Reviewerly/exHarmony huggingface dataset:
Online Education in Russia. Modern Trends and Tendencies

Online education is extremely important in Russia due to country geography. There are many students in distinct areas. Online education for them usually is the only opportunity for getting high quality education in their region. During the last several years due to Internet coverage development online education has become more wide spread. Modern trends and tendencies in this field are development of humanitarian and social courses, search of opportunities to provide group discussions in this format, foundation of many online schools and universities. The main problem in this area is low government regulation and as a result low quality of education.
Creating Web Applications and Site Collections

In previous chapters, we have learned how to create Service Applications in order to enable additional functionality for our users. In this chapter, we will learn how to create Web Applications and Path-based as well as Host Named Site Collections and how to customize Alternate Access Mappings, create Content Databases for our Site Collections, and enable Fast Site Collection Creation.
Online Document Verification System

In this modern era, there has been a need for the reliable and fastest mode to verify a document or certificate, to reduce the level of duplicity which is done manually till now. Document or certificate verification plays an important role whereas a document or certificate issued by an authority will be verified for its authenticity. In a typical system, it will be performed through the interchange of mails or post, as it is not an effective and efficient process due to the human intervention. In this proposed system, a common database is organized between the admin and user to operate frequently and it will be more secured as there is no need for any third party involvement which provides direct communication between client and user. Using the proposed system, the authenticity as well as the integrity of the certificate can be verified, and the admin can verify the document and upload it to the portal.
Getting started

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.
Spatial Regularization and Adaptive Distance Metric Methods through DST for Tumor Segmentation

The process of segmentation in MRI pictures is turning into a significant assignment to be considered in clinical oncology applications, in view of the noise and blur that is available normally in MRI images. To minimize this natural disadvantages in the MRI images an imaging tool called belief theory is taken as the base alongside the proposed evidential clustering algorithm (ECM-MS) .This proposed technique joins the adaptive distance metric in so as to limit the clustering distortions and the comparability that happen between the voxels. The local homogeneity is measured by the spatial regularization dependent on the belief theory called Dempster Shafer Theory (DST). To get definite division the surface highlights are extricated from the data picture and is incorporated with the force of the voxels in the proposed strategy, thusly giving a decent presentation contrasted with different strategies.
Convergence in security management

This chapter examines the practical experience of merging or combining the departments of two different operations under one umbrella, thus streamlining operations and reducing expenses. First the notion of how the idea of convergence came about is discussed, followed by the specifics of merging functions between companies, the convergence of security and IT security, the tasks of security, and the benefits of convergence. Finally, a discussion of return on investment (ROI) is included.
A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimisation algorithm

Cloud computing is the latest emerging trend of distributed computing, in which distributed resources are delivered based on user's demand. In the cloud environment, computing resources need to be scheduled so that providers make the most use of the resources and users will find the applications they need at the lowest cost. In this paper, an inverted ant colony optimisation (IACO) algorithm has been used to solve the task scheduling problem in the cloud environment with the goal of reducing runtime and increasing load balancing. In the proposed method, pheromone repellent is used instead of pheromone gravity, so the effect of pheromone prevents the wrong choice. In order to observe load balance and the effect of pheromone repulsion, fuzzy logic and weight definition have been used. The results have shown that the proposed algorithm while reducing the total time and cost of execution, could also increase load balance.
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values

Machine learning and artificial intelligence methods are often referred to as \black boxes" when compared to traditional regression-based approaches. However both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables. The fitted models are themselves functionals of the data,about which we can do statistical inference using computationally intensive methods such as the bootstrap. Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial dependence functions. Bootstrapping these first-differenced functionals provides standard errors and confidence intervals for the estimated relationships. We show that this approach replicates the point estimates of coefficients attained in a linear OLS models, and demonstrate how this generalizes to marginal relationships in ML/AI models. This paper extends the partial dependence plot described in Friedman (2001), and visualizes the marginal distribution used to construct the PDP as described in Goldstein et al. (2015) before applying the steps described above. We further discuss the extension of PDP into a Shapley value decomposition and explore how it can be used to further explain modeloutputs. We conclude with a hedonic house pricing example, which illustrates how machine learning methods such as random forests, deep neural net, and support vector regression automatically capture nonlinearities and shed light on inconsistencies revealed by meta-studies of hedonic house pricing.
Understanding the Energy vs. Adversarial Robustness Trade-Off in Deep Neural Networks

Adversarial examples, which are crafted by adding small perturbations to typical inputs in order to fool the prediction of a deep neural network (DNN), pose a threat to security-critical applications, and robustness against adversarial examples is becoming an important design factor. In this work, we first examine the methodology for evaluating adversarial robustness that uses the first-order attack methods, and analyze three cases when this evaluation methodology overestimates robustness: 1) numerical saturation of cross-entropy loss, 2) non-differentiable functions in DNNs, and 3) ineffective initialization of the attack methods. For each case, we propose compensation methods that can be easily combined with the existing attack methods, thus provide a more precise evaluation methodology for robustness. Second, we benchmark the relationship between adversarial robustness and inference-time energy at an embedded hardware platform using our proposed evaluation methodology, and demonstrate that this relationship can be obscured by the three cases behind overestimation. Finally, we examine the gap between robustness measured with the attack methods and the verification methods, and show this gap is reduced by our proposed compensation methods. Overall, our work shows that the robustness-energy trade-off has differences from the conventional accuracy-energy trade-off, and highlights importance of the precise evaluation methodology.
Incentive Stackelberg game-based path planning for payload transportation with two quadrotors

This paper proposes a path planning approach to the task of transporting a cable-suspended payload with two quadrotors. The transporting system is provided with a desired payload path which is predefined by the user without the consideration of system dynamics. The path planning problem is to find the optimal paths for the quadrotors, which can make the payload move along the closest path to the desired one under the constraint of system dynamics. This problem is formulated as an incentive dynamic Stackelberg game where one quadrotor plays a leader role while the other plays a follower role. By using an incentive strategy, a game solution can be found based on the linearised dynamic system. Some simulation results are provided to show the performance of the proposed path planning approach. The planned paths are also used in real experiments where the paths are tracked by two quadrotors using PID controllers.
Analysis and Prediction of Student Evaluation Scores Based on Bias SVD

Students' evaluation scores for teachers are significant indicators in the teaching evaluation process of a university or online course.There is a disadvantage in all existing evaluation methods, which regard students as the same and ignore the individual differences.To solve this problem, we propose a novel teaching evaluation method which is based on Bias SVD.Firstly, we convert the evaluation scores of teachers into a matrix.Then decompose this matrix by gradient descent and the biases of students in the evaluation process are iteratively obtained.By analyzing 63,193 evaluation records from 15 schools in Beijing Institute of Technology.We find that students who tend to give high scores have corresponding high offset values.We use a sentiment lexicon in the field of education to verify this method.By calculating emotion scores for teachers, we find that biases and scoring features are considerably correlative.Finally, we filtered the really too subjective scores through a certain threshold, and then used the XGBoost model to predict scores from the filtered data.It was shown that the combination method of Bias SVD and XGBoost can improve the accuracy of the prediction experimentally.
URL Based Phishing Detection

Damage caused due to phishing attacks is that which targets the user's personal information. Phishing includes sending a user an email, or causing a phishing page to steal personal information from a user. Blacklist-based detection techniques can detect this form of attack; however, these approaches have certain limitations, and the number of people affecting have continued to grow. The aim of a technique for phishing detection using machine learning to identify each URL into either a legitimate URL or a phished URL. Data availability here in this proposed solution is the key to executing the solution and if there is any issue with data availability it can cost the project accuracy. Data used for model testing must be reliable and appropriate to almost identify all the websites that the user wants to check. Model consistency is another factor that may trigger project failure so the model has to be accurate in determining a true identity of URLs. This technique employs features of a standardized resource locator (URL). The features have been defined which contain URLs for the phishing site. The suggested approach employs certain characteristics to detect phishing. The strategy was tested with a data collection of 3,000 URLs for the phishing site and 3,000 valid URLs for the site. The findings show that more than 90 percent of phishing sites can be identified by the proposed technique.
Privacy Preservation in Blockchain IoT

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Altana buys OCS (Yokohama) &amp; Gardco (Florida)

The Gaussian curve is derived from the binomial theorem and illustrated by Galton's quincunx. The difference between populations and samples is again illustrated by comparing z- and t-test statistics. Non-Gaussian distributions often demonstrate skewness, especially with a few large measurement, and may show abnormal kurtosis, that is, the degree of peaking. Methods of testing for these abnormalities are described. Methods of testing for nonnormality are introduced, and the importance of normality is discussed.
Linear Two-Class Classification

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
ART Surveillance in Africa

ART was pioneered in Africa in the 1980’s. Subsequently, ART centres emerged around the continent, mostly by specialists acquiring skills abroad. Despite this, ART activity remains scant in most African countries and absent in several others.
Small Business Innovation Research Program—Phase I (USDA)

Federal Grants & ContractsVolume 43, Issue 18 p. 5-5 Grant Alerts Small Business Innovation Research Program—Phase I (USDA) First published: 20 August 2019 https://doi.org/10.1002/fgc.30603Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume43, Issue1822 August 2019Pages 5-5 RelatedInformation
Optimization of Text Generation Method for Task-based Human-machine Dialogue System

The human-machine dialogue system is now used in all aspects of life, but the task-based system reply relies on manually marked rules and templates, which makes the text rigid and monotonous, and can not form coherent paragraphs when there is more information, but simply combine sentences together. In order to solve the above problems, this article first analyzes the construction method of the task-based dialogue system, and then proposes an optimized text generation method for the pipeline method. This paper uses the Planning-based Hierarchical Variational Model (PHVM) in the text generation phase. This model will make a global plan before generating the text, split the long text generation task into clauses, and retain the relevance between the clauses, and then generate clauses one by one. In addition, it uses sampling to introduce latent variables globally and locally to provide a variety of options for text generation. Experiments show that the model performs well in generating coherent text and rich text diversity, and solves the existing problems of task-based dialogue systems.
Image Processing Method on Radiographic Image of Piping using MATLAB: Enhancement and Detection Process

Image enhancement is a pre-processing process to enhance the quality and information content of original data. This paper investigates two methods of image augmentation that is deployed to remove noise and improve radiographic images. The first method is image filtering, which consists of smoothing, sharpening and edge enhancement (Sobel &amp; Prewitt) operations. The filtering method emphasizes certain characteristics or eliminates other details. While the second method is morphological technique that utilizes the opening and closing operation, which employed to removed distorted noise and imperfection on the processed images. Each method and operation applied to the image is evaluated subjectively based on the enhance image quality. The image quality measured using MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) which is a full reference metrics. The image quality results are compared to give a wide picture on the performance of the enhanced images. The image processing operations accomplished by using MATLAB image processing toolbox.
Intellectual Property Law and Extra-Contractual Liability

Abstract This chapter discusses intellectual property (IP) and extra-contractual liability by highlighting general comparative analysis issues within civil and common law systems, with some consideration given also to major theoretical clusters that might influence the different legal regimes. The chapter focuses on emerging issues of extra-contractual liability for intellectual property infringement in the platform economy, with special emphasis on copyright and trademark infringement, seeking to co-ordinate miscellaneous approaches from the United States (US), the European Union (EU), and selected European countries’ experiences. In doing so, this chapter highlights research and methodological issues related to limited harmonization at a regional level in secondary and extra-contractual liability doctrines when applied to IP. Finally, this chapter describes the World Intermediary Liability Maps (WILMap) as an attempt to provide consistency within a fragmented research framework while also presenting other miscellaneous endeavours seeking the same goal.
Logic and Data Analysis

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.
Statistical Signal Processing Application of on Line Examination and Result Publication in Organization using Algorithm

Online examination is performed through the internet or intranet using computer. It is an effective conducting and evaluating solution for examination process in organization. Online examination process is developed based on MAT LAB which carries out the examination evaluation and result publications offer greater flexibility than the traditional examination pattern. The main purpose of examination is that generating multiple choice tests, database design, software implementation of a multiple choice examination and is better idea to make an online interface for evaluation. Performance of the proposed algorithm is estimated by using statistical signal processing based on students data and by computing average value, standard deviation (SD), signal-to-noise ratio (SNR), coefficient of variance (CV) and typical error (TE). It is assumed that organization authority manages the institutional resources and the academic and administrative services in such a manner that a large number of students can appear the examination without any hazard inside the own organization safely and properly used.
Nuts and Bolts of Linking: Understanding Context Sensitive Linking Services and Implementation

SUMMARY. The popularity of context sensitive linking services has grown exponentially over the past couple of years. The process of implementing and maintaining these services can require a good deal of labor. Those involved need solid knowledge of serials to provide successful context sensitive linking service (CSL) to their patrons. Frick and Duncan recently managed the implementation of CSL services at their institutions. Their presentation provided an overview of some of the tools that go into providing CSL services, such as Digital Object Identifiers, OpenURLs and link resolvers. In addition, it took a practical look at the actual work required to set up and maintain CSL services. It examined some common issues and problems associated with providing them. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]