Reviewerly/exHarmony huggingface dataset:
A Study on the Determinants of the General Public’s Behavioral Intention to Prevent COVID-19

본 연구는 일반 대중들 327명을 대상으로 코로나19 감염병 예방행동 의도에 영향을 미치는 요인을탐색하였다. 주요 결과는 다음과 같다. 첫째, 코로나19와 관련하여 정부와 질병관리청, 그리고 의료/ 감염병 전문가가 제공하는 정보의 신뢰와 일반 대중의 코로나19에 대한 지식이 위험인식 요인인지각된 민감성과 지각된 심각성에 미치는 영향을 검증한 결과, 정보에 대한 신뢰는 지각된 심각성에유효한 영향을 미치지 못하였으나 지각된 민감성에는 정적 영향을 미쳤다. 일반 대중의 코로나19에대한 지식은 지각된 민감성과 지각된 심각성 모두에 정적 영향을 미치는 것으로 확인되었다. 둘째, 정보에 대한 신뢰와 지식이 코로나19 감염병 예방행동 의도에 미치는 영향을 검증한 결과, 정보에대한 신뢰와 지식은 예방행동 정적 영향을 미치는 것으로 확인되었다. 셋째, 위험인식 요인인 지각된민감성과 지각된 심각성이 예방행동 의도에 미치는 영향을 검증한 결과, 지각된 민감성과 지각된심각성은 코로나19 예방행동 의도에 정적 영향을 미쳤다. 본 연구는 일반 대중의 코로나19 예방행동을 이해할 수 있는 모델을 제안하였다는 점에서 학술적, 실무적 의의가 있을 것이다.This study investigated the factors affecting the intention to prevent COVID-19 infectious diseases, targeting 327 general public. The main results are as follows. First, trust in information provided by the government, the Korea Centers for Disease Control and Prevention, and medical/infectious disease experts related to COVID-19 had a positive effect on perceived susceptibility, and the general public’s knowledge about COVID-19 had a positive effect on perceived susceptibility and perceived severity. Second, trust and knowledge of information had a positive effect on behavioral intention to prevent COVID-19. Third, perceived susceptibility and perceived severity, which are risk perception factors, had a positive effect on behavioral intention to prevent COVID-19. In conclusion, this study will have academic and practical significance in that it proposes a model that can understand the general public’s COVID-19 prevention behavior.
HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.

BERT-based models are currently used for solving nearly all Natural Language Processing (NLP) tasks and most often achieve state-of-the-art results. Therefore, the NLP community conducts extensive research on understanding these models, but above all on designing effective and efficient training procedures. Several ablation studies investigating how to train BERT-like models have been carried out, but the vast majority of them concerned only the English language. A training procedure designed for English does not have to be universal and applicable to other especially typologically different languages. Therefore, this paper presents the first ablation study focused on Polish, which, unlike the isolating English language, is a fusional language. We design and thoroughly evaluate a pretraining procedure of transferring knowledge from multilingual to monolingual BERT-based models. In addition to multilingual model initialization, other factors that possibly influence pretraining are also explored, i.e. training objective, corpus size, BPE-Dropout, and pretraining length. Based on the proposed procedure, a Polish BERT-based language model – HerBERT – is trained. This model achieves state-of-the-art results on multiple downstream tasks.
Online web navigation assistant

The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.
My Virtual Self: The Role of Movement in Children's Sense of Embodiment

There are vast potential applications for children's entertainment and education with modern virtual reality (VR) experiences, yet we know very little about how the movement or form of such a virtual body can influence children's feelings of control (agency) or the sensation that they own the virtual body (ownership). In two experiments, we gave a total of 197 children aged 4-14 years a virtual hand which moved synchronously or asynchronously with their own movements and had them interact with a VR environment. We found that movement synchrony influenced feelings of control and ownership at all ages. In Experiment 1 only, participants additionally felt haptic feedback either congruently, delayed or not at all – this did not influence feelings of control or ownership. In Experiment 2 only, participants used either a virtual hand or non-human virtual block. Participants embodied both forms to some degree, provided visuomotor signals were synchronous (as indicated by ownership, agency, and location ratings). Yet, only the hand in the synchronous movement condition was described as feeling like part of the body, rather than like a tool (e.g., a mouse or controller). Collectively, these findings highlight the overall dominance of visuomotor synchrony for children's own-body representation; that children can embody non-human forms to some degree; and that embodiment is also somewhat constrained by prior expectations of body form.
Application of Computer Information Processing Technology in Teaching Management Information System of Colleges and Universities

Abstract Human beings have entered a new era — information age, with the rapid development of information technology with network communication technology and multimedia technology as the core, which has caused various profound changes in many fields and accelerated the progress of human beings to the information society. At the same time, with the acceleration of the pace of global economic integration, the level of informatization has become an important symbol to measure the international competitiveness, modernization, comprehensive national strength and economic growth ability of a country and a region. The demand of social development for informatization is becoming more and more intense, and the development of informatization has received unprecedented attention in all countries of the world. As the front position of educational informatization, the process of higher education informatization has had an unprecedented impact on college teaching. Campus, classroom, library and other traditional teaching environment and space-time concept has been adopted and quietly changed. Statistics from the Science and Technology Development Center of the Ministry of Education show that some schools in colleges and universities have built their own campus network, and other schools are under construction, and 98.4% of the colleges and universities with campus network have realized office automation and scientific research networking.
Can We Define Design? Analyzing Twenty Years of Debate on a Large Email Discussion List

We explore the discipline’s attempts to define design in extant literature and in an analysis of 20 years of discussion extracted from the PhD Design email list hosted by JISCMail. We describe in detail our novel method using data mining and cluster visualization, and we present and compare the topics of discussion that emerged from the 20-year data sample. We identified four highly influential term labels based on their common occurrence: theory, knowledge, activity, and science. Our analysis of the text associated with these terms revealed prevailing perspectives of what design is within the design research community conversation. Despite robust discourse around relevant perspectives on design, the list discussions are and have been repetitive, with no significant progress made towards a consolidated definition of design. We propose that it may not be possible to define design in this way, and that the field should move away from reiteration and discuss the importance of design’s role as a leader of the global conversation about transdisciplinary approaches to research and design of the future scenarios and emerging pathways of humankind that such pathways point to.
Hybrid Image-based Rendering for Free-view Synthesis

Image-based rendering (IBR) provides a rich toolset for free-viewpoint navigation in captured scenes. Many methods exist, usually with an emphasis either on image quality or rendering speed. In this paper we identify common IBR artifacts and combine the strengths of different algorithms to strike a good balance in the speed/quality tradeoff. First, we address the problem of visible color seams that arise from blending casually-captured input images by explicitly treating view-dependent effects. Second, we compensate for geometric reconstruction errors by refining per-view information using a novel clustering and filtering approach. Finally, we devise a practical hybrid IBR algorithm, which locally identifies and utilizes the rendering method best suited for an image region while retaining interactive rates. We compare our method against classical and modern (neural) approaches in indoor and outdoor scenes and demonstrate superiority in quality and/or speed.
Federated f-Differential Privacy.

Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on record level: it provides the privacy guarantee on each individual record of one client's data against adversaries. We then propose a generic private federated learning framework {PriFedSync} that accommodates a large family of state-of-the-art FL algorithms, which provably achieves federated $f$-differential privacy. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by {PriFedSync} in computer vision tasks.
Entropy Partial Transport with Tree Metrics: Theory and Practice

Optimal transport (OT) theory provides powerful tools to compare probability measures. However, OT is limited to nonnegative measures having the same mass, and suffers serious drawbacks about its computation and statistics. This leads to several proposals of regularized variants of OT in the recent literature. In this work, we consider an \textit{entropy partial transport} (EPT) problem for nonnegative measures on a tree having different masses. The EPT is shown to be equivalent to a standard complete OT problem on a one-node extended tree. We derive its dual formulation, then leverage this to propose a novel regularization for EPT which admits fast computation and negative definiteness. To our knowledge, the proposed regularized EPT is the first approach that yields a \textit{closed-form} solution among available variants of unbalanced OT. For practical applications without priori knowledge about the tree structure for measures, we propose tree-sliced variants of the regularized EPT, computed by averaging the regularized EPT between these measures using random tree metrics, built adaptively from support data points. Exploiting the negative definiteness of our regularized EPT, we introduce a positive definite kernel, and evaluate it against other baselines on benchmark tasks such as document classification with word embedding and topological data analysis. In addition, we empirically demonstrate that our regularization also provides effective approximations.
Boosting the Restoring Performance of Deduplication Data by Classifying Backup Metadata

Restoring data is the main purpose of data backup in storage systems. The fragmentation issue, caused by physically scattering logically continuous data across a variety of disk locations, poses a negative impact on the restoring performance of a deduplication system. Rewriting algorithms are used to alleviate the fragmentation problem by improving the restoring speed of a deduplication system. However, rewriting methods give birth to a big sacrifice in terms of deduplication ratio, leading to a huge storage space waste. Furthermore, traditional backup approaches treat file metadata and chunk metadata as the same, which causes frequent on-disk metadata accesses. In this article, we start by analyzing storage characteristics of backup metadata. An intriguing finding shows that with 10 million files, the file metadata merely takes up approximately 340 MB. Motivated by this finding, we propose a Classified-Metadata based Restoring method (CMR) that classifies backup metadata into file metadata and chunk metadata . Because the file metadata merely takes up a meager amount of space, CMR maintains all file metadata in memory, whereas chunk metadata are aggressively prefetched to memory in a greedy manner. A deduplication system with CMR in place exhibits three salient features: (i) It avoids rewriting algorithms’ additional overhead by reducing the number of disk reads in a restoring process, (ii) it increases the restoring throughput without sacrificing the deduplication ratio, and (iii) it thoroughly leverages the hardware resources to boost the restoring performance. To quantitatively evaluate the performance of CMR, we compare our CMR against two state-of-the-art approaches, namely, a history-aware rewriting method (HAR) and a context-based rewriting scheme (CAP). The experimental results show that compared to HAR and CAP, CMR reduces the restoring time by 27.2% and 29.3%, respectively. Moreover, the deduplication ratio is improved by 1.91% and 4.36%, respectively.
Learning from error episodes in dialogue-videos: The influence of prior knowledge

In laboratory study environments, dialogue-videos, or videos of a tutor and a tutee solving problems together, have been shown to more effectively improve student learning than monologue-videos, or videos of tutors solving problems alone. Yet, few studies have replicated these findings in the context of authentic university classrooms. Here, we investigate the impact of dialogue-videos, and more specifically the effect of errors made by tutees in dialogue-videos, on student learning in the context of an undergraduate biology course. To understand why, we investigated students’ effort spent on watching videos, perceived influence of dialogue-videos, and worksheet completion rates. We found that higher-performing students perceived that they used the dialogue-videos to review content. We also found that higher-performing, but not lower-performing, students learned better from dialogue videos where tutees made errors. We also discuss the complexities of replicating laboratory studies in the classroom and implications of our findings.&#x0D; Implications for practice or policy:&#x0D; &#x0D; Tutee errors can be intentionally included in dialogue-videos to promote student learning.&#x0D; When students lack the necessary prior knowledge, monologue-videos may be more effective in presenting the course content.&#x0D; When using dialogue-videos, instructors can encourage students to collaborate to resolve any confusion in time to maximise the benefit of dialogue-videos in teaching and learning.&#x0D;
Working Remotely During the COVID-19 Pandemic: Efforts of the Ministry of Education in the United Arab Emirates

Following the global outbreak of COVID-19 pandemic, work is no longer being conducted in the traditional manner. This has created pressure on businesses to shift into a mode of remote working in order to avoid severe disruption. The United Arab Emirates (UAE) has attempted to face challenges accompanying this crisis and explore solutions with the wise vision and constant support of its leadership. This paper examines the transition of working procedures at one of the most influential entities in the UAE; the Ministry of Education (MOE). The purpose here is to shed light on how obstacles have been turned into opportunities in order for MOE employees to continue working remotely during such critical conditions. This is illustrated by the efforts of ministry departments that were interviewed for their feedback during the study. This paper provides insights into the activation of remote work in response to sudden incidents by highlighting measurements and factors that contribute to entities’ readiness for such a transition. This study analyzes the value and originality of MOE remote working practices and their impact on the wider community.
A Quality Evaluation Scheme for Curriculum in Ideological and Political Education Based on Data Mining

A large amount of data has been accumulated in the teaching and management of colleges and universities, while these data have not been effectively used. Thus, this paper introduces the design scheme of integrating data mining technology into the Curriculum in Ideological and Political teaching evaluation system, as well as the key technologies in the system development process. When the evaluation data is acquired, the data mining model of teaching quality evaluation is established, and the indicators of teaching quality evaluation are listed. Then the mining process and specific classification are provided in the analysis of association rules. This paper also designs an improved Aprior algorithm and describes its application in teaching evaluation. Finally, the mining object, data selection, data mining process and specific mining steps are determined in the cluster analysis. The relationship between teachers' basic information, teaching methods and teaching evaluation results is analyzed, and the clustering results are generated. Thus, the selection of evaluation methods, the formulation of relevant policies and the improvement strategies of teachers' teaching can be expected.
Convolutional Neural Networks for Automatic Detection of Colon Adenocarcinoma Based on Histopathological Images

Colorectal cancer is the second type of cancer that causes death and the third in terms of prevalence and number of cases. Due to the absence of symptoms in the early stages of the injury, several types of tests must be performed to discover the cancer, but these methods take a lot of time, cost and require a specialized expert. So in this paper, we proposed a Convolutional Neural Network (CNN) model that characterized by speed of diagnosis and high accuracy with few number of parameters for diagnosing colon adenocarcinoma since it is the most common of colorectal cancer, where it represents 95% of the total cases of colorectal cancer, depending on dataset of 10000 histopathological images divided into 5000 images for colon adenocarcinoma and 5000 images for benign colon. Our model consists of two paths each path is responsible for creating 256 feature maps to increase the number of features at different level in order to improve the accuracy and sensitivity of the classification. To compare the performance of the proposed model, Visual geometry Group (VGG16) model was prepared and trained on the same dataset. After training the two models we obtained an accuracy of 99.6%, 96.2% for the proposed model and VGG16 respectively, we also obtained from the proposed model a sensitivity of 99.6% and Area Under Curve (AUC) of 99.6% which indicates the effectiveness of this model in diagnosing colon adenocarcinoma.
Educational Leadership Doctoral Faculty Academic Qualifications and Practitioner Experiences in Georgia

Aim/Purpose: This study examined doctoral faculty of educational leadership within the state of Georgia in the United States. The aim was to illustrate the academic qualifications and practitioner experiences of the faculty that develop students in educational leadership programs to be scholarly practitioners and future educational leaders. Background: Faculty of educational leadership programs prepare their students to hold imminent senior leadership roles in P-12 school administration and higher education administration. In this apprenticeship model, doctoral faculty utilize their academic qualifications and/or practitioner experiences to develop students into scholarly practitioners. Methodology: A descriptive quantitative study utilizing content analysis was conducted to examine faculty of doctoral programs in educational leadership (n=83). True to this methodology, the inquiry of this study sought to better understand the academic qualifications and practitioner experiences of doctoral faculty in the field of educational leadership. Contribution: This study serves as a primer for faculty and researchers to visualize the doctoral faculty of educational leadership programs. It can serve as a catalyst to encourage empirical studies of educational leadership faculty and their effectiveness in preparing scholarly practitioners. Findings: Key findings included that nearly 2/3 of the faculty have their terminal degrees from a Research 1 institution, 3/5 hold a PhD, and 3/4 have practitioner experience in their respective field. Recommendations for Practitioners: Doctoral programs should examine the diversity of the academic qualifications and practitioner experiences of their faculty and develop strategies to enhance their programs with these complimenting skill sets. Recommendation for Researchers: Descriptive studies effectively “dip our toe” into a new area of inquiry. Considerations for future research includes examining student perceptions of their faculty who hold either a PhD or Ed.D, as well as those who are academics versus practitioners to better understand their effectiveness. Impact on Society: True to the work of John Dewey, education serves as the vehicle to promote a democratic society. Recognizing these doctoral faculty are preparing the future leaders of education, understanding the experiences of faculty will allow for better insight into those who are ultimately shaping the future of education. Future Research: Future research should focus on empirical studies that explore the effectiveness of faculty based on their academic qualifications and practitioner experiences through the lens of student perceptions.
The Role of Motivation and Desire in Explaining Students’ VR Games Addiction: A Cognitive-Behavioral Perspective

Virtual Reality games create an interactive platform for gaming and education for young people. While some longitudinal study has studied the beneficial effects of VR games on learning, the problematic use of VR games by a significant number of learners has become increasingly serious. The current study investigated the mediating effect of behavioral desire and moderation of cyber aggression on consumers’ VAD, which contributes to behavioral and psychological urge to use VR games. Data are from 367 VR games users collected. Findings suggest that behavioral desire influences addictive behavior in the presence of a positive flow experience. Furthermore, theoretical and practical implications in the context of VR-based games are also discussed in this current research.
The Impact of Pair Programming on College Students’ Interest, Perceptions, and Achievement in Computer Science

Active and collaborative learning has shown considerable promise for improving student outcomes and reducing group disparities. As one common form of collaborative learning, pair programming is an adapted work practice implemented widely in higher education computing programs. In the classroom setting, it typically involves two computer science students working together on the same programming assignment. The present study examined a cluster-randomized trial of 1,198 undergraduates in 96 lab sections. Overall, pair programming had no significant effect on students’ course performance; subject matter interest; plans for future coursework; or their confidence, comfort, and anxiety with computer science. These findings were consistent across various student characteristics, except that students with favorable pretest scores exhibited negative effects from pair programming.