Reviewerly/exHarmony huggingface dataset:
Data Deduplication Techniques for Big Data Storage Systems

The enormous growth of digital data, especially the data in unstructured format has brought a tremendous challenge on data analysis as well as the data storage systems which are essentially increasing the cost and performance of the backup systems. The traditional systems do not provide any optimization techniques to keep the duplicated data from being backed up. Deduplication of data has become an essential and financial way of the capacity optimization technique which replaces the redundant data. The following paper reviews the deduplication process, types of deduplication and techniques available for data deduplication. Also, many approaches proposed by various researchers on deduplication in Big data storage systems are studied and compared.
The Effectiveness of Mobile Application of Earthquake and Tsunami Early Warning System in Community Based Disaster Risk Reduction

Community Based Disaster Risk Reduction (Community Based DRR) needs participatory of the society in disaster prone area to get involved in the process and goals.Saving live and also reducing devastated infrastructures is the highest priority in Early Warning System (EWS) in earthquake and tsunami prone areas.Unfortunately, there is little availability for EWS that is very user-friendly, easy to access, and have local information and content.Hence, this research wants to develop a Mobile Application for earthquake and tsunami early warning system which is support the Community Based Disaster Risk Reduction.This Mobile Application designed for local content of Bandar Lampung City, Lampung Province Indonesia.The research conducted by two stages: (1) to make design and to build Mobile Application for earthquake and tsunami early warning system; and (2) to deliver questionnaire a to some respondents for Mobile Application Try-Out, before and after using the mobile application.The mobile application built by this research named "Integrated Early Warning System of Lampung (Sistem Peringatan Dini Terpadu Lampung)".The results found that the "Integrated Early Warning System of Lampung" Application is also effective to give knowledge of disaster risk reduction as well as earthquake and tsunami.As conclusions, the "Integrated Early Warning System of Lampung" Application is effective to give alert and knowledge as early warning system and can be developed for national purposes.
Determining the Effectiveness of ‘Cloud Computing’ on Human Resource Management by Structural Equation Modeling (SEM) in Manufacturing Sector of West Bengal, India

The manufacturing industry is very much crucial for driving the economy of the nation. It has a direct impact on the GDP of any nation and also, it is very beneficial for a country to be strong in the manufacturing industry. This is also the case for a developing nation such as India. Hence, it is imperative for the organization to effectively manage their human resources to optimally utilize them as they are the non-perishable resources of the company. This could be done with the help of Cloud-Computing which is one of the advanced fields of elastic IT (Information Technology). This technology is continuously gaining momentum as it is relatively easier to use in E-HRM (Electronic-Human Resource Management) and also contains less initial investment. The objective of the study is to identify the factors that have an impact on the effectiveness of Human Resource Management by the help of Cloud computing. Various statistical tools like Exploratory Factor Analysis and Structural Equation Modeling have been executed in this study by SPSS 21 and AMOS 21 software.
A Machine Learning Based Improved LogisticRegression Method for Prostate Cancer Diagnosis

The objective of this paper is to review and investigate machine learning methods and propose an improved Logistic regression method for Prostate Cancer (PrC) diagnosis and prediction.The paper compares proposed method with existing supervised classification techniques for a prostate cancer data set.An Improved Logistic Regression method is applied on patients vulnerable for PrC showing considerable improvement in prediction rate.The proposed methodincorporates clinical as well as tumor stages with patient ethnic characteristics.The comparative analysis of improved Logistic method show improvement on prediction accuracy rate and records a better Sensitivity and Specificity compared to other popular classification methods.
Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 2. Identification

Introduction. The article continues a series of publications on the linguistics of relations (hereinafter R-linguistics) and is devoted to the origin of signs, their independence and determination of the dimension of linguistic spaces. Methodology and sources. The article is devoted to the continuation of the axiomatic approach, but for the identification process. Research methods are to develop the necessary mathematical concepts for linguistics in the field of identification. Results and discussion. The concept of a sign is defined and its interrelation with decomposition of linguistic spaces is established. This radically changes the attitude to signs in linguistics, where the “external” origin of signs is assumed. It is shown that the decomposition of linguistic spaces into signs spaces entails the independence of signs and the possibility of decomposition of objects of identification. It is fundamentally distinguished by the signs on the parameters. On the basis of the independence of signs it is possible to formulate the notion of dimension of linguistic spaces, which is defined as the smallest number of signs describing the linguistic space. In the lattice of linguistic spaces there is a division operation, which allows to simplify the selection of signs. Conclusion. The main conclusions are as follows. Signs, on the basis of which the identification of objects in the category, are abstract mathematical objects associated with the decomposition of linguistic spaces. Signs are independent from each other and allow not only to make decomposition of spaces, but also to decompose on parts objects of identification. Their origin is not related to their presence in the “outside world”, so it, as will be shown later, creates the basis for the emergence of language.
Random Shaped Image Inpainting using Dilated Convolution

Over the past few years, Deep learning-based methods have shown encouraging and inspiring results for one of the most complex tasks of computer vision and image processing; Image Inpainting. The difficulty of image inpainting is derived from its’ need to fully and deeply understand of the structure and texture of images for producing accurate and visibly plausible results especially for the cases of inpainting a relatively larger region. Deep learning methods usually employ convolution neural network (CNN) for processing and analyzing images using filters that consider all image pixels as valid ones and usually use the mean value to substitute the missing pixels. This result in artifacts and blurry inpainted regions inconsistent with the rest of the image. In this paper, a new novel-based method is proposed for image inpainting of random-shaped missing regions with variable size and arbitrary locations across the image. We employed the use of dilated convolutions for composing multiscale context information without any loss in resolution as well as including a modification mask step after each convolution operation. The proposed method also includes a global discriminator that also considers the scale of patches as well as the whole image. The global discriminator is responsible for capturing local continuity of images texture as well as the overall global images’ features. The performance of the proposed method is evaluated using two datasets (Places2 and Paris Street View). Also, a comparison with the recent state-of-the-art is preformed to demonstrate and prove the effectiveness of our model in both qualitative and quantitative evaluations.
Breast Cancer Diagnosis Using MLP Back Propagation

Breast Cancer (BC) is one of the most prevalent forms of Cancer among women.Premature diagnosis of BC is crucial to the survival of the patient.Here we implement an algorithm designed to diagnose and forecast breast cancer using a multi-layer perceptron (MLP) back-propagation technique that will help doctors diagnose the disease (benign, malignant).The proposed MLP includes an input layer, and, has inputs linked to the ten attributes of the data set.It has a hidden layer with five nodes (neurons).It leads to the pair outcomes: benign and malignant.The objective of our projected algorithm is to diagnose and classify the disease.MLP can help timely recognition of the cancer, and, therefore, can help to go for proper medication at early stage of cancerous development.This approach is tested on the (WBC) Wisconsin Breast Cancer dataset, resulted in 98.9 percent accuracy of classification using MLP back propagation.
Lung Cancer Detection using Local Energy-Based Shape Histogram (LESH) Feature Extraction Using Adaboost Machine Learning Techniques

It is difficult to find the exact symptoms of lung cancer due to the formation of the majority of cancer tissues in which the large tissue structure intersects differently. With digital images, this question can be evaluated. Images with the basic operation of the LESH Algorithm will be examined in this strategy. GLCM approach is used in this paper to pre-process the snap shots and feature extraction system and to check a patient's disease rate at its it's premature or unnatural to know it. The cancer stage will be assessed with the aid of the results . Using the data set and the cancer patient's survival rate can be calculated. The conclusion is based entirely on the accurate and incorrect arrangement of tissue patterns.
Exploring Methods for Generating Feedback Comments for Writing Learning

The task of generating explanatory notes for language learners is known as feedback comment generation.Although various generation techniques are available, little is known about which methods are appropriate for this task.Nagata (2019) demonstrates the effectiveness of neural-retrieval-based methods in generating feedback comments for preposition use.Retrieval-based methods have limitations in that they can only output feedback comments existing in a given training data.Furthermore, feedback comments can be made on other grammatical and writing items than preposition use, which is still unaddressed.To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study.Our close analysis of the type of task leads us to investigate three different architectures for comment generation: (i) a neural-retrieval-based method as a baseline, (ii) a pointer-generator-based generation method as a neural seq2seq method, (iii) a retrieve-and-edit method, a hybrid of (i) and (ii).Intuitively, the pointer-generator should outperform neural-retrieval, and retrieve-andedit should perform best.However, in our experiments, this expectation is completely overturned.We closely analyze the results to reveal the major causes of these counter-intuitive results and report on our findings from the experiments. 1
Reversible data hiding methods in integer wavelet transform

Reversible data hiding is art of concealing secret information such that cover media and secret information are both recovered without any information loss. In this paper high frequency sub-bands of integer wavelet transform are used for data embedding. All coefficients are used for embedding and to improve the security the embedding is carried out in frequency domain using spiral, sequential and random embedding method. The main objective of this research is to hide the maximum data with minimal distortion and to attain reversible hiding phenomenon both in cover and secret image. The experimental result shows the improved capacity, imperceptibility and complete reversibility attained on standard and medical images. The parameter of robustness has not been vastly studied for reversible data hiding and an attempt is made to check the same for basic attacks and results shows that it can withstand geometrical attack.
Teaching Writing Using Google Apps For Education (GAFE)

This paper presents how an English teacher can use Google Apps for Education (GAFE) technology to enhance the quality of teaching writing in the context of teaching English for students in a higher education in Indonesia. This paper will briefly introduce some Google applications which can be used for (Engliah) teachers to enhance their teaching. It specifically reports a technology based teaching technique which has been practiced in a teacher education program in a university in Indonesia. It explores the prerequisite condition to use this application. This is followed by detailed stages of using this application, particularly in teaching writing: what teachers and students should do during the lesson. The explanation is enriched by a discussion on some possible challenges an English teacher or students may face in the field. The discussion is finally closed by proposing some suggestions on how to make use the technology and how to deal with the potential problems.
Efficiency of Probabilistic Network Model for Assessment in E-Learning System

The knowledge acquirement by the learner is a major assignment of an E-Learning framework. Evaluation is required in order to adapt knowledge resources and task to learner ability. Assessment provides learner’s an approach to evaluate the skills gained through the e-learning domain they are accessing. A dissimilar method can be used to assess the information acquirement, such as probabilistic Bayesian Network model. A Bayesian Network is a graphical representation of the probabilistic relationships of a complex system. This network can be used for reasoning with uncertainty. Bayesian Network is the most challenging task in e-learning system as learner evaluation model are an element of uncertainty. In this paper the current proposed scheme is constructed on Bayesian Network to deduce the stage of knowledge possessed by the learner. It also proposes type of assessment to identify the knowledge whatever the learner identifies. Throughout the assessment, it can be performed by two approaches namely Sequential and Random. In Sequential approach, questions can be displayed on the learner machine in sequential order. In Random approach, questions can be displayed on the learner machine in random order. However, both have their inherent limitations. Questions that are considered to be answered easily by the learner may also be presented to the learner who is not desirable. This system determined on the illustration of Bayesian Network model and algorithm for inference about learner’s knowledge. The Bayesian Network model was efficiently implemented for three levels of learner called Higher Learners (HL), Regular Learners (RL) and Irregular Learners (IL) for learner’s assessment and was successfully implemented with 81.1% of probabilities for learner’s assessment.
Imaging &amp; Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Breast cancer has become a major concern of women health throughout the world and has an important cause of death among women. The important radiographic signs of cancer are the masses visible in the breast. In the initial stage, the masses in the women breast are very strenuous to detect. In many cases, it has been proven that a manual attempt of treatment methods are time consuming and inefficient. Hence there is a basic demand for well-planned methods for diagnosis of the cancerous cells with minimal human participation resulting high in precision. Mammography has been proven as an efficient technique for the identification of cancer in women breast. Automated detection of masses in breast mammogram is the major goal in the identification of cancer in women breast. Machine learning techniques can be used as an effective mechanism by the physician for the early detection of cancer in the breast. By early recognition of malignancy in the breast, patients will get treatment right from the initial stage of cancer which can save their lives.
Automated Gesture Based Wireless Wheelchair Control by Means of Accelerometer

According to a research there are about 6 million populations in the world who are paralysed and needs a wheelchair for their mobility. Earlier the wheel chairs had to be moved and be externally supported by any person. To help overcome this “joystick-controlled wheelchairs” are developed. But in regular use, these joystick-controlled wheelchairs became difficult to use. Especially in the case of paralysed people, the use of joystick became more difficult due to the hard buttons and unidirectional use of the joysticks. To overcome these problems, we’ve tried to develop a “gesture-controlled wheelchair” which can be moved with a slight tilt of the hand. This can be used in both hands and can be controlled to come to the user from a distance. The current work is implemented with Arduino based devices such as Arsuino NANO and UNO processors and programmed through Arduino IDE.
Denoising of MRI Brain Images using Adaptive Clahe Filtering Method

Image processing is a method of making the quality of an image better after removing unwanted information from image in various applications and domains to process computer effectively. Enhancement is, used to improve the quality effects of an image for further analysis. Enhancement of image can be done by filtering, de noising and contrast enhancement. Even though contrast enhancement of images is applied in different fields it is used effectively in the medical field. Medical Imaging is now recently used in most of the applications like Radiography, MRI, Nuclear medicine, Ultrasound Imaging, Tomography, Cardiograph, and Fundus Imagery and so on. The main problem in analysis of medical images is the poor contras .in medical image analysis the detection of tumor, cancerous cells, malignant or benign has to be classified effectively. In this paper various spatial domain techniques and their effectiveness in terms of quality improvement are discussed. The measuring metrics used for comparing different methods are parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), DICE coefficient, etc,.
Image Encryption and Decryption using RSA Algorithm with Share Creation Techniques

visual cryptography system proposed a image encryption and decryption method. In the proposed method Red, Green, Blue color images using visual cryptography. In existing system is working for share created, it is encrypted separately by using visual secret share creation (VSS) algorithms. The proposed work is original images share1 and ahare2 created XOR-Based visual cryptography. This proposed schemes share1 encryption and share2 encryption included in RSA algorithm. The share1 and ahare2 decryption process is enable secret image sharing and then stacking. The proposed system is value calculate the PSNR and MSE formula and then image security using NPCR and UACI formula. The visual cryptography existing work to compare the proposed work and better results quality of RGB color images. The color image encryption and decryption using RSA algorithm and matlab coding.
An Ensemble Model of Outlier Detection with Random Tree Data Classification for Financial Credit Scoring Prediction System

Recently, Financial Credit Scoring (FCS) becomes an essential process in the finance industry for assessing the creditworth of individual or financial firms. Several artificial intelligence (AI) models have been already presented for the classification of financial data. However, the credit as well as financial data generally comprises unwanted and repetitive features which lead to inefficient classification performance. To overcome this issue, in this paper, a new financial credit scoring (FCS) prediction model is developed by incorporating the process of outlier detection (OD) process (i.e. misclassified instance removal) prior to data classification. The presented FCS model involves two main phases namely misclassified instance removal using Naïve Bayes (NB) Tree and Random Tree (RT) based data classification. The presented NB-RT model is validated using the Benchmark German Credit dataset under different validation parameters. The extensive experiments exhibited that a maximum classification accuracy of 90.3% has been achieved by the proposed NB-RT model.
Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.
Development of a Game-Based e-Learning System with Augmented Reality for Improving Students’ Learning Performance

Currently, the school children usually spend a lot of time on the games in their recreational activities and some of them are even addicted to the games. Compared with other extracurricular activities, the e-Learning system reflects the fact that school children are very interested in the games. As a result, educators have lately craved to develop effective teaching activities that allow the school children to learn some subjects and to play the games simultaneously. Therefore, this study is based on an e-Learning system which combines the serious game by Unity3D Game Engine with augmented reality (AR). Students are able to acquire their knowledge and to foster logical skills via this game-based e-Learning system. According to its efficacy and utilities, this study has assessed and compared the game-based e-Learning system with the traditional learning and other e-Learning systems. The experimental results have indicated that the proposed game-based e-Learning system can outperform other existing systems.
IDENTIFYING SKYLINES IN CLOUD DATABASES WITH INCOMPLETE DATA

Skyline queries is a rich area of research in the database community.Due to its great benefits, it has been integrated into many database applications including but not limited to personalized recommendation, multi-objective, decision support and decision-making systems.Many variations of skyline technique have been proposed in the literature addressing the issue of handling skyline queries in incomplete database.Nevertheless, these solutions are designed to fit with centralized incomplete database (single access).However, in many realworld database systems, this might not be the case, particularly for a database with a large amount of incomplete data distributed over various remote locations such as cloud databases.It is inadequate to directly apply skyline solutions designed for the centralized incomplete database to work on cloud due to the prohibitive cost.Thus, this paper introduces a new approach called Incomplete-data Cloud Skylines (ICS) aiming at processing skyline queries in cloud databases with incomplete data.This approach emphasizes on reducing the amount of data transfer and