Reviewerly/exHarmony huggingface dataset:
A New Image Encryption Algorithm Based on Modified Optically Injected Semiconductor Laser Chaotic System

Compared with general chaotic systems, laser chaotic systems have more complex dynamic behaviors and extremely high sensitivity to parameters and initial values. So far, there are few studies on the application of laser chaotic systems to image encryption. Therefore, this paper proposes a new image encryption scheme based on an optical injection semiconductor laser chaotic system. The encryption scheme mainly includes two processes of scrambling and diffusion. The scrambling process converts the pseudo-random sequences generated by the laser chaotic system into chaotic matrixes and scrambling each pixel through a random point scrambling algorithm. In the diffusion process, the chaotic sequences generated by the laser chaotic system are quantized into random numbers and applied to the improved gravitational model algorithm to achieve the effect of pixel diffusion. The experimental results and security analysis indicate that the proposed algorithm has good image encryption performance and can effectively resist various common attacks on image encryption systems.
Review Paper on Transform Domains Techniques for Face Recognition

In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.
CLASSIFICATION OF ANALYZABLE METAPHASE IMAGES BY EXTREME LEARNING MACHINES

A chromosome is a DNA molecule that contains the genetic material of an organism.Possible defects in chromosomes can cause structural and functional disorders in living things.Identifying the metaphase stages of cells is a critical step to identify problems in chromosomes.In this proposed study, the discriminative features of possible metaphase images were extracted with Gray level co-occurrence matrix and classified with the Extreme Learning Machines classification method.When the results were evaluated, it was observed that the proposed method was as successful as the deep learning methods in the literature.Especially in recent years, when online learning has become important, the need for retraining of deep learning-based algorithms after each validation will increase the importance of the proposed method in this field.The rapid increase in unlabelled data from each patient every day affects the duration of training and creates time and resource constraints.Fast and accurate modelling of such data with alternative machine learning methods will contribute to the studies in this area..
Improving CPU utilization of interleaving generation parallel evolutionary algorithm with precedence evaluation of tentative solutions and their suspension

This paper proposes a new mechanism to improve the CPU efficiency of parallel evolutionary algorithms (PEAs). The proposed method is based on interleaving generation evolutionary algorithm (IGEA) that was proposed in a previous study. Whereas PEA generates offspring after all individuals are evaluated, IGEA generates offspring of which all parents have been determined before other evaluations are completed. The proposed method introduced a precedence evaluation of tentative offspring and their suspension mechanism into IGEA. In particular, while IGEA generates offspring of which all parents have been determined, the proposed method tentatively generates offspring when one of two parents has been determined and then begins their evaluations. The evaluation of unnecessary offspring is suspended when the other parent of tentative offspring is determined. We compare the proposed method with the original IGEA and a simple PEA to investigate the effectiveness of the proposed method. This paper considers two replacement schemes of PEAs, (λ,λ)-PEA and (λ+λ)-PEA. The experimental results reveal that the proposed method has higher CPU utilization than the original IGEA and the simple PEA on both schemes.
E-Business BitPos-Start Up Online Cashier by using Internet Technology Development

The rate of technology adoption and online food ordering transactions in Indonesia is increasing over time. However, restaurant service practices, especially those in shopping centers, tend to rely on physical point-of-sale systems. This causes certain restaurants to lose leverage over balk and renege, as well as lowering the restaurant's reputation in the eyes of its customers. The writer has a note based on this phenomenon to solve the problem by using existing technology to create a POS product that combines digital POS with food ordering application named BIT. Methods that used in this research uses Porter’s Five Forces Model, Lean Canvas, and smart PLS. For analysis and design, writer use Object Oriented Analysis and Design with Unified Modelling Language. The result of this research is BIT will be digital mediator between restaurant and their customer, so that the queueing and food ordering process can be done digitally and massively, also eliminating problems that exists in the use of traditional POS.
Performance of compression algorithms for radio signal data

In the face of the shortage of radio spectrum resources, the contradiction between supply and demand and other issues, data compression technology can ensure data integrity while saving storage space, effectively improving the utilization of spectrum resources. This article first makes lossless Huffman coding, LZ77, LZ78, and LZW algorithms. The compression technology is briefly introduced, and the application of various algorithms and the corresponding advantages and disadvantages are analyzed with examples. Secondly, for the radio signal data of the radio monitoring station in Gansu Province, the performance of these commonly used lossless compression algorithms is compared. Finally, the experimental test result data proves that when the compressed file is large, the LZ series algorithm has a better compression effect than the Huffman algorithm. In LZ series, LZW algorithm has the best compression effect, LZ77 algorithm has the worst compression effect, and the compression effect of LZ78 is between them. When the compressed file is small, because the data in the compressed file is less, the compression effect of several algorithms is not obvious.
Improvement of Image Super Resolution by Deep Neural Networks

The modern methods and architectures for image super resolution which are based on deep neural networks (DNNs) are considered. Several ways of their improvements were proposed and demonstrated. It was shown that the perception models built on MobileNet and EfficientNet families of DNNs turned out to be faster in training and have a better perception loss rate than previously used VGG family. In the more general context the usage of the smaller DNNs with the higher performance and lower size allow researchers to use and deploy them on devices with the limited computational resources for Edge Computing layer.
An Efficient Method to Measure Robustness of ReLU-Based Classifiers via Search Space Pruning

Deep Neural Networks (DNNs) have achieved high accuracy on image classification. However, a small disturbance to an input may fool the networks to misclassify the label, which can cause a series of security and social problems. Thus, the robustness of DNNs must be ensured, particularly to those safety-critical systems. In this paper, we focus on the problem of measuring the robustness of ReLU-based DNNs, which can be equivalently formulated to solve a Mixed Integer Linear Programming problem (MILP). The complexity of solving MILP is directly related to the number of integer variables. We propose an efficient method for robustness measurement and verification by pruning the search space of MILP problems. Particularly, we design a greedy algorithm based on linear programming (LP) to determine the reasonable boundary. Then the search space is pruned by setting the boundary to integer variables in MILP. The comparison experiments on five classifiers trained on MNIST and CIFAR-10 datasets show our method outperforms other related tools in terms of efficiency and accuracy.
Music Player Based on Human Emotions

Emotions are responses to ones internal and external well being. Emotions need to be balanced for a peaceful life. Unconditional feelings can lead to depression, suicidal attacks, mental illness etc. Now a days people are busy in their own life’s and have no one to share their feelings, in such cases music can be a best companion.This emotionally-based music player project recognizes the user’s facial expressions and plays songs based on their emotions. Emotions are perceived through machine learning. We’ve reclaimed the last layer of MobileNet architecture created by Google through our split. The training dataset we used contains 400 faces per class. Webcam captures user image using Opencv library. This image was processed using a Haar cascade classifier. The emotions used in this project are Smile, Sad, Angry, Fear. Depending on the mood, the music will be played from pre-defined directories.
A New Symmetric Key Cryptographic Algorithm using Paley Graphs and ASCII Values

The main objective of the study conducted in this article is to introduce a new algorithm of encryption and decryption of a sensitive message after transforming it into a binary message. Our proposed encryption algorithm is based on the study of a particular graph constructed algebraically from the quadratic residues. We have exploited the Paley graph to introduce an abstract way of encryption of such message bit according to the other message bits by the intermidiate study of the neighborhood of a graph vertex. The strong regularity of the Paley graphs and the unknown behavior of the quadratic residues will play a very important role in the cryptanalysis part which allows to say that the brute force attack remains for the moment the only way to obtain the set of possible messages.
What kind of support did the faculty receive during emergency distance education at universities?

The COVID-19 epidemic has been a major challenge for departments that provide methodological and technical support for e-learning at universities. From day to day, these workplaces have encountered a huge increase in the requirements and interest of teachers in supporting the preparation and implementation of emergency distance education. From a technical point of view, it was necessary to ensure the smooth operation of platforms for both asynchronous and synchronous teaching, and to assist even complete beginners in the field of distance education. Methodological support included the implementation of courses aimed at quickly obtaining basic information about the authorial and tutorial work, sharing examples, instructions, and infographics, as well as personal online consultations. The chapter will present examples of good practice, tools, and procedures in support that have been tried and tested by university teachers, and have proved effective. Furthermore, a case study of the implementation of methodological and technical support at the University of West Bohemia in Pilsen will be presented. Specific forms of support will be presented at the same time as the results of the evaluation research which, after six weeks of a state of emergency, have provided feedback on the individual measures implemented. The results show that teachers especially appreciate enough up-to-date instructions for various platforms, speed in providing support, and the personal approach of methodological and technical support staff.
An Ensemble Approach with Clustering-Based Under-sampling for Imbalanced Classification

Class imbalance widely occurs in many real-world applications, which affects the recognition of important class to a certain extent. Ensemble methods that combined resampling are effective to alleviate the class imbalance problems. This paper presents a new ensemble approach with clustering-based under-sampling, called CNBoost, for learning from imbalanced data. This algorithm is based on the combination of Centers NN and boosting procedure. Centers NN, as an under-sampling utilizing the nearest neighbors of cluster centers, is used to provide a new training subset in each iteration of boosting, which makes the base learner learn the overall data distribution in each iteration of boosting. We compared the performance of the proposed algorithm with 3 popular ensemble methods. Out of 10 datasets and 3 measurements, CNBoost performs equally well or better than the other 3 methods in 25/30 categories. In addition, we discussed the effect of the base learner used in boosting on the performance of these algorithms. The results show that CNBoost is a promising approach with high classification accuracy and stability for dealing with imbalanced datasets.
Probing Negative Sampling for Contrastive Learning to Learn Graph Representations

Graph representation learning has long been an important yet challenging task for various real-world applications. However, its downstream tasks are mainly performed in the settings of supervised or semi-supervised learning. Inspired by recent advances in unsupervised contrastive learning, this paper is thus motivated to investigate how the node-wise contrastive learning could be performed. Particularly, we respectively resolve the class collision issue and the imbalanced negative data distribution issue. Extensive experiments are performed on three real-world datasets and the proposed approach achieves the SOTA model performance.
Binary Classification with Supervised-like Biclustering and Adaboost

Biclustering based classification methods have achieved great success. In existing biclustering based classification methods the residue score threshold used for searching biclusters is fixed and the number of mined useful biclusters is small. To solve the two problems, in this study we proposed a novel binary classification method based on supervised-like biclustering and adaboost. Supervised-like means the biclusters is searched twice with two different bicluster quality indicators MSD and weights instead of unique indicator MSR in other biclustering based classification methods. In initial search, the residue score threshold is identical for all biclusters. In the second search, some biclusters of low quality are searched again with smaller residue threshold. Besides, due to some limitations, many biclusters cannot be found. To obtain more biclusters, two additional operations are adopted. In the proposed method, we initially mine column constant biclusters from datasets, then transform the biclusters to weak classifiers. Through adaboost, the initial strong binary classifier can be constructed. Finally, with supervised-like strategy, the better final strong binary classifier can be obtained. To verify the performance of the proposed method, it is compared with seven binary classification methods on two datasets. Experimental results demonstrated that the proposed method outperformed other binary classification methods.
Influence of Source Parameters on the Polarization Properties of Beams for Practical Free-Space Quantum Key Distribution

Polarization encoding has been extensively used in quantum key distribution (QKD) implementations along free-space links. However, the calculation model to characterize channel transmittance and quantum bit error rate (QBER) for free-space QKD has not been systematically studied. As a result, it is often assumed that misalignment error is equal to a fixed value, which is not theoretically rigorous. In this paper, we investigate the depolarization and rotation of the signal beams resulting from spatially-dependent polarization effects of the use of curved optics in an off-axis configuration, where decoherence can be characterized by the Huygens–Fresnel principle and the cross-spectral density matrix (CSDM). The transmittance and misalignment error in a practical free-space QKD can thus be estimated using the method. Furthermore, the numerical simulations clearly show that the polarization effect caused by turbulence can be effectively mitigated when maintaining good beam coherence properties.
AGE INVARIANT FACE RECOGNITION TECHNIQUES: A CRITICAL REVIEW STUDY -

Face recognition (FR) has drawn intensified interest and attention in the domain of computervision.FR has been researched for the last three decades but, still it is a challenging research area because theface recognition accuracy is limited by the variability of personal appearances, variant poses, facial expressions,illumination, and aging. Age Invariant face recognition (AIFR) is a significant problem that has not beenextensively studied until recently. In this paper, we survey some well-known techniques for cross-age facerecognition. We present a comparative analysis of various approaches in terms of robustness to aging. Also, theimportant facial aging databases together with their number of images per each subject are briefly described.Finally, we present the discussions on the experimental results from these methods, challenges, conclusions, andguidelines for future work.
About the bibliography of Tajikistan

The article deals in brief with the history of Tajik bibliography. Basic attention is paid at the development of bibliography in the Republic in the Soviet period. Characteristics of the main State centers of bibliographic activity – the Book Chamber of the Republic of Tajikistan, State Republican Library named after Firdousi, from 1993 – the National Library of Tajikistan, Central Scientific Library of the Academy of Sciences of Tajikistan named after I. Gandy are given. Bibliographic and bio-bibliography works are enumerated, published in the Republic in different historical periods.
WPS: Application for Generating Answer of Word Problem in Bahasa Indonesia

In education field, a lot of automation has been carried out, for example an automatic assessment system. For example in math, there is word problem that has higher complexity than algebraic problem. To be able to create an automatic assessment system, it is necessary to have a system that can automatically issue answers to word problems first. This work introduces WPS: Application of Word Problem Solver, a system that will generate an answer for a word problem. This system will give an answer of word problem that user inputs based on the algorithm and rules inside the program. We used corpus built specifically for word problems which have different characteristics for each problem, depending on the type of operator used. The result of this research is an application that can automatically generate an answer from word problem issues by user with accuracy of 82.5%.
sorn: A Python package for Self Organizing Recurrent Neural Network

The self-organizing recurrent neural (SORN) network is a class of neuro-inspired artificial networks.This class of networks has been shown to mimic the ability of neocortical circuits to learn and adapt through neuroplasticity mechanisms.Structurally, the SORN network consists of a pool of excitatory neurons and a small population of inhibitory neurons.The network uses five basic plasticity mechanisms found in the neocortex of the brain, namely, spike-timing-dependent plasticity, intrinsic plasticity, synaptic scaling, inhibitory spike-timingdependent plasticity, and structural plasticity (Lazar et al., 2009;Papa et al., 2017;Zheng et al., 2013) to optimize its parameters.Using mathematical tools, a SORN network simplifies the underlying structural and functional connectivity mechanisms responsible for learning and memory in the brain.
Shared-latent Variable Network Alignment

The increasing popularity and diversity of social media sites, has encouraged many people to participate in different online social networks to enjoy a variety of services. Linking the same users across different social networks, also known as social network alignment, is a critical task of great research challenges. Many existing works usually focus on finding a projection function from one subspace to another for network alignment, however, the projection functions proposed in their papers are independent and updated individually, which could not effectively exploit the non-parallel data, and yield inferior alignment performance. In this paper, we propose a Shared-latent Variable Network Alignment (SVNA) architecture to effectively exploit the non-parallel data for network alignment, and jointly train projection functions and decoders in a unified framework with the shared latent variable z. Specifically, SVNA first employs the graph convolutional networks to preserve the structural information of the network. By introducing the shared latent variable z, SVNA simultaneously integrates two projection functions and two decoders for jointly training. Both projection functions and decoders share the same latent space, therefore both projection directions can learn from the non-parallel data more effectively. Thereafter, SVNA utilizes the Generative Adversarial Networks (GANs) framework to further train the projection functions, and adopts a probability-based semi-supervised method to achieve the network alignment. Experiments on three real-world datasets show that SVNA generally outperforms the state-of-the-art methods in network alignment task.
Optical Character Recognition using Backpropagation Neural Network for Handwritten Digit Characters

Recognizing handwritten characters, the accuracy of the optical character recognition is usually not relatively high due to every person having their unique way of writing characters. Therefore, we focus on finding a high recognition accuracy of optical character recognition by using a backpropagation neural network. The input layer of the backpropagation neural network is the pixel number of the one-character image, which is 784 input nodes that will be the input layer of the neural network. Then the output layer of the neural network will be the 10-digit characters which are 0 to 9. The dataset that used for this research has a total of 280,000 data. The output of the neural network will a computerized digit representing the recognized digit characters. The performance measurement is the recognition accuracy where the recognized data and the expected output data are compared and calculated. Additionally, the dataset was applied with salt and pepper noise to represent the corrupted data and use a median filter to repair the image. The recognition accuracy for the corrupted image and the corrected image are obtained and discussed.