Reviewerly/exHarmony huggingface dataset:
Hyper Elliptical Curve Cryptography (Hecc) For Cloud

This system provides an insight into developing a distributed system which is secure, robust and user friendly. This thesis suggests a design and implementation of a digital envelope that combines the hashing algorithm of MD5, the symmetric key algorithm of AES and the asymmetric key algorithm of Hyper Elliptic Curve. A hybrid algorithm is designed, combining the best of both AES and ECC over GF(p) cryptography. The MD5 hash algorithm is adopted to ensure integrity of the data. Cryptography (HECC). This paper discusses securing the data in clouds through implementing the key for encryption and decryption using hyper elliptical curve cryptography. The focus is on Advanced Encryption Standard (AES), the most commonly used secret key cryptographic algorithm, and Hyper Elliptic Curve Cryptography (HECC), public key cryptographic algorithms which have gained popularity in the recent years and are replacing traditional public key cryptosystems, such as RSA and ECC. Such techniques are necessary in order to use high security cryptographic algorithms in real world applications.
Classifying Handwritten Digit Recognition using CNN and PSO

A normal human can easily recognize any written or typed or scanned text, numbers, etc., but when it comes to a machine, it is difficult to find out what exactly that given text or numbers. It will be difficult to recognize a handwritten digit for a machine. Many machine learning methods were used to fix the handwritten digit recognition issue. It is growing in more convoluted domains, so its training complexity is also increasing. To overcome this complexity problem, many algorithms have been implemented. In this paper, the Convolutional Neural Network (CNN) and Particle Swarm Optimization (PSO), those two approaches do use for recognition of the isolated handwritten digit. Customized PSO is used to reduce the overall computation time of the proposed system. The customized PSO used with CNN, to decreases the required number of epochs for training. It is used to identify digits in the MNIST handwritten digital database to predict the number. The system has achieved an average of 94.90% accuracy.
Multi-Query Image Retrieval Based on Deep Learning and Pareto Optimality

In this study, a method for fast and efficient multiquery image retrieval from large scale databases is introduced. Images used as queries are semantically different from each other. In order to obtain similarity between multiple queries and each item in the database, image features are extracted from a deep networks and then they are converted into binary codes. The database items that simultaneously most closely resemble multiple queries are obtained by the Pareto front method. Furthermore, the method is tested on a designed graphical user interface.
NONLINEAR REGRESSION APPLICATIONS IN MODELING OVER-DISPERSION OF BIRD POPULATIONS

The aim of this study was to statistically evaluate bird populations in Akdoğan Lakes by means of Poisson and negative binomial regression models.The over-dispersion value in Poisson regression was much higher than 1.0 (33.827).In contrast, the value of over-dispersion in the negative binomial regression was very close to 1.0 (1.598).Therefore, the parameter estimates were interpreted considering the negative binomial regression.When spring season was considered as a reference parameter, the change in population densities in other seasons was not statistically significant.The population changes in other habitats were not statistically significant, when reed area was considered as a reference parameter.The change in the population density of 13 ordo groups is non significant when the Anseriformes order was evaluated as reference parameter.The population change in the Gruiiformes population was 11.951 times higher compared with the change in reference parameter and the change was statistically significant (p <0.01).As a result, it is recommendable to use negative binomial regression with the scope of removing over-dispersion problem in the bird population modeling.
Citrix flaw remains critical

The critical vulnerability affecting the Citrix Application Delivery Controller (ADC) and Gateway (CVE-2019-19781) is still a major cause for concern even though the flaw has been patched.
Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the real-world domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy.
A new approximate image verification mechanism in cloud computing

With the growing prevalence of cloud computing, more and more data especially images and videos are stored in cloud servers. To ensure the security of private data, data owners usually encrypt their private data before outsourcing the data to cloud servers. It is discovered that highly correlated data exist in storage outsourcing and much useful information can be extracted from these correlated data and used for cloud-based services. In the paper, we propose a scheme of encrypted image verification in cloud computing for mobile devices. Many existing schemes focus on verification of query results for outsourced text data or identical images. Different from that, the proposed scheme aims to verify the correctness of query results for similar images. Through the successful query of similar images, power and memory resources of mobile devices can be saved. The security of our scheme is analysed in the random oracle model, and analysis shows that the scheme is secure against adaptive chosen-keyword attack. And what's more, the experimental results demonstrate that our scheme is an efficient one.
Perl ustr ation on Recent Research into Multi Clouds integrated with Multi Party Computation

Recent advances in multi cloud technologies and multi-party computations have improved State of art usage of Cloud computing in real time scenarios. Primary reason behind using any service offered by others is ease of use with lesser economics. Cloud Computing is technological advancement which is in usage for last two decades because of its Pay-per-Usage policy offering enormous benefits across the user community. In spite of its enormous benefits, single factor which is stepping it back from its wider adoption throughout the digital society is its Security. Tremendous research work was done across industry and academia in association with cloud security. This paper focuses on brief history, real time deployment of cloud, usage, benefits, risks associated and Surveys various studies done by national and international organizations related to cloud security concerns and dwell upon the advantages of integrating multi clouds and multi-party computation techniques and emphasizes on recent research done across multi cloud environment and give a short note of future work to enhance security paradigm.
Protocols Using Shared Key Cryptography

The majority of protocols for key establishment and entity authentication that have been proposed in the literature concentrate on the case where there are exactly two users who wish to communicate or establish a session key. This is commonly referred to as the two-party case. In this chapter we discuss two-party key establishment and authentication protocols based on symmetric algorithms. The next chapter discusses two-party protocols using public key algorithms, while the multi-party case is covered in Chap. 6.
Data mining model based on user reviews and star ratings

With the rapid development of e-commerce, the research on sentiment analysis of online reviews has been paid more and more attention. This paper presents an Aspect-Level Sentiment Analysis Method based on long short-term memory (LSTM) and boot-strapping, which performs semantic mining and prediction on time-based data patterns and data combinations of text, star rating and helpful votes. A high prediction accuracy rate is obtained in the open data set. Compared with the traditional methods, which single analysis comment or evaluation, merchants can gain a deeper understanding of user feedback from sentiment analysis.
Optimal Hesitation Rule Mining using weighted Apriori with Genetic Algorithm

Weighted Apriori algorithm practices the itemsets that are frequently generated in particular databases for statistical analysis. Traditional association rule mining only deals with the items that are actually present in the transaction and disregards the items that customers hesitated to purchase such items can considered as almost sold items that contains valuable information which can be used in enhancing the decision making capabilities. This paper focuses on the weighted apriory with genetic algorithm because with the help of weighted apriory there are some hesitation patterns are define on these rules the genetic algorithm is applied which gives the optimal results(Newly generated valid rules). This exertion portrays that if the cause of yielding the things is known and settled, we can without much of a extend expel this hesitation status of a client and thinking about recently developed rules as the intriguing ones for increase offers of the entity or item.
Multi-Frame Joint Tracking and Shape Estimation Method for Weak Extended Targets

This paper addresses the joint tracking and shape estimation (JTSE) problem of elliptical extended targets in low signal-to-noise (SNR) scenarios using multi-frame joint processing. Considering the weak target echoes and unknown parameters of elliptical extended targets, it is challenging to achieve effective detection and tracking. To solve these problems, a multi-frame tracking and shape estimation (MF-JTSE) method is proposed. This method achieves accurate estimation of motion trajectories and shape parameters including semi-axis lengths simultaneously for unknown priori information. By comparing with single-frame joint tracking and shape estimation (SF-JTSE) methods, simulation results show that the proposed algorithm is able to achieve superior tracking performance and estimation accuracy for extended targets in low SNR scenarios.
An Intelligent Wellness Assessment System for the Elderly Healthcare

Due to the rapidly increasing aged population in China, the demand for institutional healthcare service is growing. In this paper, we proposed an intelligent system which could use the daily activity data of the elderly living in a stable environment, like nursing home, to predict their wellness conditions. This system has many advantages, such as high flexibility, high reliability, and low cost. Daily activities of the elderly could be recognized by analyzing their location information, and the location data can be collected by using Radio Frequency Identification (RFID) devices. The wellness conditions could be determined by using a novel framework integrating temporal and spatial contextual information. We initiated this research with the development of the intelligent system and conducted the testing experiments at the geriatrics department in the third people's hospital of Lanzhou. Daily location data of 5 male elderly patients were collected for data analysis. A Support Vector Machine (SVM) model and a Back Propagation Neural Network (BPNN) model were constructed to determine the wellness conditions of the subjects. The experimental results were encouraging and it indicated that this proposed intelligent system could assist in providing prompt healthcare services to elderly people.
E-Commerce Performance with Cloud Computing

Electronic Commerce or E-Commerce is a term for a business that incorporates the trading of information over the web. It covers an extent of different kind of business from buyer based retail goals, trough closeout or music site, to business exchanges trading items and ventures between undertakings. The web of things is the arrangement of a physical inquiry embedded with devices, programming, sensors and framework accessibility that engages to assemble and exchange data. This paper will illuminate how IOT is executed in online business experience nearby the delineation of Amazon dash an IOT advancement made by Amazon
Spring Data

Spring Data’s mission is to provide a familiar and consistent Spring-based programming model for data access while still retaining the special traits of the underlying data store.
Design development and performance analysis of distributed least square twin support vector machine for binary classification

Machine learning (ML) on Big Data has gone beyond the capacity of traditional machines and technologies. ML for large scale datasets is the current focus of researchers. Most of the ML algorithms primarily suffer from memory constraints, complex computation, and scalability issues.The least square twin support vector machine (LSTSVM) technique is an extended version of support vector machine (SVM). It is much faster as compared to SVM and is widely used for classification tasks. However, when applied to large scale datasets having millions or billions of samples and/or large number of classes, it causes computational and storage bottlenecks. This paper proposes a novel scalable design for LSTSVM named distributed LSTSVM (DLSTSVM). This design exploits distributed computation on cluster of machines to provide a scalable solution to LSTSVM. Very large datasets are partitioned and distributed in the form of resilient distributed datasets on top of Spark cluster computing engine. LSTSVM is trained to generate two nonparallel hyper-planes. These hyper-planes are achieved by solving two systems of linear equations each of which involves data instances from either class. While designing DLSTSVM we employed distributed matrix operations using the MapReduce paradigm of computing to distribute the tasks over multiple machines in the cluster. Thus, memory constraints with extremely large datasets are averted. Experimental results show the reduction in time complexity as compared to existing scalable solutions to SVM and its variants. Moreover, detailed experiments depict the scalability of the proposed design with respect to large datasets.
On approximate k-nearest neighbor searches based on the earth mover’s distance for efficient content-based multimedia information retrieval

The Earth Mover's Distance (EMD) is one of the most-widely used distance functions to measure the similarity between two multimedia objects. While providing good search results, the EMD is too much time consuming to be used in large multimedia databases. To solve the problem, we propose an approximate k-nearest neighbor (k-NN) search method based on the EMD. In the proposed method, the overhead for both disk accesses and EMD computations is reduced significantly, thanks to the approximation. First, the proposed method builds an index using the M-tree, a distance-based multi-dimensional index structure, to reduce the disk access overhead. When building the index, we reduce the number of features in the multimedia objects through dimensionalityreduction. When performing the k-NN search on the M-tree, we find a small set of candidates from the disk using the index and then perform the post-processing on them. Second, the proposed method uses the approximate EMD for index retrieval and post-processing to reduce the computational overhead of the EMD. To compensate the errors due to the approximation, the method provides a way of accuracy improvement of the approximate EMD. We performed extensive experiments to show the efficiency of the proposed method. As a result, the method achieves significant improvement in performance with only small errors: the proposed method outperforms the previous method by up to 67.3% with only 3.5% error.
Database Migration using Data Synchronization and Transactional Replication

Database migration is one of the important thing to make the data can be stored optimally and minimize risk of data lost. Data Synchronization and Transactional Replication can be used to do it. Transactional Replication is working on services area to migrate database stored in cloud to another cloud storage. Data Synchronization is used to synchronize migrated database with origin database. Data lost concerns remain as the big consideration to widespread the adoption. Many ways can be done to minimize the risks, one of them is by migrate the database that is already stored in cloud to another database in cloud. This research will conduct database migration using data synchronization and transaction replication. By using Azure and Amazon Web Services as Database Migration Services, these techniques allow SQL Database data to be easily replicated, and allows multiple SQL Databases to isolate mission-critical workloads from analytic queries that run relatively longer. The evaluation is by comparing these two migration services based on the synchronizing time of full loaded database migration. Migration result shows that database migration using transactional replication and data synchronization is influenced by the number of rows as well as size of the table, server location, also uploading and downloading speed in each database migration service.
Research on image denoising algorithm based on non-local block matching

In order to study how to better suppress image noise, improve image resolution, and enhance the visual effect of images, three-dimensional joint filtering – block matching (BM3D) algorithm is used to explore image denoising and image detail retention, and in image denoising, sparse expression and low rank recovery theory are introduced. The results show that BM3D algorithm has a good effect in removing Gaussian white noise, but it is lacking in suppressing impulse noise and mixed noise. The non-local BM3D algorithm is superior to sparse expression and low rank recovery in removing Gaussian white noise, but sparse expression and low rank recovery has a good effect in removing impulse noise.
USING A MULTILINGUAL CHATBOT FOR FOREIGN LANGUAGE PRACTICE

Chatbots, or conversational agents, are computer programs that emulate the way people talk to each other, providing a more natural user interface.Students learning a foreign language might benefit from using the systems to practice their skills.In this study, we asked 49 students to evaluate transcripts of conversations with a multilingual chatbot to assess the accuracy of translations and appropriateness of replies.Results show that translations and responses were generally very good.Students gave poor ratings for only four of 24 responses, and the vast majority thought the system would help with acquiring a new language.
Implementation of Morphological Operation through Intercept Based Euclidean Distance Algorithm

Skeletonization is the process of extracting the region-based shape features that represents a common form of original object. It can be employed to binary images that produces alternative binary image as the resulting output. The main goal of thinning is to degrade particular foreground pixels from original input image. A pruning focused on removing short spurs present in an image. By Employing morphological operation for the process of extracting skeleton can be used in wide range of application. In this paper, we proposed a model which is a robust intercept based Euclidean distance (IBED) algorithm to perform morphological operation to extract the surface of the skeleton from the computed Tomography image. In this edge points of the skeleton have been analyzed over set extraction, hence the characterization has been improved by presenting a novel skeleton with edge points. The index value, edge points, Euclidean distance and fracture length have been calculated. The proposed technique is employed to four men and two women with different fractures in CT scanned image.
Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment

Concurrency and Computation: Practice and ExperienceVolume 32, Issue 17 e5978 SPECIAL ISSUE NOTE Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment Rathinaraja Jeyaraj, Rathinaraja Jeyaraj orcid.org/0000-0003-0165-181X Department of IT, National Institute of Technology Karnataka, Karnataka, IndiaSearch for more papers by this authorAnanthanarayana V S, Ananthanarayana V S orcid.org/0000-0001-6593-8938 Department of IT, National Institute of Technology Karnataka, Karnataka, IndiaSearch for more papers by this authorAnand Paul, Corresponding Author Anand Paul paul.editor@gmail.com orcid.org/0000-0002-0737-2021 School of Computer Science and Engineering, Kyungpook National University 80-Daehakro, Daegu, South KoreaSearch for more papers by this author Rathinaraja Jeyaraj, Rathinaraja Jeyaraj orcid.org/0000-0003-0165-181X Department of IT, National Institute of Technology Karnataka, Karnataka, IndiaSearch for more papers by this authorAnanthanarayana V S, Ananthanarayana V S orcid.org/0000-0001-6593-8938 Department of IT, National Institute of Technology Karnataka, Karnataka, IndiaSearch for more papers by this authorAnand Paul, Corresponding Author Anand Paul paul.editor@gmail.com orcid.org/0000-0002-0737-2021 School of Computer Science and Engineering, Kyungpook National University 80-Daehakro, Daegu, South KoreaSearch for more papers by this author First published: 19 August 2020 https://doi.org/10.1002/cpe.5978 The article from this special issue was previously published in Concurrency & Computation: Practice & Experience, Volume 32, Issue 7, 2020. For completeness, we are including the title page of the article below. The full text of the article can be read in Issue 32:7 on Wiley Online Library: https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5558 AboutPDF 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. Volume32, Issue17Special Issue: Special Issue on Computational Intelligence Techniques for Industrial and Medical Applications (CITIMA2018). Special Issue on Cloud Computing, IoT, and Big Data: Technologies and Applications (CloudTech17)September 10, 2020e5978 RelatedInformation