Reviewerly/exHarmony huggingface dataset:
An analysis of the trends of managers in commercial banks in Taif Governorate towards the application knowledge management processes: دراسة تحليلية لاتجاهات المديرين بالبنوك التجارية بمحافظة الطائف نحو تطبيق عمليات إدارة المعرفة

This study aims to identify the trends of managers in commercial banks towards the application of knowledge management processes in Taif governorate in Saudi Arabia. To achieve the objectives of the study, the researcher developed a questionnaire of (27) paragraphs divided into three dimensions. The study community consisted of all managers in commercial banks in Taif. The resolution analyzed using (SPSS). The study found a set of results, the most prominent of which were: The existence of significant effect at the level of statistical indication (a=0.05)of knowledge management processes diagnosis, generation, storage, transport and application) in commercial banks in Taif. There is a statistical difference of (a=0.05) to arrange the areas of knowledge management processes from the point of view of managers in commercial banks in Taif governorate. The most important findings of the study: The need to urge all managers working in commercial banks in Taif governorate to use their own knowledge to solve the problems and obstacles they are exposed to so that there is creativity and innovation. Create a database of experts and consultants from managers and knowledge- conscious, and their knowledge innovations so that they can benefit from their expertise and opinions in developing knowledge them to commercial banks in Taif.
A DCA-resistant Implementation of SM4 for the White-box Context

SM4 [ISO.IEC.18033-3.AMD2] is a cryptographic standard issued by the Organization of State Commercial Administration of China as an authorized block cipher for the use within China. Based on threshold implementations and operations in composite fields, we propose and implement a fixed-key white-box SM4, where all intermediate values (inputs and outputs of the lookup tables) are masked. We express the SM4 S-box in term of operations in composite fields, thus reducing the total size of the lookup tables. The threshold implementation makes the distribution of the masked values uniform and independent of the original values being represented. The white-box SM4 implementation provides resistance against traditional white-box attacks, such as the affine equivalence attack, the BGE-like attack. For DCA-like attacks, our test shows that for aggregating 2048 single-bit correlations, an identified DCA leakage requires billions of microseconds of processor time to extract the secret key, which increases 1st order DCA resistance by at least thousands of times compared with resistance against a single-bit correlation used in a classical DCA attack.
Research on feature extraction algorithm of pavement disease

In order to improve the accuracy and robustness of pavement crack image segmentation, an adaptive image enhancement algorithm is proposed. Based on the histogram of the disease image, linear transformation and equalization are performed to enhance the gray features. The image is divided into high and low frequency sub bands by using non subsampled contourlet transform, and the detail features are enhanced by using sigmoid function. At the same time, the corresponding adaptive order adjustment model and edge extraction algorithm are constructed to obtain the binary feature image. The experimental results show that the proposed algorithm has strong de-noising ability and can extract pavement crack features well. Compared with the traditional methods such as region growing and watershed, it has the advantages of fast speed and high accuracy.
Higher Order c-Differentials

In [], the notion of c-differentials was introduced as a potential expansion of differential cryptanalysis against block ciphers utilizing substitution boxes. Drawing inspiration from the technique of higher order differential cryptanalysis, in this paper we propose the notion of higher order c-derivatives and differentials and investigate their properties. Additionally, we consider how several classes of functions, namely the multiplicative inverse function and the Gold function, perform under higher order c-differential uniformity.
An adaptive two phase blind image deconvolution algorithm for an iterative regularization model

This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L0-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L0-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods.
Supersingular j-invariants and the class number of ℚ(−p)

Let [Formula: see text] be a prime. Let [Formula: see text] be the discriminant of an imaginary quadratic order. Assume that [Formula: see text] and [Formula: see text]. We compute the number of [Formula: see text]-roots of the class polynomials [Formula: see text]. Suppose [Formula: see text], we prove that two class polynomials [Formula: see text] and [Formula: see text] have a common root in [Formula: see text] if and only if [Formula: see text] is a perfect square. Furthermore, any three class polynomials do not have a common root in [Formula: see text]. As an application, we propose a deterministic algorithm for computing the class number of [Formula: see text].
Mobile Augmented Reality as a Field-Assistance Tool in Urban Maintenance

We present a mobile AR application designed to be used by urban maintenance workers as a field-assistance tool. Using any standard smartphone camera, our system can accurately detect the desired equipment and augment it with relevant information and step-by-step instructions on how to do any required maintenance jobs. Alongside this mobile application, we also developed a desktop application for creating and authoring the data and augmentations that should be displayed during a given job (Archer). This paper proposes a novel approach to automatically detect and minimize the amount of points (checkpoints) at which the app will ask the user to perform a new equipment recognition, which are useful in order to maintain tracking stability as the user modifies the real-world object during the course of the job. The experiments and user tests demonstrate the accuracy and practicality of the developed systems, which can effectively be used to greatly improve the workflow of urban maintenance workers.
Access to the Information and Communication Technologies in South America in Times of COVID-19

COVID 19 pandemic caused by SARS-CoV-2, has managed to deepen and to put on evidence, the great inequalities existing between countries such as the internal social strata regarding accessibility to education and technological services that are required to have effective distance learning. Although rural areas still have precarious basic services throughout the continent, it is enough with a single non-studying child to conclude that there is no democratization of learning access and technology. The research methodology was a bibliographic review, supported on electronic means to obtain all the relevant and available information about the subject of study. The general conclusion is that regardless of the accessibility to learning that exist in our countries, the important thing is the knowledge that remains, if this is really effective, if the teacher do the class online with the available applications, and also if he has all the technological resources, to teach the class and not only to send the weekly planning of the assignment either by mail, messaging or web platforms, without an evaluation to verify that the student has learned and passed the class or subject
Design and implementation of parallel processing for embedded system online experiment teaching platform

As the core course of information engineering specialty, embedded system course has the characteristics of great theoretical difficulty and strong practical operation. It is an important part of this course to cultivate students’ practical ability through experimental teaching. In order to overcome the impact of the lack of time and room on traditional experimental teaching, an online simulation experimental teaching platform for embedded system course rose in response to the proper time and conditions. Concurrent sharing and remote fault detection are the key to realize the system. This paper designs and implements an embedded system online simulation experiment teaching platform oriented to the combination of virtual and real. The platform supports concurrent access by multiple users based on the Secure Shell (SSH) protocol, and realizes the sharing of experimental resources by constructing a task pool. The method of fault detection and recovery using the heartbeat mechanism improves the reliability of the system. Practical applications have shown that the system can run stably for a long time, support a large number of students in online experiments at the same time, and improve the utilization of experimental equipment and the efficiency of students’ experiments.
Evaluation of an Accreditation Variable for University Institutions Using 2 Tuple Linguistic Representation Model

The evaluation of higher education institutions and their corresponding accreditation constitute tools of proven effectiveness for the insurance of the quality in this level. There are different ways to develop these accreditation processes, with different visions and parameters and there are quantitative and qualitative approaches for their evaluation. In Cuba, there is a System of Evaluation and Accreditation for High Education, which establishes the variables, components, standards and indicators that define the main activities to be evaluated in a university. The present paper is directed to propose the use of the pattern of the linguistic representation model based on 2-tuples to achieve a more objective evaluation according to the different parameters that are evaluated and the experts’ characteristics. It is analyzed the variable of Human resources as a case of study to apply this model to their different indicators and to propose a valuation of this variable, through the use of the mentioned tools.
Incident early warning based on sparse autoencoder and decision fusion for drilling process

Complicated geological environments lead to a high risk of drilling incidents. Incident early warning for drilling process is in demand for industry field. An incident early warning method for loss and kick based on sparse autoencoder and decision fusion is proposed in this paper. Sparse autoencoder is employed to detect the abnormality of the drilling parameter time series. Mann-Kendall trend test approach is performed to extract the trend of the time series that is detected as abnormal. The abnormality detection and trend extraction results of each drilling parameter are fused to get the final incident early warning result. Experiments are executed with the actual data collected from a practical drilling process. The experiment results indicate the effectiveness of the proposed method.
Non-Linear and Non-Gaussian State Space Models

This chapter discusses estimation for non-linear and non-Gaussian state space methods. We start by defining conditionally Gaussian and more general non-Gaussian and non-linear state space models. The text reviews some classes of the many possibilities of non-Gaussian models. In particular, dynamic generalised linear models (DGLM) are discussed aimed at categorical time series, count data, data for positive-valued time series, continuous proportions and so forth. Other models such as bearings-only tracking and stochastic volatility models are also considered. The first attempts to model non-Gaussian state space models includes the power local level models and these are described for historical and pedagogical purposes. The chapter moves on discussing approximate inference such as the extended Kalman filter and the unscented Kalman filter. Sequential Monte Carlo methods are reviewed and some illustrative examples are presented. Markov chain Monte Carlo is discussed for the class of DGLMs, and the chapter concludes by considering dynamic survival modelling.
Enhanced Data Privacy Algorithm to Protect the Data in Smart Grid

Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.
Formation of trees of optimal subsets of random variables of various degrees of detalization

The paper describes numerical methods of search engine optimization of combinations of alphabets of random variables and the search for optimal modes of complex processes with the formation of optimal subsets trees. A method for solving discrete optimization problems using optimal subsets of random variables of various degrees of detail is considered. The combinations of alphabets are adjusted at the subsequent stages of the process, depending on the dynamics of its flow at the previous stages with the help of the optimal set tree of complex shape.
A study of ballot anomaly detection with a transparent voting machine

research-article Share on A study of ballot anomaly detection with a transparent voting machine Authors: Juan E. Gilbert University of Florida University of FloridaView Profile , Isabel Laurenceau University of Florida University of FloridaView Profile , Jean Louis University of Florida University of FloridaView Profile Authors Info & Claims InteractionsVolume 28Issue 6November - December 2021 pp 56–61https://doi.org/10.1145/3484937Online:10 November 2021Publication History 0citation498DownloadsMetricsTotal Citations0Total Downloads498Last 12 Months498Last 6 weeks53 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
E-Learning Design Using Iconix Process for Distance Learning

E-learning is an important advance in the modern education system. While an Illustration is an art of visual writing artwork, drawing, painting, photography, or another type of art. Most people who want to be an illustrator usually must learn their skill basic with self-taught or learning on art of school. However most people specifically for known illustration and want to learn their skill on art school but constrained with distance or time or another reason with their tools art. Until the most people can not get their skill with fast. By looking at this times with the Design Online Learning Of Education On Illustration At Website, which has contained more information and education about illustration and the illustrator can make it easier for others to learn or to hone their skill with complete material which comes from most experienced illustrator. The design will become a place of sharing content about illustration, it was built with PHP, and DBMS MySql. This research resulted in an e-learning application using Iconix process.
Professional Success of a Teacher in Modern Educational Realities

The key problems of the article are determined by the increased requirements for the professional development of modern teachers. Based on the methods of theoretical analysis of scientific literature, a characteristic of the main components of the professional success of a modern teacher was proposed. Special attention is paid to the professional development of young teachers. As a result, conditions that ensure the professional success of teachers in modern educational practice were presented.
Research on Detection of Chinese Microblog Public Opinion Analysis System

Microblog is becoming more and more important in people’s life. But at present, there are also some disharmonious and false uncivilized behaviors in micro blog, even reactionary remarks. Based on the above situation, public opinion monitoring and analysis technology came into being. In order to correctly guide public opinion and purify the network environment, the government or relevant management departments need to provide effective management means. Public opinion monitoring and analysis platform has become an important solution. Before the analysis of microblog data, the original data will be preprocessed to avoid affecting the results of subsequent analysis. The analysis method in this paper is based on the combination of K-means algorithm and word co-occurrence degree to identify hot topics and obtain public opinion. The specific process is as follows: after data preprocessing, by dividing the time window and calculating the word frequency increase rate and relative frequency of each time window, extracting the subject words in microblog information, then clustering the keywords according to the co-occurrence degree of the subject words, and selecting the appropriate topic word clusters to describe the hot topics, so as to obtain the hot network public opinion.
Theoretical and methodological basis of music education: structure and contents

The research subject of the article is the theoretical and methodological basis of music education represented in a traditional four-level structure (developed by A. Yudin within the system approach), consisting of: 1) a philosophical, 2) a general scientific, 3) a specific scientific, 4) a methodological level. Based on the thesis about the connection between theory and practice in music education, the second part of the article briefly describes the contents of the first three levels of the structure of the theoretical and methodological basis which are believed to be the most academical. The author focuses on two aspects: 1. The goals and potentials of the level under consideration; 2. The problems arising at each level. The author arrives at the following conclusions: 1) the philosophical level of the theoretical and methodological basis of music education, being the most general and therefore the leading one, is necessary for training prospective musicians for the formation of the value, moral and spiritual, and esthetic components of education; 2) the general scientific level helps systematize the philosophic provisions and concepts into certain approaches and principles which can be introduced into the pedagogical practice as models of integral formation of education processes; 3) the level of socio-scientific disciplines, which is the most thoroughly developed due to the close connection with practical aspects and problems, consists in the aspects of the contents of education (musicology), the methodology of teaching (pedagogics), and the study subject/instrument (performance art).&amp;nbsp; &amp;nbsp;
The use of entropy based fuzzy membership on weighted logistic regression for the unbalanced data

Abstract Logistic regression is a popular and powerful classification method. The addition of ridge regularization and optimization using a combination of linear conjugate gradients and IRLS, called Truncated Regularized Iteratively Re-weighted Least Square (TR-IRLS), can outperform Support Vector Machine (SVM) in terms of processing speed, especially when applied to large data and have competitive accuracy. However, neither SVM nor TR-IRLS is good enough when applied to unbalanced data. Fuzzy Support Vector Machine (FSVM) is an SVM development for unbalanced data that adds fuzzy membership to each observation. The fuzzy membership makes the interest of each observation in the minority class higher than the majority class. Meanwhile, TR-IRLS developed into a Rare Event Weighted Logistic Regression (RE-WLR) by adding weight to logistic regression and bias correction. The weighting of the RE-WLR depends on the undersampling scheme. It allows an “information loss”. Between FSVM and RE-WLR has a similarity, the weight based only on class differences (minority or majority). Entropy Based Fuzzy Support Vector Machine (EFSVM) is a method used to accommodate the weaknesses of FSVM by considering the class certainty of class observations. As a result, EFSVM is able to improve SVM performance for unbalanced data, even beating FSVM. For this reason, we use EF on the TR-IRLS algorithm to classify large and unbalanced data, as a proposed method. This method is called Entropy-Based Fuzzy Weighted Logistic Regression (EF-WLR). This Research shows the review of EF-WLR for unbalanced data classification.
Informisana saglasnost kao etičko pitanje onlajn-istraživanja - perspektiva ispitanika

The paper discusses informed consent as a component of online research ethics. It begins from the analysis of understanding, from the preconditions on which the realization depends, and from the difficulties that accompany the application of the informed consent in practice. The aim of the empirical part of the research is to determine the characteristics of the process of obtaining the informed consent in online research and to examine the factors on which they depend. The sample included 153 teachers. Data were collected with the survey-type questionnaires and with the descriptive assessment scales. For data processing, besides the frequencies and percentages, the ch 2 test and Kramer 's V correlation coefficient were performed. The results indicate that most of the respondents have had experience of giving informed consent in online surveys in which they have participated so far; that the text often contained information about the purpose and the goal of the research, apart from the tasks of the respondents in the research; that the majority of the teachers evaluate the content of the text as understandable and useful; and also that most of the characteristics depend on the environment of the school in which the teachers are employed. The results are discussed in the context of the possibility of improvement of the process of obtaining informed consent from the respondents in the online survey.
Analysis of key technology of computer artificial intelligence recognition

Abstract With the continuous development of science and technology, people for the research and development of artificial intelligence gradually in-depth, intelligent technology is widely used, can help people to improve the state of life, improve people’s quality of life. This paper will analyze the computer artificial intelligence identification technology, explore its practical application in life, and put forward some suggestions for the development of intelligent technology.