Reviewerly/exHarmony huggingface dataset:
AXIOLOGICAL MODELS OF EDUCATIONIN THE CONTEXT OF ITS CONTINUITY

The purpose of the study. The article discusses various axiological models of education in the aspect of ensuring the continuity of the educational process. Education is analyzed as a process that combines organizational, process and resultant components. The aim of the study is to identify and substantiate educational models from the point of view of value bases. This is due to the need to choose a specific model or a combination of them, depending on social conditions and institutional factors. Conclusions. As a result of the conducted research, the author comes to the conclusion that at present it is advisable to define a number of axiological educational models, each of which is based on certain value ideas about the images of the ideal personality of students: the traditional Christian image, the image based on socio-pedagogical determinism; the image based on natural determinism; the humanistic image. The philosophical-axiological approach allows us to distinguish four axiological models of education: theocentric, sociocentric, nature-centric and anthropocentric. The choice of a particular model is determined by the ideological orientation of the subject of the learning process, the specific historical conditions of the development of society, the state's requests for educational results, personal and professional qualities of students. The author concludes that there is a need for a differentiated approach to the definition of a specific axiological model of education, which will be more effective if it is enriched with elements of other models.
Modeling Volatility in the Stock Market for Accuracy in Forecasting

In this paper, the best GARCH type model was selected and compared with the machine learning models, such as Extreme Learning Machine (ELM) and Multilayer Perceptron Neural Network (MLP-NN) models in modeling and forecasting monthly return of the financial market data. The objective of the study was to compare the best model in forecasting New York and Shanghai Stock Composite indices, for the period 01.01.1996 to 01.09.2019. The GJR-GARCH model outperformed other GARCH type models based on the Schwarz Bayesian Information Criterion (SSBIC). The Monte Carlo simulation carried at 1000, 2000, 3000, 4000 and 5000 finite sample (window) sizes to test the consistency of the GJR-GARCH model parameters has shown perfect results between true and the simulated coefficients. Finally, the GJR-GARCH model was compared with the MLP-NN and ELM machine learning models. The monthly return forecasting of two years (24 months) was done starting from period 01.09.2019 to 01.09.2021. The study found the MLP-NN model as the best in the modeling and forecasting monthly returns of the two composite stock indices for the two years by considering the Root Mean Square Error (RMSE).The study recommends that further research should focus on the formulation of the hybrid model that combines machine learning and the GJR-GARCH models in forecasting stock market volatility.
Counterfactual Causal Analysis on Structured Data

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference, it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph.
The Effect of Organizational Culture on the Organizational Commitment and Organizational Performance in the Resort Corporation

초록 · 키워드 목차 오류제보하기 The purpose of this study was to analyze the effect of resort corporation organizational culture on employees" organizational commitment and organizational performance. The subjects of this study are those who work in resorts in Gyeonggi-do and Gangwon-do. For the final analysis, 220 data were used. Hypothesis verification was performed by Structural Equation Modeling (SEM), and as a result, the following conclusions were obtained. First, the innovation-oriented organizational culture had a positive effect on employees" organizational commitment. However, unity-oriented organizational culture did not have a significant effect on employees" organizational commitment. Second, innovation-oriented organizational culture had a positive effect on organizational performance. However, unity-oriented organizational culture did not have a significant effect on organizational performance. Third, organizational commitment of resort corporation employees had a positive effect on organizational performance. Therefore, organizational performance should be created by improving organizational commitment of employees through innovative organizational culture. #리조트 기업(Resort Corporation) #조직문화(Organizational Culture) #조직몰입(Organizational Commitment) #조직성과(Organizational Performance) AbstractⅠ. 서론Ⅱ. 이론적 배경Ⅲ. 연구방법Ⅳ. 실증분석 결과Ⅴ. 결론참고문헌
Digital Competence and Mysteries of Learning: Use of Technology in Classroom

The idea of digital competence has emerged in recent years around the globe. It entails a wide range of abilities and knowledge in every field. The current research paper is to investigate the digital competence and mysteries of learning: use of technology in classrooms by the teachers. University teachers were used as population and sixty teachers were randomly selected as a sam-ple from different campuses of education university Lahore. Self-constructed instrument, Teach-ers' Digital Competencies Survey [TDCS] instrument, was adapted and used for data collection. The results revealed a statistically apparent difference in gender of teachers about different indi-cators of digital competencies and learning with technology. It was concluded that male teachers had used more digital technology with more confidence than that female teachers. There search-ers recommended that digital competence may be promoted among teachers for the better teaching and learning environment. The universities should provide better technological facilities for better learning.
Learning Students’ Intents for Better Conversations

Learning students’ intents is a crucial step in understanding students’ utterances towards building a human-machine conversational system in the education domain. Such kinds of systems could benefit both students and universities in exchanging information automatically and efficiently. However, the research on this field has seen little attention, especially in resource-poor languages like Vietnamese. This paper pioneers the study on this interesting yet unexplored field in three main aspects. Firstly, we develop a corpus about students’ intents consisting of 3,548 real conversational utterances which are manually annotated. Secondly, we carefully investigate the impacts of different machine learning techniques on the performance of the detection model using this corpus. Specifically, the latest innovation in deep learning techniques (CNNs, LSTMs, attention mechanism, and Transformers), and recent advances in pre-trained language models, namely BERT, have been extensively exploited. Moreover, we also demonstrated additional improvement to the performance by using a data augmentation technique to enrich the training data in Vietnamese. Thirdly, to verify the effectiveness of the proposed methods, we perform extensive experiments. Experimental results showed that the proposed methods outperform the traditional machine learning algorithms by a large margin. Furthermore, we also see that fine-tuning a monolingual BERT on an augmented training dataset for this task outperformed other methods and yielded the best performance. We achieved 94.89% accuracy averaged on five folds. The F1 score measured on each intent type was also very high with more than 90% in most cases. These achievements are promising in developing a real Vietnamese conversational system in the education domain.
Recode-Decode-and-Compare: An Efficient Verification Scheme for Coded Edge Computing Against Collusion Attack

Recently, coded computing has great breakthroughs in edge computing, which can support and optimise many latency-sensitive and computation-intensive applications. In edge computing, a practical issue is that, comparing to computing devices configured in the cloud data center, edge devices may be unreliable and untrustworthy. Since coded computing has potential in speeding up the computation and protecting the confidentiality of computing data, coded edge computing (CEC) emerges. For a major security issue: verify the correctness of computation result in CEC, the existing efficient work performs verification by comparing multiple final results. The attackers (malicious edge devices) studied before are independent, while collusion attack is the realistic and important scenario in distributed computing and edge computing. To improve the security of CEC, in this paper, we study the verification issue of collusion attack and design the efficient verification scheme: Recode-Decode-and-Compare (RDC). By re-encoding the encoded data, collusive attackers cannot infer the linear relationship between different intermediate results. Therefore, the user can verify the correctness by comparing multiple final results. We also conduct solid theoretical analyses to show the successful verification probability and verification overheads. Finally, extensive simulation experiments demonstrate that the RDC verification scheme can efficiently verify the computation result and outperforms the state-of-the-art works.
Enabling a 3-D Cyberspace Experience Online

The allure of a 3-D cyberspace grows again as communication channels connect a growing number of virtual reality (VR) head mounted displays and interaction-enabling controllers. Computer graphics enable 3-D cyberspace as much as any component critical to providing satisfactory experiences. Historically, the usefulness of 2-D interfaces entrenched when developers demonstrated the feasibility of 3-D cyberspace. Now a certain amount of prolonged inertia seems necessary to promote 3-D graphics to the participation level of 2-D graphics. The HTML 5 stack and other technologies enable 3-D integration into our planetary shared cyberspace. This chapter provides a historical perspective of one researcher’s methods used to provide 3-D web experiences, the lack of critical mass for their use in the past, and how the latest virtual reality integration trajectories will bring a critical mass to the 3-D cyberspace of the future.
Introduction: What Are the Goals of AI Research?

Abstract Debates rage about the AI goals, with two primary camps. One sees the goal of AI research as long-term curiosity-driven research to understand how computational agents, humans or machines, perceive, decide, and act in the real world. They do their work by building programs that perform well in laboratory studies on specified tasks. The second camp of AI researchers is devoted to creating successful products and services by using many forms of algorithms, especially machine and deep learning. They are devoted to mission-oriented tasks that could be widely deployed.
Scatterplot Variations Seen in Malaria Using Automated Hematological Analyzers: A Series of Ten Cases

Background: Malaria is a major health problem in India. Complete blood count and peripheral blood smear (PBS) is important for its diagnosis. Inter observer variation makes PBS fallible. Rapid diagnostic tests cannot detect low parasitemia and mixed infections. Scatterplot from automated analyzers have shown variations previously which might be exploited.&#x0D; Methods: Scatterplot patterns of ten samples of confirmed malaria and 100 control samples were derived and other infections ruled out by culture and serology as a part of descriptive study between July and August 2018. Each malarial scatterplot was compared with the control pattern for abnormalities and their frequency noted.&#x0D; Results: All the ten samples belonged to Plasmodium vivax species. Abnormalities detected included split in neutrophilic region, eosinophil-neutrophil merge, neutrophil graying, lymphopenia, ghost red blood cells (RBC), eosinophil split, reactive lymphocytes, monocytosis, pseudoeosinophilia, neutrophilic leukocytosis&#x0D; Conclusion: Variations in scatterplot patterns are seen in malaria and provide clues to the diagnosis of malaria.
Geographic Information System Mapping and Management of Child with the Highest Nutritional Potential in Prabumulih City Using K-Means Clustering Method

Malnutrition is one of the diseases that Indonesia is worried about, not only a burden on the state, but also a burden on the family.Of course this is also a concern of the local government, including Prabumulih City.Based on a report from the family health and community nutrition section at the Prabumulih City Health Office, in 2019 the number of cases of malnutrition in children under five reached 1%.And to maximize health monitoring in toddlers so that they are not potentially bad, clustering is carried out using the K-Means algorithm which aims to provide information for the parties involved in decision making.The system development method used is eXtreme Programming (XP).The data used is a recap of data on toddlers with bad potential from 2018 -2020.This study resulted in clusters (groups) of bad disaster distribution areas with a level of C1 for areas with high nutritional potential, C2 for areas with moderate nutrition levels.potential, for C3 for areas of low potential malnutrition.
Securing IoT through Blockchain in Big Data Environment

Over the years, both major corporations and individual customers have lauded IoT. The widespread implementation of this new technology, however, poses many security issues. Simply put, anything associated with the “Internet” poses a possible security threat as a whole to the network. Also, 57% of IoT devices are considered effectively venerable by programmers since they admire medium-severity attacks. Given the preferences and exponentially expanding notoriety of IoT, it is noteworthy to evaluate how we approach IoT devices and networks’ security management. The use of blockchain technology is one possible way to improve security and reliability in an IoT ecosystem. Via decentralizing blockchain IoT networks and removing single failure points, associated devices gain additional security and are less susceptible to malware and other attacks. More independent operations and deeper network and foundation maintenance costs are other benefits of a decentralized IoT infrastructure. This chapter offers a summary of blockchain principles and addresses, in particular, how IoT is secured with blockchain technology.
Grammar in 5D: The interface design of a mismatching grammar

This chapter deals with grammar design, interfaces, mismatches and metatheory. The core question is: how can one describe and assess grammar design and, in particular, interface design? The distribution, derivation and direction of linguistic information being computed both between and within levels are hereby analysed in order to see how these parameters may combine in any theory of language modelling. More particularly, this chapter looks into the Functional Discourse Grammar framework and its hybrid approach to these features - non-derivational yet strongly directional - and advocates for a complete elimination of empty nodes in the search for a fully modular approach. This work further examines the architectural compatibility of Functional Discourse Grammar with various modular and constraint-based approaches with a strong lexical emphasis such as the Parallel architecture, autolexical Syntax, Head-Driven Phrase Structure Grammar or Lexical Functional Grammar. accordingly, it argues for the following design features for the functional framework: an interface-based lexicon which links distinct types of fully encapsulated information by means of bi-directional interfaces within lexical entries and with the grammar proper; and unification for intra-level construal in the search for weaker directionality.
Research on Sensation Recognition using Mobile Phones

The accessibility of implicit sensors in cellular telephones has empowered a large agency of imaginitive applications. individual elegance of use manages spotting a purchaser's feelings. beyond programs have essentially depended on account and showing self-introduced emotions. This paper indicates a practical feeling recognition framework for cell phones finished as a terrific console that surmises a patron's enthusiastic nation using device mastering structures. The framework utilizes accelerometer readings and exceptional a part of composing behavior like pace and postponement among letters to prepare a classifier to foresee emotions. Credulous Bayes, J48, IBK, Multi-reaction without delay relapse and SVM were assessed and J48 modified into seemed to be the finest classifier with over 90% exactness and accuracy. but giving emotive input to singular customers, the framework likewise makes use of geo-categorized facts to collect and show passionate conditions of locales or countries via a website.
Dynamic route planning for scenic spots in wisdom tourisms

In this paper, we designed a dynamic route planning scheme for scenic spots. Specifically, in this scheme, the initial value of each scenic spot is determined in the scenic spot. Then, the map containing the relative distance data between the scenic spots in the scenic spot is processed to obtain the path information between the scenic spots. The tourist’s time requirements are obtained for the scenic area visit in order to get the default optimal route. Furthermore, the default optimal path is obtained to meet the expectations of tourists. Further, based on the current time point, the precise value of the remaining scenic spots in the tourist's selection requirements, and the path information between the remaining scenic spots, the optimal value solving operation is performed to obtain the optimal path for the current time point. Based on the known data, combined with the multi-definition algorithm to solve the optimal path, the best tour route matching the current location can be obtained in real time according to the actual tour mode of the tourists, thereby avoiding time waste and improving the tour experience.
Cyber-Physical Security

This chapter explains how cybersecurity increasingly connects to consumer safety and critical industrial infrastructure, as well as the digital economy and systems of democracy. Thus, the stakes of cyber-physical security have never been higher. From attacks on the energy sector to the attacks on the consumer Internet of things and democracy, cybersecurity governance is an existential concern in society. Regrettably, security is woefully inadequate. Market incentives privilege rapid product introduction rather than strong security. The chapter then suggests baseline recommendations, across all stakeholders, necessary for improving the cyber-physical ecosystem. It also looks at how cyber-physical systems complicate and increasingly shape already-difficult global cybersecurity governance questions such as when governments choose to stockpile knowledge of software vulnerabilities for cyber offense, rather than disclose them to secure critical infrastructure.
Anchor constrained refinement network with Intersection-over-Union-aware and scale-aware loss for oriented object detection

Object detection has achieved good progress in the last few years. However, there are many challenges in the field of remote sensing imagery. Objects in remote sensing images usually have arbitrary orientations and various scales. In addition, some objects are easily overwhelmed by a cluttered background. To take advantage of single-stage object detectors that have fast speed, many cascaded structures based on single-stage detectors have been proposed to improve detection performance. However, feature inconsistency in cascade structure results in poor detection performance. To address these problems, we propose an innovative model in terms of both model improvement and loss function refinement. This model consists of an attention module to highlight useful information in cluttered scenes, a multi-scale feature fusion module, and a cascade refinement module with anchor constrained convolution to address feature inconsistency. Furthermore, Intersection-over-Union (IoU) classification loss is proposed to enhance the correlation between classification and localization, and a scale-aware regression loss is proposed to improve the detection performance on objects with different scales. We conducted extensive experiments on both the DOTA dataset and the HRSC2016 dataset, and the experimental results show that our model has advantages compared with current state-of-the-art methods.
Dissimilarity functions for rank-invariant hierarchical clustering of  continuous variables

A theoretical framework is presented for a (copula-based) notion of dissimilarity between continuous random vectors and its main properties are studied. The proposed dissimilarity assigns the smallest value to a pair of random vectors that are comonotonic. Various properties of this dissimilarity are studied, with special attention to those that are prone to the hierarchical agglomerative methods, such as reducibility. Some insights are provided for the use of such a measure in clustering algorithms and a simulation study is presented. Real case studies illustrate the main features of the whole methodology.
Blockchain Technology in the Banking System in Developing Countries

Blockchain technology has now made a significant contribution to a broader range of societies and promoted economic growth in general, particularly in the financial sector. Therefore, over the past decade, blockchain technology has received a lot of attention, especially since 2009, as a milestone in the Bitcoin transaction tracking database. This study aims to tackle blockchain technology’s potential and future developments in the banking system of developing countries. The positive, negative and trending usage of blockchain technology in developing countries is addressed in this chapter. Several banks are planning to own blockchain technology in the banking system to minimize their operating costs, increase security and performance, and create confidence in transactions. However, blockchain technology has many risks, such as security, infrastructure and legal risks, and other obstacles, such as operating costs and transaction fees. Storage will increase dramatically over time. Banks in developing countries also need to carefully weigh the benefits and challenges to optimize the advantages and minimize the risks that blockchain technology brings.