Reviewerly/exHarmony huggingface dataset:
Adaptive Particle Sampling and Resampling in Parallel/distributed Particle Filters

Particle filters have been widely used in estimating the states of dynamic systems by using Bayesian interference and stochastic sampling techniques. Parallel computing techniques were introduced to improve the performance of sequential particle filters with multiple processing units (PUs). However, the unavoidable communications between Pus, lower the performance. The hybrid and adaptive resampling algorithms were proposed to improve the performance of parallel/distributed particle filters by reducing the communication costs without loss of estimation accuracy. In this paper, we propose an adaptive sampling and resampling technique in particle filters. In the proposed algorithm, the number of particle is dynamically adjustable based on the model convergence. As a result, less particles will be used if the current convergence is good and more particles will be used if the convergence is getting bad. The experimental results show the improved performance by using less particles and reducing the communication cost compared with other algorithms.
Social Media with Language Acquisition Working Memory in Elementary School Students

This study aims to describe the role of social media in the acquisition of language of primary school age children.Language acquisition of children will be associated with the acquisition of words stored in working memory.Getting words can be stimulated by using social media.This study uses a quantitative approach.The research subjects were fourth grade elementary school students totaling 70 students.analysis of the study was carried out using the Pearson correlation coefficient and hierarchical regression performed.The results of the study show that social media has a role in acquiring languages from four classes in elementary school.
Privacy-preserving public auditing with data deduplication in cloud computing

Storage represents one of the most commonly used cloud services. Data integrity and storage efficiency are two key requirements when storing users’ data. Public auditability, where users can employ a Third Part Auditor (TPA) to ensure data integrity, and efficient data deduplication which can be used to eliminate duplicate data and their corresponding authentication tags before sending the data to the cloud, offer possible solutions to address these requirements. In this thesis, we propose a privacy preserving public auditing scheme with data deduplication. We also present an extension of our proposed scheme that enables the TPA to perform multiple auditing tasks at the same time. Our analytical and experimental results show the efficiency of the batch auditing by reducing the number of pairing operations need for the auditing. Then, we extend our work to support user revocation where one of the users wants to leave the enterprise.
An Optimal Aggregation of Product Data using Vector Space Model

In the current scenario there exists many versions of a particular product. A product might be laptop, mobile or any other gadget. With increase in number of versions there is a need to analyze the reason for release of the new version of the product. This can be done by the study of reviews and ratings provided by consumers. To get a more accurate output we first relate the rating and review usingSentiment Analysis (SA). SA is a form of text mining that helps us to understand the attitude and behavior of a customer towards a product/service. The ratings given by the customer may not be in the same level of agreement as in the review text. Customer may have issues with the product and has explained in the review but can be generous and give decent rating, such circumstances often depends on the emotional quotient of the customer. Therefore, there is a need for a system which can elicit the polarity among the reviews and check if there is proper agreement between the ratings and reviews given by user till the product become obsolete.In order to provide the correlation between the ratings and reviews lexicon method of sentiment analysis is used to generate the sentiment score for each review. Based on the sentiment score obtained the reviews are further classified into extreme negative, negative, neutral, positive, and extreme positive and compared to the ratings given by the customer. With this reviews as input, feature selection is done using vector space model. The output obtained depicts the success factors and failures of a product which helps to build a better version.
ZIMBABWE: Hyperinflation Fears

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 1 p. 22438C-22439C Policy and Practice ZIMBABWE: Hyperinflation Fears First published: 06 March 2019 https://doi.org/10.1111/j.1467-6346.2019.08757.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue1March 2019Pages 22438C-22439C RelatedInformation
Real-Time Image Privacy Preservation using AES with Salt key and Gaussian Blur Algorithms with the application of Data Perturbation in Cloud

Rapid increase in image data produced by individuals and companies provoke privacy hitches such as unauthorized revelation of personal data and person’s individuality stealing, but it can be used to protect privacy of the user and also personal information. Data provider and Cloud Administrator are two types of users who have privilege and access. However, there is absence of research examining the effectiveness of implementing techniques to images as a technology that protects privacy. The Advanced Encryption Standard (AES) algorithm is selected for the login authentication process, which ensures the protection of information from unauthorized access and is proposed to create a random key generation. Data perturbation is used to add noise in databases, and Gaussian blur is done to introduce some degree of degradation in the original image. The obtained test results show that an AES with salt key can be randomly generated. In this process, security authentication is reinforced in the ciphertext changes that make up the key words for each encryption process. The result shows that the model is relatively fast with a time average of 0.034 s occupying less than 100kB of memory space.
Incrementing Bit Fields of Subscriber Identity Module (SIM)

The overall markets for SIM cards is massive and comparatively Stable. In fact, the amount of SIM cards shipped globally is forecast to extend a huge range and still increasing at once. So, increasing the bit field of MSIN(Mobile Subscriber Identification Number) is additionally an helpful choice to choose. however in present bit size of nine or ten (depends on the country) the MSIN have over trillion mixtures of obtainable numbers. In our proposed work we have a tendency to makes a group of home MNCs that are connects in usual, and Keeps record of attributes like ‘Signal Strength’, ‘Internet Speed’, ‘Call drop. After that, Orders or sorts the set of MNCs as per any alternative of those attributes, then selects the most effective result for home MNCs each time and Updates the database contiguously.As the population is increasing of the globe and additionally there is no any regulation or an individual is not restricted to use just one SIM card so, it would be a secure possibility for future use.
Frame Prediction- Noise Removal using Denoising Autoencoders

In the current times, tasks like Object Detection, Object tracking, Gesture prediction, Video prediction in computer vision are being solved effectively with models of deep learning . Video frame prediction involves predicting the next few frames of a video given the previous frame or frames as input. Currently, the challenge in video frame prediction is that the predicted future frames are blurry. This paper focuses on the removal of noise from the predicted image using Denoising Autoencoders, solve the above-addressed issue. The proposed work, trains LSTM model which generates future frames by giving a sequence of input frames. The predicted output is given as an input to the Denoising Autoencoders which tries to remove the blurry predictions. Our approach is implemented on Moving MNIST Dataset. The result of our proposed method improved accuracy and is compared with the accuracy of Denoising Autoencoders, LSTM, and LSTM along with Denoising Autoencoders.
Imprint: Geomechanics and Tunneling 4/2021

Geomechanics and TunnellingVolume 14, Issue 4 p. 419-419 ImprintFree Access Imprint: Geomechanics and Tunneling 4/2021 First published: 04 August 2021 https://doi.org/10.1002/geot.202170407AboutPDF 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume14, Issue4August 2021Pages 419-419 RelatedInformation
Color image watermarking using multidimensional Fourier transforms

This thesis presents two vector watermarking schemes that are based on the use of complex and quaternion Fourier transforms and demonstrates, for the first time, how to embed watermarks into the coefficients consistent with our human visual systems (HVS). Watermark casting is performed by estimating the Just-Noticeable distortion (JND) of the images, to ensure watermark invisibility. The first method encodes the chromatic content of a color image as CIE a*b* chromaticity coordinates whereas the achromatic content is encoded as CIE L tristimulus value. Color watermarks (yellow and blue) are embedded in the frequency domain of the chromatic channels by using Spatio Chromatic Discrete Fourier Transform (SCDFT). It first encodes a* and b* as complex values, followed by a single discrete Fourier Transform. The most interesting characteristic of the scheme is the possibility of performing watermarking in the frequency domain of chromatic components. The second method encodes the L*a*b* components of color images and color watermarks are embedded as vectors in the frequency domain of the channels by using the Quaternion Fourier Transform (QFT). The idea is twofold: Robustness is achieved by embedding a color watermark in the coefficient with positive frequency, which spreads it to all components in the spatial domain. On the other hand, invisibility is satisfied by modifying the coefficient with negative frequency, such that the combined effects of the two are insensitive to human eyes
Cloud Data Security Protection Technique using Internet on Things

The internet of things is turning into an appealing framework worldview to acknowledge inter-connections throughout corporeal, digital as well as communal gaps. Through the connections amid the IoT, safety concerns befall important, along with it is huge to set up improved resolutions for safety protections. The IoT apparition of unlock data sharing is expert through using cloud registering concepts. Since IoT is depends on the web, safety concerns of internet will similarly emerge in IoT as well as IoT enclose three layers for example perception, transportation and application layers. The safety concerns, modernism along with solution recognized by the application layer are conversed about in this Paper. The principle focal point of this examination work is on Data Security Protection procedure for application layer.
ROBOT JOURNALIST OR HUMAN JOURNALIST?: AN ANALYSIS IS OVER NEWS ARTICLES

Technology and humans have been in constant interaction in the historical process.So much so that with every technological progress, mankind has entered a new revolutionary era; first the Agricultural Revolution, then the Industrial Revolution and finally the Information Revolution.Thanks to information technologies, society is connected with information networks.The information society demands the continuous production of information with an insatiable appetite.The media, which has been undertaking the responsibility of enlightening the public for hundreds of years, is experiencing a transformation with technology and as a result, practices in journalism are constantly changing.The profession of journalism has a convergent relationship with computer engineering."Robot journalists" can now produce the news that were made by humans for hundreds of years.The "Algorithms" which were previously used to edit/arrange the news, can now be used in writing the news.Thousands of news from the world's most important agencies and newspapers, such as Associated Press, Reuters, and The Washington Post, are now being written by robot journalists.With the developed software, these robots can organize financial data, write news about sports and even election results, and provide information to the public in just a few minutes after an earthquake.The Los Angeles Times was able to write news about all the homocides in the California area without any delay, with the use of A.I. journalism.In short, "robot Journalism" revoked the previous argument that "robots can only be manuel labour workers" and this argument is now replaced by; "robots are capable of intellectual work."The development of "robot journalism" that marked the first quarter of the 21st century scares human journalists day after day; for many remark that they will face the risk of losing their jobs in the near future.This study comprises the comparison of the news sample written by robot journalists with the news written by human journalists and its analysis by a group of journalist candidates.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 May 2019; 124 (3): 286–287. doi: https://doi.org/10.1352/1944-7558-124.3.286 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
High-definition human visual attention mapping using wavelets

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.
Review of article on TraceME

This paper discusses the so-called traceability analysis that the authors have published and applied since 2013.The motivation for the paper is the development of a cloudbased software built on the CAFE framework which implements their method and is launched via web interface.The authors position their analysis as the answer to what they consider are the deficiencies in the current state of model evaluation, describe how their software and algorithms work, and then show analysis results from a selection of CMIP6 models.
PENERAPAN SISTEM ANTRIAN PELAYANAN (SIAP)

SIAP (Service Queuing System) application is an innovation in terms of service queues. SIAP is an android based application developed as a form of innovation for better service by considering technological developments to the public. This application provides a registration menu to people who want to do service. By entering the ID number, name and type of service, the registrant (community) can get a queue number. After the community registers, the registrants will be verified by the administrator. After being verified, the registrant gets a queue number. After the registrant gets a queue number then he can immediately come to the Administrative Services and Civil Registration Service to receive services in accordance with the order of the queue number in the SIAP Application. After carrying out the service, the community can see the service status. In addition, there is also a feature of complaints from the public if there are irregularities in the service that are directly conveyed to the leadership without going through the administrator or staff.&#x0D; Keyword : SIAP (Service Queuing System), service, application, administrator.
A Comparative Study of Online and Offline Teaching Modes of Accounting Courses Based on Convolutional Neural Networks

At present, the accounting teaching in many colleges and universities is mainly based on traditional classroom lectures. In the course of teaching, teachers often use single-filled, instilled, and one-to-one explanations for knowledge transfer. Although this method can enable students to understand and master the content of the textbook, it cannot apply the learned theories to practice well. Therefore, starting from the convolutional neural network(CNN), this article uses big data to conduct a comparative study of online and offline accounting teaching. The purpose is to compare the strength and weakness of online and offline teaching(OOT). This article mainly uses investigation method, data method and comparative method to conduct in-depth research on students' attitudes towards OOT, (CNN)s and OOT modes. The survey results show that 68% of students accept both OOT modes. The time flexibility of online teaching cannot be compared with offline teaching, but the interactivity of online teaching cannot be compared with offline teaching.
Big Data Security Analytics in Clinical Data using Cryptographic Algorithms

With increasing apprehension and concerns of cloud computing and information security, awareness about the use of security algorithms in data systems and processes is indispensable. The objective of this paper is to examine a set of cryptographic algorithms for cloud platforms to secure clinical data. The main benefits of cloud storage are scalability, resilience, cost efficiency, high reliability and easy access to your knowledge anyplace, anytime. Because of these benefits every organization is moving its data to the cloud. So there is a necessity to protect that data against unauthorized access, modification or denial of access etc. we analyzed the use of cryptography in securing clinical data sets using evaluation parameters such as computing memory, encryption time and decryption time
Collaborative Learning as A Strategy to Improve the English Reading Comprehension of Indonesian Learners in The Agribusiness Department at Muhammadiyah University of Parepare

This research sheds some light on collaborative learning to increase the agribusiness students' reading ability at the Muhammadiyah University of Parepare. It tries to find answers to two questions. First, to what extent does the collaborative learning strategy improve the students reading comprehension in the ESP course at UMPAR? Second, what are the students' perceptions of collaborative learning strategies in teaching reading comprehension at UMPAR? To answer these questions, the researcher adopted a Quasi-Experimental design by using the experimental and control group. The data were collected through a reading test and a questionnaire. The research findings indicated that using a collaborative learning strategy improved the students' reading comprehension at agribusiness students at UMPAR. The t-test result of the Pre-test was -1.25 and 2.50 in the Post-test. The Post-test result was more significant than the t-table (2.50&gt;2.023). It indicated that H0 was rejected and H1 was accepted. The results of the study also revealed that a collaborative learning strategy improves the students' ability in teaching Reading Comprehension.
Low Cost Ecg Monitoring using Internet of Things

Patient monitoring is the heart of the health care domain in day to day life either at home or at hospital. This paper focuses an Intelligent ECG Monitoring System to monitor the heart patients residing at distant places at low cost and complexity using Internet of Things. The proposed system automatically screens the health condition of the patient and records their Electrocardiogram through heart rate sensor and ATtiny Board. ATtiny is a low cost IoT device used along with heart rate sensor and ESP8266 to record the electrocardiogram of the patients. The ECG is sent to the doctors, nurses or the patient’s relatives residing in the remote places through the internet to help them in remote monitoring of the patients with ease.
Ensuring Efficient Data Storage using Fully Mature Homomorphic Encryption Technique in the Cloud Environment

In cloud computing, user database is stored at remote site instead of user computer’s hard disk where the connection between remote site and user computer is provided by internet connection. As cloud computing essentially places data outside the custody of owner of data, it inexorably hosts security disputes. The distance among the physical and the client location of data generates a barrier as the data can be accessed by an unauthorized party and this would influence the solitude of client’s data. The utilization of traditional encryption systems to encrypt the data prior to transmitting to the cloud provider has been most extensively utilized technique to link this security gap. Be that as it may, the customer will require offering the private key to the server to unscramble the information in front of playing out the figuring’s fundamental. Homomorphic encryption techniques permits computations on encrypted data devoid of decryption. This paper deals with the utilization of Fully Mature Homomorphic Encryption (FMHE) to encode the client’s data on cloud server and as well it facilitates to perform required computations on the encrypted data