Reviewerly/exHarmony huggingface dataset:
The Structural Relationship between College Students' Personal Characteristics, Learning Experiences, Learning Satisfaction, and Learning Outcomes

Objectives The purpose of this study is to analyze the structural relationships between college students' personal characteristics (self-efficacy, self-directedness, learning motivation, major selection motivation) and their learn-ing experiences, learning satisfaction, and learning outcomes based on the I-E-O learning process model.&#x0D; Methods A total of 1,042 students from A University in Seoul were surveyed to measure their self-efficacy, self- directedness, learning motivation, major selection motivation, learning experiences, learning satisfaction, and learning outcomes. The structural equation modeling was employed to analyze the structural model.&#x0D; Results Among the personal characteristics, self-efficacy had a positive influence on peer interaction, learning satisfaction, GPA, and cognitive and non-cognitive learning outcomes. Self-directedness had a positive impact on learning experiences during class but showed a negative effect on learning satisfaction and GPA. Both external and internal learning motivations had a positive influence on learning experiences during class, and external learn-ing motivation had a negative impact on peer interaction. External major selection motivation had a positive effect on peer interaction but negatively affected grades and learning experiences during class. On the other hand, in-ternal major choice motivation had a positive influence on learning satisfaction and peer interaction. Learning ex-periences during class and peer interaction had a positive impact on cognitive and non-cognitive learning out-comes, and learning experiences during class also positively affected GPA. Peer interaction did not significantly affect grades but had a positive influence on learning satisfaction. Lastly, the relationship between self-efficacy and cognitive and non-cognitive learning outcomes was mediated by peer interaction and learning satisfaction, while the relationship between internal learning motivation and cognitive and non-cognitive learning outcomes was mediated by learning experiences during class and learning satisfaction.&#x0D; Conclusions The findings suggest that universities should provide information to enhance students' internal learning motivation and major selection motivation to improve their learning experiences and outcomes.
An Analysis of Career Concerns and Exploration Process of Practical Dance Students

Objectives The purpose of the study is to provide basic data necessary to improve and develop the curriculum for career exploration and revitalize career education by understanding the actual experiences of students in the field of practical dance and examining the process of contemplating and exploring their careers.&#x0D; Methods This study selected 6 participants based on the theoretical sampling method and conducted in-depth interview. As for the analysis, the study adopted 3-step analysis proposed by Strauss and Corbin(1998), and con-ducted open coding, axial coding, selective coding, and also conducted member check and peer debriefing in en-tire process of analysis and interpretation to secure the veracity of the data.&#x0D; Results 132 concepts, 31 subcategories, and 20 categories were derived through ‘categorization of the career worries and exploration process of practical dance students’, which is an open coding. In ‘Structuring the Career Concerns and Exploration Process of Practical Dance Students’, an axis coding, a paradigm model was presented by conducting a paradigm analysis, and the attributes and dimensions of the categories corresponding to each component were analyzed. Finally, the core category of “Constructing the Career Concerns and Exploration Process of Practical Dance Students” was derived, and the connection between them was reaffirmed in the proc-ess of describing the results derived from the story outline development as a single story. Based on this, it was found that in the process of contemplating and exploring career paths, the contextual condition of “university edu-cation environment” and the mediating condition of “changed practical use environment” are important factors that affect career decisions.&#x0D; Conclusions The process of practical use students contemplating and exploring their careers deepened their worries about their careers and showed that they were determined by various attempts and efforts as they faced unstable reality amid various personal, social, and environmental factors. In the future, it is necessary to provide high-quality education to develop various career-related capabilities of practical dance majors and further re-search that can serve as the basis for improving the working environment and expanding the industry.
Analysis of the Effects of Tinkering for Enhancing Digital Literacy Competence in University General Education

Objectives The purpose of this study is to examine the impact of tinkering activities as digital literacy practices in general education courses on the digital literacy competencies of university students.&#x0D; Methods To achieve the research objective, 197 students enrolled in a maker general education course at D University in Gyeongsangbuk-do were selected. Among them, 168 students were assigned to the experimental group, and 29 students were assigned to the control group. The experimental group engaged in tinkering activities online for 12 weeks while taking the maker general education course, after completing a pre-test for digital literacy. The control group only participated in the maker general education course and underwent a post-test for digital literacy.&#x0D; Results The post-test digital literacy scores of the experimental group were significantly higher than those of the control group (t=2.280, p=.012), and they were also significantly higher than the pre-test scores of the ex-perimental group (t=2.837, p=.003). Furthermore, an analysis of the differences in digital literacy scores based on students' major, grade, and gender revealed significant differences in gender in the pre-test results of the ex-perimental group (F=4.026, p=.047), as well as a significant interaction effect between major and gender (F=3.101, p=.048). In the post-test results of the experimental group, significant differences were found in digital literacy scores based on gender (F=5.278, p=.023).&#x0D; Conclusions Based on the analysis results, it can be concluded that the incorporation of tinkering activities in uni-versity maker general education courses can contribute to enhancing students' digital literacy levels. The im-provement effects were observed among students with various characteristics, particularly among male students in engineering and fine arts colleges. Therefore, it is necessary to consider the characteristics of participating stu-dents when implementing tinkering activities in each course.
PENGARUH MANAJEMEN KEPALA SEKOLAH DAN DISIPLIN KERJA TERHADAP KINERJA GURU

The aim of the research is to determine the significant influence between school principal management and work discipline on teacher performance. The research method used was descriptive quantitative with 33 samples, then data analysis was carried out using the SPSS software analysis tool version 25. The research results showed that from two independent variables and one dependent variable, there was a very significant influence on the dependent variable. The results of the data that have been analyzed for the 3 variables of this research show that there is a significant influence between the principal's management on teacher performance, and the influence of teacher discipline has a significant influence on teacher performance. So it can be concluded that teacher performance is greatly influenced by the principal's management and teacher performance.
An Analysis of Teachers' and Experts' Perceptions of Guidelines for Guaranteeing Minimum Achievement Level Following the Introduction of High School Credit System: Focusing on the Case of Gangwon Metropolitan City Self-Governing Provincial Office of Education

Objectives This study is expected to face many difficulties in operating guidance that guarantees the minimum level of achievement following the introduction of the high school credit system, so it seeks to listen to the opin-ions of teachers and experts, focusing on the case of the Gangwon Special Self-Governing Provincial Office of Education, and propose support measures to prevent failure to reach the minimum level of achievement.&#x0D; Methods To this end, in-depth interviews (2023. 4. - 2023. 6.) was carried out. The questions of the in-depth in-terview consisted of the suitability of the purpose of preventive guidance to guarantee the minimum level of ach-ievement, differences in teachers' perceptions and practices, the actual state of operation and impediments, and the performance of suitable operation plans and policies.&#x0D; Results Through interview analysis on the cases of the Gangwon-do Office of Education’s guidance for guaran-teeing the minimum achievement level of the high school credit system, the results were analyzed from five as-pects: (1) perception, (2) practice, (3) class, (4) evaluation, and (5) environment. Opinions were understood. In ad-dition, implications were drawn by analyzing the current status of guidance for guaranteeing the minimum ach-ievement level of the high school credit system of other city and provincial offices of education, and cases of pre-vention and supplementary guidance for guaranteeing the minimum achievement level for research and prepara-tion schools at the Gangwon Metropolitan Autonomous Office of Education.&#x0D; Conclusions Firstly, as reviewed in the 2022 achievement evaluation results of high schools (107 schools) of the Gangwon-do Office of Education, it was confirmed that more than 40% of the students had academic achieve-ment E. Secondly, as a result of analyzing the operational tasks of research schools and preparatory schools of other city/provincial offices of education and the Gangwon Metropolitan Autonomous Provincial Office of Education and identifying their characteristics, the main factors were methods of selecting low-achieving students, af-ter-school operation of non-attainment prevention guidance, and emotional support programs. Thirdly, measures to improve and support classes and assessments were proposed to have the subject competencies presented in the 2015 revised curriculum based on everyday life and familiar subjects, rather than just achieving the quantita-tive goal of the subject to reach the minimum achievement level.
Hierarchical deconvolution dehazing method based on transmission map segmentation

Images captured in fog are often affected by scattering. Due to the absorption and scattering of light by aerosols and water droplets, the image quality will be seriously degraded. The specific manifests are brightness decrease, contrast decrease, image blur, and noise increase. In the single-image dehazing method, the image degradation model is essential. In this paper, an effective image degradation model is proposed, in which the hierarchical deconvolution strategy based on transmission map segmentation can effectively improve the accuracy of image restoration. Specifically, the transmission map is obtained by using the dark channel prior (DCP) method, then the transmission histogram is fitted. The next step is to divide the image region according to the fitting results. Furthermore, to more accurately recover images of complex objects with a large depth of field, different levels of inverse convolution are adopted for different regions. Finally, the sub-images of different regions are fused to get the dehazing image. We tested the proposed method using synthetic fog images and natural fog images respectively. The proposed method is compared with eight advanced image dehazing methods on quantitative rating indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image entropy, natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). Both subjective and objective evaluations show that the proposed method achieves competitive results.
Sharding Technologies in Blockchain: Basics, State of the Art, and Challenges

Sharding is one of the key technologies of blockchain scalable characteristics widely used in cryptocurrencies, Internet of Things, supply chain management and other fields. It can achieve high performance scaling without reducing the decentralization of blockchain, thus solving the problems of insufficient scalability and low throughput of blockchain. In this paper, the characteristics of some classical sharding technologies are analyzed in terms of performance and implementation. The key mechanisms of sharding are summarized, including sharding formation, reshuffle, intra-shard consensus protocol, and cross-shard protocol. Also, the advantages and shortcomings of typical scalability schemes are summarized, including state channel, DAG, and side chain. We summarize the current challenges faced by sharding technology from intra-shard, and cross-shard, and provide an outlook on the development prospects and future research directions in this field.
A Kind of Optimization Method for Committee and Reward Mechanism

Consensus protocols play a vital role in blockchain by determining block generation speed and ensuring blocks’ consistency and security. To address issues such as low efficiency and inadequate throughput in consensus algorithms, we have conducted research and proposed a novel Byzantine Fault Tolerant (BFT) protocol called CF-BFT, which operates in a dual-mode fashion and relies on node identity authentication. The CF-BFT protocol encompasses two sub-protocols: CheckBFT and FastBFT. Initially, the system assumes a pessimistic environment and executes the CheckBFT protocol, wherein a committee verifies the current environment’s safety and the integrity of the leader node. Upon confirming the safety of the current environment, the CF-BFT protocol transitions to the FastBFT protocol for message handling. By optimizing the number of participating nodes, we optimized and reduced the participant nodes from the conventional $$3f+1$$ to a more efficient $$2f+1$$ committee members. This mitigates the communication overhead. By incorporating a hash-based selection mechanism for choosing the primary, we have enhanced the decentralization and randomness of the protocol. Moreover, the adoption of a ring topology for selecting primary nodes and committee members greatly enhances the scalability of the blockchain network. Furthermore, we introduce the concepts of cumulative reputation value and consumable reputation value to optimize the reward mechanism, resulting in a more rational tenure of the primary node within the FastBFT sub-protocol. Experimental results demonstrate that the proposed algorithm outperforms traditional BFT algorithms and other dual-mode algorithms across various performance indicators. While ensuring consistency and security, it significantly reduces communication costs and improves consensus efficiency. The experiment showed that in the best-case scenario, we improved by 47% to 2.5 times compared to SAZyzz, while in the worst-case environment, we achieved a maximum performance increase of 20% to 70%.
Research on an underwater image segmentation algorithm based on YOLOv8

Abstract Underwater image segmentation faces complex problems such as target type, number, scale, and environmental interference. The traditional segmentation method relies on much manual annotation and expertise, extracts only the surface information of the image, and has low segmentation accuracy and efficiency, so the image segmentation method based on deep learning is chosen. Deep learning algorithms are used on top of the YOLOv8, and the adaptive self-supervised learning (Convnext V2) module, the lightweight network (Slim Neck), and the dynamic sparse attention mechanism (bilevel routing attention, Biformer) are added, which improves the YOLOv8 algorithm for single-stage instance segmentation of different underwater targets. The Convnext V2 module introduces the global response normalization (GRN) unit, which enhances the feature competition and reduces the model computation. A slim neck network is designed, which achieves a double improvement in model inference speed and accuracy by the operation of dense convolution in the lightweight convolution (GSConv) module and the design of a cross-level network structure (VoV-GSCSP). The introduction of Biformer’s dynamic sparse attention mechanism achieves more flexible computational allocation and content awareness. Comparison experiments show that the improved algorithm dramatically increases the segmentation speed while improving the segmentation accuracy. On the dual-target dataset, the mAP is improved by 3.5% from 0.753 to 0.779, and the FPS is improved by 86% from 63 to 117. The experiments validate that the improved algorithm can be used for the segmentation of different underwater images.
Image Water Ripple Detection Method Based on Constraint Convolution and Attention Mechanism

Abstract Noise can introduce irrelevant interference signals, reduce the signal-to-noise ratio of the image, weaken the contrast between the target and the background, and make it more difficult to detect the target in the image, thus increasing the difficulty of water ripple detection. Therefore, a method for image water ripple detection based on constraint convolution and attention mechanism is proposed. Using the attention mechanism for image denoising, the “attention map” is calculated from both channel and spatial aspects, and the calculated “attention map” is multiplied by the image feature map for adaptive feature learning to achieve image denoising processing. The convolutional neural network is used to extract the features of the input image. Based on feature extraction, the constrained convolution operation is applied to highlight the detailed features of water ripples. The features obtained from the constrained convolution operation are input into the support vector machine classifier for the classification and detection of water ripples. According to the relationship between the patterns and features learned by the classifier, whether the image belongs to the category of water ripples is judged, to achieve water ripple detection. The experimental results show that the proposed method has a good image denoising effect and water ripple detection effect.
Correlations of Flow, Usability, Workload, and Presence with Task Performance in a Spatially Distributed Memory Task

It requires an elaborated set of measurements to assess how the performance of tasks conducted in VR is composed. Flow is a measurement widely used in other domains as a predictor of performance and has been shown to be positively correlated with performance. However, some previous works indicate that flow might bring different results in VR than in traditional environments. It is not well known for VR environments how this phenomenon relates to usability, workload, and presence. Therefore, this work reports on an experiment that allows positioning flow among the other measurements, using a spatially distributed task. The results suggest that flow correlates negatively with performance, workload, and presence, which supports VR-specific previous work while it contradicts findings from other domains. Flow is positively correlated with usability in this experiment, which is in line with expectations from previous work.
Computing Topological Indices of 3-Layered Artificial Neural Network

Let η be a network graph with vertex and edge sets P(η) and E(η), respectively. This study aims to find the expected value (obtained during training and testing data) for Artificial Neural Networks (ANN) through indices. A three-layer artificial neural network is considered here, which we call ANN(m, n, o). Moreover, a comparison is given between the topological indices (TI) of ANN with topological indices (TI) of the Probabilistic Neural Network (PNN). By comparing the indices, we can assess the effect of network structure on ANN model accuracy. The comparison between the two approaches helps us understand the accuracy and performance of ANN and PNN models. We can also gain insights into the differences between ANN and PNN in terms of their ability to learn and generalize.
Comparing Cinematic Conventions through Emotional Responses in Cinematic VR and Traditional Mediums

This paper compares emotional responses to cinematic conventions in cinematic virtual reality (CVR) versus traditional mediums. We conducted a between-subjects experiment to evaluate viewer experiences across 10 cinematic shot types displayed through three viewing mediums: computer screen, cinema projector, and VR headset (N=15 per condition). Using the Self-Assessment Manikin scale (SAM), we measured pleasure, arousal, and dominance reactions. The statistical analysis showed that CVR elicited significantly higher arousal and lower dominance than traditional formats, indicating enhanced emotional engagement. Overall patterns were found to be similar, but the viewers’ emotions were intensified in CVR for shots like close-up and ground-level. Our findings suggest CVR's embodied, interactive qualities altered the impact of cinematic techniques. This comparative study reveals CVR invokes different emotional resonance versus traditional mediums, contributing insights on adapting storytelling strategies for the VR cinematographic experience.
The Effects of Avatar Voice and Facial Expression Intensity on Emotional Recognition and User Perception

The use of avatars of various rendering styles (e.g., abstract, cartoon, realistic) in virtual reality is ever-increasing. However, little is known about the effects of auditory stimuli, specifically avatar voices, on users’ perceived realism. This paper aims to investigate and better understand the role of a look-alike avatar’s vocal and facial expression intensity on users’ perceived realism and emotional recognition using a virtual bystander scenario. Results show that avatars’ vocal intensity generally affected study participants’ emotional recognition while facial expression intensity affected their perceived realism. The results have implications for the perception and effectiveness of look-alike avatars in virtual environments, specifically industry training for dangerous or non-replicable situations, such as school shootings and exposure therapy.