Reviewerly/exHarmony huggingface dataset:
Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites

The orientation of fibers in composites reinforced with short fibers can provide insight into the microstructure of the material and considerably affect its macroscopic characteristics. However, the present standard techniques for detecting fiber orientation and length based on microscopic image processing have faults in practical applications, including high effort, low efficiency, and unreliable measurement results. In this study, a method for measuring fiber orientation based on 3D reconstructions of scanning electron microscope (SEM) images is provided. The geodesic active contour (GAC) model is applied to segment the fibers in the SEM images. Matching the fiber contours with the scale-invariant feature transformation (SIFT) algorithm successfully extracts 3D orientation information from the fiber contours. The unit vector of the fiber axis is fitted from the extracted point cloud using the ordinary least squares (OLS) method. With a maximum deviation of 3.83° and an average deviation of 1.50°, the measurement findings of this method are substantially comparable to those of the image-measuring instrument. This paper offers a quantitative approach to studying the microstructure of short fiber-reinforced composites, thereby furnishing objective evidence to support the development and research of such materials.
Kinematic Calibration of Robot Manipulator using RGB-D Camera

This paper proposed the robot manipulator kinematic calibration method based on a distance error model using an easy-touse RGB-D(Depth) camera. The RGB-D camera attached to the robot end effector measures the checkerboard’s corner 3-D position in two different positions. The homogeneous transformation matrix having rotation matrix and translation vector between two different RGB-D camera positions is calculated from the acquired position data from the RGB-D camera. The distance error is measured by comparing the distance moved by the RGB-D camera from the derived homogeneous transformation matrix with the distance moved by the robot manipulator. Simulation and experiments have been conducted to verify the proposed robot manipulator kinematic calibration method with a 6-DOF robot manipulator.
Toward an intentional model aware of learner cognitive traits for pedagogical process guidance

Abstract Previous research on adaptive learning successfully demonstrated that use of Intentional Model for Educational Processes Guidance has gained popularity in Intelligent Tutoring System. Within this context, the novelty of this version of Intentional Model of Pedagogical Process Guidance (IMPPG) is the effectiveness use of Cognitive Trait Model to be aware of different traits of learner. The proposed model leads to generate an individualized learning for each learner by selecting the most appropriate pedagogical process according to the actual preferences of the tutor and the cognitive traits of learner. In fact, this model couples the pedagogical intention with the pedagogical strategies and provides a multitude of paths between learner intentions. Based on the cognitive traits of the individual learners and the corresponding pedagogical method or the individual teaching style, the ITS adopts the proposed model to adaptively support the tutor to achieve his/he intention through the selected strategy. This model has been experimented and assessed with tutors and students learning spreadsheet management in a first-year studying in applied license’s degree in Business English and applied license’s degree in Education. The first results show that the intentions and the strategies selected by our model were relevant according to students’ needs and tutors’ preferences. In the initial tests, the process model has met what was expected, however, more studies with experiments must be carried out. The primary results are reported and discussed in the paper; several lessons learned from the experience and potential improvements are also included.
The benefits of a cyber-resilience posture on negative public reaction following data theft

Research shows that customers are insufficiently motivated to protect themselves from crimes that may derive from data theft within an organisation. Instead, the burden of security is placed upon the businesses that host their personal information. Companies that fail to sufficiently secure their customers’ information thus risk experiencing potentially ruinous reputational harm. There is a relative dearth of research examining why some businesses that have been breached stay resilient in the face of negative public reaction while others do not. To bridge this knowledge gap, this study tackles the concept of cyber-resilience, defined as the ability to limit, endure, and eventually bounce back from the impact of a cyber incident. A vignette-based experimental study was conducted and featured: (1) a breached business described as having a strong cyber-resilience posture; (2) a breached business described as having a weak cyber-resilience posture. Overall, a convenience sample of 605 students in Canada were randomly assigned to one of the two main experimental conditions. The results show that a strong cyber-resilience posture reduces negative customer attitudes and promotes positive customer behavioral intentions, in comparison to a weak cyber-resilience posture. Similarly, the more negative attitudes a customer holds toward a breached business, the less likely they are to behave favorably toward it. As a result of this study, cyber-resilience, which has hitherto primarily received conceptual attention, gains explanatory power. Furthermore, this research project contributes more generally to business victimology, which is an underdeveloped field of criminology.
Effects of Video Conferencing-based Class on College Students’ English Reading Ability and Their Attitude According to Students’ Proficiency

This study aims to examine the effects of video conferencing-based learning on students’ reading competence of English and their attitude. For the goal of the study, both quantitative and qualitative research have been analyzed. Pre-test and post-test were conducted for the quantitative analysis. And for the qualitative research, the result of open-ended questions was examined. The samples were 56 university students; 26 were high-level students and 30 were at low-level. Both group were participated in class for 16 weeks through video conferencing. And were required to take two tests. Survey was implemented to investigate students’ attitude toward video conferencing-based learning. This study proposes the needs of utilizing video conferencing-based learning to foster university students’ reading competence of English and their attitude.
Reading engagement of Indonesian secondary EFL teachers as literacy indicators perceived over reading resources and pleasure reading

Nurturing students to become engaged readers for literacy development may need teachers who can play roles as models of keen readers. This descriptive quantitative study aims to profile the reading engagement of Indonesian EFL teachers as perceived from reading resources and pleasure reading. This study employed a survey questionnaire, requesting the respondents to reflect on their personal and school reading collection and their habits in reading for pleasure. Through a convenience sampling technique, voluntary responses were received from 183 secondary EFL teachers, mostly from East Java Province. The data were descriptively tabulated to result in frequencies and percentages. Research tool SPSS ver.24 was used to analyze the raw data for means, correlations, and compared means. Overall, this study found that reading engagement among secondary EFL teachers reflects moderately positive directions. The statistical analyses demonstrate that possessing personal reading resources may result in a slightly significant impact in assuring EFL teachers to read for pleasure yet better than having school reading resources. It has also been proven that both types of reading resources are weakly, yet significantly, correlated with reading for pleasure. This means that the more EFL teachers have access to reading resources, the more they will read and indirectly improve themselves. Future research may uncover the implications of having teachers engaged in reading on the design of more responsive reading instruction for the development of literacy culture at schools.
THE INFLUENCE OF MODERN TYPES OF LECTURES ON INFORMATION AND COGNITIVE INDEPENDENCE OF STUDENTS

The influence of the lecture on the development of information and cognitive independence of students is considered. The urgency of the problem is determined by the need to master the methods of working with information for the implementation of independent cognitive learning activities and the successful development of competencies. The purpose of the article is to identify the impact of the lecture on the development of information and cognitive independence of students. The results of the study. The positive influence of modern forms of lectures on the development of information and cognitive independence of students has been revealed and proved.
DIGITAL ADAPTATION OF EDUCATIONAL PROCESS PARTICIPANTS TO DISTANCE LEARNING (AS SEEN IN ELEMENTARY SCHOOL)

The article examines digital adaptation of elementary school teachers and students during distance learning in the context of the 2020-21 global COVID-19 pandemic in Russia. The authors conducted an empirical study among elementary school teachers using an authentic digital questionnaire as the methodological basis. The questionnaire concerned applied distance learning technologies, their impact on the educational process and the emotional well-being of young learners, as well as the teacher satisfaction with the remote form of interaction with students.
Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

This research modelled the effect of pH on the remediation of crude oil-polluted soil using biochar blend. The biochar blends, PL-500, pW-500, and RS-400, were made by pyrolyzing poultry litter, pine wood, and rice straw at varied temperatures and times. The pH of the crude oil polluted soil was 4.72. Response surface experimental design mixed biochar to remediate total petroleum hydrocarbons (TPH). Following 30 days of bioremediation, 15g PL-500, 3g PW-500 and 6g RS-400, removed a maximum of 46% TPH. The experimental data were statistically modelled and optimized using design expert software and response surface methods. Analysis of variance (ANOVA) was used to determine the significance of each regression coefficient. Biochar blend improved soil pH to 6.9 following remediation. ANOVA indicated that PL-500 was significant for predicting TPH % degradation at p =0.0290, suggesting that its high pH, nutrient, and soil water conservation values made it more effective in remediating TPH. The quadratic model predicts with R2 =0.8567. A model fit statistics were used to examine soil pH influence on TPH remediation. RSM study indicated a good positive association between statistical model and experiment with R2 = 0.7612. The model fits experimental data and predicts that . Remediation requires soil pH and biochar's alkalinity raised soil pH to 6.9, which promoted hydrocarbon-utilizing bacteria.
Propagating pendulum beams to simulate the quantum-pendulum time evolution

Physical analogies are particularly useful to study not easily accessible systems. This is the case of the quantum pendulum, whose evolution in time is not easily observed. Fortunately, its mathematical description is shared by the Pendulum beams, which can be readily created in optical laboratories. We engineered mode superpositions that upon propagation mimic the time evolution of the quantum pendulum. We stablished experimental conditions in which the analogy is valid and present results for several relevant cases, finding excellent agreement between experiment and theoretical predictions. We expect to explore other superpositions to extend our analogy to pendulums with more complex dynamics.
Processing of singular agreement controller in Turkish

The present study investigates whether the properties of the singular agreement controller in Turkish comitative construction ease the processing of the comitative construction and the singular agreement controller checks the agreement type on the verb (singular vs. plural).The data collected from 134 native Turkish speakers indicated that only one of the singular agreement controllers: the second person sen 'you' (both singular and plural agreement) was processed slower than the others, and there was no significant main effect of the agreement type (plural vs. singular).
Implementation of Inlislite Application Based on Management Information Systems at SMKN 1 Praya Tengah

This study aims to describe what impacts occur when implementing the Inlislite application system based on a management information system in the library of SMKN 1 Praya Tengah. This study uses a qualitative research design and is analyzed by making rich and thick descriptions. Research at the data collection stage uses a grounded theory approach. Based on the results of the research, it can be concluded that Inlislite has a positive impact, because it can improve library services at SMKN 1 Praya Tengah because librarians process data on borrowing and returning books faster. In addition, there were technical problems in implementing inlislite at SMKN 1 Praya Tengah which resulted in the existing system running slowly. The recommendations for this research are: 1) To separate schools from Inlislite, and add library facilities in the form of the latest generation of computers; 2) Provide regular Inlislite training and guidance to Librarians, so that Librarians are more adept at operating Inlislite.
INTERACTIVE KNOWLEDGE TESTING SOFTWARE MODULE

At the present time, the process of rapid filling with new means of information technologies of higher education, which lead to qualitative assessment of knowledge, using all possible modern methods of the educational process, continues in Ukraine. The rapid development of information technologies fills society with the latest opportunities, which, in turn, facilitate human activity in all spheres of life. The creation of a unified educational information environment, when using new information technologies, significantly increases the quality of education in Ukraine and is a priority task of informatization of the education system of our country and will provide equal opportunities for citizens to receive education of various degrees and levels, as well as integrate into the world educational and information space of Ukraine. Control over the educational activity of students is the most important component of the pedagogical system and an integral part of the educational process, which is intended to determine the success of education for each student or student, to analyze the results obtained, and, subsequently, to correct the educational process. Improvement of the assessment system, modernization of the diagnosis of student learning is a tool for improving the quality of education along with reforming its content. In modern conditions of the development of the latest technologies, the task of constantly updating the knowledge of specialists is urgent. Computer-based learning tools play an important role in solving this problem. The use of information technologies in education allows for the individualization of educational processes, providing operational self-monitoring and control with the help of error diagnosis and feedback.Testing as a form of control and diagnosis of students' knowledge is becoming increasingly widespread in the educational process and in the field of professional pedagogical activity, as it has certain advantages over other forms of control of knowledge and skills, in particular, the possibility of covering a large amount of material. In the modern conditions of professional and technical education in Ukraine, the possibility of automated formation of the test, the possibility of automated verification of test results, increasing the objectivity of evaluation and reducing bias in the evaluation of knowledge is gaining importance.
Deep Learning based Handwritten Arithmetic Equation Solver

Manual recognition of different styles of handwritten characters, digits, symbols, and operators is quite intricate. This paper attempts to automate the process of recognition of digits (0 to 9), decimal point, opening and closing parenthesis (‘(’ and ‘)’) brackets, and five binary arithmetic operators: sum (+), difference (-), product (×), exponent (A) and division in input handwritten equation followed by evaluation of expression. Firstly the segmentation based on binary threshold and contouring is applied to segregate the components of input handwritten equation. The individual segregated components are further input to Convolution Neural Networks(CNN) for recognition as part of second step, followed by evaluation of valid expression as the final step. The publicly available MNIST dataset handwritten digits (0–9) are considered and rest of the images for parenthesis, decimal point and five arithmetic operators are manually generated. The CNN model identified the handwritten expression with a high accuracy of 99% followed by a correct evaluation of the valid expression.
Lake Ice Infrared Image De-noising using Deep Learning Convolutional Neural Network

Elimination of noise from the acquired images is one of the fundamental tasks so as to restore the high quality of images to increase the results of segmentation, classification, recognition, detection etc. In this research work, Infrared imaging is used to capture the images with various temperature ranges during day and night time. Infrared thermography is used as a nondestructive image acquisition sensor. So infrared imaging sensors are used to identify and classify different ice types formed on the rivers during winter season in western countries. However, you can observe blur and degradation in the acquired images through infrared imaging, as the climatic nature is involved in the process. These factors affect the quality of the image and also the quantitative analyses of the images. Especially, when there are deeper faults which are found in an under laying substance and high thermal conductivity materials can also be studied. Acquired infrared images are usually affected with noises. In this paper, de-noising of infrared images is carried out using convolution neural network model in deep learning. In this work Gaussian white noise is added to the infrared images with varying noise intensities from 1% to 10% and then the convolution neural network model is applied to de-noise the infrared images. In the qualitative analysis of the infrared images edge factor, uniform region, texture, smoothness, non-uniform region, structure of the region of interest is considered. For quantitative analysis, mean square error, peak-signal- noise-ratio and structure similarity index measure is used. The results of traditional methods of image de-noising are compared with convolution neural network based method. It is concluded from the experimental work that the convolution neural network based method proves to be better for removal of Gaussian white noise when compared with traditional methods.
Enhanced Thyroid Nodule Classification Adopting Significant Features Selection

Appropriate selection of features play a crucial role for refining precision of classification systems. The classification accuracy and training speed may be significantly intensified by elimination of superfluous features. The present paper addresses the high dimensional data analysis problem through feature selection approach for refining the classification accuracy of Thyroid Nodules (TNs) as benign and malignant. Thyroid Ultrasound Images (TUS) containing nodules are first de-speckled and further improved using Canny Edge Detection (CED) method. This process is followed by application of segmentation technique Adaptive Regularized Kernel Fuzzy C-means (ARKFCM) where relevant Area of Interest (AOI) is obtained and using AOI, nineteen texture features are mined. Finally, feature subset selection is carried out using five different search methods- Genetic Search (GS), Best First (BF), Linear Forward Selection (LFS), Greedy Step Wise (GSW), and Subset Size Forward Selection (SSFS). Selected features are assessed using ten different classifiers Bayes Net, Naïve Bayes, Logistic, Multilayer Perceptron, Radial Basis Function, Sequential Minimal Optimization, Instance Based K-nearest neighbor, K-star, J-48 and Random Tree. Experimental evaluation revealed, features listed using five search techniques have boosted performance of all considered classifiers in comparison to their performance using original nineteen features.
Item Layout for Building Materials Management with Apriori Algorithm

The initial stage of opening a business is one of the business activities that can have an impact on the economy. TB. Jasa Manis is a trading business that sells various kinds of building materials, electrical equipment, materials and others. A sales strategy is needed to maintain business and marketing. Data Mining answers this problem by analyzing customer transaction data, then creating a certain rule, pattern or model. Products that are frequently purchased by customers will be used as a reference point in viewing consumer spending patterns. The association technique that can be used is an a priori algorithm designed to find item combinations. The a priori algorithm is guided by the min support and min confidence values. The combination pattern of sales transaction data itemset for two months on TB. Jasa Manis, it can be found that with a combination pattern the minimum support value is 14% and the minimum confidence is 40%. The rules for the highest association or goods that are often purchased together by customers are found in the Lotus Geese/LBR and Roof Nails. Through the analysis of customer shopping habits obtained, the layout of building materials can be adjusted to the customer's shopping pattern. With the data mining socialization rules, patterns of consumer shopping behavior can be identified and can increase sales at TB. Sweet Service
APLIKASI PEMBELAJARAN SISTEM GERAK MANUSIA DENGAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID

This study aims to produce learning media for human motion system lessons by utilizing android-based augmented reality technology. This learning media can increase students' absorption of the motion system material and as a practical teaching aid that can help teachers in the teaching and learning process in schools. The research method used is the Software Development Life Cycle (SWDLC) method with the stages of planning, analysis, design, implementation, and testing. The results are android-based applications with augmented reality technology with the lesson of the human motion system. The applications have been tested on smartphones with Android nine and ten systems with successful test results, and the application running properly
PERANCANGAN JARINGAN VIRTUAL LAN MENGGUNAKAN METODE PROTOKOL PEER-VLAN SPANNING TREE

The role of computer networks today is very important, starting from the need to share data, software, and communication lines (internet). PT. XYZ is a company engaged in event organizer services. As a modern company, of course, it already has computer network technology to support operational activities. However, along with the increasing needs and users, problems related to security arise and there is no redundancy path between switches. The purpose of this research is to design a VLAN network and create a redundancy path so that it can divide the network segment of each division with broadcast storm prevention technology. Network implementation with Vlan Trunking Protocol to divide network segments between divisions is going well so that there is a backup path between switches so that when one of the lines dies, the network will remain connected