Reviewerly/exHarmony huggingface dataset:
A Conceptual Model of Nurse Retention Integrating Quality of Work Life and Human Resource Management: The Case of Jordan

Worldwide, the health sector faces a major challenge in retaining its staff. In Jordan, both public and university hospitals struggle with high turnover among nursing staff, which ultimately affects their overall performance. This study aims to discuss an integrated conceptual model that is developed based on the impact of quality of nursing work life (QNWL) and human resource management (HRM) practices on nursing staff retention. The model is drawn using the socio-technical system (STS) and social exchange (SET) theories to support the significance of QNWL and nurse retention. In addition, HRM practices involving training, compensation, performance appraisal, and recruitment were used. This framework contributes to research by combining the perspectives of QNWL and HRM to understand employee retention comprehensively. The practical insights derived from this model can be used to improve nurse retention strategies in the Jordanian healthcare system and as a foundation for future empirical research.
Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.
The Problem of Classifying AI as a Modern Cultural Phenomenon

The article examines the question of whether artificial intelligence (AI) is a phenomenon of modern culture. The criteria for the phenomenon of culture are highlighted: the criterion of value-normative certainty, the criterion of developmental impact on members of society, the criterion of compliance with the creative component of hu-man nature, the criterion of helping a person understand and express his own individuality, the criterion of pop-ularity and recognition. The compliance of AI with each of these criteria is analyzed. It is revealed that AI is in-cluded in the process of preserving and transmitting the values and norms of society, contributes to the devel-opment of society members, including creativity, as well as human expression of individuality, is very popular in modern society. Conclusion dwells upon the fact that AI corresponds to the specified criteria, so that at this stage of societal development it can be considered a cultural phenomenon.
Aligned ICTs to the CEFR in the Cuban foreign language teaching program.

This study examines integrating information and communication technologies (ICTs) into foreign language teacher preparation aligned with the Common European Framework of Reference (CEFR) standards at a Cuban university. A mixed methods approach combined surveys, interviews, assessments and observations to evaluate effects on trainees’ communicative competence development, interpreting uneven gains across reading, writing, listening and speaking abilities. Questionnaire and assessment analysis evidenced perceived and actual progress towards CEFR alignment among fifth year versus first year students resulting from sustained ICT incorporation over their progression. However variability disadvantages persisted in some competencies. Qualitative insights emphasized technology’s enablement of contextualized practice and real-world application as instrumental for competence growth. Despite affirmative outcomes overall, optimizing consistency by motivation and engagement-focused implementations is recommended. Intensive, systematic ICT integration shows potential for helping future foreign language teachers attain key standards-based competencies. Findings inform initial teacher training advancement in Cuba and beyond.&#x0D;
Neuroscience as a basis for the improvement of educational ICTs

Neuroscience, with its in-depth study of the human brain, has been identified as a vital tool for improving Information and Communication Technologies (ICT) in education. In order to provide more efficient and personalized didactic tools, the integration of neuroscientific findings into ICT design has been essential. Using qualitative methodologies involving documentary analysis, case studies, and systematic reviews, the importance of adapting ICT to the ways in which the brain naturally learns has been identified. Results indicate that adapting ICT based on neuroscientific principles leads to increased engagement, retention, and knowledge transfer among students. In addition, the use of digital tools aligned with neurodidactic strategies has proven to be more effective in creating inclusive and diversified learning environments, addressing the individual needs of each learner and promoting holistic learning.
Heavy element abundances in galactic globular clusters

Globular clusters are considered key objects for understanding the formation and evolution of the Milky Way. In this sense, the characterisation of their chemical and orbital parameters can provide constraints on chemical evolution models of the Galaxy. We use the heavy element abundances of globular clusters to trace their overall behaviour in the Galaxy, with the aim to analyse potential relations between the hot H-burning and s-process elements. We measured the content of Cu I and s- and r-process elements (Y II, Ba II, La II, and Eu II) in a sample of 210 giant stars in 18 galactic globular clusters from high-quality UVES spectra. These clusters span a broad metallicity range and the sample is the largest that has been uniformly analysed to date, with respect to heavy elements in globular clusters. The Cu abundances did not show a considerable spread in the sample, nor any correlation with Na, indicating that the Na nucleosynthesis process does not affect the Cu abundance. Most GCs closely follow the Cu, Y, Ba, La, and Eu field stars' distribution, revealing a similar chemical evolution. The Y abundances in mid-metallicity regime GCs (-1.10 dex $&lt;$ Fe/H $&lt;$-1.80 dex) display a mildly significant correlation with the Na abundance, which ought to be further investigated. Finally, we do not find any significant difference between the n-capture abundances among GCs with either Galactic and extragalactic origins.
Exploring Convolutional Neural Networks for the Classification of Acute Lymphoblast Leukemia Blood Cell Images

This paper introduces a novel approach to blood cell classification using convolutional neural networks (CNNs). Our emphasis lies in methodological insights derived from comparative analyses across various CNN architectures, implemented in Python with PyTorch such as ResNet152V2, Xception, EfficientNetB5, and EfficientNetV2M. In exploring model capabilities and limitations, we observe that the simple and shallow architectures is not sufficient to learn patterns compared to more complex networks. During research we have discovered that EfficientNetV2M demonstrate stable results with mean F 1 =0.891 score, which is higher compare to other models. Saliency maps are applied to reveal significant regions within or near cells, offering nuanced insights into morphological influences like cell shape. Extensive data augmentation allows one effectively mitigate overfitting, aligning train learning curves with validation and test splits. Our study is methodologically rich, comprising a meticulous data-specific overview, a robustness analysis through multiple model training, a systematic architecture comparison, and an in-depth examination using saliency maps.
Pressure, Opportunnity, Rationalization, dan Capability terhadap Terjadinya Fraud : Studi di Salah Satu Dinas Propinsi Sulawesi Selatan

This study examines the influence of Pressure, Opportunity, Rationalization, and Capability on Fraud within a department in South Sulawesi Province over two months, from December to January 2022. The department's entire staff constitutes the study population, with a saturated sampling method employed to ensure a representative sample. Field research methodology was utilized, employing quantitative data collection through questionnaire surveys. Data analysis was conducted using multiple linear regression with the assistance of statistical software.The results indicate that Pressure (Opportunity), Opportunity, Rationalization, and Capability significantly impact Fraud. The results indicate that Pressure (Opportunity), Opportunity, Rationalization, and Capability significantly impact Fraud. Practical recommendations for the study's subject include; conducting training on work ethics, stress management, and understanding of fraud prevention; implementing clear and stringent anti-fraud policies; enhancing communication and transparency throughout the organization; strengthening internal supervision mechanisms and fostering a work culture that values integrity and ethics.
Sequential Polar Decoding with Cost Metric Threshold

Polar codes have established themselves as a cornerstone in modern error correction coding due to their capacity-achieving properties and practical implementation advantages. However, decoding polar codes remains a computationally intensive task. In this paper, we introduce a novel approach to improve the decoding efficiency of polar codes by integrating the threshold-based SC-Creeper decoding algorithm, originally designed for convolutional codes. Our proposed decoder with an additional cost function seamlessly merges two established decoding paradigms, namely the stack and Fano approaches. The core idea is to leverage the strengths of both decoding techniques to strike a balance between computational efficiency and performance, with an additional method of controlling movement along a code tree. Simulations demonstrate the superiority of the proposed improved SC-Creeper decoder with tuned parameters. The improved SC-Creeper decoder achieves the performance of the CA-SCL-8 decoder in terms of high code rates and overcomes it in terms of the N=1024 code length, while simultaneously surpassing the efficiency of the traditional Fano decoding algorithm.
The Impact of HRM Practices on Employee Creativity Through the Mediating Role of Employee Engagement in the Egyptian Hotels Context

Human Resource Management (HRM) practices play a pivotal role in organizational enhancement and association development. This study investigates the relationship between HRM practices, external factors, and their impact on employee satisfaction, performance, and creativity within Egyptian hotels. Key HRM practices considered include recruitment, selection, market-oriented training, productivity enhancement, and social security assessments.The research methodology involved a unit of analysis focusing on employees in Egyptian hotels. A convenient sampling technique was employed, with 331 respondents participating in the study. Data was collected through a questionnaire comprising 42 statements adapted from previous studies. The proposed framework positions HRM practices as independent variables, employee creativity as the dependent variable, and employee engagement as a mediator.Results indicate that recruitment, selection, performance appraisal, and career planning significantly influence employee satisfaction, while compensation, training, development, employment security, and employee involvement show partial support. Additionally, recruitment, selection, compensation, performance appraisal, and career planning significantly affect employee performance, with other factors showing partial support.Moreover, employee engagement is found to mediate the relationship between HRM practices and employee creativity. Employee satisfaction fully mediates the relationship between specific HRM practices and creativity, while employee performance partially mediates this relationship. These findings provide insights into the nuanced impact of HRM practices on employee outcomes, emphasizing the critical role of employee engagement in fostering creativity.The study concludes with practical recommendations for organizations to optimize HRM practices and enhance employee engagement for improved creativity and overall performance.
Pre-Segmented Down-Sampling Accelerates Graph Neural Network-Based 3D Object Detection in Autonomous Driving

Graph neural networks (GNNs) have been proven to be an ideal approach to deal with irregular point clouds, but involve massive computations for searching neighboring points in the graph, which limits their application in large-scale LiDAR point cloud processing. Down-sampling is a straightforward and indispensable step in current GNN-based 3D detectors to reduce the computational burden of the model, but the commonly used down-sampling methods cannot distinguish the categories of the LiDAR points, which leads to an inability to effectively improve the computational efficiency of the GNN models without affecting their detection accuracy. In this paper, we propose (1) a LiDAR point cloud pre-segmented down-sampling (PSD) method that can selectively reduce background points while preserving the foreground object points during the process, greatly improving the computational efficiency of the model without affecting its 3D detection accuracy. (2) A lightweight GNN-based 3D detector that can extract point features and detect objects from the raw down-sampled LiDAR point cloud directly without any pre-transformation. We test the proposed model on the KITTI 3D Object Detection Benchmark, and the results demonstrate its effectiveness and efficiency for autonomous driving 3D object detection.
A Study of Online Teaching Strategies During Covid-19 of Secondary School Teachers

During the advent of COVID-19, it became inevitable to switch from traditional face-to-face teaching to online teaching mode. Switching to the online teaching process was the only option left for uninterrupted learning. One of the ways to make the online teaching-learning experience was the ability to energize the class environment. Creating a classroom community where students can communicate after lectures and can share their ideas and thoughts. Recording of the lectures must be given to students so that they can see them later for better understanding. Effective teaching strategies implementation was utmost required to improve the online teaching and make online learning a successful experience for both teachers and the students. Descriptive survey method was used for the present study. Objectives were to study the role of teacher in online class during covid -19 pandemic, to find different types of educational strategies for making online classes, to study the role of parents and students in implementation during online classes. Sample was selected from the 7 Secondary Schools affiliated to the State Board of Gujarat in Vadodara city taking as random sampling technique which included 70 teachers. Self made questionnaire was used to achieve the objectives of the study. It has been found in present study that the maximum number of teachers were able to perform their role in helping students according to their needs, perform as a mentor, guide and friend, to inspire the students and the remaining have faced difficulty motivating their students. Able to maintain the proper interpersonal relationships, interactions were also possible between the teacher &amp; students in online mode of teaching, but very few teachers face the difficulty in fulfilling or maintaining the interpersonal relationships. Different types of educational strategies were used in which maximum number of teachers were able to perform their interactive classroom though various activities. All the teachers were able to perform stories as strategies to clear the concept, majority of teachers are able to inspire the students and the remaining have faced difficulty with an inductive deductive approach &amp; ICT based lesson plan in online mode of teaching. Majority of teachers were able to make good rapport with the students &amp; Use teaching aids, various online quiz platforms in online mode of teaching, but very few teachers face the difficulty in fulfilling open forum or discussions &amp; encourage Group feedback for engagement. The maximum number of teachers agreed that parents and students were found able to perform their roles, to perform online cultural programs, competitions &amp; activities, to motivate online morning assemblies, dignity of online assessments, to connect in online parents and teacher meeting during online class.
Seeding Gaussian boson samplers with single photons for enhanced state generation

Non-Gaussian quantum states are crucial to fault-tolerant quantum computation with continuous-variable systems. Usually, generation of such states involves trade-offs between success probability and quality of the resultant state. For example, injecting squeezed light into a multimode interferometer and postselecting on certain patterns of photon-number outputs in all but one mode, a fundamentally probabilistic task, can herald the creation of cat states, Gottesman-Kitaev-Preskill (GKP) states, and more. We consider the addition of a non-Gaussian resource state, particularly single photons, to this configuration and show how it improves the qualities and generation probabilities of desired states. With only two modes, adding a single-photon source improves GKP-state fidelity from 0.68 to 0.95 and adding a second then increases the success probability eightfold; for cat states with a fixed target fidelity, the probability of success can be improved by factors of up to four by adding single-photon sources. These demonstrate the usefulness of additional commonplace non-Gaussian resources for generating desirable states of light.
DEVELOPING A MEASUREMENT INSTRUMENT FOR TECHNICAL AND ANALYTICAL SKILLS IN AUDITING FOR ENHANCED FRAUD DETECTION

This study bridges a significant gap in forensic accounting and fraud detection by establishing a standardized measure for Technical and Analytical Skills (TAS) in external auditing. Despite the acknowledged importance of TAS in fraud detection, the absence of a universally accepted definition and measurement instrument has limited the field's advancement. This research introduces a validated TAS measurement instrument, underpinned by a novel framework that categorizes TAS into six critical dimensions: Substantive Analytical Procedures, Technical Tools and Software, Critical Thinking, Innovation and Solution Implementation, Professional Development, and Quantitative and Statistical Analysis. A structured survey among 360 auditors from international firms in Southern Africa confirmed the instrument's reliability, with Cronbach's alpha values exceeding 0.70 across all dimensions, and supported the distinctiveness of the six-factor structure through confirmatory factor analysis. The instrument's potential to enhance auditing practices and fraud detection capabilities is considerable. It offers a foundation for future research to explore its cross-cultural applicability, predictive validity, and adaptation to technological advancements. This contribution not only provides a robust tool for auditing professionals but also fosters a culture of innovation and continuous learning within the field.&#x0D; Keywords: Technical and Analytical Skills, Forensic Accounting, Fraud Detection, External Auditing, Skill Measurement, Professional Development, Confirmatory Factor Analysis, Auditing Education.
Research of Digital Characteristics of the Step Cycle in Forensic Medicine

This article presents the results of a study devoted to the analysis of the characteristics of the human step cycle as an indicator that allows you to identify a person on video by the signs of gait. Video recordings of volunteers walking on an electric treadmill were used as the research material. A software package has been developed that allows calculating the parameters of the step cycle used for processing the material. The average values of such characteristics of the step cycle were obtained and analyzed, such as the duration of the transition period and double support as a percentage of the step cycle and seconds, the length of the step cycle in seconds, walking frequency, walking pace separately for men, women and for the entire sample as a whole. The change of the listed indicators depending on the walking speed is investigated, the change in the frequency of steps with increasing speed is analyzed. A method of identifying a person in a video based on the characteristics of a step cycle is proposed, which allows comparing the digital characteristics of steps and making a reasonable conclusion about their similarity or difference. A software package has been developed to determine the anthropometric parameters of an individual in a video based on the characteristics of the step cycle. The obtained results can be used in forensic medicine for conducting expertise on identification of a person by signs of gait.
Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a “3+X” talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China’s hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.
Machine Learning Driven Strategies for Safeguarding India’s Digital Environment Against Cyberstalking

The ubiquity of the internet and mobile phones has drastically shifted the physical world into a virtual realm, encompassing individuals of all age groups, from schoolchildren to seniors. People now utilize social media and various internet-based platforms to connect and invest their time. However, within this virtual environment, the ability to conceal or impersonate one’s identity presents an opportunity for criminal activity to go unnoticed. Despite India’s progression towards becoming a digital nation, there exists a shortage of regulations and technological resources to address the challenges stemming from this digital paradigm shift. Cybercrime refers to any illicit behavior involving the use of a computer as a tool, target, or weapon. One subset of cybercrime is cyberstalking, which encompasses actions where a perpetrator employs a computer or associated technologies to induce fear in their victim. This chapter aims to delve into the definition, typology, detection, prevention, and the imperative need for cybercrime education. Its primary objective is to investigate the surge in internet stalking cases in India, exploring the definition, categories, and underlying causes of cyberstalking. Additionally, this article scrutinizes the Indian legal framework and judicial precedents related to these issues.