Reviewerly/exHarmony huggingface dataset:
Pandemics and the Protection of Privacy and PersonalInformation: Issues with Regard to the Use and Protection of Information on the Basis of Improving Public Health in the Fight Against Infectious Diseases

In order to make use of the response to the new the coronavirus disease (COVID-19) for pandemic countermeasures due to infectious diseases that may have a serious impact on the lives and health of the people, which may occur in the future, the handling of personal information and the protection of privacy necessary to ensure the effectiveness of countermeasures against infectious diseases will be examined.This paper intends to establish to comprehensively understand the systems for the collection and publication of information related to infectious diseases based on the Infectious Diseases Control Law, and to clarify the structure and issues of the current systems and mechanisms for the collection and management of information on infected persons.
Security enhancement of an auditing scheme for shared cloud data

In cloud storage services, public auditing mechanisms allow a third party to verify integrity of the outsourced data on behalf of data owners without the need to retrieve data from the cloud server. In some applications, the identity of data users should be kept private from the third party auditor. Oruta is a privacy preserving public auditing scheme for shared data in the cloud which exploits ring signatures to protect the identity privacy. In this paper, we propose two attacks and demonstrate that the scheme is insecure and a dishonest server can arbitrarily tamper the outsourced data without being detected by the auditor. We also propose a solution to remedy this weakness with the minimum overhead and without losing any desirable features of the scheme. Performance evaluation demonstrates acceptable efficiency of improved scheme in comparison to the original protocol.
The development of the English domino game in basic spoken English class

The goal of this study was to develop the domino game as a learning tool for Basic Spoken English students. The ADDIE (Analysis, Design, Development, Implement, and Evaluate) model was used for this investigation. This study was conducted in the English Department, with 30 students serving as subjects. These pupils were enrolled in a class called Basic Spoken English. The data collection instrument for this study was a questionnaire, a validation sheet, and an observation sheet. The validation was completed by subject matter experts and media specialists for learning and teaching English to determine whether or not the English Domino game can be used as a teaching and learning medium. The trial was done in a large group setting, with 30 students from STKIP Muhammadiyah Muara Bungo's English Department participating. The results indicate that the Domino game is an effective medium for teaching and learning to speak. The score for content validation was adequate, as was the score for media validation. Furthermore, the evaluation conducted by the lecturer of Basic Spoken English revealed that the English domino game was an excellent teaching and learning medium. Additionally, the observation score was 87.5, which is considered to be extremely good. As a result of the observation, it can be concluded that the developed Domino game not only encouraged students to be highly motivated to learn but also assisted them in improving their speaking abilities.
Construction of University Teachers’ Digital Competency Model Based on New Media Communication Technology

Under the background of new media communication technology, digital technology and the new crown epidemic, the use of digital technology to teach, such as online teaching, has become the main teaching method in the epidemic era. This has greatly initiated our country’s long-term offline teaching method. How to adapt to the current teaching methods and improve the digital competence of college teachers has become the main problem to be solved in contemporary education. The purpose of this article is to study the construction of the digital competency model of college teachers based on new media communication technology. Based on the new media communication technology, this article first analyzes the digital competency needs of college teachers, and through questionnaire surveys and interviews, establishes the three dimensions of the digital competency model for college teachers, namely: digital technology capabilities, digital professional development and innovation and the three dimensions of digital value and pursuit, and give the countermeasures for colleges and universities to improve the digital competence of teachers. According to the survey data, 8.7% of college teachers said that they did not meet the situation of frequently using new media communication technology or digital resources for teaching special research, and 21% of college teachers said that they did not meet the situation of frequently using media for teaching special research. This shows that college teachers’ performance in using digital value for teaching research and construction of digital education is not ideal.
METHODS OF DEVELOPMENT OF RESEARCH AND RESEARCH ACTIVITIES OF STUDENTS ON THE SUBJECT "ALGEBRA AND THE BEGINNING OF ANALYSIS"

The article discusses the theoretical foundations and pedagogical features of the organization of search and research activities of students when teaching the subject "Algebra and the beginning of analysis. The concept of "search and research activity" in teaching mathematics has been clarified. The concept of " search and research task" is considered and tasks are complied. The pedagogical analysis of textbooks of natural and mathematical direction is carried out and the advantages of textbooks are shown. The research used the methods of pedagogical analysis, observation, questioning, pedagogical experiment, pedagogical analysis of the content of the subject "Algebra and the beginning of analysis", analysis of pedagogical and psychological literature. As a result of the study, research tasks were developed aimed at developing the search and research activities of students. In the course of the study, a survey was conducted. As a result of the analysis of the polls, it turned out that the student who work with additional literature, interested in independent research, creativity, were able to complete the tasks. The results of the research can be used by specialists-mathematicians who teach algebra and the beginnings of analysis in the school curriculum, they can use tasks as a tool for the development of search and research activities of students.
Translation and Interpretation in Bulgarian Language and Literature Teaching

Changing the focus in modern education from teaching knowledge to acquiring key competences requires the development of new, practically oriented, interactive methodological solutions. The article outlines theoretical perspectives for future investigation, based on the results from lessons carried out in line with the Waldorf pedagogy, which is innovative for Bulgaria. They point to opportunities of developing interdisciplinary teaching in English, Bulgarian (Music, Visual Arts, Philosophy, Citizenship) through (literary) translation as a learning activity. Thus conditions could be created for going beyond a particular subject, hence for learning in the context of universal human themes. This could allow for the acquiring of basic life skills and the integrated development of key competences: literacy competence, multilingual competence, cultural awareness and expression competence, citizenship competence, including intercultural competence.
Disaster Management Using Artificial Intelligence

Natural calamities are known to inject a feeling of helplessness into the minds of the people involved with it, and that is exactly the situation for the personnel who are in charge of helping with the situation. Primarily focusing on the issue of rescue personnel not being able to locate the affected casualties, a solution to the same is proposed via two distinct technologies, social media analytics and aerial person detection. When any kind of disaster occurs, social media gets flooded with different kinds of data such as images, videos, texts, etc. These could be posted by people who are directly or indirectly affected by the occurrence of the disaster. In this paper, a social media analytics method is proposed to monitor social media for disaster-based posts and give detailed insights to take necessary action. Another major challenge experienced by search and rescue teams during a disaster situation is the search for survivors and victims and also to reach out to distant areas. Drones can be used for person detection in case of emergency situations. It can fly through a particular area and help in the detection of people stranded in emergency situations such as floods, isolated buildings, etc. Here, another method is also proposed for performing person detection from an aerial viewpoint, and all of these are integrated into a web app from which the user can select the features according to the need.
From Simulation to Deployment: Transfer Learning of a Reinforcement Learning Model for Self-balancing Robot

Reinforcement learning algorithms are time and resource-intensive and can be influenced by the setup of the physical robot environment and its hardware capabilities. Often, in small-scale projects, it is not workable to build a physical robot equipped with good processing capabilities and to set up a fully controlled and monitored environment for reinforcement learning. During this period, it will be more cost-effective for most of the training to be conducted in a simulated environment and then transferred to a physical robot. In this project, two RL experiments were conducted on a simple two-wheeled robot model in a simulated environment. The first was to make the robot start from a completely fallen down position and learn to stand, and the second to start from a balanced position and learn to maintain the position. It was found that starting from a balanced position gave a better performance, and hence, this learned model was used as a baseline for testing on a physical robot such as the LEGO Mindstorms, but it could be seen that the LEGO hardware was not well suited for this kind of intense reinforcement learning algorithms.
Information Security Governance in Civil Aviation

This chapter focuses mainly to proactive means in information security and more specifically governance of information security in civil aviation. The reason is that, to find sustainable, coherent and holistic way to implement information security through the complete civil aviation ecosystem, the governance plays a key role when creating sufficient framework enabling information security by design environment. The study will help aviation and other critical infrastructure sectors to consider, understand and coordinate the information security governance. The study will test how to apply information security governance with ISO27014 through such a safety critical, interconnected infrastructure sector like civil aviation.
Image Deblurring for CCTV Captured Images of Fast Moving Vehicles

A common problem in consumer-level photography is image blur due to unbalanced camera shakes. The blurring of images is caused by movement during photo capture, out-of-focus image, noise in the image, and multiple objects motion. Deblurring can be used to detect the Car License plate by capturing the image from CCTV cameras. In image deblurring, we gradually restore the images to different resolutions. They follow a coarse-to-fine framework. Neural Networks allow us to find relationships between sets of data by extracting similar features from them. Using RESNET Encoders with the neural network will give us a sharp image as an output which we would further pass to the License plate detector. This would give us the license plate from the image. The License plate found would be searched through the database and sent the desired message to the owner. This would increase efficiency by not needing human intervention when a vehicle is overspeeding.
Magmatic Processes

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
VGG FACE Fine-Tuning for Classification of Facial Expression Images of Emotion

The human facial expression of emotions is an important way of nonverbal communication. Six facial expressions of emotions are considered distinguishable by the face image from a neutral expression: anger, disgust, fear, happiness, sadness, and surprise. Accurately recognizing and distinguishing these emotions is a complex and still an open research question. In this work, a deep convolutional neural network (CNN) is applied to extract facial expressions features from images. This CNN originates from a previously trained deep neural network (DNN). A multiclass support vector machine classifier (mSVM) replaced the fully-connected and softmax layers of DNN. One specific dataset is used to fine-tune the CNN; another dataset is applied to train the mSVM and assess system performance. The system reached an average accuracy of 92.1%. The happiness class got the highest classification performance with an F1 score of 98.9%. The lowest F1 score is 87.2% and belongs to sadness class.
Importance of Self-Learning Algorithms for Fraud Detection Under Concept Drift

Fraud detection has been a difficult problem in the industry for the past many years which has caused a massive financial loss for individuals/organizations. Machine learning techniques have proved to be an efficient technique to identify and detect fraud. The major problem which exists in this domain is “Concept drift”. Fraudsters tend to evolve their habits over time which eventually leads to a pattern change. Machine learning models normally depend on the reliable set of labels which classifies whether the transaction is fraud or legitimate. When there happens a change in the patterns, the model tends to lose its performance in predicting new patterns. A model which adapt to this changing behaviour is inevitable in such scenarios. In this paper, we have reviewed several articles which discusses the problem of concept drift in fraud detection, and we have also surveyed different types of methods and techniques used so far by the researchers to deal with it. Above that the paper also proposes a procedure and steps to make a machine learning model adaptive.
Blockchain-Based Drug Regulation System

Unauthorised access to drugs and the sale of counterfeit or substandard drugs are severe global problems with considerable risks to consumers and the public in general. In India, about 300 acid attack cases are reported every year due to a lack of proper implementation of restricted acid sales. According to the WHO report, around 3 million people die every year due to alcohol abuse as no purchase limit restrictions exist. The counterfeit drug problem has also led to millions of deaths around the world. These issues develop a critical need to use technologies to regulate the sales of these products to only an authorised set of people. Tracking of drugs is also essential to prevent fake drugs from entering the supply chain. This paper proposes a potential solution by building a distributed Blockchain-based pharmacosurveillance application to track the supply chain of restricted products like drugs, acid, chemicals, and other related products. Smart contracts set rules for all transactions occurring between different participants in the supply chain. The proposed solution records every transaction among manufacturers, wholesalers, and retailers on a verifiable and immutable Ethereum blockchain network.
Analysis of research trends on English vocabulary teaching and learning: Focused on the KCI journals

The present study intends to explore the research trends of studies conducted on English vocabulary teaching and learning. With this goal, 2,573 KCI-indexed English education articles published between 1990 and 2021 in major Korean academic journals were collected. After data screening, the selected 1,354 abstracts were analyzed using Netminer 4.4. The researcher categorized these data from various perspectives, including years of publication, target research subjects, research methods and instruments, and research topics. The results revealed that: 1) the number of published papers incrementally increased between 1990 and 2021, reaching a record high of 129 in 2017; 2) the most frequently utilized participation group was university students; 3) data were collected via both qualitative and quantitative methods utilizing questionnaires, interviews, tests, surveys, and experiments; and 4) the research topics were on teaching and learning model-development, methods, and strategies. The study has meaningful implications in that: 1) teaching and learning vocabulary has gained more attention over the past 10 years; 2) most of the research participants have been EFL learners in Korea; 3) the overall research interest has been mainly geared towards practical vocabulary teaching and learning, and 4) vocabulary education has been considered to have the strongest connection to reading comprehension.
On the number of solutions of two-variable diagonal sextic equations over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ p $ be a prime, $ k $ a positive integer, $ q = p^k $, and $ \mathbb{F}_q $ be the finite field with $ q $ elements. In this paper, by using the Jacobi sums, we give an explicit formula for the number of solutions of the two-variable diagonal sextic equations $ x_1^6+x_2^6 = c $ over $ \mathbb{F}_q $, with $ c\in\mathbb{F}_q^* $ and $ p\equiv1({\rm{mod}} \ 6) $. Furthermore, by using the reduction formula for Jacobi sums, the number of solutions of the diagonal sextic equations $ x_1^6+x_2^6+\cdots+x_n^6 = c $ of $ n\geq3 $ variables with $ c\in\mathbb{F}_q^* $ and $ p\equiv1({\rm{mod}} \ 6) $, can also be deduced.&lt;/p&gt;&lt;/abstract&gt;
Visual Image Authentication

Malware plays a critical role in breaching computer systems. Cybersecurity research has primarily focused on malware detection. Detection methods are currently up against fundamental limits in theoretical computer science. The purpose of visual image authentication is to create Artificial Intelligence (AI) problems that are easy for humans to understand, yet also hinder malware from understanding or capturing a human user’s credentials or keys. Human visual intelligence still surpasses AI visual recognition systems, particularly when a problem is undefined, or visual occlusions or distracting objects occur in the image. Our technology can exploit AI weaknesses because overlapping, outlining and distracting can help hinder AI recognition of visual images.
Position Estimation and Tracking with a Fuzzy-Logic-Based Adaptive Strong Tracking Kalman Filter for Capacitive Touch Panels

A capacitive touch panel (CTP) is one of most important applications that is used in most of the human controlled electronic devices. The sensor detects CTP and decide if there is error or not. The error might be caused by the speed of touching the CTP. However, the low touching speeds cause more errors than the fast ones. The paper of consideration introduces an algorithm with smooth tracking and predict the position. Many methods have been studied to mitigate the errors, including moving average filter (MAF) method, Kalman filter (KF) and a strong tracking Kalman filter (STKF). Generally, STKF succeed to estimate the normal and fast speed of touching the CTP, whereas it fails in estimating the low speed. The paper of consideration proposes a method that integrates fuzzy logic with STKF to decrease the error produced from low speed of finger movement. Fuzzy logic provides a solution for this problem that results in a smooth tracking at various speeds (low, high, normal and nonlinear cases).
Experience in analyzing a student's digital footprint in LMS Moodle.

Experience in analyzing a student's digital footprint in the Learning management System (LMS) Moodle. The analysis of the digital footprint is carried out in the cancers of the course "Project Management". The project management course is an actual course for future students of architectural specialties, due to the requirements of the professional standard. The data source was students' feedback about the course, as well as questionnaire data. The questionnaire is filled out by the listeners without fail. The questionnaire questions are post-project analysis questions from the MSF (Microsoft FrameWork Solutions) project development methodology. Unlike the questionnaire, feedback about the course is left at will, the content of the reviews is not regulated by anything. The main conclusion of the study can be attributed to the conclusion that the analysis of the digital footprint allows you to identify and determine the skills, abilities and the experience that students receive during the course. Thus, the analysis of the digital footprint serves as an independent confirmation of the correctness of the decision on which competencies the course should form. Also, the analysis of the digital footprint allows us to conclude whether the training course forms the skills and abilities required by professional standards in this specialty. The methodology of digital footprint analysis demonstrated in this study is of independent value.
Neural Network Analysis for Image Classification

The article considers the possibility of modeling artificial neural networks using the mathematical apparatus of information theory. The issues of pattern recognition, classification and clustering of images using neural networks are represented by two main architectures: a direct distribution network and convolutional networks. The possibility of using orthogonal transformations to increase the efficiency of neural networks, the use of wavelet transformations in convolutional networks is investigated. Based on the theoretical studies carried out, the directions on practical application of the obtained results are proposed.
Use Influencer Maps and Personas As Marketing Tools

Nonprofit Communications ReportVolume 20, Issue 5 p. 5-5 Developing Your Communications Plan Use Influencer Maps and Personas As Marketing Tools First published: 05 April 2022 https://doi.org/10.1002/npcr.31939Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume20, Issue5May 2022Pages 5-5 RelatedInformation