Reviewerly/exHarmony huggingface dataset:
An error compensation method for 3D measurement of step edge in fringe projection profilometry

Fringe projection profilometry is an efficient, fast and non-contact 3D measurement technique, widely used in industrial parts measurement. However, invalid phases are common when measuring step edges such as holes, ribs and steps in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. In this paper, an error compensation method on 3D measurement of step edge is proposed. 3D measurement space is firstly divided into multiple subspaces based on binocular camera system parameters. Then a calibration method is proposed to calculate the amount of compensation for each subspace and obtain parameters of edge error model. After calibration, point cloud of object with step edge is reconstructed by projecting phase-shifting structured light fringe patterns. By using Principal Component Analysis (PCA), normal estimation of point cloud is implemented. And edge feature is extracted with combination of eigenvalues variation of the covariance matrix and first- and second-order fitting based on two-dimensional projection. At the same time, the corresponding deviation amount of each edge point is solved on the basis of aforementioned edge error model. Finally, the accurate step edge is obtained after error compensation according to the normal direction and deviation amount. Experimental results show that, the method proposed can effectively improve the 3D measurement accuracy of step edge.
Research on the optical video extensometer with large field of view and high accuracy

Optical video extensometer has been widely used in the field of strain measurement because of its features of non-contact, large range, high precision, being suitable for special environments and materials. A new optical video extensometer with the large field of view and high accuracy was designed in this paper. The hardware includes dual telecentric lens, CCD camera, ring light source, tripod, slide rail and some supporting fixtures. This integrated hardware has the advantages of low cost and high imaging quality. Also, the supporting software is developed. By sub-pixel tracking of selected markers, it can automatically calculate the data of transversal and longitudinal strain values. Poisson's ratio of the material can also be obtained. The measured results of the video extensometer are proven to be correct and accurate by experimental verification. Compared with strain gauges, the maximum strain error of the optical video extensometer is about 15με, and the standard deviation error is 8.38με. This optical video extensometer can be used for high-precision strain measurement of large size samples of metal, polymer, film and large deformation materials.
Image registration method for full-Stokes-vector division-of-aperture polarimetric camera

Polarimetric imaging can significantly improve the quality of images captured under poor imaging conditions, these include enhancing visibility in fog, increasing image contrast under complicated background, highlighting details of targets by removing strong surface glare, etc. Compared with other polarimetric imaging systems, the full-Stokes-vector division-of-aperture polarimetric camera has obvious advantages such as the real-time imaging and the structural compactness, owing to its inherent capability of capturing the same scene’s four images related to its full-Stokes vector simultaneously. However, the deviations among four polarized images are inevitable due to assembly errors of four sub-apertures and the corresponding imaging channels. In this paper, we propose a method to solve the image registration problem in such a kind camera. Firstly, the coarse image registration is implemented through the phase-only correlation algorithm; secondly, the feature points of the reference image and other three images under registration are extracted and matched by using the Speeded-Up Robust Features (SURF) algorithm; finally, the affine transformation matrix between the reference image and each of the three images to be registered is obtained by using the RANSAC (i.e., Random Sample Consensus) optimization algorithm, respectively. The experimental results demonstrate that four polarized images are registered, which effectively enhance the image quality in terms of the Degree-of-Polarization (DoP), the Angle-of-Polarization (AoP), the structural similarity (SSIM) index, and the Normalized Mutual Information (NMI) index.
Fringe projection based 3D contour measurement method for holes on sheet metal parts

Holes are common and important feature information in sheet metal parts and play a decisive role in product production and assembly. The 3D contour reconstruction method based on passive binocular vision has been widely used in hole measurement due to its high accuracy and robustness. It can obtain dimensional data such as diameter and center of round holes, but there are still problems in measurement accuracy and integrity, and it is difficult to accurately measure special-shaped holes. Aiming at these issues, a fringe projection-based 3D contour measurement method for holes on sheet metal parts is proposed. In this method, two constant movements are performed on the extracted contour curve to the surrounding area, and the 3D contour points can be reconstructed according to the phase matching. Therefore, the real 3D contour is reconstructed inversely according to the corresponding points of the previous two reconstructed contours. With this method, contours only need to be extracted on one image, so it can reduce the effect of inconsistency in contour extraction. In addition, the accuracy and integrity of the 3D contour can be guaranteed by using high-quality phase matching of the sheet metal area. The experimental results show that this method can achieve accurate and complete 3D contour reconstruction for sheet metal holes of various shapes, and the accuracy can reach 0.08mm.
Depth constraints in two-step calibration method based on line features

Calibration is essential for the three-dimensional light field endoscope, and the two-step calibration method based on line features was proposed by us to accomplish it before. In the second step of the two-step calibration method, the relationship between the projections of virtual feature points on the microlens image and the central sub-aperture image was used to calibrate the parameters about the microlens array, but we find that the feasibility of the calibration method has a depth constraint on the border of the checkerboard. In this paper, we deduce and demonstrate this constraint, and an optimization algorithm is designed to improve the accuracy of the second step calibration due to the inaccurate black-and-white boundary detection. Experimental results show that our method is effective and accurate for the calibration of the three-dimensional light field endoscope.
Student Database Management System

The main objective of this project is to add mobility and automation to the process of managing Student information in an institute. In a real-world environment, such as a college campus, information is disseminated to students in the form of notices, handwritten manuals, and vocal messages. Today, it is critical to employ not only the traditional forms of statement, but also newer ones, such as cell phone technology, to facilitate faster and easier communication among students. The college staff uses android devices to upload results and college information to a secure android application. Before any record changes, all data is thoroughly examined and validated on the server. The college Administrator manages the MYSql Database, where all data is safely kept. The method reduces paperwork and the amount of time it takes to access student records. In the previous system, college depended mainly on paper records, which had its own set of drawbacks. At the same time while searching any information it is too difficult to access and takes a lot of time to search the particular website. Hence, in order to overcome this problem a smart phone-based application using Android can be used to make this process easy and secure. Keywords: Student Database Management System, Android SDK, User Android Application.
Fusion-based restoration method for uneven illumination images in deep sea

Light is almost exhausted in the deep sea due to the attenuation of light caused by absorption and scattering, so artificial light is often required for deep water exploration. Under the influence of artificial illumination, light scattering and absorption, the image captured by imaging system often suffer from uneven illumination and contrast reduction. To address these issues, this paper proposes an effective method for deep-sea image recovery based on multi-scale fusion. Firstly, the luminance channel of the image is decomposed into a base layer and a detail layer through Weighted Least Squares (WLS) filter, and the luminance-adjustment image is obtained by combining adaptive luminance mapping and detail enhancement techniques. Next, the model-based dehazing method is introduced to obtain a global contrast-enhanced image. Finally, two weights are designed based on the above two inputs to produce an enhanced underwater image by multi-scale fusion. Simulation and experimental results show that the method can effectively improve the details in the shadow region of deep water images and eliminating the effects of uneven illumination.
Light field camera decoding based on two-plane ray model

2D raw image of Light Field (LF) camera needs to be decoded into 4D LF data for representation and processing. In decoding, the main lens is usually modeled as a thin lens while the micro-lens array is modeled as a pinhole array. Although this model takes into account the main lens distortion, it is still difficult to accurately characterize the complex imaging relationship of the LF camera. In order to obtain more accurate 4D LF data, this paper proposes a LF camera decoding method based on a two-plane ray model. By calibrating the ray corresponding to the pixel point of the LF camera, the mapping relationship between the real object point and the image point is established to obtain a new two- plane model of LF; then by interpolating and resampling on this two-plane, the correction of 4D LF data is achieved. Compared with traditional methods, our method improves the decoding accuracy of LF camera, and provides new ideas and methods for studying LF imaging.
Camera vibration correction of stereo-DIC systems for field measurements

Camera vibration is one of the main factors affecting the measurement accuracy of stereo-DIC. It is difficult to keep fixed of the cameras in the field because of wind loads and ground vibrates. Currently, the research on camera vibration correction for Stereo-DIC in field measurements is fairly limited. This study proposes a camera vibration correction method based on fixed background points and motion model parameters. The background points are used to calculate the motion parameters of two cameras, and then the image coordinates of the interesting points that eliminate the global motion are estimated. The proposed method avoids repeated calibration of extrinsic parameters by correcting the motion of each camera separately. To verify the proposed method, an experiment in which the stereo cameras occurred relative movement was performed. Experiments shown that the proposed method has effectively corrected the effects of camera movements.
A ticket-voting-based strain calculation method for digital volume correlation

Digital volume correlation (DVC) is a powerful technique to measure the deformation inside the material or a structure. In the DVC working steps, the displacements of points of interest (POIs) are first calculated by tracking the correspondences between the volumes acquired before and after deformation using speckle correlation, which is then followed by strain calculation using the derivative of these displacements. Generally, displacement accuracy of the POIs heavily depends on the speckle quality. Unlike the speckles on the surface of test object for digital image correlation analysis can be painted or fabricated, however, the speckles inside the material or a structure for DVC analysis are often not controllable. In addition, the volume acquired by the common volumetric imaging equipment generally has limited resolution and large image noise. As a result, the poor speckle quality and large image noise introduce great displacement outliers. Strain calculation is vulnerable to displacement noise because of the high sensitivity of differential calculation to noise. Therefore, it is critical to discard the displacement outliers to guarantee the strain calculation accuracy. In this work, I proposed a ticket voting method to detect displacement outliers and then exclude them in the strain calculation. In the ticket voting method, each POI is voted by its neighboring POIs based on the residual errors in strain calculation. If this POI obtains more than half of the total tickets, it will be included in strain calculation, otherwise excluded. Both simulation and experiment results showed that the proposed method had significantly higher accuracy than the conventional method; and our DVC method can avoid the miscalculated strain concentration.
A new approach for solving the minimum vertex cover problem using artificial bee colony algorithm

The minimum vertex cover problem is a well-known problem in classical graph theory. It is known as one of the NP-Hard optimization problems, meaning that the best solution cannot be found within an acceptable time. Therefore, to handle this optimization problem effectively, various alternative approaches based on either approximation or metaheuristic techniques have been considered and proposed in the previous literature. However, developing more effective methods to deal with the problem of finding the minimum vertex cover is still challenging. This study proposes a strategy for tackling the minimum vertex cover problem based on the artificial bee colony (ABC) algorithm. The main novelty and contribution of this research are to show that the ABC algorithm can be used as another useful and efficient way to solve the minimum vertex cover problem and also to demonstrate the application of the minimum vertex cover solutions to real-world wireless sensor network problems. The proposed method is evaluated by assessing the algorithm’s performance based on an obtained set of optimal vertices and the amount of computation time in terms of the number of iterations. The empirical results show that the proposed approach can yield satisfactory results and outperform other existing algorithms in finding a set of optimal vertices.
A personalized feedback system to support teacher training

Abstract This paper aims to illustrate an automated system developed to give formative and personalized feedback to teachers in training. It is an expert system (Paviotti, Rossi &amp; Zarka, 2012) that uses concrete examples, cases and scenarios to guide the engaged learners (Leake, 1996). In this regard, this system is able to create questionnaires, deliver them, collect and analyze data, send feedback to the participants to provide information about their beliefs and behaviors about teaching and learning processes. Far from constituting an assessment of teaching practices, the automated feedback demonstrates its usefulness in identifying teachers’ mindframes at an early stage, so as to be able to implement more specific and personalized training. This allows its application to be extended to further training areas as well as constituting an effective approach for need analysis and a preparatory action for numerous training activities (guided discussion with experts, observation on practice, modeling, etc.).
PGTCN: A novel password-guessing model based on temporal convolution network

Text passwords are the primary method for identity authentication. However, easy-to-remember passwords are vulnerable to password-guessing attacks. The study of password-guessing not only enhances the understanding of password security, but also promotes the improvement of password library security. Nowadays, deep learning based models have demonstrated their promising ability for password-guessing, e.g., Recurrent Neural Network (RNN) and its variants. However, RNNs are failed to parallelize and lack long memory, resulting in unsatisfactory guessing efficiency and effectiveness. Aiming to generate a high-quality password dictionary for password checking, we propose a temporal convolutional network-based password-guessing model named PGTCN. Specifically, in PGTCN we improve the password-guessing efficiency by introducing the Improved Temporal Convolutional Network (ITCN) which adopts residual learning and feature fusion techniques. PGTCN can automatically study the structure and characteristics of passwords and generate new passwords based on the learned knowledge. To verify the performance of PGTCN, we compare it with state-of-the-art models on six public password datasets. Evaluation results show that the password dictionary generated by the proposed PGTCN achieves the structure coverage rate up to 84%, and boosts the matching rate up to 22%. We conclude that the PGTCN could be regarded as a robust and efficient model for password guessing.
Counterfeiting

This chapter deals with the age-old problem of counterfeiting, from the ancient world to the modern day, including a look at Isaac Newton's work with the Royal Mint in devising early methods to detect counterfeiting.
Paving the way to a software-supported requirements prioritization in distributed scrum projects

Both, agile software development (e.g., Scrum) and the geographical distribution of project stakeholders have gained increasing significance in recent years. Establishing agile methods in distributed software development projects challenges science and practice as the characteristics of agile software development are not compatible with distributed project environments. Especially the task of requirements prioritization that incorporates a significant role in Scrum and that requires high communication and collaboration between stakeholders lacks an extensive software-supported methodology. This research-in-progress paper presents the first three steps of a design science research project. Based on an initial literature review combined with expert interviews addressing two software companies, the paper presents issues, requirements, and solution characteristics. Finally, it proposes a solution concept in form of a process model. These artifacts offer a solid foundation for further research, especially instantiating and evaluating the concept using a functional software prototype.
The impact of the COVID-19 on the formation of a new type of higher school

The article analyzes the problems of modern higher school that trains personnel for the agro-industrial complex functioning, which were manifested by the COVID-19 coronavirus pandemic, which with the 1st and 2nd World Wars, has become an event of a planetary scale, revealing not only the school problems, but also the societal problems of the entire global megasocium. It has generated a lot of discussions about creation of a new person type, a new educational space and universities without teachers. "Pedagogical relations" are a type of social relations with all the internal contradictions of society (political, economic, demographic, social, ethnic, religious, gender, etc.) and external contradictions of nature and man himself. The mental, existential and moral security of the individual is under threat. Such problems include the forced return to home schooling, transition to distance learning, which in turn has certain inconveniences for students and teachers. At the same time, attention is paid to such a social background as the growth of conditions for working remotely at home, alternating work at home with work in institutions. The factor of working at home allows family members, including students, not only to communicate more, but also to bring closer the learning process and material and spiritual production in the likely prospect.
Filtering grayscale images using the Kalman filter

This article aims to explore the possibility of using the Kalman filter to filter images. The relevance of the study lies in the fact that at present the tasks of image processing have become of great importance in many areas, such as industry, science, medicine, space industry and agriculture. Methods for improving image quality are of great applied and scientific interest for the agricultural sector, since machine vision methods are now widely used in assessing the condition of agricultural plants, soil condition, sorting of agricultural products, controlling unmanned agricultural machines, etc. The purpose of this work is to develop an algorithm and software for filtering grayscale images. The article consists of four parts: Introduction, Materials and methods, Results, Conclusions. The first part describes the relevance of the topic, discusses the reasons for obtaining noisy images. The second part describes the Kalman filter algorithm as applied to image filtering problems. In the third part, the results of the software implementation of the developed algorithm are considered, which make it possible to evaluate the quality of image filtering. In the fourth part conclusions are drawn and summed up. The main results of the work are the algorithmic implementation of noise removal from halftone images grayscale images using a software tool developed as part of these studies.
Research on Innovation of Online Financing Process based on Blockchain Technology

Blockchain technology has the technical characteristics of unforgeable data and traceability of information, which is of pioneering significance in enhancing the convenience of financing activities and reducing financing costs. One behavior conducts prediction and risk analysis to provide corresponding solutions, and uses the binary Logistic regression model to reconstruct the index system used, and collects a large number of financial operation data of enterprises, and establishes a set of prediction system evaluation models for later stages. The research on related blockchain technology and online financing provides a new reference..
Image Dehazing Algorithm Based on Atmospheric Scattering Model

With the development of electronic circuit technology and optical imaging technology, the resolution and clarity of captured images are getting higher and higher. However, in hazardous weather conditions such as heavy fog, the photos taken can be extremely damaging. The main cause of image damage is that the surface light of the venue interacts with particles suspended in the air during the approximation process, resulting in loss of image detail, tone conversion and reduced satisfaction, and the problem of not being able to cover the surface. This paper aims to study the image dehazing algorithm based on atmospheric scattering model. In this paper , an image dehazing algorithm based on atmospheric scattering model is studied in depth , and a material-robust and highly stable blur removal algorithm is proposed. In this case, the custom blur polarization algorithm treats the magnitude of the polarization as a general variable, resulting in a generally dull, low-level, low-toned image, with loss of image information. Filtering is recommended. Initially, two polarization maps were obtained under optimal and worst conditions using a polarization imaging system, and then the intensity of the surface light was calculated by division of the quadtree and proper optimization of the appropriate parameters as intensity. And using a general filter , the scattered light around the surface is calculated by the distorted path, then the image without blur is restored according to the deblurring pattern, the final image is destroyed. Experiments have shown that the algorithm in this paper is about 400 times faster than the DCP algorithm, and the user satisfaction is also above 70%.