Reviewerly/exHarmony huggingface dataset:
Lightweight Vision Transformers for Face Verification in the Wild

Facial verification is an important task in biometrics, particularly as it is increasingly being extended to wearable devices. It is therefore essential to study technologies that can adapt to resource-limited environments. This poses a challenge to current research trends, as the deep learning models that achieve the best results typically have millions of parameters. Specifically, convolutional neural networks (CNN) dominate the state of the art in computer vision, although transformer models have recently shown promise in tackling image problems. Vision transformers (ViT) have been successfully applied to various computer vision tasks, outperforming CNNs in classification and segmentation tasks. However, as ViT models have a high number of parameters, it is crucial to investigate their lightweight variants. The Knowledge Distillation (KD) training paradigm enables the training of student models using teacher models to transfer knowledge. In this study, we demonstrate how to train a lightweight version of ViT using KD to create one of the most competitive lightweight models in the state of the art. Our analysis of ViT models for facial verification sheds light on their suitability for resource-constrained environments such as smartphones, smartwatches, and wearables of all kinds.
DeepArUco: Marker Detection and Classification in Challenging Lighting Conditions

Detection of fiducial markers in challenging lighting conditions can be useful in fields such as industry, medicine, or any other setting in which lighting cannot be controlled (e.g., outdoor environments or indoors with poor lighting). However, explicitly dealing with such conditions has not been done before. Hence, we propose DeepArUco, a deep learning-based framework that aims to detect ArUco markers in lighting conditions where the classical ArUco implementation fails. The system is built around Convolutional Neural Networks, performing the job of detecting and decoding ArUco markers. A method to generate synthetic data to train the networks is also proposed. Furthermore, a real-life dataset of ArUco markers in challenging lighting conditions is introduced and used to evaluate our system, which will be made publicly available alongside the implementation. Code available in GitHub: https://github.com/AVAuco/deeparuco/ .
Enhancing Transferability of Adversarial Audio in Speaker Recognition Systems

Although deep neural networks have demonstrated state-of-the-art performance in several tasks such as speaker recognition among others, they are highly vulnerable to adversarial attacks. These attacks involve the transformation of the original speech signal in order to fool the trained model with minimal alteration in the auditory perception. These attacks have been shown to succeed in white-box settings, however, they are less likely to succeed in a realistic black-box setting. However, it is imperative to investigate the extent of the threat posed by transferability of such attacks to target models to strengthen the defense against them. Therefore, in this work, to enhance the transferability of adversarial examples in black-box setting, the source model’s architecture has been minimally modified. Particularly, by skipping selected ReLU activation functions during backpropagation. Experiments on the VoxCeleb dataset resulted in average transferability of 18.7% and 20.5% on two target models.
The Quantum Advantage

Abstract “The Quantum Advantage: Interferometric Computing” describes the development of quantum algorithms and quantum computing, beginning with the first quantum algorithm invented by David Deutsch as a side effect of his attempt to prove the many-worlds interpretation of quantum theory. Peter Shor found a quantum algorithm that could factor the product of primes, threatening all secure communications in the world. Once the usefulness of quantum algorithms was recognized, quantum computing hardware ideas developed rapidly into quantum circuits supported by quantum logic gates. The limitation of optical interactions, that hampered the development of controlled quantum gates, led to the proposal of linear optical quantum computing. Boson sampling, consisting of a complex cascade of single-photon interferometers, has been used to demonstrate quantum supremacy, also known as quantum computational advantage, using photonic integrated circuits.
SOCIAL MEDIA USE AND ITS EFFECTS ON STUDENTS’ BEHAVIOR IN THE SECONDARY SCHOOLS IN MACHAKOS SUB- COUNTY, MACHAKOS COUNTY, KENYA

Social media is one of the most used interactive communication tools in the current world. Almost every person above the age of ten years is operating an account of any social media platform. The study was guided by two objectives; to examine prevalence of social media use among secondary students and, to determine behavior displayed by the students as a result of social media use. The study was anchored on the social learning theory. A mixed-method research design was adopted. Purposive sampling was used to select 15 teacher counselors, while 392 students were selected using a simple random sampling. The study found that 97.4% of the secondary school students in Machakos Sub- County were regular social media users. The students preferred using WhatsApp, YouTube, Instagram and Facebook. The study also found that social media use inculcated both positive (60% ) and negative (40%) behaviors respectively. The study thus concluded a high prevalence of social media use among secondary school students in Machakos Sub-County. Based on these findings, the study recommends that school administrators should come up with stringent strategies to control the use of social media among secondary school students and, the Ministry of Education should develop policies for curbing the use and challenges associated with social media by secondary school students.
Intelligent Analysis of Components with Regard to Significant Features for Subsequent Classification

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The knowledge explosion in all areas is both an opportunity and a risk. In order to shape the effects as positively as possible and create sustainable added value, the amount of information must be processed in a targeted manner and important content must be separated from ignorable content.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In the automotive industry and especially in the development of new components, it is possible to look back on many past projects and thus knowledge. However, the decisive factor is whether this information is available in a suitably processed form or the knowledge is even held by just a single expert. A new and intelligent method is therefore required to analyze existing data appropriately and at the same time prepare it ideally for further applications, such as use within forecast models based on Artificial Intelligence (AI). To achieve this, several steps need to be taken.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Firstly, it is possible to perform a suitable segmentation of the component. The aim is to detect areas in a component where features and form elements are found. Other remaining regions are ignored after the inspection by segmentation and voxelization: There is no sustainable valuable knowledge here.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Subsequently, the voxelization of the component takes place, which results in the three-dimensional component or Computer-Aided-Design (CAD) file being mathematically readable and thus a kind of translation takes place. This is done by rasterizing the component based on a previously selected resolution and other upcoming steps.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Finally, the segmented and relevant areas are analyzed accordingly. This can be done according to corresponding previously defined guidelines or, for example, by using a suitably trained AI. The advantage is based in the mathematical readability, which is now given by the voxelization that has taken place.&lt;/div&gt;&lt;/div&gt;
The Method and Possibility of Building a Community in Women-Testimony Poetry

이 논문에서는 증언문학의 하위 범주로 ‘증언시’를 설정하고 여성주의적 관점에서 쓰인 증언시를 살펴보았다. 특히 에밀리 정민 윤의 첫 시집 『우리 종족의 특별한 잔인함』을 중심으로 일본군 ‘위안부 문제를 다룬 증언시가 우리 시사에서 어떤 위치에 놓일 수 있는지 확인하고자 하였다. 허수경이 독일에서 한국어로 시를 썼듯이, 에밀리 정민 윤은 ‘소수적 감정’을 느낄 수 있는 이민자 여성으로서 국가의 경계를 벗어나 ‘위안부’ 문제를 다루었다. 이때 주목되는 여성주의적 관점은 민족주의적 시각과 거리를 두며 증언시의 새로운 모습을 보여준다. ‘위안부’ 여성들을 바라보는 시인의 시선은 전 세계 여성들의 문제뿐 아니라 환경 문제로까지 확장되며 더 커다란 기억의 공동체를 이룬다. 이렇듯 에밀리 정민 윤은 여성-증언시를 통해 새로운 연대의 가능성을 모색한다. In this paper, ‘testimony poetry’ was set as a sub-category of testimony literature and testimony poetry written from a feminist perspective was examined. In particular, focusing on Emily Jungmin Yoon"s first collection of poems, “A Cruelty Special to Our Species” we tried to find out where the testimony dealing with the “Japanese Military Sexual Slavery” could be placed in our current affairs. Just as Heo Soo-Kyung wrote a poem in Korean in Germany, Emily Jungmin Yoon dealt with the issue of “Japanese Military Sexual Slavery” outside the boundaries of the country as an immigrant woman who could feel “Minor feelings” The feminist perspective that draws attention at this time distanced itself from the nationalist perspective and showed a new aspect of testimony. The poet"s gaze at “Japanese Military Sexual Slavery” women extends not only to women"s problems around the world but also to environmental problems, forming a larger community of memories. As such, Emily Jungmin Yoon seeks the possibility of a new solidarity through women-testimony poetry.
My Heartful Songlikes

My Heartful Songlikes Nora Hikari (bio) poetry, Nora Hikari, home, nature, hope, song, time, peace High hearts, little lights!The welcome sky sings sudden!I dream to echoes that you are freshand brilliant today! Listen hard! Do you cheeka windchuckle against a coldyear? I promise you,in a place with everyone,there is patience, and a warmthready to give you full home. I have a something! It's for you.A quiver of to-happens, whichI twirled long and true,waiting for you to remember. There's a small to-happen,where we rush liquid and laughacross a sunbeam of wildflowers.Another to-happen, where we holdhand over moment, bound lip,hard felt, into a bright world bell. One more to-happen, for justmy final and specific:nothing rains, nothing clouds,nothing suns, only starlight,and again, down, into another.A long ride into the next,where we still are songful. [End Page 160] Nora Hikari nora hikari (she/her) is a Chinese and Japanese transgender poet and artist based in Philadelphia. She was a 2022 Lambda Literary fellow, and her work has been published in Ploughshares, Palette Poetry, Foglifter, The Journal, The Shade Journal, and others. Her chapbook, GIRL 2.0 (Seven Kitchens Press), was a Robin Becker Series winner. She was a reader at the 2022 Dodge Poetry Festival and a finalist for the Red Hen Press Benjamin Saltman Award. Her chapbook The Small Lights of Her Heart is forthcoming from Finishing Line Press in 2023, and her chapbook Let's Burst Like Stars is forthcoming from swallow::tale press in 2024. Copyright © 2023 The Massachusetts Review, Inc.
A Study on Exploring Educational Possibilities of Borderline Intelligence Students Based on the Metaverse

본 연구의 목적은 경계선지능 학습자의 메타버스공간의 교육적 활용방안을 탐색하는 것이다. 문헌연구를 통하여 경계선지능 학습자의 특성과 학업실패 요인을 파악하였다. 지적장애와 경계를 이루는 지능수준으로 인지 및 이해도가 낮은 이들에게 메타버스의 가상공간은 물리적 한계를 넘어서 현실에서 가능하지 못한 활동이 가능하게 할 수 있다. 연구 결과 경계선지능 학습자의 학습지원을 구체화하기 위해 메타버스의 현실체감 기술을 이용하여 경계선지능 학습자들이 필요로 하는 기능의 보안 가능성을 제시하였다. 경계선지능 학습자에게 현실적인 학습지원을 시도해 볼 수 있는 메타버스 교육 환경을 제시하였으나 정량적 평가가 되지 못하였다. 후속 연구는 경계선지능 학습자의 학습 환경 및 인지기능의 세분화를 통한 학습전략과 콘텐츠프로그램 연구가 필요하다.The purpose of this study is to explore the possibility of educational utilization of metaverse space for borderline intelligence learners. To this end, the characteristics of borderline intelligence learners and the factors of academic failure were identified through a qualitative approach. For those with low levels of cognition and understanding at the level of intelligence that borders on intellectual disability, the virtual space of the metaverse can enable activities that are not possible in reality beyond physical limits. As a result of the study, in order to materialize learning support for borderline intelligence learners, the possibility of security of functions required by borderline intelligence students was presented by using the metaverse's reality experience technology. Although a metaverse education environment in which borderline intelligence learners can try realistic learning support was presented, it was not evaluated quantitatively. Follow-up research requires research on learning strategies and content programs through the segmentation of the learning environment and cognitive functions of borderline intelligence learners.
Development of Inventory of Learning Information Processing Process

본 연구의 목적은 초등학생, 중학생을 위한 학습정보 처리과정의 정확한 개념을 세우고, 하위요소를 구성하여, 측정도구를 개발하고, 척도의 타당성을 확보를 하는 것이다. 척도 구성요소 구인 및 정의 구성을 위해 전문가 자문을 실시하였으며, 척도 문항 확보를 위해 문항개발 위원회의 도움을 받았다. 타당한 척도문항을 확보하기 위해 예비조사를 통한 내용타당도, 구인타당도를 실시하였으며, 타당도의 선정기준을 충족한 문항을 최종 선정하였다. 학습정보 처리과정의 정의는 대상을 언어로 지각, 인식하여 긍정적 정서를 통해 공감, 반응함으로써 문제를 처리하는 학습단계이고, 문항은 입력기능에서 28개, 정서기능에서 18개, 인지기능에서 19개 총 65개 문항을 개발하였다. 최종 구성된 척도모형의 적합도를 확인하기 위해 확인적 요인분석을 실시했으며 절대적 적합도 지수와 증분적 적합도 지수의 선전기준을 모두 통과하였다. 척도의 준거 예측정도를 검증하기 위해 준거타당도를 확인하였으며, 같은 측정개념을 위해 제작된 LMI(학습처리능력) 척도와 적률상관분석에서 정적상관을 보였다. 이로써 본 연구를 통해 개발된 초등학생, 중학생 대상 학습정보처리과정 척도는 내용타당도, 구성타당도, 준거타당도를 모두 충족하였음을 확인하였다. 향후 본 연구결과를 통해 초등부 중등부 학생이 학습정도 처리과정에서 어려움을 겪어 학습부진으로 이어지는 문제를 해결할 수 있기를 기대한다.The purpose of this study is to develop an accurate concept of the learning information processing process for elementary and middle school students, construct sub-elements, develop measurement tools, and secure the validity of the scale. Expert consultation was conducted to identify and define scale components, and the committee of item development helped to secure scale items. In order to secure valid scale questions, content validity and construct validity were conducted through a preliminary survey, and items that met the selection criteria for validity were finally selected. The definition of the learning information processing process is a learning stage in which problems are processed by perceiving and recognizing objects in language, empathizing and responding through positive emotions, and the questions are 28 in the input function, 18 in the emotional function, and 19 in the cognitive function. A total of 65 items were developed. Confirmatory factor analysis was conducted to confirm the goodness of fit of the finally constructed scale model, and both the absolute goodness of fit index and the incremental goodness of fit index passed the propaganda criteria. In order to verify the criterion prediction degree of the scale, the criterion validity was confirmed with the LMI (Learning Processing Ability) scale and the positive correlation analysis, which were designed for the same measurement concept, showed a positive correlation. As a result, it was confirmed that the learning information processing process scale for elementary and middle school students developed through this study satisfied all of content validity, construct validity, and criterion validity. In the future, it is hoped that through the results of this study, elementary and middle school students will be able to solve the problem that leads to poor learning due to difficulties in the process of processing their learning level.
Comparison of the Qualitative Difference between Offline and Online Accounting Lecture Results before and after COVID-19

본 연구에서는 COVID-19로 인하여 온라인 강의를 경험한 학생들을 대상으로 회계강의 온라인 강의와 오프라인 강의평가 차이를 비교하고자 한다. 회계강의는 일반적인 사회과학 및 인문강의와 달라 온라인 강의와 오프라인 강의 결과가 다를 것으로 생각된다. 또한 회계과목의 특징에 따라 재무회계, 전산회계, 원가회계로 분류한 후 분석하여 회계강의의 과목별 특성에도 어떠한 차이가 있는지 확인하고자 한다. 본 연구에서는 A대학의 오프라인 대면강의와 온라인 비대면강의에 있어 학생들의 강의 후 시험점수와 각 과목의 교수 강의평가점수를 확인하여, COVID 19 전후에 어떠한 차이가 있는지 확인한다. 본 연구의 분석결과, A대학의 경영학과 학생들의 온라인과 오프라인 회계강의 후 재무회계, 전산회계, 원가회계 과목들의 시험점수에 있어서 오프라인 점수가 더 높게 나타났으며, 이는 통계적으로 유의한 차이가 있는 것으로 확인되었다. 추가적인 분석으로 팬데믹 기간동안 온라인 강의 방법의 차이에 따라 과목별로 차이를 보였다. 또한 추가적인 교수강의 평가에 대한 상관관계 분석결과, 온라인 강의평가점수가 더 높게 나타나, 교수의 강의평가에 있어서는 강의시험점수가 낮을수록 강의평가 점수가 높은 것으로 나타났다. 본 연구의 결과로 보아 회계강의의 경우 온라인 강의보다는 오프라인 강의가 보다 더 적합하다는 것을 사례 분석을 통해 확인하였다. 또한 회계과목의 종류와 온라인 강의방법에 따라 강의 결과의 학업성취도 차이를 확인하였다.In this study, we want to compare the qualitative differences between online and offline lectures for accounting courses for students who have had to experience online lectures due to the COVID-19 pandemic. Accounting courses are different from general social science and humanities courses, so it is believed that the results of online and offline lectures would be different. In addition, accounting courses are classified into financial accounting, computerized accounting, and cost accounting, and then analyzed to see what differences there are in the characteristics of each subject. In this study, in the offline face-to-face lectures and online non-face-to-face lectures of University A, we want to check the test scores of students after the lectures and the evaluation of the professors of each subject to see what differences there are before and after COVID-19. As a result of this analysis, it was found that after the online and offline accounting lectures of business administration students of University A, the offline scores of the financial accounting, computerized accounting, and cost accounting subjects were higher, and this was statistically significant. Further analysis showed differences by subject according to differences in online teaching methods during the pandemic. In addition, the correlation analysis of additional professor lecture evaluations showed that the online lecture evaluation score was higher, and the lower the lecture test score in the professor's lecture evaluation, the higher the lecture evaluation score. Judging from the results of this study, it was confirmed through empirical case analysis that offline lectures are more suitable than online lectures in the case of accounting lectures. As a result of this study, it was confirmed through case analysis that offline lectures are more appropriate than online lectures in the case of accounting lectures. In addition, it was confirmed that the academic achievement of the lecture results differed according to the type of accounting subject and the online lecture method.
A Study of Research Trend on Flipped Learning in Korea Higher Education: Focusing on the Instructional Process

이 연구는 최근 5년(2018~2022년) 동안 우리나라에서 대학수업을 대상으로 수행된 플립러닝의 연구 경향을 한국연구재단의 학술연구정보서비스(RISS)를 통해 수집, 분석하였다. 첫째, 교양교과(외국어 등)를 포함한 인문학, 교직교과를 포함한 교육학, 그리고 간호학 전공에서 상대적으로 많은 논문이 발표되었다. 둘째, 수업 과정을 기준으로 보면, 수업 실행(43.4%)과 수업 효과(40.5%)에 관한 연구가 주로 수행되었다. 수업 설계를 할 때 역량기반의 교육과정과 교수설계의 토대인 백워드 설계모형을 적용하는 연구가 등장하였다. 학습 만족, 학습 동기, 학습효능감을 포함하는 학습과정을 종속변인으로 삼아 효과를 분석하는 연구가 가장 많았고, 자기주도학습, 문제해결력, 의사소통 등 학생들의 핵심역량을 종속변인으로 삼은 연구도 많았다. 새로운 역량으로 강조되는 행위자 주체성(agency)을 탐구한 연구도 등장하였다. 플립러닝을 다른 학습자 중심의 수업전략과 결합하여 설계하고 운영하는 연구, 대학 자체적으로 설정한 역량체계를 활용하여 수행하는 연구가 요구된다.The purpose of this study is to analyze the research trends of flipped learning on higher education on in Korea. The results of this research are follows. First, In the last five years, the academic articles was mostly published on the Human science(liberal education), Education(teaching profession, Nursing area. Second, In recent, The Design of flipped learning was generated by Backward by Design(by Wiggins and McTighe) which support Competency Education. Third, the research papers on flipped learning on the implementing stage was published by Phenomenological research method in department of nursing, and teaching profession area. Fourth, The researchers designed dependent variables of the Learning process as academic satisfaction, academic efficacy, learning motive mostly.
COMPUTER VISION-BASED ROAD VEHICLE TRACKING FOR SELF-DRIVING CAR SYSTEMS

Road object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems to achieve the automation/autonomy. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) and autonomous car subsystems. The implementation is a sequence of computer vision techniques fused together and merged with machine learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%. Keywords: Autonomous Driving, Self-Driving Vehicle, Computer Vision, Car Detection, Car Tracking, Advanced Driving Assistance System, Machine Learning DOI： https://doi.org/10.35741/issn.0258-2724.58.3.66
A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment. They provide numerous benefits to vehicle owners, manufacturers, vehicle service providers, insurance companies, etc. These vehicles generate a large amount of data, which makes privacy and security a major challenge to their success. The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors. This could have a negative impact on how well-liked CAVs are with the general public, give them a poor name at this early stage of their development, put obstacles in the way of their adoption and expanded use, and complicate the economic models for their future operations. On the other hand, congestion is still a bottleneck for traffic management and planning. This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain, which will be used further for congestion detection and mitigation. Numerous devices placed along the road are used to communicate with passing cars and collect their data. The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment. Furthermore, this data will be stored in the memory pool, where other devices will also store their data. After a predetermined amount of time, the memory pool will be mined, and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics. The information is then used in two different ways. First, the blockchain’s final block will provide real-time traffic data, triggering an intelligent traffic signal system to reduce congestion. Secondly, the data stored on the blockchain will provide historical, statistical data that can facilitate the analysis of traffic conditions according to past behavior.