Reviewerly/exHarmony huggingface dataset:
WORK VALUES, JOB INVOLVEMENT AND ORGANIZATIONAL COMMITMENT AMONG NURSES IN PRIVATE AND GOVERNMENT HOSPITAL: ITS IMPLICATION TO HOSPITAL ADMINISTRATION

Introduction: This study sought to determine the work value, job involvement and organizational commitment of nurses working in the government hospital and private hospital. A total of 60 registered nurses, 30 from the government hospital and 30 from the private hospital were the participants of this study which utilized the descriptive survey approach. A questionnaire designed by the researcher was the main data gathering tool used. This was divided into two parts, first part is the demographic characteristics of the respondents and the second part are the main variables being studied, the work values, job involvement and organizational commitment. Furthermore the findings of this research uncovered that work values, job involvement and organizational commitment had a stronger impact in every organization. Finally the practical implication of this research to hospital administration Results of the study disclosed the following: Socio-demographic profile The respondents of the study (n=60) were generally female (66.7%) with a mean age of 27 years, single, BSN degree holder (90%) with an employment contractual status of 63% and monthly income of below 12k (66%). Subjects of the study were currently connected with their present employer below one year (66.7%). Work value, job involvement and organizational commitment. The study variables revealed work value obtained an average weighted mean value of x=4.21 with a description of Important and x= 4.38 interpreted as Important from government and private hospital nurses respectively. For job involvement obtained an average weighted mean value of x= 3.38 described as Neutral and x= 3.52 interpreted as Agree both from government and private hospital respectively. And organizational commitment obtained an average weighted mean value of x= 3.54 as Agree and x= 3.37 described as Neutral from government and private hospital respectively. Significant findings showed that there was no significant difference on the job involvement and organizational commitment between the government and private hospital nurses using 0.05 level of significance and 2 as the degree of freedom obtained, the researcher determined the X2 critical value @ 5.99. And there is significant difference on work value between the nurses in government and private hospital It is mainly recommended that the administration should pay more attention to knowing the work values, job involvement and organizational commitment of the nurses to promote loyalty, committed nurses and work satisfaction of the nurses and provision of positive environment.
"A Study on the Learning Motivation through the Narratives of Korean Language Learners from European Countries –Focusing on Learners Staying in South Korea–"

Recently, as the number of Korean learners with various backgrounds and purposes has increased, the number of Korean language learners from European countries is also increasing. However, studies on Korean language learners from European countries mainly focus on curriculum, textbook development, and teaching methods, and there are not many studies that focus on the learner itself to identify the motivation for learning. Therefore, this study attempted to investigate with what motivation European Korean language learners, who wanted to learn Korean in South Korea, had through narrative research. As a result, it was found that learners, who were exposed to several languages due to the regional specificity of Europe, had a strong interest in learning a new language. Also, through various occasions, they became curious about Korean culture, and their interest in the language and their curiosity about culture led them to start learning Korean. In this process, various experiences in life in South Korea were working as another strong motivation for learning Korean. In addition to this, the social context that did not adequately cope with the unstable public security or the pandemic in Europe became one of the factors that encouraged learners to come to South Korea to study Korean. This study is significant in that it studied learners’ learning motives from various linguistic and cultural backgrounds and can be a reference for the composition of educational content for Korean language learners from European countries. (Yonsei University)
Digital Currency

We have enteredDigital currency the third decade of the twenty-first century. Nobody knows what events will affect and change the economic, political, social and ecological balance of the world in the third decade. However, what one can be sure of is that economic activities will continue and there is no possibility that currency will disappear in the next decade, only that it will change significantly, all the signs of which have become fully visible in the second decade of the twenty-first century, especially in the last two or three years.
An Information Flow Security Logic for Permission-Based Declassification Strategy

With the increasing popularity of smartphones and the rapid development of mobile network, ensuring the security of mobile applications becomes more and more important, which has received substantial attention from both academia and industry.Information flow security, as a prominent approach to system and network security, aims at ensuring high security level information would not be accessed by analyzing the information with lower security levels.In this paper, we design a novel information flow security logic to reason about the security of mobile applications, leveraging on the idea of permission based declassification.Firstly, we propose a formal language with permission check branches, through which the access to the confidential information can be controlled.Then we present our novel information flow security logic based on the permission based declassification strategy, which can make the reasoning more precise by degrading the security level of the specific information.Finally, we demonstrate the usability of our logic via examples.
Construction of "TPACK+V" Education Model in Advanced English under the Concept of Holographic Education

In the context of the construction of "new liberal arts", Advanced English should be equipped with new integrated features.Combined with the concept of holographic education, starting from the four levels of TK, CK, PK, and VK, the design idea of "five modes" (proceduralization, modularization, theme-based, case-based, hybrid) is proposed based on the study of the whole process of online and offline teaching.The study is conducted to locate the entry point, theme selection and key elements extraction of curriculum values in each teaching link, and explore the hybrid education model by selecting typical chapters in the textbook.A systematic mode for value eduction will be condusive to promoting the course development, and offer reference to the fellow teachers.
A Study on the Determinants of Digital Transformation(DX) of MICE Corperations

In 2004, Professor Erik Stolterman of Sweden first referred to 'a phenomenon in which human life is improved in a better direction using IT technology' as Digital Transformation (DX). If you look at the transformation, you can use digital technology to improve your business and expand and change your business area. In other words, the core of the 4th industrial revolution can be seen as digital transformation in all areas of individuals and businesses. A company's digital transformation requires a cool-headed, correct awareness and desirable response to situations that can be both an opportunity and a threat depending on the circumstances and judgment. The MICE industry is no exception. Since 2010, the MICE industry has been combined with digital technology and has become a driving force for innovative growth in industries that have entered a low-growth phase, leading the growth and innovation of the MICE industry. In particular, a number of events in the form of Untact and hybrids were held in the context of COVID-19, accelerating the digital transformation of MICE companies. This study reveals that in order to implement a successful digital transformation that increases business performance and efficiency, a positive organizational culture of a company is a prerequisite, and it studies the perception and attitude of MICE company members to develop a strategic perspective for digital transformation of MICE companies. is to establish Against this background, the purpose of this study is to examine the perception of digital transformation of MICE companies and to empirically analyze the factors that promote digital transformation. To this end, literature research was conducted, and prior research was conducted on the relationship between digital transformation awareness and technology acceptance, external responses to digital transformation and digital transformation readiness, digital transformation intention and digital transformation implementation, and data on MICE company employees were collected. I want to analyze Through this, we intend to present theoretical and practical implications for digital transformation of MICE companies preparing for the 4th industrial innovation and post-corona era.
Analysis of Social Media Utilization based on Big Data-Focusing on the Chinese Government Weibo

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research.This study uses Python language to analyze the big data of the Chinese provincial government Weibo.First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022.Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships.Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm.The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly.Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo.Third, the contents of government Weibo account for more than 30% of negative sentiments.The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.
Development Strategy of Internet Plus Modern Seed Industry

The seed industry is the chip of agriculture and seed industry modernization is a significant symbol for agricultural modernization. Internet Plus Modern Seed Industry is an important application scenario of modern agriculture and a concentrated embodiment of scientific and technological innovation in the seed industry. Based on the concept definition and main characteristics of Internet Plus Modern Seed Industry and using field investigation and expert consultation, this study analyzes the characteristics, supporting technologies, and typical applications of application scenarios for different subjects such as government departments, scientific research institutions, and breeding bases. The challenges and demand for the infrastructure, data sharing, key technologies, and commercialization system of the Internet Plus Modern Seed Industry in China are analyzed. Additionally, we propose the development strategy, technical roadmap, and major demonstration projects, to provide a scientific reference for the development of modern seed industry. Specifically, major demonstration projects are urgently required for big data platforms for germplasm resources, new infrastructure for Internet Plus Modern Seed Industry bases, Internet Plus Modern Seed Industry data sharing platforms, and big data intelligent services for the seed industry. Moreover, the intelligent equipment research, development, and manufacturing industry as well as the commercialized breeding software industry should be encouraged to comprehensively promote the intelligent development of modern seed industry.
GH-CNN: A New CNN for Coherent Hierarchical Classification

Hierarchical multi-label classification is a challenging task implying the encoding of a high level constraint in the neural network model. Before the rise of this field, the classification was done without paying attention to the hierarchical links existing between data. Nevertheless, information relating the classes and subclasses may be very useful for improving the network performances. Recently, some works have integrated the hierarchy information by proposing new neural network architectures (called B-CNN or H-CNN), achieving promising results. However with these architectures, the network is separated into blocks where each block is responsible for predicting only the classes of a given level in the hierarchy. In this paper, we propose a novel architecture such that the whole network layers are involved in the prediction of the entire labels of a sample, i.e., from its class in the top level of the hierarchy to its class in the bottom level. The proposed solution is based on a Bayesian adjustment encoding the hierarchy in terms of conditional probabilities, together with a customized semantic loss function that penalizes drastically the hierarchy violation. A teacher forcing strategy learning is used to enhance the learning quality. Thanks to this approach, we could outperform the state of the art results in terms of accuracy (improved for all levels) and also in terms of hierarchy coherence.
JointFusionNet: Parallel Learning Human Structural Local and Global Joint Features for 3D Human Pose Estimation

3D human pose estimation plays important roles in various human-machine interactive applications, but how to efficiently utilize the joint structural global and local features of human pose in deep-learning-based methods has always been a challenge. In this paper, we propose a parallel structural global and local joint features fusion network based on inspiring observation pattern of human pose. To be specific, it is observed that there are common similar global features and local features in human pose cross actions. Therefore, we design global-local capture modules separately to capture features and finally fuse them. The proposed parallel global and local joint features fusion network, entitled JointFusionNet, significantly improve state-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4 mm in MPJPE (relative 6.8 $$\%$$ improvement) over the previous best feature fusion based method [22] on H36M dataset in 3D human pose estimation.
Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing

Moiré often appears when photographing textured objects, which can seriously degrade the quality of the captured photos. Due to the wide distribution of moiré and the dynamic nature of the moiré textures, it is difficult to effectively remove the moiré patterns. In this paper, we propose a multi-spectral dynamic feature encoding (MSDFE) network for image demoiréing. To solve the issue of moiré with distributed frequency spectrum, we design a multi-spectral dynamic feature encoding module to dynamically encode moiré textures. To remedy the issue of moiré textures with dynamic nature, we utilize a multi-scale network structure to process moiré images at different spatial resolutions. Extensive experimental results indicate that our proposed MSDFE outperforms the state-of-the-art in terms of fidelity and perceptual on benchmarks.
Change from Above in a 16th-Century Corpus of Tuscan Correspondence: The Spread of the Codified Form of the Masculine Determiner

Sixteenth-century Italy saw the codification of a literary variety based on archaic Florentine. This process was at first guided by non-Tuscans, and the codified variety did not entirely coincide with the variety spoken at the time in Florence, going back instead to the language of fourteenth-century Florentine authors. This archaising variety was at first rejected by Tuscan writers as inauthentic, but in the course of the century attitudes changed, and this variety was eventually promoted by the Accademia della Crusca. Whereas most attention has focused on the literary sphere, the reception of this archaising norm in Tuscany at the level of informal writings warrants further investigation. This paper examines the Buonarroti letter corpus, tracking the spread of the archaising form of the masculine determiner, a highly salient feature that distinguished archaic and contemporary Tuscan. The analysis shows a dramatic increase in the use of this form throughout the sixteenth century, with the change being led by high-ranking individuals and progressively adopted by the ranks below. Italian historiography has been criticised for the centrality usually attributed to change from above. The results presented here, however, suggest that, for a highly salient feature, change from above is detectable in the informal written language of sixteenth-century Tuscans.
Nonlinear gradient-based feature selection for precise prediction of diseases

Developing accurate predictive models can profoundly help healthcare providers improve the quality of their services. However, medical data often contain several variables, and not all the data equally contribute towards the prediction. The existence of irrelevant and redundant features in a dataset can unnecessarily increase computational cost and complexity while deteriorating the performance of the predictive model. In this study, we employ the gradient-based prediction attribution as a general tool to identify important features in differentiable predictive models, such as neural networks (NN) and linear regression. Built upon this approach, we analyse single-stage and multi-stage scenarios for feature selection using ten medical datasets. Through extensive experiments, we demonstrate that the combination of the gradient-based approach with NN provides a powerful nonlinear technique to identify important features contributing to the prediction. In particular, nonlinear gradient-based feature selection achieves competitive results or significant improvements over previously reported results on all datasets.
Problems on polarization aberrations in large aperture dynamic interferometry based on the polarization phase shifting technique

The polarization based phase shifting method is an effective way for dynamic measurements. However, when this technique is applied to the measurements of large optics, the interferometric results are easily limited by the birefringence of large optics. The birefringence changes the polarization states of reference light and test light, and brings constant polarization aberrations into the measurement results independent of the phase shifting procedure. In this article, the detailed theoretical analysis on the mechanism of polarization aberration is presented. Afterwards, we propose a new interferometric method to determine the birefringence effects by measuring the transmitted wavefronts of the large optics, which are considered as birefringent samples. Theoretical analysis shows that the polarization error in the linearly polarized system can be corrected by two independent measurements with orthogonal polarization states. The phase retardance can be obtained from the wavefront difference of the transmitted wavefronts when switching the polarization states of the incident lights. The birefringence distribution obtained is used to calibrate the polarization aberrations in the measurement result of a homemade large aperture measurement platform and the correction result is compared with the result via the wavelength tuning phase shifting method. The elimination of the polarization aberrations can be observed in the final results.
A Critical Analysis of the Core Determinants of Central Bank Digital Currency in Zambia

In this study, we analyze the main determinant of a Central Bank Digital Currency (CBDC) in Zambia using a critical literature review.There are many determinants of Central Bank Digital Currency but this critical literature review only focuses on the main ones namely, how the introduction of a CBDC can improve the efficiency of payments systems platforms, make a stronger national defense against AML/CFT, ensure nondiscriminatory access to domestic financial services and whether the introduction of a CBDC can mitigate the high cost of currency processing in Zambia.The methodology follows a critical review of CBDC literature from central Banks that have already launched, performing proof of concepts or piloting CBDCs in their jurisdictions.The literature also considers publications and working papers from global standards setting organizations such as the IMF, World Bank, WEF, ITU, ECB and many others.The study focuses on the Zambian jurisdiction and reviewed whether these main determinants may warrant the introduction of a CBDC in Zambia.
Application of blockchain technology in smarter energy transaction

Blockchain is currently one of the most suitable technical means to open up physical entities and virtual data to the value level. Its core is to rely on a distributed consensus mechanism to achieve reliable reconstruction and transfer of value. The technical principles and value characteristics of the blockchain coincide with the inherent demands of smart energy. In order to break through the traditional electricity market-oriented trading model and improve the application level of blockchain technology in smart energy transactions, this article describes the application of blockchain technology in smart energy transactions, analyzes its key technical difficulties, and analyzes the key technical difficulties. The prospects are put forward to provide a reference for the application of blockchain technology in the field of electricity trading.
Anomaly value detection in intelligent operation and maintenance

At present, most of the existing network monitoring and fault repair rely on the rule system or manual processing, however, with the increasing scale of the network and the diversification of business, this approach is difficult to meet the requirements. With the rapid development of artificial intelligence technology, Artificial Intelligence for IT Operations (AIOps) has also made great progress. Anomaly detection of KPI (Key Performance Indicator) is a core technology of AIOps, through which the manual workload and O&M cost can be reduced, meanwhile, O&M efficiency can be enhanced. In this paper, using the performance of the KPI of operator base stations as research data, a simple and efficient algorithm is proposed to detect the anomalous values and periods of three core indicators, namely, the average number of users, the PDCP flow, and the average number of activated users. At last, 1014 anomalous values and 203 anomalous periods were detected. Unlike the current network O&M which commonly detects anomalies based on human-defined rules and fixed thresholds, the anomaly detection algorithm used in this paper is practical and has good operability, which can help O&M personnel to better solve the network operation and maintenance problems.