Reviewerly/exHarmony huggingface dataset:
Document Management System

In this project, it is designed a "Document Management System (DMS)" to make web-based and systematic management and follow-up of documents in the General Directorate of Personnel of Ministry of National Education. This project aims to ensure that the decrees prepared as a result of the appointments made by the General Directorate of Personnel are kept in a safe way in electronic environment, monitored, managed and delivered to the related units. Also this application; it is designed to be used as a document management system in public institutions and universities. Web-based applications make access to content easier, reducing storage and printing costs.
Memes, Confession Pages and Revenge Porn- The Novel Forms of Cyberbullying

In today’s era, technology and the internet is influencing every aspect of our lives. From kids, to adults, to even elderly, we are exceedingly dependent on technology. Technology has its immense benefits, we can send messages from one part of the planet to the other with a click of a button. But with good, comes bad. With the emergence of social media, we have seen various transgressions, with Cyberbullying and online harassment being the prime examples of it. Cyberbullying means bullying an individual or a group using technology. The present article has four chapters to it. The former part of the first chapter aims to discuss the concept and modes of Cyberbullying, the latter part of it deals with the reasons and effects of Cyberbullying on the victims. The chapter also discusses the impact of COVID-19 pandemic on the Cyberbullying menace in India. The last chunk of the first chapter explains the legal aspects of Cyberbullying in India through legislations and case laws. The objective of the second chapter is to discuss the most recent forms of Cyberbullying, mainly memes, confession pages and revenge porn. These have been explained through real-life cases that have happened in premier educational institutions of India. The penultimate chapter of the article deliberates upon the problem of Cyberbullying through a survey which has been done with a sample size of 100 students from various parts of the country. Students from all fields of education, such as Law, Medical and Engineering have participated in the survey. The questions asked in the survey are revolving around the various aspects of Cyberbullying and revenge porn. The final leg of the article discusses the remedies and solutions to the problem of Cyberbullying and Cyber trolling.
Web application with Python and security of the information system

The aim of the research is to develop a database management system for collecting, processing, storing and using information for the teaching of PhD students at a university using the high-level Python language.Studied and researched in the process of development are the main characteristics of the most widely used database management systems. The practical aspects of the design, creation and use of databases were analysed. Has been formulated the requirements to the functional capabilities of the developed database. For the development of the web-application was used Python programming language. The database model, the user interface and a set of reports were developed. A physical data model, oriented towards the design and the development of a database management system using the Python programming language was proposed. The main risks and threats to the security of information in the web-application are characterized. Guidelines for infrastructure protection are proposed.
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becoming hardly distinguishable from real ones, raising serious concerns about the spread of fake or manipulated information over the Internet. In this context, it is important to develop automated tools to reliably and timely detect synthetic media. In this work, we analyze the state-of-the-art methods for the detection of synthetic images, highlighting the key ingredients of the most successful approaches, and comparing their performance over existing generative architectures. We will devote special attention to realistic and challenging scenarios, like media uploaded on social networks or generated by new and unseen architectures, analyzing the impact of suitable augmentation and training strategies on the detectors' generalization ability.
Analysis of sentiment based movie reviews using machine learning techniques

The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a scale of (–) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%.
Structured Representation Learning using Structural Autoencoders and Hybridization

We study the problem of self-supervised structured representation learning using autoencoders for generative modeling. Unlike most methods which focus on using side information like weak supervision or defining new regularization objectives, we focus on improving the representation using a novel decoder architecture and an improved sampling technique. Our structural decoder architecture learns a hierarchy of latent variables, akin to structural causal models, and learns a natural ordering of the latent mechanisms without any additional regularization. We propose a novel framework to characterize the quality of the learned representation by applying interventions in the latent space and evaluating the effects to gain insight in the causal structure learned by the model which also enables us to quantify how disentangled the representation is. We evaluate our architecture and sampling method on several challenging natural image datasets and compare to several canonical baselines.
Artificial intelligence-empowered technology in the home

Nowadays artificial intelligence (AI) is playing an increasingly important and seemingly ubiquitous role in society, with applications ranging from autonomous cars to decision support in healthcare and law, to mention a few. But what is the impact of AI in the home, and how are its inhabitants benefiting from AI in this time and era? This overview will focus on AI-empowered technology in the home along two dimensions: management of the home (including smart devices and domestic robots) and knowledge enrichment (including information gathering and question answering, e.g., via chatbots). We evaluate available technologies as to the functionalities they deliver and the AI methodologies they deploy, and consider possible future developments, including benefits and threats, as the AI-supported functionalities become more sophisticated with time and additional research and industrial investment are channelled into empowering home technology with AI.
Subspace Clustering Using Matrix Factorization

High-dimensional data suffers from the curse of dimensionality and sparsity problems. Since all samples seem equidistant from each other in high-dimensional space, low-dimensional structures need to be found for cluster formation. This paper proposes a top-down approach for subspace clustering called projective clustering to identify clusters in low-dimensional subspaces using best low-rank matrix factorization strategy, singular value decomposition. The advantages of this approach are twofold. First is to obtain multiple low-dimensional substructures using the best low-rank approximation, thereby reducing the storage requirements. Second is the usage of the obtained projective clusters to retrieve approximate results of a given query in time-efficient manner. Experimentation on six real-world datasets proves the feasibility of our model for approximate information retrieval.
Towards document-level human MT evaluation: On the Issues of annotator agreement, effort and misevaluation

Document-level human evaluation of machine translation (MT) has been raising interest in the community. However, little is known about the issues of using document-level methodologies to assess MT quality. In this article, wecompare the inter-annotator agreement (IAA) scores, the effort to assess the quality in different document-level methodologies, and the issue of misevaluation when sentences are evaluated out of context.
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models

Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task.We present a method that leverages the second phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training.We conduct an extensive inter-and intradataset evaluation, showing that our method improves the generalization ability of BERT, sometimes leading to a +9% gain in accuracy.These results highlight the importance of a proper fine-tuning procedure, especially for relatively smaller-sized datasets.Our code is attached as supplementary.
Cross-modal Pedestrian Re-identification Based on Generative Confrontation Network

Pedestrian re-recognition is a very important research direction in video surveillance. With the emphasis on night video surveillance, pedestrian re-recognition is also being studied from a single mode to a cross-mode direction. Since the images taken by the camera at night are generally divided into two types, thermal imaging and infrared images, corresponding to the RegDB data set and the SYSU-MM01 data set respectively. In order to make the trained model have good performance in both data sets, GAN network is used in this article. The visible light image is generated by CycleGAN to generate the corresponding infrared image, and then the infrared image is generated by PTGAN to generate a thermal imaging style image. Then input the image into the single-stream network for training and learning, and finally optimize the network in an end-to-end manner.
Prognosis of of gold mineralization phases by multifractal modeling in the Zehabad epithermal deposit, NW Iran

Concentration–Number (C–N) fractal method has been used for determining and separating mineralization phases based on surface lithogeochemical Au, Ag, Cu, Pb, Zn, As and Sb data in the Zehabad epithermal deposit, NW Iran. Five mineralization phases are demonstrated by multifractal modeling for the mentioned elements correlating with geological studies. The extreme phase of Au mineralization is higher than 7.9 ppm, which is correlated with hematite deposition in silicic veins and veinlets, whereas Ag (˃79.43 ppm), Cu (˃15.85%), Pb (˃63.1%), Zn (˃11.2%) extreme phases are associated with the main stage sulfidation phases. The results show that Au, Cu, Pb, Zn and Ag have two different mineralization trends based on the multifractal nature in this area. These trends are presented based on oxidic and sulfidic mineralization. According to mineralogical studies, the main stages of mineralization include: 1) formation of chalcopyrite ± sphalerite in silicic veins in sulfidic trend; 2) deposition of native gold and specular hematite in silicic veins in response to boilling, in oxidic trend; 3) next phase of fluid penetration and replacing chalcopyrite by galena, sphalerite and tetrahedrite-tennantite in the sulfide veins, in sulfic trend. Neighbouring copper and silver are due to the formation of tetrahedrite-tennantite solid solution. The obtained results show a positive correlation between mineralization phases and the faults present at the deposit. Moreover, mineralization phases of these elements demonstrate a good correlation with silicification and silicic veins and veinlets.
The effect of the electronic word of mouth on purchase intention via the brand image as a mediating factor: an empirical study

This study aims to investigate the impact of electronic word of mouth (eWOM) on the brand image and purchase intention in the telecommunication sector in Jordan. A quantitative-based approach was used for data collection, and a research model was suggested and tested empirically using a sample of 354 respondents who had experiences with the internet and social media. Measurement items are adopted and modified from existing scales found in the relevant prior studies to fit the study's objectives. Using multiple regression analysis, the findings show that eWOM has a positive significant effect on both purchase intention and brand image; the brand image has a significant impact on purchase intention, and eWOM has a fully indirect impact on purchase intention through the brand image as a mediating factor. The current study suggested that telecommunication service providers should wisely and professionally focus on eWOM's favourable contacts to promote a brand image that will later promote the customers' purchase intention.
Optimizing the Valid Transaction Using Reinforcement Learning-Based Blockchain Ecosystem in WSN

In virtual transactions, blockchain technology is a new effective transmission technology that integrates decentralized and distributed database. Blockchain allows transparency in the whole chain of data, and it stores all the transactions into a ledger over four building blocks. However, to the best of our knowledge, there are no techniques available to optimize the transactions occurring between the source and destination of blockchain ecosystem. Here, the transactions are transmitted and received through a self-generated address or digital identities that serve an anonymous user. This paper has done a work on an optimal distribution of blockchain transactions in wireless sensor networks (WSNs) using Reinforcement Learning (RIL). The RIL helps in transacting the value of transactions from source to destination address and helps in recording the public transaction history. The RIL helps in verifying carefully the transactions since it is unaltered or rejected after verification. Further, the experimental results validate the blockchain transactions based on its transmission rate, packet delivery ratio, etc.
A Car-Following Model Using Online Sequential Extreme Learning Machine

Abstract To prevent the sedimentation and loss of real-time data reflecting the car-following behavior and use it for modeling services, a car-following model using online sequential extreme learning machine is proposed. This model based on car-following behavior is affected by the driver’s memory effect, and a forgetting factor is introduced according to the timeliness of incremental data in different traffic conditions. It is trained, validated, and tested by NGSIM project data. Compared with other data-driven car-following models, the model has higher predictive accuracy and better real-time performance. With the help of experimental simulation, it verifies that the average time of the model’s online update is about 0.004s, which can fully meet the real-time requirements of the online traffic simulation system. The research results show that the car-following model proposed in this article is reasonable and feasible.
Interactive Web-Based Data Visualization with R, Plotly, and Shiny

Journal of the Royal Statistical Society: Series A (Statistics in Society)Volume 184, Issue 3 p. 1150-1150 BOOK REVIEW Interactive web-based data visualization with R, plotly, and shiny , Sievert, Carson; Chapman & Hall/CRC; 2020; ISBN 978-1-138-33145-7 Shalabh, Corresponding Author shalab@iitk.ac.in Indian Institute of Technology Kanpur, Kanpur, IndiaSearch for more papers by this author Shalabh, Corresponding Author shalab@iitk.ac.in Indian Institute of Technology Kanpur, Kanpur, IndiaSearch for more papers by this author First published: 11 April 2021 https://doi.org/10.1111/rssa.12692Read 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume184, Issue3July 2021Pages 1150-1150 RelatedInformation
Research of Knowledge Graph Technology and its Applications in Agricultural Information Consultation Field

As a hot research field of big data intelligence, knowledge graph is widely concerned and discussed in recent years. This thesis elaborated the definition and architecture of knowledge graph, the development drive and its importance, analyzed the seven technologies in the knowledge graph: knowledge acquisition, knowledge representation, knowledge storage, knowledge fusion, knowledge modeling, knowledge computation and knowledge operation and maintenance, and introduced and prospected the application of knowledge graph in the agricultural information consultation and the challenges faced in its future development.
Unsupervised Learning of Explainable Parse Trees for Improved Generalisation

Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate tree representation. In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures. We also demonstrate the superior performance of our proposed model on natural language inference, semantic relatedness, and sentiment analysis tasks and compare them with other state-of-the-art RvNN based methods. Further, we present a detailed qualitative and quantitative analysis of the learned parse trees and show that the discovered linguistic structures are more explainable, semantically meaningful, and grammatically correct than recent approaches. The source code of the paper is available at this https URL.
A Simplified Architecture to Integrate and Interoperate Heterogeneous and Distributed Healthcare Data

The different health organizations are using various databases having different data models to store healthcare data. Those electronic data are significant to integrate for providing urgent information about the patient and better quality treatment. Information retrieval is the first condition from a heterogeneous environment to integrate and interoperate data. Three main challenges are facing in the time of information retrieval from heterogeneous and distributed databases: (i) resolving the structural or data model variability, (ii) bridging among various data querying syntaxes, and (iii) mitigating semantic heterogeneity of data created in data storing time. The aim of this paper is how to retrieve data effectively from heterogeneous and distributed healthcare databases, whether to use standard or various other data models. We here propose a simplified architecture to integrate and interoperate data among different databases having distinct nature and query language developed by different vendors. Also, a client API is implemented pragmatically to retrieve data from MySQL, MarkLogic, and Apache Cassandra. The primarily implemented part of our proposed architecture retrieves data from different databases successfully and efficiently.
Learning Implicit 3D Representations of Dressed Humans from Sparse Views

Recently, data-driven single-view reconstruction methods have shown great progress in modeling 3D dressed humans. However, such methods suffer heavily from depth ambiguities and occlusions inherent to single view inputs. In this paper, we address such issues by lifting the single-view input with additional views and investigate the best strategy to suitably exploit information from multiple views. We propose an end-to-end approach that learns an implicit 3D representation of dressed humans from sparse camera views. Specifically, we introduce two key components: first an attention-based fusion layer that learns to aggregate visual information from several viewpoints; second a mechanism that encodes local 3D patterns under the multi-view context. In the experiments, we show the proposed approach outperforms the state of the art on standard data both quantitatively and qualitatively. Additionally, we apply our method on real data acquired with a multi-camera platform and demonstrate our approach can obtain results comparable to multi-view stereo with dramatically less views.
New normal policy on the Rupiah exchange rate using Long Short-Term Memory

Abstract Since the world is facing health problems due to COVID-19, Indonesia was not success to surge the outbreak. In addition to the risk of health problems, this pandemic is also disrupting the global economy. The rupiah exchange rate also saw a devaluation during the COVID 19 outbreak. The Indonesian economy is expected to grow negatively in the third quarter of 2020 and predicted to continue until the end of the year. The measure from the government impacted the economic circumstances then along with the decline in new cases of the spread of COVID-19 the government was implementing a new normal, after the completion of large scale social restrictions weakened the economic development, with one of its goals to improve and try to save Indonesia’s economy from a possible worse recession. This study attempts to use the forecasting method to find out whether the application of the new normal will strengthen the Rupiah exchange rate in the coming period. The methods that will be used is Long Short Term Memory as the best model to overcome long-term dependencies to obtain a predictive model that most closely approximates existing data patterns. The most suitable model is Long Short Term Memory with 50 epochs using five hidden neurons. Based on the results, it seems that the Rupiah exchange rate tends to weaken in the next five-day.
Application of Travel Service Recommendation Algorithm Based on Cloud Computing

Abstract In the context of big data, traditional collaborative filtering recommendation algorithms cannot provide users with accurate recommendation services, making the sparsity of user data become an important factor affecting the accuracy of recommendation in the complex social network environment. This paper mainly studies the design and application of travel service recommendation algorithm based on cloud computing. In this paper, the data is processed based on MapReduce parallel computing framework to improve the performance and speed of the algorithm. The Linux-equipped cluster is deployed under the Hadoop framework. The cluster feasibility test is carried out to realize the recommendation function of the algorithm, and the function of the algorithm is realized according to the actual problems. On the Hadoop big data platform, the traditional algorithm was improved and the hadoop-based collaborative filtering recommendation algorithm was implemented to further improve the recommendation rate and accuracy of the traditional recommendation filtering algorithm.