Reviewerly/exHarmony huggingface dataset:
Development of the Enhanced Piece-Wise Linear Neural Network Algorithm

This chapter discusses development, application, and enhancement of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The dual objectives of developing the algorithms are (1) to generate good predictive models comparable in performance to the original artificial neural network (ANN) models and (2) to “open up” the black box of a neural network model and provide explicit information in the form of rules that are expressed as linear equations. The enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm because it can locate more than two breakpoints and better approximate the hidden sigmoid activation functions of the ANN. Comparison of the results produced by the two versions of the PWL-ANN algorithm showed that the enhanced PWL-ANN models provide higher predictive accuracies and improved fidelities compared to the originally trained ANN models than the PWL-ANN models.
Automatic synonym and quasi-synonym extraction from user reviews on mobile devices

In this paper we explore 5 similarity measures on the task of synonym extraction. We apply these measures to distributional word vectors created by word2vec model trained on a Russian-language corpus of user reviews. Through series of comprehensive experiments we have established that Cosine measure demonstrates, in general, the best performance in synonym extraction task. However, we found the way to improve Cosine measure performance correcting its value with the help of Gower distance. Our new corrective measure which we call Cosine-Gower, outperforms Cosine similarity for all meaningful parts of speech excluding verbs.
Algorithms for Inversion Mod $p^k$

This article describes and analyzes all existing algorithms for computing x = a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (mod p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ) for a prime p, and also introduces a new algorithm based on the exact solution of linear equations using p-adic expansions. The algorithm starts with the initial value c = a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (mod p) and iterativelycomputes the digits of the inverse x = a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (mod p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ) in base p. The mod 2 version of the algorithm is more efficient than all existing algorithms for small values of k. Moreover, it stands out as being the only one that works for any p, any k, and digit-by-digit. While the new algorithm is asymptotically worse off, it requires the minimal number of arithmetic operations (just a single addition) per step, as compared to all existing algorithms.
Wearable interaction and home automation to improve the patient wellness

The concept of Health Smart House aims at giving an autonomous life, in their own home, to people who suffering from a chronic disease, older, handicapped people, etc. However, developing a smart health system for home is a challenge. A long-term, it must meet the specific needs of any user. In addition, it must provide the most important daily services. That is, to design and develop smart health system is important to know patients' needs and the point of view of caregivers, doctors, nurses, etc. In this paper, we have based on a smart-home environment that use home automation and tangible and wearable interaction to improve the patients' quality of life. In order to know the point of view the patient, caregivers, doctors, etc. We conducted an expert survey to identify challenges and advantages in home automation and system interaction. The survey is divided into three sections: the patients' load cognitive using the system; the most useful variables related to data gathering through sensors, and the system social acceptance. Thanks to the results obtained, we contribute to a better understanding about important factors in healthcare, home automation and user interaction.
Tool Wear Volume and Residual Life Prediction Based on Extreme Learning Machine

Since the tool wear state has a great influence on processing quality, an online monitoring method for tool wear state based on single-hidden layer feed-forward neural network (SLFN)-extreme learning machine (ELM) is proposed. According to the real-time working condition data of the milling tool, a variety of feature extraction methods including statistical analysis, fast Fourier transform and wavelet transform are used to extract 34 features. The 34 features are mixed features of time domain, frequency domain and time-frequency domain and they are sensitive to tool wear. The extracted features and wear volume are input into the extreme learning machine network framework for training to get the ELM model. Then put the test set features into the ELM model for prediction and evaluation, and the corresponding predicted tool wear volume and remaining number of passes are obtained. By comparing the prediction results of SVR and BPNN optimized by genetic algorithm, it is found that ELM has the advantages of fast learning speed, high prediction accuracy and strong generalization ability, and can realize online monitoring of tool wear state.
Manifold Learning Based on Sparse Neighbourhood Classification

Manifold learning and its various algorithms have attracted more and more attention in recent years. Locally linear embedding (LLE) algorithm, as a classical nonlinear dimensionality reduction algorithm, has been widely used in many technical fields, such as image classification and object recognition. This paper proposes an improved algorithm based on LLE algorithm-Sparse LLE, which applies a new method of classification in the process of finding K-nearest neighbours with original LLE algorithm. According to the experiment results, the new algorithm can not only guarantee the effect of dimensionality reduction, but also greatly reduce the processing time of the dimensionality reduction of datasets.
Elliptic-Curve Basics

This chapter covers the essentials of elliptic curves as used in cryptography. The first section of the chapter gives the basics concepts of elliptic curves: the main defining equations for the curves of interest and an explanation of the arithmetic operations of “addition” and “multiplication” in the context of elliptic curves. We shall follow standard practice and first define elliptic curves over the field of real numbers, with geometric and algebraic interpretations of the arithmetic operations in relation to points on a curve. Elliptic curves over the field of real numbers are not useful in cryptography, but the initial interpretations given are useful as a means of visualizing and understanding the arithmetic operations and the derivation of the relevant equations that are ultimately used in practice. In cryptography, the elliptic curves used are defined over finite fields, and the second section of the chapter covers that, with a focus on the two most commonly used fields: GF(p), with p prime, and GF(2m), with m a positive integer Point addition Addition point Point multiplication Multiplication point (The main aspect of the first two sections is the definition of point addition and point multiplication, the latter being the primary operation in elliptic-curve cryptosystems.) The third section is on the implementation of point multiplication. And the last section is on projective coordinates, which simply inversion relative to the “normal” affine coordinates. Projective coordinates Affine coordinates
An Efficient Algorithm for Reducing Vision Bleach in HDR Imaging

This paper addresses the problem of loss in detail due to a phenomenon known as vision bleach when photographing a wide dynamic range scene. Vision bleach is caused due to over-exposure of highlights in a scene and is reduced to a certain degree by High Dynamic Range (HDR) algorithms. However, HDR images aren’t noise- proof, involve tone-mapping (which reduces color accuracy) and show a loss in sharpness when capturing the scenery with light sources in it. These problems are addressed in the proposed algorithm by using sample images captured by focusing the camera on different POI (point of interest) in the scene. After shift elimination using Motion Homography and ECC (Enhanced Correlation Coefficient) algorithm in individual samples, a high-quality image is constructed by merging them and applying a seam elimination routine. A comparison between the images obtained using the proposed algorithm, an automatic brightness adjustment algorithm and the HDR algorithms show better color accuracy and a greater reduction in vision bleach around highlights.
A Module-Level Pipeline Implementation Based on Inter-Board Heterogeneous

With the internet of things, autonomous driving, and smart security rapid development, data growth puts higher and more urgent requirements on processor computing performance. A single chip is hard to meet the real-time computing requirements in many scenarios, so it is necessary to expand multiple chips. This paper proposes a module-level pipeline FPGA cluster structure based on inter-board heterogeneous. The method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between boards. In addition, considering the impact of communication delay between boards, it is proposed to use the delay between development boards as a separate stage of the pipeline. In this way, we can minimize a stage's runtime of module level pipeline. In order to evaluate the calculate capacity of per hardware resource, the concept of resource conversion efficiency (RCE) is proposed. Experimental results of LeNet -5 neural network with the module-level pipeline structure design demonstrates that proposed method increased processing capacity and resource utilization efficiency. Compared with the method of sequential execution, the execution speed and RCE of the proposed method are increased by 46.1% and 84.7% respectively.
Feedback Recommendation System Based on Structured Feedback Acquisition

Abstract Users’ feedback are increasingly becoming a vital factor during the software evolution process. Users’ feedback are widely considered to be an indication of how satisfied are the system’s users. It was found that large percentage of feedback represents problems reported by users who face problems in using the system. Users’ feedback are usually communicated in an ad-hoc manner with no defined structure. The ad-hoc nature of the feedback makes it difficult to study and analyse feedback. Also, it usually happens that similar problems are reported by different users, so engineers spend wasted time and effort in duplicate issues. In this paper we propose a recommendation system which recommends to the user reported problems that are similar to his from prevoius feedback threads to reuse existing solutions. We based our work on a structured feedback system to ensure we get better results. Structured feedback ensures minimum level of useful and meanigful information that helps enhance the analysis results. Initial evaluation was conducted and the results are promising.
EFL 학습자의 어휘 지식과 어휘 학습 전략 사용 간의 구조적 관계

The Journal of Studies in Language 35.1, 039-052. The current study aims at investigating the structural relationships between EFL learners’ vocabulary knowledge and their vocabulary learning strategy use depending upon their level of vocabulary knowledge. For the study, 1,124 high-school students were divided into two groups according to their vocabulary knowledge. The results showed that there were significant differences in the students’ vocabulary learning strategy use according to their vocabulary knowledge, and that three factors for high-level students (context use, repetition in writing, and learning range of words) and two for those in low level (meaning checking and learning range of words) had direct influence on the enhancement of their vocabulary knowledge, and other factors in each level exerted indirect influence through the direct factors. Teaching implications are discussed. (Chungnam National University)
Multi-level Thresholding Using Adaptive Gravitational Search Algorithm and Fuzzy Entropy

Conventional multilevel thresholding methods are computationally expensive when applied to color images since they exhaustively search the optimal thresholds by optimizing the objective functions. To address this problem, this paper presents an adaptive gravitational search algorithm (AGSA) based multi-level thresholding for color image. In AGSA, a dynamic neighborhood learning strategy which incorporates the local and global neighborhood topologies is introduced to achieve adaptive balance of exploration and exploitation. Moreover, a sinusoidal chaotic based gravitational constants adjusting operator is embedded to further promote the performance of AGSA. When extending AGSA to solve the multi-level thresholding problem, the fuzzy entropy is adopted as the objective function. Experiments were conducted on two color images to investigate the efficiency of the proposed method. The obtained results are compared with that of the particle swarm optimization (PSO) and gbest-guided GSA (GGSA). The experimental results are validated qualitatively and quantitatively by evaluating the mean of the objective function values and the total CPU time required for the execution of each optimization algorithm. Comparison results showed that the AGSA produced superior or comparative segmentation accuracy in almost all of the tested images and the algorithm largely reduce the computational efficiency of GSA.
Application of the Source Encryption Algorithm Model in the Power Industry

Abstract With the development of Internet technology and Internet of Things technology, the Internet of Everything has become a hot topic, and in March 2019, the National Grid for the first time clarified the definition of the Pan-In Power Internet of Things, pointing out that the company’s most urgent and important task is to accelerate the construction of the Pan-In Power Internet of Things. The security of Data Transfer on-line at any time is particularly important, in order to ensure the security of data, in the process of data transmission, data needs to be encrypted. This paper expounds a model of the information source data encryption algorithm, analyzes the encryption algorithm and the encryption method, and then provides a reference basis for the data transmission data security of power system.
Security for the Cloud

Today's people are moving towards the internet services through cloud computing to acquire their required service, but they have less confidence about cloud computing because all the tasks are handled by the service provider. Cloud system provides features to the owner to store their data on some remote locations and allow only authorized users to access their data according to their access capability. Data security becomes particularly serious in the cloud computing environment because data are scattered in different machines and storage devices including servers, PCs, and various mobile devices such as smart phones. To make the cloud computing be adopted by enterprise, the security concerns of users should be rectified first to make cloud environment trustworthy. The trustworthy environment is the basic prerequisite to win the confidence of users to adopt this technology. However, there are various security concerns that need to be taken care of regarding the trust maintenance between various parties, authorized access of confidential data, data storage privacy, and integrity.
Identity Retaining and Redundancy Reducing Gan for Person Re-Identification

Person re-identification (ReID) models trained on one domain suffer performance degradation when tested on other domains. The existing works address this problem by domain translation with identity information preserving. However, these methods focused on adding pixel constraints to preserve identity, which also preserves a lot of redundant information. Therefore, this paper propose an identity retaining and redundancy reducing generative adversarial network (IRGAN), a domain translation method for person ReID. IRGAN is implemented by an unequal-cycle strategy, which imposes both foreground and feature constraints to domain translation. By imposing part-level feature constraints, the redundant information generated by pixel constraints can be reduced. Thus the performance of the domain translation is significantly improved. Experimental results indicate that our method is effective.
Design and Construction of Intelligent Voice Control System

With the development of speech recognition technology, there are wide applications of intelligent voice around the world. In the paper, we propose an intelligent voice control system which can realize the functions of voice activity detection, speech recognition, speaker authentication, instruction analysis, and automatic answer. A new voice activity detection (VAD) algorithm and backtracking algorithm are proposed to improve system performance. Then, we test our system in our own silumation corpus, we create the system evaluation indexes and the text result is satisfactory.
STEM in Natural Science Education in After-School Education

Review of new and innovative approach in education, which integrate Science, Technology, Engineering and Mathematics (STEM) and project based learning. The use of STEM in educational systems of different countries is considered. Effectivity and benefits of using STEM approach in pedagogical activity for learning are analyzed. Specific attention paid to possibilities of STEM in activity of after-school education. Thus, STEM in student learning is an important promising direction that promotes student development. The international community is paying close attention to the development and implementation of the STEM approach to school and university study, especially the United States of America. In article represented example of program with implementation of STEM approach for physical and mathematical workshops of after-school education. The need of further development of STEM approach consists in the competitiveness of education systems and the demands of modern society.
Dissemination of Scholarly Information Among the Research Scholars Through Social Networks and Media in Madurai Kamaraj University and Manonmaniam Sun

This chapter tries to analyse the impact and use of social media among the research scholars in Madurai Kamaraj University and Manonmaniam Sundaranar University. The result of the study found that 66 (56.90%) were Manonmaniam Sundaranar University and the Residual respondents 50 (43.10%) were Madurai Kamaraj University. Thirty-three (66%) Madurai Kamaraj University were male, and 29 (43.94%) were male from Manonmaniam Sundaranar University. Seventy-three (62.93%) come under the category of 26 to 35 years; 22 (18.97%) were in the category of up to 25 years. Thirty (25.86%) belong to the department of management/commerce; 14 (12.07%) were from the department of education/physical education. Seventy-eight (67.24%) were seeing Facebook, and 33 (28.45%) of the respondents used Twitter.
Aircraft Wing Deformation Measurement Method Based on Three-dimensional Speckle Image

Wing deformation has a direct impact on aircraft performance and personnel safety, so it is of great significance to test the deformation law. Traditional methods of wing deformation measurement generally have some shortcomings, such as fewer measuring points and poor real-time performance. In recent years, the developed three-dimensional speckle correlation vision measurement technology has many advantages, such as high accuracy, strong real-time performance, small environmental impact and non-contact full-field measurement. It can overcome the traditional methods in wing deformation measurement. The deficiency of law. Therefore, the method of wing deformation measurement based on three-dimensional speckle correlation method is studied in this paper.
IDTree: Concise Hierarchical Data Representation for Resource-Constrained Smart Objects

For Smart Object (SO) in Internet of Everything, especially for barcode and RFID tags in supply Chains, the interoperability, scalability and high efficiency is critical to store and exchange semi-structured data among SOs. However, current methods either are too heavy-weight, or cannot represent hierarchical data model flexibly on demand, and they cannot meet the requirement of resource-constrained SOs. This paper presents a byte-aligned concise IDTree method to represent hierarchical data based on metadata identifiers, with ID-Value associations and specific nonprinting characters to control data structure, for resource-constrained SOs. After introducing the overview of IDTree method, the paper presents the representations for two types of node-data, then represent tree-like structure with generalized list method. Finally, a practical hierarchical data example is given to illustrate the method in detail.
Target Detection on the basis of Empirical Wavelet Transform using Seismic Signal

A novel approach is proposed to detect moving ground target using empirical wavelet transform (EWT) as a time-frequency technique. In order to analyze the performance of EWT, the seismic dataset is generated by acquiring the seismic signature of the moving vehicle, i.e., bus. EWT based time-frequency coefficients have been computed from the seismic signals. The number of statistical features has been calculated from EWT based time-frequency coefficients. With the statistical features, bus and noise have been classified using SVM as a classifier. Accuracy, true positive rate, and area under the curve (AUC) have been used as the performance parameters of the algorithm. The AUC of approximately 95%, true positive rate, and accuracy of about 89% have been achieved.
Digital image correlation based on variable circle template in dual camera matching

In the measurement of the 3-D shape of a large curved object using traditional 3-D-digital image correlation (DIC), the left and right camera images are relatively rotated or deformed too much, which will cause the matching to fail. We propose a 3D-DIC matching method. Compared with traditional methods having fixed-size templates, this method is robust to relative rotation and deformation between dual cameras and is suitable for 3D-DIC measurement of nonplanar objects. In the proposed method, we use polar coordinates to define the matching template as a variable circle, which is convenient for interpolation calculation and changing the template size. The zero-mean normalized cross-correlation method is used to determine the position of an entire pixel on the distorted image. This step can provide an initial value close to the true value for subsequent iterations of the inverse compositional Gauss–Newton algorithm. The performance of the proposed method is verified by experiments. Compared with the traditional matching method, the proposed method is suitable for matching large curved surfaces between 3D-DIC dual cameras and extends the application of DIC technology.