Reviewerly/exHarmony huggingface dataset:
An Analysis of Research Trends in Mobile Learning through Comparison between Korea and China using Semantic Network Analysis

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.
MOST VULNERABLE CHILDREN: A CASE STUDY OF THE GOVERNMENT SCHOOLS IN MADHYA PRADESH AND CHHATTISGARH

It still found that dropping out is a serious problem in the government schools of poor states in India. The causes may be categorized into two broad categories individual level and institutional level. Along with a host of causative factors of dropping out from the school, there are a passive segment ofchildren who suffers a lot both emotionally and cognitively in compare to other risk children owes to their inappropriate schooling behaviour. Such types of children were found to a great extent in the government schools of Madhya Pradesh and Chhattisgarh States of India. These children were categorised as ‘most vulnerable children’ who are although exist in our education system but they were omitted silently without much attention to address their problem. In such context, this paper aims to explore the nature and extent of conditions of most vulnerable children in elementary education in Indian education system.
CONDITIONS FOR SELF-DETERMINATION AS AN EDUCATIONAL RESULT IN NEW UNDERGRADUATE DEGREE PROGRAMS

Introduction. This paper is concerned with the conditions and mechanisms necessary to boost the process of self-determination, and promote self-determination as an educational outcome in the undergraduate degree programs. Materials and Methods. The research employed the method of theoretical analysis and synthesis of the literature in the field of philosophy, psychology, and pedagogy on the topic of self-determination, and the modeling method to describe the research results. Results of research. The study – provides a detailed description of the conditions under which self-determination is promoted in the undergraduate degree programs; – demonstrates in which educational programs (linear or nonlinear), self-determination as educational outcome is hardly possible to achieve and why; – presents methodological recommendations for the development of educational programs aimed at self-determination of students. Conclusion. The conclusion of the paper indicates practical value and prospects. The main contribution of the paper lies in the critical analysis of existing research on self-determination, the synthesis of various theoretical works for the creation of a two-circuit model of self-determination, and the integration of individualization practices into this process. Keywords: self-determination, educational programs, positioning, activities, individualization practices, subjectivity.
Deep learning-based volumetric image generation from projection imaging for prostate radiotherapy

Due to the inter-fraction variation of anatomy, it is highly desired to provide fast and low-dose volumetric imaging during prostate radiation therapy treatment for patient setup and daily treatment dose estimation. In this study, we propose a novel generative adversarial network integrated with perceptual supervision to derive 3D volumetric images from two orthogonal 2D projections. Our proposed network, named TransNet, consists of three modules, i.e., encoding, transformation and decoding modules. Rather than only using image distance loss between the generated 3D images and the ground truth 3D CT images to supervise the network, adversarial loss is used to improve the realism of generated 3D images. We conducted a study on 20 patient cases, who had received prostate radiotherapy in our institution, and evaluated the efficacy and consistency of our method for two orthogonal projection angles, i.e., 0° and 90°. For each 3D CT image, we simulated its 2D projections at these two angles. The TransNet takes the two angles as input and output the 3D CT. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) achieved by our method are 117.5±15.3HU, 22.7±3.8dB and 0.904±0.27, respectively. These results demonstrate the feasibility and efficacy of our 2D-to-3D method for prostate cancer patients, which provides a potential solution for fast on-board volumetric imaging for patient setup and adaptive radiation therapy.
藏頭詩生成系統:Seq2Seq 控制訊號的應用

An acrostic is a form of writing that the first token of each line (or other recurring features in the text) forms a meaningful sequence. In this paper we present a generalized acrostic generation system that can hide certain message in a flexible pattern specified by the users. Different from previous works that focus on rule-based solutions, this work adopts a neural-based sequence-to-sequence model to achieve this goal. Besides acrostic, users are also allowed to specify the rhyme and length of the output sequences. Based on our knowledge, this is the first neural-based natural language generation system that demonstrates the capability of performing micro-level control over output sentences.
Mobile learning: It’s factors and challenges: A literature review

The adoption of mobile learning has become a working area in research.The study aims to provide a better viewpoint of the concept, evolution, determinants and the issues of mobile learning adoption.In this study, researchers have used non-systematic review techniques.Through this literature review, the researchers identify those factors of M-Learning adoption that can be effective in education system, though these are indicate previously by many researchers.This study also focuses on different M-Learning adoption models (TAM, UTAUT, TPB) which help to determine the factors for accepting this trend learning.It indicates the challenges for its implementation which need further and future studies.
WITHDRAWN: FreeHand sketch-based authenticated security system – A comparative study on convolutional neural network, Levenshtein distance and sequence matcher procedure

The main issue in the present situation is how to secure the data in the online digital world. Most of the online accounts are protected using text passwords, OTP, and biometric systems. But they have their own limitations. Therefore to increase the security in a different direction, three different approaches are proposed. And they are Convolutional Neural Network, Levenshtein Distance, and Sequence Matcher. These three are used to implement the FreeHand Sketch-based Authenticated Security System to check the image password similarity for authentication purposes. These three proposed methodologies commonly request the user to draw the image password for registration and if all the credentials are satisfied then the image password will be stored in the system database. The user gets login with his/her image password if it got matched with the registered image password. The accurate results of each proposed approach are compared with the recall and precision metrics.
THE EFFECT OF EDUCATIVE GAME TOOLS (PUZZLE FLANEL) ON THE EARNING ABILITY OF CHILDREN AT 4-5 YEARS AGE IN PAUD RENGGALI, EAST BEKASI, WEST JAVA

Research on improving early numeracy process of children aged 4-5 at Early Childhood Education (ECE) programs in Renggali by using flannel puzzle, ice cream sticks, and buttons stucked on flannel media. Early numeracy isfundamental for understanding concept and meaning of numbers in order to solve daily logic problems of early-aged children. The games tools puzzles used in this study are made of flannel, dakron, and chlid-friendly duplex. Innovative flannel puzzles were created and used in order to stimulate the children’s learning so that fun learning method can be applied. An action research was employed in this study of which 15 students of ECE were selected as the subject. Data of the study was collected from a classroom action research design consisting of four steps: planning, action, observation, as well as reflection and was analyzed by means of descriptive, qualitative, and quantitative methods. Findings of the study reveal that student’s early numeracy skill can be seen frompercentage of children’s skill that was 44.66%prior to treatment and has increasedto 49.25% in the end of intervention. Findings show that the children’s skillis relatively high which isabout 90.16%as investigated in this study.
Facebook removes thousands of fake accounts from Russia, Iran

The social media giant said it removed a total of 2,632 pages, groups and accounts in massive cleanup.
Touch Gestures and Interaction

So far, we’ve looked at different ways of adding virtual objects to our Augmented Reality scenes. Wouldn’t it be great if you could interact with them too? Oh, wait. You can, and that is what we will look at in this chapter.
THE INFLUENCE OF GROUP TASK TOWARDS STUDENTS’ WRITING ABILITY IN NARRATIVE TEXT

Language is a very important thing for human around the world because language is used in all aspects of human life. People use language to communicate with each other and to get knowledge. The objective of this research is to find out the influence of using group task as a technique in teaching writing ability at SMP level. The writer conducted the research at the first semester of the eighth class of SMP Muhammadiyah 1 Way Sulan South Lampung in 2017/2018. There were two classes namely class VIIIA for experimental class and class VIIIB for control class. As the sample consists of 59 students. The sample was taken by using total random sampling technique. After analyzing the data by using t-test formula, it was found that ttest > ttable = 7.11 > 2.00. It  means that there was positive influence of group task towards students’ writing narrative text ability at the first semester of the eighth class at SMP Muhammadiyah 1 Way Sulan South Lampung in 2017/2018.
Airport Target Detection Based on Deep Learning in Remote Sensing Image

This article focuses on the extraction of airport target in remote sensing images. We propose a Guided Edge Net Model based on Convolutional Neural Network. In the first step, we use six convolutional layers to convolve the original image to obtain a preliminary feature image. In the second step, we concatenate the convolution results of 2-6 layers and put it into the VGG-19 model. At the same time, we use the target edge image as a reference to train the edge guidance network. In the third step, we obtain a result image with the same size as the input image by deconvolution. The Guide Edge Net Model reduces the false detection rate of the candidate airport area for a certain extent. From the perspective of MAE, the algorithm proposed in this paper performs better than other methods, only 0.0063, Compared with the method based machine learning, it reduced by 3%. Experimental results demonstrate that the network can achieve higher detection accuracy and segment the target accurately. The results are highly consistent with the actual object and easy to accomplish.
NEW WAYS OF IMPLEMENTING CYBER SECURITY TO HELP IN PROTECTING AMERICA

Cybersecurity is increasingly becoming an area of great concern the world over, and America mainly as in the context of this research paper. With the advent and subsequent advancement of technology, computer systems, and the numerous benefits it has brought about, an ugly side of cyber-attacks has tagged along. Cybersecurity can be seen as the intentional and deliberate act of guarding information science such as computers, servers, mobile devices, computer systems, computer networks, and electronic systems from malicious attacks. Malicious attacks may come in many different forms, as we witnessed in recent times, particularly in America, where this issue has continued to deter efforts against cybersecurity aggressively. Some of the most notable or recognizable incidents of cyberattacks in the United States include one that just happened recently in which the USA Government through its State Department and the Department of Defense reported a surge of cyber attacks by the Chinese government launched against its healthcare providers, pharmaceutical companies and Human Services to hamper sustained efforts of battling the COVID-19 pandemic. Hence, cybersecurity plays a pivotal role in guarding the national interest of America, and the present paper provides insight importance and notable events coupled with emerging tools implemented in America's cybersecurity.
Evaluating Different Machine Learning Models for Predicting the Likelihood of Breast Cancer

Objective: With the continuous development of medical equipment and advancements in data collection, the size and features of the medical database are also increasing rapidly. However, the majority of current cancer detection still relies on doctors’ observation and test with cell tissue samples which does not seem to utilize the medical database to its full potential. Therefore, the research focus for this study was to examine if we can use artificial intelligence to identify patterns in medical databases that can be predictive of cancer detection. Accordingly, various machine learning (ML) methods were examined for their predictive performance with the perspective that how can they help the doctors in cancer diagnosis. Methods: From the Breast Cancer Surveillance Consortium (BCSC) dataset, 154,899 screening mammograms records are applied in this research. This dataset includes 12 independent variables and 1 dependent variable, which was in the form of labeled data. We developed four prediction models using well-established machine learning algorithms: Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), and Bayesian Network, by training them on 80% of the data and examined their prediction performance on remaining 20% data. We performed a comparative analysis of the performance of these machine learning models. Results: Among the four models, Naïve Bayes showed the best prediction accuracy for the malignant samples, which is predictive of cancer likelihood. The Bayesian Network model performed the second best. Both Logistic Regression and SVM yielded poor prediction performance for predicting malignant cases and thus the breast cancer likelihood.
Non-robust Features through the Lens of Universal Perturbations

Recent work ties adversarial examples to existence of non-robust features: features which are susceptible to small perturbations and believed to be unintelligible to humans, but still useful for prediction. We study universal adversarial perturbations and demonstrate that the above picture is more nuanced. Specifically, even though universal perturbations---similarly to standard adversarial perturbations---do leverage non-robust features, these features tend to be fundamentally different from the ``standard'' ones and, in particular, non-trivially human-aligned. Namely, universal perturbations have more human-aligned locality and spatial invariance properties. However, we also show that these human-aligned non-robust features have much less predictive signal than general non-robust features. Our findings thus take a step towards improving our understanding of these previously unintelligible features.
Regimes de informação e bibliotecas comunitárias: elementos para uma reflexão

The regimes of information constitute one conceptual framework which refers to environments that have relationship between politics and information at Information Society. In this connection, the article has the aim to discuss about the variables present in conceptualization of regimes of information with the community libraries’s phenomenon. The article presents an qualitative approach based on bibliographic research. Initially, is presented a conceptual panorama of regimes of information with your elements, then, expose the conception of community libraries. With both concepts, a framework has been created to connect both phenomenons from the similarities. Based on the analysis carried, it was possible to observe that regimes of information aspects can be considered to understand the phenomenon, proposition and maintaining of community libraries.
Teaching Writing Through Canva Application at MTS Al-Islamiyah Ciledug

This study aims to determine how effective the use of Canva application media is in teaching writing. This research was conducted in July-August at Mts Al-Islamiyah, Ciledug. Researchers used quantitative methods with pre-experimental methods by applying pre-test and post-test as instruments in collecting data. Researchers used one class in class IX-1 MTs Al-Islamiyah Ciledug as the population. The method in this study using the rendom sampling technique used in class IX-1. The mean score on the post test was 70.83 and the pre-test was 64.73. It also shows that the Tcal value (9,585) is higher than the Ttable (2.021), at the 5% significance level. If the result t-test is higher than t-table, the alternative hypothesis (Ha) is accepted, and (H0) is rejected. Based on the results of the t-test calculation above, because the positive hypothesis (Ha) is accepted, it can be concluded that the Canva Application is an effective use of media in teaching writing skills.
On the Geometry of Deep Bayesian Active Learning

We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its geometric interpretation interacting with a deep learning model. There are two main components in GBALD: initial acquisitions based on core-set construction and model uncertainty estimation with those initial acquisitions. Our key innovation is to construct the core-set on an ellipsoid, not typical sphere, preventing its updates towards the boundary regions of the distributions. Main improvements over BALD are twofold: relieving sensitivity to uninformative prior and reducing redundant information of model uncertainty. To guarantee the improvements, our generalization analysis proves that, compared to typical Bayesian spherical interpretation, geodesic search with ellipsoid can derive a tighter lower error bound and achieve higher probability to obtain a nearly zero error. Experiments on acquisitions with several scenarios demonstrate that, yielding slight perturbations to noisy and repeated samples, GBALD further achieves significant accuracy improvements than BALD, BatchBALD and other baselines.
KONTRIBUSI KETERAMPILAN MEMBACA PEMAHAMAN TEKS FABEL TERHADAP KETERAMPILAN MENULIS TEKS FABEL SISWA KELAS VII SMP NEGERI 25 PADANG

ABSTRACT The purpose of this study is three. First, describe the reading skills of fable text comprehension of class VII students of SMP Negeri 25 Padang. Second, describing fable text writing skills of class VII students of SMP Negeri 25 Padang. Third, describe the contribution of reading skills to fable text comprehension on fable text writing skills of class VII students of SMP Negeri 25 Padang. This type of research is quantitative with descriptive method. The design used in this study is design contribution. The population of this study was the seventh grade students of SMP Negeri 25 Padang who were enrolled in the 2018/2019 school year, namely as many as 250 students. The sample in this study was determined by proportional random sampling of 20% of the population, which is 48 students. The data of this study were scores on the results of reading skills tests of fable text comprehension and scores on the results of fable text writing skills. The data was obtained through two types of tests, namely objektive to measure reading skills fable text comprehension and performance tests to measure fable text writing skills. The results of this study are three. First, reading skills of fable text comprehension of class VII students of SMP Negeri 25 Padang are in good qualification (B) with an average value of 81.31. Second, fable text writing skills of grade VII students of Padang Public Middle School 25 are in good qualification (B) with an average of 77.26. Third, there is a contribution of reading skills in fable text comprehension towards fable text writing skills of class VII students of Padang State Middle School at 53.29%. Kata Kunci: Kontribusi, keterampilan membaca pemahaman, keterampilan menulis teks fabel
Optimal Predictive Model for Large Scale Classification

Biosensors calculate the expression pattern of multi- ple genes in experimental work. A unique genomic chip is possible to produce levels of expression from multiple genes. The ability to interpret these high-dimensional samples fuels the creation of methods of automated analysis. Even though the existing methods undergo imbalanced problems and less classification accuracy over gene expression datasets.Therefore, a novel computational method has been developed inorder to increase the classification performance of gene expression dataset and accurate disease prediction.By adding fuzzy memberships, we take into account the features of imbalanced data. Within our work, both the sample entropies and the expense for each class decide the fuzzy memberships in order to understand the different samples with various contributors to the judgment boundary. Thus, on imbalanced genomic datasets, the current proposed approach will result in more desirable classification outcomes. In addition, to build a new algorithm, we integrate the fuzzy memberships into current MKL. The results show that the proposed approach will tackle the imbalanced problem and achieve high accuracy rate.
Financial Development and Economic Growth in Nigeria: New Evidence from a Threshold Autoregressive and Asymmetric Analysis

Financial development is concerned with policies, procedures and initiatives to increase the financial access, depth and productivity of financial institutions and financial markets. To that regards, this paper investigates the asymmetric relationship between financial development and economic growth by incorporating financial globalization uncertainty and inflation into a production function covering the Nigerian economy from 1980–2019. The threshold Autoregressive co-integration and nonlinear autoregressive distributed lag techniques are applied to examine the asymmetric cointegration between the variables, as well as the degree of the relationship among the variables. The results indicate long-run relationship between the parameters in the light of asymmetries. The asymmetric estimation results show that positive shock of financial development dampen economic growth. By comparison, symmetrically, the negative shock of the financial development improves the productivity growth of the Nigerian economy. Finally, while inflation shows a positive relationship, the financial globalization uncertainty indicates insignificant result. The implications of the said findings for productivity policies in Nigeria are also discussed.