Reviewerly/exHarmony huggingface dataset:
Shifted power-GMRES method accelerated by extrapolation for solving PageRank with multiple damping factors

Starting from the seminal paper published by Brin and Page in 1998, the PageRank model has been extended to many fields far beyond search engine rankings, such as chemistry, biology, bioinformatics, social network analysis, to name a few. Due to the large dimension of PageRank problems, in the past decade or so, considerable research efforts have been devoted to their efficient solution especially for the difficult cases where the damping factors are close to 1. However, there exists few research work concerning about the solution of the case where several PageRank problems with the same network structure and various damping factors need to be solved. In this paper, we generalize the Power method to solving the PageRank problem with multiple damping factors. We demonstrate that the solution has almost the equative cost of solving the most difficult PageRank system of the sequence, and the residual vectors of the PageRank systems after running this method are collinear. Based upon these results, we develop a more efficient method that combines this Power method with the shifted GMRES method. For further accelerating the solving phase, we present a seed system choosing strategy combined with an extrapolation technique, and analyze their effect. Numerical experiments demonstrate the potential of the proposed iterative solver for accelerating realistic PageRank computations with multiple damping factors.
About the system of games in teaching high school students algorithms and mathematical structures

The article considers the issue of increasing the motivation of students to study computer science and mathematics by introducing gamifcation elements into the educational process. Special attention is paid to the possibility of introducing computer games into the educational process as part of extracurricular activities in computer science. A brief overview of the training software environment developed by the authors is given, containing a set of paired fnite mathematical games ("Determinant", "24 cards", "Even", "The Bachet's game", "The Bergson's matches", "The Kayles' game", "Nim"). The authors indicate the possibility of using the developed system of games to prepare students for the state fnal certifcation in computer science in the 11th grade. The main approaches to solving the games listed above, as well as their applications for determining the winning strategies of some other games, are considered. In conclusion, the rationale for the expediency of using the developed educational software environment is given.
Decision Support System of Direct Cash-Village Fund Recipients Using Multi Attribute Utility Theory

Direct Village Fund Cash Assistance (BLT-Dana Desa) is a form of assistance from the government in the form of cash to poor families in villages sourced from the Village Fund to reduce the impact of the COVID-19 pandemic. To facilitate village officials in determining aid recipients quickly, accurately and on target, the MAUT method was chosen which was deemed suitable for use in the Decision Support System (DSS) which had many criteria so that it could easily calculate each alternative based on the many types of criteria and sub-criteria used and with a predetermined weight. There are 148 data samples of BLT recipients registered in the Social Welfare Integrated Data (DTKS) of Loa Janan Ulu village. The criteria in this study are building floor Size, type of house floor, types of house walls, sanitary facilities, power source, source of drinking water, cooking fuel, consumption of chicken/meat/milk, clothing needs, consumption in a day, do not have savings max. 500.000 rupiah. Based on the results of calculations using the MAUT method, a recommendation for direct cash assistance recipients was obtained with an accuracy value of 92.57%.
Semantic Segmentation of Mammograms Using Pre-Trained Deep Neural Networks

Anatomic regions like breast and pectoral muscle are common regions that need to be segmented before determining abnormalities in the mammographic image. For this task, we explore five convolutional neural network models, namely, Resnet50-Unet, Mobilenet-Unet, Vgg-Unet, Unet, and Segnet. A classic technique that uses an MLP network is compared with the convolutional methods. The MIAS and INbreast datasets are used for evaluating Deep Neural Networks on the segmentation of these regions. Several evaluation metrics are used to compare the results of convolutional approach and a traditional method based on hand-crafted features. The results confirm the superiority of the deep networks over the traditional method. The best Jaccard value for Deep Learning algorithms is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.945\pm 0.068$</tex> . The Mobilenet-Unet network presents the best trade-off between computational cost and segmentation capacity; therefore, it is the best choice to compose a CAD system.
Real time face mask detection with SSD

This paper proposes the Neural Network SSD architecture for the task of face mask detection. We trained this model to detect 3 different categories of people: those wearing a mask, those wearing it incorrectly and those not wearing it. For this proposal, a dataset with 853 images was used with annotations of these 3 classes. For the Neural Network, the weights were initialized from a pretrained model on the image dataset Pascal VOC and fine-tuned to finally achieve a mAP of 70.2% on the validation set. The mAP achieved on the test set was 66.7% which shows that despite the lack of a huge dataset, the architecture SSD is suitable for this task. Additionally, our experiments showed that the MultiBox Loss Function parameters can be modified to improve the performance on small and unbalanced datasets such as the one presented.Current work has focused on the task of classification or detection for a single class. Our work is a novel approach as it performs real-time detection and classification in 3 classes, making it suitable for natural scenarios such as public spaces.
Prediction of the local cloud cover to optimize photovoltaic system power forecast

A major influence on the generated power of a photovoltaic (PV) system is the cloud cover over the system. For this purpose, this paper presents a forecast model that predicts the local cloud cover over a PV system. Based on satellite images, moving clouds are detected and their vectors are calculated using the optical flow method. The forecast model consists of an artificial neural network (ANN), which is trained with the motion vectors of clouds and can predict motion vectors for ten minutes. To determine the cloud cover, an artificial image is generated using the predicted motion vectors, which shows the future cloud positions. Over a cutout for a local forecast, the cloud cover is calculated by a thresholding method. The forecast model shows that cloud positions of known days can be predicted very accurately. The accuracy of the model decreases as expected for days that are unknown to the ANN. The predicted cloud cover is integrated into a PV forecast model. It is shown that this results in a lower deviation between the prediction and the measurement of the PV power under cloudy conditions.
Lung Cancer Segmentation and Detection using Cuckoo Search Evolutionary Algorithms in Early Stage

In all around the world the main reason of death among people is the lung cancer. If the detection of lung cancer is done at the initial stage it can increase the probabilities of survival in humans. If this disease is detected on time, the complete 5-year rate of survival of patients injured with lung cancer from 14% to 49%. The detection approach of lung cancer that uses image processing is utilized in this research for classification in the presence of lung cancer as a CT scan. The designed model used some steps like pre-processing, image segmentation, and feature extraction along with classification. For segmentation K-means along with Cuckoo Search Algorithm (CSA) is used. Feature extraction technique is utilized for extraction purpose of features from lung Region of Interest (ROI) data by using Maximally Stable Extrenal Regions (MSER) and these features are used to train Convolutional Neural Network (CNN), which is used for testing later. At the end to see the type of lung cancer diseases are present by using classification technique of neural network named as Convolutional Neural Network. The work has been executed in MATLAB software and performance has been assessed in terms of True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), Classification Accuracy, Error, and Execution Time.
DETERMINATION OF TEMPERATURE VARIABILITIES AND TRENDS IN TURKEY

A comprehensive study has been carried out for Turkey to investigate temperature variabilities and trends in a 50-year period of 1969-2018.Daily temperature data belonging to 174 meteorological stations which was collected from Turkish Meteorological State Service was used in this research.Temporal and spatial variability of temperature have been studied by linear regression, 3-yr moving average and non-parametric Mann-Kendall test.Yearly and seasonally average temperature of each region has been formed from analysis of daily average temperature for this time period.Temperature increase in this recent half century time period is found to be in the Eastern Anatolia Region with a value of 1.74°C/50 years.The regions with the highest seasonal temperature increases are estimated to be the Aegean Region (1.61°C) in autumn, Eastern Anatolia Region (2.37°C) in winter, Eastern Anatolia Region (1.84°C) in spring and Marmara Region in summer (2.79°C).Moreover, yearly temperature trends showed increasing trends at 99% confidence interval in most stations.Increasing trends at 99% confidence interval have been confirmed in most regions from analyzing seasonally and yearly temperature trends.
Recurrent Sparse MIMO Detection Network Based on Modified Projected Gradient Descent Method

Deep learning (DL) has emerged as a powerful tool for signal detection in large-scale multiple-input multiple-output (MIMO) systems. In this paper, the recurrent sparse detection network (RS-Net) is proposed for performance improvement and complexity reduction. First of all, in order to reduce complexity, RS-Net unfolds the projected gradient descent (PGD) method in a modified way, which consists of a projection and a gradient descent (GD) part. Meanwhile, an RNN with the parameter-sharing structure is adopted to its projection, which significantly eases the training burden. Then, to improve the detection performance, we regularize RS-Net by introducing sparse representation. Besides, the step size of iterations in GD part is also investigated for better convergence efficiency. Finally, simulations demonstrate a better decoding trade-off between performance and complexity in RS-Net.
Data entropy decomposition into functional and uncertainty components

Information entropy is a fundamental property of measurement data and can be decomposed into functional and relative uncertainty components. The purpose of this paper is to mathematically isolate functional entropy and to demonstrate its application to model data. The equation for functional entropy is developed and its utility is demonstrated using hypothetical data that describes different processes that contribute to the same effect. Functional entropy assigns an effectiveness value to these processes in the fundamental units of information, which makes it a useful tool for comparing different data sets and their associated processes.
Study on the Automatic Basketball Shooting System Based on the Background Subtraction Method

There are many drawbacks such as clustering, background updating, inaccurate testing results, and low anti-interference performance in traditional moving target detection theory. In our study, a background subtraction method to automatically capture the basketball shooting trajectory was used to eliminate the drawbacks of the fixed-point shooting system such as cumbersome installation and time and manpower consumption. It also can improve the accuracy and efficiency of moving target detection. We also synthetically compared to common methods including the optical flow method and interframe difference method. Results showed that the background subtraction method has better accuracy with an accuracy rate over about 90% than the interframe subtraction method (88%) and the optimal flow method (85%) and presents excellent robustness with considering variable speed and nonrigid objects. Meanwhile, the automatic detection system for basketball shooting based on background subtraction is built by coupling background subtraction with detection characteristics. The system detection speed built is further accelerated, and the image denoising is improved. The trajectory error rate is about 0.3, 0.4, and 0.5 for the background subtraction method, interframe subtraction method, and optimal flow method, respectively.
INSPECTORMAP: A Spatial Data Infrastructure Applied to the Maintenance of Solar Plants Using Free Satellite Imagery

Photovoltaic solar plants are one of the main facilities away from urban centers for the generation of clean energy. Since its appropriate maintenance ensures its suitable operation, optimizing their maintenance tasks in a preventive way is key. This article presents a spatial data infrastructure called INSPECTORMAP that, based on the analysis of free satellite images within the optical spectrum, can detect unusual vegetation and bodies of water in the vicinity of photovoltaic plants that can affect their correct operation. Thanks to the implementation of a monitoring and alert system, it is possible to know and map the status of the photovoltaic plant in terms of unusual coverages appearing, both natural and artificial, at any moment. Thus, maintenance workers would travel to the solar plant to carry out their maintenance tasks in this regard only when the system detects a risk.
Electric Power Supply Fault Tolerant Analysis for a Naval System

This paper presents an analysis for the fault tolerant system of the electric power supply naval, taking in consideration the electric power computer simulation and design technology. The main switchboards configuration alternatives are taken in consideration to increase the reliability by improving the fault tolerant aspects of power system in its functional stages. The paper points to the most fault tolerant alternative.
What really matters in Code Review? A study about challenges and opportunities related to code review in industry

Code review is a widely known process where developers assess each other’s codes. Since the Modern Code Review (MCR) activities bring countless opportunities for the industry, numerous aspects can be evaluated about the result of the MCR on the quality of the software. Based on that, we evaluated the perspective of software practitioners on code review by applying a survey to understand more about the aspects of the MCR in their software development routine and their impressions about how it brings quality to the software. Our results reaffirm that the MCR is seen not only as important but also is associated with the quality of the product. Besides that, we highlight the aspects of the MCR that bring benefits to the software and the development process. We also found results that highlight which aspects of the MCR can negatively impact the product and which factors allow the MCR to be executed properly. This research contributes to expanding the perspective of quality aspects, challenges, and benefits that can be obtained with the MCR process applied in the software development industry.
Extracting experimental parameter entities from scientific articles

Systematic reviews are labor-intensive processes to combine all knowledge about a given topic into a coherent summary. Despite the high labor investment, they are necessary to create an exhaustive overview of current evidence relevant to a research question. In this work, we evaluate three state-of-the-art supervised multi-label sequence classification systems to automatically identify 24 different experimental design factors for the categories of Animal, Dose, Exposure, and Endpoint from journal articles describing the experiments related to toxicity and health effects of environmental agents. We then present an in depth analysis of the results evaluating the lexical diversity of the design parameters with respect to model performance, evaluating the impact of tokenization and non-contiguous mentions, and finally evaluating the dependencies between entities within the category entities. We demonstrate that in general, algorithms that use embedded representations of the sequences out-perform statistical algorithms, but that even these algorithms struggle with lexically diverse entities.
A Scientometric Analysis on Aggregate Blending

An all-encompassing approach for the first scientometric analysis on aggregate blending or proportions of aggregates is described in this study. The investigation accepted a comprehensive survey idea utilizing scientometric examination and science planning innovation, and extensive conversation to feature the most powerful distribution sources, most dynamic analysts, and foundations, effect on the field of Aggregate blending, to inspect the present status of-the-workmanship research center, and flow research gaps. The study gives a brief note on 19,732,021 related bibliographic data taken from the Scopus database. This examination will assist scientists, policymakers, professionals, research institutions with understanding the latest technology in aggregate blending and opening more space for additional exploration.
Video- Text Embedding based Multimedia Recommendation for Intelligent Vehicular Environments

Recently, technologies related to autonomous vehicles have been developing rapidly. In line with the advancement in autonomous driving, fully autonomous vehicles that can be driven without human intervention will soon be commercialized. When fully autonomous vehicles appear, various in-vehicle services will change along with automobile culture. Autonomous vehicles may offer services for enjoying multimedia content and entertainment in the vehicle. In this paper, therefore, we propose a method for recommending multimedia to passengers in the vehicle for new services of this kind. We used video-text embedding as a method for recommending multimedia and conducted experiments using movie trailers and plots. Previous studies on video-text embedding have offered new data, such as audio, or proposed new models based on high-complexity deep learning methods. In this paper, however, video-text embedding is performed based on a pretrained model and multiplication operations between features and vectors. The proposed method shows that video-text embedding can be performed without consuming much time or computing power. Similar movies and dissimilar movies can be identified through the proposed videotext embedding. This was verified by measuring genome matrix similarity, and the information about the movies used for the recommendation confirmed that the results were significant.
Getting Started

In the previous chapter, you learned how to set up the Python 3 environment on Linux, macOS, and Windows computers. You also looked at a few popular IDEs for Python. In this chapter, you will get started with concepts of test automation. Then you will explore a light and easy way to learn the test automation framework in Python 3, called doctest.
A study on Rényi entropy and Shannon entropy of image segmentation based on finite multivariate skew <i>t</i> distribution mixture model

Image segmentation technology has been widely used in various business and social fields. In recent years, more and more scholars have studied the theories in this field. Many models and methods have good effects in image segmentation. However, as people's demand for image is getting higher and higher, people often face images with complex structure and multimode, which makes us need to study and analyze the theory of image segmentation more deeply. In this paper, we study the Rényi entropy and Shannon entropy of finite multivariate skew t mixture distribution (this distribution was proposed based on Sahu and Branco (2003; https://doi.org/10.2307/3316064 ), and it has better properties and wider application range than the traditional skew t distribution). In addition to the specific calculation results of the two kinds of entropy, we use Hölder inequality and polynomial theorem to obtain the upper bound and lower bound of the two kinds of entropy of finite multivariate skew t mixture distribution.
Development of interactive gadgets: pedagogical, methodological and ergonomic aspects for industrial design

The Industry 4.0 is a consequence of the evolution in technological advances, which has allowed and the use of new tools for simulation, digital integration, fabrication flexibility, and personalization to achieve new product design solutions. The importance and actuality of this revolution have had a great impact on the engineering and design education system, and this is the case of the Faculty of Engineering and Technology Sciences (FCITEC), from the Autonomous University of Baja California (UABC), where the implementation of gadget prototyping has been encouraged. This ongoing work is intended to delineate the methodological, pedagogical, and ergonomic aspects of gadget prototyping with platforms such as Arduino and NodeMCU, and its benefits to the Industrial Design (ID) Discipline. It is a project that started in 2018 with the scope of understanding interactivity, usability, and multidisciplinary collaboration, which are key for a designer’s profile. In this sense, User-Centered Design methodology is used as a framework for usable product development, with the aid of task, interface, and housing design. Specific tools of particular interest are persona design, interface analysis, and cognitive architecture outline. Important results so far include 1) student-made prototypes, 2) usability workshops in international congresses, 3) intellectual property registration, and 4) academic course designs.
Demixing and Topology Identification for Mixed Graph Signals

Graph learning (GL) plays a pivotal role in graph signal processing (GSP) for inferring the underlying signal structure. However, most of the recent works only focus on the single-graph learning situation. This work studies the multiple-graph learning problem for mixed graph signals, and a joint blind signal separation (BSS) and GL algorithm is proposed. To eliminate the ordering uncertainty of BSS, an approach based on maximum graph correlation (MGC) is also put forward such that the unmixed graph signals can be partitioned into a set of clusters, with a learned topology for each cluster. Numerical experiments demonstrate the effectiveness of this algorithm, in both signal separation and graph topology recovery.
High Payload Capacity using Steganography Combined with Cryptography

In today’s world, information plays a vital role in any company. Most of the employees, end-users are creating more data about their business. In this case, every company faces a difficulty while protecting the data. For example, when storing the data on the cloud, many issues are encountered, they are data loss, confidentiality, integrity, availability. A variety of data security techniques are there to solve these issues. The most popular techniques are cryptography and steganography. By taking help of these techniques, this research study has proposed a new approach for providing better security and achieving high information storage capacity. The first step is to encrypt the cover image. As a result, the ciphered image will be obtained. Then, steganography will be applied for embedding the information. Two different algorithms have been used in this proposed model. Further, the proposed method will be analyzed by calculating the Peak Signal to Noise Ratio (PSNR) values and histogram.