Reviewerly/exHarmony huggingface dataset:
Proficient Justification of Data Accuracy for Cloud Storage Using Dual Protection

The cloud security is one of the essential roles in cloud, here we can preserve our data into cloud storage. More and more clients would like to keep their data to PCS (public cloud servers) along with the rapid development of cloud computing. Cloud storage services allow users to outsource their data to cloud servers to save local data storage costs. Multiple verification tasks from different users can be performed efficiently by the auditor and the cloud-stored data can be updated dynamically. It makes the clients check whether their outsourced data is kept intact without downloading the whole data. In our system we are using the own auditing based on the token generation. Using this key generation technique compare the key values from original keys we can find out the changes about the file. A novel public verification scheme for cloud storage using in distinguishability obfuscation, which requires a lightweight computation on the auditor and delegate most computation to the cloud. Not only stored also the content will be encrypted in the cloud server. If anyone try to hack at the cloud end is not possible to break the two different blocks. The security of our scheme under the strongest security model. They need first decrypt the files and also combine the splitted files from three different locations. This is not possible by anyone. Anyone can download the files from the server with file holder permission. At the time of download key generated (code based key generation) and it will send to the file owner. We can download the file need to use the key for authentication and some other users want to download file owner permission is necessary.
Vision Based Mouse Control System using different Colour Coding

In this project, Human computer Interaction approach (HCI) is done, where we are trying to control the movement of mouse cursor and its click events using hand gestures with different colors. Hand gestures were acquired using a camera based on color detection technique. This method is mainly focused on the use of Web Camera to develop the visual based interaction between a computer and human in a cost-efficient manner. These day’s intelligent machine are being developed which can be used along with the computer and helps in friendly Human Computer Interaction (HCI). In the previous year’s many technologies are used for developing the virtual mouse. In this project, we have tried to provide an upgraded technology for the virtual mouse. To work with a computer mouse and Keyboard are the very essential input devices. To solve this problem virtual keyboard and mouse is developed.
Smart Voting through UID Verification by using Face Recognition

Image processing is a process, method, technology which is used to perform some operations on an image, in order to get an upgrade image or to extract some useful information from it. In urban region voting is the major issue, identification of person is most important. Sometime people can use fake voter identity for voting. For avoiding this problem we have to develop a system which can identify the face of person and check in present database if face is match in that point the system can return output as UID of the person. After verification of valid face and UID the voter can enter to vote. Data will be store at only one place, so winning party information can be announced within short time period.
A Study on Improvement of Sampling Design of the National Assessment of Educational Achievement

The purpose of the present study is to improve the sampling design of the National Assessment of Educational Achievement (NAEA) to enhance the representativeness of the sampled data and the accuracy of statistical estimates. Based on the census data of 2016 NAEA, we conducted a simulation study by varying sampling methods, sample size, explicit stratification variable, and the expected class size, and compared the results from sampled data with the census results. The study found the appropriateness of stratified sampling relative to cluster sampling and efficiency of 3% sampling, and suggests revision of the expected class size reflecting the real educational settings, and inclusion of area size as an explicit stratification to improve the representation of the sampled data. The study implications can be used for improving sampling design in large scale assessments.
A new heuristic for full trie minimization

Trie is a data structure with many applications.High space usage is the major drawback of the trie.The order-containing trie optimizes the space usage of the trie by rearranging symbols of strings.We present a new heuristic for building order-containing tries with small space.The heuristic is based on the following observation.For a given string set P, by moving the symbols on a position to the first position of strings in P, the trie of the resulting pattern set may have fewer nodes than that of the trie of P. We present an algorithm to find positions that yield the smallest such trie.The algorithm runs in O( P ) time and uses O(|P| log|P|) bits space, where P is the number of total symbols in P, and |P| is the number of patterns in P. By using this method recursively in trie constructions, we can build a trie with fewer nodes than the trie of P. We conduct several experiments that show the new heuristic builds smaller tires than previous work.
Human Capital and Its Impact on Total Productivity within Malian Enterprises: Case of the Enterprise PRIMA

This study highlights the benefit of human capital as generator of the competitiveness for an enterprise. Its overall objective is to set a sufficient guideline for Malian enterprises to implement the impact of human capital on the total productivity. The recommendations could serve as guidelines for Malian companies. Qualitative and quantitative methods, primary and secondary data are used. Secondary data was collected from books, school archives, articles on the internet. Primary data are collected by interview, survey questionnaires and observations. The framework of this research was analyzed using the multiple regression models. Hypothesis tests are used to reject or accept a hypothesis. The student test is used for the individual coefficients evaluation and the Fisher test for the overall evaluation of the model. Excel software was used to calculate p-values and coefficients of determination. The results of the research confirmed that: human capital is a generator of competitiveness for an enterprise. Those results suggest that human capital has a significant impact on an enterprise’s total productivity. The workers can improve their total productivity through voluntary investment in human capital. This study has improved the existing theory of human capital and productivity. A great understanding of the relationship between human capital and the total productivity of an enterprise was made. The theoretical model developed in this study can be used by another enterprise taking into account its specificity. Through this model an enterprise can easily identify its weak points and make the necessary improvements. The enterprise PRIMA needs a substantial investment in health and especially in the training of its workers. The theoretical model is applicable in practice.
REGION HOMOGENEITY IN THE LOGARITHMIC IMAGE PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS

In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.
Analisis Eksistensi Benda Cagar Budaya Dalam Tata Ruang Kota Guna Mendukung Pelestariannya di Kota Surakarta.

The City of Surakarta, one of National Activity Center and also known as Eco Cultural City, has 171 cultural heritage objects have to be preserved. City development that leads to modernization, physically, is a threat to the existence of those cultural heritage objects.To minimize lost of these valuable objects, risk management method, which is based on the interaction between their vulnerability and hazard, could be carried out. This study was aimed to identify and categorize cultural heritage objects at the city, to analyze the risk of losing of cultural heritage objects, and to recommend future city spatial planning in relation to cultural heritage objects. This research was conducted through visual classification techniques on high resolution satellite imagery, Weighted Overlay, Overlay Analysis and Descriptive Analysis. Surakarta City's cultural heritage objects mostly have 100-200 years old which is dominated by traditional Javanese architectural styles. Cultural heritage objects are not fully in good condition, as many as 35 units were partially damaged, 9 units suffered total damage, and 5 units have experienced modernization. A total of 33 cultural heritage objects covered an area of 886,556 square meters (46.09%) are at a high risk of losing their existence. To protect the existence of cultural heritage, the result research should to considered of the city developtment program.
simpeg/discretize: Add representation methods for `TensorMesh`

from pr: #143 commits from: @prisae, @banesullivan review from: @lheagy This PR adds html and non-html representations which should be more generally applicable, for small and big TensorMesh 's.Based on work by @banesullivan on vtki and @prisae on the printversion -tool.
Author Correction: Retrieval of retrained and reconsolidated memories are associated with a distinct neural network

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
Text mining in insurance: from unstructured data to meaning

Every day, insurance companies collect an enormous quantity of text data from multiple sources. We present a strategy to make beneficial use of the large amount of information available in documents by exploiting natural language processing. After a brief review of the basics of text mining, we describe a case study in which, by analyzing the accident narratives written by the researchers of the National Highway Traffic Safety Administration of the U.S. Department of Transportation, we aim to extract latent information that can be used to fine-tune policy premiums. The process involves two steps. First, we classify the reports according to the relevance of their content to determine the risk profiles of the people involved. Next, we use these profiles to create new latent risk covariates for a company’s ratemaking process.
PARENTS’ ATTITUDES TOWARDS THE INTRODUCTION OF INFORMATICS AS A COMPULSORY SUBJECT IN PRIMARY EDUCATION IN THE REPUBLIC OF CROATIA

PARENTS’ ATTITUDES TOWARDS THE INTRODUCTION OF INFORMATICS AS A COMPULSORY SUBJECT IN PRIMARY EDUCATION IN THE REPUBLIC OF CROATIA
THE STATUS AND IMPORTANCE OF MATHEMATICS IN UNIVERSITY EDUCATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 5236-5240ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1303Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
How to solve spatial problems using linked-data: the case of planning a shopping center in Delft

The promotion of the data re-usability was the main motivation behind the conception of linked data. Spatial data can also be expressed as linked data. This thesis explores different approaches to utilize spatial data in linked data format, in order to determine if the technologies have matured enough to assist solving spatial problems. More precisely, it attempts to select a position to build a new shopping center in Delft, Netherlands by using relational data and linked data. Five different Linked-data implementations are identified and examined; two of them, a Geographic SPARQL Protocol and Resource Description Framework (RDF) Query Language (a recursive acronym for GeoSPARQL) (GeoSPARQL) endpoint and an API, are offered from Publieke Dienstverlening Op de Kaart (PDOK). The other three approaches are designed using Oracle Spatial and Graph. From the latter three, one approach utilizes linked data stored in a local database while the rest utilize relational data mapped into linked-data. All five approaches are assessed for GeoSPARQL compliance and are compared to a QGIS solution. From the aforementioned approaches, only the two mapping ones have achieved the desired solution. However, none of them fully complies with GeoSPARQL specification hence, further research is necessary.
AVOIDING DESIGN FIXATION TO IMPROVE THE STUDENTS’ CREATIVE CAPABILITIES

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 6312-6317ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1535Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
GETTING IT RIGHT: A DIGITAL STRATEGY TO SUPPORT NURSING STUDENTS SAFELY ADMINISTER MEDICATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 1220-1225ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0392Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
An Outline of the Semantic Network of the Preposition Up in American English : A Corpus Study

In this study an outline is presented of the semantic network of the preposition up in American English in sentences extracted from the Corpus of Contemporary American English (COCA), which was don ...
Marker Quality Improvement in Augmented Reality Using Grey-scale Method

Augmented Reality (AR) is a technology which combines the real world and virtual world, where users can feel the interaction between the two worlds. Marker Augmented reality or known as marker-based tracking technique uses a marker as media to display this technology. The marker is used as an object that contains images with unique patterns to identify each different object. Because it has a unique pattern, not any objects can be used as a marker. In Vuforia SDK, image recognition is done using a grayscale technique to search for angular features of the pixel image. If the image was taken directly using the camera in the old times in the form of Black and White (BNW), it has bad quality and most likely cannot be used as a marker because it has a few features. In BNW images taken directly from the camera, the Vuforia SDK detects only a few features in the image. This journal explains how the technique of making a BNW image into a marker can be detected by Augmented Reality Technology.
Preventing Plagiarism: Overview of Plagiarism Detection Software’s

Plagiarism is a burning issue. Plagiarism detection software’s are the need of today’s academic field. There are various plagiarism detection softwares available (free or paid). Current research paper is focused on what is plagiarism? Historical background, importance and types of plagiarism tools and overview of few plagiarism detection software’s such as copyleaks, viper, plagiarism.Net etc. Keywords: Plagiarism, copyleaks, viper checker, plagiarism.net, plagium Cite this Article Sunayana D. Jadhav. Preventing Plagiarism: Overview of Plagiarism Detection Software’s. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 247s–253s.
A Short Survey of Data Compression Techniques for Column Oriented Databases

Abstract Column oriented data-stores has all values of a single column stored as a row followed by all values of the next column. Such way of storing records helps in data compression since values of the same column are of the similar type and may repeat. This paper surveys the various data compression techniques in column oriented databases. Data compression is efficiently used to save storage space and network bandwidth. It improves the performance of query execution. Specialized algorithms based on the type of the data stored in column results in immense improvements in compression ratios. Higher-up ratios result in more efficient bandwidth usage. K eywords: Compression, column oriented databases, decompression, NoSQL, HBase
Attribute Based Storage Supporting with Secure De-Duplication in Cloud

zAttribute based Encryption has been widely used in cloud computing for secure encryption of our files into the cloud. It controls the users who can access our data. It enables the users only with specific credentials to access our data. But this standard Attribute based Encryption does not support Deduplication of data, which helps in saving both the storage and network bandwidth of an organization. In this paper, we propose a system setting in a hybrid cloud, which will provide both features of secure encryption and data Deduplication.
Multi-Scale Adaptive Connected Component Labeling for Binary Images

A multi-scale adaptive connected component labeling Algorithm was proposed. The image was first shrunk by levels of down-sampling, and the smallest image was labeled. Then the labeling result was propagated back to the original image level by level. With the design of joint memory structure, this algorithm needs no extra memory. During the backward propagation, double-layer decision tree was used to speed up searching. The backward propagation could be terminated earlier for application-specific constrains to fulfill application requirements
Immediate Open Access Is Welcome But... [President's Message]

Presents the President's message for this issue of the publication.
A physician’s guide to preventing data breaches

While newsmaking cyberattacks often involve external hacks, many breaches result from mistakes made within the practice.
Lyapunov Exponent Analysis for Stochastic Ion Motion in a Non-Adiabatic Trap

The Lyapunov exponent analysis is carried out for ion motions in a non-adiabatic trap where the axial magnetic field from a solenoid is partially cancelled by that from a Helmholtz coil.In particular, relation between the Lyapunov exponents and the trapped rate of ions injected in the axial direction is numerically studied.It is found that there exist energy minimizing the particle trapped rate, and the Lyapunov exponent for the axial velocity is found to be minimized with that energy.
Real Time Object Recognition and Classification using Deep Learning

Navigation in indoor environments is highly challenging for visually impaired person, particularly in spaces visited for the first time. Various solutions have been proposed to deal with this challenge. In this project consider as the real time object Recognition and classification using deep learning algorithms. Object detection mainly deals with identification of real time objects such as people, animals, and objects. Object detection algorithm uses a wide range of image processing applications for extracting the object's desired portion. This enables one to identify the objects and calculate the accuracy of the object and deliver through voice. Using this information, the system determines the user's trajectory and can locate possible obstacles in that route.
A risk-aware approach to cybersecurity

If you are concerned that your security program is non-compliant, you should be—because you're worried about the wrong issue.
Natural Disaster Relief Management Using Dynamic Key Algorithm in Cloud Environment

Natural disaster is happening all around the world with vulnerable effects to life and property.The disaster occurred in Chennai and Kerala cause severe effects and there arise a necessity for developing a New Internet based Android application that provides a platform for donating basic necessary items like food, clothes etc.., to all needy people/organizations.The number of smart phone users are increased thus the disaster management system was implemented as a smart phone application.During the period of disaster, the activity relief is not well-coordinated.For the efficient provision of rescue and relief to disaster -affected areas, this system has been developed.The disaster management system Android application determines the optimum route using geographical locations that help the volunteers and rescuers to serve the most number of people who affected in the disaster area at shortest possible time.Dynamic key algorithm was applied for secured cloud environment and geographic location were accessed by the Google Map service.
Blockchain for Cryptocurrencies – A Literature Survey

Bitcoin has ended up being the most proficient cryptocurrency ever. Precluding the impedance of any third party which monitors the cash flow, Bitcoin is a blockchain technology based decentralized form of online currency which has come into wide acceptance for all internet transactions across the world. Hence, a literature review is conducted to understand the technical know-how of the working of the blockchain for a financial application such as the bitcoin. Cite this Article Heenal Sapovadia, Adarsh Sutariya. Blockchain for Cryptocurrencies: A Literature Survey. Journal of Network Security . 2018; 6(3): 23–27p.
The Hybrid parameter learning algorithm development for dynamic Bayesian network in the context of the Metropolis — Hastings approach

Bayesian networks are an efficient tool for analyzing complex stochastic processes. The generic methods of analysis based on Bayesian networks are probabilistic inference procedures that aimed at solving filtering, prediction and smoothing problems. An essential stage in the probabilistic inference methods implementation is network structure and parameters learning carried out on the special training samples obtained as a result of passive observation of the analyzed process and/or active experiment. Effectiveness of the Bayesian networks parameters learning algorithms directly depends on the preprocessing and organization of the training sample processing, which optimizes the classical learning algorithms capabilities. In the context of research introduced in this article, a hybrid algorithm for the parameters learning of dynamic Bayesian networks with observed and hidden variables has been developed. Algorithm allows increasing the efficiency of probabilities calculation procedure for the initial state and the probabilities of transition between the networks slices involving the variety of special methods for organizing training sample preprocessing. The questions of convergence of the proposed algorithm are also considered.