Reviewerly/exHarmony huggingface dataset:
Design of Gamification Teaching and Learning Model to Improve Concept Learning and Evaluation of Student Satisfaction in this Class

Objectives The purpose of this study were to design a gamification teaching-learning model to improve concept learning classes. And to compare and evaluate the satisfaction of students who took a gamification class according to this model and the gamification method and the lecture method. Methods The gamification teaching-learning model was designed by analyzing the contents of 34 academic papers and the syllabus of the research subject. The data for analysis are 74 cases that responded to all 6 questions of the student satisfaction survey. Differences in student satisfaction were analyzed by the paired sample method. Student satisfaction factors included class satisfaction factors that considered positive changes in class preparation, class operation method, knowledge, skills, and attitude. The evaluation factors for the reasons for class satisfaction were learning motivation, learning recall, and learning flow, and were analyzed by the matched sampling method for each item. Results The gamification instructional model was designed as learning activities of In-Class, Out-Class, and In-Class. The gamified class was conducted according to the designed instructional model. Student satisfaction with the gamification method was higher than the lecture method in learning motivation, learning memory, and learning flow. Conclusions The gamification teaching and learning model was appropriately designed. According to the designed teaching-learning model, the gamification method applied to class was more positive than the lecture method in terms of improving learning motivation, promoting thinking ability, and arousing interest in learning. This study provides researchers who are contemplating the educational effects of classroom instruction with an opportunity to explore instructional strategy tips and gamification instructional materials.
Differences of Daily Life and Psychological &amp; Emotional Experiences between School Violence-related Students and Ordinary Students in the COVID-19 Situation

이 연구의 목적은 코로나19 상황에서 일상생활, 심리･정서적 경험, 필요한 도움 등에 있어 학교폭력 피해학생, 가해학생 그리고 일반학생 간에 차이가 있는지 살펴보고 재난 상황에서 학교폭력 관련 학생을 도울 수 있는 시사점을 찾는 데 있다. 이를 위해 서울시 ○○구 청소년 위기실태조사 원자료를 활용하여 2차 분석하였다. 분석대상은 관내 초중고 학생 1139명이다. 집단 간 차이를 알아보기 위해 교차분석을 실시하였다. 또 집단 간 차이가 어느 집단의 차이인지 알기 위해 두 집단별로 교차분석을 재실시하여 사후검증 하였다. 분석 결과 코로나19 상황에서 일상생활의 경험은 가해학생의 집밖에서 친구를 만나며 시간을 보낸 경험이 일반학생 및 피해학생보다 유의하게 많았다. 정서적 경험의 경우 피해학생이 부정적 정서나 대인관계의 힘든 점을 일반학생 및 가해학생보다 유의하게 많이 느꼈다. 자살 관련 경험은 가해학생과 피해학생이 일반학생보다 유의하게 높았고, 특히 피해학생의 경우 일반학생보다 코로나19로 인해 자살 관련 경험이 더 심각해진 것으로 나타났다. 코로나19 상황에서 심리･정서적 도움에 대한 요구는 일반학생보다 가해학생에게 유의하게 높았다. 연구결과를 토대로 국가적 재난 상황에서 학교폭력 관련 학생을 위한 지도 및 개입 방안의 시사점을 논의하였다.This study aims to find out whether there is a difference in experiences - like the daily lives, psychological and emotional experiences, necessary help, etc - between school violence-related students and ordinary students during the COVID-19 situation, to find implications to help students related to school violence. Therefore, the second analysis was conducted using data from the 00-gu, Seoul Youth Crisis Survey Institute. The analysis targets were 1,139 jurisdiction students of elementary, middle, and high school. chi-square(χ2) test was conducted to analyze the differences between groups. In addition, in order to know where comes the difference (from which group), cross-analysis was re-conducted for each of the two groups and then post-verified. Analysis results showed that the perpetrator had more daily experience of meeting friends outside the house than the ordinary/victim students, as a significantly meaningful difference. In terms of emotional experience, the victim students felt significantly lot of negative emotions or difficulties in interpersonal relationships, showing differences from ordinary/perpetrators students. Suicide-related experiences were significantly higher in perpetrators/victim students than in ordinary students, and in particular, COVID-19 made suicide-related experiences of victim students more serious than ordinary students. In the COVID-19 situation, the demand for psychological/emotional help was meaningfully higher among perpetrators students than ordinary students. Based on the results of the study, the implications of guidance for school violence-related students and the intervention strategies in national disaster situations were discussed.
Implementation of Python Interoperability in Java through TCP Socket Communication

Programming language interoperability is highly desirable for a variety of reasons, such as the fact that if a programmer implements specific functionality that has previously been implemented in another language, the software component can simply be reused. Because they are particularly well-suited and efficient at implementing features, certain languages regularly arise to handle issue areas. There are numerous third-party programs available for a variety of languages. When programmers have experience with and preferences for several programming languages, collaboration on complex projects is easier. A range of techniques and methods have been used to handle various cross-language communication challenges. The importance of interoperability and cross-language communication between Java and Python via socket programming is examined in this research article through an empirical model of different execution environment paradigms that can help guide the development of improved approaches for integrating Python libraries with Java without the need for extra libraries or third-party libraries. The interoperability strategy benefits from the quality and availability of Python libraries in Java by cutting down on development time, maintenance needs, general usability, upkeep, and system integration without incurring additional costs. It is versatile to use this interoperability strategy since identical scripts are run in Java client contexts in the same way that they were used in Python. There are different Python modules used in the research article to exemplify and evaluate the expressions, built-in functions, strings, collections, data exploration, statistical data analysis using NumPy, SciPy, and Pandas, and Scikit-Learn for machine learning with linear regression.
Swiss Year of Scientometrics Lecture: Opportunities and Challenges of Scientometrics–Part II

&lt;em&gt; This blog post is the second in a four part series based on the keynote presentation by Stefanie Haustein at the Swiss Year of Scientometrics &lt;/em&gt; &lt;em&gt; lecture &lt;/em&gt; &lt;em&gt; and &lt;/em&gt; &lt;em&gt; workshop &lt;/em&gt; &lt;em&gt; series at ETH Zurich on June 7, 2023. &lt;/em&gt;
Concluding remarks: Harnessing non-covalent interactions for synthesis and catalysis

An overview of the Faraday Discussion meeting on harnessing non-covalent interactions for synthesis and catalysis, is presented.
A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on FormSpring in Textual Modality

Social media usage has increased tremendously with the rise of the internet and it has evolved into the most powerful networking platform of the twenty-first century. However, a number of undesirable phenomena are associated with increased use of social networking, such as cyberbullying (CB), cybercrime, online abuse and online trolling. Especially for children and women, cyberbullying can have severe psychological and physical effects, even leading to self-harm or suicide. Because of its significant detrimental social impact, the detection of CB text or messages on social media has attracted more research work. To mitigate CB, we have proposed an automated cyberbullying detection model that detects and classifies cyberbullying content as either bullying or non-bullying (binary classification model), creating a more secure social media experience. The proposed model uses Natural Language Processing (NLP) techniques and Machine Learning (ML) approaches to assess cyberbullying contents. Our main goal is to assess different machine learning algorithms for their performance in cyberbullying detection based on a labelled dataset from Formspring [1]. Nine popular machine learning classifiers namely Bootstrap Aggregation or Bagging, Stochastic Gradient Descent (SGD), Random Forest (RF), Decision Tree (DT), Linear Support Vector Classifier (Linear SVC), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Multinomial Naive Bayes (MNB) and K-Nearest Neighbour (KNN) are considered for the work. In addition, we have experimented with a feature extraction method namely CountVectorizer to obtain features that aid for better classification. The results show that the classification accuracy of AdaBoost classifier is 86.52% which is found better than all other machine learning algorithms used in this study. The proposed work demonstrates the effectiveness of machine learning algorithms in automatic cyberbullying detection as against the very intense and time-consuming approaches for the same problem, thereby by facilitating easy incorporation of an effective approach as tools across different platforms enabling people to use social media safely.
Three questions with… Cecilia!

Our lab is growing! In our &lt;em&gt;Three Questions&lt;/em&gt;&lt;em&gt; &lt;/em&gt;series, we’re profiling each of our members and the amazing work they’re doing. In this week’s post we’re highlighting Cecilia Rozemblum, a PhD student in the ScholCommLab based at the Universidad Nacional de La Plata (UNLP), Argentina. With two degrees under her belt already—one in librarianship and documentation—she’s excited to be almost done her third.
Schema Translation Infrastructure

Repurposing data is hard sometimes. Given a current application’s data-worldview – i.e., its schema – one cannot in general pull in historical data collected for different applications because those applications had different worldviews – i.e., they used different data schemas. One may perform one-off or ongoing transformations – e.g. ETL jobs – as part of a hub-and-spoke strategy to bring data from past worlds into the “present” world so that...
A Study on Online Multiple Document Reading-writing Teaching and Learning Methods Based on the RESOLV Model

In this study, we sought to find implications from the RESOLV model (Reader's Representation of Reading Contexts and Tasks) for designing online multi-text literacy lessons that engage students and improve their literacy skills. To understand the RESOLV model, we first examined its components, the Context Model and the Task Model, together. Based on our understanding of the RESOLV model, we proposed three things to consider when organizing online multidisciplinary literacy classes. First, we proposed how to organize tasks in online literacy classes based on the Context Model. Second, we proposed how to organize educational content in online literacy classes based on the Task Model. Third, we proposed a teaching and learning method that can promote collaborative interaction through team discussions with fellow learners as a teaching method for online reading and writing classes based on the integration of the situation model and the task model.
A Study on Aspect of the Expressive Writing by University Students: Using Text Mining and Semantic Network Analysis

The purpose of this study is to analyze writing aspects in 110 expressive writing results conducted in liberal arts at D University using text mining and semantic network analysis. To this end, 110 pieces were finally selected for analysis. The analysis target went through a text preprocessing process such as synonymy combination and elimination of non-terms, then extracted key words and analyzed the connection relationship through network analysis. As a result of the analysis of the frequency of keywords, 'thought', 'exercise', 'friend', 'people', and 'life' were emphasized, and as a result of the analysis of the language network, “Exercise-Health-Soccer-Military-Taekwondo-Confidence,” “Picture-Draw-Admission,” “Friends-Friendly -Thanks,” and “Music-Enjoy-Singing-Sharing” were the main cluster words. As a result of the study, the author's self-expressive writing as a university student appeared in ways of individual internal reflection, self-identity formation, self-healing, emotional expression, and self-consolation. Examining university students' expressive writing aspects in liberal arts is meaningful in that it can be an opportunity to identify the characteristics of university students with clear environmental changes, seek classes necessary for future liberal arts education, and further explore their individual lives.
Exploration of advanced computer technology to address analytical and noise improvement issues in machine learning

Computer-based visual recognition technology combined with deep learning enables accurate image matching through interactive, multi-level comparison. Currently, popular search methods such as RNN, Faster RNN, etc. are used in computer vision to compute image information by performing hierarchical comparison of image objects. In today's machine learning is used to construct training curves to predict corresponding results, but the accuracy rate will cause some data distortion due to overformation. Therefore, to solve the problem of interference is now physical, the abandonment method is developed to extract certain neurons and reduce the number of feature values. In this study, a visual image recognition framework is designed that uses advanced computer technology to mark and compare images, and to mark and eliminate blurred images. The experimental method successfully improves the prediction of accuracy after a judgment error by comparing the results of training with the results of deep learning verification. The accuracy of matrix formation and result prediction can reach 0.9812 without eliminating the image produced by the light-emitting elements. We remove noisy images based on the same sampling information. After re-training and re-predicting, the accuracy can reach 0.9847.
Text Mining In Online Transportation User Sentiment Analysis On Social Media Twitter Using The Multinomial Naive Bayesian Classifier Method And K-nearest Neighbor Method

Text mining is the process of detecting information or something new and researching large information. Text mining can also usually perform an analysis of unstructured text. Social media users in Indonesia, which currently almost reach 200 million users, have resulted in a flood of data. This condition makes text mining a solution to extract knowledge from the flood of data [1] . In exploring knowledge, there are various techniques or methods that can be adopted including the Multinomial Naive Bayesian Clasifier and K-Nearest Neighbor methods. Both of these methods have several phases that are able to explore the potential knowledge of a flood of supervised and unsupervised learning data. It is hoped that the combination of these two methods will help analyze public sentiment or perception towards online motorcycle taxi users in Indonesia [2].
Target Detection Based on Taylor Expansion and Bilateral Symmetric Network

In the task of target detection, traditional detection algorithms are prone to weak generalization and low detection accuracy due to the small number of parameters and fixed parameter values. Conversely, CNN-based detection algorithms are more accurate, but cannot be developed on mobile due to model issues. In this paper, we refer to the gradient features of HOG and propose the Taly preprocessing method which can preprocess images using Taylor extensions and extract multi-order gradient features of the images. Then TaylorNet is designed under the bilateral symmetric network. Multi - gradient features contain rich edge feature information of images. Then, the gradient feature is fused through the bilateral symmetrical network structure to achieve the fusion of low resolution and high resolution, so as to realize the accurate positioning and detection of edge features, and finally achieve the accurate detection of the target. Through the training and testing of the dataset SBD and VOC2012, the comparison results show that compared with some SOTA algorithms, TaylorNet effectively reduces the size of the model while ensuring high accuracy, so it can also effectively implement mobile development.
Principal Component selections and filtering by spatial information criteria for multi-acquisition CEST MRI denoising

This work provides a new denoising methodology for multi-acquisition magnetic resonance images (MRI) based on principal components analysis (PCA). We are proposing a new principal component selection criterion that identifies spatial information in the extracted component coefficients, leading to a better preservation of anatomical structures and pathological information. In addition, our adaptive filtering step allows us to further denoise the MRI data, rejecting persistent spatial noise from the extracted component coefficients. In our investigations the proposed method outperformed the eigenvalue based selection criteria on Amide Proton Transfer weighted CEST data.
Cycle-Consistent Adversarial Transformers for Unpaired MR Image Translation

Translating acquired sequences to missing ones in multi-contrast MRI protocols can dramatically reduce scan costs. Neural network models devised for this purpose are characteristically trained on paired datasets, which can be difficult to compile. Moreover, these models exclusively rely on convolutional operators with undesirable biases towards feature locality and spatial invariance. Here, we present a cycle-consistent translation model, ResViT, to enable training on unpaired datasets. ResViT combines localization power of convolution operators with contextual sensitivity of transformers. Demonstrations on multi-contrast MRI datasets indicate the superiority of ResViT against state-of-the-art translation models.
Evaluation of Neural Network Reconstruction of Undersampled Data using a Human Observer Model of Signal Detection

We evaluated images from undersampled data using a U-Net with common metrics (SSIM and NRMSE) and with a model for human observer detection, the sparse difference-of-Gaussians (S-DOG). We also studied how the results vary when changing the loss function and training set size. We saw that the S-DOG model would choose an undersampling of 2X while SSIM and NRMSE would choose 3X. In previous work, human observers also chose a 2X acceleration. The S-DOG model led to the same conclusion as the human observers. This result was consistent with changes in training set size and loss function.
Learning White Matter Streamline Representations Using Transformer-based Siamese Networks with Triplet Margin Loss

Robust latent representation of white matter streamlines are critical for parcellating streamlines. This work introduced a novel transformer-based siamese network with triplet margin loss, that learns to embed any lengths of streamlines into fixed-length latent representations. Results showed that a minimum of two layers of transformer encoders were sufficient to model streamlines with a very limited number of training data.
4D-MRI image enhancement via a deep learning-based adversarial network

We developed and evaluated a deep learning technique for enhancing four-dimensional MRI (4D-MRI) image quality based on conditional adversarial networks. The quantitative and qualitative evaluative results demonstrated that the proposed model was able to reduce artifacts in low-quality 4D-MRI images and recover the details obtained from high-quality MR images, and performed better as compared with a state-of-the-art method.
High-quality Reconstruction of Volumetric Diffusion Kurtosis Metrics via Residual Learning Network and Perceptual Loss

This study proposed a novel 3D residual network to learn end-to-end reconstruction from as few as eight DWIs to volumetric DKI parameters. The weighted loss function combining perceptual loss is utilized, which helps the network capture in-depth feature of DKI parameters. The results show that our method achieves superior performance over state-of-the-art methods for providing accurate DKI parameters as well as preserves rich textural details and improves the visual quality of reconstructions.
Automatic background phase correction on 4D Flow using M-estimate SAmple Consensus (MSAC)

4D Flow data requires background phase removal to correctly quantify flow velocities. Typical methods are semi-automatic, requiring manual parameter input and remain time-consuming. Furthermore, correction methods based on stationary tissue segmentation fail when in the presence of wrap-around artifacts. In contrast, the proposed M-Estimate SAmple Consensus (MSAC) algorithm robustly and fully automatically corrects background phases by rejecting pixels that show large deviations from the most probable phase offset fit. A parameter sensitivity analysis is proposed following in vivo application in 13 datasets.
Diffeomorphic Image Registration for CINE Cardio MR images using deep learning

Diagnostic applications often require the estimation of organ motion. Image registration enables motion estimation by computing deformation fields for an image pair. In this work, voxelmorph, a framework for deep learning-based diffeomorphic image registration is used to register CINE cardiac MR images in four-chamber view. Additionally, the framework is extended to a one-to-many registration to also utilize temporal information within a time-resolved MR scan. Registration performance as well as the performance of a valve tracking application using this approach are evaluated. The results are comparable to a state-of-the-art registration method, while noticeably reducing the computation time.
Didactic bases of formation and systematization of the content of the training course ‘Preparation for Competition’

The article deals with the design and implementation of the training course ‘Preparation for Competition’. The article sets out the general concept of the course, specifies the effectiveness of the preparation of specialists for a competitive process, and explains the possibilities of shaping the personal competitiveness of future specialists. The author points out that in the process of systematization and construction of the course content, it is necessary to strictly observe the didactic principles. According to the author, it is necessary to understand cognitive objects and phenomena of reality, embodied in the form of learning elements: concepts, essential features, relationships, laws, rules, principles. In the article, the first group includes objects, objects, processes and phenomena of reality included in the content of the training course. All the signs that reveal the essence and features of these objects and processes, the author refers to the second group. In addition, the article also talks about the need to determine the content according to its focus on the development of the competitiveness of the future specialist with the help of logical and psychological analysis.