Reviewerly/exHarmony huggingface dataset:
QoE Comparison of AL-FEC Algorithms on H.265/HEVC Video and Audio Transmission with MMT

This study assesses QoE of H.265/HEVC video and audio IP transmission over MMT with the two AL-FEC coding methods.We employ Reed-Solomon and Structured Low Density Parity Check.We perform multidimensional QoE assessment with three adjective pairs.We then find the appropriate selection of the coding method and the code rate according to the content and network condition can enhance QoE.
Robust video event recognition

Video Event Recognition is an important problem in video semantic analysis. In Video Event Recognition, video scenes can be summarized into video event through understanding their contents. Previous research has proposed many solutions to solve this problem. However, so far, all of them only target high-quality videos. In this thesis, we find, with the constraints in modern applications, that low-quality videos also deserve our attention. Compared to previous works, this brings a greater challenge, as low-quality videos are lacking essential information for previous methods to work. With the quality degraded, technical assumptions made by previous works no longer stay intact. Thus, this thesis provides a solution to address this problem. Based on the generic framework proposed by previous work, we propose a novel feature extraction technique that can work well with low-quality videos. We also improve the sequence summary model based on previous work. As a result, comparing to previous works, our method reaches a similar accuracy but is tested with a much lower video quality.
Estimation of Parameters in Erlang Distribution using Prior Information

This paper focuses on the estimation of parameters of an Erlang density using known Coefficients of variation and Kurtosis of the population basing on past experience and a simple random sample of size from the population. Estimators using Searle D.T approach are proposed and their bias and mean squared error are calculated. The relative efficiency of over conventional estimator is also tabulated for various sample sizes and various C.V values, Kurtosis values. The proposed estimators are observed to be more efficient than under the conditions established. Subject Classification: AMS 62J13
A TRUSTED DATABASE ON ENQUIPMENT WITH PROTECTION AND INFORMATION PRIVACY 

Customarily, when classification turns into a worry information is scrambled before outsourcing to an administration supplier. Any product built crypto-logic develops later sent, to server type question handling with the encoded information, characteristically constrain inquiry expressiveness. Here, we present Trusted DB, an database outsourcing model which permits customers to complete sql inquiries use of security to execute also below administrative consistence imperatives along utilizing facilitated server, sealed trusted equipment in basic question preparing stages, in this manner expelling any constraints on the sort of bolstered questions. Regardless of the over cost, execution constraints equipment with trusted, here demonstrate expenses in particular inquiry which requests the greatness smaller compared to (present nor) future programming just instruments. Trusted DB is keep running with genuiene equipment and manufactured, and expenses were listed here
DR CONGO: Money Laundering

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 57, Issue 6 p. 23033C-23034B Continental Developments DR CONGO: Money Laundering First published: 04 August 2020 https://doi.org/10.1111/j.1467-6346.2020.09562.xRead 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume57, Issue6August 2020Pages 23033C-23034B RelatedInformation
Supplementary material to &amp;quot;Impact of an educational program on earthquake awareness and preparedness in Nepal&amp;quot;

of questions asked in the 2018 and 2020 surveys, followed by statistical indices of change in 2 years of our program: the corresponding calculated and tabulated chi-square values, and p-values at 0.05 significance level.
Data Governance

In this chapter, we’re going to talk about strategies to ensure the long-term success of your data program and actions you should take to ensure it is relevant and successful. The first of these topics is the drafting and implementation of an organizational data governance strategy.
Retraction

This article refers to:RETRACTED ARTICLE: Application of deep learning algorithm in college English teaching process evaluation
New Parallel Technics for GPU, Fast SURF Algorithm

Computer vision algorithms, especially real-time tasks, require intensive computation and reduced time. That’s why many algorithms are developed for interest point detection and description. For instance, SURF (Speeded Up Robust Feature) is extensively adopted in tracking or detecting forms and objects. SURF algorithm remains complex and massive in term of computation. So, it’s a challenge for real time usage on CPU. In this paper we propose a fast SURF parallel computation algorithm designed for Graphics-Processing-Unit (GPU). We describe different states of the algorithm in detail, using several optimizations. Our method can improve significantly the original application by reducing the computation time. Thus, it presents a good performance for real-time processing.
Iteratively weighted principal component analysis and orientation consistency for normal estimation in point cloud

In this paper, we present a method to robustly estimate normal of unorganised point clouds, namely Iterative Weighted Principal Component Analysis (IWPCA). Since the neighbourhood of a point in a smooth region can be well approximated by a plane, the classical Principal Component Analysis (PCA) is a widely used approach for normal estimation. Iterations are applied and bilateral spatial normal weights are introduced in each iteration for the local plane fitting to enhance the reliability of the PCA results. Minimal Spanning Tree (MST) is used to reorient flipped normals. We demonstrate the effectiveness and robustness of the proposed method on a variety of examples.
Improved Bayesian inverse reinforcement learning based on demonstration and feedback

A major obstacle to traditional reinforcement learning is that rewards need to be artificially set to have a strong subjectivity. The inverse reinforcement learning algorithm solves this problem. Traditional inverse reinforcement learning requires an optimised demonstration, which is often not met in reality. Therefore, an interactive learning method was proposed to enhance the learned reward function by combining the feedback with the demonstration and using the improved Bayesian rule iteration of the imagery to improve the Agent strategy. The proposed method was tested in experimental and simulation tasks. The results showed that the efficiency of the method was significantly improved under different degrees of non-optimal proof.
Perancangan Sistem Informasi Penjualan Dan Jasa Perbaikan Mobil Pada Bengkel Alam Jaya

Alam Jaya Workshop is an individual company engaged in services or repairs of four-wheeled vehicles located on 32 Tidore Street. In processing the data, both sales data, customer data, goods data, purchase data and reports on Alam Jaya Workshop are still not well computerized. The data processing system used in Alam Jaya Workshop is currently still manual, where the service data search process is slow and difficult because the data is not well coordinated, making reports slow if the report is needed immediately, the accuracy of the data is not guaranteed. All these problems arise because of the lack of good organization in processing data and in making reports, it is still recorded in the agenda book, resulting in slow performance. Therefore, this study aims to provide a solution to the problems that occur by offering sales information and maintenance service along with purchasing system using Unified Modeling Language (UML) to design the system modeling such as Use Case Diagram, Activity Diagram and Class Diagram as well PHP programming language, MySQL DBMS with Adobe DreamweaverCS5 editor and waterfall system development method. There are several stages in doing this research that is problem identification, literature study, data collection, data analysis, system design and report writing. The result of this study is a system that can provide effectiveness and efficiency in processing data at Alam Jaya Workshop.
A Case for Link-Level Acknowledgements

Bound together secure designs have prompted numerous broad advances, including engineering and flip-tumble entryways. Following quite a while of affirmed inquire about into compilers, we disconfirm the combination of A* seek. In our exploration we affirm not just that model checking and IPv7 are constantly incongruent, yet that the same is valid for the World Wide Web
Credit Analysis in Banking Industry

Our economy depends on data which is everywhere, in every section, in every country. We produce and convert it into useable data. Information allows us to enhance the business processes and provide our customers and partners with the best quality standards, services and products. The phenomenal rise in information, and problems encountered while dealing with huge amount of data, make it necessary for an organization to introduce a technique that can overcome these problems and provide an effective solution. Every year the banking organizations, generate enormous amount of valuable data from their customers and their transactions. These valuable data need to be saved and analyzed effectively using big data analytic techniques so as to get the necessary and useful insights for the banking sector. Big Data Analytics (BDA) provides a better consumer experience with better data management creating transparency, collecting more accurate and detailed performance data, setting up controlling experiments, segmenting populations to customize actions, and replacing/supporting human decisions making with automated algorithms. The primary focus of our proposed work will be on identifying the issues that the banking sector faces in decision making while granting loans to customers, in detail and providing an optimal solution using the Big Data approach and tools like Hadoop, HDFS, Spark etc.
Correction to: The effect of source claims on statement believability and speaker accountability

A Correction to this paper has been published: 10.3758/s13421-021-01215-9
The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Sentiment Analysis is an application of Natural Langue Processing to analyze social media corpora to extract insights of corpora. Sentiment analytical results are the real feedback of the customers, which enables the organizations and companies to take appropriate decision on their products and business policies. Stemming plays in-evitable and vital role in sentiment analysis. Stemming is one of the phase of preprocessing the social media corpora. Today most of the researches uses strong stemmers to identify stem words of social media corpora. The most popular stemming algorithms such as Lancaster and Porter stemming algorithms causes prejudiced the meaning of the words. The over-stemmed words mislead the sentiment classification process. To prevent the over-stemming the Unprejudiced lighter stemming algorithm is proposed to sustain the meaning of the stemmed words. The propose Un-prejudiced algorithm uses lexical database and Parts of speech of Python Natural Language Tool Kit. There are a few stemming algorithm accuracy evaluation methods, in this paper we focused on Paice Error-rate relative to truncation (ERRT) measure to evaluate the accuracy of Lancaster, Porter and Unprejudiced stemming algorithms. The experiments were conducted on 25,758 source words and results were evaluated using Paice stem evaluation method and Sirsat method. The Paice Evaluation ERRT values 0.47209, 0.28703, 0.15502 of Lancaster, Porter, Unprejudiced respectively are proved that the Unprejudiced stemmer is more accurate than Lancaster and Porter. Sirsat’s stem evaluation method Average Words Conflation Factor (AWCF) results 10310.31, 14031.17, 23349.87 of Lancaster, Porter, Unprejudiced respectively are also proved the Unprejudiced stemming algorithm is more accurate than Lancaster and Porter stemming algorithms.
Preface: 3rd International Conference on Earth Science, Mineral, and Energy

First Page
Surrogate-assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules

© 2013 IEEE. Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.
Research on lightweight detection model of fake domain name

In recent years, phishing websites and other fake domain name attacks have become more frequent, posing a serious threat to the security of society and individuals. Fake domain name detection has become an important part of network protection. At present, fake domain name detection is mainly for public domain names, and detection methods are mainly based on edit distance. It is difficult to express the visual characteristics of domain names fully. Based on that, this paper studies the lightweight detection strategy of domain name string for the educational fake domain names and improves the detection efficiency by comprehensively considering the effect of character position, character similarity and operation type on the vision of domain name. Experimental results show that our method has a higher precision rate and recall rate on the position of the character, the character similarity, the type of operation than the traditional edit distance algorithms.
Concept Relation Knowledge Visualization with CR Logic using Neo4j

Knowledge Visualization using graphical structure is a field in which the researchers have a strong influence on comprehensibility. Based on the information provided by the users, the knowledge is created. By mapping the logic generated based on the knowledge, we have created a new graphical structure using Cypher Query Language that shows the connectivity between each concept and their relationship. These knowledge visualization techniques are mainly suitable in finding the outcome of a situation and also in discovering new knowledge from the existing or by adding new information. An automated code is generated to build the graph structure for visualization in Neo4j. The reduction in content size and graphical structure helps the user to easily gain knowledge about the system.
What is the risk to the uk?

Veterinary RecordVolume 184, Issue 25 p. 750-751 News and reports What is the risk to the uk? First published: 22 June 2019 https://doi.org/10.1136/vr.l4315Read 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. Volume184, Issue25June 2019Pages 750-751 RelatedInformation
Enhancement of Satellite Imagesusing Image Sharpening Technique

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.
Model Free Optimal Control of Two Whole Buildings using Deep Q-Learning

This study introduces an application of deep Q-networks (DQN) for whole building energy optimization.The DQN was coupled to an office building and a retail building: an EnergyPlus simulation model provided by the U.S. Department of Energy (DOE).The optimal controls can successfully minimize total building energy use with appropriate indoor CO2 concentrations maintained (less than 1,000 ppm).It was shown that the DQN can find balanced control actions between different energy consumers in the building such as chillers, pumps, and air handling units (AHUs), leading to a whole building global optimization.
Selection Structures

As one shouldSelection structures \b have learned in Computer Science I, there are two basic types of control structures available regardless of the language used. These two types of control structures are selection structures and iteration structures, also commonly known as ifs and loops.
Preface for “Irreversible Processes in Nature and Technics”

Since 2001 Bauman Moscow State Technical University has regularly hosted the All-Russian Conference “Irreversible Processes in Nature and Technology”. The coming conference will be the tenth anniversary. In 2019, the conference focused on discussing new diverse experimental and theoretical findings recently obtained in the field of irreversible processes. At that conference researchers presented their latest achievements, results of their studies in various scientific fields and made their proposals for making new practical devices based on the implementation of irreversible processes. The conference participants had an opportunity to meet and talk to colleagues from different countries, and establish business and technical contacts. The conference focused on discussing the results in the field of space technology, aircraft, vehicles and energy devices, as well as the problems of creating new materials which give the possibility of converting the energy of solar radiation into electrical energy with a high conversion coefficient. The participants also covered the issues of changes in external conditions in a condensed medium, which form new phase states of matter, characterized by the manifestation of properties: spontaneous polarization, magnetization, “swirling” of the medium in the form of stopped vortices, superconductivity, superfluidity, etc. Nonlinear-optical processes were studied: multi-frequency forced combination scattering, multiple generation of optical harmonics, etc. In recent years, the scientific community has become interested in the problems related to the properties of gravitational waves, whose detection was possible in ground conditions using the Fabry–Pérot laser interferometer. Chairman of the Organizing Committee A.N. Morozov Chairman of the Program Committee V.S. Gorelik
Retraction Note: DOOR: Distributed Object Oriented Software Restructuring Approach Using Neural Network

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1134/S0361768820220016
The Role of Narrative Format in Improving Narrative Transport and Empathy among Fiction and Non Fiction Readers

The purpose of this study was to determine whether non-fiction (news) and fiction (short story) narrative formatscould have different effects in increasing narrative transport and readers’ empathy. The study was conductedusing an experimental pre-posttest method and two groups designed. Participants of 62 students (male = 19,female = 43, Mage= 21.6) were randomly assigned to two groups of reading conditions (non-fiction vs. fiction),then their level of empathy and narrative transport were measured after manipulation/treatment. The measuresused were the Narrative Transport Questionnaire and the Interpersonal Reactivity Index. The statistical analysismethod used was the MANCOVA test. Differences in conditions (non-fiction vs. fiction) as independentvariables, baseline narrative transport score and empathy as covariates to be controlled. The test results betweenthe group multivariate test showed that there was a significant difference between the reading groups of non-fiction and fiction on narrative transport and readers’ empathy simultaneously after controlling for the pre-testnarrative transport and pre-test empathy variables, F(2, 57) = 3.291, p &lt; .05, Wilks’ Λ = .896, with power ηp2 =.104 (10.4%). The univariate test on each dependent variable (empathy, narrative transport) in terms of textreading (non-fiction vs. fiction), after controlling for covariates (pre-test empathy, pre-test narrative transport),only differed significantly in the dependent variable narrative transport, F(5, 55) , p &lt; 0.05, with ηp2 = .087 (8.7%),but not significant on the dependent variable empathy. The discussion is related to the familiarity of the readertowards the main character in non-fiction and fiction texts
Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink

Wireless network sensors based on the mobile sink are regarded to be a common network and used in various fields in the last few years, they are thought to be easy to use, but contain the problem of energy loss and are affected by an energy hole problem, as it depends on batteries.This paper proposes a solution to this problem by using an innovative objective function for a consistent distributing of cluster heads, the enhanced harmony search based routing protocols based on energy equilibrated node clustering protocol.In order to route the data packet among the sink and cluster heads, an enhanced modified deep echo state neural network is suggested.The efficiency of a projected integrated clustering and routing protocol has been investigated at 500 nodes, and the 96 per cent success data for the proposed algorithm is given using the average energy consumption, send and receive packaged and optimum numbers of CH.