Reviewerly/exHarmony huggingface dataset:
Peace Education through Dark History for Reconciliation in East Asia :A Case Study of Japanese Elementary School’s Social Studies

이 연구의 목적은 과거사를 중심으로 역사 수업을 하는 초등학교 사회과교육의 특징과 의미를 분석하는 것이다. 일본 S 초등학교의 사회과교육 중 역사 수업은 조몬시대부터 시작하는 국가 교육과정 달리 1945년 8월 15일 패전 날부터 어두운 과거사를 중심으로 구 성되어 있다. 사례연구 방법으로 S 초등학교의 독특한 사례를 살펴보았다. 2019년부터 2020년까지 학교를 직접 방문하여 학교 교육과정, 수업 자료, 교사 수업 일지, 학생 수업 활동지, 소감문, 수업 보고서 등을 수집하였고, 현장의 경험과 관찰 내용을 현장 일지로 공책에 기록하거나 녹음하여 의미와 특징을 분석하였다. 분석 결과는 다음과 같이 정리할 수 있다. 첫째, S 초등학교는 동아시아 타자들과의 공생을 중요한 교육 목표로 여긴다. 이를 위해 국가 중심의 집단 기억을 해체하고, 평화 시민으로 성장할 수 있도록 교육과정을 구성한다. 일본의 역사를 비판적으로 바라보고 해석과 구축으로서의 역사를 이해할 수 있도록 안내하고 있다. 둘째, 어두운 과거사를 중심으로 일본의 역사를 재구성하여 평화교육을 실천하고자 하였다. 동아시아 공생을 위해서는 가해의 역사, 과거사를 제대로 마주하고 다시는 이와 같은 일이 일어나지 않도록 교육하는 것이 중요하다고 여긴다. 이를 위해 천황의 전쟁 책임, 난징대학살 등 국가 교육과정에서 다루지 않는 다양한 기억을 통해 집단 기억을 해체하고 재구성하는 역사교육을 통해 동아시아의 타자들과 정서적 유대감이 형성될 수 있도록 계획하였다. 이와 같은 교육을 받은 학생들은 다른 나라의 아픈 역사에 공감하고 과거사를 기억･ 전달하며 평화를 위하여 노력하는 것이 중요하다는 것을 깨닫는다. S 초등학교의 사례는 과거사를 제대로 마주하는 교육과정이 혐오를 넘어 동아시아 역사 화해와 평화로 이어지는 하나의 방법이 될 수 있다는 것을 보여준다.This study attempted to derive directions and implications for teaching dark history for peace education in East Asia through the case study of S Elementary School in Japan. The curriculum of S Elementary School, which actively conducts peace education focusing on teaching dark hisotry, was examined as a case study method. From 2019 to 2020, I visited the school and collected the school curriculum, class materials, teacher's class journals, student class activity sheets, and reflections. I analyzed the meaning and characteristics by reading and re-reading all the research data. Unlike Japan's national curriculum, S Elementary School's history curriculum starts from August 15, 1945. In addition, students try ‘doing history’ in which they collect data themselves, analyze historical data, and actively participate and explore. Research implications are as follows. First, the curriculum of S elementary school was oriented toward peace education in a way that dismantled the nation-centered collective memory. It guides students to critically look at Japanese history and understand history as an interpretation and construction so that they can grow into citizens of peace rather than citizens with patriotism. Second, it tried to practice peace education by reconstructing Japanese history centering on the dark past. Through history education that deconstruct and reconstructs collective memory through various memories that are not covered in the national curriculum, such as the Emperor's responsibility for war and the Nanjing Massacre, it is planned to form an emotional bond with others in East Asia. The case of S Elementary School showed that a curriculum that correctly faces the past can be a way to go beyond hatred and lead to historical reconciliation and peace in East Asia.
PERANCANGAN ANIMASI 3 DIMENSI SEBAGAI UPAYA PENGEDUKASIAN MASYARAKAT MENGENAI PENTINGNYA MENCINTAI RUPIAH SEBAGAI MATA UANG NEGARA INDONESIA

Rupiah is the official currency of the Indonesian people. Love for the rupiah is an important thing for the people of Indonesia, because the rupiah is a symbol of Indonesian ownership. However, over time, the love for the Rupiah has faded. Not caring for rupiah currency, not appreciating or even destroying it are some signs of depletion of love for rupiah currency. 3D animation can be an effective means of instilling certain values, especially among children. Through 3D animation, the love for the Rupiah currency can be instilled indirectly in the general public and children in particular. Based on this, this study will describe the design of 3D animation in an effort to educate the public about the importance of loving the Rupiah as the nation's currency. This study uses a qualitative method with a descriptive approach. The purpose of this research is to find out the ways and stages in designing audio-visual educational media in the form of 3D animation. The result of this research is the formation of a 3D animation titled “Because of Rupiah for Indonesia” which is then disseminated to the public through Instagram social media. The distribution of this 3D animation has a positive response from the public so that it can be said as a proper educational medium.
Crime Detection in Surveillance Videos

During the most recent couple of decades, surveillance cameras have been introduced in numerous areas. Examination of the data caught utilizing these cameras can assume powerful jobs in web based observing different occasion expectation and objective driven applications including inconsistencies and interruption identification. Wrongdoing has raised in our everyday lives, observation recordings are utilized to catch an assortment of true irregularities. Observing consequently a wide basic open zone is a test to be tended to. We can abuse ongoing PC vision calculations so as to supplant human work. The video observation framework is two-dimensional spatial data over a third measurement, that recognizes and predicts strange practices expecting to accomplish a shrewd reconnaissance idea. In this paper, we audit various methodologies used to learn inconsistencies by abusing both ordinary and atypical recordings. To abstain from clarifying the peculiar fragments or clasps in preparing recordings, which is very tedious, the learning calculation adapts irregularity through the different examples of positioning structures by utilizing the feebly marked preparing recordings.
Changes in Religious Subjects and the Direction of Textbook Policy according to the Revision of The 2022 Curriculum

[연구 목적] 본 연구는 2022 개정 교육과정에서 종교 교과가 ‘종교학’에서 ‘삶과 종교’로 변화한 것의 의미를 살펴보고, 그에 적합한 교과서 정책의 방향을 제시하는데 그 목적이 있다. [연구 내용] 2022 교육과정이 ‘종교학’에서 ‘삶과 종교’로 개정된 것은 단지 명칭의 변화가 아닌 정체성의 변화이며, 이전 교육과정이 드러낸 한계에 대한 반성임을 강조하였다. 선행연구들을 토대로, 새 교육과정에서는 ‘특수성에 기반한 종교교육’과 ‘보편성에 기반한 종교교육’의 조화가 필요함을 설명했다. 그러면서 ‘국․공립학교에서의 종교교육’과 ‘종교계 사립학교에서의 종교교육’을 구분하고, 사립학교도 학교의 유형에 따라 각각 차이를 두고 다양한 형태의 종교교육이 가능하도록 할 필요가 있음을 제시하였다. 교과서 정책과 관련해서는, 지난 교육과정에서 종교학 1종만이 교과서로 인정되면서, 종교 교육과정이 획일화되어 버리는 문제가 발생했음을 지적하면서, 다양한 교과서들, 특히 개별 종교계에서 개발한 교과서들의 인정이 필요함을 강조하였다. 2022 개정 교육과정은 2025 고교학점제를 중심으로 한 교육과정 개정이었기 때문에, 고교학점제를 고려한 종교교육 고민이 필요함도 강조했다. 고시된 교과목인 ‘삶과 종교’에 따른 교과서는 물론이고, 고시 외 신설과목의 개설과 그에 따른 교과서 인정 시도의 2가지 전략이 필요함을 강조했다. 종교계 사학들이 종교교육 특성화를 위한 신설 교과 개설을 통해, ‘종교교육 중점학교’로서의 역할을 감당할 수 있도록 할 필요가 있음을 제안하였다. [결론] 본 연구의 결론은 다음과 같다. 새 교육과정과 교과서 정책에서는 특수성과 보편성의 조화를 이루는 종교교육 인정이 필요하며, 국․공립학교와 종교계 사립학교의 차별된 종교교육의 정책이 필요하다. 개별 종교계에서 개발한 교과서를 포함한 다양한 형태의 교과서 인정이 필요하며, 고교학점제를 고려한 종교교육 체제 고민이 필요하다.[Objective] The purpose of this study is to examine the meaning of the change in the religious subject in the 2022 revised curriculum, and to suggest the direction of textbook policy suitable for it. [Contents] This study revealed that the attempt to replace ‘religious education’ with ‘liberal arts education’ shrank and distorted religious education. The 2022 curriculum revision means reflection on the limitations of the previous curriculum. However, if the textbook accreditation review process is based on the paradigm of the previous curriculum, religious textbooks developed by each religion will still be rejected. [Conclusions] The conclusions of this study are as follows. It is necessary to recognize religious education in harmony with specificity and universality, and a policy of differentiated religious education between public schools and religious private schools is needed. Recognition of various types of textbooks, including textbooks developed by each religion, is required, and it is necessary to consider the religious education system considering the high school credit system.
A Figure-Ground Discrimination Algorithm Inspired by Border-Ownership Selective Cells

We developed a figure-ground discrimination algorithm that is suitable for robot vision.The algorithm is inspired by the response properties of border-ownership (BO)-selective cells, which possibly support figure-ground discrimination in the visual nervous system.The output of our algorithm is affected by spatial parameters such as the characteristics of the spatial filter used for edge enhancement and the interval of model BO-selective cells.Therefore, we investigated the relationship between the algorithm output with various spatial parameters and object sizes.
Collaborative Content-Dependent Modeling: A Return to the Roots of Salient Object Detection

Salient object detection (SOD) aims to identify the most visually distinctive object(s) from each given image. Most recent progresses focus on either adding elaborative connections among different convolution blocks or introducing boundary-aware supervision to help achieve better segmentation, which is actually moving away from the essence of SOD, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., distinctiveness/salience. This paper goes back to the roots of SOD and investigates the principles of how to identify distinctive object(s) in a more effective and efficient way. Intuitively, the salience of one object should largely depend on its global context within the input image. Based on this, we devise a clean yet effective architecture for SOD, named Collaborative Content-Dependent Networks (CCD-Net). In detail, we propose a collaborative content-dependent head whose parameters are conditioned on the input image’s global context information. Within the content-dependent head, a hand-crafted multi-scale (HMS) module and a self-induced (SI) module are carefully designed to collaboratively generate content-aware convolution kernels for prediction. Benefited from the content-dependent head, CCD-Net is capable of leveraging global context to detect distinctive object(s) while keeping a simple encoder-decoder design. Extensive experimental results demonstrate that our CCD-Net achieves state-of-the-art results on various benchmarks. Our architecture is simple and intuitive compared to previous solutions, resulting in competitive characteristics with respect to model complexity, operating efficiency, and segmentation accuracy.
Constructing Structured SSA from FJ

We propose a novel approach of constructing structured SSA forms. Specifically, our declarative algorithm converts a Featherweight Java (FJ) program into its structured SSA form in a single pass. We prove that the proposed algorithm produces valid SSA forms which are semantically consistent with respect to the original source programs. We verify the resulting SSA forms are minimal. We demonstrate that structured SSA form can serve as a unified intermediate representation for both compiler optimization and program verification pipelines. We implemented the algorithm as a library.
Error-Correcting Codes on Projective Bundles over Deligne–Lusztig Varieties

The aim of this article is to give lower bounds on the parameters of algebraic geometric error-correcting codes constructed from projective bundles over Deligne–Lusztig surfaces. The methods based on an intensive use of the intersection theory allow us to extend the codes previously constructed from higher-dimensional varieties, as well as those coming from curves. General bounds are obtained for the case of projective bundles of rank 2 over standard Deligne–Lusztig surfaces, and some explicit examples coming from surfaces of type A2 and 2A4 are given.
Critical analysis on the function and limitations of the ‘standardization’-based school system in the era of ‘personalization’

최근 인공지능을 비롯한 첨단 기술의 눈부신 발달에 힘입어 사회 각 분야에서 개인의 관심, 특성, 요구 등에 맞춘 최적화된 서비스를 제공하기 위한 ‘개인화’ 노력이 고도화되고 있다. 아울러 다른 한편에서는 제품이나 서비스를 일정한 기준에 따라 통일하고 규격화하는 ‘표준화’ 전략도 시행되고 있다. 개인화와 표준화는 서로 상반되는 목적과 논리를 통해 여러 제도와 영역의 운영과 발달에 기여하고 있다. 따라서 각 분야별로 어떠한 접근이 중심이 되어야 하는지, 그리고 그 근거는 무엇인지에 대한 진지하고 면밀한 논의가 요청된다. 이러한 맥락에서 본 연구는 학교 제도가 줄곧 표준화 전략을 고수해오고 있다는 점에 대해 문제를 제기하고자 하였다. 이를 위해 그 기반이 되어 온 표준화 전략과 Taylor의 과학적 관리운동의 특성을 살펴본 후, 학교의 교육과정, 교수방법, 평가방법 등을 중심으로 표준화 전략의 실제와 한계를 비판적으로 검토하였다. 또한 개인화 시대의 도래가 던지는 메시지를 내재적 동기와 자기 결정권의 가치, 차이와 다양성의 의의, 그리고 측정치로 감환될 수 없는 전인적 자아의 중요성 등을 중심으로 분석, 제시하였다. 논의를 통해, 학교 체제를 작동시키고 있는 표준화-측정-비교-경쟁 등으로 이루어진 굳건한 폐쇄적 연결 고리는 교육이 아닌, 교육 외적 기능을 위해 봉사하는 논리라는 점을 확인하고, 이를 극복할 수 있는 새로운 관점의 필요성이 제시되었다.Based on the recent remarkable development of advanced technologies including artificial intelligence, the era of ‘personalization' has arrived to provide optimized services tailored to individual interests, characteristics, and needs in various fields of society. On the other hand, a ‘standardization’ strategy is being implemented to unify products or services according to certain standards. Personalization and standardization do not necessarily mean one is right and the other is wrong, but they contribute to the operation and development of various systems and areas through respective purposes and logic. Therefore, a serious and thorough discussion on which approach should be followed in respective areas is required. In this context, this study aims to raise a question about the fact that the school system has been sticking to a standardization strategy since its birth. For this purpose, after examining the standardization strategy that has been the basis of Taylor's scientific management movement, the reality and limitations of the standardization strategy were critically reviewed, focusing on school curriculum, teaching methods, and evaluation methods. In addition, the message of the advent of the era of personalization was analyzed, focusing on the value of intrinsic motivation and self-determination, the significance of uniqueness and diversity, and the importance of the whole person, which cannot be reduced into the number. Through the discussion, it is confirmed that the strong closed link consisting of standardization, measurement, comparison, and competition that operates the school system serves not the value of education but the political and sociological function, and the need for a new perspective is suggested.
Enhancing Learning Motivation and Thematic Learning Outcomes of Fifth-Grade Students through the Quantum Teaching Model

Based on a preliminary study conducted in class V of UPT SDN 04 Siparayo, it was observed that teachers were not optimizing the use of learning models, which resulted in a decrease in students' learning motivation and low learning outcomes. Teachers tended to still use conventional learning models. This study aims to determine the effect of using the Quantum Teaching model on students' motivation and learning outcomes. The steps of the Quantum Teaching model are: (1) grow, (2) experience, (3) name, (4) demonstrate, (5) repeat, and (6) celebrate. Students experience enjoyable learning and find it easier to understand the subject matter. This study is an experimental study with a quasi-experiment design in the form of a pretest-Post-test non-equivalent group design. Data were obtained from quantitative research results. The results showed that the level of learning motivation of students taught with the Quantum Teaching model was higher compared to students taught with conventional learning. Furthermore, for learning outcomes, students taught with Quantum Teaching model obtained higher average scores compared to students taught with conventional learning. To see the effect of the Quantum Teaching model on students' motivation and learning outcomes, a One-Way ANOVA test was conducted, which obtained a higher F-value compared to the F-table, is an F value of 4.098 is greater than the tables F value of 3.555 at a significance level of 0.05 This indicates that there is an influence of using the Quantum Teaching model on students' learning motivation and outcomes. The application of the Quantum Teaching model in learning has a positive impact on students' motivation and learning outcomes. When learning using the Quantum Teaching model, students are seen to be motivated to participate in learning and their learning outcomes also improve. Therefore, the use of the Quantum Teaching model has a significant effect on students' motivation and learning outcomes.
Multi-modal Content Summarization

Over the past few years, content has been generated at unimaginable rates in multiple domains such as news, education, entertainment, etc. This content takes on innumerable forms such as videos, audio clips, text based articles, etc. and becomes an important source of information to people in their day to day lives. However, consumers cannot cope with the amount of information being produced around them and are always seeking quicker and more efficient ways of consuming and understanding said content. In this paper, we present our Multi-Modal Content Summarization tool which allows you to generate concise and relevant summaries of long video lectures or information heavy content by providing visual and text summaries. While work has been done along the lines of video summarization and text summarization separately, we have been able to produce a combined summary where the visual and the audio content has been synchronized to provide the end-user a holistic summary. In status quo, work revolving around multi-modal summarization targets relatively shorter videos, whereas we attempt to summarize longer videos which are more information heavy since summaries are most relevant in such cases. We adopted an unsupervised learning approach combined with feature extraction from pre-trained models such as BERT, using which we extracted text feature vectors and ResNeXt to extract features from video frames using a 3D CNN architecture. In terms of evaluation, we performed uni-modal evaluation for both, video and text. We observed the highest ROUGE score of 37.18 for text summarization after using K-Means, Latent Semantic Analysis (LSA), and necessary context sentences. For video-summarization, we used ResNext-101 to generate feature vectors, followed by clustering on top of it. We recorded an F1 score of 69.19 after experimenting and fine-tuning.
Breaking the Performance Gap of Fully and Semi-Supervised Learning in Electromagnetic Signature Recognition

Intelligent electromagnetic signature recognition is one of the key technologies in Internet-of-Things (IoT) device connection, which can improve system security and speed up the authentication process. In practical scenarios, as the number of IoT devices increases, electromagnetic features such as fingerprint and modulation signals also increase substantially. However, since intelligent recognition technology, such as Automatic Modulation Classification (AMC), requires a large amount of labeled data to train the neural network classifier, it is challenging to collect so much labeled data. To address the performance degradation challenges with small training data, we propose an efficient semi-supervised electromagnetic recognition framework to break the performance gap with the fully supervised learning scheme. This framework can fully use the unlabeled electromagnetic data collected during the authentication process for self-training to improve the classifier’s performance. According to the idea of consistency regularization, we design a signal augmentation method and propose an ensemble pseudo-label design algorithm to improve confidence. Moreover, we perform a convex combination of electromagnetic features to smooth the model decision boundary while generalizing to unknown data distribution regions. Experimental results on the modulated data demonstrate the performance superiority of the proposed algorithm, i.e., use less than 5% of data with no more than 10% performance drop.
Using a Digital Educational Environment to Monitor Student Learning Results

The use of digital educational environments contributes to improving the quality of education, forms information and communication competence of students and teachers, optimizes the process of control and evaluation of the results obtained, makes it automated and objective. Since testing is one of the most effective ways to check the level of competence of students, the problem of creating a high-quality digital test is still a burning issue of e-learning. One of the main methodological tasks of the teacher is to create a valid, objective, representative and reliable digital test that reflects the real knowledge of students. This article deals with digital testing in the digital educational environment. The quality of the test, mainly determined by the quality of the test questions. Test questions have to meet the requirements for their level of difficulty. In this paper, an analytical expression is used to determine the degree of difficulty of test questions. Using this expression allows the teacher to analyze the questions, identify and eliminate their imperfections in order to create an optimal digital test. #CSOC1120.
Human Behaviour Recognition using Fuzzy System in Videos

Human behavior can be detected and analyzed using video sequence is a latest research topic in computer vision &amp; machine learning. Human behavior is used as a basis for many modern applications, such as video surveillance, content-based information retrieval from videos etc. HBA (Human behaviour analysis) is tricky to design and develop due to uncertainty and ambiguity involved in people’s daily activities. To address this gap, we propose hierarchical structure combining TDNN, tracking algorithms, and fuzzy systems. As a result, HBA system performance will be improved in terms of robustness, effectiveness and scalability.
Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements

The key issue of multiple extended target tracking is to differentiate the origins of the measurements. The association of measurements with the possible origins within the target’s extent is difficult, especially for occlusions or detection blind zones, which cause intermittent measurements. To solve this problem, a hierarchical network-based tracklet data association algorithm (ET-HT) is proposed. At the low association level, a min-cost network flow model based on the divided measurement sets is built to extract the possible tracklets. At the high association level, these tracklets are further associated with the final trajectories. The association is formulated as an integral programming problem for finding the maximum a posterior probability in the network flow model based on the tracklets. Moreover, the state of the extended target is calculated using the in-coordinate interval Kalman smoother. Simulation and experimental results show the superiority of the proposed ET-HT algorithm over JPDA- and RFS-based methods when measurements are intermittently unavailable.
From art and circuit design to geometry and combinatorics

These notes provide a detailed insight on the interplay between crossing numbers of graphs and random geodesic drawings, and try to explain a relationship with the main fundamental open questions about crossing numbers of graphs. A very general class of geodesic drawings on the sphere attaining the Hill bound is presented.
sPellorama: An Immersive Prototyping Tool using Generative Panorama and Voice-to-Prompts

We proposed sPellorama, an immersive tool that enables Virtual Reality (VR) content creators to quickly prototype scenes based on verbal input. The system first converts voice input to text, then utilizes a text-guided panorama generation model to produce the described scene. The panorama is later applied to Skybox in Unity. Previously generated panorama will be preserved in the photosphere and ready to be viewed. The pilot study shows that our tool can enhance the process of discussion and prototyping for VR content creators.
Combinatorial Generation of Planar Sets

We introduce a multi-dimensional generalization of the Euclidean algorithm and show how it is related to digital geometry and particularly to the generation and recognition of digital planes. We show how to associate with the steps of the algorithm geometrical extensions of substitutions, i.e., rules that replace faces by unions of faces, to build finite sets called patterns. We examine several of their combinatorial, geometrical and topological properties. This work is a first step toward the incremental computation of patterns that locally fit a digital surface for the accurate approximation of tangent planes.