Reviewerly/exHarmony huggingface dataset:
INTEGRATION OF “WRITE AND IMPROVE” AWE TOOL INTO EFL AT HIGHER EDUCATIONAL ESTABLISHMENT: CASE STUDY

Dramatic changes in political, economic, cultural, scientific, and technological spheres, which have taken place in recent decades, have caused noticeable alterations in different societal domains, including education. Through the prism of the modern educational paradigm that mainly focuses on meeting the needs of human beings, nowadays, a special place is occupied by ICTs and their integration into the educational process. The present research is devoted to both theoretical evidence and practical implementation of automated writing evaluation (AWE) tool “Write and Improve” into language learning at a higher educational establishment. We use experimental method titled case study, which allows finding out ways to incorporate the software mentioned above into EFL. First and foremost, the author analyses fundamental and contemporary studies in the realm of ICT integration into language teaching, learning, and assessment. Special attention is paid to AWE as cutting-edge software, which utilizes artificial intelligence to assess students’ writing. The data under investigation indicate that using AWE computer-assisted programs facilitates students’ management and increases motivation for writing and revision. The article sets a goal to demonstrate that “Write and Improve” is an example of innovative technology to enhance writing as an essential academic competency. Based on profound theoretical substantiations, the author conducts the experiment to test the research hypothesis in the case study. Within the framework of the article's written tasks, university students who study English as their major subject at the Taras Shevchenko National University of Kyiv have been chosen. The attempted investigation proved “Write and Improve” to be highly effective among language learners, thus worth being introduced not only into teaching and learning but also assessment.
Application of the variation operator in a genetic algorithm for the synthesis of fuzzy controllers

Abstract . Objective. This article studies the problem of increasing the efficiency of fuzzy controller synthesis in a control system using a genetic algorithm. The best parameters of the fuzzy controller are selected using the crossing-over and mutation operators in the genetic algorithm. The operation of the mutation operator can lead to the formation of an incorrect set of parameters, which complicates the procedure for synthesizing a fuzzy controller. Methods . Arrays of parameter sets of membership functions, conclusions, and rule weights that are included in the fuzzy controller are compiled using mathematical simulation. The mechanism of operation of single-point and two-point variation operators in the genetic algorithm is described by the simulation modeling. Results . Mathematical models of single-point and two-point variation operators for the genetic algorithm are proposed. The mechanism for changing the values of elements in the array of a set of parameters of a fuzzy controller with one input and output variable is presented. Conclusion . Replacing the mutation operator with the variation operator eliminates the formation of incorrect sets of parameters of the fuzzy controller in the control system.
Student Voice: Photography, COVID-19, and our collective memory

Children have the right to give their opinions freely on issues that affect them and the right to share freely with others what they learn, think, and feel. Photography can be a very effective way of providing young people opportunities to share their voice.
O HiperDia no contexto da pandemia da COVID-19

Introduction: In the pandemic context of Covid-19, chronic diseases like arterial hypertension and diabetes mellitus, their multiple aspects and the impact that produces in the health system maki it important the discussion about the control strategies of these conditions. In this perspective, the national program of arterial hypertension and diabetes mellitus (HyperDia) in the health space demand of the professional nurses the development of strategies to control of these conditions in a moment of overload of the health system, considering that users registered with HyperDia are a risk group to severe forms of the Covid-19. Objective: To analyze the performance of nurses inserted in the HyperDia program and to know the main points of the nurse consultation during the pandemic. Methods: The methodology runs through qualitative, descriptive research with content analysis. This research involved four reference nurses of the HyperDia on different Units of Primary Health Care of one sanitary district on Salvador, Bahia. Results:  The strategies developed for the prevention and control of users registered in the program are aimed at maintaining social distance, validation, and extension of the prescription. Conclusion: The Covid-19 pandemic puts hypertensive and diabetic patients in conditions to enhance the risk factors and complications inherent to hypertension and diabetes mellitus. In this field, nurses struggle to mitigate the consequences and difficulties imposed on workers, exacerbated by the lack of personal protective equipment and testing.
Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

Libraries have the main task in the processing of library materials by classifying books according to certain ways. Dewey Decimal Classification (DDC) is the method most commonly used in the world to determine book classification (labeling) in libraries. The advantages of this DDC method are universal and more systematic. However, this method is less efficient considering the large number of books that must be classified in a library, as well as labeling that must follow label updates on the DDC. An automatic classification system will be the perfect solution to this problem. Automatic classification can be done by applying the text mining method. In this study, searching for words in the book title was carried out with N-Gram (Unigram, Bigram, Trigram) as a feature generation. The features that have been raised are then selected for features. The process of book title classification is carried out using the Naïve Bayes Multinomial algorithm. This study examines the effect of Unigram, Bigram, Trigram on the classification of book titles using the feature extraction and selection feature on Multinomial Naïve Bayes algorithm. The test results show Unigram has the highest accuracy value of 74.4%.
Automated filtering and polygonal reconstruction of virtual skull model based on computed tomography

Abstract A method of automated filtering and polygonal reconstruction of the skull virtual model using the methods of “Marching cubes” and “Connected component labeling” is considered. An algorithm for effective software implementation is described. A computational experiment on computed tomography data consisting of 244 DICOM images is presented. The experimental results confirm the correctness and computational efficiency of the algorithm.
360 Degree Fish Eye Optical Construction For Equirectangular Projection of Panoramic Images

Intelligent transportation systems is widely studied in autonomous driving car recently. To identify the location of Autonomous driving car and verify the environment, Simultaneous Localization and Mapping (SLAM) is a concept for constructing the unknown environment model and estimating sensor motion during movement. One of the major challenges of monocular SLAM is fast movement and video image suture on dual fisheye camera. A field of view of a general monocular camera is limited to barely 50 to 70 degrees. For these reasons, this paper chooses dual back-to-back fisheye cameras to overcome the narrow field of view and capture 360-degree images for monitor and SLAM applications. However, the fisheye lens poses a challenge of large distortion and the angle of the two fisheye camera causes image difference while unwrapping and blending to get a 360-degree panoramic image. This paper proposes an algorithm to successfully calibrate, project, stitch, and blend to acquire the equirectangular panoramic images. The experimental result shows that this algorithm is effective, fast (27.7 FPS), and overcomes the difficulties of significant changes in the scene of continuous frames when the dual eye fish camera is in motion on Xavier without using the graphic card. This fast and accurate image stitching method can also apply to the bird view of an autonomous driving car on fast-moving status.
Combat data shift in few-shot learning with knowledge graph

Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is nontrivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.
Exploration of Early Childhood Literacy Ability based on Analytical Hierarchy Process (AHP)

Strong early literacy skills will affect children's thinking skills in the future.The results of existing research indicate that learning in schools has not been able to make this happen.This research is a survey research to see the gap between children's literacy skills in the field and expected literacy abilities.So that it will be seen the components that can affect the literacy abilities of children aged 5-6 years.Currently, there are not many studies examining the influence of literacy components in developing children's literacy, because many researchers consider that all components are important.Therefore, a deeper study is needed to see the components that affect the development of children's literacy.The expected literacy ability is expert judgment which has been validated using the Analitycal Hierarchy Process (AHP).The data analysis technique used is descriptive analysis.The data collection techniques used were observation and questionnaires.The population in this study used simple random sampling.Respondents in this study were 150 children aged 5-6 years.The results showed that the children's ability to understand language components was 6.76%, phonological awareness reached 5.73%, basic reading skills 6.5%, basic writing skills 7.8%, and children's motivation in reading 3.37%, so it can be concluded that based on the components of children's early literacy skills, the accumulated age of 5-6 years has only reached 30.16% of the expected.The contribution of this research is to determine the appropriate literacy stimulation for children.
Three Emerging Innovative Technologies Required for Cyber Operations to Execute Commander’s Intent at Machine Speed

Decision advantage for the DoD and Combined Cyber Operations results from the secure, seamless, and rapid maneuver of data and information. In the DoD’s 2018 Artificial Intelligence Strategy, the DoD recognized that it must, “put in place key building blocks and platforms to scale and democratize access to AI. This includes creating a common foundation of shared data, reusable tools, frameworks and standards, and cloud and edge services.” More than ever, integrated, adaptive cyber operations provide the means of maneuver for data to enable DoD’s decision advantage-based goals. To support this vision, the integrated implementation three innovative cyber technologies must be rapidly realized across DoD Networks in order to execute cyber operations according to Commander’s Intent—at machine speed. Getting Information to the “edge” is what makes DoD competitive and provides advantage. The word “edge” in this context reflects the distributed individual platforms, sensors, and people who comprise the scale and scope of today’s globally networked DoD operations. That edge is creating the demand to access data and consume information as never before, and a greater need for more innovation to support DoD cyber operations on the DoD Information Network (DODIN). At the heart of the need for innovation is an increased demand for data and information, as well as the size and scale of networks and networking exploding without a proportionate growth in the IT resources to support today’s cyber operational demand. If the network continues to grow exponentially and must function as the medium of maneuver for the data that provides DoD decision advantage to the edge, then the DoD must deploy revolutionary innovations to reinvent the network as an integrated platform for cyber operations—across the enterprise and to the edge and implemented natively as hybrid multicloud-ready. Three innovative, next-generation networking technologies, integrated tightly together, offer the opportunity for DoD to provide revolutionary cyber operations capabilities across the DODIN and produce improved, data-enabled mission results. The scalable and seamless integration of: (1) advanced identity management, (2) software-defined networking, and (3) hybrid multicloud capabilities provides a Commander’s Intent-driven Cyber Platform implemented in a zero trust architecture that operates at machine speed and ensures decision advantage for the DoD.
An Empirical Study on The Impact of The Interaction between Feature Selection and Sampling in Defect Prediction

With the gradual growth of software engineering, it is more and more important to predict the defects in software in advance, which can reduce the major human and financial losses caused by software defects. There are two main problems in software defect prediction: feature redundancy and class imbalance (the number of defective modules and no defective modules is unbalanced, the number of no-defective modules is much higher than defective modules). There are different feature selection methods and sampling methods to address issues. However, the task of feature selection and sampling interact with each other has not been studied. We study the impact of the interaction in terms of the five performance measures of AUC, accuracy, precision, recall and F1 of defect prediction. Chi-square, IG (Information Gain) and relief are used for feature selection to remove redundant features, SMOTE (synthetic minority oversampling method) and RUS (random under-sampling) are used to sample the defective data and four classifiers, NB (Naive Bayes), LR (Logistic Regression), DT and SVM (Support Vector Machine) are used. The studying result shows that when chi-square and IG are used for feature selection, it is better to sample first for both sampling methods in terms of AUC and recall, when relief is used and NB, LR and SVM are used, it is a good choice to perform feature selection first in terms of AUC and recall, and it is better to sample first when using DT classification.
Regulation of Cross-Border Copyright Relations related to Works with an Unknown Copyright Holder (Orphan Works)

The existing options for regulating the admission of third parties to the use of orphan works are not unified and tend to the legal presumptions opt-in (the copyright holder disagrees by default) or opt-out (the copyright holder agrees by default). There are certain common directions of the development of regulation concerning such issues as key understanding of an orphan works, preliminary search for a copyright holder, providing the copyright holder with the opportunity within a certain period to restore his rights and receive compensation for the use of a work, limitation of compensation for the use of an orphan works. Some states also show a tendency to the development of regulation of access to orphaned works specifically for cross-border relations: the creation of joint storage banks and cooperation of such banks (EU); recognition of the status of an orphan work established in one state by other (contracting) states (EU); the perception (In the established case) by national courts of supporting documents issued in a foreign jurisdiction on the search for the copyright holder as evidence of the orphanhood of the work (USA); establishment of conflict of laws regulation for finding the law applicable to relations with works restored under copyright protection (USA). Due to the fact that the activities of states to regulate access to works with an unknown rightholder contradicts the concept of the exclusivity of copyright and the imperativeness of their validity, the creation of a legal regime for the protection of orphan works should be initiated at the conventional (unifying) level.
Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage

The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. We derive novel algorithms for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage, where the return process and innovations of the volatility process are allowed to correlate. Moreover, based on the idea of ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers in order to guarantee stable sampling efficiency irrespective of the baseline parameterization.We carry out an extensive comparison to already existing sampling methods for this model using simulated as well as real world data.
On $-1$-differential uniformity of ternary APN power functions

Very recently, a new concept called multiplicative differential and the corresponding $c$-differential uniformity were introduced by Ellingsen et al. A function $F(x)$ over finite field $\mathrm{GF}(p^n)$ to itself is called $c$-differential uniformity $\delta$, or equivalent, $F(x)$ is differentially $(c,\delta)$ uniform, when the maximum number of solutions $x\in\mathrm{GF}(p^n)$ of $F(x+a)-F(cx)=b$, $a,b,c\in\mathrm{GF}(p^n)$, $c\neq1$ if $a=0$, is equal to $\delta$. The objective of this paper is to study the $-1$-differential uniformity of ternary APN power functions $F(x)=x^d$ over $\mathrm{GF}(3^n)$. We obtain ternary power functions with low $-1$-differential uniformity, and some of them are almost perfect $-1$-nonlinear.
Syllable-Based Indonesian Automatic Speech Recognition

The syllable-based automatic speech recognition (ASR) systems commonly perform better than the phoneme-based ones. This paper focuses on developing an Indonesian monosyllable-based ASR (MSASR) system using an ASR engine called SPRAAK and comparing it to a phoneme-based one. The Mozilla DeepSpeech-based end-to-end ASR (MDSE2EASR), one of the state-of-the-art models based on character (similar to the phoneme-based model), is also investigated to confirm the result. Besides, a novel Kaituoxu SpeechTransformer (KST) E2EASR is also examined. Testing on the Indonesian speech corpus of 5,439 words shows that the proposed MSASR produces much higher word accuracy (76.57%) than the monophone-based one (63.36%). Its performance is comparable to the character-based MDS-E2EASR, which produces 76.90%, and the character-based KST-E2EASR (78.00%). In the future, this monosyllable-based ASR is possible to be improved to the bisyllable-based one to give higher word accuracy. Nevertheless, extensive bisyllable acoustic models must be handled using an advanced method.
An ICSC Model for Detecting Human Skin in JPEG Images

The detection of human skin color has proven to be a useful and robust technique for detecting nude images, face detection, localization and tracking. This paper presents an Improved Chromatic Skin Color model to detect the human skin in JPEG images; the ICSC model detected the human skin with detection rates more than 90%. A threshold method and 2D Gaussian model will improve the accuracy of skin regions detected
When Sentiment Is News: The Polarity Pattern Approach

We present a new approach and set of dictionaries for the analysis of sentiments in financial and business news headlines at the firm level. The proposed Polarity Pattern Approach (PPA) captures the deep structure of context and syntax and reduces measurement errors evident in previous approaches for classifying the tone of words and sentences in news headlines. We demonstrate the superior predictive validity of PPA using an out-of-sample dataset. As expected, negative news headlines are negatively related to stock returns and positively related to trading volumes, while positive news headlines are positively related to both, especially for finance news. Prior studies report a non-significant or, counter intuitively, negative relationship between positive news and stock returns, which we attribute to measurement error in previous approaches. Additionally, using market activities, we jointly estimate cross-sectional regression to compare the explanatory power of the PPA approach with commonly used methods. We show how PPA’s tone assignment significantly outperforms other dictionaries and approaches, including the commercial package (RavenPack) that uses both machine learning and deep learning. The PPA, including dictionaries, provides finance researchers with a new, more valid method of sentiment analysis for media news.Link to PPA tone assignment dataset: https://www.whensentimentisnews.com
Bayesian Reasoning and Artificial Intelligence against COVID-19

The article focuses on the potential role of Probability Theory and Artificial Intelligence in the battle against the pandemic of COVID-19, which, starting from China on December 2019, has created a chaos in the world economy and the lives of people, causing hundreds of thousands of deaths until now. After discussing the importance of the reproduction number Ro of the viruses, the Bayesian Probabilities are used for measuring the creditability of the diagnostic tests for the novel coronavirus. Artificial Intelligence designs are also described which are used as tools against COVID-19 and a Case-Based Reasoning expert system is proposed for the COVID-19 diagnosis.