Reviewerly/exHarmony huggingface dataset:
In Improved Algorithm for Load Balancing techniques Over Cloud Computing

The term "cloud computing" describes the services and programmes that data centres make available to users all over the world. Online access is provided for all services and programmes. Virtualization is a crucial component of the cloud computing environment, and virtual machine migration is one of its key components. Virtual machine migration is the process of moving an operational virtual machine to a destination computer where it continues to function normally with all of its installed programmes and operating system. Making server balancing easier. By dispersing the workload among available servers, this boosts performance. There are several load balancing algorithms, which may be divided into static and dynamic load balancing methods.
Iterative Method to Register Longitudinal MRI Acquisitions in Neurosurgical Context

The visual comparison of MRI images obtained before and after neurosurgical procedures is useful to identify tumor recurrences in postoperative images. Image registration algorithms are utilized to establish precise correspondences between subsequent acquisitions. The changes observable in subsequent MRI acquisitions can be tackled by methods combining rigid and non-rigid registration. A rigid step is useful to accommodate global transformations, due, for example, to the different positions and orientations of the patient’s head within the scanning device. Furthermore, brain shift caused by tumor resection can only be tackled by non-rigid approaches. In this work, we propose an automatic iterative method to register pre- and postoperative MRI acquisitions. First, the solution rigidly registers two subsequent images. Then, a deformable registration is computed. The T1-CE and T2 MRI sequences are used to guide the registration process. The method is proposed as a solution to the BraTS-Reg challenge. The method improves the average median absolute error from 7.8 mm to 1.98 mm in the validation set.
Robust Image Registration with Absent Correspondences in Pre-operative and Follow-Up Brain MRI Scans of Diffuse Glioma Patients

Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Laplacian pyramid image registration network (cLapIRN) with forward-backward consistency constraint, and 3) a non-linear instance optimization method. We apply the method to the Brain Tumor Sequence Registration (BraTS-Reg) Challenge. Our method achieves accurate and robust registration of brain MRI scans with pathologies, which achieves a median absolute error of 1.64 mm and 88% of successful registration rate in the validation set of BraTS-Reg challenge. Our method ranks 1 $$^\text {st}$$ place in the 2022 MICCAI BraTS-Reg challenge.
What Does Job Applicants' Body Art Signal to Employers?

In this study, we present a state-of-the-art scenario experiment which, for the first time in the literature, directly measures the stigma surrounding job candidates with tattoos and piercings using real recruiters. We find that job candidates with body art are perceived as less pleasant to work with, less honest, less emotionally stable, less agreeable, less conscientious and less manageable. This goes hand in hand with lower hireability for men with body art but not for women. Compared to candidates who reveal obesity, a characteristic we also randomise, those with body art score better overall in terms of hireability and rated personality, similar in terms of rated taste to collaborate but worse in terms of rated direct productivity drivers.
The Capacity Development Opportunities of Sri Lankan Government School Principals

Human resource is more important than any other resource in every institution or organization. In a school, the principal is the chief manager who supervise the human resources. Therefore, there should be proper professional development programmes in the system of Education to enhance the knowledge, skills and attitudes of the principal. Through that more systematic, effective and productive Education service can be delivered to the community. It has become a necessity to pay attention to the capacity development of principals. Therefore, it was necessary to do a research on identifying the capacity development areas of Sri Lankan government school principals. The main aim of this study was to find out the capacity development opportunities for Sri Lankan government school principals. With special focus to 60 principals in Hambantota district, 6 directors of Education and 5 trainers of principals data had been collected through questionnaires, interviews and documents. The methodology used was mixed method approach and the data were analyzed both quantitatively and qualitatively. Through this study, the necessary capacity development areas for Sri Lankan government school principals were identified where suitable suggestions were made for modifications. It could be concluded that the principals were in need of appropriate pre-service or in service professional development. The awareness of the principals regarding the nature and the duration of the existing capacity development programmes was not satisfactory. Over half of the selected principals in the sample have pointed out that the capacity development programmes for the school principals are not conducted based on their capacity development needs.
Of Flesh and Steel: Computational Creativity in Music and the Body Issue

Could machines ever take our place in the creation of art, and particularly music? The outstanding results of some well-known AIs (e.g. EMI, Flow Machines) might make us believe that this is the case. However, despite this evidence it seems that machines present some intrinsic limits both in creative and non-creative contexts (already highlighted by John Searle and the debate around mechanism). The arguments of this paper are centred around this very belief: we are convinced that the utopian claims regarding all-round machine intelligence are not plausible and that our attention should be directed towards more relevant issues in the field of computational creativity. In particular, we focus our attention on what we call the “body issue”, i.e. the role of the body in the experience and creation of music, that we consider problematic for the idea of a truly creative machine (even if we take into consideration weaker renditions of artificial intelligence). Our argument is based on contemporary findings in neuroscience (especially on embodied cognition) and on the theories of Maurice Merleau-Ponty and Roland Barthes.
Needs Analysis of Middle School Underachieving English Language Learners

영어학습부진아를 지도할 수 있는 역량 배양을 목적으로 설계된 ‘영어학습부진아 이해와 지도’ 과목의 교육과정을 개선하기 위해 영어학습부진 중학생의 학습자 요구를 조사하였다. 본 연구의 설문조사는 영어 학습에 대한 7개 영역의 14개 문항을 5점 리커트 척도로 설계하여 수준별 수업을 진행하는 재단 내 산하 중학교의 영어 기초반 학생을 대상으로 2022년 11월과 12월에 걸쳐 온라인으로 진행되었다. 응답자 32명의 응답에 의하면, 모르는 단어를 발음대로 읽는 학습, 4 단어 이상의 복잡하고 긴 영어 어구 학습, 짧고 간단한 영어 문장 학습은 1∼3학년 공통적으로 가장 배우고 싶어 하였다. 반면, 1학년 학생은 영어 알파벳 쓰기와 영어 자음과 모음 등의 발음 학습을 원하지 않았고, 2∼3학년 학생은 영어 알파벳 쓰기, 영미문화 학습 및 영어로 진행하는 영어 수업을 가장 원하지 않는 것으로 조사되었다. 그러나 각 학년마다 영어 알파벳 쓰기가 필요하다고 응답한 응답자의 비율은 23.5∼33.3%로 나타나 학습자별 맞춤식 지도가 필요해 보인다. 마지막으로 조사 결과를 바탕으로 본 연구는 ‘영어학습부진아 이해와 지도’ 과목의 교육과정 단계별로 반영해야 할 사항을 제시하고 연구의 한계점도 서술하였다.In order to narrow the educational gap in school, the educational needs of middle school underachieving English language learners (U-ELLs) were investigated to improve the curriculum of a course of “Understanding and Guiding of Underachieving English language Learners” in a college of education. During November and December, 2022, the survey of this study was conducted via online for ELL students in low-level classes at a middle school. The final 15 questions were designed with 14 questions (on a 5-point Likert scale) in 7 areas about English learning and 1 question about student’s grade. The responses showed that 32 ELLs wanted to learn 1) pronunciation of unfamiliar words, 2) complex and long English phrases with more than four words, and 3) short and simple English sentences. Meanwhile, 1st grade ELLs were not interested in learning English alphabet writing and the correspondence between English alphabets and sounds. 2nd and 3rd grade ELLs were not interested in learning English alphabet writing, British and American cultures, and TEE. The participating ELLs did not want to learn English alphabet writing, but 23.5∼33.3% of the respondents wanted to learn English alphabets, which seems that pre-service teachers should be prepared to teach U-ELLs with customized pedagogy. Finally, the researchers suggested how to improve pre-service teacher eduction and added the study limitations.
Capacity Enhancement of Preparatory Teachers by Field Understanding

제 4차산업혁명이 진행됨에 따라 초·중등학교에도 그에 걸맞은 교육의 변화가 필요하다는 인식하에 2021년 말 교육부는 교원양성체제 발전방안을 발표하였다. 그 방안 중의 하나가 ‘현장이해력 제고’이고, 이를 위해 대학교원과 현장교사와의 공동연구를 제시하였다. 본 연구는 사범대학 교원이 현장교사와 공동으로 현재 중·고등학교의 현직교사들로부터 현장의 목소리를 듣고, 그 내용을 토대로 예비교사의 현장이해력을 높이는 방법을 찾는 것을 목적으로 하였다. 이를 위해 학습자, 교육정책, 직무환경 세 가지 변인으로 구성된 설문지를 현직교사로 하여금 응답하게 한 뒤 그 내용을 분석하였다. 응답에 따르면, 요즘 학습자들은 디지털 네이티브 세대이고 개인주의적 성향이 강하다. 교육정책에서는 고교학점제에 대해 관심이 쏠리고 있고, 디지털화를 두드러진 변화 중 하나로 지목하였다. 직무환경에서는 학교폭력, 학부모의 무분별한 민원, 교권의 추락이 주된 스트레스 요소였다. 결론적으로 예비교사가 현장이해력을 높이려면 중·고등학생과 함께하는 프로그램에 많이 참여하고, 학습자에 대한 이해와 소통을 위해 요즘 세대의 관심사에 관심을 가질 필요가 있다. 고교학점제와 같은 현재 교육이슈에도 관심을 갖고 그에 따른 변화에도 주의를 기울여야 한다. 무엇보다도 자신의 교사철학을 세우고 그것을 늘 검토하는 것이 필요하다.As the fourth industrial revolution is in progress, the Education Ministry announced development plan of teacher training system at the end of year of 2021 in recognition of the necessity of changes in elementary, middle and high schools. It proposed enhancement of field understanding by joint researches between college researchers and incumbent teachers. This study aims at enhancing field understanding by listening to teachers on the job. We asked 11 incumbent teachers to complete the questionnaire composed of 3 factors of students, curriculum and working environments as a research method. As a result, the students of these days are digital natives, and have a strong individualistic tendencies. Teachers have strong interest in high school credit system, and digital instruments. They felt much stress in school violence, excessive interference of parents of students, and the fall of teaching authority. In conclusion, they proposed the preparatory teachers to participate in the programs with teenagers, And they proposed them to have interests in the interests of teenagers in order to understand them. Above of all, they emphasized at planning up the their own philosophies of teachers.
A Study on the Subjectivity of College Students on the Learners-Based Classes in Competency-Based Liberal Arts Education

본 연구에서는 대학생들이 역량기반 교양교육의 학습자 중심 수업에 대해 어떻게 인식하는지 파악하여 역량기반 교양교육에 적극적으로 참여시킬 수 있는 학습자 관점의 교수학습 개선 방안을 마련하고자 하였다. 연구의 목적을 달성하기 위해 Q 방법론을 활용하였으며, Q표본은 31개의 진술문으로 구성되었고, P표본은 J대학교에서 동일한 A교양교과목을 수강하는 1학년 28명 학생으로 구성되었다. 연구결과 총 6가지의 유형이 도출되었으며 유형 1은 자기 성장을 중요하게 생각하는 유형, 유형 2는 수업환경을 중요하게 인식하는 유형, 유형 3은 직무능력 향상을 중요하게 생각하는 유형, 유형 4는 실용지식 교육을 중요하게 생각하는 유형, 유형 5는 수업활동을 중요하게 생각하는 유형, 유형 6은 개인적인 역량 향상을 중요하게 생각하는 유형으로 나타났다. 이에 본 연구는 대학생들의 역량기반 교양교육의 학습자 중심 수업에 대한 교육요구도를 파악하여 그에 필요한 적합한 교수학습 개선에 필요한 기초자료를 마련하였다는 것에 의의가 있다.This study tried to figure out how college students perceive learner-centered classes in competency-based liberal arts education, and to prepare ways to improve teaching and learning from the learner's perspective so that they can actively participate in competency-based liberal arts education. To achieve the purpose of the study, the Q methodology was used, the Q sample consisted of 31 statements, and the P sample consisted of 28 first-year students taking the same liberal arts course A at J University. As a result of the study, a total of 6 types were derived. Type 1 is the type that considers self-growth as important, Type 2 is the type that considers the classroom environment important, Type 3 is the type that considers job ability improvement as important, and Type 4 is the type that values practical knowledge education as important, Type 5 considers class activities as important, and Type 6 considers personal competency improvement as important. Therefore, this study is meaningful in that it prepares the basic data necessary to improve teaching and learning by identifying the educational needs of college students for learner-centered classes in competency-based liberal arts education.
Objective understanding of history and exploration of educational possibilities through multiperspectivity -Focusing on the source of different views of the same subject-

본 연구의 목적은 다중시각을 통한 객관적 역사 이해의 교육 가능성에 대해 탐색해 보는 것에 있다. 이러한 문제의식 하에서 연구자는 ‘인간은 각자의 위치성, 입장구속성, 지향성에 기반하여 동일한 대상을 서로 다르게 본다’라는 가설을 먼저 설정한 다음, 동일한 대상을 서로 다르게 보는 근원적 원인에 대해 논증하였다. 그리고 각자의 시각에서는 다르게 보이더라도 동일한 특정 대상을 ‘객관적 대상’으로, 상호주관적 공통성을 ‘대상의 객관성’으로 나누어 개념을 탐색하고, 이러한 기초 개념들을 바탕으로 대상과 시각자 간에 삼각측량의 상관관계로 추론하는 객관적 역사 인식 구도에 대해 설명하였다. 나아가 역사학습에 활용할 수 있도록 다중시각을 통한 객관적 역사이해 6단계를 모형으로 제시하고, 이를 역사교실에서 적용할 수 있도록 안내하였다. 현재 사용하는 용례 중 적확한 용어가 없다는 점에서 연구자는 ‘입장구속성’이라는 개념어를 창안하였다. 본 연구는 다중시각을 통한 객관적 역사 이해와 교육 가능성을 탐색하기 위해 가설을 설정· 논증하고, 관련 개념들을 정리하는 동시에 이를 체계적으로 구조화하는 작업을 시도한 점에서 기존 연구와는 차별성이 있다.The purpose of this study is to explore the educational possibilities of objective historical understanding through multiple perspectives. Therefore, for the fundamental cause of seeing the same object differently, hypotheses were set up with three factors: each person's location, position constraint, and directivity, and then, in Chapter 2, the fundamental layer of seeing the same object differently was analyzed as a case. In Chapter 3, the concept was explored by dividing the same object into 'objective object' and the intersubjective commonality of the object into 'objectivity of object', even if viewed differently. Based on these basic concepts, the objective historical recognition process of deducing the correlation between the subject and the viewer through triangulation was described. In Chapter 4, the structure of multi-visual historical recognition was presented as a model so that it could be used for history learning. History teachers teach students living in the future how to think historically along with historical knowledge, and can design multi-visual history so that students can reflect on their own perspective. Therefore, this study analyzed several concepts and structured them in order to explore the possibility of objective historical recognition and education through multiperspectives.
Community-based education and teacher agency

본 연구는 지역 교육과정을 교사의 관점에서 분석하는 것을 목적으로 한다. 최근 지역을 기반으로 한 교육은 전 세계적으로 확산되는 추세이다. 가령 2022 교육과정 개정의 주요한 특징 중 하나는 지역사회 중심 교육의 강화이며 OECD 2030 프로젝트 역시 학교와 지역사회의 협력을 강조한다는 특징이 있다. 이는 불확실한 미래에서 학생들이 자기 주변 지역사회에 충분한 관심을 두고 책임감 있게 참여할 수 있도록 학교와 지역사회가 같이 협력하는 것을 강조하는 지속가능형 패러다임과도 관련이 있다. 지역 교육과정을 강조하는 국내외적 상황 속에서 학교와 지역 연계와 관련된 국내 연구는 지속적으로 이루어져 왔지만, 교육 운영 주체인 교사들이 지역사회 맥락에서 행위 주체성을 어떻게 발휘하는지를 다룬 질적 연구는 거의 이루어지지 않았다. 이와 같은 상황에서 연구자는 지역사회 연계 의식을 가진 개별 교사들이 개인의 전문적 그리고 단위학교 공동의 협력을 통해서 지역 교육과정을 운영시켜나가는 외국 사례를 발굴하고 이를 통해 국내 지역 교육과정을 위한 시사점을 제공하고자 한다. 본 연구는 미국 내에서도 지역분권의 성격이 강한 지역에 해당하는 미국 중서부 주(state)의 미들타운 지역사회 사례를 통해 교사들의 경험을 분석하고자 하였다. 본 연구는 미들타운 초등학교 교사 6명을 대상으로 개별면담을 실시하였다. 면담 결과, 교사들은 미들타운 지역과 다양한 형태로 연결되어 있었다. 특히 지역 자원에 해당하는 지역 텃밭, 지역 숲, 그리고 지역 센터등을 통해 지역 교육과정에 참여하고 있었다. 교사들은 지역과 연계의식을 가지고 지역과 공존한다는 공통의 가치를 가지고 지역사회와 협업하는 과정을 경험하고 있었다.The purpose of this study is to analyze community-based education from teachers’ perspective. The recent 2022 National Curriculum Revision emphasizes community-based education, and the OECD Education 2030 project also highlights cooperation between schools and local communities. In a future of rapid change and uncertainty, the cooperation of schools and communities will be more emphasized in order to provide a sustainable environment which helps students to be more deeply involved with their community. Under the international atmosphere of community-based education, there have been previous studies which examine school and community connection. However few qualitative research is practiced on how teachers achieved and exercise agency in improving local communities. In such a situation, the researcher conducts a case study with individual teachers in the US. This study is intended to provide implications for community-based education in Korea. This study has targeted the Middletown city in W state in the midwest of the United States. the Middletown city is one of regions which have strong community involvement. This study conducted individual interviews with 6 elementary school teachers in the Middletown city. Through the analysis of the interview results, the researcher derived the essential features that enable sustainable, community-based education. The research findings are expected to provide important implications for future research and policies for Korean community-based education.
Computational Versus Perceived Popularity Miscalibration in Recommender Systems

Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users' recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users' perception of popularity bias.
Calibration Learning for Few-shot Novel Product Description

In the field of E-commerce, the rapid introduction of new products poses challenges for product description generation. Traditional approaches rely on large labelled datasets, which are often unavailable for novel products with limited data. To address this issue, we propose a calibration learning approach for few-shot novel product description. Our method leverages a small amount of labelled data for calibration and utilizes the novel product's semantic representation as prompts to generate accurate and informative descriptions. We evaluate our approach on three large-scale e-commerce datasets of novel products and demonstrate its effectiveness in significantly improving the quality of generated product descriptions compared to existing methods, especially when only limited data is available. We also conduct the analysis to understand the impact of different modules on the performance.
EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation

The point-of-interest (POI) recommendation predicts users' destinations, which might be of interest to users and has attracted considerable attention as one of the major applications in location-based social networks (LBSNs). Recent work on graph-based neural networks (GNN) or matrix factorization-based (MF) approaches has resulted in better representations of users and POIs to forecast users' latent preferences. However, they still suffer from the implicit feedback and cold-start problems of check-in data, as they cannot capture both local and global graph-based relations among users (or POIs) simultaneously, and the cold-start neighbors are not handled properly during graph convolution in GNN. In this paper, we propose an enhanced encoder-decoder network (EEDN) to exploit rich latent features between users, POIs, and interactions between users and POIs for POI recommendation. The encoder of EEDN utilizes a hybrid hypergraph convolution to enhance the aggregation ability of each graph convolution step and learns to derive more robust cold-start-aware user representations. In contrast, the decoder mines local and global interactions by both graph- and sequential-based patterns for modeling implicit feedback, especially to alleviate exposure bias. Extensive experiments in three public real-world datasets demonstrate that EEDN outperforms state-of-the-art methods. Our source codes and data are released at https://github.com/WangXFng/EEDN
MDI: A Debiasing Method Combining Unbiased and Biased Data

In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.
From Stuck in a Dark Tunnel to Shining New Light: The Stories of Early Childhood Practitioners During the Pandemic

South Korea was one of the few countries in the world to face a major outbreak in the first phase of the COVID-19 pandemic. This study portrays the lived experiences of Korean early childhood practitioners striving to thrive between 2020 and 2021. Their stories offer their reflections on their challenges, struggles, and lessons learned throughout the pandemic. Findings reveal that, as time passed, there were longstanding struggles and newly emerging challenges. This study sheds light on early childhood practitioners’ experiences of teaching during the unique and uncertain time of the pandemic, which can offer in-depth understanding for the betterment of future teaching.
TMML: Text-Guided MuliModal Product Location For Alleviating Retrieval Inconsistency in E-Commerce

Image retrieval system (IRS) is commonly used in E-Commerce platforms for a wide range of applications such as price comparison and commodity recommendation. However, customers may experience inconsistent retrieval problems. Although the retrieved image contains the query object, the main product of the retrieved image is not associated with the query product. This is caused by the wrong product instance location when building the product image retrieval library. We can easily determine which product is on sale through the hint of the title, so we propose Text-Guided MuliModal Product Location (TMML) to use additional product titles to assist in locating the actual selling product instance. We design a weakly-aligned region-text data collection method to generate region-text pseudo-label by utilizing the IRS and user behavior from the E-commerce platform. To mitigate the impact of data noise, we propose a Mutual-Aware Contrastive Loss. Our results show that the proposed TMML outperforms the state-of-the-art method GLIP [11] by 3.95% in top-1 precision on our multi-objects test set, and 2.53% error located images in AliExpress has been corrected, which greatly alleviates the retrieval inconsistencies in IRS.