Reviewerly/exHarmony huggingface dataset:
Design of Non-Intrusive Bus Data Monitoring and Intelligent Processing Unit

State estimation and fault detection of complex equipment are of great importance to ensure operational reliability and safety. However, some key information is transferred between the sub-systems via different kinds of communication protocols. In other words, it is hard to measure or obtain the system state by additional sensors. On the other hand, limited by the bandwidth of the telemetry link, the real-time state estimation and monitor can only be implemented on-board. This brings more challenging issues for complex equipment state estimation, prognosis, and health management. Therefore, this work designs a non-intrusive bus data monitoring and intelligent processing unit (BMIP) for complex equipment online state estimation and anomaly detection. According to the communication protocol of the researched equipment, the design (BMIP) realizes the multi-channel 1553B and RS-485 communication. At the same time, the anomalies of the navigation system is online detected based on the least square support vector machine. The bus data is monitored and processed by an embedded system. Experiments have verified that the device has a good fault diagnosis result for constant discrepancy, drift and stuck. The use of this device can effectively reduce the cost of finding faults in industrial systems, and has a good application prospect.
Novelty Search in Particle Swarm Optimization

This paper presents a novel approach to implementing the Novelty search technique (introduced by Kenneth O. Stanley) into the Particle Swarm optimization algorithm (PSO). PSO is well-known for its impaired ability to operate in multidimensional spaces due to its inclination towards premature convergence and possible stagnation. This presented research aims to try various implementations of Novelty Search that could remove this inability and enhance the PSO algorithm. In total, we present five different modifications. The CEC 2020 single objective bound-constrained optimization benchmark testbed was used to evaluate the different Novelty Search-based modifications of the algorithm. All results were compared and tested for statistical significance against the original variant of PSO using the Friedman rank test. This work aims to increase understanding of implementing new approaches for population dynamics control, which are not driven purely by a gradient, and inspire other researchers working with different evolutionary computation methods.
DIVINIA: Rare Object Localization and Search in Overhead Imagery

This work introduces DIVINIA, a feature extractor and novel training objective for content-based image retrieval. DIVINIA combines a semantic matching objective with a ranking objective to produce a feature extractor that is able to retrieve semantically relevant regions from a large search corpus. It further ranks them appropriately according to visual similarity. Furthermore, DIVINIA provides a mechanism for performing one-shot and even zero-shot object localization without the need to fine-tune the feature extraction model or re-index the corpus of search features. We demonstrate the capabilities of the DIVINIA system in the context of object localization in satellite imagery. We present quantitative and qualitative results that show robust domain transfer between satellite image optics and sensor modalities. We show good precision and search relevance ordering when returning areas of interest to specific object classes.
OCR Using Convolution Neural Network in Python with Keras and TensorFlow

We aim to design an expert system for,” OCR using Neural Network” that can effectively recognize specific character of type style using the Artificial Neural Network Approach. We are pre-processing the input image, extracting the features, and then using the classification schema along with training of system to acknowledge the text. During this approach, we have trained the system to seek out the similarities, and also the differences among various handwritten samples. It takes the image of a hand transcription and converts it into a digital text. The extension of MNIST digits dataset has been used and A-Z characters in both uppercase and lowercase to detect handwritten text and convert it into digital form using Convolutional Neural Networks model, abbreviated as CNN, for text classification and detection also we are using keras graph to predict alphanumeric characters drawn using a finger and linked our handwriting text recognition program using keras and TensorFlow librar.
Uploading Files

PHP can be used to upload files to a server. However, allowing others to upload material to your web site could expose you to all sorts of problems. After describing the mechanics of file uploads using the $_FILES array, this chapter builds a custom class that allows you to restrict the size and type of uploads, as well as preventing files from being overwritten. The finished class is capable of handling multiple files in a single operation.
REALIZATION OF THE IDEA OF INTERDISCIPLINARY INTEGRATION OF FINE ART AND FOREIGN LANGUAGE LESSONS IN PRIMARY SCHOOL

The integrative approach allows students to form the competence to transfer knowledge, skills, and abilities from one subject to another. For this article, it is relevant to design and conduct a lesson by one teacher who has basic training in the relevant disciplines and create integrated topics, sections, and even courses on this basis. The possibilities of using interdisciplinary links between the lessons of fine education and a foreign language on the example of specially selected and synchronously built thematic training modules are considered.
Contagious Patient Tracking Application Spotlight: Privacy and Security Rights

As the Covid-19 outbreak spreads across the globe and has killed many lives, many applications have been created to track patients and fight this pandemic. However, several applications lack safety and privacy. This paper designs and develops a mobile app to track patients with the Covid-19 or any other pandemic disease through using GPS in Iraq. Moreover, the app maintains a privacy for users by encrypting their personal data before sending them to the cloud using a MODE CBC AES block encryption algorithm. The app keeps the identity and location of the users, supports two language interfaces English and Arabic, and works in Android and iOS environments. Only the health care providers can decrypt these data and know about the patient's location. Also, to make the patient trusts the application, his/her information will be deleted after sending his/her negative test after 21 days. In addition, the app provides users with information regarding healthcare places in the case of emergency. For the evaluation of this app, a data was collected from 20 users, including males and females and their ages were between (20–50) in Mosul city. The results showed that the app works properly and the users are notified when they are in close with other registered infected people. In addition, the users found that the app was simple, easy to use, and useful to do contact safely. To convince the users to utilize this app, the app is provided with button trial option to try it.
Authentication and Integrity of E-documents based on Zero-Watermarking Method

During the last period, due increase the use of e-government applications and the health conditions that occurred as a result of the spread of the Corona virus, the using of e-documents has become increasingly common. Therefore, there was an urgent need to ensure the authenticity of the source and integrity of the e-documents and that they are free from forgery and manipulation. In this paper, the zero-watermarking method has been proposed in order to achieve the authentication and integrity of e-documents and to determine the regions which tampered. The main idea is based on segment the e-documents into small square areas with 8×8 and then generate the watermark information by computing the eigenvalues and eigenvectors. The generated watermark is insert into the e-documents with a specific relationship among regions. The experimental results prove the efficiency and effectiveness of the proposed method in detecting areas that have been tampered by various types of attacks. In addition, a high level of quality for e-documents watermarked was obtained.
Unsupervised Learning &amp; Reservoir Computing Leveraging Analog Spintronic Phenomena

We have proposed three distinct spintronic neural network approaches that leverage analog spintronic phenomena: 1) Unsupervised learning systems with spin-transfer torque magnetoresistive random-access memory (STT-MRAM) in which analog behavior is produced by stochastic STT switching; 2) Unsupervised learning systems with three- and four-terminal MTJs in which analog behavior is produced by magnetic domain wall motion; and 3) Reservoir computing systems with irregular arrays of nanomagnets in which analog behavior is produced by frustrated nanomagnetism. All three spintronic neural network approaches exploit the hysteresis intrinsic to binary spintronic memory devices while providing analog behavior with significant advantages over analog memory devices.
Semantic Embedding-Based Entity Alignment for Cybersecurity Knowledge Graphs

This paper proposes a new framework that can semantically align the two or more entities in cybersecurity-related Knowledge Graphs (KGs) using an external resource. To do so, we identify four main principles that the external resources must have and then use them to analyze various external resources. The resource is used to find sentences that are needed to understand the usage context of the entities. The entity alignment is performed by semantic embedding with BERT. At this time, semantic embedding is defined as a vector that contains the latent semantic features of the sentences only with similar usage context from the external resource encoded with the language model BERT. To identify the sentences with similar usage context, we first classify the informative entities related to the target entities. Using the informative entities, we generate a set of sentences that have used similar usage context. Finally, to predict semantic relationships (equivalence) between the entities, we employ pre-trained BERT with the set of sentences as input. To prove the superiority of the framework, we perform the experiments to evaluate the accuracy of prediction of equivalence of entities from the different KGs.
Tutor support as a tool for the adaptation of foreign students in Russian and Foreign Universities

This study analyzed the Russian and foreign experience of foreign students tutor support. It is shown that the increasing the number of international students around the world showed that universities faced the problems of creating a favorable environment and adaptation for them. Tutor support is common in foreign universities, but its growth is also observed in Russia. The purpose of this study is to analyze the Russian and foreign experience of tutoring foreign students. The tasks include: to show an understanding of tutoring in domestic and foreign scientific discourse; to analyze foreign and Russian experience on the example of leading universities; to show the positive possibilities of tutor support of foreign students in the process of their adaptation. This work uses such research methods as analysis of domestic and foreign literature, analysis of websites of foreign universities, comparative analysis of the practices of tutor support of foreign students in Russian and foreign universities. The author analyzes the websites of leading foreign and Russian universities. The most successful practices of tutor support of foreign students abroad and in Russia have been identified. The author also describes the experience of conducting a School of Volunteer Tutors to work with students from among foreign citizens of the Siberian Institute of Management — the branch of RANEPA over the past three years. Recommendations on the organization of tutor support of foreign students at the university and its effectiveness in the adaptation process are given.
Identify the types &amp; reason of code mixing found in whatsapp group cargo department staff at ngurah rai airport

The aims of this study are to find out (1) the types of code mixing found in WhatsApp Group cargo department by the staffs at Ngurah Rai Airport and (2) to analyse the reason of code mixing found in the WhatsApp Group by the staff at Ngurah Rai Airport. The data were taken from chat of cargo department staff in WhatsApp Group at Ngurah Rai Airport. The method of collecting data was starting from March 1 to 30, 2021. The analysis was done by applying descriptive qualitative method requires documentation to identify the types of code-mixing, purposed theory by Hoffman (1991) about the types of code mixing and the purposed reason of code-mixing theory from Holmes (2013). The result of this study shows that there two of three types that found in the WhatsApp Group cargo department by the staff at Ngurah Rai Airport, there are: Intra-sentential code mixing, intra-lexical code mixing and involvement in a change of pronunciation. The type of Intra-Sentential code-mixing was more commonly found in chat conversations by the staffs on WhatsApp group, who stated that it was easier not to change the language in the cargo world, which primarily used English, to keep it easier for interact and avoid misunderstandings in the handover of their assignments or job desks. Because this research is based on chat messages in a WhatsApp Group, types of involvement in a change in pronunciation can be identified because that type is part of speech. The reason of code-mixing purposed theory by Holmes (2013) there are four reasons, namely: Topic, participant, solidarity and status. Because the relationship between employees provided information about the duties being carried out between divisions, the topic became one of the most common causes for identifying code mixing in chat chats in WhatsApp groups by cargo department staff.
Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)

One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector machine. Here in the research, the novel Rain optimization algorithm and support vector machine algorithm (ROA-SVM)-based method of face recognition is provided. In ROA-SVM, Rain optimization algorithm is applied for optimizing SVM parameters at the same time. The average classification accuracy for the YALE dataset in the proposed method was 86% and for the base paper was 81%. Furthermore, in order to find optimal parameters in support vector machine, proposed ROA-SVM method efficiency has been improved by 5 percent in comparison with PSO-SVM basic research.
Publishing Historical Records in Documentary Editions (NARA)

Federal Grants & ContractsVolume 45, Issue 3 p. 3-3 Grants Alerts Publishing Historical Records in Documentary Editions (NARA) First published: 04 January 2021 https://doi.org/10.1002/fgc.31478Read 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. Volume45, Issue37 January 2021Pages 3-3 RelatedInformation
THE THEORETICAL STUDY OF THIRD-GRADERS’ COGNITIVE UNIVERSAL LEARNING SKILLS FORMATION TO SOLVE PROBLEMS IN MATH LESSONS

This article shows the theoretical basis of third-graders’ cognitive universal learning skills formation to solve problems. It describes the structure of problem-solving skills, classification of problems, solutions of problems, techniques that are used by primary school teachers, coordination between cognitive universal learning skills, and stages of problem-solving.
Attenuated and normalized item-item product network for sequential recommendation

Sequential recommendation has become a research trending that exploits user's recent behaviors for recommendation. The user-item interactions contain a sequential dependency that we need to capture to better recommend. Item-item Product (IIP), which models item co-occurrence, has shown good potential by characterizing the pairwise item relationships. Generally, recent behaviors have a greater impact on the current than long-term historical behaviors. And the decaying rate of influence around infrequent behaviors is fast. However, IIP ignores such a phenomenon when considering item-item relevance and leads to suboptimal performance. In this paper, we propose an attenuated IIP mechanism which is position-aware and decays the influence of historical items at an exponential rate. Besides, In order to make up for scenarios where the influence is not in a monotonous decline trend, we add another normalized IIP mechanism to complement the attenuated IIP mechanism. It also strengthen the model's ability in discriminating favorite items under the sparse data condition by enlarging the gap of matching degree between items. Experiments conducted on five real-world datasets demonstrate that our proposed model achieves better performance than a set of state-of-the-art sequential recommendation models.
THEORETICAL ASPECTS OF THE PROBLEM OF FORMATION OF DIGITAL COMPETENCE OF TEACHERS

Due to the socio-economic changes, there was a need to revise the value content of education. One of the directions is digitalization. At the same time, the question arises about the development of digital competencies among specialists of educational organizations. The article considers the concept of digital competence, describes its essence. The levels of development of digital competence, the types of the interrelation of teachers with the digital environment, the possibilities of the digital educational environment for the formation of digital competencies are described.
Types of derivational affixes in new idea magazine: a morphological analysis

This study was aimed to identify the derivational affixes that can be found in “New Idea”magazine. Specifically, this study was focused on analyzing the types and the process of derivational affixes that were found in the data source. This study used qualitative descriptive method to analyze the data. The data were analyzed based on the theory proposed by Katamba (1993) to solve the problem which about the types of derivational affixes. Theory that proposed by Rochelle Lieber (2009) was used to solve the problem about the morphological process of derivational affixes. This study found 427 data that contains derivational affixes. Derivational prefixes were found in 52 data with percentage 12% such as prefix in-, un-, dis- , re-, ex-, en-, and pre-. Then, derivational suffixes were found in 375 data with percentage 88% such as suffix –hood, -ship, -ness, -ity, -ment, -less, -ful, -ic, -al, -er, -ly, -ion, -ish, -ing and –ed. This study concluded that derivational suffix –ly was the most dominant type of derivational affixes that found in“New Idea”magazine.
Exploration of the evolution-based footprint on the Generative Adversarial Networks

In the last few years, the Generative Adversarial Networks (GAN) have proved to be an excellent tool for image generation and found a variety of practical applications. In this paper, the specific evolutional concept of mutation for the generative model training improvement is proposed and examined. The idea behind the research is that the generative model learning can be optionally tackled as a complex optimization task. Such an approach is associated with a two-fold problem raised in the paper, meaning that comparing different generators to each other is hard to define, so is the fitness function of the weight vectors. It can be assumed that the fitness function, if existed, might have an unknown but potentially high number of local minima, which could restrict the gradient descent to move in some directions. The vast number of degrees of freedom usually seen in the image processing layers exceeds the standard problems for intelligent optimization algorithms. Therefore the hybrid combination of the gradient descent with the smart usage of mutations is considered and examined, in particular with the proposed novel approach for the mutation masking strategy.
Components of readiness for educational and cognitive activity of future teachers in the process of professional training

The article analyzes the problem of readiness for educational and cognitive activity of future teachers in the process of professional training in higher education establishment, theoretical and methodological foundations of formation and development of the teacher’s personality in the system of continuing pedagogical education. A number of contradictions of the research are identified: between the objective need for specialists who are ready for integrated educational and cognitive activity and the lack of available knowledge about the essence, content and organization of pedagogical process that ensures its implementation; focusing on the advanced nature of future teachers’ training, ready for systemic innovative transformations in the field of their professional activity and insufficient development of technologies for preparing students for such activity. The essence of the concept of readiness for certain types of activity, including educational and cognitive activity of applicants for higher education is revealed. The main theoretical approaches to the definition of «readiness» are characterized: historical, functional, personal. The concept of long-term and situational (temporary) readiness, their components and indicators are revealed. The concept of readiness for educational and cognitive activity is studied as a complex holistic formation; dynamic phenomenon; a system that is determined by internal and external factors and has its own structure. The essential characteristics of the components of readiness for educational and cognitive activity are represented: necessary-motivational, information-cognitive and reflexive-procedural.