Reviewerly/exHarmony huggingface dataset:
Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems

Abstract One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output Non-Orthogonal (MIMO-NOMA), is to correctly estimate the channel state information for coherent detection at the receiver. A hybrid deep learning model, called convolutional fuzzy deep neural networks, is proposed in this study for accurately estimating channel state information and detecting symbols in MIMO-NOMA systems. The performance of this proposal has been compared to traditional algorithms like Least Square Error- Successive Interference Cancelation (LS-SIC) and linear minimum mean square (LMMSE-SIC), as well as to other deep learning methods such as convolutional neural networks. With this proposed scheme, significantly less bit error rate is obtained in both Rician and Rayleigh channel environment compared to other algorithms. In addition to the high performance of this scheme, the fact that it does not need channel statistics is another important advantage.
Synthesis of a Generalized Algorithm for Processing and Generating Data on Reflected Signals from Complex Targets

Introduction. The quality of input information for trajectory processing (TP) systems can be improved by increasing the measurement accuracy of radar sensors (RS). However, in such a case, radar targets acquire the characteristics of complex targets having several marks at the output of the detector. This makes it difficult to accurately assess the kinetic parameters of targets in a TP system. In this respect, the development of a generalized algorithm for processing and generating data from the reflected signals of complex targets seems a relevant research task. Aim. To investigate reasons for the formation of complex targets and, using the theory of radar image processing, to synthesize an algorithm for processing and generating data on reflected signals from a complex target. Materials and methods. The following methodological approaches were used: the theory of digital signal processing; applied theory of radar image processing; MATLAB Simulink Toolboxes for simulating radar image processing; some prerequisites for fuzzy clustering methods. Results. Following an analysis of some characteristics of complex targets and the theory of radar image processing, an generalized algorithm was synthesized for processing and generating data of reflected signals from this class of targets. The results can be used to improve the measurement accuracy of their representative point when solving the TP problem. Conclusion. Reasons for the formation of complex targets in radar technology were analyzed. Their specific features consist in the need to accurately assess a true mark. A generalized algorithm for processing and generating these signals reflected from complex targets was proposed. The results can serve as a basis for solving the TP problem.
Dark light enhancement for dark scene urban object recognition

Abstract This paper presents a low‐light image enhancement method to improve the performance of autonomous piloting tasks based on deep learning methods. In the low light environment, camera sensors cannot capture enough effective photon signals causing poor performance in vision‐based autonomous piloting. Moreover, the lack of training data makes single‐frame low‐light enhancement and denoising algorithms hard to generalize in real‐world scenarios. By analyzing the noise patterns of real‐world cameras under low‐light environments, a noise generation method is proposed to mimic real‐world dark‐light noise and generate noisy‐clean data pairs for training. A method that can calibrate the camera shot noise parameters is then designed to learn low‐light enhancement from the synthesized noisy data pairs. The neural network trained on a synthesized dataset can effectively enhance the dark light image quality. Consequently, the network improves the downstream auto‐piloting applications such as object detection and semantic segmentation. Qualitative and quantitative experiments have been conducted to demonstrate that the method outperforms previous methods in low‐light enhancement.
A Novel Steganography Approach Using S-CycleGAN With an Improvement of Loss Function

Technologies related to the internet of things (IoT) have been extensively employed in business operations, military research, networking technologies, etc. With the proliferation of the IoT, data theft and leakage have gradually increased because the data communication system is public. Various novel steganography algorithms have been proposed for data hiding. But in this process, the quality of the hidden image decreases badly. We want to give an accurate color to the image instead of severe color changes. To change it, we have to perform a colorization process by mapping the original image to the stego image. It gains more characteristics according to the actual embodiment. Here, the objective is to improve the appearance by calculating the loss function. The stego image can withstand steganalysis by using the authors' proposed scheme by maintaining its integrity while to some extent improving the quality of the image. The experimental results confirmed the proposed method outperforms the existing methods (SGAN and CycleGAN) for information hiding with enhanced image transmission quality.
Wavelet Energy-Based Adaptive Retinex Algorithm for Low Light Mobile Video Enhancement

In today's world, smartphones are ubiquitous and feature high-quality cameras and video recording capabilities. The camera shake, shadows, or other environmental factors lead to poor video, even in low-lighting situations with a high-end camera. To preserve and enhance low-light mobile video, a wavelet energy-based adaptive retinex algorithm is proposed. The first step in the method is to extract frames from the composite video before it is fed into the algorithm in HSV space. The authors then apply the wavelet energy-based adaptive retinex algorithm to the dynamic range-extended value channel. Lighting conditions that cause video degradation are restored adaptively to their natural brightness. A color restoration technique that improves the conventional retinex method fixes the gray world violation problem. Wavelet energy is used as a metric in concurrence with the retinex algorithm to objectively validate image enhancement quality. An experimental work of more than 300 still images and videos show that the proposed technique outperforms the current state of the art.
AI-Based DBMS Controlled Speech Recognition Model for Some Common Computing Commands

Speech recognition has become an important part of the AI model. It plays a vital role in the correct function of different AI enabled devices that we see today. Many automobile industries use this voice recognition technology in their AI to develop modern voice enabled cars. To that end we have implemented a deep learning-based voice recognition model in the MySQL database to execute various DBMS command automatically through speech recognition by using intelligent Google Speech API and match-LSTM model. Our proposed model accept speech as input and by using intelligent Google Speech API service we construct MySQL database and perform training using match-LSTM. The latency rate and data transfer speed of this model is very fast because of its prior training approach. This type of methodical approach can be made in any DBMS systems to make it more convenient, smart and easy to use for normal user.
An Image Steganography Approach Using Arnold Transformation

Sleuthing images that conceal information has continuously been a stimulating and difficult problem in cyber security. Finding hidden data in nursing image is called steganalysis. In this chapter, the authors explore the method employed in the study. The investigational results are have included followed by a discussion with prospect analysis. Several 2×2 blocks are recycled to implant the data bits within the stego-image, and using the Arnold Map, four quadrants of the pixels of each block is selected to embed the data in nonlinear bit position. Different measures with the change of BPP (bits per pixel) are also worked out.
Study of the Image Segmentation Process Using the Optimized U-Net Model for Drone-Captured Images

Aerial views of the scenes captured by UAV or drone have become very familiar as they easily cover the wide view of the scene with different terrain types and landscapes. The detection of the scene images captured by drone and their subparts have been done on the basis of simple image processing approach involving the pixel intensity information. Many computer vision-based algorithms have successfully performed the tasks of segmentation. The manual approach of such segmentation has become time consuming, resource intensive, and laborious. Moreover, the perfection of segmentation on the irregular and noisy images captured by the drones have been lowered to greater extents with application of machine learning algorithms. The machine learning-based UNet model has successfully performed the task of segmentation, and the performance has been enhanced due to optimization. This chapter highlights the different variations of the model and its optimization towards the betterment of accuracy.
Secure Cryptography Using Chaotic Algorithm

A chaotic cryptographic method and bit shuffle methodology for image encryption and decoding have both been suggested. In order to evaluate the encryption's effectiveness and determine whether it met the desired standard, a number of performance analysis tools were also used. These included the number of pixel change rate (NPCR), the unified average changing intensity (UACI), the entropy analysis, which is a component of an encryption scheme that shows how random the image is, and the correlation coefficient. These results reveal the safety of the suggested cryptographic technique.
Counterfeiting Money and Anti-Counterfeit Measures

This study is designed to investigate the use of counterfeit money in the Ottoman Empire. Firstly, the concept of money and the reasons for the use of counterfeit money in the Ottoman Empire are described. Secondly, the reasons for the formation of counterfeit money are examined. Thirdly, the precautions taken against counterfeit money in the Ottoman Empire are discussed. The goal of this study is to investigate the counterfeit money printing incidents that had taken place. Moreover, the anti-counterfeit measures are identified that are implemented by Ottoman Empire to curb this crime and to remove counterfeit money from the market.
Automated Finite Element Solution of Diffusion Models for Image Denoising

Abstract We present in this paper a numerical solution of a generalized diffusion-based image denoising model, using the finite element computing platform FEniCS. The generalized model contains as special cases three classical denoising techniques: linear isotropic diffusion, total variation, and Perona-Malik method. The numerical simulation using four classical grayscale images demonstrates the superior performance of the finite element method over the finite difference method in terms of both the denoising quality and the computational work.
Tower Building Technique on Elliptic Curve with Embedding Degree 18

Abstract Pairing based cryptography is one of the best security solution that devote a lot of attention. So, to make pairing practical, secure and computationally effcient, we choose to work with extension finite field of the form 𝔽 p k with k ≥ 12. In this paper, we focus on the case of curves with embedding degree 18. We use the tower building technique, and study the case of degree 2 or 3 twist to carry out most arithmetics operations in 𝔽 p 2 , 𝔽 p 3 , 𝔽 p 6 , 𝔽 p 9 and 𝔽 p 18 , thus we speed up the computation in optimal ate pairing.
A flexible approach for biometric menagerie on user classification of keystroke data

Abstract Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.
Twisted Edwards Curve Over the Ring

Abstract Let 𝔽 q be a finite field of q elements, where q is a power of an odd prime number. In this paper, we study the twisted Edwards curves denoted E E a,d over the local ring 𝔽 q [ e ], where e 2 = 0. In the first time, we study the arithmetic of the ring 𝔽 q [ e ], e 2 = 0. After that we define the twisted Edwards curves E E a,d over this ring and we give essential properties and we define the group E E a,d , these properties. Precisely, we give a bijection between the groups E E a,d and E E a,d 0 × F q ,where E E a,d 0 is the twisted Edwards curves over the finite field 𝔽 q .
On the Construction of Short Addition-Subtraction Chains and their Applications

Abstract The problem of computing x n effciently, such that x and n are known to be very interesting, specially when n is very large. In order to find effcient methods to solve this problem, addition chains have been much studied, and generalized to addition-subtraction chains. These various chains have been useful in finding effcient exponentiation algorithms. In this paper, we present a new method to recover all existing exponentiation algorithms. It will be applied to design a new fast exponentiation method.
On classifier performance for remote sensing images compressed by different coders

Remote sensing data are widely used in numerous applications. A conventional task solved using remote sensing images is their classification. The classification maps are commonly produced by some pre-trained classifiers applied either to uncompressed or compressed images where lossy compression is often needed and employed in practice due to the necessity to reduce data volume at stages of image transfer and storage. Then, the classification accuracy depends on the characteristics of an image, a classifier, and a coder used. The main subject of this paper is the factors that determine classification accuracy. One of them is compressed image quality. We fix the quality of compressed image quality characterized by the peak signal-to-noise ratio for several coders and rely on the same training approach. Our goal is twofold. First, we would like to consider classification accuracy for two approaches to classifier training: based on undistorted data and images with simulated distortions. Second, our desire is to compare the performance of different techniques of image compression. The task of this paper is to obtain an idea is it worth training the neural network classifier for uncompressed images or images of similar quality to the quality of compressed data to be classified. The coder’s influence on classification results is of special interest as well. The main results are the following. First, the classification accuracy is almost the same for classifiers trained for uncompressed and simulated compressed data for the general distortion model. Second, there is a certain difference in the classification results for different compression techniques studied. Lightly better classification results are observed for data produced by more sophisticated (modern) coders. Experiments have been carried out for two real-life three-channel Sentinel-2 images of Kharkiv and the Kharkiv region having different complexity. Four typical classes have been considered. As a conclusion, it is possible to state that either the general model of distortions must be modified or the classifier training should be performed for data produced by the corresponding compression technique.
Semi‐supervised learning for ABUS tumor detection using deep learning method

Abstract Automated breast ultrasound (ABUS) imaging system is a practical technique to automatically scan the whole breast. Automatic tumor detection plays a significant role in the clinic. However, training deep convolutional neural networks (CNNs) for tumor detection needs a large quantity of labeled data. It is time‐consuming and expensive to manually annotate tumor positions in ABUS images. In this paper, a novel semi‐supervised learning EfficientDet (SSL‐E) model is proposed for ABUS tumor detection. Our SSL‐E model solves the tumor detection problem from high similarity and serious unbalance between tumors and backgrounds. Considering the image contrast variation and tumor scale variations in ABUS images, color transformation and geometric transformation are employed for data augmentations. Then the consistency between image and its augmented version is developed, thus the robustness of the detector can be improved. Aiming at the problem of serious unbalance between tumors and backgrounds, a novel copy‐paste synthesis strategy is designed, which can generate more tumor samples and enhance tumor diversity. This method is tested on 68 tumor volumes and 68 normal volumes, including 43,248 slices (1683 tumor slices and 41,565 normal slices). It obtains a promising result with sensitivity of 90.2% and false positives per image (FPs/I) at 0.15.
Academic entrepreneurship system in the Russian Federation

Aim. The presented study aims to investigate the features of the academic entrepreneurship system in the Russian Federation through the example of leading Russian universities and to determine the main prospects for its development. Tasks. The author defines the theoretical foundations of the academic entrepreneurship system; examines the legal and regulatory framework of academic entrepreneurship; comparatively analyzes the entrepreneurial activity of leading Russian universities; determines the prospects and trends of the development of the academic entrepreneurship system in Russia. Methods. This study uses general scientific methods, including comparison, generalization, benchmarking, abstraction, deduction, and induction. Results. The theoretical foundations of academic entrepreneurship are investigated, and the author’s original definition of the term is formulated. The regulatory and legal framework of university entrepreneurship through the regulation of small innovative enterprises established at universities is examined. The level of academic entrepreneurship in leading Russian universities at the level of desk research is analyzed. Major directions for the development of the academic entrepreneurship system in Russia are determined. Conclusions. The system of academic entrepreneurship is implemented through the combined efforts of educational organizations and representatives of the business community to simplify the commercialization of the results of innovative activity. The legal and regulatory framework of academic entrepreneurship in Russia is underdeveloped considering the needs of the modern economy. The main legal acts regulating the activities of small innovative enterprises are federal laws, regulations of the Government of the Russian Federation, and the decree of the Ministry of Education and Science of the Russian Federation. University entrepreneurship in domestic higher education institutions continues to develop. A peculiar feature of the existing system is the desire to create an innovative infrastructure within the university and to stimulate entrepreneurial activity among the teaching staff and students. The prospects for the development of academic entrepreneurship in Russia are associated with the development of innovative university infrastructure and increased interaction with manufacturing organizations.
The development of university research scholars’ and faculty’s competence in academic writing

Introduction. The development of competence in the field of academic writing is one of the requirements for university research scholars and faculty at the current stage. The number of research articles published in academic journals indexed in Scopus and Web of Science is one of the indicators of the effectiveness of the university in general and the effectiveness of each individual scholar in particular. The purpose of the study is to develop a course program for the university research scholars and faculty for the development of competence in the field of academic writing and to test its effectiveness in the course of experiential learning. Materials and methods. The study involved young faculty of various scientific specialties of Derzhavin Tambov State University (N=40). For 13 weeks, they were trained in writing the structural components of an IMRAD research article, and also studied issues related to the use of modern information and analytical resources for the selection of research literature on topics and the promotion of research articles after their publication. The objects of control were micro skills associated with writing structural fragments of an article in the IMRAD format. Statistical analysis of the data was carried out using the Student's t-test. Median data (M) were used in the analysis. Research results. It was revealed that the proposed course has confirmed its effectiveness in all controlled aspects. In addition, the study showed that the greatest difficulties for most learners, predominantly representatives of humanities, were caused by writing the sections “Methods and materials” (M = 3.05), “Results” (M = 2.95) and “Discussion of the results” (M = 3.45). The least difficulties for the participants were caused by writing the sections "Introduction" (M=4.16), "Literature Review" (M=4.8), "Conclusion" (M=4.43) and compiling a list of references (M=4.87). The paper presents in detail the subject content of the course and tasks for independent implementation by students. Conclusion. The novelty of the study is in the development of the subject content of the course for the development of students’ and scholars’ competence in academic writing. The results of the study can be used in the development of specialized academic writing courses for bachelor and master degree students, involved in research activity.