Reviewerly/exHarmony huggingface dataset:
A Study on the Development of STEAM Program for Topical Discussion Using Data Based on Blended Learning in High School

Objectives The purpose of this study is to design a 10-hour STEAM program with the subject of data and to develop a blended learning-based high school STEAM program through the application of a pilot school. Specifically, in order to improve learners' data literacy, we develop a topic discussion STEAM program using data so that learners can directly select discussion topics, find data as a basis to support their thoughts, and critically analyze other learners' claims. Methods To this end, a study was conducted to develop and apply a topic discussion program based on data that can be actually applied on a blended learning basis according to the ADDIE model, a general model of program development. Specifically, in the analysis stage, literature research on data literacy and topic discussion was conducted, and the topic discussion STEAM program using data was selected as the main learning element. In the design stage, the entire subsequent schedule of the blended learning-based STEAM program was designed and student activity sheets were produced. In the development stage, the instruction plan for each class and the student study paper were embodied, and the first revision was made through the first expert review. The implementation stage was conducted from September to December 2021, and nine teachers applied the revised STEAM program to 717 students in the first and second grades of high school. In the evaluation stage, The results of the program application were analyzed, and the final STEAM program was prepared through the second expert review process. Results In order to help students improve data literacy, five stages of discussion using data were developed based on the STEAM framework. In consideration of the pandemic situation, step-by-step activity papers and evaluation papers for online and offline classes were developed, respectively, and it was confirmed that they had a positive effect on learners' communication capabilities, discussion capabilities, collaboration, and problem-solving skills. Conclusions Through the results of this study, the topic discussion STEAM program using data will be able to improve the data literacy of learners who will live in a future society where big data is commonplace. In addition, the STEAM program designed based on blended learning is expected to be available in various learning environments in the future.
The Historical Value and Educational Significance of Social Equality Movement

Objectives This study aims to reconsider the historical value and educational significance of social equality movement by analyzing the history curriculum and textbooks based on the research trends and characteristics of the social equality movement. Methods To this end, the research trend of the social equality movement was followed and the characteristics of the social equality movement were categorized into two categories. The main research flow is the case of emphasizing the nature of the national liberation movement and focusing on the nation, and the other trend is the case of focusing on the movement to overcome discrimination in the social equality movement and treating civic values as important. The descriptions of the history curriculum (7th, 2009 revision, 2015 revision) and textbooks (2015 revision high school Korean history) were analyzed based on national values and civic values. Results As a result of the review in this study, first, in the history curriculum (7th Korean Modern and Contemporary History, 2009, 2015 Revised Korean History), the social equality movement emphasized the characteristics of the national liberation movement, giving priority to national values. However, there was a change that gradually approached the social equality movement in terms of social movement in terms of unit organization and achievement technology. Next, in the history textbook (2015 High School Korean History), the view of the national liberation movement centered on the confrontation between the social equality movement and the Japanese Empire was dominant, but a narrative focused on civic values by approaching the social equality movement as a social movement appeared. Conclusions By reconsidering the historical value of the social equality movement, the educational significance of the social equality movement as a citizenship education is improved.
A Brief Study on Principles for the Content and Orgarnization of KFL/KSL Teacher’s Professional Ethics

This paper aims to lay a theoretical foundation and find an effective approach for establishing KFL/KSL teacher’s professional ethics through a literature review on teaching ethics. The results are as follows. First, teaching ethics first identified the basic characteristics of ethics required by teachers through the analysis of literature data in related fields, and then categorized them to describe the details. Second, teaching ethics integrally approached the universal teacher’s values and its practice in terms of cognitive, affective, and behavioral function. Third, as all occupational groups are currently specialized, teaching ethics is also being reinterpreted from the perspective of expert ethics. Referring to that, the following conclusions can be drawn. First, in order to establish KFL/KSL teacher’s professional ethics, it is necessary to approach from the aspects of ‘duty ethics’ and ‘utilitarian ethics’, and the specificity of KFL/KSL field should be considered. Next, its details should be described with a focus on ethical practices, and should be integrated with the universal teacher’s values(clergy, labor, and profession). Lastly, it must be practiced in a behavioral function, so an integrated approach is required.
Evaluation of collection development and management in selected university libraries in Ogun State, Nigeria

Collections of five university libraries in Ogun State, Nigeria were evaluated using SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis. Results showed that the libraries have strengths in having a collection development policy, users’ involvement in collection development, balanced, relevant and high-quality collections and up-to-date online databases. Lack of weeding policy and non - participation in the consortium are weaknesses in three libraries, opportunities include the use of other library resources, technology, open access resources, and acquisition through the Tertiary Education Trust Fund (Tetfund) in the government-owned institutions while the high cost of online subscriptions, other information services providers, inadequate funding of collection development in four libraries, reliance on Tetfund, gifts and exchange by public universities constitute threats. Findings further revealed no significant difference between collection development of public and private university libraries (t(74) = 0.20, p&gt; 0.05). The study recommends the development of a weeding policy that must be adhered to as well as collaboration amongst libraries.
Extractive Long-Form Question Answering for Annual Reports Using BERT

This paper suggests a strategy to obtain extractive long-form answers from Annual Reports. Perceiving most previous usage, BERT has been used to get short answers with either a phrase or a sentence. Annual reports are simply not sufficient and most queries require a more well-rounded answer. In this paper, TF-IDF, FinBERT, and BERT are used to build extractive long answers from annual reports—which is a very large context. The BERT model that is used in the pipeline is pre-trained on unsupervised textual data from annual reports and fine-tuned on FiQA.
A Learning Automata-Based Approach to Improve the Scalability of Clustering-Based Recommender Systems

One of the common techniques to reduce the scalability problem in collaborative filtering (CF)-based recommender systems is the clustering technique, which accelerates finding the nearest neighbor users in the recommendation process. Different clustering algorithms lead to improved accuracy and diversity in recommender systems. It is challenging to develop recommender systems based on clustering with decreasing scalability and simultaneously increasing accuracy. This article proposes a new clustering-based recommender system that takes into account the theoretical properties of the Learning Automata (LA) technique. The presented clustering novelty lies in the fact that employing LA technique for the user clustering in a CF-based recommender system has not been set forth so far in a way that addresses the scalability while improving accuracy. In addition, a novel similarity metric is embedded in the proposed algorithm to measure the similarity value between users. This metric is developed as like/dislike (LD) which can significantly improve the accuracy by reducing the computational cost. Extensive simulations have been performed on real-world datasets such as MovieLens and FilmTrust, which confirm the effectiveness of the proposed algorithm. In this regard, the proposed algorithm has improved the precision between 5 and 16% on average compared to the existing state-of-the-art methods such as GA-GELS, NUSCCF, NNMF, and KL-KM.
Research on substation engineering cost prediction method based on index theory

The substation project is one of the key investment points of power grid enterprises. Therefore, it is of great significance to strengthen the cost control level of the substation project. This paper presents a substation engineering prediction method based on engineering cost index theory and verifies the effectiveness combined with practical case analysis.
Considerations for a Successful Cybersecurity Program

A successful cybersecurity program aligns with the organization’s strategy and protects the company’s data assets. Cybersecurity professionals secure organizations by combining their technical expertise with strong general, risk and project management skills. Business data owners and management team members also play a pivotal role in ensuring data security because strong cybersecurity is essentially a business requirement. This chapter provides a high-level overview of considerations for building a world-class cybersecurity, risk-based program to secure your organization’s data, regardless of your organization’s size. A cybersecurity risk management program using risk analysis, monitoring and response tools is key to evaluating the adequacy of your organization’s existing cybersecurity program. Inputs to the risk analysis include threats, vulnerabilities, laws, regulations, industry standards and best-practice frameworks. Organizations select, fund and execute projects to mitigate risks and to close the gap between the current state of the organization’s cybersecurity program and the desired state.
Fundamental Graph Neural Networks

As the most representative GNNs, convolutional graph neural networks generalize the operation of convolution from grid data to graph data, falling into two categories, spectral based (from the perspective of graph signal processing) and spatial based (from the perspective of information propagation). Since Graph Convolution Network (GCN) bridges the gap between them, spatial-based methods have developed rapidly recently due to their efficiency and flexibility. In this chapter, we first introduce GCN from the view of spectral, then we provide the flexible and efficient spatial-based variants of GCN, including GraphSAGE (with an inductive framework for unseen data), graph attention network (with the attention mechanism for aggregating neighbors), and heterogeneous graph attention network (with semantic-level attention for heterogeneous graphs).
Homogeneous Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used in various graph-related tasks and exhibited competitive performance. Existing GNNs follow a message-passing rule that aggregates the information of neighbors to update node representations. The design of message-passing function is the most fundamental part of GNNs. In this chapter, we will introduce the message-passing functions of three representative homogeneous GNNs. Further, we show that most existing homogeneous GNNs can be unified as a closed-form framework, which may help the researchers understand and interpret the principles behind message-passing mechanism.
Hyperbolic Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep representation learning methods for graphs. Most GNNs learn the node representations in Euclidean spaces. However, some studies find that compared with Euclidean geometry, hyperbolic geometry actually can provide more powerful ability to embed graphs with scale-free or hierarchical structure. As a consequence, some recent efforts begin to design GNNs in hyperbolic spaces. In this chapter, we will introduce three hyperbolic GNNs, which learn hyperbolic graph representations to get better performance.
Distilling Graph Neural Networks

As shown in the previous chapters, Graph Neural Networks (GNNs) have been successfully used on many data mining tasks. Previous works mostly focus on the improvement of a single GNN model, and another idea is to design a universal framework for enhancing an arbitrary GNN. Knowledge distillation provides a general solution for this purpose, which injects the knowledge of a teacher model into a student model by aligning their outputs. In this chapter, we will introduce three typical knowledge distillation frameworks for graph neural networks.
Platforms and Practice of Graph Neural Networks

Graph Neural Networks (GNNs) are widely used in structured data analysis. However, most typical deep learning platforms, such as PyTorch and TensorFlow, have inadequate support for the manipulation of sparse matrices, resulting in inefficiency in building GNNs. For these reasons, some platforms are designed for GNNs. In this chapter, we will give descriptions of some mature platforms of GNNs with their features, and present a multi-backend graph learning platform GammaGL, which can use the same code implementation to support TensorFlow, PyTorch, PaddlePaddle, and MindSpore. Finally, we will give a practical demonstration of GammaGL.
Changing “theatre-going” behaviours during COVID-19

The COVID-19 pandemic along with social distancing measures has been with human society for over two years now. The leisure activities that used to be considered normal become something unreachable; theatre is one of them. Since March 2020, theatre and performance festivals were cancelled globally, and theatre-going by the pre-COVID-19 definition was no longer a leisure option for the audience. Meanwhile, the digital engagement of “theatre-going” became the “new norm” during the pandemic. Various ways of “digital theatre-going” are provided for the audience all over the world. Who is the “theatre” audience during the pandemic? How do they participate and experience the performing arts at home during lockdowns? Have these experiences changed theatre-going behaviour? This chapter presents the results of the first two phrases a research project on the theatre-going experience during the pandemic: the quantitative findings from an audience survey and first-round in-depth interviews. This chapter is one of the first empirical research projects focusing on the theatre audience during the pandemic and reveals the ambiguous image of the audience profile behind the screens across the world.
Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption

Credit card fraud is a problem continuously faced by financial institutions and their customers, which is mitigated by fraud detection systems. However, these systems require the use of sensitive customer transaction data, which introduces both a lack of privacy for the customer and a data breach vulnerability to the card provider. This paper proposes a system for private fraud detection on encrypted transactions using homomorphic encryption. Two models, XGBoost and a feedforward classifier neural network, are trained as fraud detectors on plaintext data. They are then converted to models which use homomorphic encryption for private inference. Latency, storage, and detection results are discussed, along with use cases and feasibility of deployment. The XGBoost model has better performance, with an encrypted inference as low as 6ms, compared to 296ms for the neural network. However, the neural network implementation may still be preferred, as it is simpler to deploy securely. A codebase for the system is also provided, for simulation and further development.
Opportunities and Challenges for Blockchain Technology in Supply Chain Management: Reflection on Society 5.0

World is quickly making the transition toward digitalization, and corporates and individuals are leading efforts toward the change in traditional practices, and the ongoing situations are also encouraging the change, which is supposed to improve humanitarian aspects loud and clear. But the role and participation of governments and regulatory bodies in adopting digital technologies are not up to the mark, which is the reason for uncertainty around the humanitarian aspect of Supply Chain (SC). On the other hand, corporates are utilizing digital technologies in SC to maximum extent, to add value to their customers, establish trust by incorporating transparency and traceability in SC activities. For all digital technologies data is the key, so the process starts with Internet of Things (IoT) for data acquisition, Blockchain Technology for secure data storage and smart contracts usage, data science and machine learning to extract the insight and predict the future, and finally artificial intelligence to automate the knowledge for greater experience. Society 5.0 is aiming to establish economic balance by linking physical space with cyberspace, which will be an advancement for the current information age, in this initiative digital technologies play a critical role. Also, the world is not yet ready to take Society 5.0 direction, many developments are still left unattended in completing the information age itself, most importantly the participation from government and regulatory bodies. This chapter discusses opportunities and challenges for digital technologies in SCs, Society 5.0 outlook, the role of government in promoting humanitarian aspects of SC, and how technology laggards can adopt them so that the world can be prepared for Society 5.0.
Blockchain Application in Cryptocurrency

The development of information technology in supply chains has shown that this digital revolution can be a source of performance for enterprises and governments. Among these technologies is the blockchain which is a stored database with the credibility and trust of all actors in the supply chain. The actors of supply chain benefit from a secure and guaranteed information to carry out their transactions. The application of blockchains in cryptocurrency not only reduces the risk of information but also eliminates several processing and transaction fees. This allows countries with volatile currencies to have a more stable currency. The objective of this chapter is to conduct a literature review to synthesize the knowledge of using the blockchains in cryptocurrency. To carry out our study, in the first part, the link between blockchain and cryptocurrency is studied, and then we discussed the technological evolution of the currency due to blockchain.
Opportunities and Challenges for Society 5.0 in Procurement

The new technologies continue to evolve at a breakneck pace. If digital solutions and artificial intelligence will not reduce us all to inactivity, their impact on our lives will increase. Digital is already reshuffling the cards for the organization of our daily lives. The supply chain is a rapidly evolving field. We don’t manage a supply chain today as we did 10 or 20 years ago because working methods are changing very fast, so are customer expectations, as well as supplier requirements in an unstable geopolitical environment subject to strong pressures. And this, not to mention the rapid development of technologies that are reshaping the world of work. This is why supply chain professionals must constantly train themselves to work more efficiently to prepare for the future. And in terms of the future, trends are emerging and becoming more refined every year. This is therefore the perfect opportunity to carry out a prospective inventory of the supply chain. Fast progressions in digital transformation are reforming global supply chains and changing the present models of the procurement inside companies. Vastly, procurement involves the exchange of data and information between the actors of the supply chains. As the complexity increases, numerous inventions are developed to support the procurement in the form of automated solutions for different industries. These solutions enclose blockchain, data analytics, robotics, Internet of goods and smart contracts, etc. Therefore, this chapter aims to explore the possibilities of these technologies in intelligent procurement and the main obstacles against their adaption.
A QFD Approach to Design a Blockchain-Based Professional Development Module in Education System for Society 5.0

In the current scenario of the development of the industries, the growth of the service sectors is on the rise. A significant player in the service sector that has come to the fore in recent years is the education sector, with many private sector players becoming part of the system. The education sector has many stakeholders who are part of the entire system, yet one of the least discussed are the faculty members. Moreover, in terms of capacity building, there is limited research that has been done. If we model the education sector as a service supply chain, the suppliers of knowledge need to be developed over time for them to provide the output in terms of knowledge imparted to the students. Teachers are sometimes believed to be all-rounders in all dimensions, and it is forgotten that they also need to be given time to develop and build their capacities to impart knowledge to the students competently. This chapter focuses on the capacity building of stakeholders for the education service supply chain with the Society 5.0 dimension. Under this scenario, we would like to understand that given newer technologies are coming in the education sector, one such being the blockchains, these stakeholders need to keep themselves up-to-date. The role of heads of institutions will play a major role in overhauling the entire education system to make it blockchain compliant. Therefore, this paper aims to understand the needs of the Heads of Institutions from the teachers in the blockchain environment. The blockchains in the education systems can only be developed if the capacity building of the system stakeholders can be done. The Quality Function Deployment (QFD) methodology is used to understand what the teachers should be able to do in a blockchain-based education institution. It then links it up with how they will be able to achieve the “what’s” through various methods of training. QFD aims to let us know which of the “How’s” is important and needs to be concentrated upon to develop an efficient blockchain-based learning system.
Blockchain Technology: A Potential to Transform Healthcare

Blockchain technology is being used in various domains to enhance solutions faced by these domains. Healthcare has also emerged as one such field where this technology is being implemented to solve many day-to-day issues. Patient Data Management is one field where hospitals, laboratories, pharmacies, and physicians have started using a Blockchain network. Using Blockchain technology enhances the healthcare system’s efficiency in terms of performance, transparency, security, and consistency. This paper elaborates two models: one for implementing Blockchain technology for patient data management and another, for tracing the drugs given to a patient. Both the models help in increasing accuracy and correctness in treating a patient.