Reviewerly/exHarmony huggingface dataset:
3D Measurement using a binocular cameras-projector system with only one shot

We propose a two-camera and single-projector three-dimensional scanning system based on the de Bruijn sequence of color stripe pattern, the purpose of which is to reconstruct the three-dimensional model of the object by one scanning method. Compared with the existing methods, the time coding method requires a lot of time, and we directly use the color to encode the image, so that an image can be captured to complete the object reconstruction. In this paper, the two-step method is used. First, the camera is simultaneously calibrated. And then the result of the camera calibration is used to correct the image position of the color stripe pattern. Under the effect of compensating light. Not only do we get excellent camera calibration results, but we also compensate for color deviations caused by external light sources and projector projections, resulting in excellent projector calibration results. Under the action of the dual camera, a more complete scanning degree of the object is obtained, and high-precision measurement can also be implemented.
NBI(narrative based instruction) 모형 개발 연구

The purpose of this study is consideration for developing narrative based instruction model. The core of curriculum in school is instruction(or teaching & learning). So, investigative survey on instructional contents, articles on method are important more than any other fields in educational research. In general, educational research conducted on the base of diversity, methodological pluralism. By this principle, this study was performed for developing new model on instruction. Making the NBI(narrative based instruction) model which is projected as a renewal model was conducted as followings. Firstly, critical review on existing instructional model, secondly, idea mining by interviewing with education experts for getting their thinking, thirdly, data mining for analysing quantitative/qualitative sources which collected by surveying for school student and academic journal, fourthly, critical review on ground theory/model for developing new model, lastly, consideration on psychology and philosophy concerned with this study. Through above process, researcher suggests a draft model. The result of critical review on concerned multi resources is the method of borrowing the idea(leadership, approach, model, narrative based) for developing new model by effect, A drawn result performed by this consideration is as followings. Firstly, existing instructional models have a merit and uniqueness respectively, nevertheless, there are not meta model as general one, so this study purposed the solution on that problem, going forward in educational research. Secondly, the necessity and potentiality on progressing instruction theory or developing narrative concerned instruction model is verified by this study.
Development of NgeXTEA: a web based learning tool for cryptography algorithm

Abstract One of the issues in teaching information security in particular cryptography is lack of tools to facilitate students’ understanding of related topics. The study specifically discusses the development of a web based learning tool for teaching symmetric cryptography in Indonesian language. The module is called NgeXTEA which contains a guide to the process of encryption and decryption in stages using Extended Tiny Encryption Algorithm (XTEA). The development of NgeXTEA including its features is presented. Based on several evaluations with various combinations of characters, it is finally concluded that NgeXTEA module works well as expected.
Moth flame optimization algorithm based on quadratic interpolation for data clustering

K-means clustering is a clustering technique based on partition. It is widely used in practice because of its simplicity and efficiency. However, it has shortcomings of highly relying on initial clustering center and possibly trapping into local optimum. Firstly, a revised moth flame optimization algorithm with quadratic interpolation is proposed to overcome the defects of K-means and to revise the quality of solution and iterative efficiency of the basic algorithm. The initial population with better diversity is generated by using tent chaotic map to ameliorate the exploration ability of the algorithm. Arithmetical crossover operation for flame location produces new flame with better diversity to guide the moth finding the optimal solution so that the iterative efficiency of the algorithm is ameliorated. Selecting the moths of population to perform quadratic interpolation is helpful for the algorithm to converge rapidly near the optimal solution. It can polish up exploitation ability of the algorithm. The high performance of the improved algorithm is then employed for optimize the location of cluster centers and is examined by five UCI datasets. The experiment results indicate that the improved technique is suitable for finishing problem clustering via k-means, and good clustering results are obtained.
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From the Experience of Using Innovative Forms to Improve the Competencies of Teachers and Specialists of Psychological and Pedagogical Support through the Implementation of the Educational Program “From Problems to Actions” in the Context of Network Communities

The article presents innovative experience aimed at improving the professional environment of teachers and specialists of the psychological and pedagogical support service of the Novosibirsk region in the context of the introduction of professional standards. The presented practice contributed to the improvement of psychological, professional skills as well as information and communication skills (ICS) of teachers and specialists of educational organizations through participation in online professional communities. A short-term educational psychological and pedagogical program “From Problems to Actions” was developed and implemented as a form of network professional culture in order to provide organizational and methodological support for the professional development and skills of teachers and specialists engaged in the field of education, upbringing and socialization of children in the conditions of digitalization of education. The practical value of the presented experience is noted as an innovative and interactive form aimed at improving the quality of training and design of methodological products by placing them in an open educational network of professional communities.
Virtual Falls

In this paper, we present an innovative application of Virtual Reality in human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts into developing reliable and effective fall detection algorithms and devices by researchers and engineers, minimal success has been seen. The lack of recorded fall data and the data quality have been identified as a major obstacle. To address this issue, we are proposing a framework for generating fall data in virtual environments. Our initial results have indicated that the virtual fall data generated using the proposed framework are of sufficient quality and could be used to improve fall detection algorithms. Although the approach proposed is to be used for fall detection, it is fully applicable to other domains that require training data.
Statistical RF/Analog Integrated Circuit Design Using Combinatorial Randomness for Hardware Security Applications

While integrated circuit technologies keep scaling aggressively, analog, mixed-signal, and radio-frequency (RF) circuits encounter challenges by creating robust designs in advanced complementary metal–oxide–semiconductor (CMOS) processes with the diminishing voltage headroom. The increasing random mismatch of smaller feature sizes in leading-edge technology nodes severely limit the benefits of scaling for (RF)/analog circuits. This paper describes the details of the combinatorial randomness by statistically selecting device elements that relies on the significant growth in subsets number of combinations. The randomness can be utilized to provide post-manufacturing reconfiguration of the selectable circuit elements to achieve required specifications for ultra-low-power systems. The calibration methodology is demonstrated with an ultra-low-voltage chaos-based true random number generator (TRNG) for energy-constrained Internet of things (IoT) devices in the secure communications.
Parameterized Wavelets for Convolutional Neural Networks

Convolutional neural networks (CNNs) have become the prominent type of machine learning approach for visual pattern recognition but suffer from the tuning of a large number of parameters. In this paper, we propose and evaluate a novel parameterized wavelet convolutional neural network architecture for image classification with a far less number of trainable parameters. This model architecture mainly consists of two parts: parameterized wavelet decomposition layers followed by convolutional layers and fully connected layers. These wavelet decompositions comprise wavelet operations with learnable parameters that are updated during the training phase using the back-propagation algorithm. We evaluate the performance of the proposed architecture using several common image datasets and compare the results with influential shallow to deep CNN models. Our findings support the possibility of reducing the number of parameters in deep CNNs without significantly compromising its accuracy.
Edge Voxel Erosion for Noise Removal in 3D Point Clouds Collected by Kinect©

When the human body point cloud is captured by Kinect©, the edge voxels with lower accuracy need to be removed to ensure the precision of the 3D point cloud patches. However, there is no existing erosion algorithm for 3D voxel. This paper proposes a 3D contour voxels erosion algorithm based on 2D image erosion algorithm, which maps the erosion mode of 2D contour pixels to the 3D contour voxels, and eliminating the contour voxels with low accuracy to optimize the 3D human body point cloud.
Zero-Copy Data Transfer for an OpenCL API Remoting System

In a cloud computing environment based on virtual machines, an OpenCL API remoting system forwards OpenCL commands issued by guest applications from the guest to the host. The forwarded commands are executed on the host side, and results are sent back to the guest. In this way, guest applications can make use of GPU devices installed on the host. If these commands require the transfer of data between guest applications and GPU devices, traditional API remoting systems usually copy the data between the guest and the host. The problem with this scheme is that the copying operation introduces additional cost and reduces data transfer bandwidth, which in turn decreases the overall performance of such systems. This paper utilizes a technique, called zero-copy data transfer, which allows sharing of memory between the guest and the host. Since the technique does not require to perform the copying operation, it can increase bandwidth significantly. With this technique, buffers of guest applications are first pinned to physical memory on the guest side, then mapped into some memory regions on the host side, and finally directly accessed by host programs. Our experiments demonstrated that the technique achieved a significantly higher transfer bandwidth than conventional network transfer. When the technique was utilized in an OpenCL API remoting system, for data-intensive OpenCL applications, overall performance was comparable to that of a physical OpenCL implementation.
Construction of News Article Evaluation System Using Language Generation Model

This study constructed a news article evaluation system that utilizes a language generation model to analyze financial markets. This system enables us to analyze the effect of news articles distributed in financial markets on the stock price of a company. We added the generated news articles as data for analysis through GPT-2 and verified the accuracy of the constructed system. As a result of empirical analyses, we confirmed that the accuracy of the model with the generated news articles improved. More detailed analyses are planned for the future.
Hyperplane sections of determinantal varieties over finite fields and linear codes

We determine the number of Fq-rational points of hyperplane sections of classical determinantal varieties defined by the vanishing of minors of a fixed size of a generic matrix, and identify the hyperplane sections giving the maximum number of Fq-rational points. Further we consider similar questions for sections by linear subvarieties of a fixed codimension in the ambient projective space. This is closely related to the study of linear codes associated to determinantal varieties, and the determination of their weight distribution, minimum distance, and generalized Hamming weights. The previously known results about these are generalized and expanded significantly. Connections to eigenvalues of certain association schemes, distance regular graphs, and rank metric codes are also indicated.
Three-dimensional particle localization method based on single light field camera

Reconstruction algorithm is a research hotspot in the application of light field camera. In this paper, the mathematical model of 3D particle light field imaging is deeply studied, and the ray tracing matrix is established to determine the imaging model of the particle in the light field camera. A mathematical model based on tomographic reconstruction of light field camera is established to realize the calculation of light field image. The multiplicative algebraic reconstruction algorithm (MART) is used to reconstruct the voxel value, and the weighting coefficient in the algorithm is also improved, which improves the operation speed. The influence of the relaxation factor in the algorithm on the three-dimensional positioning of particles is studied, and the selection range is determined. Relevant experimental studies have been carried out, and it is found that the relaxation factor in the range of this paper can improve the operation speed and determine the reasonable iteration number.
Training PhD students for teaching of engineering disciplines: Study of Russian Universities Experience

Abstract The paper contains several findings of the study of the Russian universities’ system related to pedagogical training of PhD students, which has been carried on in the framework of the ERASMUS+ Capacity Building for HE project EXTEND (# 586060-EPP-1-2017-1-RO-EPPKA2- CBHE-JP) - “Excellence in Engineering Education through Teacher Training and New Pedagogic Approaches in Russia and Tajikistan”. The project itself is devoted to the training of new personnel for the engineering universities. The strengths and weaknesses of the Russian system of pedagogical training of young teachers are under discussion in this article. The Introduction part of the paper describes the Russian system of education, EXTEND project main activities, Project Consortium participants, objective of the analysis of curriculum contents of PhD programs in engineering in Russian universities, including courses and internships on pedagogy, learning outcomes and PhD student’s teaching competences and motivation. The methodology part of the paper draws the four stages of the research. The main part of the paper presents the results of the analysis of the 22 PhD programs collected by Russian universities’ team. The Discussion part consists of four recommendations on how to enhance the Russian teacher preparation.
The Deceptive Efficiency of Smart Contracts: Hidden Opportunism in Certainty

The optimism for smart contracts is derived from the cost-saving efficiency benefits in contractual construction, enforcement, and provisions of legal certainty. However, this paper analyzes from a behavioral standpoint the problem of opportunism in incomplete contract theory and relation-based contract theory, leading to an evaluation of how smart contracts might consequently lead to the amplification of such costs of opportunism, overall making them less efficient compared to conventional natural language contracts.
Living Jiagu: Enabling Constructive Etymology for Chinese Learning

Living Jiagu is an interactive, wall-sized exhibition for the engaging learning of Chinese writing. Living Jiagu leverages state-of-the-art machine learning technologies to facilitate the recognition and recall of Chinese characters via constructive etymology in context - i.e., learning the writing and meaning of pictographic characters by designing them from image prompts similar to the creators of Oracle Bone Script (OBS) 3000 years ago and experiencing how these characters function and interact in natural scenes. An installation of Living Jiagu received positive feedback from over one thousand users.
Current State of Cloud Computing Risk Assessment in Malaysian Private Sector

The potential benefits of cloud computing, such as flexibility and cost-effective, have attracted many companies to use it. Nevertheless, security issues are still prominent in cloud computing. Measuring the security of cloud computing can be effectively implemented by performing cloud computing risk assessments. Since the traditional risk assessment method is no longer applicable to cloud computing, the effective method in assessing cloud computing becomes the debated issues among scholars. This study reveals the current state of cloud computing risk assessment conducted in the private sector in Malaysia. We performed a semi-structured interview session with five private companies and highlighted the issues debated by the scholar.
Concept Drift for Big Data

The term “concept drift” refers to a change in statistical distribution of the data. In machine learning and predictive analysis, a fundamental assumption exits which reasons that the data is a random variable which is being generated independently from an underlying stationary distribution. In this chapter we present discussions on concept drifts that are inherent in the context big data. We discuss different forms of concept drifts that are evident in streaming data and outline different techniques for handling them. Handling concept drift is important for big data where the data flow occurs continuously causing existing learned models to lose their predictive accuracy. This chapter will serve as a reference to academicians and industry practitioners who are interested in the niche area of handling concept drift for big data applications.
Geographic Information Systems Search Test Location For Admission Of New Students In Sriwijaya State Polytechnic Using Location Based Service (LBS) Method

Abstract New Student Admission Selection or in Indonesia called New Student Admission Selection (SPMB) is one of the new student admission selection tests for universities. The problem is usually found during the admission of new students because the students have come from outside the city. Another problem that could be a supervisor might make a mistake when they found the room they were late. And also the Admin is difficult to coordinate with supervisors and new students during the test. This is a geographic information system to search the test locations. This system is built using the Location Based Service (LBS) method by utilizing GPS in Maps. The system development method that is used is RUP, this system will provide the direction of the user’s mobile device access. The results of research are made in a system that can facilitate students in finding the location of exam rooms. The geographical information system in mapping is the test location for the students who are going to do the test for the test location, and make it easier for supervisors to find out the location of the new student admission selection test.
Neighborhood density correlation clustering

In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters is proposed that avoids the clustering errors caused by iso-density points between clusters. Because the density of the center region of any cluster sample dataset is greater than that of the edge region, the data points can be divided into core, edge, and noise data points, and then the density correlation of the core data points in their neighborhood can be used to form a cluster. The clustering results of a sample dataset can be formed according to the connection between the core data points and edge points. By constructing an objective function and optimizing the parameters automatically, a locally optimal result that is close to the globally optimal solution can be obtained. This algorithm solves the difficulty of parameter optimization and aspheric clustering in unsupervised clustering to some extent and has excellent potential for application in data analysis and mining.
Design and Implementation of Collision Module in Virtual Reality Scene

The core problem of collision detection is the intersection of virtual objects. The most direct algorithm is to detect the position between two objects in the scene. However, due to the complexity of the virtual reality scene, this most direct detection algorithm cannot meet the real-time requirements of the role interaction in the virtual reality scene. Therefore, aiming at the collision detection problem in the virtual reality scene, an improved OBB level bounding box algorithm is proposed, which uses the hierarchical construction of the bounding tree to delete the nodes that are not added, and improves the accuracy of collision detection in the scene. Finally, through the performance and function test of the collision module in the system, the result shows that the system has achieved the expected effect, has certain practicability, and has certain reference and reference significance for the collision detection problem.
Synner: Generating Realistic Synthetic Data

Synner allows users to generate realistic-looking data. With Synner users can visually and declaratively specify properties of the dataset they wish to generate. Such properties include the domain, and statistical distribution of each field, and relationships between fields. User can also sketch custom distributions and relationships. Synner provides instant feedback on every user interaction by visualizing a preview of the generated data. It also suggests generation specifications from a few user-provided examples of data to generate, column labels and other user interactions. In this demonstration, we showcase Synner and summarize results from our evaluation of Synner's effectiveness at generating realistic-looking data.
Investigation of Medical Image Segmentation Using Machine Learning Based Fuzzy Reliability Function for MIoT

Abstract Nowadays the image segmentation is placed a crucial role in the medical image analysis process. The automatic image segmentation process using Medical Internet of Things (MIoT) is applied to the image for analyzing the various directions such as horizontal and vertical to identify the abnormal growth of the cells present in the human part. In the recent past less accurate, more noise, high error rate and false segmentation, lead to reduce the entire disease identification process. In this research work, automatic image segmentation process using multimodal machine learning based segmentation with fuzzy reliability function techniques used to minimize the false segmentation rate and increase the recognition accuracy. This research presenting the medical imaging of the brain and retina has been segmented for clinical experimental analysis by applying the various unsupervised clustering technique and edge detection techniques to improve efficiency of the system in MIoT environment.