Reviewerly/exHarmony huggingface dataset:
Research on Local Employment Intention of College Graduates Facing Urban Economic Development: Based on R City in China

Purpose - Based on the highly coupled relationship between local college graduates’ localized employment and urban economic development, this paper mainly studies the willingness and influencing factors of local college graduates’ localized employment.&#x0D; Design/Methodology/Approach - Based on theoretical research, this paper takes R city in China as the specific research object. It uses a combination of academic research and empirical analysis methods to conduct a questionnaire survey on 306 students from four local universities in R city. A total of 231 valid questionnaires were collected, and the collected data was statistically analyzed using Spss.22.&#x0D; Findings - This paper found that there are significant differences in the willingness of local employment among students from different universities in R city. Factors such as understanding of employment policies, attractiveness of employment policies, natural environment, convenience of life, housing prices, levels, wage levels, urban development prospects, and employment opportunities affect the willingness of local university students in R city to work locally. Among them, the positive impact of urban development prospects is the most prominent, followed by the understanding of employment policies. The attractiveness of employment policies and convenience of life, while housing prices have had a negative impact.&#x0D; Research Implications - The research results of this article provide valuable references for local cities such as R city to formulate talent policies, attract talent aggregation, enhance the willingness of local university graduates to localize employment and build a virtuous cycle mechanism between education investment and economic output. R city should tell the future development story of the city well, formulate talent policies that are more conducive to talent aggregation without realizing the story, and better promote talent policies.
Research on the Personnel System Reform of College Teachers in China during the Digital Transformation of Education

Purpose - Deeply implementing the Digital transformation of education is the trend of educational reform and development in the world, and it is also an urgent requirement for China to build a high-quality education system. Based on the current situation of personnel system reform in China’s colleges and universities during the Digital transformation of education, this paper explores the reform of the personnel system in colleges and universities and puts forward reform strategies.&#x0D; Design/Methodology/Approach - Based on human resource management theory, this paper studies the problems existing in the reform of personnel systems in colleges and universities during the Digital transformation period, including three problems: imperfect top-level design and lack of digital strategic planning; The degree of data governance is low, and the integration of digital technology and system is not close; The awareness of Digital transformation is weak and digital literacy is lacking. Therefore, we propose reform strategies.&#x0D; Findings - This article investigates the optimization of top-level design and deep integration of digital technology and personnel systems. Proposes reform strategies for job appointment, assessment and evaluation, and salary incentive systems. Strengthen the awareness of transformation; Promote innovative reform of the university personnel system.&#x0D; Research Implications - This study is of great significance to the personnel system reform of Chinese universities in the Digital transformation of education. Also, it provides a reference for other scholars’ follow-up research.
Investigating Students’ Attitudes Toward Learning based on Metaverse

By merging augmented Reality (AR) and Virtual Reality (VR), the real world becomes a metaverse. This study aims to determine students’ attitudes toward learning based on a metaverse platform depending on different variables. The researchers applied a Quasi-experimental method of one group post-test design to a random sample of 100 students from the Faculty of Information Technology, Mutah University. The system of the human body was selected and three topics were introduced: the circulatory system, the nervous system, and the digestive system. A scale was developed to measure the students' attitudes after watching the lessons prepared by the proposed AR platform and then to analyze their feedback from the answers collected in a well-prepared questionnaire. The results showed that there is a statistically significant difference in students’ attitudes toward learning based on the metaverse. Furthermore, the study recommends adopting the use of the metaverse platform in learning and promoting research in the areas of AR and metaverse
Analisa Perbandingan Hasil Trombosit Pada Alat Hematologi Analyzer Metode Impedance dan Metode Optik Pada Kasus Anemia Hipokrom Mikrositer (Berdasarkan Indeks Eritrosit)

Platelets have an important role in hemostasis, namely the formation and stabilization of platelet plugs. There are several methods in the counting of platelets. They can be carried out in a direct and an indirect manner. The direct method can be carried out in an automatic and automatic way. The automatic method uses an hematology analyzer which functions to measure and examine blood cells in blood samples. Counting the number of platelets is done automatically by two methods, namely optical and impedance which have different principles. The optical method is based on cell size and morphology, while impedance is based on cell size only. Hypochrome is a condition where the Hb concentration is less than normal so that the central achromia widens (&gt; 1/3 of the cell) and there is a decrease in the color of the erythrocytes, namely an increase in the diameter of the central pallor above normal so that it looks paler. Microcyte is a condition where the cell size is smaller than normal. The purpose of this study was to determine the difference in the number of platelets in the hematology analyzer using the impedance method and the optical method in the case of microcytic hypochromic anemia (based on erythrocyte index) with a descriptive analytical research design. The test used is the paired t-test with a total sample of 30 samples. The results of platelets using the impedance method have an average of 339.37 * 103 / uL with a standard deviation of 120.294. The results of platelets using the optical method have an average of 314.70 * 103 / uL with a standard deviation of 113.569. A significant value of 0.000 significant value of (sig. &lt; 0.05) can be concluded that there is a difference between the results of platelets on the hematology analyzer using the impedance method and the optical method in cases of microcytic hypochromic anemia (based on erythrocyte index). Keywords : Trombosit, Hipokrom Mikrositer, Hematology analyzer, Impedance, Optik.
Features of the Ukrainian popular science blogosphere

The object of the research is popular science blogs — a specific direction of Internet communication aimed at spreading scientific knowledge in an accessible way. The relevance of the study is determined by the active development of the Ukrainian popular science blogosphere and the identification of the need to analyze the main trends in this area. The purpose of the article is to analyze the popular science blogosphere using the example of blogs on the YouTube platform. The methodology of researching the topic includes social communication, axiological approaches, as well as statistical methodological techniques. Content analysis, synthesis and generalization are the main methods that were used in the process of scientific research. The novelty of the article lies in highlighting the features of blogs with audiovisual and textual possibilities for creating popular science content, as well as revealing the specifics and trends of functioning of popular science blogs. In this regard, popular science blogs on the YouTube platform became the object of this scientific research. As a result of the research, it was concluded that blogs, in which one can use audiovisual and textual components, are the most convenient means of creating popular scientific content. Actually, this meets the demands of the modern consumer of information. Based on the ranking and content analysis, it was found that historical topics tend to be the most popular. They are followed by natural sciences, astrology, medicine and psychology. In particular, the top ten popular blogs include: “Named after T. H. Shevchenko”, “History Without Myths”, “Damn Rationalist”, “WAS: Popular History”, “Interesting Science”, “History for Adults”, “Your Underground Humanitarka”, “Khmarynka Science”, “Universe UA”, “Pitiatko”. On the basis of the analyzed popular science blogs, the following trends have been identified: the presentation of material in the blogs has the characteristics of the author’s journalistic programs; the use of emotionally charged vocabulary, subjective judgments and other artistic techniques in the materials (in their titles, on the covers or in the videos themselves); forms of citation of sources of information used in the blog differs in differently themed blogs (their indication in the description is mostly absent in materials on historical topics, but present in the rest of the studied blogs).
Cuban Children in Quarantine: Their Life Experiences Through Pictorial Representations

The emergence of COVID-19 caused a global health crisis and the implementation of preventative measures such as social distancing and quarantine. Faced with this situation, at risk population, such as children and adolescents experienced a variety of psychological responses. An exploratory qualitative study was carried out with the purpose of documenting life experiences of Cuban children and adolescents during the COVID-19 quarantine period, using children's drawings as an instrument. The results obtained show experiences related to the reproduction of messages aimed at reinforcing the perception of risk and health measures, the ambulance as a symbol of anguish and fear, moods related to sadness and boredom related to confinement.
Assessment of Data Security Implementation in the Supply Chain Enterprises in Latvia

Abstract The importance of data security in the supply chain has increased in the last few years. The study identifies recent trends and shortcomings in data security, assessing enterprises operating in Latvia’s transport and storage industry. The research aims to study and identify the current trends in data security in the Transport and Storage industry to develop recommendations for improving data security in the supply chain. The research focuses on studying data security in the supply chain in Latvia. The theoretical literature review was used to learn the main aspects of the supply chain concept and the data security measures applied. Statistical and content analysis shows the industry’s primary data security trends and issues. The results of the research concluded that in Latvia, enterprises providing supply chain operations are focusing on something other than mandatory education about the data security of their employees; thus, they need to apply sophisticated measures to protect data through all supply chain processes.
Scalable Point Cloud Attribute Compression

This paper develops a Scalable Point Cloud Attribute Compression solution, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ScalablePCAC</i> . In a two-layer example, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ScalablePCAC</i> uses the standard G-PCC at the base layer to directly encode the thumbnail point cloud that is downscaled from the original input, and a learning-based model at the enhancement layer to compress and restore the full-resolution input point cloud conditioned on the base layer reconstruction. As such, the base layer provides a coarse reconstruction of the input point cloud and the enhancement layer further improves the quality. We then adopt a cross-layer rate allocation strategy that flexibly determines the resolution downscaling factor, the quantization parameter of the base layer, and the quality controlling factor of the enhancement layer to adapt the bitrate of the two layers for approximately optimal Rate-Distortion (R-D) performance. We conduct extensive experiments on popular point clouds following the MPEG common test conditions. Results demonstrate that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ScalablePCAC</i> achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 10% BD-BR reduction against the latest G-PCC version 22 (TMC13v22) on the Y component; it also significantly outperforms existing learning-based solutions for point cloud attribute compression, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , compared with a recent work showing state-of-the-art performance, it achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 20% BD-BR reduction.
On the Role of Entropy-Based Loss for Learning Causal Structure With Continuous Optimization

Causal discovery from observational data is an important but challenging task in many scientific fields. A recent line of work formulates the structure learning problem as a continuous constrained optimization task using an algebraic characterization of directed acyclic graphs (DAGs) and the least-square loss function. Though the least-square loss function is well justified under the standard Gaussian noise assumption, it is limited if the assumption does not hold. In this work, we theoretically show that the violation of the Gaussian noise assumption will hinder the causal direction identification, making the causal orientation fully determined by the causal strength as well as the variances of noises in the linear case and by the strong non-Gaussian noises in the nonlinear case. Consequently, we propose a more general entropy-based loss that is theoretically consistent with the likelihood score under any noise distribution. We run extensive empirical evaluations on both synthetic data and real-world data to validate the effectiveness of the proposed method and show that our method achieves the best in structure Hamming distance, false discovery rate (FDR), and true-positive rate (TPR) matrices.
Federated Anomaly Detection with Sparse Attentive Aggregation for Energy Consumption in Buildings

Anomaly detection in energy consumption for buildings plays a critical role in improving energy efficiency. In distributed systems, federated learning (FL) has gained widespread adoption due to its ability to train robust models while preserving privacy. However, most FL algorithms face challenges in handling heterogeneous data across different buildings, where distribution gaps exist. Moreover, addressing the heterogeneity in data necessitates additional efforts in model personalization, resulting in significant computational overhead, especially in large-scale FL settings. To address these challenges, we propose a sparse attentive aggregation-based federated anomaly detection approach (SAA-FAD). SAA-FAD comprises an extraction network and a lightweight recognition network. The extraction network employs a variational Bayes scheme to extract statistical characteristics from the raw data for computing similarities between clients while preserving privacy. Then the recognition network is trained with the extracted statistical features. The model is trained in a sparse attentive aggregation manner, which leads to superior anomaly detection performance on heterogeneous data. Additionally, SAA-FAD achieves a time and memory complexity of O(N∙logN) for computing similarity. Simulation results demonstrate that SAA-FAD outperforms other baseline models, particularly in scenarios with heterogeneous data, while offering a significant advantage in terms of computation time.
Virtual Reality in Workplace Learning

The interest and use of Virtual Reality (VR) technology for workplace learning has been increasing and is projected to increase further based insights from scholarly literature, industry reports, and interviews with organization and learning leaders. We present and discuss perceived affordances, limitations, and future directions of VR learning based on interviews with 21 workplace learning leaders across sectors and industries. Perceived affordances include: (1) simulation of dangerous or difficult real life scenarios, (2) interpersonal and leadership skill development, (3) affordability, (4) data and assessments, (5) social learning, and (6) bridging the real and virtual. On the other hand, perceived limitations include: (1) limitations of experiences in VR, (2) hardware bottlenecks, (3) costs, and (4) limited acceptance of VR learning. Many study participants anticipated improvements in VR hardware fidelity and comfort, simplification of VR learning creation, and expansion of AR learning opportunities, while some anticipate no significant changes in VR learning opportunities and adoption. We also discuss recommendations for VR instructional design and implementation and various directions for further research.
Socio and macroeconomic determinants of mobile money adoption: a cross-cultural study of selected MENA and Sub-Saharan countries

Purpose This study aims to investigate the factors contributing to the low adoption rate of mobile money services (MMS) in the Middle East and North Africa (MENA) region compared to other regions. The study focussed on socio-demographic factors and macro-level determinants in several selected MENA and Sub-Saharan African (SSA) regions where MMS have been successful. Design/methodology/approach This study analysed 23 countries across MENA and SSA to establish the correlation between socio and macroeconomic factors and MMS adoption using a quantitative approach. The analysis used the generalized least square (GLS) method. Findings The study revealed that gender and income are factors that positively influence the adoption of MMS in MENA and SSA regions. Additionally, the study found that the affordability index, which measures macroeconomic indicators, correlates with MMS adoption in both regions but in an inversed way. On the other hand, political stability appears to have a positive correlation with MMS adoption in the MENA region. The correlation between the regulatory index and MMS adoption positively impacts the entire study group, although it is insignificant in the SSA region. Research limitations/implications Future studies should assess market competition among MMS providers and the psychological aspect of user adoption behaviour. Additionally, conducting a focus group discussion with stakeholders in the MMS industry can assist in uncovering potential factors contributing to low MMS adoption in the MENA region. Originality/value This study contributes to understanding the role of the socio-demographic and macroeconomic determinants in promoting digital transformation through adopting MMS.
Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features

Facial Expression Recognition (FER) is currently widely explored by researchers in the field of Computer Vision. The application of Machine Learning and Deep Learning methods is useful in developing an intelligent system that is accurate in recognizing facial expressions such as emotions. This is inseparable from the type of dataset and classification method used which certainly affects the desired results. To choose the right method, it is necessary to compare the performance of these methods. This study focuses on comparing the performance results of four classification methods namely, Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) on a multiclass dataset for seven classes of facial emotion labels based on Eigenface feature selection uses the Personal Component Analysis (PCA) algorithm. The test parameters used to perform method comparisons are accuracy, recall, precision, f1-score, as well as the Receiving Operating Characteristic (ROC) and Area Under Curve (AUC) curves. The results of the analysis state that the SVM method has the highest accuracy value, while other methods show varying performance based on recall, precision, f1-score, and ROC and AUC analysis. This research was conducted on the FER 2013 dataset which showed that the classification method tested had quite good performance according to the test parameters.
The Effect of Scrabble Game on Students' Vocabulary Mastery of Grade Eight at SMP Negeri 12 Pematang Siantar

The aim of this research is to determine the effect of the reading aloud strategy on the pronunciation abilities of seventh grade students at SMP Negeri 3 Pematangsiantar. In this research, researchers used quantitative research methods with a quasi-experimental design and pre-test, post-test and control designs. The sample for this research consisted of class VIII students at SMP Negeri 12 Pematang Siantar with an average class size of 32 in the 2022–2023 academic year. There are around 288 students in class VIII. By using this technique, the sample that will be used in this research consists of two classes, namely class VIII-8 as the experimental class and class VIII-9 as the control class at SMP Negeri 12 Pematang Siantar. From the results of research conducted at SMP Negeri 12 Pematang Siantar, it was found that there was an influence of the application of the Scrabble game on mastery of mathematics. This can be seen from the students' average score in the test, namely pre-test 54.21 and post-test 76.09, while the researchers' learning results found that the t-test in this study was 2.14. Based on the previously designed hypothesis formulation, the hypothesis is not rejected if tcount is greater than ttable. The meaning of these results is that students who were taught to use the game Scrabble were better than students who did not use the game Scrabble in the classroom treatment process. After carrying out calculations, the researchers found that in this study the t-count was higher than the t-table. Referring to this, we can put forward the following hypothesis. t-count &gt; t-table 5%, namely 2.14 &gt; 1.670. Because the calculated t-value (2.14) is higher than the t-table (1.670) at the 5% significance level for a two-sided test, the null hypothesis is rejected and the alternative hypothesis is accepted. Therefore, the Scrabble Game is very effective as a learning support medium, especially in improving mathematics mastery.