Reviewerly/exHarmony huggingface dataset:
Multipartite Spin Coherent States and Spinor States

Multipartite generalizations of spin coherent states are introduced and analyzed. These are the spin analogues of multimode optical coherent states as used in continuous variable quantum information, but generalized to possess full spin symmetry. Two possible generalizations are given, one which is a simple tensor product of a given multipartite quantum state. The second generalization uses the bosonic formulation in the Jordan-Schwinger map, which we call spinor states. In the unipartite case, spinor states are equivalent to spin coherent states, however in the multipartite case, they are no longer equivalent. Some fundamental properties of these states are discussed, such as their observables and covariances with respect to symmetric operators, form preserving transformations, and entanglement. We discuss the utility of such multipartite spin coherent and spinor states as a way of storing quantum information.
ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning

Learning-based approaches to monocular motion capture have recently shown promising results by learning to regress in a data-driven manner. However, due to the challenges in data collection and network designs, it remains challenging for existing solutions to achieve real-time full-body capture while being accurate in world space. In this work, we introduce ProxyCap, a human-centric proxy-to-motion learning scheme to learn world-space motions from a proxy dataset of 2D skeleton sequences and 3D rotational motions. Such proxy data enables us to build a learning-based network with accurate world-space supervision while also mitigating the generalization issues. For more accurate and physically plausible predictions in world space, our network is designed to learn human motions from a human-centric perspective, which enables the understanding of the same motion captured with different camera trajectories. Moreover, a contact-aware neural motion descent module is proposed in our network so that it can be aware of foot-ground contact and motion misalignment with the proxy observations. With the proposed learning-based solution, we demonstrate the first real-time monocular full-body capture system with plausible foot-ground contact in world space even using hand-held moving cameras. Our project page is https://zhangyux15.github.io/ProxyCapV2.
Reply to: “Expanding Biopsy Options for Suspicious Breast Lesions: CT-Guided Biopsy Using a 3D-Printed Device”

Please see the accompanying Letter by Almir G. V. Bitencourt, Paula N. V. P. Barbosa, and Rubens Chojniak.
Low frequency sparse adversarial attack

Deep neural network (DNN) is found to be vulnerable to adversarial attacks. Gradient-based adversarial attacks usually craft perturbation on the whole pixels of input image, making them easier to be perceived or detected by the detection-based defenses, in the case of using large perturbation budget. In this paper, we present a new sparse approach to perturb image, which we call Robust Gradient-based Low Frequency search (RGLF). We show that RGLF provides a convenient way to modify any adversarial attack so that its imperceptibility can be improved significantly, not only to human eyes, but also to some popular detection-based defenses. Technically, RGLF first uses a robust gradient mask (RGM) to localize the semantic regions of input image, and then, applies a technique, called adaptive frequency search (AFS), to generate the low-frequency perturbations we expect, and finally, embeds the perturbations in the semantic regions. Extensive experiments are conducted to show that, i) the proposed RGLF technique can remarkably reduce the detection rates of the gradient-based adversarial attacks by 25%, 36%, and 30%, respectively for three SOTA detection-based defenses, namely, SBD, LiBRe, and SimCat; ii) The imperceptibility of the adversarial attacks modified by RGLF is improved significantly, even in the case of using large perturbation budget.
Development of Competences for Describing One's Homeland

The article presents the process and results of the students' training in the academic course on “Methodology of teaching about man, nature and society” to describe and present the homeland, as an integrative core of the education in the subject and a context for the formation of key and specific competencies. A rationale is given to the applicability of the created algorithm for description and adapted cognitive cycle for training, aimed at developing key and subject-specific competencies that pre-service primary teachers need to demonstrate in their teaching. The results of the study support the effectiveness of the tested didactic model for the effective development of competencies.
Global Citizenship Education Programs for Elementary, Middle, and High School Students: A Systematic Review

본 연구는 체계적 문헌고찰을 통해 초･중･고등학생을 대상으로 실행된 세계시민교육 프로그램 관련 연구물을 분석하였으며, 향후 세계시민교육의 실천과 후속연구를 위한 시사점을 제공하고자 하였다. 이를 위해 2011년부터 2022년까지 등재지 및 등재후보지에 출판된 44편의 국내 문헌을 선정한 뒤, 다음과 같은 분석 틀 - (1) 세계시민교육의 실행 방식(UNESCO, 2015), (2) 세계시민교육의 관점(나장함, 조대훈, 2017), (3) 프로그램의 연구 방법 - 을 기준으로 문헌의 전문을 검토하였다. 분석 결과 및 제언은 다음과 같다. 첫째, 세계시민교육의 실행 방식을 분석한 결과, 대부분의 연구가 부가적 접근법인 ‘특정 교과에 포함된 주제’와 ‘독립 교과’ 방식을 적용하였으며, ‘전학교적 접근’과 ‘범교과 주제’ 방식의 세계시민교육 연구는 상대적으로 적었다. 둘째, 세계시민교육의 관점을 분석한 결과, 연구의 대부분이 ‘여행자적 관점’과 ‘인도주의적 관점’을 반영하였으며, ‘신자유주의적 관점’과 ‘비판적 관점’의 연구는 상대적으로 적었다. 셋째, 프로그램의 연구 방법을 분석한 결과, 많은 연구가 개발된 지 오래되고 타당성이 부족한 척도를 활용하여 프로그램 효과를 분석하고 있었다. 또한, 연구마다 상이한 척도를 사용하여 세계시민교육 프로그램 연구의 효과를 일관성 있게 해석하기 어려웠으며, 문항을 재구성하는 과정에서 신뢰도와 타당도 검증 절차를 보고하지 않은 연구들이 많았다. 그리고 엄격한 연구 방법을 사용하여 프로그램 효과를 분석한 연구과 그렇지 않은 연구를 분석하였다. 앞선 연구 결과를 토대로 세계시민교육 프로그램 관련 연구를 위한 제언을 제시하였다.This study conducted a systematic review on global citizenship education (GCE) programs for Korean students in elementary, middle, and high schools. A total of 44 articles published in Korean journal from 2011 to 2022 were analyzed based on the framework: (1) approaches to delivery (UNESCO, 2015), (2) perspectives on GCE (Na & Jho, 2017), (3) research methods. The outcomes are as follows. First, GCE programs was mainly delivered by approaches of ‘integrated within certain subjects’ and ‘separate, stand-alone subject’, while few programs were delivered by ‘school-wide approach’ and ‘cross-curricular approach’. Second, while many GCE programs were delivered by ‘tourist perspective’ and ‘humanitarian perspective’, there were few studies based on ‘Neo-liberal perspective’ and ‘critical perspective’. Third, one out-dated scale has been used up to recently. And it was difficult to consistently interpret the effects of GCE program because different scales were used for each study. In addition, many studies did not report verification procedures about reliability and validity in the process of reorganizing questionnaire. Also, many did not use rigid research methods to analyze the effects of GCE program. Based on these findings, suggestions for future research related to GCE program were presented.
The Effects of a Program for Positive Self-Concept and Emotion of Older Adults

본 연구는 자전적 기억과 자기개념 간 관계에 초점을 맞추어 노인을 대상으로 건강하고 긍정적인 자기개념 고취를 위한 프로그램을 실시하고 그 효과를 검증하기 위해 수행되었다. 프로그램은 아동기부터 현재까지 다양한 역할 수행자로서 경험한 긍/부정적 중요 생활사건을 회상하고 재조명함으로써 자기 이해를 높이고 자기를 수용하면서 내일을 계획하도록 구성되었다. 연구 참여자는 실험집단 20명(남 6, 여 14)과 통제집단 20명(남 7, 여 13)으로 총 40명이었으며 평균연령이 72.55세였다. 프로그램의 효과를 검증하기 위하여 자기개념과 관련된 척도 및 정서 상태를 측정하는 척도를 사용하였고 사전, 사후검사 점수 차이를 SPSS Statistics 21로 공변량분석하였다. 분석 결과, 정서 변화는 나타나지 않았으나 자기지각이 더 긍정적으로 변하였고 자아통합감 수준이 증가하였다. 이러한 결과는 자기상이 부정적일 수 있는 노년기에 본 프로그램이 건강한 자기개념 고취에 도움이 될 수 있음을 말해준다. 논의에서 본 연구의 의의, 제한점, 추후연구에 대해 기술하였다.The present study investigates the effects of a program for the positive selfconcept of older adults. The program is based on the relationship of autobiographical memories and self-concept and is composed of understanding oneself by recalling important life events, accepting oneself, learning communication skills, and planning for the future. Twenty older adults of an experimental group participated in the program for seven weeks and they were compared with 20 older adults of the control group without any treatment. Before and after the intervention, all participants were tested using the self-perception, self-acceptance, self-integrity, self-esteem, depression, and subjective well-being scales. ANCOVA was performed using SPSS 21 to verify the effects of the program. The results showed positive change in self-perception and self-integrity. These findings suggest that this program may help the self-concept of elderly people to be more positive. Study implications, research limitations, and suggestions for further research were discussed.
A Study on the Congruence between College Major Hopes and Actual Choices of High School Students

고등학교 시기는 학생이 진로계획에 있어서 가치와 관점을 설정하고, 대학에서 심화학습할 전공을 계획하고 준비하는 시간이라는 점에서 효과적인 진로교육이 요구된다. 이에 따라 본 연구는 고등학교 시기 학생의 희망 전공과 실제 대학 전공의 일치가 진로교육의 단기적 목표 달성 척도라는 전제하에 전공일치 영향요인을 탐색하였다. 경기교육종단연구 초등학교 패널의 고등학교 3학년 및 대학교 1학년 시기의 자료를 바탕으로, 평균차이검정과 로지스틱 회귀분석을 활용하여 분석하였다. 먼저, 진로계획 수립은 고등학생이 희망 전공으로 진학하는 데 효과적이다. 상대적으로 일찍 희망 전공을 계획하고 준비하는 예체능 계열, 수시전형에서 전공일치율이 높게 나타나고 있으며, 진로계획성이 높을 때 전공일치 확률 또한 높은 것으로 나타났다. 둘째, 희망 전공에 진학한 학생은 대학생활을 긍정적으로 인식한다. 전공일치 대학생은 학습참여도와 대학에 대한 만족감이 높으며, 대학과 전공을 다시 선택하는 것을 희망하는 정도가 낮은 것으로 확인되었다. 셋째, 학생 특성에 따라 직업적, 개인적, 환경적 요인이 전공일치에 미치는 영향은 차이가 있다. 진로계획성, 진로활동참여도, 학업성적, 교육포부, 부모자녀일치, 부모학력과 전공일치확률 간의 관계가 통계적으로 유의하였으나, 이러한 관계는 학업수준을 세 수준으로 나누어 분석할 때 달라지는 것으로 나타났다. 끝으로, 진로교육에 있어 자기이해, 진로탐색, 진로설계의 각 진로활동 영역이 균형 있게 이루어져야 하며, 학생의 특성을 고려하여 개별화된 진로교육이 제공될 필요가 있으며, 진로교육의 지지자로서 학부모에 대한 연수 제공이 필요함 등을 제언하였다.This study explored the factors influencing major congruence under the premise that the congruence of students' desired college majors and actual majors measures short-term goal achievement in career education. Mean difference tests and logistic regression analyses were used based on data from the Gyeonggi Education Panel Study’s 9th and 10th panel data. First, it was found that career planning is effective in helping high school students enter their desired majors. Students who plan and prepare for their majors relatively early have a higher rate of major congruence in the arts and sciences. The probability of major congruence is higher when career planning is high. Second, students who enter their desired majors perceive college life positively. It was found that college students who entered their desired majors were more likely to be engaged in learning and satisfied with their college and less likely to want to choose another college or major. Third, there were differences in the influence of occupational, personal, and environmental factors on major congruence according to student characteristics. The relationship between career planning, participation in career activities, academic performance, educational aspirations, adolescent−parent career congruence, parents' educational background, and major congruence probability was statistically significant. Still, these relationships were found to be different when the academic level was divided into three groups for analysis.
Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System

These days, the application of image processing in computer vision is becoming more crucial. Some situations necessitate a solution based on computer vision and growing deep learning. One method continuously developed in deep learning is the Convolutional Neural Network, with MobileNet, EfficientNet, VGG16, and others being widely used architectures. Using the CNN architecture, the dataset consists primarily of images; the more datasets there are, the more image storage space will be required. Compression via the discrete cosine transform technique is a method to address this issue. We implement the DCT compression method in the present research to get around the system's limited storage space. Using DCT, we also compare compressed and uncompressed images. All users who had been trained with each test 5 times for a total of 150 tests were given the test. Based on testing findings, the size reduction rate for compressed and uncompressed images is measured at 25%. The case study presented is face recognition, and the training results indicate that the accuracy of compressed images using the DCT approach ranges from 91.33% to 100%. Still, the accuracy of uncompressed facial images ranges from 98.17% to 100%. In addition, the accuracy of the proposed CNN architecture has increased to 87.43%, while the accuracy of MobileNet has increased by 16.75%. The accuracy of EfficientNetB1 with noisy-student weights is measured at 74.91%, and the accuracy of EfficientNetB1 with imageNet weights can reach 100%. Facial biometric authentication using a deep learning algorithm and DCT-compressed images was successfully accomplished with an accuracy value of 95.33% and an error value of 4.67%.
Computer Vision-Based Fall Detection for Enhancing Safety in Daily Living Activities

This research paper presents a comprehensive study on the application of computer vision techniques for fall detection in the context of daily living activities. The research aims to explore the potential of computer vision in accurately detecting falls and preventing potential injuries. The methodology involves the use of machine learning algorithms, specifically convolutional neural networks, for analyzing body motion and identifying fall events. Experimental results show promising performance with high accuracy and recall rates. The findings of this research contribute to the field of fall detection and provide insights into the use of computer vision for improving safety and well-being.
An Experimental Study on Second-Order Trust in Algorithms: How Does Algorithm Aversion Spill Over During the Pandemic?

Cooperation between artificial intelligence (AI) and humans is one of the most hotly-debated issues in theory and practice. Previous studies have indicated algorithm aversion, in which individuals are more uncooperative when dealing with an AI. However, most previous studies have examined only trust in AIs (“first-order trust”), with a shortage of research on trust in other individuals who use AIs (“second-order trust”). Therefore, we examine how algorithm aversion spill over to trust in leaders who use algorithms during the pandemic. We conducted a pre-registered experiment to verify our question using Mechanical Turk (N = 1,400). We manipulated the attributes of the adviser (human or Intelligent Agent) and the environment (pandemic or stable) and measured the degree to which participants in the subordinate role trust their leader in cognitive and affective terms.The results revealed that a spillover effect of algorithm aversion occurred especially in the affective terms and management risks amplified the effect. Our study provides new insights not only into trust in leadership research but also into organizational and state policies in the digital age. Studying trust through AI in organizations can contribute to achieving Sustainable Development Goals No. 8, which is decent work.
The Effect of Resilience on Self-Efficacy in University Students

This study examined the effect of resilience on self-efficacy in students attending four-year universities living in Chungcheongbuk-do. For this study, frequency analysis of basic variables, correlation analysis to confirm the correlation between variables, and multiple regression analysis were conducted to analyze the effect between variables. The analysis results are as follows. First, as a result of analyzing the correlation between resilience and self-efficacy, the correlation between self-efficacy and resilience was found to be positive (+). Second, multiple regression analysis was conducted to verify the effect of resilience on self-efficacy of college students, and it was found that resilience, an independent variable, had a positive effect on self-efficacy and was statistically significant (=0.624 p&lt;0.001). Through this study, it is important to lead college students to take appropriate actions in any given situation by increasing their self-efficacy through educational programs that improve their resilience.
The NYU Data Catalog: a modular, flexible infrastructure for data discovery

Abstract Objective Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response, the library developed and maintained the NYU Data Catalog, a public-facing data catalog that has supported not only faculty acquisition of data but also the dissemination of the products of their research in various ways. Materials and Methods The current NYU Data Catalog is built upon the Symfony framework with a tailored metadata schema reflecting the scope of faculty research areas. The project team curates new resources, including datasets and supporting software code, and conducts quarterly and annual evaluations to assess user interactions with the NYU Data Catalog and opportunities for growth. Results Since its launch in 2015, the NYU Data Catalog underwent a number of changes prompted by an increase in the disciplines represented by faculty contributors. The catalog has also utilized faculty feedback to enhance support of data reuse and researcher collaboration through alterations to its schema, layout, and visibility of records. Discussion These findings demonstrate the flexibility of data catalogs as a platform for enabling the discovery of disparate sources of data. While not a repository, the NYU Data Catalog is well-positioned to support mandates for data sharing from study sponsors and publishers. Conclusion The NYU Data Catalog makes the most of the data that researchers share and can be harnessed as a modular and adaptable platform to promote data sharing as a cultural practice.
Two-stage processing of image restoration problem based on generative adversarial network

Aiming at the problem that texture details and edge information of images are easily lost in the process of image recovery, a two-stage processing algorithm is proposed that combines denoising processing and super-resolution reconstruction processing. The algorithm is divided into two stages, the first stage uses the improved DEGAN network to denoise the image, and the training set and the test set are denoising the training set and the test set respectively through the optimal path obtained by training, and the result is directly transferred to the dataset for the second stage of super-resolution processing. In the second stage, the denoising training set is reconstructed with the improved SRGAN network, and a clean image dataset with the noise processing needs to be added as the target image for super-resolution processing. Compared with single denoising and super-resolution reconstruction, the images recovered by two-stage training processing have significantly improved subjective perception and objective evaluation indicators.
Maneuvering target filtering method based on the constraints of energy change rate and tangential acceleration

Aiming at the problem of poor tracking performance of traditional radar filtering algorithm in terminal high maneuvering of the target, a maneuvering target filtering method based on energy change rate and tangential acceleration constraints is proposed. Firstly, the traditional extended Kalman filter (EKF) is used according to the measured ranges, azimuth angles and elevation angles of the detected target. Then, applying the maneuvering characteristics of target, the constraints of energy change rate and tangential acceleration are designed, and the form of pseudo-measurement equation is integrated into the filtering process. The proposed method has the advantages of simple implementation and small computation. Compared with the traditional EKF method, the proposed method can effectively improve the filtering precision of position and velocity of targets in large maneuvers. Simulation results show the effectiveness and good performance of the proposed method.
Gameplay Automation

Reinforcement Learning has performed remarkably well in various games and has the potential to surpass human level gameplay. Although it does good in turn-based games, complex game genres like fighting or much more complex 3D shooters are still a challenge. The created ML model can teach itself how to play a specific game so that it can be used to test the games for completeness, bugs, and irregularities. The model is also used to find out if there are any ways to speed run games by exploiting certain in-game mechanisms or to check if any characters or abilities are overpowered. Models were created for a few games to identify how well these AI models can perform and see what kind of differences were required to switch between the games.
The Role of Chatgpt to Reduce the High Overhead Issues of Blockchain Technology in Healthcare

Open artificial intelligence (AI) applications, including ChatGPT, are gaining recognition across diverse research domains, including healthcare, due to their effective handling of inquiries related to AI implementation in healthcare. This study aims to investigate four key areas of blockchain technology (BCT) in healthcare: identifying overhead challenges, proposing ChatGPT-based solutions, analysing ethical risks, and exploring the synergistic potential of ChatGPT integration with BCT. A literature review was conducted using Google Scholar, and ChatGPT was utilized by posing questions to gather information. This approach allowed for an examination of the potential synergy between ChatGPT and BCT in efficiently addressing overhead challenges in healthcare. The research identifies seven primary overhead challenges in BCT, with maintenance cost being a less-explored aspect. The study proposes ChatGPT-based solutions such as optimizing smart contracts and compressing code size. Ethical implications, including privacy, biases, transparency, and academic integrity, are recognized. ChatGPT demonstrates synergistic capabilities when integrated with BCT. ChatGPT holds promise in addressing overhead challenges in healthcare BCT. Its potential role in healthcare presents valuable applications to improve the efficiency and effectiveness of BCT in the healthcare domain.
The Empirical Subject in the Contextualism Program: The Case of Atomic Physics

Non-classical turns, the revision of the classical categories of subject and object, open up broad prospects for science and indicate difficulties. The non-classical turn of epistemology, changes in the under-standing of the category of the subject do not give the understanding of what functions the subject performs in science. The article analyzes the problems of changing the methodological category of the subject (I.G. An-tipova). The study is carried out in line with contextualism. In view of the wide breadth of the problems of methodology, the study focuses on the analysis of difficulties in contextualism. These difficulties lie in the contextual explanation of the subject. On the example of quantum mechanics (E.R. Redkin), the problems of the subject in technoscience are considered. The research methodology is the philosophical works of L.A. Mikeshina, research on the problems of the subject of I.G. Antipova. The difficulties of turning to the empirical subject are explained by the inconsistency in the methodological program of contextualism. The empirical subject of science is not given by the context. The empirical subject performs the cultural function of forming the context of science.