Reviewerly/exHarmony huggingface dataset:
Pattern and Predictors of Adult Learner’s Student Engagement in University through Latent Profile Analysis: Based on the Case of University A

In this study, we try to understand how adult learners are currently engaged in learning activities in university from the perspective of 'student engagement', and explore what factors influence adult learner’s engagement patterns. A latent profile analysis on the engagement pattern of adult learners features the following four points: first, the engagement pattern can be classified into three groups: 'low-level and the lowest interaction participants(low-level)', 'middle-level and instructor and self-directed participants(middle-level)', and 'high-level and balanced participants(high-level)', where the degree of student engagement is clearly distinguished. Second, the 'low-level’ and 'middle-level’ participants have insufficient experience in interacting with instructors and colleagues. Third, self-directed and instructor-led learning experiences are high overall irrespective of the classification. Fourth, the 'low-level’ participants group mainly includes women, 30s and 40s, and the 'high-level’ participants mainly consists of men, 60s, and first graders. Finally, our multinomial logistic regression analysis indicates that gender, supportive environment of university, commitment of university, and expectation on the performance of university are the major factors affecting the degree of engagement. This study suggests that 'adult learners' engagement’ should be highlighted as a new accountability of university. In addition, given that university-related experiences are a predictor of future engagement, this study emphasizes the role and effort of the university that can vary adult learners' engagement.
English Literature Education in the Knowledge Information Society

This study explores diverse achievements concerning the relationship between technology and English literature education in the academic papers contributed to the Korean Society for Teaching English Literature(KSTEL). KSTEL has paid attention to science and technology, while maintaining a critical yet receptive perspective. In particular, its research has not been limited to published works but expanded to the diverse visual texts supported by new technologies. English literature education through multiple media encourages interdisciplinary studies including study of the media itself and on generative culture. Interest in the field of cybernetics is rising, as the convergence of academics has been encouraged. In the second generation of cybernetics, the various fields in humanities, including art, have actively contributed to establishing cybernetics theories and practices, suggesting a new turning point in the study of control and communication of living things and machines. In addition, digital humanities, which appeared at the beginning of the 21st century, are continuously developing, demonstrating its rich potential not only in the study of English literature, but also in education.
Witnessing multi-observer steering on both sides simultaneously via weak measurements

The quantum steering as an intermediate form of quantum correlations, has various peculiarities differing from the Bell nonlocality and the quantum entanglement. In a specific scenario, each side of the observer shares one of the two entangled qubits, steering can be witnessed simultaneously between any two observers from different sides by performing weak or projective measurements successively, with independently chosen and unbiased inputs. Here, we demonstrate this steering scenario experimentally to simultaneously witness quartic Einstein-Podolsky-Rosen steerings of two successive observers between each side with the entangled pair with a state fidelity of 97.6% compared with a maximally-entangled one.
Differentiated Didactic Approach To Teaching The Arts (D.D.A.T.A.)

Differentiated Didactic Approach To Teaching The Arts (D.D.A.T.A.) Finally, the Chapter Nine presents pedagogical and psychological folds using Differentiated Didactic Approach to Teaching the Arts (D.D.A.T.A.) to improve the life of special needs learners by means of culture, self-knowledge, self-actualization, and the sense of belonging. The D.D.A.T.A. is a relatively new teaching approach in the field of Special Education, developed during the period 2012 to 2020. One of its main goals is to help Special Education professionals to teach persons with Intellectual Developmental Disorders (IDD) or Autism Spectrum Disorders (ASD) music, dance, and drama, thus cultivating their cognitive, emotional, mobility, expression, communication, social, and artistic skills. Its second key goal is to help teachers form art ensembles comprised solely of special needs individuals which, in turn, could work in harmony with ordinary art ensembles. The D.D.A.T.A. model provides individualized programs adjusted to each special needs person’s abilities; and creates the right, positive psychology and motives for all participants.
Emerson, Thoreau, Fuller, and Transcendentalism

Research Article| September 01 2022 Emerson, Thoreau, Fuller, and Transcendentalism James S. Finley James S. Finley Texas A&M University–San Antonio Search for other works by this author on: This Site Google American Literary Scholarship (2022) 2020 (1): 3–25. https://doi.org/10.1215/00659142-9580484 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation James S. Finley; Emerson, Thoreau, Fuller, and Transcendentalism. American Literary Scholarship 1 September 2022; 2020 (1): 3–25. doi: https://doi.org/10.1215/00659142-9580484 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsAmerican Literary Scholarship Search Advanced Search The text of this article is only available as a PDF. Copyright © 2022 Duke University Press2022 Article PDF first page preview Close Modal Issue Section: Part I You do not currently have access to this content.
Comparison of digital learning barriers in university students

The general objective of the research was to identify the variables that integrate the learning barriers in students at the higher level of the Autonomous University of Coahuila, the methodology used was quantitative, transversal, exploratory, descriptive and explanatory with a comparative and integrational methodical derivation. The collection of responses was carried out from a virtual instrument composed of 23 signalitic variables and 105 variables on a decimal scale of ratio. The sample consisted of 320 subjects, for which a non-probabilistic sample was used through the convenience strategy. The statistical levels that were processed were: frequencies and percentages in addition to comparison. The main results indicated that students are in family contexts within which there is helplessness, disinterest, violence and detachment, which generates limitations and complications that are reflected in the teaching-learning process. The outstanding conclusions are that most of the havoc and complexities for the educational process of the university student come from digital skills, abilities and abilities, which can be said given that tools, platforms and virtual sites have symbolized limitations and arduous obstacles in the generation of student knowledge, as well as.
Object detection algorithms based on deep learning

The object detection algorithm has experienced the transition from the traditional algorithm to the object detection method based on convolutional neural network. The traditional object detection algorithm mainly includes VJ detector, HOG, DPM, etc. It mainly uses the sliding window method to detect and locate the object, and there is a certain bottleneck in the running speed. With the increase of training data and computer performance, the object detection algorithm based on deep learning has a good effect in both detection accuracy and detection speed, and has become the mainstream algorithm of object detection. Through the author's own understanding, this paper summarizes various object detection algorithms in a more understandable language.
Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans

Unsupervised anomaly detection models that are trained solely by healthy data, have gained importance in recent years, as the annotation of medical data is a tedious task. Autoencoders and generative adversarial networks are the standard anomaly detection methods that are utilized to learn the data distribution. However, they fall short when it comes to inference and evaluation of the likelihood of test samples. We propose a novel combination of generative models and a probabilistic graphical model. After encoding image samples by autoencoders, the distribution of data is modeled by Random and Tensorized Sum-Product Networks ensuring exact and efficient inference at test time. We evaluate different autoencoder architectures in combination with Random and Tensorized Sum-Product Networks on mammography images using patch-wise processing and observe superior performance over utilizing the models standalone and state-of-the-art in anomaly detection for medical data.
Analysis of Knowledge Transfer in Kernel Regime

Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network. It has been observed that classifiers learn much faster and more reliably via knowledge transfer. However, there has been little or no theoretical analysis of this phenomenon. To bridge this gap, we propose to approach the problem of knowledge transfer by regularizing the fit between the teacher and the student with PI provided by the teacher. Using tools from dynamical systems theory, we show that when the student is an extremely wide two layer network, we can analyze it in the kernel regime and show that it is able to interpolate between PI and the given data. This characterization sheds new light on the relation between the training error and capacity of the student relative to the teacher. Another contribution of the paper is a quantitative statement on the convergence of student network. We prove that the teacher reduces the number of required iterations for a student to learn, and consequently improves the generalization power of the student. We give corresponding experimental analysis that validates the theoretical results and yield additional insights.
Proving, Refuting, Improving—Looking for a Theorem

Exploring the proofs and refutations of an abstract statement, conjecture with the aim to give a formal syntactic treatment of its proving–refuting process, we introduce the notion of extrapolation of a possibly unprovable statement having the form if A, then B, and propose a procedure that should result in the new statement if A′, then B′, which is similar to the starting one, but provable. We think that this procedure, based on the extrapolation method, can be considered a basic methodological tool applicable to prove–refute–improve any conjecture. This new notion, extrapolation, presents a dual counterpart of the well-known interpolation introduced in traditional logic sixty-five years ago.
Expert Systems: Web-Based Motorcycle Detection Solutions

Motorcycles are one of the main means of transportation for some people in carrying out their daily activities. Efficient, cost-effective time to the destination, as well as maintenance tools that are quite easy to obtain, make this motorcycle a priority among the community, and this is evidenced by the number of motorcycle users compared to other means of transportation on the road. From the description above, the author makes a system that can make it easier for users to find out problems that occur in the motor, which cause damage so that it can interfere with the activities to be carried out. The system that will be made is an expert system for detecting web-based motorcycle damage. This expert system is made using the BFS method, which is a combination of the two methods so as to produce the best decision from the two searches. The Best First Search method is a merger or combination of the Depth First Search and Breadth First Search methods. Depth First Search method or in-depth search, in this method the expert system will conduct an intensive and in-depth search of the given value or variable. And the Breadth First Search method works by analyzing each root node for immediate testing. From these results it can be concluded that the system is feasible to use and can be used as an alternative solution for the community. Make it easy for motorcycle users to find solutions for motorcycle damage, and make it easier for mechanics to repair motorcycles if they forget the symptoms of motorcycle damage experienced.
The primary and secondary mineral resources of Montenegro and their mapping into the European data model

Primary and secondary mineral resources are of strategic importance to the EU economy. Montenegro, as a country candidate for membership in the EU, is required to follow (and later to implement) European policies, strategies as well as initiatives, including those related to mineral resources and the mining sector. The importance of providing access to mineral raw materials in the future is recognized by the EU, as well as meeting the needs of European industry, maintaining employment and ensuring further development. Considering the overall economic situation in Montenegro, it is important to encourge the mining sector and other industries based on the use of mineral resources in making a greater contribution to the development and sustainability of society as a whole and also increase the share of national GDP. The potential for discovery and utilization of primary and secondary mineral resources in Montenegro is demonstrated. The most important metallic mineral resources are bauxite, lead and zinc, while conventional energy resources include coal (oil and gas potential has yet to be proven). In addition, there are abundant non-metallic mineral raw materials - industrial minerals and construction materials. Secondary mineral resources, especially aluminous red mud (bauxite residue), are also significant and have been the subject of research in recent years. Tailings from flotation processes at operating and abandoned lead and zinc mines might also be of interest for metal recovery. Bottom and flay ash from thermal power plants, slag from steel production, as well as marlstone and limestone from the hanging wall of coal deposits may also have potential. Waste rocks could be used particularly for secondary aggregate production. A database was developed and the most important deposits of primary and secondary mineral resources in Montenegro were mapped during the RESEERVE project. Mineral data were harmonised so as to be INSPIRE compliant. In addition, some novel geochemical exploration results of secondary mineral resources are presented.
Using language to empower joint diabetes decision making in the paediatric diabetes clinic

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
A Study on the Curriculum of the Graduate School of Department of Education Consulting in South Korea

The purpose of this study was to suggest implications for the curriculum of the graduate school of department of education consulting and education consulting major in South Korea through the analysis of the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea.&#x0D; In this study, in order to investigate the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea, the keywords were limited to ‘education consulting’, and data were collected in the order of Daehakalrimi(https://www.academyinfo.go.kr), Naver and Google keyword search, and each university and graduate school’s website. As a result of the analysis of the collected data, it was confirmed that three master’s and doctoral programs in general graduate schools in South Korea, and nine master’s programs in graduate schools of education in South Korea were in operation, and the curriculum of the graduate school of department of education consulting and education consulting major in South Korea were organized based on the faculty members’s major and expertise according to new department and major or name changed department and major.&#x0D; This study is the first study to analysis the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea and is meaningful as a basic study of the development of education consulting as disciplines.
A Benchmark for Compositional Visual Reasoning.

A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality - allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluid intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abstract rules and generating image datasets corresponding to these rules at scale. Our proposed benchmark includes measures of sample efficiency, generalization, compositionality, and transfer across task rules. We systematically evaluate modern neural architectures and find that convolutional architectures surpass transformer-based architectures across all performance measures in most data regimes. However, all computational models are much less data efficient than humans, even after learning informative visual representations using self-supervision. Overall, we hope our challenge will spur interest in developing neural architectures that can learn to harness compositionality for more efficient learning.
ASK-ViT: A Model with Improved ViT Robustness through Incorporating SK Modules Using Adversarial Training

The ViTs model has been widely used since it was proposed, and its performance on large-scale datasets has surpassed that of CNN models. In order to deploy the ViTs model safely in practical application scenarios, its robustness needs to be investigated. There are few studies on the robustness of ViT model, therefore, this study investigates the robustness of the ViT model in the face of adversarial example attacks, and proposes the ASK-ViT model with improving robustness by introducing the SK module. The SK module consists of three steps, Split, Fuse and Select, which adaptively select the size of the perceptual field according to the input of multi-scale information, and extracts the features that help the model to classify examples. In addition, adversarial training is used in the training process. Experimental results show that the accuracy of the proposed defense method against C&amp;W, DI2FGSM, MDI2FGSM, ImageNet-A, and ImageNet-R attacks is 30.104%, 29.146%, 28.885%, 29.573%, and 48.867%, respectively. Compared with other methods, the method in this study shows stronger robustness.
A Study on the Shift of the Cohesive Device ‘Personal Pronoun’ in English-Korean Translation

This paper aims to investigate the shift of the cohesive device ‘personal pronoun’ in English-Korean translation and the importance of the instruction about cohesive devices including personal pronouns to help students achieve better translation quality in English-Korean translation. In order to compare the translation errors of the cohesive device ‘personal pronouns’ between student and machine translations, we examined what errors both the translations made in the cohesive device translation. Machine translation produced a lot of literal translation and hence created awkward and unpractical sentences in the target text. The comparison of the first and second translations produced by Korean students shows that there is a big difference in the error number between the two translations, which proves their quality in translation. Hence, the difference in the translation quality about cohesive devices between the first and second translations implies that instructions about cohesive devices are necessary to enhance their cohesion-related translation skills.
Comparative study of official manufacturers, automotive journalists, and Indonesian citizens preferences in choosing 150cc sports motorcycle products using topic modeling with LDA Algorithm (Study of Honda CBR150R and Yamaha All New R15 V3)

The high potential of the Indonesian motorcycle automobile sector market has increased the level of competitiveness between motorbike manufacturers. As two of Japan's big four manufacturers with the most wholesalers, Honda and Yamaha must maintain a continual innovation approach to maintain a competitive advantage over competitors. Knowing consumer's preferences is essential for protecting the business and gaining a competitive advantage in the market. Consumers must choose their product above others, and companies must ensure that this happens. This study investigates the factors of Indonesian citizens considering choosing a 150cc sports motorcycle, and examines the variations in the features of the 150cc sports motorcycle market, based on claims from motorcycle manufacturers, automotive media journalists, and social media comments from Indonesian citizens. This study is categorized as qualitative research, using crawling and scraping of 4,554 online user-generated content data from Twitter and YouTube social media, five automotive review websites, and two official manufacturer websites. Data processed by Latent Dirichlet allocation (LDA) topic modeling algorithm uses a coherence score to explain the dataset's interpretability and the topic's output quality. According to the official manufacturer, the result indicates that the factors of engine performance, braking grip, handling stability, and sporty design were the priority characteristics of the ideal 150cc sports motorcycle. Furthermore, engine performance, sporty design, and handling stability factors are priority characteristics of the ideal 150cc sports motorcycle according to automotive journalists. Meanwhile, the sporty “Moge” look design factor, engine performance, on the road (OTR) prices, and consumer reference prices are the priority characteristics of the ideal 150cc sports motorcycle according to Indonesian citizens. Businesses can use these findings to establish and manage corporate innovation and influence consumers to buy the product by considering their preferences.
Reviewer Paper Assignment Problem – A Brief Review

Since last two decades, conferences are flooded with papers receiving excessively high numbers of submissions. Assignment of submitted papers to appropriate expert reviewers, known as Reviewer Assignment Problem (RAP), is a crucial and challenging task for journal editors and conference management committee. Core objective of RAP solution is fair and timely review process. A brief review of the literature is presented in this paper covering a typical conference management system, popular techniques, performance measures, data sets and research opportunities along with challenges in domain. Adaptation of 5G and use of machine learning algorithms will contribute to efficient conference management system.