Reviewerly/exHarmony huggingface dataset:
Three-dimensional profilometry method with single-frame multifrequency composite projection fringe

An improved composite Fourier transform profilometry is proposed in this study to quickly measure the three-dimensional (3D) contour of an object. Three groups of fringe patterns with different frequencies are combined in this method. Compared with the other composite profilometry, it solves the problem of phase error propagation in phase unwrapping. The absolute phase of the object is accurately calculated and the measuring accuracy is improved greatly by filtering out the background direct current noise. Both simulation and experiment results show that the 3D contour of the measured object can be reconstructed quickly and accurately by this method. It has great application potential in the field of real-time 3D measurement.
Sign up for the science blog archive waitlist

The science blog archive that I have started to work on (see previous posts) finally has a name: the &lt;em&gt; Rogue Scholar &lt;/em&gt; . I picked this name because I liked the description in the Urban Dictionary.A person with extensive knowledge pertaining to various subject matters that extends beyond formal education. This person often &lt;strong&gt; gathers &lt;/strong&gt; knowledge from various sources, such as media, friends, casual reading or the internet.
Zeroing Neural Networks Combined with Gradient for Solving Time-Varying Linear Matrix Equations in Finite Time with Noise Resistance

Due to the time delay and some unavoidable noise factors, obtaining a real-time solution of dynamic time-varying linear matrix equation (LME) problems is of great importance in the scientific and engineering fields. In this paper, based on the philosophy of zeroing neural networks (ZNN), we propose an integration-enhanced combined accelerating zeroing neural network (IEAZNN) model to solve LME problem accurately and efficiently. Different from most of the existing ZNNs research, there are two error functions combined in the IEAZNN model, among which the gradient of the energy function is the first design for the purpose of decreasing the norm-based error to zero and the second one is adding an integral term to resist additive noise. On the strength of novel combination in two error functions, the IEAZNN model is capable of converging in finite time and resisting noise at the same time. Moreover, theoretical proof and numerical verification results show that the IEAZNN model can achieve high accuracy and fast convergence speed in solving time-varying LME problems compared with the conventional ZNN (CZNN) and integration-enhanced ZNN (IEZNN) models, even in various kinds of noise environments.
Marketing technologies in educational management: current problems

The article considers in detail the possibilities and significance of marketing technologies for additional education programs as the most flexible and adaptive component of the educational process. Moreover, additional education programs are the most market-defined. Also at this stage, we have developed the structure of the e-mail message about ongoing additional educational programs in order to promote them, and on its basis, we have prepared an example of a letter for e-mail informational and educational mailing using techniques to attract the attention of the addressee in order to promote additional educational programs. The content of the letter was also formed for students of the electronic course "Education Marketing", sent out in order to monitor the educational process.
Factors That Promoted The Non-Inclusion Of Basic Education Students In Presence In A Post-Pandemic Context : Case: Factors identified in the Technical Secondary School no.51 of the municipality of Tepic, Nayarit

This research was focused on secondary school students due its importance on their growing processes from childhood to teens, on this stage the experiences lived build their character and personality. Therefore, it is needed to take care their issues and emotional needs generated by a pandemic context. Students results show several behaviors coming from anxiety and stress situations according to the results, the attitudes observed are socializing fear, anxiety and concern about spaces completely disinfected, isolation and lack of interest in attending face-to-face classes.
The effects of teaching experience on the teacher efficacy of pre-service English language teachers

This study investigates the impact of the 2022 Pre-service teachers' tutoring program on the self-efficacy of pre-service English language teachers. Online surveys, teacher logs, and interview results were analyzed to examine the changes in teacher efficacy. Online surveys based on Kwon's (2017) teaching efficacy scale were conducted before and after the tutoring program. The teaching efficacy scale consisted of the following constructs: motivational strategy, practice and feedback, material development and utilization, explanation and questioning, and encouragement of self-directed learning. In addition, follow-up interviews were conducted with each teacher and transcribed. A deductive coding scheme was developed based on the teaching efficacy constructs, and the interview transcripts were repeatedly read and coded according to the scheme. The results indicated that teaching experience improved the efficacy of pre-service English language teachers. Teacher efficacy improved when teachers enhanced their learners' motivation and achievement using appropriate teaching strategies and learning materials. Enactive experiences were the most influential factor in teacher efficacy. On the contrary, teacher efficacy decreased because of the difficulty in communicating with students and providing feedback in speaking activities. This result implies that it is important to learn how pre-service teachers can increase learners’ motivation and their self-efficacy by practicing it through teaching practice.
HYBRID LEARNING IN PHOTOGRAPHY CLASS AMIDST PANDEMICS

Arguably, recreating virtual space has been a slow-developing concept to many skeptical creative media communities and scholars. With the rapid growth of new media technologies, analogue or digital, society must exercise critical thinking in how these realizes the former impossibilities. Contemporarily, technologies are embedded in our everyday life, and yet we were not certain if these strongly transformed us. Unexpectedly, the COVID-19 pandemic radically disrupted every aspect of life, including education, and as a result educational institution demanding for new systems and structures that ensure uninterrupted learning for all students. We were forced to modify our activities, and it is a fact that many struggles and postponed. It is an entirely diverse challenge experienced by creative media educators. The direct hands-on instructions needed to be converted to live and/or recorded virtual simulation. Countless educational institutions have experimented with a hybrid learning approach to deliver teaching and evaluated their learning outcomes. In this article, I will share my experience in hybrid learning for one of the visual communication design courses, specifically photography.
Research on Multi-depth-of-field Electronic Image Stabilization Algorithm for In-Vehicle Videos

The vehicle-mounted cameras provide video streaming information for the post-processing phase of the intelligent vehicle, such as control and decision making. According to the characteristics of in-vehicle videos, this paper studies a video stabilization algorithm considering different depths of field. On the motion estimation stage, for the problem of inaccurate estimation inter-frame motion vectors caused by different depths of field of feature points, this paper proposes a method to repair inter-frame motion vectors. The method can correct the feature point position of the current frame and improve the estimation accuracy of the global motion vector. In the motion smoothing phase, a real-time online optimization framework and adaptive weights are used to balance the relationship between motion smoothing and motion following. Experiments prove that the accuracy of motion estimation of the algorithm in this paper is better than other improved algorithm, and the average value of PSNR after video stabilization is increased by more than 3dB compared with the original videos.
Graph-Based Taxonomic Semantic Class Labeling

We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is first constructed with clusters of conceptually associated lexemes representing related senses and conceptual domains of a source lexeme. The label abstraction is based on the syntactic patterns of the x is_a y dependency relation. For each sense cluster, an additional lexical graph is constructed by extracting label candidates from a corpus and selecting the most prominent is_a collocates in the constructed label graph. The obtained label lexemes represent the sense abstraction of the cluster of conceptually associated lexemes. In a similar graph-based procedure, the semantic class representation is validated by constructing a WordNet hypernym relation graph. These additional labels indicate the most appropriate hypernym category of a lexical sense community. The proposed labeling method extracts hierarchically abstract conceptual content and the sense semantic features of the polysemous source lexeme, which can facilitate lexical understanding and build corpus-based taxonomies.
Job Recommendation System Using the Content-Based Filtering Method

On the xyz application that is already running, it is a job vacancy portal. In this system there are has two users, as business accounts and job seekers. Where business accounts can post vacancies and job seekers can apply for vacancies. In this running system, when job seekers display the vacancies list, there is a list of the latest vacancies that have been posted by business people. The problem with the list of vacancies is that the list of vacancies that appear is still not in accordance with the wishes of the user as the job seeker account, thus reducing the interest in application users. There-fore, a job vacancy recommendation system is needed that can correct deficiencies in the list of vacancies that are displayed to users as job seekers. The purpose of this research to be able to pro-duce recommendations for suitable job vacancies. In this research, the authors use the content-based filtering method. Keywords: Recommendation System, Machine Learning, Content-based Filtering
Jumpiness Index based Prediction Model for Precipitation Using Evolutionary Computation

This paper investigates the jumpiness index based modeling for precipitation prediction. The jumpiness index (JI) represents different forecast jumps and the inconsistency correlation for forecasts issued at different times but valid for the same time. Unlike researches that improve the accuracy of predictions mainly, this study attempts to analyze the characteristics of prediction consistency for the numerical model GDAPS using the jumpiness index. In this work, we try to minimize prediction errors through a combination of JI elements and symbolic functions using Cartesian Genetic Programming (CGP), which is a kind of evolutionary computation to solve nonlinear regression problems. The relationship between the time-by-time jumpiness indices and the prediction error is analyzed. Also, the applicability of a combination of jumpiness indices to improve the accuracy of future forecasts is examined. Experiments are conducted to model the relationship between JI and UM error values calculated by GDAPS data for 2013 and 2014 for ground precipitation data using CGP-based symbolic nonlinear regression.
Improvement on Management of Tags Using an Event Queue

The tag is a snippet of webpage scripts tracking interactions of users on the website consisting of webpages. In these days, most companies and organizations even individuals use the tags to understand the effects of their websites and the intends of web users. Because sequential loading of tags on a website worsens the performance of the website, most tag management systems use asynchronous tag loading schemes on the websites. In this paper, we propose an asynchronous tag loading scheme using the event queue. Evaluation with the Google Analytics shows that the proposed tag loading scheme can reduce the average webpage loading time by 61.47% and reduce the average tag omission count by 5.1% against the synchronous tag loading scheme.
Two-level Approach for Heterogeneous Multi-Robot Optimal Navigation

This paper proposes a two-level optimization approach for a multi-robot navigation problem. Our aim is to achieve tasks with optimal strategies, optimal trajectory planning and to give better and feasible solutions. Optimal alternatives are selected by combining game theory strategic decision with an optimal trajectory planning method. This is solved at the top level by applying Sequential Quadratic Programming (SQP). At the bottom, Pure Nash equilibrium is solved using the Simulated Annealing (SA) algorithm. The trajectory planning for the multi-robot system is performed by decomposition where the b-spline technique is initially used to describe the robots path in an environment with obstacles. The cubic spline technique is then introduced to set up the motion along the desired path. The kinematic and dynamic constraints inherent to the robot behavior are taken into account and collision constraints are handled by penalization. The proposed approach is applied to heterogeneous Unicycle Wheeled Mobile Robots and simulated in endowed obstacles environment. Optimal collision-free trajectories of the robot team are obtained with smooth line paths and good-quality solutions. Simulation results are given to show the effectiveness and robustness of proposed algorithm.
Blockchain and Smart Grid

Because of the increase in the population and advancements in the industrialization, the requirement of electricity is also increasing gradually. So, this increased demand of electricity requires some sophisticated way of secure distribution of high-quality electricity while reducing the losses. A new approach is proposed, which is known as smart grid, which satisfies the above conditions. By using this approach, any person can generate and sell electricity to the grid. But it is very difficult to record the verified transactions of electricity generation and sale in between the users and the generators. Blockchain’s immutability feature can be used to solve the above-said problems in the smart grid.
Energy consumption, palmar grip strength and lean mass of hemodialysis patients

CKD is a complex disease with a significant impact on quality of life, longevity, use of medical resources and public health expenditures. Patients with CKD experience a drastic change in their lives, living with many limitations that include painful hemodialysis treatment, rigid diet ototherapy, changes in family, professional and social daily life and more frequent thinking in death, which leads the individual to have a negative perception about his health (SANTOS et al., 2019).
AUDIT OF ATTENDANCE INFORMATION SYSTEM AT MOTORCYCLE FACTORY USING COBIT 5

The change in the fingerprint attendance system into an attendance application is called "Mobile Attendance" according to research at motorcycle factory. The reason is work activities that are usually carried out in the office have now changed to being at home due to the COVID-19 pandemic. Employees are required to carry out the attendance process at certain times by taking photos of themselves or the environment in which they are located. Audit attendance applications in companies using COBIT 5, namely the DSS subdomain on DSS02 Manage Service Requests and Incidents and DSS04 Manage Continuity. The audit is carried out through the company's employee interview method, then measures every management process involved in it. The results obtained in the form of the average value achieved by each process is still less than expected and the value of the gap is quite far. Therefore, it is expected to conduct further research related to the management process in the company
Natural Language Processing for Data Science Workforce Analysis

As the demand for people with Data Science and Data Analysis skills are rising at a very high rate, periodic exploration of the skill sets for jobs in these fields have become essential. This research presents the use of Natural Language Processing for Human Resource Management. It presents the application of such techniques and tools as Python Libraries with Beautiful Soup and Selinimum, Web Scrapping, Topic Analysis, Sentiment Analysis, and Natural Language Processing.
A Multi-scale Video Denoising Algorithm for Raw Image

Video denoising for raw image has always been the difficulty of camera image processing. On the one hand, image denoising performance largely determines the image quality; moreover, denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with image, video has motion information in time sequence; thus, motion estimation which is complex and computationally expensive is needed in video denoising. In view of the above problems, this paper proposes a video denoising algorithm for raw image, performing multiple cascading processing stages on raw-RGB image based on convolutional neural network, and carries out implicit motion estimation in the network. The denoising performance is far superior to that of traditional algorithms with minimal computation and bandwidth, and has computational advantages compared with most deep learning algorithms.
Data Clustering Approach on the Basis of Data Sensitivity for Implementation of Secure Cloud Computing Environment

In the work illustrated, an effective approach has been proposed for handling data for the cloud computing environment. Here, the sensitivity of data is derived from data weights that in turn based on the size of the file and attribute scale for the preprocessing. The categorization of the sensitivity is based on the 10 attributes, and it has been selected on the basis of the size and information of data. Afterward, for clustering of similar elements, K-means clustering algorithm is applied which clusters the data in maximum of 5 clusters. It is been used to perform data sensitivity analysis and encryption. In terms of data categorization and processing in a cloud computing environment, the approach demonstrates its efficacy.
Exploring the Impact of Indian Revenues During COVID-19 Using Social Network Analysis

The second wave of the COVID-19 pandemic affected economy resulting in job loss for many throughout the world. The main objective is to analyze the Indian revenues before COVID-19, i.e., before December 2019 and the ongoing COVID-19 pandemic. A comparison is done between the revenues that are collected from various departments sectors for the years 2019–2020 and 2020–2021 to find out how the COVID-19 has affected the Indian economy as well as jobs for millions of people. Studying the network through social network analysis helps us to understand clearly how the Indian economy has changed during the COVID-19 period. The network is studied using an average path along with a weighted path to ascertain the small word characteristics. Centrality measures helps us to understand the characteristics of the dataset. The tools used to analyze them are designed using Gephi.
COVID-19 Recovery Prediction Using Regression-Based Machine Learning Approaches

The number of people affected by the ongoing coronavirus has fluctuated rapidly and it has become strenuous to predict when will this pandemic end. To impede the spread of this virus, it is the need of the hour to maintain a social distance, wear masks and sanitize regularly. No doubt, the mortality rate has escalated, summing to a large percentage of population and destroying lives and economies with it. In India, mortality has climbed to as high as 3.2% as per the Indian Express, with recovery rate summing to 81.55% according to Times of India. Therefore, it has become prudent to determine and predict the effect and drawbacks of various factors such as testing, mortality rate and confirmed cases on the recovery rate. Due to progression and evolution in the discipline of machine learning, it has become practicable to get a middling figure of effects of these factors on death rate. Regression, one of the most broadly exhausted machine learning and statistics algorithm, is used to make predictions from data by learning the relationship between the features. In this article, regression algorithms are used to anticipate the same by using a cumulative data of all states in India. Study compares the ramification of the number of testing done and their impact on the recuperation of life due to the virus. Therefore, based on the research and computing, it was found that ridge regression gave the highest accuracy equivalent to 99.6%.
A Novel Approach for Age and Gender Estimation in Surveillance Videos

Nowadays surveillance cameras are everywhere and it produce a tremendous amount of data every day, making manual detection of abnormal activities a laborious one. So, various automatic abnormal human activities prediction techniques have been proposed. Rain Streaks Removal—Enhanced Object Detection and Tracking—Spatio-temporal Frequent Object Mining (RSR-EODT-STFOM) predicted the abnormal human activity in surveillance videos; STFOM-Face Detection-Pose-Invariant Orthogonal Locality Preserving Projection (STFOM-FD-PIOLPP) recognized face of those persons. In this paper, the STFOM-FD-PIOLPP is extended by detecting the age and gender of the unknown faces. Stacked Trimmed Active Shape Model (STASM) is introduced to locate facial landmarks for efficient face recognition. Speeded Up Robust Features (SURF), OLPP, HOG, Weighted LBP, HFO, and HFD are used to classify the age and gender of the unknown faces using CNN. Estimated age and gender of unknown faces are saved in database for further clarification of human abnormal activity in surveillance videos.
Designing REACTION Media of Reading Skills for Islamic Senior High School

This researcher aimed to design media for reading skill for Islamic Senior High School. The research procedure uses research and development by using ADDIE’S method. ADDIE’S method has five steps. They are analysis, design, development, and implementation. This learning media in this research was designing REACTION (Reading Easy Application) media of reading skills for Islamic Senior High School. Sampling using product validation techniques with media experts, Material experts. Based on the results of validations and trials on multimedia using Hyperlink products, it can be reported validation of the material shows a percentage of 76 % which mean the media into good category. Based on the media validation shows a percentage of 72 % which mean the media into good category. Based on the tried out or implantation with students, the researcher got 88,28% of its mean include to the category of strongly agree. It can be concluded that the use of media-based Hyperlinks was valid and effective when implemented in English language learning in the classroom.