Reviewerly/exHarmony huggingface dataset:
A Comparison Study of a DIC and Extensometer on Stress-Strain Curve for AL5052 Aluminum Sheets and Its FEM Applications

The digital image correlation (DIC)Digital Image Correlation (DIC) method is an advanced technical method to measure material strain by comparing several captured figures during deformation. DICDigital Image Correlation (DIC) provides full-field displacements and full-field strains in recorded images. In fact, there is much software using the DICDigital Image Correlation (DIC) algorithm. However, a commercial DICDigital Image Correlation (DIC) system requires a high initial investment. In this study, the DICDigital Image Correlation (DIC) algorithm was built based on the open-source of Matlab software, temporarily called NCORR DICDigital Image Correlation (DIC) . A DICDigital Image Correlation (DIC) system was built including a computer system, extensometer, tensile testingTensile testing machine, high-resolution camera, NCORR DICDigital Image Correlation (DIC) software and applied for AL5052AL5052 sheet metal test sample. After obtaining the images from the tensile testTensile test , the entire image will be included in the NCORR DICDigital Image Correlation (DIC) software for processing. To check the accuracy of the DICDigital Image Correlation (DIC) system, the results of the stress-strain curve of NCORR software were compared with the standard test method using extensometer. The obtained results measured from NCORR DICDigital Image Correlation (DIC) software were imported into a finite element method (FEM)Finite Element Method (FEM) software to check the accuracy of this method. Moreover, the hardening model (Voce, Swift, Kim-Tuan) provides empirical evidence and prerequisite conditions for the development of new fractureFractures criteria to describe behavior fracturesFractures .
Improving Customer Satisfaction Through Reduction in Post Release Defect Density (PRDD)

Quality of software delivered is a matter of significant concern to all stakeholders, especially the customers. Post release defect density (PRDD) is commonly used as a measure of quality of software delivered and is computationally defined as the number of defects leaked to the customer divided by the size of the software. PRDD is a major determinant of customer satisfaction and hence, improving PRDD would generally result in increase in the level of customer satisfaction. This paper presents a case study that illustrates how to improve PRDD in enhancement and maintenance type of software projects. It describes the approach followed, which involved two stages and applied lean six sigma methodology. Improvements and changes were identified and implemented in selected software project in stage one of this case study. After successful validation in the selected project, these improvements and changes were extended and replicated to other similar software projects in stage two. This paper postulates that improvements and changes in planning of certain engineering activities in the projects, selected based on statistical tools such as process capability analysis, scenario analysis, and sensitivity analysis, would result in reduction in PRDD. It also postulates that tracking of the above activities using statistical tools such as control chart and cause and effect diagram would ensure the sustenance of improvements in PRDD. Finally, it illustrates how reduction in PRDD would eventually result in higher level of customer satisfaction.
Case Report of Anomalous Head Posture Correction with Low Vision Aid Using Virtual Reality

Purpose: To report the successful rehabilitation of a patient with anomalous head posture by using a virtual reality low vision aid (VRLVA).Case summary: A 75-year-old male diagnosed with age-related macular degeneration 15 years prior presented with an anomalous head posture for eccentric viewing.He had central scotoma within the central 10 degrees, and the preferred retinal locus (PRL) was localized to the inferonasal retina.The patient underwent a training session on the use of the VRLVA, which has a remapping function to remap distorted images, and those falling on the scotoma, to another location on the screen.After explaining the purpose of the VRLVA, the patient was given the device to use at home for two weeks.At two weeks after training, the patient could look straight ahead when using the VRLVA, without anomalous head posture.In addition, reading performance, including speed and accuracy, improved; moreover, he experienced no device-related adverse events.Conclusions: By using the VRLVA, low-vision patients with anomalous head posture can shift images falling on the central scotoma to the PRL, thereby minimizing their anomalous head posture and improving reading performance.We expect that future technological advances, such as a wearable design and the use of lightweight material, will further improve the performance and acceptability of the VRLVA.
Enhancement and Tampering Detection for Surveillance System

Surveillance systems are prevalent in our lives, and surveillance videos are often used as significant evidence for judicial forensics. Good quality surveillance videos have become essential, due to the increase in the rate of malpractice and misconduct. therefore, there arises a need for high-resolution videos in law enforcement. Along with bad quality, there is one more problem that is faced that is of low light in the image or the video. To tackle these problems, an integrated system ResMaster is proposed which would be a web application, wherein along with video enhancement, video tampering will also be detected. Video enhancement will be done using exposure fusion framework which works on the pixels depending on their exposure. With the use of this framework, results are obtained with enhanced regions that were previously overexposed or underexposed, whereas tampering will be detected using correlation of noise residue for which Gaussian Mixture Model will be used.
Deep Learning Architectures for Modeling Communication Systems

Recently, deep learning for physical layer has been modeled using autoencoders to model the entire communication system end-to-end. We extend these methods to improve the overall performance by adopting various learning strategies when multiple users try to communicate over a shared channel. We consider cooperative and non-cooperative schemes in the 2-user Gaussian interference channel. Additionally, a simple neural network architecture is provided for wireless communication systems where channel gain matrix either attenuates or, in some cases, results in fading of the message signal sent by the transmitter.
Evaluation and analysis of bio-inspired optimization algorithms for feature selection

Wrapper-based feature (subset) selection is widely used as an effective means for decreasing the dimensionality of datasets. However, it is not the most efficient approach in terms of computational cost. Hence, the choice of the wrapper is paramount. Ideally, the wrapper should be simple to use and understand, whilst yielding good solutions as fast as possible. Bioinspired optimisation algorithms are a common choice in that regard, but not all are made equally. This paper investigates a number of optimisers on diverse datasets in order to provide an insight into their efficiency and behaviour with respect to the problem of dimensionality reduction for classification needs. Correspondingly, some guidelines concerning the choice of the wrapper are given.
Case-Based Reasoning Tools for Identification of Acoustic-Emission Monitoring Signals of Complex Technical Objects

The article discusses actual issues of developing a tool for identifying signals received during acoustic emission (AE) monitoring of complex technical objects using case-based reasoning (CBR – Case-Based Reasoning). The main task of AE-monitoring is to detect AE-sources that are associated with defects at the monitored objects, information about them is contained in the parameters of AE-signals. Unfortunately, at each individual object, the difference between AE-signals cannot be quantitatively described, but can be established empirically with the help of an expert operator. For a qualitative analysis of AE-monitoring data, it is necessary to accumulate a sufficient database for each individual object, which is a difficult rather time-consuming task. To improve the efficiency of the operator in accumulating the database for identifying AE-signals, it is proposed to use case-based reasoning methods and systems (CBR-systems) that can work with relatively small training samples and at the same time produce high-quality results. Developed in MS Visual Studio in C# language, the CBR-tools for identifying acoustic emission signals allows not only to accumulate a sufficient database for identifying AE-signals, but also to use it later as an independent tool for identification. The work of the proposed tool was tested on valid expert data obtained during AE-monitoring of metal structures.
Demonstration of Teleoperated Bumblebee-Quadcopter System for Collision Avoidance

In this study, we investigate whether an animal-in-the-loop (AIL) system can be applied to the collision avoidance ability of a bumblebee bombus ignitus The AIL system is a novel experimental system for extracting the ability of animals to adapt to the environment and their own body by remotely controlling an autonomous mobile robot. The bumblebee causes an optomotor reflex to the optical flow and turns in the same yaw direction as it. However, it is unclear what kind of collision avoidance behavior is performed when flying toward a stationary obstacle. Therefore, we first measured collision avoidance behavior when flying toward a stationary obstacle. As a result, we found that the turning movement was elicited according to the expansion of the edge of the obstacle. Accordingly, we used this phenomenon for the AIL system in which the bumblebee remotely controls the quadcopter. As a result of presenting quadcopter first-person camera images to the bumblebee, we found avoidance behaviors elicited near the obstacle. This suggested that the AIL system can be applied to the collision avoidance ability of the bumblebee and that the behavior is changed by the expansion of the edge of the object.
Activity Recognition using 1D convolution from Accelerometers Data

Abstract Aiming at the problem of activity a recognition method based on a convolutional neural network was proposed in this papaer, which can effectively classify 6 types of human movements: Downstaris, Jogging, Sitting, Standing, Upstairs and Working. The network consists of an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer. The sliding window is used to transform the sensor data into a three-channel RGB image format, and the features of the three-axis speed sensor data are automatically extracted to classify each action. The model was reproduced using Tensorflow, and the recognition rate of 89.35% was achieved on the open source database WISDM. The experimental results show that the amplifier has a better effect on human movement recognition.
The Experience and Gender Differences Between Handheld and Eye-Controlled Virtual Reality

Since the word maker was introduced to Taiwan, it has caused a great wave of education and activities. The maker movement has been rapidly launched in Taiwan. Both government units and the private sector are building maker space, and also refer to foreign makers. Space all the tools, design courses and publicity Maker education. This study is a study of the operation of each of the founding centers. After waves of the maker movement, the two centers of the Maker Center, which can continue to operate, discuss the operational difficulties and business models, and conduct research on the nine aspects of the business model, trying to analyze the key ways in which it operates to make recommendations.
Planar Tracking based on Deep Learning

Planar tracking is an essential function in computer vision to address the problem of tracking a planar target affected by illumination, deformation and blurring with real-time performance. To achieve this goal, the tracking algorithm has to train a model of the planar target and uses this model to estimate the change of target pose. However, when the target is changed, the algorithm needs to retrain a model for the new target. To address this problem, this paper presents a novel deep learning based planar tracking algorithm, which can efficiently estimate the pose change of the target without retraining a new model when selecting a new target. The proposed convolutional neural network (CNN) model only requires two input images, one is the reference image, and the other one is the query image. Then, the pose change of the planar target between the two images can be directly estimated from the output of the proposed CNN model. Experimental results show that the proposed algorithm performs well and has the potential to improve the applicability of the planar tracking algorithm.
Design and Implementation of a Simple Online Court Case Management System Based on the Android Platform

The main purpose of this research is to create a new application in Android Operating System for a complainant who cannot visit his/her lawyer regularly or unable to go out due to fear of his/her life. Thanks to this research, a new application was established to make a bridge between a complainant and his/her lawyer. Everything is done automatically after necessary data entered into the application. So, costs are reduced and wasting of the time is diminished.
DESIGN OF MOBILE TRAFFIC INCIDENT RESPONSE SYSTEM

The study aims to design an emergency response system for mobile applications to enhance the capability of emergency response units to respondto traffic incidents. The location area, number of accidents and list of top barangays prone to accidents were presented and identified. The accidentprone areas and nearby response units have been mapped on the mobile application to develop the application and integrate spatial data analysis,and can be viewed by the registered users. The result shows that it can be accessed on any mobile device that is enabled and useful to any users orcommunity members for information on optimized routes to nearby response units, in a user-friendly interface. The user could easily report theaccident and get the alert, while the response unit would review the report and determine the coordinates for the area of accident. The feature of thestudy was designed to enhance the reporting process of an incident, enabling the responding units to easily locate the accident area and respondimmediately. The evaluation of the mapped accident-prone areas and reporting process on the mobile application of the emergency responsesystem for traffic incidents was also provided. The respondents evaluated the proposed system with a total weighted mean of 4.29 as moderatelyacceptable.
Analysis, Design and Implementation of an End-to-End QKD Link

This manuscript discusses the most relevant aspects of the practical implementation of a long-range Quantum Key Distribution (QKD) link with trusted nodes, achieving the highest possible secret key rate generation within the security and system level constraints. To this purpose, we report on recent pilot studies for the measurements of detection efficiency and source photon statistics for validating the calibration facilities (i) at telecom wavelength for realistic quantum backbone implementation through standard telecommunications grade optical fiber, and (ii) for the telecom and VIS-NIR regime. In addition, since there are circumstances when a fiber optical link may not be available, we will also discuss the characterization of a Free Space Optics (FSO) QKD link. Finally, the manuscript also discusses the problem of information reconciliation in Continuous Variable QKD (CV-QKD) scenarios.
Combination of analytical hierarchy processes with fuzzy c- means in selecting quality broiler chicken

Abstract One of the key points in broiler chicken farming is in the initial process of selecting chicken seeds or known as DOC. The problem that emerged in the field is there are so many options of broiler breeds available from Breeding Farm, making it difficult for farmers and companies to choose the good quality broiler chickens. This research uses Analytical Hierarchy Combination Process for knowing which sequence is influenced on choosing process, in order to produce the good quality disease-free chicken seeds with standard variables; the highest weight is 0.207, followed by normal body 0.174 then weight according to the standard with a value of 0.172. Fuzzy C-Means is used for cluster processes in monitoring the quality levels of each chicken breeding process with results divided into 3 clusters. They are cluster 1 with as many as 410 breeders (96.70%), cluster 2 with 12 breeders (2.83%) and clusters 3 with 2 farmers (0.47%). With this method, companies can reduce losses caused by mistakes from these three parts and company profits increases.
An Efficient Technique for Large Mini-batch Challenge of DNNs Training on Large Scale Cluster

Distributed deep learning using large mini-batches is a key strategy to perform the deep learning as fast as possible, but it represents a great challenge as it is difficult to achieve high scaling efficiency when using large clusters without compromising accuracy. The particular problem in this challenge is decreasing the number of model update iterations in whole of training. Thus, we need a technique which can converge the validation accuracy with a small number of iterations to address this challenge. In this paper, we introduce a novel technique, Final Polishing. This technique adjusts the means and variances in the batch normalization and mitigates the difference of normalization between validation datasets and augmented training datasets. By applying the technique, we achieved top-1 validation accuracy of 75.08% with mini-batch size of 81,920, with 2,048 GPUs and completed the training of ResNet-50 in 74.7 seconds.In addition, targeting top-1 validation accuracy of 75.9% or more, we tried additional parameters tuning. Then, we adjusted the number of GPUs and hyperparameters of DNNs with Final Polishing, and we also achieved top-1 validation accuracy of 75.97% with mini-batch size of 86,016, with 3,072 GPUs and completed the training of ResNet-50 in 62.1 seconds.
Tracking Objects Using Stereo Vision System with Vergence and Gaze Control Mechanism

This paper describes Image-Based (IBVS) and Pose-Based (PBVS) Visual Servo control approaches used in stereo vision system with vergence and gaze control mechanism. Using IBVS or PBVS controller in cameras positioning mechanism ensures that cameras are fixed on a common visual target. Keeping the tracking object fixated causes the images of the target to lie near the principal points of both cameras. For this case disparity of target object is very close to zero, so for 3D reconstruction of target neighborhood, the stereo matching algorithm which accepts only limited range of disparities can be used.
Improved hierarchical models for non-native Chinese handwriting recognition using hidden conditional random fields

Hierarchical models with HMM has the advantage of recognizing Chinese characters in digital ink from non-native language writers. However, the recognition performance has been limited by the attribute of generative model of HMM. In this paper, we apply Hidden Conditional Random Field to improve the performance of hierarchical models. First, strokes in one Chinese character are classified with HCRF and then concatenated to the stroke symbol sequence. In the meantime, the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The approach proposed is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.
Encoder–Decoder Couplet Generation Model Based on ‘Trapezoidal Context’ Character Vector

Abstract This paper studies the couplet generation model which automatically generates the second line of a couplet by giving the first line. Unlike other sequence generation problems, couplet generation not only considers the sequential context within a sentence line but also emphasizes the relationships between the corresponding words of first and second lines. Therefore, a trapezoidal context character embedding the vector model has been developed firstly, which considers the ‘sequence context’ and the ‘corresponding word context’ simultaneously. Afterwards, we chose the typical encoder–decoder framework to solve the sequence–sequence problems, of which the encoder and decoder are used by bi-directional GRU and GRU, respectively. In order to further increase the semantic consistency of the first and second lines of couplets, the pre-trained sentence vector of the first line is added to the attention mechanism in the model. To verify the effectiveness of the method, it is applied to the real data set. Experimental results show that our proposed model can compete with the up-to-date methods, and both adding sentence vectors to attention and using trapezoidal context character vectors can improve the effectiveness of the algorithm.
Generalized Net Model Raw Materials Loading and Transportation Processes of Open Construction Sites

A generalized net model of the process of loading and transportation of raw materials is constructed. Its possible applications and extensions are discussed.
THE IMPACT OF E-LEARNING TECHNOLOGIES ON QUALITY OF HIGHER EDUCATION

Digital transformation of society and economy led to the rapid spread of information and communication technologies (ICT) in higher education, which became a new driver of development for global education and for the emergence of the phenomena of e-learning and blended learning, introduction of new types of educational resources, and increased diversity of information and communication technologies in higher education, which also led to a rise in the number of publications in this field. The article considers the essence and offers a classification of technical tools of e-learning. The authors conducted a comparative analysis of approaches to the formation of models of quality of electronic services and information technologies, as well as specific models of e-learning quality. This allowed the authors to propose a model of e-learning quality and determine the set of consumer properties of e-learning technology. The reported study was funded by RFBR, project number 20-010-00571 "The Impact of Digital Transformation on Improving the Quality and Innovation of Services".
Experimenting with Reordering Model of Phrase-Based Machine Translation System for English to Hindi

Hindi is the national language of India. However, most of the government records, resolutions, news, etc. are documented in English which native urban users may not understand. This fact motivates to develop an automatic language translation system from English to Hindi. Grammatical structure of the Hindi language is very much complex than the English language. This structural difference makes it difficult to achieve good quality translation results. Translation, reordering and language model are the main working components of a translation system. The translation quality depends on how these individual components of the system are configured. Many times the values of these components are language-dependent. Hence, proper settings of these components are very much essential. This paper discusses various settings of the reordering model and through experimentation demonstrates the proper values of the parameters for getting a quality translation from English to Hindi. The freely available n-gram-based BLEU metric and TER metric is used for evaluating the results.