Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Electronic Health Record System Enforced by Ensemble Multi-Contract Approach

Electronic health records (EHRs) are predominantly shared among radiologists, clinicians, pharmacists, healthcare providers and researchers for effective treatment and diagnosis, as they contain confidential and critical information about patients. The proposed solution requires the use of blockchain technology for the efficient management of health records. The electronic health record would have to be stored in such a way that it follows the norms of the blockchain architecture using the concept of proof of authority. The implementation results in dealing with privacy and security concerns and achieves scalability, providing role-based access, event traceability and an off-chain storage system using interplanetary file system. It operates as a distributed database that is validated by a decentralized community rather than a central entity. It’s a collection of records that a crowd oversees and maintains rather than relying on a single entity with a centralized server, leading to the creation of a decentralized ledger. Each of the blocks comprises numerous transactions with a link created among different blocks using a hash function to form a chain. When a record is created, it is verified across the distributed network of nodes and added to a block which is linked to the previous block, leading to a chain of blocks or blockchain. The management of health records utilizes a multi-contract approach aimed at securing the access control paradigm with better traceability.
Comparative analysis of the teachers’ migration

This article analyses Russian and foreign academic research in the field of teacher migration. The study aims to determine the volume of research on the given subject area. We have used a comparative and structural-functional analysis and statistical means. The objectives were to study the concept of migration, to identify positive and negative aspects of labor migration, to examine the meaning of the terms of educational and intellectual migration, to identify the problems faced by teachers when moving to other countries based on the works of foreign authors. The teachers’ experience in such countries as the USA, Belgium, and Norway was analyzed for this purpose. The main results of the study confirm the relevance of the article’s topic. We came to the conclusion that the narrow focus “migration of teachers” is barely studied and requires additional research to identify the causes and factors in the teacher professional activities.
Adaptive Network Compression using Block Removal and Recycling Mechanisms

Recent advancements in deep learning has improved the performance of various visual tasks; however, the inference of deep learning models requires large computations, where expensive GPUs are essential. In this study, we propose a novel scheme to compress the computation and storage memories of deep learning models, even without the supervision of the testing environment. To obtain general robustness across various domains, the proposed algorithm trains the deep learning model using two novel mechanisms: block removal and block recycling. In addition, the trained network can robustly function even after compression. We validated the classification performance of the proposed algorithm using CIFAR10. The obtained results proved impressive performance of the presented algorithm with reasonable compression rates.
Multi-Layer Encryption Algorithm

In recent years, security has become a big issue for many applications to defend attacks from intruders. Exchanging credentials in plaintext might expose it to stealers. Many techniques are required to protect the data of the consumers from attackers. Cryptography has come up with a solution to provide security for the users to exchange data securely by the means of the process called as Encryption/ Decryption. In this field, there are basically two techniques of cryptography i.e Symmetric and asymmetric, developed to achieve a secure connection between the sender and receiver. These techniques provide specific goals in maintaining privacy by converting original message to non-readable form and sends it over a communication channel. The unauthorized members try to break the non-readable form but the difficulty depends upon the techniques that were used to encrypt the data. In this paper, we proposed a quadruple encryption algorithm consists of novel phase-shift algorithm, AES (Advanced Encryption Standard), TwoFish and RC4 and making it hard to attack by common methods.
INFLUENCE OF ACOUSTIC PRESSURE WAVE ON THE MOVEMENT OF THE UNDERWATER

The forced movement of the submarine under the action of an acoustic pressure wave at rectilinear and uniform movement of the device is analyzed. The analysis of the dynamics of translational movement of the hull under the action of an acoustic pressure wave in an ideal environment, which makes it possible to assess the physical properties of the environment and the elastic properties of the outer hull on the value of the maximum movement of the submarine. It is proved that if the total pressure pulse is limited, then the water particles will receive certain displacements and it can be expected that under these conditions the displacement of the submarine will be determined. The results of the analysis make it possible to conduct a comparative analysis of the translational movement of the submarine under the action of an acoustic pressure wave, taking into account the characteristics of the moving medium, more precisely, taking into account the viscosity of the real medium
Application of Encryption Technology in Data Acquisition and Communication of Energy Saving and Emission Reduction

With the development of information technology, the scale of networks has become more and more complex and huge, and the complexity of the network environment makes reliable data transmission in key facilities more and more important. The reliability of data and the security of data transmission affect the normal operation of energy saving and emission reduction. A data acquisition preprocessing system based on RSA encryption algorithm is proposed to realize the encryption of data transmission process. Multi-threading technology is used in data processing to alleviate the problem of data efficiency in the transmission process, and to ensure the safety and efficiency of data transmission when a large amount of data is input. Through field tests in a power plant in northern Jiangsu, the results show that the system can effectively improve the security of the data transmission process.
Fake Face Image Detection Using Deep Learning-Based Local and Global Matching

The widespread adoption of face recognition systems in practice has provoked multiple attempts to fail these systems in order to impersonate another person.The range of such fake attacks is wide, and methods which can be used to compensate for one type of attacks are not adapted against other attacks.In this study, we propose a method for detecting fake face images based on local and global matching provided by deep neural networks.Also we do not discard the background analysis as a pre-processing stage.The idea is to assess the depth of the face in a still image as one of the main features of liveliness, which is not an easy task.The proposed method is directed against presentation attacks and attacks of adversarial perturbations.The experiments were conducted with and without deep neural networks.The use of deep learning increased the true accept rate and significantly reduced the error values.
What's Coming in Future Issues

AORN JournalVolume 115, Issue 1 p. 118-118 What's Coming in Future IssuesFree to Read What's Coming in Future Issues First published: 27 December 2021 https://doi.org/10.1002/aorn.13602AboutPDF 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. Volume115, Issue1Special Focus Issue: AORN Pre‐ConferenceJanuary 2022Pages 118-118 RelatedInformation
Novel And Optimized Successive Breast Lesion Detection with Ultrasound Imagery

Breast cancer is one of the world's most diagnosed cancers in women. To analyze breast cancer, mammography is crucial in breast cancer showing, while ultra-sound plays a significant role in imaging modalities of screening & diagnosis of cancer. It is proposed in the paper about the novel method to perceive the breast cancer lesions with ultrasound and images using texton filters groups, the local conformation of design structures, and cataloging lacking using the dissection method. The proposed successive breast lesion detection algorithm is optimized for exact accurate output for breast cancer images. The developing process can detect accurately & configure breast lesions & was achieved with perfect detection for the proposed methodology. This method will be an efficient tool for detecting breast nodules at the time of screening & breast imaging, making it easy for physicians to concentrate on images in the presence of a lesion. This advanced technique will play a critical component for detecting automatic breast nodules at the classification of lesion in-kind versus malevolent.
Developing a listening English learning model using text2speech application

The research on the development of "Listening" practice using Text2Speech Applications aims to develop teaching of English, ‘Listening’ for teachers and students who are not experts in the field of information technology. This study tries to discuss how to adopt and adapt Text-to-Speech software / applications in teaching and learning of English. Text2Speech is a computer application (software) that makes it possible to convert written text into spoken text so that it can be heard. By using the Text2Speech application, written text in MS Word format can be converted into spoken text. Text2Speech allows any text to be read in a computer-synthesized voice. By utilizing Text2Speech, written text can be read and sounded so that it can be heard by the user. Text2Speech allows users to determine how to pronounce a word, phrase, sentence, and even text or parts of text. By utilizing the Text2Speech application, teachers and students get "listening" learning materials in native speakers' voices, so that students get used to hearing "listening" teaching materials from native speakers.
Pemanfaatan Teknologi Informasi dan Komunikasi Dalam pelaksanaan Kurikulum 2013 di MI Tarbiyatul Athfal

Information and communication technology (ICT) has advantages over traditional methods in effectiveness and efficiency. Mastering ICT is one of the most important competencies possessed by educators. The purpose of this research is to study how the use of "information and communication technology" (ICT) is used as the implementation of the 2013 curriculum at MI Tarbiyatul Athfal, as well as the supporting and inhibiting components of the use of ICT in the implementation of the 2013 curriculum. This is a “qualitative descriptive” research project. . Research data collection is done through observation, interviews, and documentation. According to the findings of this study, ICT was used in the implementation of the 2013 curriculum at MI Tarbiyatul Athfal by providing ICT facilities to assist the process of “learning planning, learning implementation, and assessment of learning outcomes”. ICT also supports instructors in the “learning process”, enabling learning materials to be more “interesting, innovative, and active” to be presented. In addition, the use of ICT has helped achieve the objectives of the 2013 curriculum. Budget allocation policies for the procurement of ICT facilities, as well as the presence of Wi-Fi network routers that spread throughout the school buildings, are supporting aspects. Physical and non-physical factors are obstacles to the use of ICT in the implementation of the 2013 curriculum.
Visual experience recognition using adaptive support vector machine

Video has more information than the isolated images. Processing, analyzing and understanding of contents present in videos are becoming very important. Consumer videos are generally captured by amateurs using handheld cameras of events and it contains considerable camera motion, occlusion, cluttered background, and large intraclass variations within the same type of events, making their visual cues highly variable and less discriminant. So visual event recognition is an extremely challenging task in computer vision. A visual event recognition framework for consumer videos is framed by leveraging a large amount of loosely labeled web videos. The videos are divided into training and testing sets manually. A simple method called the Aligned Space-Time Pyramid Matching method was proposed to effectively measure the distances between two video clips from different domains. Each video is divided into space-time volumes over multiple levels. A new transfer learning method is referred to as Adaptive Multiple Kernel Learning fuse the information from multiple pyramid levels, features, and copes with the considerable variation in feature distributions between videos from two domains web video domain and consumer video domain.With the help of MATLAB Simulink videos are divided and compared with web domain videos. The inputs are taken from the Kodak data set and the results are given in the form of MATLAB simulation.
Using AutoTutor to Track Performance and Engagement in a Reading Comprehension Intervention for Adult Literacy Students

A large percentage of adults throughout the world have low reading skills. Computer technologies can potentially help these adults improve their literacy in addition to instructors at literacy centers. AutoTutor was designed to teach comprehension strategies by implementing conversational âtrialoguesâ in which two computer agents (tutor and peer) hold spoken interactions with the adult about words, sentences, and text in digital lessons. The agents model comprehension strategies, ask questions, and give feedback on adult answers. AutoTutor records in log files the adultsâ performance, namely the time and accuracy of answering questions in the conversation. We assessed the value of AutoTutor in a study with 52 adult literacy students in the United States and Canada who interacted with AutoTutor as part of a 4-month intervention with human instructors. Performance in AutoTutor was tracked at four theoretical discourse levels (words, explicit textbase, conceptual situation model, rhetorical structure) and also engagement, with an objective psychometric measure of comprehension skill both before and after the intervention. The results showed that AutoTutor provides nuanced performance and engagement measures that predicted comprehension improvements and can be used to guide formative assessment for instructors.
Barcode Location in Financial Statement System Based on the Partial Differential Equation Image Recognition Algorithm

Financial statements are the basis of financial analysis. Most of them are still using traditional financial statement software, and financial data cannot achieve better information sharing. Using a barcode to improve the efficiency of data sharing is undoubtedly the most convenient and fast way to realize financial statement information sharing. This paper studies the problem of the barcode image location and recognition in the financial statement system and tries to apply the partial differential equation image recognition algorithm to the barcode location in the financial statement system. In this paper, image technology is used to preprocess the code 39 barcode label image in the captured image, including color space conversion, image enhancement, threshold segmentation, binarization, and edge detection. The barcode position location is realized by using the method based on integral projection peak analysis and Hough transform line detection. It is proved that the positioning function of the barcode makes the data in financial statements realize resource sharing. Then, the performance of the completed barcode positioning and recognition algorithm is tested. The reliability and effectiveness of the algorithm are verified on the manually made test set.
Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection

Abstract Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.
Blockchain

Distributed ledger technology, a method of storing and maintaining the integrity of multiple copies of critical data using a massively redundant network of participating machines, has found a “killer application” in blockchain, a type of distributed ledger. A blockchain consists of sequential blocks that may never be modified or reordered, leaving a public, auditable record that is consistent and highly resistant to tampering and deletion. These qualities make blockchain eminently suitable for its most common use: cryptocurrency, and its variants in the form of cryptocurrency tokens, used to represent ownership or some other right to virtual or physical goods and capabilities. Blockchain also enables smart contracts, bodies of software code that may be written to serve as the memorial and means of execution of an agreement between parties. Smart contracts can have all the elements of a traditional contract, and as jurisdictions legislate or jurists rule on the fine points of enforceability and the acceptability of smart contracts as traditional contracts, applications in nearly every area of commerce begin to emerge. Digital lawyers may not need to become software developers, but deepening their understanding of the capabilities and limitations of the technology, developing a keen awareness of the issues at the intersection between code and the law, as well as the law’s readiness in this area will be of great advantage to them and their clients.
Deep graph convolutional network for US birth data harmonization

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more information were more similar to each other.), and developed an active learning mechanism to conduct the harmonization. Toward a total of 9,321 variables from 49 databases (i.e., 783 stemmed variables, from 1970 to 2018), we applied our model iteratively together with human reviews for four rounds, then obtained 323 hyperchains of variables. During the harmonization, the first round of our model achieved recall and precision of 87.56%, 57.70%, respectively. Our harmonized graph neural network (HGNN) method provides a feasible and efficient way to connect relevant databases at a meta-level. Adapting to the database's property and characteristics, HGNN can learn patterns globally, which is powerful to discover the similarity between variables among databases. Our proposed method provides an effective way to reduce the manual effort in database harmonization and integration of fragmented data into useful databases for future research.
Effect of Online Classes on English Phonology

본 연구는 현재 대학 교육 현장에서 진행하고 있는 온라인 수업 중 영어전공 교과인 영어음운론의 실시간 동영상 수업이 어떤 학습적 효과가 있는지에 대해 분석하는데 있다. 이에 다양한 음성 기호, 음소의 구별기호, 음운규칙의 설정과 적용 방법 등을 어떻게 하면 온라인 수업에서 효과적으로 표기하며 이해시킬 수 있는지에 집중하며 진행한 교수 방법의 효율성을 점검한다. 본 연구는 마이크로소프트 팀즈 프로그램을 이용한 실시간 온라인 강의를 진행하며 얻은 결과에 기반을 두고 있다. 아울러 이러한 영어전공 교과 교육에 있어서 어떤 교육방식을 채택하는 것이 효과적이며, 학습자들의 온라인 수업 적응 정도, 학습내용 습득의 정도, 그리고 비대면 온라인 수업을 통한 음운론 교육의 실효성에 대한 반응 등에 대해 설문조사를 통해 분석하였다. 이것은 앞으로 더 효율적인 온라인 교수방법을 모색하는 계기를 마련하는데 기여할 것이다.The purpose of this study is to investigate the effect of current online classes on English phonology and analyze the operation of the teaching method as well as the factors related to the improvement of learning effectiveness that English phonology learners can catch. This study was performed based on the results from live online classes conducted through the Microsoft Teams program. For various phonetic symbols involving diacritic marks and the manner of application of phonological rules, the method of the live online class was found to trigger influential positive results in terms of sharing textbook files, sharing PPT files of summarized learning contents, and using PC tablets. The result of a questionnaire reflects that the majority of learners preferred the current learning method in the online phonology class and were comparatively satisfied with student-directed learning and mutual communication online, supporting that the class operation of instructors and learning motivation are significant.
Image captioning using relevance attention and ITEM encoding

The image caption task aims to generate a corresponding semantic description for an image. The current algorithm of generation captioning has these problems, that is insufficient information understanding in the process of generation image description and insufficient research of the relationship between image features. In order to solve these problems, we propose an image description method using the relevance attention mechanism and ITEM encoding. This model uses ITEM encoding to obtain text features containing contextual information and image information, so it can obtain richer semantic information. Then the relevant attention module is used to capture the relevant features which is a correlation between image features and text semantic information. Finally, the LSTM is used for decoding the relevant features to generate image caption. This paper conducts experiments to verify the effectiveness of the model on the MS COCO data set and Flickr 30k data set. The experimental results show that compared with the baseline model using visual attention, various evaluation indicators of our model have improved significantly.