Reviewerly/exHarmony huggingface dataset:
Prospects and Tasks for the Introduction of the 2022 Revised National Curriculum for the Subject of &lt;Religion in Human Life&gt;: Focusing on Teachers' Autonomy and Expertise

[연구 목적] 본 연구의 목적은 2022 개정 교육과정 개편을 통해 <삶과 종교>로 교과명이 변경된 종교교과에 대한 이해를 도모하기 위하여 학교에서의 종교교육의 주요 주체인 교사를 중심으로 변화를 전망하고 향후 과제를 제시하는 데 있다. [연구 내용] 본 연구에서는 종교교과 교사 양성의 특수성을 바탕으로 발현되는 종립학교의 문화를 고찰한다. 더불어 2022 개정 교육과정에서 제시하는 자율권 확대가 현장에 미치는 영향들을 예상하고, 종교교과 교사의 전문성과 어떠한 관계가 있는지 정리한다. [결론] 본 연구의 결론은 다음과 같다. 학교에서의 종교교육은 교육과정, 교과서, 교원 양성등 다양한 영역에서 제도화와 통일화가 지속되었다. 교육법에 의거한 종교교육의 제도화는 어느 정도 보편성을 담보하였지만, 개별 종교교사에 따른 다양한 특수성은 여전히 존재한다. 또한 반복되는 교육과정 개정 과정에서 교과서 개발을 포함한 실질적인 변화 적용의 주체는 종교교사였으며, 그 전문성에 대해서는 성실히 기록되고 연구되지 못 하였다. 지속가능한 학교에서의 종교교육을 위해서는 지나온 역사와 현재의 실질적 현황을 종합할 수 있는 연구가 진행되어야 할 것이다. 동시에 초종교 종교교사 협의회가 구성되어 사회적으로 의미 있는 공동의 활동을 통해 학교종교교육이 특정 종교의 신앙교육이라는 오해와 편견을 극복할 것을 제안하였다.[Objective] The purpose of this study is to promote understanding of the subject of religion, which was renamed from <Religious Studies> to <Religion in Human Life> through the 2022 revised National curriculum reorganization, to forecast changes centered on teachers. [Contents] The study examined the culture of religious teachers. Furthermore, it predicted how the expansion of autonomy presented in the 2022 revised curriculum would impact the field, and summarized its relevance to the expertise of religious subject teachers. [Conclusions] Religious education in schools has been continually institutionalized and standardized across various aspects. The institutionalization, guided by the Education Act, ensures a certain degree of universality, but distinct variations are still attributable to individuals. Moreover, throughout the iterative process of curriculum revision, religious teachers have implemented practical changes, including textbook development, without their expertise being adequately documented or studied. Comprehensive research at the Ministry of Education level is required to establish sustainable religious education in schools. Also, an inter-religious council of religious teachers needs to be established to propose initiatives aimed at overcoming misunderstandings and prejudices surrounding school religious education.
Secure and Resilient Authenticated Encryption Approach Based on Chaotic Neural Networks and Duplex Construction

This chapter delves deep into the intricate interplay of procedures that underline the foundations of Authenticated Encryption (AE) and its significance in preserving the confidentiality and authenticity of our digital communications. As the digital age progresses, telecommunication systems have shifted towards digital paradigms, driven not only by the affordability and accessibility of digital components but also by the inherent benefits they bring. Herein, we introduce a distinct approach from the Standard Duplex Construction (SDC) known as the Modified Duplex Construction (MDC). The MDC incorporates two pivotal phases: the initialization phase and the duplexing phase, each encompassing a Chaotic Neural Network Revised (CNNR) defined by a singular-layered neural structure enriched with non-linear functionalities. The chapter further discusses the implementation of MDC in two specific widths of 512 and 1024 bits. A rigorous evaluation of this construction against various cryptanalytic threats showcases its resilience and robustness. In this discourse, readers will encounter the development, realization, and analysis of a novel Authenticated Encryption with Associated Data Scheme (AEADS), conceived from the chaotic realms of neural networks. The chapter explicates the encryption and decryption processes of AEADS, emphasizing the crucial role of variables such as IV, K, AD, and M in encryption, and C and T in decryption. The reliability of the decryption is contingent on the alignment of computed and received tags, dictating either the decryption of the original message or the generation of an error. The decryption intricacies, encompassing variables like C, T, IV, K, and AD, are also elucidated. Two distinct processes have been instituted for message lengths spanning 64 and 128 bytes, providing a comprehensive view of the scheme’s versatility.
An Analysis of the 2022 Revised Curriculum from the perspective of school peace and unification education

본 연구는 학교 평화·통일교육의 관점에서 2022 개정 교육과정을 분석하고 학교 통일교육의 개선 방향을 모색하는 것을 목적으로 한다. 이전 정부의 ‘평화·통일교육’ 에서 현 정부의 ‘자유민주주의 통일교육’으로 정부 정책 변화에 따라 강조점이 다소 달라졌다고 하더라도, ‘평화’는 학교 통일교육에서 지속적으로 강조되어야 할 보편적 가치임에 틀림없다. 학교 평화·통일교육은 평화와 통일 중 어느 하나를 위해 다른 하나를 포기하지도, 통일교육을 평화교육으로 대체하지도 않으면서도 동시에 민주시민교육과의 연계라는 더 광범위한 교육 지평 속에서, 평화라는 보편 가치와 통일이라는 특수 가치를 분리 불가능한 유기적 관계 속에서 바라보며 평화의 가치를 지향점 내지 준거점으로 삼는 교육이라고 할 수 있다. 본 연구는 학계와 학교 현장의 진지한 연구와 성찰을 통해 함께 만들어 온 학교 평화·통일교육의 비전 자체는 학교 통일교육 내실화를 위해 여전히 유효한 지침을 제공할 수 있다는 합당한 가정 하에서, 학교 평화·통일교육의 관점에서 2022 개정 교육과정 총론을 분석하였다. 그리고 이를 바탕으로 향후 교육과정 적용 및 개정 시에 참고 자료가 될 수 있도록 학교 평화·통일교육 내실화를 위한 교육과정 개선 방향을 제시하였다.The purpose of this study is to analyze the 2022 revised curriculum from the perspective of school peace and unification education and to seek directions for improving school unification education. Even if the emphasis has changed somewhat from the previous government's “peace· unification education” to the current government's “liberal democratic unification education”, peace must be a universal value to be continuously emphasized in school unification education. School peace and unification education does not give up one of the other for peace or unification, nor replaces unification education with peace education, but at the same time sees the universal value of peace and the special value of unification in an inseparable organic relationship. This study analyzed the general outline of the 2022 revised curriculum from the perspective of school peace and unification education under the reasonable assumption that the vision of school peace and unification education, which has been created together through serious research and reflection. In addition, based on this, the directions of curriculum improvement for the internalization of school peace and unification education were suggested so that it could be a reference material when applying and revising the curriculum in the future.
The regulation of cloud computing: why the European Union failed to get it right

Cloud computing brings important benefits and it is expected to play a key role in facilitating the uptake of emerging technologies and applications, including artificial intelligence, blockchain, and high-performance computing. Despite its potential to deliver cost and time-efficient services, the majority of businesses in the EU have still not implemented cloud computing. This illustrates the need for a more widespread adoption of the technology. Yet, recent regulatory initiatives may obstruct the uptake of cloud services. This is arguably because such initiatives do not reflect a proper understanding of the market, which our paper intends to provide. To that end, the paper examines what cloud computing is and how it works. It subsequently discusses the EU’s attempts to regulate cloud computing, including the Digital Markets Act, the Digital Services Act, and the Data Act proposal. Our analysis demonstrates that the logic of these instruments and the obligations they establish do not fit the characteristics and workings of cloud computing. The paper concludes by noting that future regulation must mirror the specificities of the cloud, which has a value chain and traits that differ significantly from other digital services, most notably online platforms.
Reconstructing elections in a digital world

The nature of elections has been transformed by the rise of digital technologies in the last few years, in large part due to the rise of social media platforms and their impact on election campaigning, dissemination of information and opinion formation. Furthermore, digital technologies have been employed in the administration of elections, and are being adopted by electoral management bodies. This article seeks to understand these transformations in light of ongoing debates regarding the regulation of elections and digital technologies by positing an approach focused on international human rights frameworks.
Impact of Communication on Employee Performance of Pharmaceutical Companies in Nepal

This study aimed to examine the impact of communication on the employee performance of Pharmaceutical Companies in Nepal. The variables included in the study are independent variables of communication, i.e., oral communication, written communication &amp; non-verbal communication, with employee performance as a dependent variable. A descriptive and analytical research design using a questionnaire method on a 5-point Likert scale was used to collect and analyze the data. The study was conducted with a sample size of 115 employees of pharmaceutical companies in Nepal. The data were analyzed using descriptive statistics, correlation, multiple regression analysis, and t-test using SPSS software. The finding of this study provides empirical evidence to the management to ensure effective oral, written &amp; non-verbal communication in the pharmaceutical companies in Nepal. The results of the study showed that only oral communication significantly impacts employee performance, and both written and non-verbal communication have an insignificant impact on employee performance of pharmaceutical companies of Nepal.
ExistenceMap-PointPillars: A Multifusion Network for Robust 3D Object Detection with Object Existence Probability Map

Recognition of surrounding objects is crucial for ensuring the safety of automated driving systems. In the realm of 3D object recognition through deep learning, several methods incorporate the fusion of Light Detection and Ranging (LiDAR) and camera data. The effectiveness of the LiDAR–camera fusion approach is widely acknowledged due to its ability to provide a richer source of information for object detection compared to methods that rely solely on individual sensors. Within the framework of the LiDAR–camera multistage fusion method, challenges arise in maintaining stable object recognition, especially under adverse conditions where object detection in camera images becomes challenging, such as during night-time or in rainy weather. In this research paper, we introduce "ExistenceMap-PointPillars", a novel and effective approach for 3D object detection that leverages information from multiple sensors. This approach involves a straightforward modification of the LiDAR-based 3D object detection network. The core concept of ExistenceMap-PointPillars revolves around the integration of pseudo 2D maps, which depict the estimated object existence regions derived from the fused sensor data in a probabilistic manner. These maps are then incorporated into a pseudo image generated from a 3D point cloud. Our experimental results, based on our proprietary dataset, demonstrate the substantial improvements achieved by ExistenceMap-PointPillars. Specifically, it enhances the mean Average Precision (mAP) by a noteworthy +4.19% compared to the conventional PointPillars method. Additionally, we conducted an evaluation of the network’s response using Grad-CAM in conjunction with ExistenceMap-PointPillars, which exhibited a heightened focus on the existence regions of objects within the pseudo 2D map. This focus resulted in a reduction in the number of false positives. In summary, our research presents ExistenceMap-PointPillars as a valuable advancement in the field of 3D object detection, offering improved performance and robustness, especially in challenging environmental conditions.
Teacher Perception on Reading Material in Blended Learning At Senior High School

The aim of this research is to observe teacher perceptions on reading material using blended learning at SMAN 4 South Solok. This research conducted using descriptive through a qualitative approach. The participant of this study is one of English teacher at SMAN. The sample was selected purposively because the teacher has implemented reading material in blended learning at her class. The data collect through interview with an audio recorder. The results of this research showed that, the perceptions of English teachers at SMAN 4 South Solok 1) Reading materials developed in blended learning to make students master their learning because the material presented is grouped in detail. 2) Reading materials developed in blended learning was very interesting for students. 3) Reading materials developed in blended learning was according to students' ability levels because students learn more about reading. 4) Reading material developed was easy for students to understand because the material presented was not floating and was more specific. 5) Reading material in blended learning made it easier for teacher and students to interact because there are questions and answers when learning took place. 6) In learning offline, there was not enough time for teacher and students to carry out the learning process, but with online students can study at home and do their exercise so as to achieve a good learning process.
Research progress and applications of image defogging algorithms

Images taken under hazy weather conditions suffer from problems such as blurring, low contrast, and low saturation due to the scattering of atmospheric light by aerosol particles in the air, which affects the performance and judgment of image analysis equipment. With the rapid development of image processing technology and computer vision technology, researchers have proposed a large number of targeted haze removal algorithms to improve the quality of images taken under hazy weather conditions. According to the haze removal principle, mainstream haze removal algorithms can be classified into three categories: image enhancement-based, physics model-based, and neural network-based. This paper introduces and explores classic haze removal algorithms from the perspectives of principles, development, advantages, and disadvantages, and outlines the prospects for the future development and application direction of haze removal algorithms.
Safe Vote–Fraudulent Vote Prevention System

The vote is the most crucial part of a country’s democracy, allowing citizens to pick their preferred candidate to lead the country. Intervening in illicit practices will lead to the country’s demise. There are several fraud practices like rigging and booth capturing happening in India. The government has adopted many measures to avoid crimes during voting. But the verification process still remains difficult, time-consuming, and effort-requiring. The proposed system helps in resolving difficulties and crimes involved in the verification process. The existing voter ID cards would be swapped out for new voter ID cards, which have radio frequency identification (RFID) built in. RFID technology utilizes electromagnetic fields to automatically identify and track tags attached to objects, allowing for the efficient and accurate collection of data. In the context of voting, RFID tags can be embedded in voter cards, which are used to verify the identity of voters and ensure that each person casts only one vote. Each RFID card will have a unique id. A database containing the details of the voter and fingerprint is collected prior and stored. The RFID card is used as a primary verification process. The unique ID will be read by the RFID reader in the EVM machine. The fingerprint module is used as a secondary verification. This system is used to verify the identity of the voter using biometrics and the number of times the person has appeared. The system can be further secured in future by implementing facial recognition.
Echo state network for features extraction and segmentation of tomography images

The paper proposes a novel approach for gray scale images segmentation. It is based on multiple features extraction from a single feature per image pixel, namely its intensity value, via a recurrent neural network from the reservoir computing family - Echo state network. The preliminary tests on the benchmark gray scale image Lena demonstrated that the newly extracted features - reservoir equilibrium states - reveal hidden image characteristics. In present work the developed approach was applied to a real life task for segmentation of a 3D tomography image of a of bone whose aim was to explore the object?s internal structure. The achieved results demonstrated the novel approach allows for clearer revealing the details of the bone internal structure thus supporting further tomography image analyses.
Trusted authentication method for privacy information

Aiming at the data security management and control requirements in big data sharing and utilization scenarios, a trusted computing based inter agent interaction architecture is designed, which utilizes integrity metrics to achieve trust chain transfer within and between nodes, ensuring the security and reliability of the nodes themselves and their interactions. Aiming at the privacy protection problem in multi agent sharing scenarios, a remote authentication scheme based on Merkle tree is proposed. The Merkle tree is constructed based on information fragmentation and sequence as the basis for remote authentication, which effectively protects the privacy of information without tampering with its structure and content during transmission.
ELECTRIC PROSPECTING FOR GEOLOGICAL AND ECOLOGICAL DIAGNSTICS OF REGIONS HAVING NUCLEAR FACILITIES

The effectiveness of field observation data processing based on differential phase parameter using the results of spectral measurements of induced polarization using VPF-8k instrument to minimize the inductive noise effects on electric prospecting data using the method of dipole sounding is shown.
Award

Learned PublishingVolume 36, Issue 4 p. 726-727 AwardFree to Read Award First published: 09 October 2023 https://doi.org/10.1002/leap.1585AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume36, Issue4October 2023Pages 726-727 RelatedInformation
Adiland Property Website Design and KPR Management Using the Requirement Prototyping Method

The property business is always in great demand and is discussed by every class of society, that's what makes all business people vying to improve their property business, one of which is by utilizing technology so that it can make it easier for businesses to develop where companies can use technology as a tool. when making a property reservation. In this research, a website-based information system was created that helps Adiland Property Company in improving services in its business. The system created consists of a property booking system and mortgage data processing. The existence of a mortgage data processing system can make it easier for companies to manage mortgage data by customers in an integrated manner, besides that with an online ordering system it can also make it easier for customers to make orders where there are several choices of online payment methods. In making the system, the software development method was used using the requirements prototyping method, while testing this application, two methods have been used, namely blackbox testing. In blackbox testing, testing is carried out by trying all the existing inputs on the system whether they are as expected by giving a questionnaire to each existing user, namely the customer, property agent admin, in-house mortgage manager. The results obtained are a website that can be used by Adiland Property to reach a wider market and ease in making property purchase transactions.
A hydraulic energy ﬂow within the moving Earth

Abstract We consider the Earth moving through empty space at 30 km/s (in the sun’s frame of reference). Associated with this motion is a convective flow of kinetic and internal energy. Since there is high pressure inside the earth, and since the earth is moving, there is yet another “hydraulic” energy flow. This latter is what this article is about. Although this energy flow is huge, it is not addressed in the textbooks. The reason is that for the explanation one needs a concept which is not introduced in traditional presentations of classical gravitation: the gravitomagnetic field. The corresponding theory, gravitoelectromagnetism, was formulated in 1893 by Heaviside in analogy to Maxwell's theory of electromagnetism.&amp;#xD;&amp;#xD;We discuss the question of what are the sources and sinks of this hydraulic, non-convective energy flow. To answer the question, we need to study the energy flow density distribution within the gravitational field. In doing so, we will make some interesting observations. The energy flow within the field is twice as large as it should be to transfer the field energy from one side of the Earth to the other. The excess flow goes back through the matter of the Earth.&amp;#xD;&amp;#xD;Since our readers may not be familiar with Heaviside’s theory, we first treat the electromagnetic analogue of our problem and then translate the results to the gravitational situation.
REAL TIME ANOMALY DETECTION IN ELECTRONIC HEALTH RECORDS

Electronic Health Records (EHRs) are invaluable in healthcare, offering a wealth of patient data. Timely anomaly detection in EHRs is critical for identifying emerging health risks, enabling early intervention, and enhancing patient care. The project addresses the need for effective anomaly detection methods in the context of coronary heart diseases using machine learning algorithms. The aim of this project is to evaluate and compare the performance of several machine learning algorithms, including Random Forest, Decision Tree, Naive Bayes, Artificial Neural Networks (ANN). Analyzing the best algorithm based on their suitability, accuracy, and adaptability to evolving data patterns. The project meticulously preprocesses EHR data, identifies relevant features, and the algorithm is used to improve anomaly detection. Each algorithm is trained and optimized, considering performance metrics such as precision, recall, and F1-score. Real-time inference capabilities are assessed, emphasizing the need for models to adapt to changing data patterns. Continuous monitoring and model updating are emphasized to minimize false alarms. The integration of an alerting mechanism facilitates timely healthcare professional intervention upon anomaly detection. In the comparative analysis, Random Forest and ANN demonstrate superior performance, capturing intricate data relationships effectively. Decision Tree and Naive Bayes show moderate performance. The project underscores the significance of real-time anomaly detection in EHRs for coronary heart diseases. It highlights the strengths and weaknesses of various machine learning algorithms in this context. Random Forest and ANN emerge as promising choices, balancing accuracy and interpretability. Ultimately, this research contributes to the advancement of healthcare systems by enhancing their ability to proactively detect anomalies, thereby improving patient outcomes. To address this, data augmentation techniques, such as random flip, random rotation, random translation, random zoom, random contrast, random hue, random brightness, and random saturation, were employed to reduce perspective variability. The study evaluated the performance of different design strategies to identify the approach that achieves the highest accuracy in monument recognition. Keyword : Artificial Neural Networks (ANN), Decision Tree, Naive Bayes, Random Forest.