lt; t-value 3.35; 5% level of significance). It follows that Philippine education has maintained an undeviating quality of ELT teachers regardless of which system of education they belong. The result showed no variations in ELT competencies despite differences in modalities, urgencies, and the attainment of quality education. Findings also unveiled that the ELT teachers’ appraisal and integration of ICT to language teaching are directly associated with their age, sex and civil status, and educational attainment. The higher the educational attainment, the higher the integration of ICT in language teaching. With these results, a policy development for the Professional Advancement Program in ELT was crafted.
An Analysis of Feature Extraction in Image Classification: A Comprehensive Study

There have been many real-life applications utilizing deep learning, especially in the area of image classification. A common finding is that some domain data are highly skewed, which means that most of the information belongs to a small number of majority classes, and there is little or no information in the minority classes. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis of imbalanced dataset is conducted by considering deep learning-based well-known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is examined. Moreover, a bibliometric analysis is carried out from 1991 to 2022 in order to identify the global scientific research on the image classification of imbalanced mushroom dataset. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.
Relationship between Technology and Economic Growth

The study analyzes the relationship between technology and economic growth in Jordan during 2009–2018 and the data are treated via Views E program. ARDL methodology are used. Results showed a co-integration relationship between the study variables (computer use in general; computer use at work; and computer use in education, training, and economic growth) and the results presented that the deviation from long-term equilibrium is corrected using an error correction model which long-term corrected as a percentage correction (−0.06) each year from the short-term to the long-term and showed the results of the structural stability test of the (ARDL) model. It is a structural stability test for long and short-term coefficients, which showed that the data used in this study are free from any structural changes has stable parameters over time The study also used CUSUM's Squerse test, where the test results showed that the study model used is economically good and can be relied upon to anticipate economic solutions in Jordan according to the situation in the coming years, and among the most important recommendations of the study are the following: the need to encourage the use of technology in work, education, and training, and the need for expansion in using these technological means as a gateway to the digital economy and the digital state.
ANALISIS KEAMANAN JARINGAN PADA FASILITAS WIFI TERHADAP SERANGAN SNIFFING DI PT DUTA COMPUTER

Technological developments and dependence on WiFi networks have brought many benefits in the business world, including at PT Duta Computer. However, network security is becoming a critical issue as cyber attacks become increasingly complex and sophisticated. One potential attack is a sniffing attack, in which the attacker tries to steal sensitive data that is sent over a WiFi network. This study aims to conduct an in-depth analysis of network security on WiFi facilities at PT Duta Computer against sniffing attacks. The results of this study are expected to provide deeper insight into the level of security of the WiFi network at PT Duta Computer and help companies to identify and address potential security vulnerabilities. By enhancing network security, PT Duta Computer can ensure that sensitive customer data and corporate information remain safe from sniffing attacks.
Analisis Regresi Multivariate Berdasarkan Latar Belakang Santri

The students of the Matholiul Anwar Islamic boarding school come from various different regions so that they have quite diverse social and economic backgrounds. Likewise with learning motivation, achievement motivation and student learning achievement are also diverse. This study aims to determine the effect of the social and economic background of students on achievement motivation, learning motivation and learning achievement. The research sample used was 49 students in the F dormitory of the Matholiul Anwar Sukodadi Lamongan Islamic boarding school. This study uses the Multivariate Regression Analysis model. From the results of the partial study, the effect of social status on the dependent variable was concluded that there was an effect of social status on achievement motivation, learning motivation and learning achievement. Meanwhile, for the variable of economic status on the dependent variable, it can be concluded that there is no effect of economic status on achievement motivation, learning motivation, and learning achievement. Simultaneously, it was obtained that the background of the students affected the achievement motivation, learning motivation, and learning achievement in the F dormitory of the Matholiul Anwar Islamic boarding school.
Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.
Training in Writing Scientific Works and Popular Science Books for Teachers at SMKN 34, Central Jakarta

Some vocational teachers still need help writing scientific papers and publications or writing textbooks and general books, which can be used as one of the requirements for promotion or position. This writing training aims to share knowledge with teachers at SMKN 34 Jakarta about techniques for writing scientific papers, textbooks, and general books, as well as the systematics of writing and how to publish scientific papers. The method is through the sharing method by giving lectures to the participants, discussions, presentations, and questions and answers. Outside of the training session, writing assistance is also given to several teachers who are ready with their articles so they can publish a work or publish it in journal form. The result of this training is that the participants are very enthusiastic about participating in the training. It can be seen from the attendance of participants as much as 100% from the opening to the final session. After the training, the participants can understand how to write and publish scientific articles in accredited national journals. Participants also understand how to make a reference book or general book so that national publishers can publish it. This training was supposed to be successful because their understanding of the world of writing increased, the participant's response to this activity was very positive, and several teachers had prepared articles from previous research to be published in one of the accredited national journals,