Reviewerly/exHarmony huggingface dataset:
Analysis of the use of LCMS Moodle in the educational process of KSPU

The article analyzes the experience of using a learning management system (LMS) Moodle in the educational process of Kryvyi Rih State Pedagogical University (KSPU) based on the results of the survey conducted in early March 2020 and the organizational and methodological support of the semester distance learning and summer credit and examination session of 2019–2020 academic year. It was found that lecturers under the age of 30 (in particular, with up to 3 years of experience) or over 60 years old need methodological assistance more often. First of all, the developed courses present digital educational resources in text format, video and audio are not used enough. LMS Moodle is mainly used during examinations and credits, tests, and students’ independent work, while it should be used more intensively during lectures, laboratory, and practical classes. The generalized results of the survey led to the need to improve the level of ICT competence of teachers and students, to popularize the potential of LMS Moodle among them. In order to solve these tasks a number of measures were taken: groups of informational-technical, organizational and methodological support were formed by the quantity of scientific and pedagogical staff who have significant experience in LMS Moodle use; long courses, periodical seminars, consultations where both general questions of educational process organization in LMS Moodle were held as well as questions due to the specialties, internal and external expert evaluation of e-learning courses quality were covered.
HOUSEN: Hybrid Over–Undersampling and Ensemble Approach for Imbalance Classification

Classification is an essential technique and is omnipresent in our day-to-day life. In real time, the datasets are often imbalanced, i.e., the majority class is dominating the minority class. Classification of data becomes tough due to the imbalanced nature of data. Identifying the impact of imbalance techniques for classification techniques, this article presents a model named the HOUSEN model. It uses a novel hybrid class imbalance sampling method and an ensemble classification approach. Based on our experimental analysis, the oversampling and undersampling techniques are used to design a hybrid sampling method. The other techniques like random forest, support vector machine (SVM), gradient boost, and AdaBoost algorithms are also used for our analysis. The performance evaluation of the HOUSEN model is conducted over five imbalanced datasets. The HOUSEN model achieved a promising result when compared to existing techniques.
Machine Learning and Deep Learning: A Comparative Review

Machine learning and deep learning are revolutionary fields in the computer science area and are widely used in business applications. Machine learning is an approach to train computers and machines to learn from past data so it can determine future data or behavior. Deep learning is a branch of machine learning where the artificial neural network techniques and algorithms are used to train and learn the unsupervised data. Due to the large volume of data points generated daily, there is a great need for automated sophisticated techniques for the proper use of data and data management to get valuable insights. We present in this paper a comparative review of machine learning (ML) and deep learning (DL) applications. The study presents an introduction to ML and DL concepts. Then, the main approaches and techniques mainly used in the technology-enabled fields are discussed. In the end, the two most common applications of ML and DL are discussed from the business perspective.
C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data

Augmented reality applications use object tracking to estimate the pose of a camera and to superimpose virtual content onto the observed object. Today, a number of tracking systems are available, ready to be used in industrial applications. However, such systems are hard to handle for a service maintenance engineer, due to obscure configuration procedures. In this article, we investigate options towards replacing the manual configuration process with a machine learning approach based on automatically synthesized data. We present an automated process of creating object tracker facilities exclusively from synthetic data. The data is highly enhanced to train a convolutional neural network, while still being able to receive reliable and robust results during real world applications only from simple RGB cameras. Comparison against related work using the LINEMOD dataset showed that we are able to outperform similar approaches. For our intended industrial applications with high accuracy demands, its performance is still lower than common object tracking methods with manual configuration. Yet, it can greatly support those as an add-on during initialization, due to its higher reliability.
Research on Risk Measurement and Early Warning of Electronic Banking Business Based on GMDH Algorithm

In order to deal with the problems of traditional e-banking risk measurement and early warning methods, such as low accuracy of e-banking risk measurement and longer early warning time, an e-banking risk measurement and early warning method based on the GMDH algorithm is proposed. This scheme mines the e-banking risk measurement and early warning indicators by the GMDH algorithm, and it will input the influencing factors and risk factors as independent variables into the GMDH modeling network and then input the e-banking business growth rate as the dependent variable into the GMDH modeling network which is standardized by the normative method of processing the e-banking business risk measurement and early warning index data. According to the processing results, it calculates the weight of the measurement and early warning index by the entropy method, and it constructs the e-banking risk measurement model with the genetic algorithm which can help to calculate the optimal solution of the parameters, formulate the risk measurement interval, and determine the risk in order to realize the risk warning of electronic banking business. The simulation results show that the proposed method has a higher accuracy of e-banking risk measurement and a shorter warning time.
Domain structure-based transfer learning for cross-domain word representation

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains. • A cross-domain word representation method for tasks with limited resources. • Fusing the latent semantics of the source and target domains. • Propagating common semantic knowledge from source domain to target domain. • Preserving domain structure in the domain adaptation process. • Effective and efficient for text-related and low-resource tasks.
Generation of Panoramic Images by Two Hemispherical Cameras Independent of Installation Location

Single-shot capture of panoramic images has become possible in recent years, expanding applications for omnidirectional cameras. Retrofitting omnidirectional cameras onto existing objects can be problematic, however, because placing lenses on either side can cause the mounting structure to be included in captured images. To address this issue, we propose using two hemispherical cameras equipped with 185º field-of-view fisheye lenses and combining the images from each while compensating for differences in separation and height to produce a panoramic image like that from an omnidirectional camera. In other words, we investigate acquisition of omnidirectional panoramic images from two hemispherical cameras installed at some distance from each other. In this article, we propose a method for generating spherical images by stitching together images from two cameras. Specifically, we prepare a circular object, include it in images from two separated hemispherical cameras, detect the circular object in each image, and match the two detected circles. We also describe multiple experiments confirming the utility and effectiveness of the proposed method. This work is expected to contribute to expanding the range of applications for omnidirectional imaging.
Discovering Categorical Main and Interaction Effects Based on Association Rule Mining

With the growing size of datasets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are discrete and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves. Drawing inspiration from association rule mining, we come up with a method that uses association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computation complexity of the proposed algorithm to show its efficiency. And the results of a series of experiments verify the effectiveness of the algorithm.
Video quality assessment with dense features and ranking pooling

Benefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorBenefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorted videos. Then, spatial pyramid pooling is equipped in the DenseNet module to process flexible inputs with arbitrary spatial resolution. Thus, this kind of input which has the same spatial resolution as the original distorted video is processed by the well-trained DenseNet to generate frame-level quality, which considers the global spatial information of videos directly. Finally, learning to rank is introduced to explore the high-level temporal correlation of distorted videos by taking the RankNet as the temporal pooling function. The experimental results on two public VQA databases show that the proposed algorithm performs consistently with human visual perception.
Evaluating Resilience of Encrypted Traffic Classification against Adversarial Evasion Attacks

Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic. Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. In this paper, we focus on investigating the effectiveness of different evasion attacks and see how resilient machine and deep learning algorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In most of our experimental results, deep learning shows better resilience against the adversarial samples in comparison to machine learning. Whereas, the impact of the attack varies depending on the type of attack.
Formally Identifying COVID-19 Patients for Providing Medical Services using Drones

The Internet of Things (IoT) is an emerging technology that allows connecting physical devices that are linked to the internet across the world to communicate and share information. Drones are flying objects without any human pilot. In many countries, drones are being used for the fast delivery of packages or products. Nowadays the whole world is fighting against the COVID-19 pandemic which is a viral disease that can be spread by the interaction of people. To fight against this pandemic, the drone's technology can be very effective to minimize the interaction of people. There exists little research work in this area because this virus has been introduced recently in 2019. In this paper, we have proposed an IoT-based model in which wearable sensors are used that will be able to identify COVID-19 patients by monitoring the temperature and respiratory system of the people. After confirmation of identification, the control room will send an alert message and sends medical kits to the residence of the patient through drones. The UML (Unified Modeling Language) is used for the representation and flow of the system. models of the proposed system is specified using Vienna Development Method-Specification language (VDM-SL). VDM-SL toolbox is used to ensure the correctness of the models.
Efficient implementation and parallelization of fuzzy density based clustering

Clustering is a commonly used tool for data management and analysis. One of the prominent group of clustering methods consists of the density-based clustering algorithms. The use of fuzzy neighborhood functions for density-based clustering algorithms are known to significantly improve the robustness, such that choosing neighborhood parameters is rather easy for the user. On the other hand, because of the overhead of the fuzzy calculations, they demand higher computing resources. This study discusses how FN-DNSCAN -a fuzzy density-based clustering algorithm- can be implemented efficiently. A rather specific FN-DBSCAN algorithm that adopts techniques used to improve classical density-based clustering algorithms is introduced. Also, a parallel version of the algorithm is proposed and their implementation details are discussed. The proposed algorithms are tested in a set of comparative experiments, along with a straightforward FN-DBSCAN implementation and a curious but unsafe modification of the parallel algorithm. The results of the experiments that are conducted in a modest parallel computing environment of 32 processing units, show a wide variety of differences in relative speed-ups ranging from 2 to 850 times.
Attendance System Using Face Recognition for Academic Education

Manual attendance system in colleges is a tedious and time taking task for both the students and faculty. The teacher has to start counting from first to last student number and the last student has to pay attention from the first number to their roll number. The face-based attendance system which is very simple and easy to understand is proposed in this paper. Face recognition is playing a vital role in the field of Biomatrix. Modern cell phones are having a lock, based on face recognition, which is playing a smart role for authenticating someone Faster RCNN algorithm is used in this proposed system. Google's TensorFlow open-source framework is used for the training data set of human faces.
Homomorphic Encryption within Lattice-Based Encryption System

In 2009 a system of fully homomorphic encryption was constructed, in the future, many works were done based on it. In this work, will be performed an analysis of the possibility to use the ideal lattices for constructing homomorphic operations over ciphertexts. This paper represents the analysis of an encryption system based on the primitive of a union in ideal lattices space. The advantage of this approach consists in the possibility of segregated analysis of encryption security and homomorphic properties of the system. The work will be based on the method of analyzing generalized operations over ciphertext using the concept of the base reducing element. It will be shown that some encryption systems can be supplemented by homomorphism between opentext and ciphertext. Thus such systems can be decomposed by encryption and homomorphic parts which would affect each other but although can be analyzed separately. Different systems (probably within one class) can be represented via an identical transform of sets. Separated from the cryptographic scheme the underlying math can be used to analyze only the homomorphic part, particularly under some simplifications. The building of ideal-based ciphertext laying on the assumption that ideals can be extracted further, it will be shown in the paper what the ”remainder theorem” can be one of the principal ways to do this.
Electric-field noise in a high-temperature superconducting surface ion trap

Scaling up trapped-ion quantum computers requires new trap materials to be explored. Here we present experiments with a surface ion trap made from the high-temperature superconductor YBCO, a promising material for future trap designs. We show that voltage noise from superconducting electrode leads is negligible within the sensitivity ${S}_{V}=9\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}20}\phantom{\rule{0.16em}{0ex}}{\mathrm{V}}^{2}/\mathrm{Hz}$ of our setup, and for lead dimensions typical for advanced trap designs. Furthermore, we investigate the frequency and temperature dependence of electric-field noise above a YBCO surface. We find a $1/f$ spectral dependence of the noise and a nontrivial temperature dependence, with a plateau in the noise stretching over roughly $60\phantom{\rule{0.16em}{0ex}}\mathrm{K}$. The onset of the plateau coincides with the superconducting transition, indicating a connection between the dominant noise and the YBCO trap material. We exclude the YBCO bulk as origin of the noise and suggest further experiments to decide between the two remaining options explaining the observed temperature dependence: noise screening within the superconducting phase, or surface noise activated by the YBCO bulk through some unknown mechanism.
Effective backscattering and absorption coefficients of light diffusing materials retrieved from reflectance and transmittance spectra of diffuse radiation

Solutions of the differential equations for the diffuse components of the four-flux model are obtained, including explicit expressions for the collimated-diffuse and diffuse-diffuse reflectance and transmittance for an optical three-layer system. We establish a method to invert collimated-diffuse reflectance and transmittance in order to obtain the spectral variation of all average pathlength parameters and forward scattering ratios, by applying a spectral simulated annealing method. The inversion procedure was tested with synthetic collimated-diffuse reflectance and transmittance spectra and showed very good convergence. Subsequently, the method was applied to measured spectra of a light scattering and absorbing medium containing TiO2 particles in a low-absorbing matrix, which was enclosed between two glass slides. Hence the present paper, together with our previous one, wherein we inverted collimated-collimated reflectance and transmittance spectra to obtain scattering and absorption coefficients, establishes a reliable inversion method for obtaining all parameters in the most general version of the four-flux theory from experimental data.
Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.
Statistical and Neural Machine Translation for Manipuri-English on Intelligence Domain

Rahul, Laishram Meetei, Loitongbam Sanayai Jayanna, H. S.This paper describes the development and results of Manipuri-English machine translation system built on an intelligence domain. Manipuri is an under-resourced Tibeto-Burman language that is spoken mainly in the North-Eastern states of India. A total of 56,678 Manipuri-English parallel corpora from intelligence domain based on the open-source intelligence (OSINT) keywords and phrases are collected for the experiment. An evaluation of statistical machine translation (SMT) and neural machine translation (NMT) is carried out in terms of BLEU score. A BLEU score of 23.91 is achieved with the SMT-based approach which is outperformed by the NMT-based system with a BLEU score of 40.67. Further, a language-specific morphological analysis based on the suffixes is investigated. The findings on the incorporation of morphological analysis report a BLEU score of 25.03 with the SMT and a BLEU score of 44 with NMT, both of which are a significant improvement.
Rapid Multimodal Image Registration Based on the Local Edge Histogram

Due to significant differences in imaging mechanisms between multimodal images, registration methods have difficulty in achieving the ideal effect in terms of time consumption and matching precision. Therefore, this paper puts forward a rapid and robust method for multimodal image registration by exploiting local edge information. The method is based on the framework of SURF and can simultaneously achieve real time and accuracy. Due to the unpredictability of multimodal images’ textures, the local edge descriptor is built based on the edge histogram of neighborhood around keypoints. Moreover, in order to increase the robustness of the whole algorithm and maintain the SURF’s fast characteristic, saliency assessment of keypoints and the concept of self-similar factor are presented and introduced. Experimental results show that the proposed method achieves higher precision and consumes less time than other multimodality registration methods. In addition, the robustness and stability of the method are also demonstrated in the presence of image blurring, rotation, noise, and luminance variations.