Reviewerly/exHarmony huggingface dataset:
Animated Video in Teaching Reading Comprehension Viewed from the Students’ Reading Interest

The purposes of this study are to find out whether (1) animated video is effective in teaching reading comprehension, (2) students with high reading interest have better reading comprehension achievement than those with low reading interest, and (3) there is an interaction between the teaching media and the students’ reading interest in teaching reading comprehension. This study is experimental research. The population of this study is the students of MTs Negeri Prambanan. Two classes were taken as the sample, and the sampling technique used was cluster random sampling. This study applied tests and a questionnaire in collecting the data and Kolmogorov-Smirnov formula, One Way ANOVA, and multifactor analysis of variance in analyzing the data. Based on the result of data analysis, the research findings are: (1) animated video is effective in teaching reading comprehension of the students in MTs Negeri Prambanan (Fobtain = 7.60, Sig. Value 0,05); (2) the students in MTs Negeri Prambanan with high reading interest have better reading comprehension achievement than those with low reading interest (Fobtain = 9.19, Sig. Value 0,05); (3) there is an interaction between the teaching media and the students’ reading interest in teaching reading comprehension of the students in MTs Negeri Prambanan (Fobtain = 6.15, Sig. Value 0,05). Based on these research findings, it can be concluded that animated video is effective in teaching reading comprehension of the students of MTs Negeri Prambanan. It brings improvement to the students’ reading comprehension achievement.
Effectiveness of education for prospective medical personnel using VR (Virtual Reality)－Review Literature as Topic (2011∼2021)

Purpose : With the development of virtual reality programs, research is increasing to verify the effectiveness of education for prospective medical personnel using VR. Accordingly, it attempted to analyze and organize prospective medical personnel education research using VR within the last 10 years as a subject range literature review method to confirm the overall trend of the study, and to use it as basic data for developing education programs using VR. Method : This study was based on a review of subject-related literature published in domestic journals to understand the research trend of papers on prospective medical personnel education using VR (Virtual Reality), and was conducted in six stages as a study using scope research methodology. Results : As a result of the study, a total of 11 papers were analyzed, and there were 5 nursing papers, 5 dentistry papers, and 1 oriental medicine paper, respectively. There were 10 studies from 2019 to 2021, and it was contributed not only to professional societies in one field but also to the convergence society. Ten studies were quantitative studies. The research field was mainly conducted in fields that were difficult to be conducted on actual subjects such as clinical technology and anatomical practice. Conclusion : VR research for prospective medical personnel can be trained in a reality-like environment, and for this, programs through environmental provision and practical level of visual and sensory interaction must be developed and operated.
Supplemental Material: Geologic map of southern Panamint Valley, southern Panamint Range and central Slate Range, California (USA)

&lt;div&gt;Digital map data files in ArcGIS shapefile format for data collection points, geologic contacts, and geologic unit polygons.&lt;br&gt;&lt;/div&gt;
DATA VISUALIZATION OF COVID-19 DATA USING TABLEAU

The world generated 52 times the amount of data in 2010 and 76 times the number of information sources in 2022. The ability to use this data creates enormous opportunities, and in order to make these opportunities a reality, people must use data to solve problems. Unfortunately, in the midst of a global pandemic, when people all over the world seek reliable, trustworthy information about COVID-19 (Coronavirus). Tableau plays a key role in this scenario because it is an extremely powerful tool for quickly visualizing large amounts of data. It has a simple drag-and-drop interface. Beautiful infographics are simple to create and take little time. Tableau works with a wide variety of data sources. COVID-19 (Coronavirus)analytics with Tableau will allow you to create dashboards that will assist you. Tableau is a tool that deals with big data analytics and generates output in a visualization technique, making it more understandable and presentable. Data blending, real-time reporting, and data collaboration are one of its features. Ultimately, this paper provides a clear picture of the growing COVID19 (Coronavirus) data and the tools that can assist more effectively, accurately, and efficiently. Keywords: Data Visualization, Tableau, Data Analysis, Covid-19 analysis, Covid-19 data
A localization strategy combined with transfer learning for image annotation

This study aims to solve the overfitting problem caused by insufficient labeled images in the automatic image annotation field. We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. The model consists of an InceptionV3 network pretrained on the ImageNet dataset and a label localization algorithm. First, the pretrained InceptionV3 network extracts features from the target dataset that are used to train a specific classifier and fine-tune the entire network to obtain an optimal model. Then, the obtained model is used to derive the probabilities of the predicted labels. For this purpose, we introduce a squeeze and excitation (SE) module into the network architecture that augments the useful feature information, inhibits useless feature information, and conducts feature reweighting. Next, we perform label localization to obtain the label probabilities and determine the final label set for each image. During this process, the number of labels must be determined. The optimal K value is obtained experimentally and used to determine the number of predicted labels, thereby solving the empty label set problem that occurs when the predicted label values of images are below a fixed threshold. Experiments on the Corel5k multilabel image dataset verify that CNN-2L improves the labeling precision by 18% and 15% compared with the traditional multiple-Bernoulli relevance model (MBRM) and joint equal contribution (JEC) algorithms, respectively, and it improves the recall by 6% compared with JEC. Additionally, it improves the precision by 20% and 11% compared with the deep learning methods Weight-KNN and adaptive hypergraph learning (AHL), respectively. Although CNN-2L fails to improve the recall compared with the semantic extension model (SEM), it improves the comprehensive index of the F1 value by 1%. The experimental results reveal that the proposed transfer learning model based on a label localization strategy is effective for automatic image annotation and substantially boosts the multilabel image annotation performance.
Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives

The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primitives), that can efficiently solve the object manipulation with visual input and sparse reward, by utilizing a non-parametric policy for reinforcement learning and appropriate behavior prior for the object manipulation. We evaluate the efficiency and the performance of the proposed AP-NPQL for four object manipulation tasks on simulation (pushing plate, stacking box, flipping cup, and picking and placing plate), and it turns out that our AP-NPQL outperforms the state-of-the-art algorithms based on parametric policy and behavior prior in terms of learning time and task success rate. We also successfully transfer and validate the learned policy of the plate pick-and-place task to the real robot in a sim-to-real manner.
ARTISTIC AND PEDAGOGICAL INNOVATIONS OF PROFESSOR O. ROSTOVSKY

The article is dedicated to highlight the artistic and pedagogical innovations of Professor&nbsp;O. Rostovsky and reveal their meaning in the context of the development of domestic art&nbsp;education.&nbsp;The authors made a brief analysis of the achievements of the musician-teacher, which&nbsp;gives reasons to speak about their undeniable value.&nbsp;It is emphasized that today it is difficult to overestimate the scientist’s contribution to school&nbsp;art education, methods of teaching lessons in general secondary education institutions,&nbsp;the system of training future teachers of music, etc. From today’s point of view, such works&nbsp;as «Methods of teaching music in primary school», «Methods of teaching music in&nbsp;secondary school», «Lectures on the history of Western European music pedagogy»,«Pedagogy of music perception», «Theory and methods of music education» not only do&nbsp;not lose their relevance, but on the contrary find more and more supporters.&nbsp;The results of musical and pedagogical activity of the scientist, who was deeply convinced&nbsp;of the importance of the profession of music teacher, the need to achieve a high level of&nbsp;pedagogical skills, gained special importance in the conditions of formation of the young&nbsp;state and development of the national education system. It substantiates new conceptual&nbsp;approaches to improving the quality of school art education, the need to update music&nbsp;programs, lesson content, rethink their goals and problems, study and generalize the best&nbsp;domestic and European experience, the widespread introduction of innovative developments&nbsp;in educational practice.&nbsp;One of the important activities of the scientist was the scientific guidance of dissertation&nbsp;research in the field of music pedagogy. The article emphasizes that the scientific school&nbsp;of Professor O. Rostovsky is characterized by innovative approaches to solving current&nbsp;problems of art pedagogy, and the results of research novelty and validity of conclusions.&nbsp;The authors of the article defend the need for further study of the achievements of the&nbsp;scientist, as they are convinced of their novelty and undeniable value for modern theory&nbsp;and practice of art education.
Analysis of physics learning media needs based on mobile augmented reality (AR) on global warming for high school students

Abstract The rapid progress of the times requires an educational approach to be innovative and adaptive to changes in technology and information. Mobile Augmented Reality (MAR) is a system that incorporates technology in education. This method facilitates learning to be interesting, effective, and easily accessible to everyone. This survey research was an initial step in developing Augmented Reality based Modules for high school students on global warming. The importance of this research is that educational media using Augmented Reality is relatively new in the field of educational media. Analysis of learning problems and analysis of students are the two indicators used in the analysis of this need. Based on this research survey, it was found that augmented reality based educational media is viewed as important and necessary as learning materials in schools especially for students of high ability. The next phase is the development of MAR-based learning media that meets these requirements.
Special Issue “Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures”

The demographic shift of the population toward an increased number of elder citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population [...].
OPPORTUNITIES FOR TEACHING MATHEMATICS IN PRIMARY SCHOOLS THROUGH OPPORTUNITIES FOR TEACHING MATHEMATICS IN PRIMARY SCHOOLS THROUGH DIGITAL EDUCATIONAL RESOURCEDIGITAL EDUCATIONAL RESOURCE

The article describes the possible aspects of the organization of the methodology of teaching mathematics in primary classes through digital educational resources, content. The theoretical analysis is carried out and the content analysis of the conceptual and categorical apparatus of digital education in the concepts of "information competence, information technologies", "digital resource" is presented. The possibilities of "digital education" in primary classes are characterized generically in all the main subjects. There will also be samples of digital content prepared for the section "learning in parts" in the 4th grade according to the methodology of teaching mathematics in primary classes. In the section Research results, the article presents an algorithm for preparing digital resources. It is explained that the preparation of digital educational resources consists of text-based annimations, multimedia components (video clips, synthesizing images), and sound objects. A scenario for the preparation of digital content will be developed. The advantages of using digital resources in teaching mathematics in primary classes in the development of students ' cognitive abilities are analyzed. For example, the advantages of accessibility of education, personality-oriented learning, creation of an information educational environment, the possibility of choosing an individual trajectory, creative activity, motivational breakthrough, demonstration situation, etc. are described.
Two-stage technology of automated terminology extraction based on topic modeling and context analysis

The paper deals with the task of automated terminology extraction. A two-stage technology for its solution is proposed, based on topic modeling and analyzing the context of the use of lexical units. The results of experimental verification of the technology and the prospects for its further development are presented.
Using digital tasks when teaching mathematics at school

The article discusses various types of digital math tasks to be performed on computers or tablets. The experience of creating digital tasks in mathematics with complex answers from the point of view of automatic verifcation: algebraic expressions, geometric constructions, graphs of functions, etc. is discussed. As an example of the use of digital tasks, the use of digital materials for preparing schoolchildren for the Unifed State Exam in mathematics of a profle level is considered. The article also discusses various forms of using simulators and tests in the educational process: supporting the independent activities of students in the full-time educational process, supporting online learning with a teacher in the context of the coronavirus pandemic, independent online learning without a teacher based on specially prepared and structured training courses. In the latter case, in addition to simulators, it is proposed to use other digital educational resources to increase the visibility of the educational material and organize self-control of learning outcomes: videos, slides, dynamic models, training exercises for problems with a detailed answer to the options for the USE control and measuring materials.
Research on the Modeling of Fast Face Recognition Against Age Disturbance under Deep Learning

With the development of computer vision technology, especially the promotion of artificial intelligence and deep learning related technologies in the past two years, face recognition technology has made rapid progress. However, in view of complex background lighting, face posture, expression, and age changes, Face recognition technology still faces many challenges. Compared with the research on other interference situations, there are relatively few researches on face recognition against age interference. This paper aims to achieve cross-age face recognition, and based on deep learning to extract age-robust face features as the theme, to achieve rapid face recognition modeling against age interference.
RISK OF POST STROKE PNEUMONIA DURING HOSPITALIZATION

Introduction: Post-stroke pneumonia is the most a common complication during the first few weeks after a stroke. Thus, a score is needed for the early identification of stroke patients with an increased risk of pneumonia to assist the nursing team in preventing the onset of pneumonia in stroke patients during hospitalization. This study aimed to assess the application of the A2DS2 score to predict pneumonia in acute ischemic stroke patients.&#x0D; Method: This is a diagnostic study that used a cross-sectional method conducted among adult acute ischemic stroke patients. Data analysis was performed to assess the calibration and discrimination performance of the A2DS2 score.&#x0D; Results: A total of 16 respondents were followed up. The incidence of post-stroke pneumonia was observed in 6 patients (37.5%).&#x0D; Conclusion: This scoring proved clinically accurate to predict the incidence of pneumonia in acute ischemic stroke patients.
Social Media Analytics

Social media (SM) platform integrates several websites and applications that permit people to share texts, events, photos, videos, opinions, audios, etc. rapidly and powerfully in real time. Popular websites in SM are WhatsApp, Tumblr, Facebook, YouTube, LinkedIn, Snapchat, Instagram, QZone, Twitter, Pinterest, and so on. Lots of data are to be developed from these SM sites that leads to the development of several techniques and algorithms to analyze these data. Social media analytics (SMA) is the collection of SM data and performs analysis from different websites using analytical tools. Significant applications of SMA are sentiment analysis, opinion mining, recommender systems, fake news analysis, etc. To get automatic and accurate data analysis, it is necessary to develop novel techniques and algorithms. The primary objective of this chapter is to discuss how machine and deep learning algorithms are used for analyzing the SM data.
Application of AI in Computer Network Technology in the Big Data Era

Emerging technologies have always been a strong driving force for the continuous development of Computer Network Technology (CNT). A variety of emerging technologies, including big data (BD), cloud computing, VR, AR, and AI, are widely used in the field of computer networks, bringing network users Here comes a rich new experience. CNT is a product of the era of BD. People can use CNT to complete ultra-long-distance information transmission in a very short time. Combining Artificial Intelligence (AI) technology with CNT can increase data security while improving data processing efficiency. Data cleansing, also called data cleansing, detects and removes errors and inconsistent data parts from the data to improve data quality. This article takes the application of AI in data cleaning as an example to study the application of AI in CNT in the era of BD. Experiments show that the data cleaning program described in this article has the characteristics of high degree of automation, less human management, clear structure, easy maintenance and high operating efficiency.
Small Object Detection in Aerial Images

Object detection is one of the main research directions in the field of computer vision. For aerial images which have many small and dense objects, more sophisticated object detection models are required to obtain satisfactory result. In this paper, S_ YOLOv5 is proposed with a more appropriate detection layer of small-scale objects. Furthermore, batch mode weighted-cluster-DIoU-NMS is adopted instead of the standard NMS to efficiently eliminate redundant bounding boxes. The experimental results show that S_YOLOv5m with improved NMS achieved 2.7mAP and 5.76mAR higher than YOLOv5m in small object detection.
Modeling and Solving the Fake News Detection Scheduling Problem

The large amount of information on social networks makes it difficult to distinguish fake news from good news. Society and individuals are often affected by the extent of the dissemination of false information. Currently the fake news detection (FND) is becoming a concern for scientific researchers. In this article, we propose a new approach to modeling the FND document processing process using a flow shop scheduling problem (FSSP). Our approach consists in modeling the FND data processing in the FSSP. In fact, in this model the process is composed of several phases arranged in series forming a FSSP in the flow work. The documents are considered as tasks to be processed available in front of the first machine of the model will be processed by all the machines in the same order. In order, to solve this problem we propose three metaheuristics: the iterated greedy (IG), the genetic (GA) and the artificial bee colony (ABC) algorithms. The goal is to minimize the maximum completion time of the set of documents called the makespan ( $$ C_{\max }$$ ). Various instances are tested by varying the number of documents in front of the queue in the all machines. The simulation results showed that the IG algorithm performs the best compared to other algorithms.
USING MATHEMATICAL DATA IN THE DESIGN OF PSYCHOLOGICAL DIAGNOSTIC TOOLS

The article presents the relationship between Mathematics and Psychology, especially Diagnostic Psychology. Based on the understanding of the design in psychological diagnostic tools, the mathematical content has always been used as data to solve problems and prove hypotheses proposed by researchers and psychosocial diagnoses. There is a close relationship between Mathematics and Psychology. Diagnostic psychology uses mathematical linguistic data in designing psychological diagnostic tools and vice versa. Mathematics through being used in psychological science demonstrates its wide range of applications not only in the General Sciences but also in the Social Sciences and Humanities.
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision  Exploit

Machine learning models are critically susceptible to evasion attacks from adversarial examples. Generally, adversarial examples, modified inputs deceptively similar to the original input, are constructed under whitebox settings by adversaries with full access to the model. However, recent attacks have shown a remarkable reduction in query numbers to craft adversarial examples using blackbox attacks. Particularly, alarming is the ability to exploit the classification decision from the access interface of a trained model provided by a growing number of Machine Learning as a Service providers including Google, Microsoft, IBM and used by a plethora of applications incorporating these models. The ability of an adversary to exploit only the predicted label from a model to craft adversarial examples is distinguished as a decision-based attack. In our study, we first deep dive into recent state-of-the-art decision-based attacks in ICLR and SP to highlight the costly nature of discovering low distortion adversarial employing gradient estimation methods. We develop a robust query efficient attack capable of avoiding entrapment in a local minimum and misdirection from noisy gradients seen in gradient estimation methods. The attack method we propose, RamBoAttack, exploits the notion of Randomized Block Coordinate Descent to explore the hidden classifier manifold, targeting perturbations to manipulate only localized input features to address the issues of gradient estimation methods. Importantly, the RamBoAttack is more robust to the different sample inputs available to an adversary and the targeted class. Overall, for a given target class, RamBoAttack is demonstrated to be more robust at achieving a lower distortion within a given query budget. We curate our extensive results using the large-scale high-resolution ImageNet dataset and open-source our attack, test samples and artifacts on GitHub.