Reviewerly/exHarmony huggingface dataset:
Methodological approaches to the formation of a teacher’s digital competence

Problem and goal. Digital transformation in education requires a new approach to traditional teaching methods and leads to an accelerated change in methods and forms of education. The study and selection of the possibilities of effective networked environments in the creation of digital educational content today is becoming one of the main needs for educators. This highlighted the need for highly qualified educators who have developed digital competencies and lifelong learning skills that creatively combine continuous professional growth. In this regard, it is important to determine the methodological foundations for the formation of digital competence of teachers. The purpose of the study is to concretize the features of methodological approaches to the formation of teachers digital competence. Methodology. General theoretical research methods were used: study and analysis of theoretical literature data; systemic structural analysis; concretization of the role of approaches to the formation of digital competence of teachers. The analysis of the features of methodological approaches to the formation of digital competence of teachers. Results. The research deals with the problem of the formation of digital competence of teachers. It is shown that the need for the development of digital competence is touched upon in many scientific works, but is not sufficiently disclosed in domestic scientific and practical research. The systematic, activity-based, axiological, personality-oriented approaches to the formation of digital competence is presented. Based on the analysis of the relevant literature, the advantages of the selected approaches in the formation of digital competencies of teachers, their main characteristics were identified. Conclusion. The approaches used as the methodological basis of the research, not only do not contradict each other, but also complement each other, act as a basis for constructing a concept and a structural-meaningful model of the formation of the teachers digital competence.
Twitter and Market Efficiency in Energy Markets: Evidence Using Lda Clustered Topic Extraction

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An Anti-forensic Method Based on RS Coding and Distributed Storage

The anti-forensics (AF) technology has become a new field of cybercrime. The problems of existing forensic technologies should be considered from criminals’ perspective, so as to make improvement to existing AF technologies. There are two types of AF methods, namely, data hiding and destruction, where most AF tools are primarily based on data hiding. If the data can be intercepted by investigators during the AF process, the remaining data may be destroyed by the criminal, which would make investigators obtain nothing about data information. To address this issue, this paper proposes an AF scheme with multi-device storage based on Reed-Solomon codes by combining data hiding and data destruction. The data is divided into multiple out-of-order data blocks and parity blocks, where these blocks are stored separately in different devices. This method can reduce the storage cost and protect the privacy of data. Even if the data is destroyed, it allows AF investigators to recover the data. Security analysis showed that this AF method can prevent malicious, erroneous or invalid files while acquired and ensure data security in data stolen. Theoretical analysis indicated that this method was difficult for investigators but easy for AFer in files recovery. Experimental results demonstrated that the proposed method is effective and has practical efficiency.
Case Study: Analysis of Autonomous Center Line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.
Digital Currencies in Financial Networks

We introduce a digital currency, either as a central bank digital currency (CBDC) or a financial crypto asset (stablecoin), in the network of financial accounts. Simulating a shift of deposits by both households and non-financial corporations from the banking sector to the digital currency, we model the different responses of the affected institutional sectors. We find that the introduction of a digital currency generates significant adjustments in the balance sheets of all sectors, may trigger large moves in securities prices and induces changes in the network structure. The economic impacts vary depending on the design of the digital innovation, the size of the deposit shift, the channels through which the balance sheet adjustments take place and the timing of the initiative.
Cost Model Based Incremental Processing in Dynamic Graphs

Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.
Mitigating Speculative Execution Attacks via Context-Sensitive Fencing

This article proposes a microcode-level defense against speculative execution attacks.
The use of augmented reality technology in primary education

In the article it had been analyzed a supplemented reality as a technology of the digital didactics, isolated the applications AR of the educated orientation, which can be useful in primary school. With an aim of checking the positive impact of the applications of a supplemented reality of the educated orientation on the development of the cognitive activity and the improvement of studying results of pupils it had been organized the pedagogical experiment. Diagnostics was used on the different levels of the forming the educated and cognitive interest, the cognitive activity, the educated motivation and success. The received results supported the hypothesis of the investigation, and its summing up let us make the conclusions about that the immersive technologies in the educated process which have become the didactic means, which need the active application.
A study of the segmentation of tumor in breast MRI using entropy thresholding and the Mayfly algorithm

Breast cancer is one of the most serious illnesses in women and its early detection is essential to reduce its impact. This chapter aims to develop an image assessment system to segment and analyze breast tumor fragments within a breast MRI slice. The proposed method combines thresholding and segmentation procedures to obtain a better experimental outcome. In this work, a mayfly algorithm-supported method is used to execute trilevel thresholding using the entropy function, and mining of the tumor is then carried out using a semi/automated practice. The obtained section is evaluated with ground truth (GT) and the necessary quality measures (QM) are then calculated. Based on the QM's superiority, the performance of this scheme is verified. In this work, breast MRI slices were collected from the TCIA-RIDER data set and the proposed investigation is implemented using MATLAB. This work also presents a detailed assessment of the thresholding process and segmentation procedures (watershed, level set, and active contour). The attained outcome confirms that the proposed method assisted in obtaining the tumor in MRI slices of axial, coronal, and sagittal views with better accuracy.
Policing Cybercrime

There is little doubt that the global scourge of cybercrime can be curbed substantially by good law enforcement. The key strategy is cyber surveillance. Police, indeed, have always had surveillance tools in their suite of cybercrime prevention strategies, but the demands of globalization have placed new strains on those resources. Hence, private companies are being called upon more and more to assist law enforcement agencies with the task of keeping abreast of developments in the world of cyber criminality. Global corporations now play a significant role in challenging and mitigating the various forms of cybercrime. But what is the effect of the extralegal regulatory actions implemented by industry? Given how easy it is for private corporations to favor tracing and tracking over privacy, do we, as citizens, need greater control over companies that, as potential “agents” of law enforcement, are now tasked with thwarting cybercrime? What policies should we, as citizens, be demanding? What answers do we need to hear? These and other questions are addressed in this chapter.
2. Topographic representation for quantum machine learning

One of the most common information representations in the brain is the topographic or computational map, in which neurons are arranged systematically according to the values they represent. By representing quantitative relationships spatially, computational maps enable the brain to compute complex, nonlinear functions to the accuracy required. This chapter proposes two approaches to quantum computation for machine learning by means of topographic representation. It shows how to construct unitary operators, implementable on quantum computers, that implement arbitrary (including nonlinear) functions via computational maps.
Towards Linguistic-based Evaluation System of Cloud Software as a Service (SaaS) Provider

Cloud Software as a Service (SaaS) is a type of delivering software application by using Cloud computing Infrastructure services.Cloud SaaS used the global Internet connection to offer its services to the client consumers.The selection of Cloud SaaS provider is based on the evaluation mechanism that the Cloud SaaS consumer manage before making the service contract.In this paper, the linguistic-based evaluation of Cloud SaaS quality attributes has been proposed to help the consumer to assess the service for optimal service selection.Our proposed approach has been developed by the combinations of fuzzy logic and TOPSIS MCDM methods.The proposed approach helps the Cloud SaaS consumer to select the optimal service Cloud SaaS service provider.The case study has been proposed in order to demonstrate the proposed approach.
Jacobi forms over number fields from linear codes

&lt;abstract&gt;&lt;p&gt;We suggest a Jacobi form over a number field $ \Bbb Q(\sqrt 5, i) $; for obtaining this, we use a linear code $ C $ over $ R: = \Bbb F_4+u\Bbb F_4 $, where $ u^2 = 0 $. We introduce MacWilliams identities for both complete weight enumerator and symmetrized weight enumerator in higher genus $ g\ge 1 $ of a linear code over $ R $. Finally, we give invariants via a self-dual code of even length over $ R $.&lt;/p&gt;&lt;/abstract&gt;
Development of Image Processing Technology for Shearography Phase Fringe Patterns

Shearography is a high-precision, non-contact optical full-field measurement method that can perform non-destructive testing of composite material components such as delamination, debonding, wrinkles, cracks, and impact damage. It has been widely used in the field of nondestructive testing of aerospace composite materials. Starting from the principle of shearography and phase shift technology, this paper discusses fringe image processing algorithms such as phase fringe pattern filtering and phase unwrapping technologies, and finally introduces the application of deep learning network in shearography.
An Errors Correction Model for the Errors of Non-word and Real-word in English Composition

&lt;p&gt;In the procedure from composing English, it is inevitable to face the phenomenon of word writing errors. In recent years, English composition automatic correcting system has attracted much attention. However, the precision of the existing word errors correcting system is vague generalization. So as to move forward the accuracy of checking and correcting word errors, this paper designs a word errors correction model based on natural language processing technology. This model designs phoneme matching method based on an improved IDM algorithm, and combined with a non-word input errors correction method based on character distance. The accuracy of correcting non-word errors in this model reached 86.5%. The study also proposes a real-word errors correction method, which is implemented basing on the real-word confusion set and combining the binary statistical model and the GloVe word vector model, improving the real-word errors correction method based on feature annotation of the real-word confusion set, with an accuracy of 77.9%. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Weather Prediction System Using KNN Classification Algorithm

Data Mining is a technology that facilitates extracting relevant and which have factors in common from the set of data. It is the process of analysis data from different perspectives and discovering problems, patterns, and correlations in data sets that are useful for predicting outcomes that help you make a correct decision. Weather Prediction is a field of meteorology that is created by collecting dynamic data related to the current state of the weather such as temperature, humidity, rainfall, wind. In this paper, we designed a system using a classification method by k-Nearest Neighbors algorithm for predict whether through previous data to determine the expected temperature and humidity the prediction results were compared with real results, the comparison was good and acceptable.
On the exponential Diophantine equation $ \left(\frac{q^{2l}-p^{2k}}{2}n\right)^x+(p^kq^ln)^y = \left(\frac{q^{2l}+p^{2k}}{2}n\right)^z $

&lt;abstract&gt;&lt;p&gt;Let $ k, l, m_1, m_2 $ be positive integers and let both $ p $ and $ q $ be odd primes such that $ p^k = 2^{m_1}-a^{m_2} $ and $ q^l = 2^{m_1}+a^{m_2} $ where $ a $ is odd prime with $ a\equiv 5\pmod 8 $ and $ a\not\equiv 1\pmod 5 $. In this paper, using only the elementary methods of factorization, congruence methods and the quadratic reciprocity law, we show that the exponential Diophantine equation $ \left(\frac{q^{2l}-p^{2k}}{2}n\right)^x+(p^kq^ln)^y = \left(\frac{q^{2l}+p^{2k}}{2}n\right)^z $ has only the positive integer solution $ (x, y, z) = (2, 2, 2) $.&lt;/p&gt;&lt;/abstract&gt;
Development of a HyFlex Defensive Security Course

conference papers, in addition to making numerous other conference presentations.Straub's research spans the gauntlet between technology, commercialization and technology policy.In particular, his research has recently focused on cybersecurity topics including intrusion detection and forensics, robotic command and control, aerospace command and 3D printing quality assurance.Straub is a member of Sigma Xi, the
Customizing WSL

Now that you know the architecture of WSL and are familiar with setting it up and configuring it, let us talk about some things you can do to customize WSL.
Speech To Speech Translation: Challenges and Future

The different languages in the world and the various native mother tongues have distinct origins. Speech is the most widely recognized method of articulation, with an average individual talking more than 11000 words each day. Speech can make each other understand actions and thoughts, be it a dialogue, conversation, or even a presentation. The passage of information would be ineffective if the other party did not understand the language of communication. As a result, a system capable of bridging the linguistic divide is required. Speech-to-speech translation is one such system that can be useful in facilitating communication between people who speak different languages. Efforts are constantly taken all around the globe to accomplish this objective and set it up as a regular occurrence to assist everybody. This paper describes a significant international and inter-institutional effort in this direction, highlighting the current challenges being faced as well as the future of the technology of Speech-to speech translation. The developed language-specific technology, parallel corpora, and speech unit (segmental) database are all been described.
Web-Based Expert System for Diagnosis of Pigeon Disease by Naïve Bayes Method

Pigeons are very close to human life, therefore doves are often pets or livestock. Caring for pigeons because of their uniqueness and care is not that difficult. Care that is not so difficult is not that doves are immune to chicks. Many people do not know how to treat pigeons with disease first. Therefore we need advice from experts on pigeons to treat the disease. Because the disease in pigeons can attack humans, it must be treated first. To overcome this problem, an expert system was built which was equipped with 10 diseases and 33 symptoms obtained from experts or experts. The Naïve Bayes method is a simple classification method which results in a probability value for each disease that appears. This system is made with the advantages of a user friendly display on the gejela selection page. The final result of the diagnosis is according to the first treatment method before being taken to the vet, using a web-based system which can make it easier for users to get information for disease diagnosis in pigeons.
A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators

A patent contains various information about a developed technology and is a form of Big data that receives millions of applications worldwide each year.Recently, there has been an increase in research that analyzes such patent Big data for use in R&D strategy establishment.Among these studies, a core patent classification is recognized as important because it can be used for a variety of management information.In the past, the core patent classification was performed qualitatively by some experts, but it was expensive and time consuming.To complement qualitative methods, quantitative methods using statistics and machine learning are being studied.Existing proposed methods utilize the quantitative indicators specified in the patent.However, quantitative indicators have different values for each elementary technology.If this characteristic is not reflected, an incorrect analysis result is produced.In addition, various values such as rights, technology scalability sustainable development, etc., must be considered in order to effectively classify core patents.In this paper, we propose an effective core patent classification model using improved patent performance indicators.The proposed model applies text mining and clustering to patent Big data to identify elementary technology and calculate improved patent performance indicators that reflect various values.Furthermore, a core patent classification model is constructed by learning various classification algorithms.In order to examine the practical applicability of the proposed model, experiments are conducted with patents registered in the USPTO.As a result of the experiment, the accuracy of three models trained with patent-improved performance indicators was high.Among them, k-nearest neighbors demonstrated the highest performance.
Retracted : Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Retraction: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model Yuantao Chen, Jin Wang, Songjie Liu, Xi Chen, Jie Xiong, Jingbo Xie, Kai Yang, 2021, 33 (15), (https://doi.org/10.1002/cpe.5533) The above article, published online on 23 October 2019 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck and Martin Berzins, and John Wiley and Sons Ltd. The retraction has been agreed due to scientific errors arising from the incorrect use of materials and data, which have led to conclusions that are unreliable.
A Machine Learning Predictive Model to Classify Severity of Breast Cancer Based on Mammographic Mass Dataset

Mammography which is recognized as a both a diagnostic and screening tool, is an x-ray imaging method used to examine the breast for the early detection of cancer and other breast diseases. Though it’s a useful technique for early cancer detection but sometimes its low predictive value resulted unnecessary biopsy of near about 70% benign case. There are also several Computer Aided Diagnostic (CAD) systems already have been developed to reduce this unnecessary breast biopsies. Hence, with this background, this project is directed towards developing a machine learning predictive model based on the “mammographic mass” dataset which contain BI-RADS assessment, the patient’s age and three BI-RADS attributes and the ground truth for severity together with the ground truth (the severity field) for 516 benign and 445 malignant masses. Machine learning approach like data cleaning, data transformation, feature engineering and finally several prediction models based on their accuracy are applied. Among all of the prediction models, Linear Discriminant Analysis (LDA) represent highest accuracy of near about 84.50% with reduced attributes after removing missing values and outliers. Finally, Receiver Operating Characteristic (ROC) curve is developed for the finding the classification skill of the developed LDA model and the outcomes shows that the almost 91% area are remained under ROC curve which is really good enough to judge the accuracy of developed model.