Reviewerly/exHarmony huggingface dataset:
A novel method for video enhancement under low light using BFR-SEQT technique

As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light areas' boundary pixels and varying low-illuminated night videos' weather conditions for removing noise and enhancing contrast. Hence, the video's visual appearance under low-light is improved using BFR-SEQT. Primarily, a video is inputted and converted into frames. Also, colour space is converted from which static and dynamic pixels are detected regarding frame differences. Using LoG-MF and KF algorithms, noise is removed from which foreground and background are separated using SD-FCM. Motion is estimated and features are extracted to enhance contrast. Then, pixel grouping using LC-GOA is done. Lastly, enhanced outputs from both phases are reconstructed and enhanced video is obtained. The proposed model improves video quality by enhancing contrast and removing noise with high PSNR values (27.6589db and 24.5478db), thus outperforming conventional methods.
Optimizing the Educational Potential of Virtual Reality: The Effects of Virtual Reality and Pop Quizzes on Spatial Presence and Efficacy Perceptions

Reflecting the trend of virtual reality (VR) studies in crisis and disaster communication, this study designed VR content on fire evacuation and aimed to investigate the effects of VR for educational purposes on perceptual and cognitive factors. Using a 2 (viewing condition: video vs. VR) × 2 (quiz: presence vs. absence) between-subjects design, an experiment was conducted with 176 university students in South Korea. Findings revealed that participants who were exposed to VR (compared with video) experienced greater spatial presence and, in turn, had higher response efficacy and self-efficacy. The inclusion of pop quizzes in the material hindered the evaluation of the direct and indirect effects of VR. The findings of this study can contribute to expanding the theoretical framework of VR research and identifying learning-related factors to develop more effective educational materials related to fire evacuation.
The effects of reading strategy training on students’ reading strategy use and critical reading ability in EFL reading classes

This research examined the impact of explicit reading strategy training on Ethiopian university students’ reading strategy use and critical reading ability in the English as a Foreign Language (EFL) context. Two intact classes, a control group (n = 35) and an experimental group (n = 45), participated in the study. The experimental group received 12 weeks of reading strategy training integrated into their English reading skills I classes. A mixed-method quasi-experimental design was employed, using a reading strategy questionnaire, a critical reading ability test, and a think-aloud technique for data collection. Students who received reading strategy instruction demonstrated notable improvements in reading strategy use and critical reading ability. The independent sample t-test and a paired sample t-test showed significant differences in both areas. Qualitative data highlighted compensation and memory strategies, which were not captured quantitatively. The think-aloud data revealed the predominant use of cognitive and metacognitive strategies, along with affective, social, memory, and compensation strategies. Cognitive strategies deepen understanding, and metacognitive techniques enhance interpretation in reading. These findings shed light on the responses of Ethiopian university students to explicit reading strategy training, emphasizing its significance in improving reading strategy use and critical reading ability in an EFL context.
An effective electricity worker identification approach based on Yolov3-Arcface

To address the issues of low efficiency and high complexity of detection models for electric power workers in distribution rooms, the electric power worker identification approach is proposed. The ArcFace loss function is used as the coordinate regression loss of the target box. According to the score, the template box with the highest score is selected for prediction, which speeds up the rate of convergence. Dimensional clustering is used to set template boxes for bounding box prediction. The experimental results show that the improved YOLOv3 is a high-performance and lightweight model. The electric power worker identification approach proposed in this paper has a high-speed recognition process, accurate recognition results. The effectiveness of the approach is verified with better detection performance and robustness.
NERSkill.Id: Annotated Dataset of Indonesian's Skill Entity Recognition

NERSkill.Id is a manually annotated named entity recognition (NER) dataset focused on skill entities in the Indonesian language. The dataset comprises 418.868 tokens, each accompanied by corresponding tags following the BIO scheme. Notably, 15,51% of these tokens represent named entities, falling into three distinct categories: hard skill, soft skill, and technology. To construct this dataset, data were gathered from a job portal and subsequently processed using open-source libraries. Given the scarcity of annotated corpora for Indonesian, NERSkill.Id fills a significant void and offers immense value to multiple stakeholders. NLP researchers can harness the dataset's richness to advance skill entity recognition technology in the Indonesian language. Companies and recruiters can benefit by employing NERSkill.Id to enhance talent acquisition and job matching processes through accurate skill identification. Furthermore, educational institutions can leverage the dataset to adapt their courses and training programs to meet the evolving needs of the job market. This dataset can be effectively utilized for training and evaluating named entity recognition systems, empowering advancements in skill entity recognition for the Indonesian language.
Effect of leadership styles on turnover intention among staff nurses in private hospitals: the moderating effect of perceived organizational support

Leadership styles have often been proven to support employees in performing their duties better and with more efficiency while enabling them to have extended organizational tenures. Staff nurses are an essential resource of hospitals to ensure proper administration and quality patient health care. The study aims to determine how transformational and authentic leadership styles affect the staff nurses' turnover intention in private hospitals. In addition, it also finds the moderating effect of perceived organizational support. An explanatory quantitative research design with a cross-sectional investigation and a stratified sampling strategy was used for the study. Data from 296 nurses from the eight chosen private hospitals in the Kingdom of Bahrain were gathered using a questionnaire with 24 items. Smart-PLS was employed to conduct PLS-SEM (partial least squares structural equation modeling) to measure direct and indirect effects. The result indicates that transformational, authentic leadership styles and perceived organizational support significantly negatively affect nurses' turnover intention. The study confirms the negative moderating effect of perceived organizational support between transformational leadership and turnover intention and the positive moderating effect of perceived organizational support between authentic leadership and turnover intention. Managers should concentrate on the leadership style to avoid its impact on turnover intention. By considering human resource practices such as communication and training strategies to cope with the negative effect of turnover intention, organizations can enhance employee engagement, improve job satisfaction, and foster a more stable and productive work environment. The present research revealed the adverse impact of turnover intention within hospitals by examining its association with leadership styles. The research made a significant contribution to the existing literature by delving into the impact of leadership styles on turnover intention, focusing on the moderating effect of perceived organizational support. The study's findings shed light on the intricate relationship between leadership practices and employee turnover, providing valuable insights for both scholars and practitioners in the field. The study used a cross-sectional design to collect data and ensured the absence of standard method variance. The research enhanced the social dominance theory (SDT) by examining how perceived organizational support moderates the relationship between leadership styles and turnover intention.
Personalized bundle recommendation using preference elicitation and the Choquet integral

Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Modeling the bundle utility for users is a non-trivial task, as it requires to account for the potential interdependencies between bundle attributes. To address this challenge, we introduce a new preference-based approach for bundle recommendation exploiting the Choquet integral. This allows us to formalize preferences for coalitions of environmental-related attributes, thus recommending product bundles accounting for synergies among product attributes. An experimental evaluation of a dataset of local food products in Northern Italy shows how the Choquet integral allows the natural formalization of a sensible notion of environmental friendliness and that standard approaches based on weighted sums of attributes end up recommending bundles with lower environmental friendliness even if weights are explicitly learned to maximize it. We further show how preference elicitation strategies can be leveraged to acquire weights of the Choquet integral from user feedback in terms of preferences over candidate bundles, and show how a handful of queries allow to recommend optimal bundles for a diverse set of user prototypes.
Model of adoption of virtual tools by university students in the context of an emerging economy

Introduction The impact of COVID-19 on higher education has necessitated a reevaluation of teaching methodologies, student engagement, and the integration of information and communication technologies (ICT) as pedagogical tools. This shift creates opportunities for innovation and collaborative learning environments among students. Methods This study aims to investigate the adoption of virtual tools by university students in Medellín. A sample of 373 students was surveyed using a quantitative approach with a descriptive scope. Factor analysis techniques were employed utilizing SPSS software to assess the associative relationships among variables and hypotheses within the framework of the Technology Acceptance Model (TAM). Results The analysis revealed significant correlations, particularly between perceived ease of use and perceived usefulness. Notably, technological devices such as laptops and smartphones were identified as valuable tools for educational purposes, contributing to a positive attitude towards their utilization among students. Discussion The findings underscore the importance of students’ perceptions in shaping their attitudes towards ICT tools for teaching and learning. Understanding these perceptions is crucial for designing effective pedagogical strategies, particularly in crisis situations such as the COVID-19 pandemic, where virtual tools play a vital role in maintaining educational continuity. This study contributes to the ongoing discourse on the integration of virtual tools in higher education, highlighting avenues for further research and practical implications for educators and policymakers.
Modeling correlated pairs of mammogram images

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.
On-manifold projected gradient descent

This study provides a computable, direct, and mathematically rigorous approximation to the differential geometry of class manifolds for high-dimensional data, along with non-linear projections from input space onto these class manifolds. The tools are applied to the setting of neural network image classifiers, where we generate novel, on-manifold data samples and implement a projected gradient descent algorithm for on-manifold adversarial training. The susceptibility of neural networks (NNs) to adversarial attack highlights the brittle nature of NN decision boundaries in input space. Introducing adversarial examples during training has been shown to reduce the susceptibility of NNs to adversarial attack; however, it has also been shown to reduce the accuracy of the classifier if the examples are not valid examples for that class. Realistic “on-manifold” examples have been previously generated from class manifolds in the latent space of an autoencoder. Our study explores these phenomena in a geometric and computational setting that is much closer to the raw, high-dimensional input space than what can be provided by VAE or other black box dimensionality reductions. We employ conformally invariant diffusion maps (CIDM) to approximate class manifolds in diffusion coordinates and develop the Nyström projection to project novel points onto class manifolds in this setting. On top of the manifold approximation, we leverage the spectral exterior calculus (SEC) to determine geometric quantities such as tangent vectors of the manifold. We use these tools to obtain adversarial examples that reside on a class manifold, yet fool a classifier. These misclassifications then become explainable in terms of human-understandable manipulations within the data, by expressing the on-manifold adversary in the semantic basis on the manifold.
A novel medical image data protection scheme for smart healthcare system

Abstract The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.
Automatic speech recognition and the transcription of indistinct forensic audio: how do the new generation of systems fare?

This study provides an update on an earlier study in the “Capturing Talk” research topic, which aimed to demonstrate how automatic speech recognition (ASR) systems work with indistinct forensic-like audio, in comparison with good-quality audio. Since that time, there has been rapid technological advancement, with newer systems having access to extremely large language models and having their performance proclaimed as being human-like in accuracy. This study compares various ASR systems, including OpenAI’s Whisper, to continue to test how well automatic speaker recognition works with forensic-like audio. The results show that the transcription of a good-quality audio file is at ceiling for some systems, with no errors. For the poor-quality (forensic-like) audio, Whisper was the best performing system but had only 50% of the entire speech material correct. The results for the poor-quality audio were also generally variable across the systems, with differences depending on whether a .wav or .mp3 file was used and differences between earlier and later versions of the same system. Additionally, and against expectations, Whisper showed a drop in performance over a 2-month period. While more material was transcribed in the later attempt, more was also incorrect. This study concludes that forensic-like audio is not suitable for automatic analysis.
Design of Human-Machine Integration System to Meet Diverse Interactive Tasks

A flexible production model in which humans and robots collaborate has become an urgent need for industrial production. Therefore, research on human-machine integration systems has received more and more attention in recent years. Human-computer interaction is widely used in industrial production and entertainment life, such as industrial control, VR games, etc. In this article, we designed and implemented a human-machine fusion system that meets different interactive tasks. Human movement estimation and robot interactive control are the key technologies. First, we propose a human posture solution method based on information fusion of multiple cameras and inertial measurement units. This method constructs an optimization problem, fuses the 2D joint detection information under multiple cameras and the measurement information of the worn inertial measurement unit, and optimizes the estimation of human kinematic posture, which improves the problems of incomplete posture information and sensitivity to noise under a single sensor. Therefore, this method improves the accuracy of pose estimation. Secondly, combining the kinematic characteristics of the robot and the characteristics of human-computer interaction, we designed a robot control strategy based on target point tracking and model predictive control, so that the robot can adapt to dynamic environments and different interaction needs by adjusting control parameters. At the same time, our design ensures the safety of robots and operators. Finally, we conducted human-computer interaction experiments such as action following, item delivery, and active obstacle avoidance. The experimental results show the effectiveness and reliability of the designed robot interaction system in a human-machine fusion environment.
Deep integrated fusion of local and global features for cervical cell classification

Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual transformer have been adopted as two branches to learn the features for image classification by simply adding local and global features. However, such the simple addition may not be effective to integrate these features. In this study, we explore the synergy of local and global features for cytology images for classification tasks. Specifically, we design a Deep Integrated Feature Fusion (DIFF) block to synergize local and global features of cytology images from a CNN branch and a transformer branch. Our proposed method is evaluated on three cervical cell image datasets (SIPaKMeD, CRIC, Herlev) and another large blood cell dataset BCCD for several multi-class and binary classification tasks. Experimental results demonstrate the effectiveness of the proposed method in cervical cell classification, which could assist medical specialists to better diagnose cervical cancer.
Attitude and behavioral intention for using metaverse in education: learner’s perspective

Purpose This study aims to understand and analyze the aspects influencing students’ attitudes and behavior toward the use of metaverse in education. The metaverse is currently viewed as technology with immense prospects. However, the practice of the metaverse for educational motives is rarely deliberated. Design/methodology/approach To assess the effect of the metaverse on students' knowledge and use of resources, general interests and attitudes toward the metaverse in education, a survey was conducted. The collected data were analyzed using a confirmatory factor analysis (CFA) in the first phase to address the various validity parameters. In the second phase, path analysis of the model was performed using structural equation modeling (SEM). Findings The study investigated how students intended to behave while using the metaverse for learning. The attitude toward adopting metaverse as technology is influenced by perceived utility and simplicity of use. This leads to behavioral intention as well. Studies reveal that the aspect of perceived usefulness is considered to be more significant in assessing the intention of use. Research limitations/implications This quantitative study contributes to the literature on metaverse, which is in the growing stage. In the educational sector, the existing studies are scarce; hence, the addition to the literature on metaverse is quite significant in the education domain. Practical implications The study benefits the students and the academicians because metaverse is largely considered an integral part of technology platforms, which has to be included in the learning systems eventually. There are few courses where the use of metaverse is already initiated at an introductory level, thus opening a broad spectrum of opportunities at all levels. It can provide scholars access to a massive array of resources, including multimedia presentations, interactive objects that support the delivery of lessons, videos, images and audio recordings. Originality/value This study adds to the existing literature by examining the impact of metaverse in education. The research focused on the students pursuing higher education who were mostly aware of metaverse and were open to the idea of learning and understanding through technology inclusion.