Reviewerly/exHarmony huggingface dataset:
Preface of ICNISC2019

5th Annual International Conference on Network and Information Systems for Computers [ICNISC2019] was held on April 19-20, 2019, Wuhan, China. ICNISC2019 aims to bring researchers, engineers, and students to the areas of computer networks and information system. We received over 204 submissions from various parts of the world. The Technical Program Committee worked very hard to have all manuscripts reviewed before the review deadline. All the accepted papers have been submitted to strict peer-review by 2-4 referees, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely rich, profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. We sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. There are three invited sessions. The keynote speakers are internationally recognized leading experts in their research fields, who have demonstrated outstanding proficiency and have achieved distinction in their profession. The proceedings would be published by IOP Conference Series: Journal of Physics: Conference Series (JPCS) We would like to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise. Our deep thanks also go to many volunteers and staffs for the long hours and hard work they have generously given to ICNISC2019. The last but not least, we would like to thank all the authors, speaker and participants for their great contributions to the success of ICNISC2019. ICNISC2019 Organizing Committee
Denoising using Self Adaptive Radial Basis Function

This paper presents an adaptive form of the Radial basis function neural network to correct the noisy image in a unified way without estimating the existing noise model in the image. Proposed method needs a single noisy image to train the adaptive radial basis function network to learn the correction of the noisy image. The gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptiveness in the radial basis function network is compared with the fixed form of spreadness and the center value of kernel function. The proposed solution can correct the image suffered from different varieties of noises like speckle noise, Gaussian noise, salt &amp; pepper noise separately or combination of noises. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.
An Efficient Hybrid Genetic-Grey Wolf Based Neural Network (G2NN) for Breast Cancer Data Classification

Machine learning is the one of the famous Artificial Intelligence (AI) technique. Data Mining or Machine Learning techniques are most popular in medical diagnosis, classification, forecasting etc. K-Nearest Neighbor, SVM (Support Vector Machine), DT (Decision Tree),RF (Random Forest),NN (Neural Network) are famous classification algorithms. Neural Network is one of the popular techniques, which is used to refine the verdict of breast cancer. A neural network is otherwise known as Artificial Neural Network(ANN), which is mimicking of biological neurons of human brain. Genetic Algorithm (GA) is emerged bio inspired technique. Selection, Crossover, and Mutation are three operations in Genetic Algorithm. The performance of a genetic algorithm depends on the genetic operators, particularly crossover operator. Grey Wolfoptimization algorithm is inspired from hunting of wolf strategy. Alphas, Beta, Gamma are the three levels of processes. In this paper, a novel hybrid Genetic Grey Wolf based Neural Network is introduced and we named it as G2NN. In the field of medical, we need more accuracy when compared to other field, because it relates to human life. Many researchers found new novel ideas for breast cancer data classification using neural network model. Among many diseases, Breast Cancer is one of the unsafe diseases among women in Indiaand in addition to the whole world. The early detection of cancer helps in curing the disease completely. In many research areas Genetic Algorithm and Grey wolf algorithm are used to train neurons in order to yield good accuracy. In this manuscript, a new Genetic Grey Wolf optimizer based Neural Network is introduced and we compare the proposed work with other techniques like SVM(Support Vector Machine),NN (Neural Network), Genetic based Neural Network, Grey wolf based Neural Network and the experimental results of proposed work produced better result. The proposed algorithm produces 98.9 % of accuracy on UCI Wisconsin breast cancer dataset.
Correction: The acceptability judgment of Chinese pseudo-modifiers with and without a sentential context

[This corrects the article DOI: 10.1371/journal.pone.0219896.].
Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor For Optimal Control Of Space Heating Systems

The previous research on adaptive neuro-fuzzy inferential sensor (ANFIS) presented an approach to estimate the average indoor temperature and proposed a new method to measure process variables which are impossible to measure directly by using physical sensors in buildings. To achieve high energy efficiency in heating systems, an accurate and robust prediction model is essential. This thesis aims to improve the conventional ANFIS indoor temperature estimator and look for an optimal control of space heating systems. A physical-rule based ANFIS prediction model is proposed. The differences between this physical-rule based ANFIS prediction model and the conventional ANFIS prediction model are presented and analyzed. Three performance measures (RMSE, RMS, and R
Conclusions and Outlook

In this thesis, we demonstrate full coherent control of the high-spin nucleus of an 123Sb donor. However, rather than driving the nuclear spin via the conventional nmr, we find that the high-spin nucleus can be driven electrically via ner. This electric driving mechanism is mediated by the nuclear quadrupole interaction, which is found to couple electric fields to the nuclear spin. The nucleus is observed to be highly coherent, with measured coherence times on the order of 0.1 s. The microscopic origins of ner are attributed to a distortion of the covalent bonds between 123Sb and the neighboring Si atoms, transducing electric fields into significant efgs at the nucleus.
A MULTI DIVERSIFIED GUI FOR ANALYSIS OF IMAGE DENOISING USING VARIOUS PARAMETRS.

Digital Image Processing is a promising area of research in the fields of electronics and communication engineering, consumer and entertainment electronics, control and instrumentation, biomedical instrumentation, remote sensing, robotics and computer vision and computer aided manufacturing (CAM). For a meaningful and useful processing such as image segmentation and object recognition, and to have very good visual display in applications like television, photo-phone, etc., the acquired image signal must be deblurred and made noise free. The deblurring and noise suppression (filtering) come under a common class of image processing tasks known as image restoration. This research work addresses several issues with image denoising taking into consideration several known parameters. For this purpose a GUI has been developed in Matlab which produced several research parameters.
Quantitative evaluation of syntax similarity

Machine learning systems are facing problem of incomparability of their results in case of different languages; one of the subarea here is quantitative analysis of syntax.In this paper, we introduce a new quantitative method based on statistics of words co-occurrence in syntactically tagged corpora.The method allows quantitatively evaluate difference and similarity among languages, select most influential phenomena.Experimental setup consists materials for more than 50 languages.Our experiments demonstrate that the introduced method correctly cluster languages among language families.
Intangible Assets, Corporate Taxes and the Relocation of Pharmaceutical Establishments: The Case of Puerto Rico

Puerto Rico operated as a tax haven under U.S. Internal Revenue Code (IRC) Section 936. Firms in the pharmaceutical industry accounted for approximately 50% of tax credits awarded and 20% of employment under the program. The U.S. Congress eliminated the tax exemption program in 2006, creating a natural experiment on the elimination of corporate taxation of intangible assets. We use panel data on establishments from the Quarterly Census of Employment and Wages and a difference in difference methodology to measure the impact of the elimination of IRC Section 936 on pharmaceutical and medical devices using establishments with low, medium and high participation in the program as controls. Survival rates of all manufacturing establishments declined after the phaseout and elimination of the tax exemption program but pharmaceutical and medical devices establishments experienced an additional 6.9% decline. Approximately 50% of the 34% decline in pharmaceutical and medical devices establishments in Puerto Rico from 1995 to 2017 can be attributed to the elimination of IRC Section 936. Employment in pharmaceutical and medical devices establishments, which also declined by 34%, decreased at the same rate as that of other industries. Plant closings accounted for most of the decline in their employment.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Preface: “Improving seismic networks performances: from site selection to data integration (EGU2019 SM5.2 session)”

Abstract. The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this EGU2019 SM5.2/GI4.13 session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different data sets including GPS and OBS.
“I Still Haven't Found What I'm Looking For”: Evidence of Directed Search from a Field Experiment

We explore the impact of wage offers on job applications, testing implications of the directed search model and trying to distinguish it from random search. We use a field experiment conducted on a Chinese job board, with real jobs for which we randomly varied the wage offers across three ranges. We find that higher wage offers raise application rates overall, which is consistent with directed search but can also arise with random search. We also find that higher wage offers raise application rates for job seekers with wage offers above reservation wages, and that – among the latter – the increase in application rates is stronger for those with higher reservation wages. The latter two types of evidence are consistent with directed search but not random search. Hence, our evidence lends support to directed search models.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

Over the past few years there has been a tremendous developments observed in the field of computer technology and artificial intelligence, especially the use of machine learning concepts in Research and Industries. The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning. Any information which has been handwritten documents consisting of digits in digital form like images, recognizing such digits is a challenging task. The proposed system can recognize any handwritten digits in the document which has been converted into digital format. The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction. In order to classify more effectively we also have used Support Vector Machine to recognize mere similar digits like 1 and 7, 5 and 6 and many others. The proposed system CNN and LBP is implemented on python language; also the system is tested with different images of handwritten digits taken from MNIST dataset. By using proposed model we could able to achieve 98.74% accuracy in predicting the digits in image format.
Affect Analysis of Multilingual Tweets for Predicting Voting Behavior

Social media has been proved as wild card for its role in election campaign across the globe. It has been used for general election of India in year 2014 and year 2019 by political parties for election campaign. Thus social media provides opportunity for electoral prediction. Users from India use regional languages in addition to English language on social media. Multilingual data likely to give better prediction compared to single language data. Affect analysis gives deeper insight compared to sentiment analysis. This research study aims to predict voting behavior for 2019 general election of India using affect analysis of multilingual tweets. Three languages namely English, Hindi and Gujarati are used for this study. Volume-based method and machine learning algorithm based method are two approaches widely used in literature for electoral prediction. In this research study hybrid approach is used along with consideration of ratio of positive count and negative count of tweets. Experiment result shows efficacy of the proposed approach.
An Exploration of Force Exerted on Moving Robot in Arcuated Path on Different Surface using Standard SLAM Algorithm

The paper investigate the pressure applied during navigation of robot on arcuated prefab and smooth/ marble path using non linear EKF (map based). The analysis part uses experiments on 6DoF robot using (vMicro) as meta software on.Net framework and XCTU multiplatform, which was not considered in Compressed EKF SLAM. In this paper, analysis on pressure exerted on smooth surface and prefab surfaces in Z direction which gives conclusive results for determination of surfaces.
High Security Mechanism: Fragmentation and Replication in the Cloud with Auto Update in the System.

Cloud Computing makes immense use of internet to store a huge amount of data. Cloud computing provides high quality service with low cost and scalability with less requirement of hardware and software management. Security plays a vital role in cloud as data is handled by third party hence security is the biggest concern to matter. This proposed mechanism focuses on the security issues on the cloud. As the file is stored at a particular location which might get affected due to attack and will lost the data. So, in this proposed work instead of storing a complete file at a particular location, the file is divided into fragments and each fragment is stored at various locations. Fragments are more secured by providing the hash key to each fragment. This mechanism will not reveal all the information regarding a particular file even after successful attack. Here, the replication of fragments is also generated with strong authentication process using key generation. The auto update of a fragment or any file is also done here. The concept of auto update of filles is done where a file or a fragment can be updated online. Instead of downloading the whole file, a fragment can be downloaded to update. More time is saved using this methodology.
An embedded platform approach to privacy-centric person re-identification

Systems capable of intelligently monitoring the traffic of people at entrances to enclosed areas enable a variety of useful applications such as improved retail store analytics. However, the real-world implementation of such a system is typically hindered by computationally expensive algorithms and privacy concerns. In this paper, a low-cost privacy-sensitive intelligent monitoring system based on an embedded platform is presented. The key components of the system include a people classification model and a people re-identification model. A detailed description of the optimisation of these components is presented. The developed system is able to detect people entering/exiting a closed area with an accuracy above 99% in real-time. In addition, the system is able to achieve re-identification accuracy above 93% in under 0.7 seconds on an embedded system. Data collected by the system was used for training and it was tested under real-world conditions.
Supplemental Material: Early syn-rift igneous dike patterns, northern Kenya Rift (Turkana, Kenya): Implications for local and regional stresses, tectonics, and magma-structure interactions

ESRI Shapefiles of the mapped dike patterns, faults, and other features for this study.&lt;br&gt;
IMPLEMENTATION OF AUTOENCODER ON MNIST HANDWRITTEN DIGITS

Autoencoders (AE) are a family of neural networks for which the input is the same as the output. They work by compressing the input into a latent-space representation and then reconstructing the output from this representation. The aim of an Autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. In this paper De-noising Autoencoder is implemented by proposing a novel approach on MNIST handwritten digits. This model is validated through training and validation losses, and observing the reconstructed test images when comparing to the original images. The proposed model is found to be working very well.
Inside the Latest Edition of the Computer Journal

Journal Article Inside the Latest Edition of the Computer Journal Get access ITNOW, Volume 61, Issue 1, Spring 2019, Page 65, https://doi.org/10.1093/itnow/bwz028 Published: 13 February 2019
Stereo correspondence using an assisted discrete cosine transform method

Many approaches have been taken towards the development of a compliant stereo correspondence algorithm that is capable of producing accurate disparity maps within a short period of time. There has been great progress over the past decade due to the vast increase in optimization techniques. Currently, the most successful algorithms contain explicit assumptions of the real world such as definitive differences in disparity among objects and constant textures within objects. This thesis starts by giving a brief description of disparity, along with descriptions of some common applications. Next, it explores various methods used in common stereo correspondence algorithms, as well as gives an in depth description and analysis of top performing algorithms. These algorithms are later used to compare with the proposed algorithm. In the proposed algorithm, frequency stereo correspondence in parallel with the traditional color intensity stereo correspondence is used to develop an initial disparity map. Frequency stereo correspondence is achieved using a winner-take-all block based Discrete Cosine Transform (DCT) to find the largest frequency components as well as their positions to use in disparity estimation. The proposed algorithm uses methods that are computationally inexpensive to reduce the computational time that plagues many of the common stereo correspondence algorithms. The proposed algorithm achieves an average correct disparity rate of 95.3%. This results in a disparity error rate of 4.07% compared to the top performing algorithms in the Middlebury website [1]; the DoubleBP, CoopRegion, AdaptingBP, and ADCensus algorithms that have error rates of 4.19%, 4.41%, 4.23%, and 3.97%, respectively. Additionally, experimental results demonstrate that the proposed algorithm is computationally efficient and significantly reduces the processing time that plagues many of the common stereo correspondence algorithms.
Data Pump

Data Pump is often described as an upgraded version of the old exp/imp utilities. It is a bit like calling a modern smartphone a replacement for an old rotary-dial landline. Although the old utilities are dependable and work well, Data Pump encompasses that functionality while adding completely new dimensions to how data can be lifted and moved between environments. This chapter will help explain how Data Pump makes your current data transfer tasks easier and will also show how to move information and solve problems in ways that you did not think were possible.
Association Mining

Chapter Objectives✓ To comprehend the concept of association mining and its applications✓ To understand the role of support, confidence and lift✓ To comprehend the Naïve algorithm, Its limitations and improvements✓ To learn about approaches for transaction database storage✓ To understand and demonstrate the use of the apriori algorithm and the direct hashing and pruning algorithm✓ To use dynamic Itemset counting to identify association rules✓ To use FP growth for mining frequent patterns without candidate generation
Preface

1 st International Virtual Conference on Environment and Natural Resources (IVCENR-2020) College of Science, University of Al-Qadisiyah 24 th -25 th March 2021 Welcome to the 1st International Virtual Conference on Environment and Natural Resources (IVCENR-2021) That held on June 24th-25th 2021 at the College of Science, University of Al- Qadisiyah, Iraq. In this Year the Unit of Environment and Protection from Pollution at the, College of Science, University of Al-Qadisiyah, Iraq, is holding its 1st International Virtual Conference on Environment and Natural Resources and as the novel coronavirus outbreak shutters businesses and disrupts everyday life for billions around the globe, the annual conference has moved into virtual. This conference is established to provide an ideal platform for researchers to share views and experiences in Environmental areas. Over a period of two days at the Unit of Environment and Protection from Pollution, College of Science, University of Al-Qadisiyah, the event will include several activities such as workshops, guest speakers, lectures, and research articles. The aims of the Conference is to bring together leading academic scientists, researchers, and knowledge-seekers to exchange and share their experiences and research results on all aspects of environmental sciences. It also provides a premier interdisciplinary platform for researchers and university professors to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered, and solutions adopted in the fields of Environmental Sciences enable the collaboration between scientists and technologists in academic and industrial fields as well as international networks. Hosted by College of Science, University of Al-Qadisiyah, the event was intended to provide scientific forum and research discussion on environment, renewable energy, ecology, and how technology is effectively employed for sustainable development of environment and green chemistry. List of Conference Photographs, Keynote Speakers, Photos, Tables are available in this pdf.
What is the “opposite” of “blue”? The language of colour wheels

© Society for Imaging Science and Technology 2019 A color wheel is a tool for ordering and understanding hue. Different color wheels differ in the spacing of the colors around the wheel. The opponent-color theory, Munsell's color system, the standard printer's primaries, the artist's primaries, and Newton's rainbow all present different variations of the color wheel. I show that some of this variation is owing to imprecise use of language, based on Berlin and Kay's theory of basic color names. I also show that the artist's color wheel is an outlier that does not match well to the technical color wheels because its principal colors are so strongly connected to the basic color names.