Reviewerly/exHarmony huggingface dataset:
Development of 2022 revised English curriculum: Issues and challenges

This study explores issues and challenges pertaining to the development of the 2022 Revised English Curriculum through an in-depth research synthesis in the field. To this end, RISS, a major electronic database in Korea, was searched, and the search results were manually screened to identify relevant studies. A total of 42 academic journal articles and policy research reports were finally analyzed to pinpoint issues and challenges that need to be addressed in restructuring the 2015 revised curriculum. Four main issues were identified through the research synthesis: 1) identifying and defining subject competences as well as reflecting them comprehensively in the curriculum document; 2) designing a valid framework for organizing learning contents not only to show the sequence and continuity of the contents but also to address a lack of clarity in the achievement standards; 3) reexamining the level and continuity of achievement standards; 4) differentiating high school electives in terms of their goals and achievement standards in addition to ensuring effective implementation of the elective curriculum. The study conclusion provides insights that can guide future research.
A canonical model for seasonal climate prediction using Big Data

Abstract This article addresses the elaboration of a canonical model, involving methods, techniques, metrics, tools, and Big Data, applied to the knowledge of seasonal climate prediction, aiming at greater dynamics, speed, conciseness, and scalability. The proposed model was hosted in an environment capable of integrating different types of meteorological data and centralizing data stores. The seasonal climate prediction method called M-PRECLIS was designed and developed for practical application. The usability and efficiency of the proposed model was tested through a case study that made use of operational data generated by an atmospheric numerical model of the climate area found in the supercomputing environment of the Center for Weather Forecasting and Climate Studies linked to the Brazilian Institute for Space Research. The seasonal climate prediction uses ensemble members method to work and the main Big Data technologies used for data processing were: Python language, Apache Hadoop, Apache Hive, and the Optimized Row Columnar (ORC) file format. The main contributions of this research are the canonical model, its modules and internal components, the proposed method M-PRECLIS, and its use in a case study. After applying the model to a practical and real experiment, it was possible to analyze the results obtained and verify: the consistency of the model by the output images, the code complexity, the performance, and also to perform the comparison with related works. Thus, it was found that the proposed canonical model, based on the best practices of Big Data, is a viable alternative that can guide new paths to be followed.
Spatial-Temporal Gating-Adjacency GCN for Human Motion Prediction

Predicting future motion based on historical motion sequence is a fundamental problem in computer vision, and it has wide applications in autonomous driving and robotics. Some recent works have shown that Graph Convolutional Networks(GCN) are instrumental in modeling the relationship between different joints. However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatial-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization. Therefore, we propose the Spatial-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex spatial-temporal dependencies over diverse action types. Specifically, we adopt gating networks to enhance the generalization of GCN via the trainable adaptive adjacency matrix obtained by blending the candidate spatial-temporal adjacency matrices. Moreover, GAGCN addresses the cross-dependency of space and time by balancing the weights of spatial-temporal modeling and fusing the decoupled spatial-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW demonstrate that GAGCN achieves state-of-the-art performance in both short-term and long-term predictions. Our code will be released in the future.
Improving Students Listening Comprehension Through Topdown Technique

The research aims to measure the implementation of topdown technique on improving students ability at listening comprehension of grade XIak1 at SMKN 1 Panyabungan.The method of this research was Classroom Action Research. The participant in this classroom action research was 25 students of grade XIak1 at SMKN 1 Panyabungan. The instruments consisted of topdown technique, listening comprehension test, observation sheet, and interview, the data of this research was analyzed two method of data analysis. Quantitative data analyzed was used to examining and interpreting data from topdown technique and listening comprehension. The data taken from observation and interview was used qualitative data analysis. The research consisted of two cycles, before first cycles started, the researcher conducted pre test to know students listening comprehension real ability. First Cycle conducted after treatment given, due to the result of first cycle doesn`t improve in all aspect. Researcher continued to second cycles at the end of cycle. Three result test to be compared to know students improvement in listening comprehension. The result shows that the topdown technique better improves the students ability at listening comprehension. Students score were obtained from total score of correct answer which is converted to Toeic conversion scale. The findings on this study proved that teaching listening applying topdown technique improved students` listening comprehension. Significance improvement particularly in students ability to answer listening part one; matching pictures with statement. Based on the result of the study topdown technique to be recommended for the teacher in Vocational High School ( SMK ) to improve students` listening comprehension.
Computable multipartite multimode Gaussian quantum correlation measure and the monogamy relations for continuous-variable systems

In this paper, definitions of the unification condition, the hierarchy condition, and three kinds of monogamy relations for multipartite quantum correlation measures are given and discussed. A computable multipartite multimode Gaussian quantum correlation measure ${\mathcal{M}}^{(k)}$ is proposed for any $k$-partite multimode continuous-variable systems with $k\ensuremath{\ge}2$. The value of ${\mathcal{M}}^{(k)}$ only depends on the covariance matrices of continuous-variable states, is invariant under any permutation of subsystems, has no ancilla problem, is nonincreasing under $k$-partite local Gaussian channels (particularly, invariant under $k$-partite locally Gaussian unitary operations), and vanishes on $k$-partite product states. For a $k$-partite Gaussian state $\ensuremath{\rho}, {\mathcal{M}}^{(k)}(\ensuremath{\rho})=0$ if and only if $\ensuremath{\rho}$ is a $k$-partite product state. Moreover, ${\mathcal{M}}^{(k)}$ satisfies the unification condition and the hierarchy condition that a multipartite quantum correlation measure should obey. We also show that ${\mathcal{M}}^{(k)}$ is not strongly monogamous but completely monogamous and tightly monogamous.
How do visitors perceive the significance of tangible cultural heritage through a 3D reconstructed immersive visual experience at the Seokguram Grotto, South Korea?

Sustainable heritage tourism is recognized as an economic activity that can ensure the careful management of tangible cultural heritage sites for future generations, and attract visitors to experience their cultural, historical, and archaeological significance. The use of 3D immersive cultural heritage visualization presentations can enhance a visitor’s perceived experience and motivation, and have received growing interest in the tourism literature. However, the impact of these cultural heritage presentations on visitor perceived experience with regard to certain psychological and social factors has not been fully explored. This study aims to fill this gap in the literature by investigating how 3D immersive visualization experiences enhance the significance and value of tangible cultural heritage sites, through considering significant factors. A cross-sectional study was conducted with visitors who experienced a 3D reconstructed immersive visual experience of the Seokguram Grotto World Heritage Site. The results showed that the 3D reconstructed immersive visualization contents increased the significance and value of the tangible cultural heritage site, by influencing visitors' perceived experiences using certain psychological factors. These findings will contribute positively to the sustainable heritage tourism development literature.
Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.
Study of institutional and professional loyalty features in future engineers

Introduction. Increasing professional loyalty attracts to the organization not only specialists that are qualified, but also committed employees that are involved, interested, motivated for long-term relations with the organization, those of high moral convictions that contribute to the adequate moral decisions in complicated professional backgrounds. The purpose of the study is to study the institutional loyalty, involvement in professional activities, as well as ideas about the organization of engineering students working in the profession and studying in the master’s program. Materials and methods. The study involved 196 students of the University of Architecture and Civil Engineering in St. Petersburg (98 females and 98 males) studying under the “Construction” Master’s program and working in the construction industry. The average age of the research subjects was 23 years, the average work experience in the current organization was 1 year 3 months. Methods: Utrecht Engagement Scale by A. Bakker, W. Shaufeli; WACMQ method (Workplace affective commitment multidimensional questionnaire) by Morin A. J. S. to measure the level of affective commitment to the workplace; test of unfinished sentences “For me, the organization is…”. Methods of mathematical statistics: Pearson correlation analysis, cluster analysis (Ward’s method), Student’s t-test for independent samples. Results. The results of the study allow to identify new facts of institutional loyalty of young specialists working in the construction industry and not having extensive professional experience. It has been determined that the degree of “involvement” in professional activities among the respondents, like energy, commitment, absorption in activities is not fully expressed. The respondents tend to have a pragmatic attitude towards their organizations. First of all, they see the organization as a platform for career growth and professional experience. At the same time, the employees demonstrate an ambivalent emotional attitude towards the organization: both positive and negative. Young people will be committed to their organization if it provides them with the opportunity to fully pursue what they love and promote their careers (r= 0.25, р ≤ 0.01). The older the employee, the more diligently he or she solves the problems of the organization, the more client-oriented and loyal to the profession he or she becomes (r= 0.15, р ≤ 0.01). The employee’s negative emotions in relation to his or her work reduce his or her professional loyalty and commitment to the organization in which he or she works (r= – 0.14, р ≤ 0.05). Conclusion. The employees’ institutional loyalty is the organization’s competitive advantage. However, the institutional loyalty of an employee may fail to be formed if it is not fed as early as at the stage of training at an educational institution when forming professional commitment.
Comparison of discretization methods for classifier decision trees and decision rules on medical data sets

Data sets in real life are given by real numbers in databases.On the other hand, many data mining methods like association rules and induction rules require only discrete attributes.Therefore, in order to use such data mining methods on datasets with continuous characteristics, these features in the dataset should be discretized.The discretization process is reducing the number of values by dividing the range interval of a continuous attribute into certain intervals.In this paper, eight discretization methods are presented with JRip, OneR, J48, and Part classifier algorithms of rules and tress.The applications within the scope of the study include the results obtained as a result of ten-fold cross validation and were carried out on real-life data obtained from the UCI repository.We show that discretization is important step to significantly increase the classification results of these algorithms.Finally, as a result of the study, it was seen that MDL and J48, CAIM and Jrip and Extended Chi and J48 methods gave the highest accuracy for PIMA, WBC and DERMA data sets, respectively.
Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

In chorus activities, the conductor leads chorus members to recreate music works. If you want to interpret music works perfectly with sound, emotion and emotional expression are particularly important. In this paper, a cloud HBD (health big data) integration system based on ensemble learning is designed to realize the high-efficiency and high-precision integration of HBD. An emotional speech database containing three emotions such as pleasure, calmness, and boredom is established, and the corpus problems such as emotional feature analysis and extraction needed for chorus emotion recognition research are solved. It also studies the classification and decision-making in emotional changes, and a DBN (deep belief network) chorus emotion recognition algorithm based on multiple emotional features is proposed. Feature DBN (Deep Belief Network) Chorus Emotion Recognition Algorithm This paper extracts various robust low-level features according to different features' ability to describe emotions and then feeds them into the DBN network to extract high-level feature descriptors. Then, the classification results of ELM (extreme learning machine) are voted and fused with the idea of ensemble learning, and the effectiveness of the algorithm is proved on three public datasets.
Enhanced Compressed Maximal Frequent Patterns from COVID-19 Streaming Data

Most supervised learning methods are currently used to solve the task of person re-identification (Re-ID) and yield excellent results.But these methods usually need manual annotation of training data.Especially for large data sets, they need too high cost of manual annotation and the data are difficult to obtain for fully pairwise labeling.So unsupervised learning becomes a necessarily trend for person Re-ID.This paper is trying to solve the problem by unsupervised learning method.Moreover, global features focus on spatial integrity of person features, and local ones help to highlight discriminative features of different patches.Therefore, fused-grained unsupervised (FGU) learning framework of global and local branches' feature learning is proposed to solve Re-ID task.Specifically, for the local branch, one extracts patches from a feature map which learned on a PatchNet network of images, and learns their fine-grained features to pull close the similar patches and push away the dissimilar ones.For the global branch, one maximizes the diversity between classes by repelled loss and similarity within classes through attracted loss, then similarity and diversity in the unlabeled data sets are used as information for unsupervised cluster merging and learning their coarse-grained features.The two branches are used to jointly achieve the effect of increasing inter-class differences and intra-class similarity.A large number of experiments verify the superiority of the proposed method for unsupervised person re-identification.
Role of LMS Assessment Tools

To address the crisis of low retention and graduation rates at two-year colleges, public policy has focused on accountability and evidence of achievement based on outcomes. Further, online learning environments and their tools have caused a major paradigm shift in the policies, practices and learning processes of higher education. However, research on the use of technology for educational purpose and the use of the LMS tools for learning outcomes is still limited. This mixed-methods action research study (MMAR) explored the use of LMS assessment tools among faculty teaching English Composition at a community college. The aim was to increase their use for informed decision-making on student outcomes through faculty-led workshops. Based on the findings, the V-FLC was able to develop a plan for further and sustained use of the tools and make recommendations to the division for wide-scale adoption and use. This study’s findings also contribute to the existing literature addressing faculty needs and the role of grass root leadership in LMS tool use to inform assessment practices.
Computer Musicking as Onto-Epistemic Playground On the Joy of Developing Complexity Literacy and Learning to Let Others Be

Theories across sciences and humanities posit a central role for musicking in the evolution of the social, biological and technical pat- terns that underpin modern humanity. In this talk I suggest that contemporary computer musicking can play a similarly critical role in supporting us through contemporary existential, ecological, technological and social crises, by providing a space for reworking our relationships with each other and the world, including the technologies that we make. Framed by Gregory Bateson’s analysis of the fundamental epistemological error which leads to interrelated existential, social and ecological crises, I will draw upon a range of personal projects to illustrate the value of computer music practices in learning to think better: from cybernetic generative art, through ecosystemic evolutionary art and feedback musicianship to the need for interactive approaches to algorithm interpretation in ma- chine listening to biodiversity. I will illustrate how computer musicking can help in three ways: firstly by developing complexity literacy, helping us to better understand the complex systems of the anthropocene; secondly by providing a space to explore other modes of relation through learning to let others be; and thirdly to clarify the importance of aligning technologies with and not against, the biosphere. As pre-historic musicking made us human, so contemporary computer musicking can help us learn to think through the challenges we face today and be better humans tomorrow.
Latent Feature Separation and Extraction with Multiple Parallel Encoders for Convolutional Autoencoder

Much of the real-world image data is unlabeled or mislabeled. Therefore, even if there is no label, if similar images can be grouped together with image data itself and used the group as a label, more image data can be effectively used in various tasks. This will be especially effective when dividing images belonging to the same domain into sub-groups. Therefore, in this study, we propose an image feature extraction method to be used for image clustering. The proposed feature extraction model is the Multi-head Convolutional Autoencoder (MCAE), which is a model composed of multiple encoders in parallel based on the Convolutional Autoencoder (CAE). The proposed model showed about 14% lower test reconstruction loss compared to CAE, and the correlation coefficient between extracted features was about 56% lower. In addition, as the results of clustering based on the extracted features, MCAE-based clustering showed about 3.5 times higher silhouette score than that CAE-based clustering.
Safety helmet wearing recognition based on improved YOLOv4 algorithm

In safety helmet wearing detection, traditional methods have problems such as low detection accuracy due to small object size. In this paper, we propose an improved helmet wearing detection algorithm YOLOv4-P. Firstly, we use k-means clustering algorithm to readjust the prior bounding box parameters to improve the matching between the prior bounding box and the object, and secondly, we introduce the Pyramid Split Attention (PSA) model to further process the multi-scale feature information. The network structure of YOLOv4 is improved by adding a layer of network features to improve the detection accuracy through feature fusion. Experimental results show that the improved model YOLOv4-P has an average accuracy improvement of 2.15% over the YOLOv4 algorithm on the SHWD helmet target detection dataset, satisfying the accuracy of the helmet wearing detection task.
Underwater Image Processing using Various Data Enhancement Methods

As the technology of underwater shooting advances, the underwater image process has become necessary. For this research, five different methods have been applied to the underwater images, including white balance, DCT contrast enhancement, CLAHE, Global and local contrast enhancement, linear fusion and Gaussian Pyramid fusion method. The results of these different methods are compared using the method of Underwater Color Image Quality Evaluation (UCIQE). UCIQE can compute a score for each of the different outputs. The images with higher score imply better outcome of underwater image processing. The result shows that the Gaussian Pyramid fusion method has the highest score of UCIQE, which means that this method can restore the underwater images in the most efficient way.