Reviewerly/exHarmony huggingface dataset:
A Novel Graph Augmentation for Semi-Supervised Learning on Skeleton-Based Action Recognition

In recent years, skeleton-based action recognition has become popular due to its higher data precision and resistance to environmental noise compared to RGB-based methods. However, the cost of annotating accurate skeleton video data is quite high. This difficulty often leads to suboptimal training in supervised learning algorithms. In this study, we propose an efficient data augmentation method for skeleton data and combine it with semi-supervised learning strategies to confront the issue of low-labeled skeleton data in action recognition. Moreover, we optimize the schedule for incorporating unlabeled data into the model training process. This approach ensures both model performance and reduces training time effectively. In experiments utilizing a low amount of labeled data from the NTU RGB+D dataset, our proposed method achieved an accuracy of 81.57% with only 10 labeled samples per class. This compares favorably to the original method's accuracy of 77.5%. Furthermore, with 600 labeled samples, our method improved the original result of 90.31% to 92.03%. Although the gap in improvement narrows as the number of labeled samples increases, our proposed augmentation method successfully enhances the performance of the semi-supervised learning method in skeleton-based action recognition. This opens the door to more efficient labeling in skeleton-based action recognition and improves the algorithm's practicality in real-world scenarios.
Trust as a Mediator: Exploring the Relationship between Social Responsibility and Corporate Reputation within Social Systems

The reputation of the organization is important for the survival of the organization. Therefore, this study aimed to examine the effect of information quality, structural assurance and corporate social responsibility on trust and corporate reputation. This study also examined the mediating role of trust as well. The data was collected from the users of the sports products manufactured in the UAE. A quantitative research methodology was adopted in the present research. The questionnaire was used to collect the data from the respondents. The usable response rate of the study was 65.14%. The collected data was analyzed using PLS-SEM. The study's findings show that trust is affected positively by information quality, structural assurance, and information quality. Moreover, trust also has a positive effect on corporate reputation. This paper provides guidelines to policymakers so they can improve the corporate reputation of their organizations.
Temporal-Dependent Features Based Inter-Action Transition State Recognition for Eldercare System

Elderly individuals are particularly vulnerable to accidents, with a significant number of incidents occurring during transition states between primitive actions such as sitting to standing and sitting to lying. This paper introduces a novel machine-learning technique in artificial intelligence, based on temporal-dependent features to assist the elderly. To ensure privacy, we employed stereo depth cameras for data acquisition from the elder care center and exclusively processed depth images. The first step of our approach involves localizing individuals using the YOLOv5 object detector. Subsequently, we employed the Segment Anything Model to segment only the person masks, excluding other areas from consideration. Temporal-dependent features were then extracted for every five frames from the subsequent person masks that enable the recognition of transition states from primitive actions. We tested various classification approaches and compared the results by defining norms and metrics. Our experimental findings demonstrated that the overall accuracy rates for classifying 2 classes and 5 classes on small segments are 91.18% and 91.67% respectively. To validate the effectiveness of our proposed method, we conducted experiments using real-life environments inside three rooms and obtained average accuracy rates of 90.17%, 97.16%, and 77.44% respectively. Overall, this model has the potential to enhance the safety and well-being of the elderly population.
Role of Social Networks to Improve Satisfaction and Performance of University Students

The use of technology is increasing rapidly in every sector of human life. Its importance is evident in the field of education as well. Therefore, the purpose of this research is to examine the effect of social networks, social influence, and perceived teacher support on academic performance through satisfaction. The present research was quantitative and cross-sectional. The data was collected from the students at universities in UAE through a self-administered questionnaire. The gathered data was analyzed using SPSS and Smart PLS. The findings confirm that social network, social influence, and perceived teacher support have positive effect on student online satisfaction. Moreover, satisfaction has a significant impact on academic performance as well. The results also supported the mediating effect of satisfaction. This study discussed theoretical and practical implications in detail. These findings are important for strategists in the higher education sector.
Space Sampling Techniques Comparison for a Synthetic Low-Pass Filter Bayesian Neural Network

This paper presents a comparative analysis of three sampling techniques for generating space points to develop a Bayesian neural network (BNN) surrogate model of a synthetic second-order low-pass filter. The objective is to assess the effectiveness and efficiency of different sampling methods in the performance of the Bayesian surrogate model. The study draws inspiration from widely used sampling techniques such as uniform distribution, uniformly distributed random numbers, and Latin Hypercube Sampling (LHS). The results reveal that the BNN surrogate model achieves the best performance when using the LHS sampling method highlighting the impact of sampling techniques on the surrogate model's performance.
A Handheld Reader with Optimized Image Analysis for HIV Lateral Flow Test

As a rapid, simple and low-cost diagnostic technology, immunochromatography has a wide range of application in point-of-care testing (POCT). Here, a handheld reader based on image analysis was developed for the detection of HIV in serum or whole blood sample using gold colloid lateral flow strips. The control and test lines from the image of the strip collected by the reader were extracted with an image analysis algorithm including image preprocessing (image crop, grayscale of image, image equalization, median filter and image denoise) and feature extraction. Based on the preprocessed image, the control line was located based on its color difference from the background, and the test line was accurately identified with both preliminary searching and precise boundary location. The performance of the reader was evaluated by experiments using a series of different concentration of HIV serum samples. Comparing to the commercial reader, comparable performance could be achieved with the handheld reader with significantly reduced size.
DeT-Net: A Two-stage Method for Accurate Automatic Heart Segmentation

Heart segmentation based on clinical data is a critical task for presymptomatic cardiovascular disease diagnosis and the following sustainable medical care. The complexity of cardiac image segmentation lies in the fact that the cardiac substructures are closely located and they only occupy a small portion of clinical MRI images, making it challenging for segmentation networks to focus on the segmentation of heart. Furthermore, the shapes and sizes of these cardiac structures in images can vary significantly due to differences among patients, imaging equipment, and other factors. The insufficient segmentation accuracy poses challenges in the clinical application of cardiac image segmentation algorithms. In this paper, to improve the segmentation accuracy between heart substructures, we present a two-stage heart detection and segmentation model called DeT-Net (Detection-based TransUNet with an improved bottleneck). The proposed detection-based method improves segmentation accuracy and efficiency by identifying and focusing only on regions likely to contain the heart. Experiment on Automated Cardiac Diagnosis Challenge (ACDC) datasets proves that our method outperforms other networks with a Dice Similarity Coefficient (DSC) of 91.55%.
Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets.
Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

The health and happiness of seniors can be influenced by the design of public spaces and buildings. Planning for age-friendly communities requires taking into account the wide variety of daily activities and public facility consumption among older persons. Yet, conventional approaches fall short when it comes to providing comprehensive, objective measures of the actions of the elderly. This research proposes an attention-based recognition approach by decomposing neural network outputs into class-dependent features using dictionary learning to improve discrimination performance. This research aimed to develop an empirical typology of activity of daily living (ADL) and its connection to health in China's ageing population. A deep learning-based model was rummaged to categorise the contributors in the Chinese Longitudinal Healthy Longevity Study (CLHLS) into subgroups based on their abilities with ADL. To be more precise, the study trains a category-based recognition network (CRN) by superimposing a basic but effective specific recognition encoding (SRE) unit on top of convolutional layers. In order to encode attention maps for each class, the SRE module uses the feature maps produced by the Convolutional Neural Networks (CNN) to learn a class-specific vocabulary. Class-wise adaptive feature refining is achieved by multiplying these attention maps by the input features. In this case, a gazelle optimization algorithm (GOA) handles the hyper-parameter tuning process of the proposed model. This case study is the first of its kind in a dense metropolitan region, and it uses objective measurements to evaluate the activity spaces of the elderly. This research provides empirical data for the promotion of healthy ageing in urban areas.
Supplementary Measures and Appropriate Safeguards for International Transfers of Health Data After Schrems II

Abstract In July 2020, the Court of Justiceof the European Union (CJEU) in Data Protection Commissioner v Facebook Ireland Limited, Maximillian Schrems (“ Schrems II ”) invalidated the EU-US Privacy Shield adequacy decision but found that Standard Contracting Clauses (SCCs) are a valid mechanism to enable GDPR-compliant transfers of personal data from the EU to jurisdictions outside the EU/EEA, as long as various unspecified “supplementary measures” are in place to compensate for any gaps in data protection arising from the third country law or practices. The effect of this decision has been to place regulators, scholars, and data protection professionals under greater pressure to identify and explain these “supplementary measures” to facilitate cross-border transfers of personal data. This chapter critically examines the current framework for cross-border transfers after Schrems II , including the new SCCs adopted by the European Commission, as well as the current European Data Protection Board (EDBP) guidance on “supplementary measures.” We argue that the so-called “supplementary measures” are not “supplementary” and that the CJEU’s characterization of such measures as “supplementary” undermines the original clarity of GDPR with regards to the required standards for the security of processing as well as the available mechanisms for cross-border transfers of personal data. We conclude that despite the legal uncertainty introduced by the CJEU several post- Schrem II developments have been helpful to increase awareness and improve the overall safeguards associated with cross-border transfers of personal data. These include the new SCCs and an increased understanding of the capabilities and limitations of the technical and organizational measures, including encryption, pseudonymization, and multi-party processing. Technical solutions such as multiparty homomorphic encryption (HE) that combine these three technical measures while still allowing for the possibility to query and analyze encrypted data without decrypting it has significant potential to provide effective security measures that facilitate cross-border transfers of personal data in high-risk settings.
Effectiveness of Using Quizizz Media as an Evaluation of Explanatory Text Learning for Students in Junior High School

The objective of this study is to assess the efficacy of employing Quizizz as a learning tool in enhancing the engagement and academic performance of eighth-grade students in comprehending explanatory texts. This research used quasi-experimental methodologies, specifically utilising a pre-test and post-test non-equivalent control group design. The study's population consisted of all pupils in the eighth grade. This study employed purposive sampling procedures to choose a total of 44 students from grade seventh in junior high school. The results of the study showed that the use of the Quizizz platform in evaluation activities was in the very good criteria (SB) of 55.91%, and student responses to evaluation activities were in the good category (B) of 44.01%. In the learning outcomes, students showed that the experimental class pretest data got an average of 65.05 and a posttest score of 82.10. In the control class, the pretest results got an average of 62.57, and the posttest results were 76.98. This shows that the average value result of the experimental class is higher than that of the control class. In calculating the hypothesis test sig 0.032 0.05, the hypothesis (H0) was rejected and (Ha) accepted. Based on the n-gain test results 56.7% which was included in the effective category. It can be concluded that the application of Quizizz media is effective in increasing interest and learning outcomes in evaluation activities for students.
The Impact of Utilizing Uno Stacko on Arabic Learning Vocabulary Acquisition

This research addresses the apparent deficiency in Arabic vocabulary mastery among eighth-grade students at MTsN 7 Blitar, as revealed through the researchers' observations. The identified challenges stem from the limited strategies and media implemented in the teaching process. This study proposes incorporating the uno stacko game as an innovative solution to enhance Arabic vocabulary acquisition. The primary objectives of this research are to assess the impact of the uno stacko game on Arabic vocabulary mastery and to explore students' perceptions regarding the game. The research design adopts a quantitative pre-experimental approach, employing a one-group pretest-posttest design to evaluate the effectiveness of the uno stacko game. Data collection methods include observation, interviews, tests, questionnaires, and documentation. The collected data exhibit normal distribution, indicating the suitability of the data for statistical analysis. The analysis reveals a significant difference in the average scores between the pretest and posttest, with a notable increase of 0.55. This change falls within the moderate category, signifying a positive influence of the uno stacko game on Arabic vocabulary acquisition. The percentage change of 56.53% further supports this conclusion, indicating a substantial impact, and the study delves into students' perceptions of the uno stacko game. The average percentage of student perceptions is 99.1667%, reflecting an overwhelmingly positive response. This high percentage categorizes student perceptions as very good, suggesting a favorable view of the uno stacko game as an effective and engaging tool for learning Arabic vocabulary. The findings of this research highlight the positive influence of the uno stacko game on enhancing Arabic vocabulary mastery among eighth-grade students. Additionally, the overwhelmingly positive student perceptions emphasize the game's potential as a valuable educational resource. Recommendations for further research could involve more in-depth analysis of specific aspects of using the uno stacko game, exploring variations in learning strategies, and evaluating the long-term impact on Arabic vocabulary acquisition.
Development and Usability of an Interactive Multimedia Kit as a Teaching Aid among Biology Trainee Teachers

This study was conducted to develop and determine the usability of an interactive multimedia kit known as "Meio-solution". This quantitative developmental research employed the ADDIE model as an instructional design. The kit contained teachers’ notes, videos, quizzes, and educational games that can be used as a teaching aid for meiosis topic. The developed kit has been validated by experts in Biology Education while the usability has been obtained from trainee teachers of Bachelor of Education (Biology). A total of 80 respondents were involved using the simple random sampling technique. Two research instruments were used, namely the expert validation form and usability questionnaire adapted from a previous study. The data were analysed descriptively using SPSS software. The findings showed that the Content Validity Index by experts scored 1.0. The perception of the Meio-solution design scored a mean value of 3.84 (SD = 0.27), while the usability scored a mean value of 3.80 (SD = 0.29). In conclusion, this research has successfully developed a validated Meio-solution kit to be used as a teaching aid. The Meio-solution kit has the potential to help teachers improve students’ understanding and visualisation of learning meiosis.
Comprehensive Exploration of Web Application Security Testing with Burp Suite Tools

This research paper investigates the pivotal role of Burp Suite tools in web application security testing, aligned with the foundational principles of secure coding practices outlined by the Open Web Application Security Project (OWASP). The paper provides an in-depth overview of Burp Suite tools, emphasizing their features and the significance of web application vulnerability scanning in enforcing robust security practices. The exploration includes a detailed examination of Burp Repeater and Burp Intruder, with a practical demonstration of a Burp Suite cluster bomb attack. This research contributes to advancing the understanding and application of Burp Suite tools for effective web application security testing within the framework of OWASP secure coding practices.
A Good Result for Blowfish Image Encryption Based on Stepic

Information security is important in the era of evolving communications. Security methods are needed to protect data. Strong cryptographic algorithms, such as Blowfish, developed by Bruce Schneier in 1993, offer fast and reliable encryption. Blowfish uses 64 bit blocks and key lengths between 32 and 443 bits. This algorithm is famous for its robustness and encryption speed, with a Feistel Network structure and 16 rounds. This research implements Blowfish with Python and integrates it with steganography to insert secret data in digital images. Evaluation involves metrics such as Mean Squared Error (MSE), Peak-Signal-to-Noise Ratio (PSNR), Average Visibility (AVA), Uniform Average Change Intensity (UACI), and Percentage Pixel Change Value (NPCR) to measure quality and reliability encryption and decryption process by Blowfish.
Bibliometric Analysis: Application of Blended Learning in the Development of Digital Skills in the Era of Digital Education

In the current era characterized by rapid and widespread technological progress, digital skills have become an important factor in shaping individual success and societal progress. Digital skills cover a wide range of proficiencies, including digital literacy, programming, data analysis, online collaboration, and information security. In the world of education, a lot of research has been carried out on digital skills, one of the most research topics is the application of blended learning to improve digital skills. Therefore, researchers conducted a bibliometric analysis of the application of blended learning in developing digital skills in the era of digital education which aims to conduct a comprehensive exploration of existing literature. This research involved a comprehensive approach combining a systematic literature review and bibliometric analysis to explore the development of digital skills through online learning and assess its impact. Additionally, the use of VOSviewer software to visualize bibliometric networks is also discussed. The research results show that six different thematic clusters were found in the literature on digital skills development in the digital education era. Each cluster summarizes a set of recurring keywords that describe existing research trends, providing valuable insight into the landscape of the field. It can be concluded that this research contributes to the growing understanding of digital skills development through online learning and highlights its transformative potential in the world of education.
Nexus Between Possibility of Fraudulent Financial Reporting and Corporate Governance: Evidence from Bangladesh

This paper intends to test the nexus between the possibility of fraudulent financial reporting and the corporate governance in Bangladesh. The content analysis of annual reports has been performed for 125 Bangladeshi listed manufacturing companies. In this study, we apply more than one fraud indicator models such as Altman Z-score and Beneish M-score models to determine more accurately the chance of fraudulent financial reporting. The chance of fraudulent financial reporting is determined based on whether either one or both of Altman Z-score and Beneish M-score models show red flags of potential fraud. This study found that board members with finance or accounting backgrounds are less likely to practice in misleading financial reporting since they may have the knowledge essential to understand fraudulent financial reporting strategies. This result implies that qualified directors may supervise financial reporting practices better, hence improving the quality of financial reports. Alternatively, some other components of corporate governance, such as board size, board independence, director ownership, gender diversity, and auditors' independence, have insignificant impacts on fraudulent financial reporting practices. This outcome indicates that these Bangladeshi corporate governance components may not be as strong in improving financial reporting credibility. In the context of Bangladesh, this is the first empirical study with management and policy implications. To the best of the writers’ knowledge, no article yet has been worked on the possibility of fraudulent financial reporting which calculated by multiple models and its relationship with the corporate governance in the context of Bangladesh.