Reviewerly/exHarmony huggingface dataset:
Exploring the Possibility of Convergence Education for Developing Youth's Character Competencies of the Future Society: Focused on Literature Museum Experience

이 연구의 목적은 미래사회의 청소년 인성역량 개발을 위한 융·복합교육의 가능성을 탐색하고, ‘문학공간’ 연계 체험중심 인성교육을 위하여 지역문학관 활성화 방안을 이론적으로 탐색하는 것이다. 연구의 이론적 기초로서 미래사회의 핵심역량, 융·복합교육을 실천하기 위한 교육환경, 교사의 역할, 문학공간과 관련된 연구를 중점적으로 고찰하였다. 연구의 결과는 다음과 같다. 첫째, 문학공간과 연계한 체험중심 인성교육은 학생들에게 친숙한 학습 환경을 제공해줄 뿐만 아니라 학습에 필요한 자원들을 지역사회로부터 제공받음으로써 교육공간이 확장되는 효과를 종합할 수 있었다. 둘째, 문학공간의 문학 콘텐츠에 주목하고 활용 양상을 국내·외 사례를 통해 밝힘으로써 청소년의 인성역량 신장을 위한 교과 연계 융·복합교육의 실천 가능성을 확인하였다. 결론적으로 미래사회의 역량 개발을 위한 융·복합교육의 일환으로 ‘문학공간’을 활용한 체험중심의 인성교육은 청소년에게 다양한 인성 덕목을 체화할 수 있을 것으로 판단되며, 융·복합교육 측면에서 긍정적인 효과가 도출될 것으로 기대된다. 후속연구로는 테크놀로지를 활용한 가상 문학공간 조성과 활성화 방안, 그 효과를 검증하는 평가 연구를 제안하였다.The purpose of the study was to explore the possibility of convergence education for developing youth’s character competencies of the future society, and to theoretically seek ways to revitalize regional literature museums for experience-centered character education linked to ‘literature space.’ As a theoretical basis of the study, we reviewed a literature review on the core competencies of the future society, the educational environment for practicing convergence education, the role of teachers, and the literature space. The results of the study were as follows. Firstly, experience-based character education in connection with literature museum not only provided students with a familiar learning environment, but also explored the effect of expanding educational space by providing resources for learning from the community. Secondly, focused on the literary contents of the literature museum and clarifying the usage patterns through domestic and international cases, the possibility of practical convergence education for the development of the character of youth was confirmed. In conclusion, experience-based character education using the literature space as a part of convergence education for the future development of future society is expected to embody various virtues for adolescents, and was expected to have a positive effect on the aspect of convergence education. As a follow-up study, the researcher proposed a method of creating and activating a virtual literary space using technologies, and proposed an evaluation study to verify the effectiveness of technology utilization.
Learning-Based Misalignment Detection for 2-D/3-D Overlays

In minimally invasive procedures, a standard routine of observing the operational site is using image guidance. X-ray fluoroscopy using C-arm systems is widely used. In complex cases, overlays of preoperative 3-D images are necessary to show structures that are not visible in the 2-D X-ray images. The alignment quality may degenerate during an intervention, e. g. due to patient motion, and a new registration needs to be performed. However, a decrease in alignment quality is not always obvious, as the clinician often focuses on structures which are not visible in the 2-D image, and only these structures are visualized in the overlay. In this paper, we propose a learning-based method for detecting different degrees of misalignment. The method is based on point-to-plane correspondences and a pre-trained neural network originally used for detecting good correspondences. The network is extended by a classification branch to detect different levels of misalignment. Compared to simply using the normalized gradient correlation similarity measure as a basis for the decision, we show a highly improved performance, e. g. improving the AUC score from 0.918 to 0.993 for detecting misalignment above 5mm of mean re-projection distance.
Analysis of Students' Academic Achievements: A Personality Test Perspective

Using the personality test perspective, this study adopts Data Mining and Association Rule Learning in Big Data Analysis as the basis for finding correlations between the personality test and students' academic performance. The personality test results are divided into 6 types of personality characteristics: Type A, B, C, D, E and F. The most personality traits of the University are Type D, and the main purpose is to find out the most appropriate students for each department, through the relationship between personality traits and academic performance, helping to guide students to master their personality traits and develop their potentials for the future.
Dizar: An Architecture of Distributed Public Key Infrastructure Based on Permissoned Blockchain

With the current blockchain-based Public Key Infrastructure (PKI) being in its early stage of R&D, it is suffering from many shortcomings, such as its reliance on the centralized Certificate Authority (CA), the faulty identity registration and verification mechanism, and the difficulty in certificate management. As a result, the existing blockchain based PKI has trouble in adapting to a distributed network. Therefore, we have proposed Dizar: A distributed PKI architecture based on permissoned blockchain. Dizar architecture is designed with a distributed ledger operation system that can verify security. Based on no certificate authentication, electronic certificates with legal identities in the network are registered in a secure and verifiable permissioned blockchain, thus realizing the full-cycle management of the issued electronic certificates. The performance of Dizar is analyzed and compared with previous protocols. The results show that the Dizar architecture has better adaptability to a distributed network.
Directional Search Algorithm and its Utilization of Simulated Annealing and Expand-Retract

We present the Directional Search Algorithm (DSA), a novel metaheuristic optimization algorithm combining a highly-simplified version of Particle Swarm Optimization (PSO) with ideas adapted from simulated annealing, and a dynamic "Expand-Retract" method, a novel technique to balance exploration and exploitation. We compare the speed and accuracy of DSA to PSO in finding the global minimum of some common artificial landscapes for single-objective optimization and find that DSA outperforms PSO for both criteria on the selected functions.
Association analysis of Primary liver cancer based on Apriori Algorithm

This paper briefly describes the Apriori algorithm for primary liver cancer data, and then performs data preprocessing based on the characteristics of primary liver cancer patients, including data import and extraction, and embedding the algorithm into primary liver cancer. The implementation of clinical warning. After that, the Apriori algorithm is used to realize the association of the data association rules of primary liver cancer, and the internal valuable association rules are obtained, which provides suggestions for improving the doctor's remediation and prevention, so as to prevent the occurrence and reduction of primary liver cancer. The incidence of primary liver cancer has important practical significance.
Encoding-Decoding Methods for Neural Machine Translation

Deep Learning techniques have significant progress in the last few years. Different designs and methods of the model have experimented to enhance the performance in various domains of Natural Language Processing (NLP). Machine Translation is a domain of NLP. In this paper, we have reviewed different Encoding-Decoding methods for Neural Machine Translation(NMT). We also, compare these various methods and put forward a past, present and future of Neural Machine Translation.
Desires and Possibilities in Virtual Education

The sudden migration towards virtual classes caused by the Covid-19 crisis has demanded the application and development of different resources.Both students and teachers had to be flexible enough to face this change.Two annual university courses in Design Education will be examined: 2019 and 2020, shifting from face-toface to online teaching.The information compared derives mainly from surveys and the final evaluation scores.The main benefits and drawbacks will be discussed, concerning the outcomes of the course and the experiences of teachers and students.The advantages of reformulating the course and the possibilities of online teaching were considered and exploited as far as possible, in the limited context of a public university in a developing country.Many positive strategies derived from this transformation should be retained when classroom meetings are allowed.Future courses should incorporate blended, hybrid methods of instruction that regard the specific requirements of design education.
SMLHADC: Security Model for Load Harmonization and Anomaly Detection in Cloud

Cloud computing (CC) emerged as one of the important utility such as water and electricity bills, where the user has to pay for whatever quantity they have utilized for their tasks. As cloud users are increasing; so the crucial challenges for the cloud service provider (CSP) is to balance the load among the resources shared by the end-users and curb increased security risks, misuse, or malicious attacks in cloud computing. The functionality of load balancing is divided into two functions, first, there will be allocation of resources and the second is the provisioning of resources along with task scheduling in the distributed system such as CC. Although many state-of-the-art approaches are available in the literature which provides load balancing and better resource utilization. In this paper, we build a dynamic prediction approach for cloud resource usage, discuss a load balancing technique, and identify sudden spikes and failures that are causes of the anomaly using a reactive and proactive approach. The final result demonstrates a comparative analysis of deep learning models for the prediction of cloud resource utilization, insight into dynamic load balancing technique and anomaly detection.
BCOA: Blockchain Open Architecture

Blockchain as an emerging computing paradigm has revolutionized the next generation internet. The design and realization of blockchain in multiple industries are very challenging tasks due to the complexity of blockchain. Firstly, it refers to the integrations of multiple technologies within communications, distributed system and security. Secondly, it lacks systematic method and theory to guide developers to design the business solutions. To facilitate the down-to-earth application of blockchain, we propose a BlockChain Open Architecture (BCOA) as a reference architecture which divides the blockchain into nine layers: interactions, scenarios, services, components, infrastructure, integration, data architecture, security and privacy and governance. It allows the designers and developers to use this methodology to speedily design solutions for blockchain applications. Finally we use two cases to demonstrate the feasibility and effectiveness of the proposed architecture.
Fuzzy logic programs as hypergraphs. Termination results

Graph theory has been a useful tool for logic programming in many aspects. In this paper, we propose an equivalent representation of multi-adjoint logic programs using hypergraphs, which are a generalization of classical graphs that allows the use of hypergraph theory in logic programming. Specifically, this representation has been considered in this paper to increase the level and flexibility of different termination results of the computation of the least model of fuzzy logic programs via the immediate consequence operator. Consequently, the least model of more general and versatile fuzzy logic programs can be obtained after finitely many iterations, although infinite programs or programs with loops and general aggregators will be considered.
Researchers’ Motivation and Its Correlates: An Empirical Study Amid COVID-19 Pandemic in Arab Region

This study aims to shape an insight towards researchers’ involvement in research projects in the Arabian region, mainly through assessing the intrinsic and extrinsic motivations towards research and their association with research attitudes and preferences. A cross-sectional study was conducted in the Arab states amid the COVID19 pandemic. A questionnaire was self-developed based on a literature review, validated, and distributed among scientific research communities. A convenience sampling strategy (basically through snowball and self-selection techniques) was adopted, focusing majorly on academic, social networking sites to increase the visibility of and response to the developed online survey. The sample size comprised 205 researchers/scholars from nine Arab countries. The collected data were analyzed using SPSS software version 25 to generate statistical findings based on descriptive, means comparison, correlational, and regression outputs. A p-value < 0.05 was considered being significant. On a scale of 5-point, most respondents exhibited acceptable levels of intrinsic (4.05 ± 0.58) and extrinsic motivation (3.67 ± 0.55) towards research conduct. 87% of total respondents were intrinsically motivated, while 72% were extrinsically motivated. Through regression analysis (p ˂ 0.05), intrinsic motivation is found a potent enabler for researchers to pursue a leading role in research, maintain continuous research activity, consider innovative research strategies, and leverage research through practical expertise and solving practice-related issues. Also, intrinsic motivation was associated with higher scientific productivity and lesser time to accomplish a publication. In the overall picture, intrinsic motivation seems of more potential influence than extrinsic motivation on researchers’ selection and preferences of research roles. The findings from this study could reinforce efforts of research policy- and decision-making dedicated to fostering Arab researchers’ involvement, attitude, and role in scholarly activities; and establishing evidence-based tactics to promote their motivation for publication and research productivity.
S<sup>2</sup> LMMD: Cross-Project Software Defect Prediction via Statement Semantic Learning and Maximum Mean Discrepancy

Different from within-project software defect prediction (WPDP), cross-project software defect prediction (CPDP) does not require sufficient training data and can help developers in the early stages of software development. Recent studies tried to learn semantic features for CPDP by feeding neural networks with abstract syntax tree (AST) token vectors. However, the ASTs directly parsed from software modules usually have complex structures, which are reflected on more nodes and deeper size, and the transfer learning is not regularly adopted to further reduce the data distribution difference between the source project and the target project. To solve these problems, we aim to joint learn the statement level trees (SLT) and alleviate data distribution difference with maximum mean discrepancy (MMD) to improve defect prediction performance on CPDP. Specifically, we propose a novel cross-project defect prediction method S<sup>2</sup>LMMD via statement semantic learning and MMD. We first construct the SLT by splitting the original AST on specified node. Then we generate more effective semantic features by learning of sequence embedding with Bi-GRU neural network. Finally, a transfer loss MMD is carried out to keep more common characteristics across different project datasets to further improve CPDP performance. To verify the effectiveness of our proposed method, we conducted experiments on ten widely used open-source projects and evaluated the experimental performance by using AUC measures. Our empirical results show that our proposed method S<sup>2</sup>LMMD can significantly outperform eight state-of-the-art baselines. In addition, for semantic learning, SLT has a higher influence on CPDP, while MMD is of great significance in transfer learning.
Software Requirement Criteria based on Human Errors

Software requirement specifications have been observed to largely impact the dependability and the cost of software systems in software development and certification phases. Inappropriate specification of software requirements can cause software developers' erroneous mental representations, thus leading to defects that propagate into subsequent development phases. Understanding the human error mechanisms of software requirement representation is significant for reducing the defects originated from requirements. This paper proposes a theory on the human error mechanism of software requirement, and derived two new criteria to avoid requirement specification triggering the human errors of developers. The criteria were validated by an experiment. Results show that: 1) once a requirement specification contained the error-prone scenarios of the two proposed criteria, developers indeed committed corresponding errors; 2) violating the proposed criteria tended to cause common defects, which are the defects introduced by two or more developers in the same way.
Sacrificial Floors and Tables

Abstract A floor or table strewn with beer cans, toys, bits of scrap metal, ceramic bowls, miscellaneous electronics, and wires: a collection of things to make sound. Instrument and sound object have been sacrificed for a music of things. DIY electronic music and maker communities have challenged what sound is made with and to what end. Within these communities, the sacredness of the musical instrument has been debunked. Percussion is examined in relation to a music of things and object-orientated performance, and the performance-table is also viewed in the context of experimental sound and performance works. In this chapter George Brecht’s The Cabinet and readymade actions, and the performance-tables of Keith Rowe and Adam Bohman are illustrated. In addition, the work of Tetsuya Umeda is used to demonstrate performance-installation where making and unmaking are seen as critical parts of performance.
Tilt angle optimisation by Taylor’s series expansion for maximum solar radiation in humid subtropical regions of India

This paper presents a novel method for maximum utilisation of solar energy on solar collectors by optimising tilt angle (β). The optimisation has been done in two steps, in first, the partial differential equation of total radiation on tilted surface (HT) is differentiated with respect to β and in second, Taylor’s series expansion method is applied to find the optimised tilt angle (βTaylor). The results so obtained are applied to different locations of India and it is found that on anisotropic atmospheric conditions the total monthly radiation falling on collector surface βTaylor is increased significantly at each location. The maximum gain of 3.19% during winters, 2.46% during spring, no change during summers and 3.14% during autumn is observed in Kolkata. Only 1.42% radiation losses on seasonal and 8.14% radiation loss on annual basis as compared to radiation on monthly basis at New Delhi station are observed.
Research on complaint prediction based on feature weighted Naive Bayes

Abstract Complaint prediction is a hot topic in current research. Due to complaint prediction is random, the accuracy of complaint prediction is not high. To solve this problem, this paper proposes a complaint prediction method based on feature weighted Naive Bayes. According to rough set theory, we first calculate the importance of each conditional attribute, combine the importance of attribute as the weight with the naive Bayes classifier to form a weighted naive Bayes classifier. We use multiple classifiers to make predictions, the latter classifier performs iterative learning on the basis of the previous classifier, and finally all classifiers are given different weights to make decisions. The experimental results show that the proposed method effectively combines the weighting method and the Naive Bayes method to achieve reliable prediction of complaints.
Fed-Tra: Improving Accuracy of Deep Learning Model on Non-iid in Federated Learning

Federated Learning (FL) has received more and more attention from researches and industries in that it can break the data island while protecting the data privacy. However, the original federated average algorithm proposed in FL ignores the difference in distribution of data from multiple participants (widespread in reality), which seriously undermine the performance of deep learning models. It is more serious when the gap of data-volume is large. In this paper, we propose a novel and universal federated learning method, named Fed-Tra, to effectively weaken biases in the model training to build high-precision models. Fed-Tra does this with dynamically adjusting the weight of local training samples for each round in all participants. Moreover, our evaluation on real-world datasets shows that the Fed-Tra achieves nearly +28% improvement of F1-Score compared with the original federated average algorithm.
Super-Resolution of Thermal Images Using GAN Network

Super-resolution (SR) reconstruction of thermal images has been one of the most active research areas specifically for industrial applications. However, most of the conventional RGB SR models available in the literature are not necessarily applicable to thermal images due to their difference in characteristics when compared to normal camera images. The recent advancement in the field of deep learning-based SR has helped achieve unbelievable results. Despite the advancement in models like deep convolution neural networks (CNN) and Generative adversarial networks, there remain multiple problems unsolved that will help improve the spatial resolution of thermal images. Not only the developed model should be computationally efficient but also easily implementable in industrial applications. Motivated to overcome the said limitations, in this work a generative adversarial network (GAN) based single images super-resolution architecture is proposed for thermal camera images. The developed model not only generates at par results with the other model but also is easy to implement and computationally efficient. The modified architecture has an identical layout inspired by SRGAN. In order to make the model faster to train while having less training parameters, the number of residual blocks was reduced to 5. The batch normalization layers were excluded from the residual blocks of both the Generator and Discriminator networks to remove the redundancy. Before each convolution layer, reflective padding is utilized at the edges to preserve the size of the feature maps. The comparative results revealed that the proposed network trained on thermal images produced high-quality images with enhanced details, while still maintaining image features and perspective throughout. The experimental results show that the proposed model has achieved a reduction in computation time compared to the State-of-the-Art method. The suggested strategy has outperformed the SOTA methods with the improvement of approximately 2dB in PSNR along with 0.9825 of SSIM.
Building a Chinese Slang Sentiment Lexicon Using Online Crowdsourcing Dictionaries

Microblogging platforms are now one of the most popular means of social media in China. Carrying sentiment analysis on those platforms can provide valuable insights for various uses. However, the heavy use of Internet slang in microblog contexts and the lack of slang vocabulary in sentiment lexicons make it problematic. Aimed at this issue, we propose a method to build a comprehensive sentiment lexicon for Chinese internet slang. We leverage online sources to acquire a list of slang words first. Then, a method based on SO-PMI (Semantic Orientation from Pointwise Mutual Information) is used to assign the sentiment polarity to each word. By Utilizing online sources, the slang lexicon has comprehensive coverage of internet slang. The sentiment categorization method based on SO-PMI guarantees the sentiment polarity we acquire from microblog flatforms is compatible with the same microblog context the lexicon aimed to analyze.