Reviewerly/exHarmony huggingface dataset:
Parallel SVM Algorithms in Big Data Environments

Nowadays, with the rapid development of information technology and data storage technology, the amount of data in various industries in society is growing rapidly, and the human society has stepped into the era of big data. How to efficiently process these massive data and extract potential value information from them has been a hot topic in recent years, and data mining technology has become the mainstream direction for people to explore this topic. Among the classification algorithms, Support VectorMachine (SVM) has become a popular research because of its good generalization ability, good ability to overcome dimensional disasters and nonlinear problem solving ability. However, traditional SVMs are only suitable for solving small sample problems, and their computational complexity grows exponentially when dealing with large data sets, while the emergence of parallelization techniques has solved this challenge. Therefore, it is of great value and significance to study parallelized SVMs. The proposed Support Vector Machine Algorithm using K-means Clustering and Whale Optimization Algorithm based on Map Reduce-–MR-KWSVM, firstly, proposes KF (K-means and Fisher) strategy to delete redundant data, and uses the data set after deleting redundant data to train SVM, which reduces the sensitivity of SVM to redundant data; secondly, The Improved Whale Optimization Algorithm based on Nonlinear convergence factor and Self-Adaptive Inertia Weight-IW-BNAW were proposed, and the optimal parameters of SVM are obtained by using “IW-BNAW” algorithm to improve the parameter optimization ability of support vector machine; finally, in the process of using Map Reduce to construct parallel SVM, Time Feedback Strategy (TFB) is proposed for load scheduling of reduce nodes to improve the parallel efficiency of the cluster and achieve high parallel SVM. Another algorithm proposed in this paper-–PKBBTSVM (Parallel Multi-Class SVM Algorithm Based on K-means and Balanced Binary Tree), firstly, according to the distribution characteristics of redundant data in multi-category data sets, a DKR strategy (Data Reduction Strategy Based on K-means and Sample Redundancy, DKR) is proposed, which speeds up the training speed of multi-classification SVM. Secondly, we propose the BSD strategy (Non-leaf node data partitioning strategy based on balanced binary tree and sample Divisibility) for building sub-classifiers of multi-category SVMs, and finally, the parallelization of multiclassification SVMs on Map Reduce is implemented.
Modeling dynamics of cryptocurrency XRP using tweets, news, and GMDH-based algorithms

This paper shows the possibilities of an averaged quantitative forecast of popular cryptocurrency XRP based on dynamics of tweets and news. The contribution of the paper are threefold: 1) mid-term forecasts (weekly and monthly ones) with typical GMDH-based algorithms unlike forecasts with other algorithms; 2) consideration of news (experts) along with tweets (population) unlike traditional attention to tweets; 3) using only contents of texts reflected in tweets and news instead of their tonality and other related characteristics. The paper describes procedures of text preprocessing and modeling. Experiments are implemented with regression and hybrid models. The data cover the period of a full year (2020). Baselines are presented by time series analysis with GMDH-based algorithms and the popular Holt-Winter (HW) method. To reveal the temporal relations between tweets and news we propose to use two-way mutual correlation. The completed research shows the following results: 1) application of tweets provides better accuracy than that of news both for month and for week forecasts; 2) GMDH-based algorithms with autoregression have a small advantages over HW-method for month and for week forecasts; 3) using tweets and news for month forecast gives the results close to those of Baseline for the best models and algorithms; 4) using tweets and news for week forecast has significant advantage comparing to Baseline, when Neuro algorithm is used on hybrid model; 5) news get ahead of tweets in their prediction of XRP dynamics but the proposed way needs an additional study.
A Joint Statistical Estimation of the RFC and CBO Metrics for Open-Source Applications Developed in Java

The response for a class (RFC) and coupling between object classes (CBO) metrics, along with other ones, are used for evaluating software complexity, including object-oriented design (OOD) complexity of open-source applications (apps), providing a measure of the third step (the relationships between classes) in OOD. Recommended values for the RFC and CBO metrics are known without correlation between them. However, there is a correlation between the RFC and CBO metrics. That is why we have performed jointly estimating the RFC and CBO metrics for 46 open-source apps developed in Java. We have constructed a prediction ellipse for the normalized RFC and CBO metrics based on the bivariate Box-Cox normalizing transformation to aid software architects and designers in managing complexity in OOD. If the squared Mahalanobis distance for normalized values of the RFC and CBO metrics for an app is greater than 12.57, it means that there is high complexity due to the relationships between classes.
Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing

We introduce dynamic auto-sizing, a novel approach to training artificial neural networks which allows the models to automatically adapt their size to the problem domain. The size of the models can be further controlled during the learning process by modifying the applied strength of regularization. The ability of dynamic auto-sizing models to expand or shrink their hidden layers is achieved by periodically growing and pruning entire units such as neurons or filters. For this purpose, we introduce weighted L1 regularization, a novel regularization method for inducing structured sparsity. Besides analyzing the behavior of dynamic auto-sizing, we evaluate predictive performance of models trained using the method and show that such models can provide a predictive advantage over traditional approaches.
Filtering of the fine-structured components of images with textual information by the TextSmooth software

In this paper we presented an overview of the especial computational method based on the inverse median filtering of the fine-structured components of images with textual information with compensation for the edge distortions, as well as the computational method for determining the constants of mutual recalculation of images with textual information from an information contained in these images. Such computational methods are implemented as a software called “TextSmooth” using the Java programming language and JavaFX technology. The software has the different parameters to be set and tuned for the better quality of processing, visualization of the computational process and the additional useful features. The TextSmooth software was designed especially for the easy and quick filtering of the different types of images with textual information using the graphical user interface (GUI) with maximum usability. The TextSmooth software was developed during research in scope of the CoLiTec project. The TextSmooth software was tested on the images with different resolutions, sizes, textual information, and observational condition.
Image Inpainting Generation Adversative Network Based on Attention Mechanism

Image inpainting is an essential issue in image processing, which aims to use computer vision technology to restore damaged or lost parts in degraded images automatically. It is widely used in film and television special effects production, such as image editing, digital heritage protection, and other fields. To aim at the problem of image inpainting with relatively complex image structure and high requirement for local image region repair, this paper proposes a deep convolution generation adversative network image repair method combining the local and global refinement network (LG-net) and Convolutional Block Attention Module (CBAM). Firstly, the idea of the generative adversarial network is adopted to capture semantic information of the missing part of the image by using the encoder-decoder network of skip connections. Secondly, to enhance the local repair effect, we built a local development network based on a small receiving field, which can also weaken the effect of distant undesired completion results. Thirdly, we use Convolutional Block Attention Module to obtain the important degree of features, enhance the ability to guide feature information, and further refine the global feature. Finally, the stability of the network is enhanced by introducing the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss, weighted reconstruction loss, perceptual loss, style loss, and TV loss. For the CelebA-HQ dataset, the proposed method obtained a PSNR value of 30.98 and an SSIM value of 0.983, respectively. Moreover, for the ParisView dataset, the proposed method achieved PSNR values of 29.60 and SSIM values of 0.986. Experimental results show that the proposed method is better than the current mainstream image restoration techniques in quality and quantity.
Research and Implementation of Power System Data Visualization

With the continuous promotion of digitalization and the intelligent transformation of the electric power industry, various data information has exploded. There is a practical requirement for power system operators to have accurate and timely access to relevant data. Data visualization technology provides a solution for the decision-makers in the electric power industry to quickly discover the intrinsic correlation of data, intuitively grasp the trend of data changes, and deeply explore the value hidden behind the data. This paper develops power system data visualization methods through web language technologies: the html, css, JavaScript, Echarts, Flask framework and Ajax technology. The ultimate goal is to dynamically monitor data changes in the power system and to visually reveal the nature of power operations.
Developing Scientific and ICT Literacy Skills when Teaching the Topic “Design and Research of Digital Comb Filters Using MATLAB”

The paper describes an approach used in the learning process in the Digital Signal Processing course, which supports the development of scientific and ICT literacy skills when studying the topic “Design and Research of Digital Comb Filters Using MATLAB” at the University of Ruse. First, students consider the theory and applications of digital comb filters in the field of telecommunications. Then, students study the responses of the comb filters using MATLAB and Signal Processing Toolbox. In the end, students investigate the main applications of comb filters in the telecommunication area. All these research tasks help develop the scientific and ICT literacy skills of the students, one of the leading 21st-century skills.
A Vietnamese Named Entity Recognition System for COVID-19 Articles

This paper presents a named entity recognition system for the specific domain of Vietnamese COVID-19 news articles. By incorporating manually selected and domain-specific features into a simple deep learning architecture, the system can identify a wide range of custom named entities relevant in the context of COVID-19 and future epidemics. Using high-dimensional embedding vectors in combination with part-of-speech tags and additional features, the system achieves an F score of about 90.41%, surpassing or coming close to results by other models that are more complicated or pre-trained and fine-tuned.
Mapping the environment at range: implications for camera calibration

In the last period Advanced Driving Assistance System are growing exponential and offering new features to drivers in the direction of achieving Autonomous Driving. Active safety is achieved with multiple sensors but still one of the most important ones is the camera sensors within the architecture. Creation of depth maps which describes the surrounding environment is a common practice in the field. In order to create an accurate map the correctness of the camera calibration is an important factor. In this paper we analyze the impact of camera calibration on the environment mapping reconstruction, by exploring the effect on the reconstruction error from short range to long range. Focusing on the latter, we show that the mean reprojection error may not be a sufficient indicator of the calibration accuracy and we propose better measurements and the associated calibration methods.
Research and Calculation on Interrupted Current of Smart Converter Valve in UHVDC Transmission

The DC transmission converter valve is an important part of the UHV DC transmission system. It has the phenomenon of intermittent current during operation. In order to ensure the reliability of the DC system operation and meet the needs of intermittent current test analysis, this paper studies the interruption Causes and countermeasures of continuous current phenomenon. This article analyzes several working conditions that may produce intermittent current, and models and simulates them. Through the analysis, the solutions to solve the problem of thyristor current discontinuity on the thyristor electronic board are given, which are the countermeasures of the automatic re-trigger protection algorithm and the hardware circuit protection scheme. The research on intermittent current and countermeasures in this paper clarified the possible effects of intermittent current conditions and provided a reference for future research on fault prevention and suppression of DC transmission systems.
The Design of Movable Garbage Sorting and Recycling Device

With the progress of social productivity and the gradual improvement of people's living standards, more and more domestic waste is produced. Aiming at the problem of household domestic waste classification, an autonomous mobile waste classification collector is designed, which combines deep learning image classification with embedded technology, integrating sound source positioning, path planning and waste classification. Taking Jetson nano as the data processing center, the time delay estimation algorithm is used to obtain the location of the sound source, the lidar is used to scan the information of the surrounding environment, the SLAM algorithm is used to model the environment in two dimensions, and the path is planned for the work of the garbage collector. Taking STM32 single chip microcomputer as the control core of the motor control system, the chassis DC motor control adopts PID algorithm for closed-loop control to achieve the function of accurate positioning. After the camera collects the image information of the garbage, it uses the Yolo algorithm to detect the target and automatically classify the garbage. This design increases the convenience of household waste classification, so that each user can classify waste more quickly and easily.
Research on the Acquisition and Processing of Multidimensional Power System Data Based on Web Crawlers

The digital transition of power companies has led to an explosion of all types of information. However, due to different standards and distinctive calibers, the information has not been effectively and deeply applied in the power systems. Electric power companies have a real demand for timely, accurate, and intelligent access to various information in the power industry. In this paper, we use the data acquisition technology, including Pandas, Numpy, BeautifulSoup, and other tool libraries, to build a data acquisition system that consists of an information data capture module, a data filtering module, and a data processing module. The related technologies of data storage and processing are also studied.
Library Engagement With Emerging Technologies in Research and Learning

Data-intensive emerging technologies manifest in learning and research in several ways, offering routes to impact student success and transform the research process. In learning, emerging technologies may enable new strategies for addressing student success. In research, emerging technologies are already impacting scientific methodologies with more disruption, and opportunity, anticipated. As the nature of research changes, so do the practices surrounding its dissemination and other issues taking on new importance. This chapter identifies strategic opportunities for libraries to adopt and engage with emerging technologies and ways in which library values and professional expertise inform and shape this engagement. The chapter builds on four cross-cutting opportunities that permeate many or all aspects of library services. Each section identifies key technologies shaping user behaviours and library services and highlights exemplary initiatives.
Optimal Elliptic-Curve Subspaces for Applications in Double-Authenticated Requests in Mobile Distributed Data Mining

Mathematical models based on elliptic curves have been intensively studied since their applicability in data security systems was discovered. In this article, the authors describe the optimal way to select particular subspaces over which elliptic curves are defined, showing the applicability of these subspaces in secure data transfer. Access to large databases and analyses of the requests made to these databases are required daily by a variety of users, including legal entities. An attack on these communication systems causes violations in privacy and damage to/theft of data that can be worth EUR tens of billions annually. For requests made between computers, encryption methods can be used as these systems have adequate computing power and energy. For requests made from fixed and mobile systems, if the data are distributed heterogeneously, the computing power required to authenticate both the users and the answering entities determines the efficiency of the proposed solution. To address this limitation, our study proposes a double-authentication method based on particular elliptic-curve systems.
An investigation on socio-cyber crime graph

Today, cyber crime has done much to harm individuals, organizations and public authorities. Cyber crime detection and classification methods have succeeded in protecting and preventing data against these attacks by varying degrees. Many rules and procedures to combat cyber crimes were put in place and offenders were subjected to severe sanctions. However, the study reveals that many countries are still dealing with this problem, with the United States of America suffering the most harm as a result of cyber crime over the years. A new report shows that the financial losses in 2018 were almost 781.84 million dollars. This chapter discusses the different forms of cyber crime today and the popular environments in which cyber crime occurs.
A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs

In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare.
A Graph Neural Network based Fake Currency Recognition Approach

In general, fake currencies are being circulated around, for collapsing the country’s economy. It is achieved by spreading the money to foreign tourist people and vision-impaired people. This proposed work aims to assist such people on detecting the genuineness of the currencies given to them. The work is designed using a customized Graph Neural Network (GNN) -based approach for estimations, and its performance is compared to the traditional Convolution Neural Network, K-Nearest Neighbor, and VGG16 network. This experimental study has been performed utilizing an openly available dataset called Indian currency notes from Kaggle website. The outcome of the study indicates that the suggested GNN model performs better than the existing approaches.
On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation

Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200-2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.
Analysis of Quantum Correlations Obtained Using Local Optimal Universal Asymmetric Cloners

We apply the local optimal universal asymmetric cloning machine on an initially pure entangled state of two qubits. As output, we obtain two final states which present quantum correlations. We analyze three types of quantum correlations among the final states, namely, concurrence, quantum discord, and consonance. A detailed comparison between concurrence, quantum discord, and consonance is made, and we find that consonance is greater than quantum discord, which is in turn greater than concurrence.