Reviewerly/exHarmony huggingface dataset:
Cloud Computing Technologies Adoption in Higher Education Inistitutes During COVID-19 Pandemic: Case Study

The global spread of Covid-19 has enhanced the adoption of virtual education and the use of technology in order to reduce the risk of contagion. Recently, the academics have turn out to be more technology-orientated and the educational establishments continue to seek opportunities in order to adopt an environment where all researchers could have access to high-quality scientific resources at anytime and anywhere in the world. Cloud computing is a model, which provides the users with services and application software on-demand, using the Internet. While the use of cloud computing applications is growing, there are ongoing research challenges related to guaranteeing security, privacy and the integration of the cloud applications. To the extent of our knowledge, the present study is the first from this type to thoroughly present the impacts of the COVID-19 pandemic on cloud computing environment in higher education institutions in Iraqi universities. This study will critically analysis the views of the faculty staff and researchers in Iraqi universities on cloud computing. It also tries to take a quick look at how Iraqi university staff responded to the epidemic in terms of research activities. The results shows that about 99% of the respondents considered the cloud applications to have great role in encouraging the research works in their institutes. Concerning the benefits of cloud services, the availability and saving cost were selected as most important factors that encouraging the adoption of cloud services. On the other hand, issues related to performance, lack of user control were selected as major problems in applying cloud services in Iraqi institutions Finally, it can be concluding that reducing the risks of the security, authorization, control and providing better performance of cloud services can enhance the use of cloud applications by researchers at Iraqi universities.
Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning.The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics.A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted.Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.
Directional Moiré Boosting Using Spatial Filtering

Abstract: Overlapping of two or more grating structures of small frequency differences gives rise to the Moiré patterns. While the overall moiré patterns are widely studied, in many cases – specially in case of a zonal fringe analysis – zone or direction based highlighting or suppression of moiré patterns are very helpful. This paper presents a simple method for directional boosting of moiré patterns, generated using Spatial Light Modulator (SLM) based interferometric setup, using the spatial filtering technique. Keywords: Moiré, Spatial Filter, Directional Boosting, Interferometry, Spatial Light Modulator, Optics
Basketball Players’ Behavior Prediction Method Based on Video

Basketball is a very competitive sport, in which the behavior of basketball players is the key factor to win. If we can fully predict the behavior of basketball players, then we can benefit more easily (successful defense or successful score). In this case, many basketball teams have set up a professional analysis team. The main work of these teams is to make statistics on the behavior of basketball players according to the basketball game video. According to the statistical results, we can predict the behavior of basketball players. However, simple statistics can not cover the complexity of basketball players’ behavior on the court, so although the statistical results have predictive utility, there are defects in the utility level. At this time, we need to use more advanced behavior prediction methods to predict basketball players’ behavior according to the basketball game video, this paper will take the improved RBF network player behavior prediction method as the research object, discuss the basic concept of RBF method, and then put forward the application scheme of the improved RBF network player behavior prediction method. Finally, taking the basketball game video as an example, the behavior of the players in the game is predicted, and then compared with the results, the effectiveness of this method is verified by the comparison results.
Non-linear Multiclass SVM Classification Optimization using Large Datasets of Geometric Motif Image

Support Vector Machine (SVM) with Radial Basis Functions (RBF) kernel is one of the methods frequently applied to nonlinear multiclass image classification.To overcome some constraints in the form of a large number of image datasets divided into nonlinear multiclass, there three stages of SVM-RBF classification process carried out i.e. 1) Determining the algorithms of feature extraction and feature value dimensions used, 2) Determining the appropriate kernel and parameter values, and 3) Using correct multiclass method for the training and testing processes.The OaO, OaA, and DAGSVM multi-class methods were tested on a large dataset of batik motif images whose geometric motifs with a variety of patterns and colors in each class and containing similar patterns in the motifs between the classes.DAGSVM has the advantage in classification accuracy value, i.e. 91%, but it takes longer during the training and testing processes.
PEDAGOGICAL CONDITIONS FOR THE FORMATION OF PIANO RELIABILITY OF ART STUDENTS OF PEDAGOGICAL UNIVERSITIES ON THE BASIS OF A HEURISTIC APPROACH

In the scientific article the author theoretically substantiates the content of pedagogical conditions of formation of piano reliability of future teachers of musical art on the basis of heuristic approach; interprets and specifies the pedagogical conditions as favorable pedagogical circumstances for introduction of methodical foundations of heuristic training in the process of piano training; investigates the content of the creative activity learning environment, takes into account the set of binary oppositions of the creative activity paradigm of piano training; defines the integration of heuristic forms, methods and techniques as the basis of pedagogical tools in the process of formation of piano reliability; justifies the use of heuristic strategies as the basis for the methodological support for the formation of piano reliability. Do evrystychnoho pedahohichnoho instrumentariyu, dotsilʹnoho dlya hrupovykh zanyatʹ fortepiannoyi pidhotovky, a takozh do hrupovykh zakhodiv fakhovoho navchannya studentiv fakulʹtetiv mystetstv pedahohichnykh universytetiv u tsilomu, nalezhatʹ: metod evrystychnoho polilohu, evrystychna besida, evrystychno-profesiynyy konkurs «Krashchyy za fakhom», sytuatsiyno-rolʹova hra «Fortepiannyy festyvalʹ «Art-klavir», metod mozkovoho shturmu shchodo vyrishennya problemy stsenichnoyi vytrymky pid chas stsenichnykh vystupiv, metod evrystychnoyi on-line konferentsiyi z pytanʹ fortepiannoho vykonavstva, dlya yakoyi v interneti stvoryuyetʹsya spetsialʹna hrupa korystuvachiv i yaka provodytʹsya v pozaurochnyy chas, mayuchy na meti rozvytok kreatyvnosti myslennya maybutnikh vchyteliv-muzykantiv; metod asotsiatsiy ta analohiy u protsesi vyyavlennya obrazno-tvorchoho y ideynoho zmistu fortepiannykh tvoriv, metod vedennya kolektyvnoho shchodennyka pedahohichnoyi praktyky, metod kontrolʹnykh zapytanʹ toshcho. Navedeni vyshche evrystychni metody i formy zaluchayutʹ sukupnistʹ evrystychnykh pryyomiv, sered yakykh: adaptatsiya do umov realʹnoho kontsertnoho vystupu, dyferentsiatsiya stresoriv fortepiannoyi vykonavsʹkoyi diyalʹnosti, inversiya frahmentiv fortepiannykh tvoriv pid chas vyvchennya yikh napamʺyatʹ ta in. Povnotsinne vykorystannya bahatoyi palitry form, metodiv zasobiv i pryyomiv evrystychnoho navchannya pid chas formuvannya fortepiannoyi nadiynosti studentiv fakulʹtetiv mystetstv pedahohichnykh universytetiv mozhe buty realizovane lyshe za umovy zabezpechennya yikh tryrivnevoho intehrovanoho zastosuvannya (z·hidno do druhoyi zasady evrystychnoho pidkhodu), yake peredbachaye yikh fundamentalizatsiyu, universalizatsiyu ta profesionalizatsiyu.
A Cryptographic Application of the M-Injectivity of 𝑀𝑛(𝑍𝑝) Over Itself

In this paper, we present a public key scheme using Discrete Logarithm problem, proposed by Diffie and Hellman (DLP)[1], particularly known as the Computational Diffie-Hellman Problem (CDH)[12]. This paper uses the Elgamal encryption scheme [6] and extends it so that more than one message can be sent. The combination of Hill Cipher[14 ] and the property of the matrix ring 𝑴𝒏(𝒁𝒑), of being left m-injective over itself, where 𝒑 is a very large prime, are major contributions towards the proposal of this scheme.
Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors

A s a result of the developments in the field of medi- cine, human life has been prolonged.For this reason, the ratio of elderly individuals to other individuals in the society is increasing day by day.According to the United Nations' World Population Ageing 2019 report, the world population is estimated to increase by 2 billion people and reach 9.7 billion by 2050(United Nations, 2019).According to the expectations of the World Health Organization (WHO), the number of individuals over 60 is estimated to exceed 2 billion in 2050 and the rate of deaths due to falls is estimated to increase from 28% to 42% (WHO, 2007;Chelli and Patzold, 2019).While falls of elderly people sometimes result in severe damage such as bone fracture, some falls do not cause any significant injury.The elderly person's health condition worsens as a result of the inability to get up without support after a long stay on the floor.For this reason, it is vital that the falls of elderly individuals must be detected while
Targeted Ads and/as Racial Discrimination: Exploring Trends in New York City Ads for College Scholarships

This paper uses and recycles data from a third-party digital marketing firm, to explore how targeted ads contribute to larger systems of racial discrimination.Focusing on a case study of targeted ads for educational searches in New York City, it discusses data visualizations and mappings of trends in the advertisements' targeted populations alongside U.S census data corresponding to these target zipcodes.We summarize and reflect on the results to consider how internet platforms systemically and differentially target advertising messages to users based on race; the tangible harms and risks that result from an internet traffic system designed to discriminate; and finally, novel approaches and frameworks for further auditing systems amid opaque, black-boxed processes forestalling transparency and accountability.
THE STUDY OF LANGUAGE DISORDER OF AN AUTISTIC SAVANT PORTRAYED IN LEVINSON’S RAIN MAN FILM (1988)

ABSTRACT Autistic savant as one of the language disorders can be found in daily life and it also can be portrayed in literary works and films. An autistic savant is a person with autism who has the limitation in most social functions but has cognitive abilities, such as possessing some outstanding memories and computational skills. This research aims to find out the types of language disorder and to find out the dominant type of language disorders found in autistic savant portrayed in Levinson’s Rain Man film. This research used a descriptive method. The data used in this research were the utterances of an autistic savant. The result of this research shows that the type of expressive language disorder was obtained with the occurrence percentage of 67.7% while the receptive language disorder type was obtained by the occurrence percentage of 32.3%. From this percentage, it can be seen that the most dominant type of language disorders experienced by autistic savant in Rain Man film is expressive language disorder. It means the autistic savant as an adult person also has difficulty in expressing or producing language. Key words: language disorder, autistic savant, expressive language disorder, receptive language disorder
Inexact Newton-CG algorithms with complexity guarantees

Abstract We consider variants of a recently developed Newton-CG algorithm for nonconvex problems (Royer, C. W. &amp; Wright, S. J. (2018) Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477) in which inexact estimates of the gradient and the Hessian information are used for various steps. Under certain conditions on the inexactness measures, we derive iteration complexity bounds for achieving $\epsilon $-approximate second-order optimality that match best-known lower bounds. Our inexactness condition on the gradient is adaptive, allowing for crude accuracy in regions with large gradients. We describe two variants of our approach, one in which the step size along the computed search direction is chosen adaptively, and another in which the step size is pre-defined. To obtain second-order optimality, our algorithms will make use of a negative curvature direction on some steps. These directions can be obtained, with high probability, using the randomized Lanczos algorithm. In this sense, all of our results hold with high probability over the run of the algorithm. We evaluate the performance of our proposed algorithms empirically on several machine learning models. Our approach is a first attempt to introduce inexact Hessian and/or gradient information into the Newton-CG algorithm of Royer &amp; Wright (2018, Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477).
Impact of Kernel-PCA on Different Features for Person Re-Identification

In the driving field of computer vision, re-identification of an individual in a camera network is very challenging task. Existing methods mainly focus on strategies based on feature learning, which provide feature space and force the same person to be closer than separate individuals. These methods rely to a large extent on high-dimensional feature vectors to achieve high re-identification accuracy. Due to computational cost and efficiency, they are difficult to achieve in practical applications. We comprehensively analyzed the effect of kernel-based principal component analysis (PCA) on some existing high-dimensional person re-identification feature extractors to solve these problems. We initially formulate a kernel function on the extracted features and then apply PCA, significantly reducing the feature dimension. After that, we have proved that the kernel is very effective on different state-of-the-art high-dimensional feature descriptors. Finally, a thorough experimental evaluation of the reference person re-identification data set determined that the prediction method was significantly superior to more advanced techniques and computationally feasible.
Recommendation System for Signal Processing in SHM

In this article, the recommendation system for processing signals was presented. It contains a database and two fuzzy modules composed within the system. Based on the contextual knowledge provided by the user, collected database, and fuzzy rules, the system suggests processing methods and features. The article presents an evaluation of the proposed system on a two-stage gearbox dataset. The system results are a list of recommended processing methods and extracted features, which allow for a more accurate data classification.
Comparative Mathematical Model for Predicting of Financial Loans  Default using Altman Z-Score and Neutrosophic AHP Methods

The current system in the bank depends only on the client's failure to pay monthly installments for three consecutive months to start moving and take the necessary actions towards the client. This routine system is the basic reason of happening the problemof loansdefault. In this paper the researcher presents a comparative mathematical model to predict the default of clients, as well as to devise a modern parallel model to measure the degree of credit risk criteria that guides the bank in the following-upof the client. Altman model is one of the famous methods for default prediction, formula is used to predict the probability of loan default by using Z-scores. The Z-score is a linear combination of five coefficient-weighted common financial criteria. The researcher applies the NeutrosophicAnalytical Hierarchy Process(NAHP) model on the same five common financial criteria which the bank can using them to provide constant following-up of the uses of the granted loan to guarantee that all terms set by the bank are met. The information was gathered in the form of neutrosophic data sets and evaluated using a novel Neutrosophic Analytic Hierarchy Process (NAHP) model.The researcher applies the proposed model in the credit department of one of the private Egyptian Banks (QNB) choosing random samples of real clients.
Multi-lingual Speaker Recognition based on Asymmetric Convolution and Central Loss Function

Abstract In order to solve the problem of poor speaker recognition performance on multi-language corpus on convolutional neural networks and large amount of calculations on multiple parameters, this paper draws on asymmetric convolution and center loss (Center Loss) functions to improve and optimize the ResNet model. And perform speaker recognition tasks in the Chinese-Uyghur corpus. The results show that, compared with the original model, the improved model has higher accuracy, fewer parameters, and reduced computational complexity.
LDA-Transformer Model in Chinese Poetry Authorship Attribution

Authorship attribution broadly is defined as an analysis of individuals’ writing styles, which has been attracting a lot of interest. Although the problem has been widely explored, no previous studies attempt to identify Chinese classical poetry. In this paper, we presented a public classical poetry corpus in Tang Dynasty for Chinese authorship attribution. As a particular literal form, the theme feature plays a crucial role in Chinese poetry authorship attribution. To integrate the topic feature of the Chinese poem, we employed the latent Dirichlet allocation model to capture the extra theme information. Meanwhile, due to the incoherent expression of poetry text, it is hard to capture incoherence information effectively from Chinese poems. To tackle this problem, we propose a combination model called LDA-Transformer to perform authorship attribution of Chinese poetry. We conduct systematical evaluations for the proposed method on three Chinese poetry datasets. The experimental results suggest that the topic feature can effectively improve the performance of authorship attribution in Chinese poetry. Our model achieves state-of-the-art results on related baseline methods.
Feedforward neural networks

The chapter considers aspects of feedforward neural networks including backpropagation algorithm, binary classifier; artificial neural network architecture, neuron activation transfer functions, data processing, and neural network based computing.
PENGARUH FRAUD TRIANGLE DAN EQUITY SENSITIVITY TERHADAP PERILAKU ETIS STUDY FROM HOME

This research has a purpose to find out and analyze the ethical behavior of accounting students during the COVID-19 pandemic which is reviewed from fraud triangle and equity sensitivity. Learning is done by online methods or commonly known as study from home. The object of this study is an Accounting student of the Faculty of Economics and Business Brawijaya University class of 2016, 2017, and 2018. Based on the purposive sampling method and calculation using the slovin formula obtained a sample of 83 respondents who can be used as a sample and in accordance with the conditions in filling out the questionnaire. Data sources are used using primary and secondary data sources. The primary data source is obtained directly from the results of filling out questionnaires that have been disseminated by researchers. While secondary data obtained from related agencies in this study is academic FEB UB. Analysis of data in this study using SPSS software with the results showed that fraud triangle affects the ethical behavior of accounting students when study from home and equity sensitivity has no effect on ethical behavior of accounting students when study from home. Penelitian ini bertujuan untuk mengetahui dan menganalisis perilaku etis mahasiswa akuntansi selama masa pandemic COVID-19 yang ditinjau dari fraud triangle dan equity sensitivity. Pembelajaran dilakukan dengan metode online atau yang biasa dikenal dengan istilah kuliah daring. Objek penelitian ini adalah mahasiswa akuntansi Fakultas Ekonomi dan Bisnis Universitas Brawijaya angkatan 2016, 2017, dan 2018. Berdasarkan metode purposive sampling dan perhitungan rumus slovin diperoleh sampel sebanyak 83 responden yang dapat dijadikan sampel dan sesuai dengan syarat-syarat dalam pengisian kuesioner. Sumber data yang digunakan menggunakan sumber data primer dan sekunder. Sumber data primer diperoleh langsung dari hasil pengisian kuesioner yang telah disebar oleh peneliti. Sedangkan data sekunder diperoleh dari instansi terkait dalam penelitian ini adalah akademik FEB UB. Analisis data dalam penelitian ini menggunakan software SPSS dengan hasil menunjukkan bahwa fraud triangle berpengaruh terhadap perilaku etis mahasiswa akuntansi saat study from home dan equity sensitivity tidak berpengaruh terhadap perilaku etis mahasiswa akuntansi saat study from home.
A Bi-directional Hierarchical Clustering (BHC) for Peak Matching of Large Mass Spectrometry Data Sets

the preprocessing of mass spectrometry (MS) data is a crucial step in every MS study, which not only makes data comparable and manageable but also makes the study more reproducible.However, an essential part of this process, which is often overlooked, is peak matching.Although existing clustering methods have been applied for peak matching, the use of these methods have been limited.For example, the use of hierarchical agglomerative clustering (HAC) for matching of mass/charge signals has been constrained to small-scale MS data sets due to the computational complexity of HAC.In this paper, we reintroduce a bi-directional hierarchical agglomerative clustering (BHC) as a scalable and accurate peak matching technique.As a result, the computational complexity of hierarchical agglomerative clustering for peak matching was optimized by BHC to O(RlogR).BHC was benchmarked against existing peak matching techniques.Finally, we propose a parallelization framework that significantly reduces the peak matching method's computation time.
A Single-Key Variant of LightMAC_Plus

LightMAC_Plus proposed by Naito (ASIACRYPT 2017) is a blockcipher-based MAC that has beyond the birthday bound security without message length in the sense of PRF (Pseudo-Random Function) security. In this paper, we present a single-key variant of LightMAC_Plus that has beyond the birthday bound security in terms of PRF security. Compared with the previous construction LightMAC_Plus1k of Naito (CT-RSA 2018), our construction is simpler and of higher efficiency.