Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Perception of the End-User on the Acceptance of Smart Government Service in UAE

The end-users’ acceptance of electronic government applications is crucial for the effective delivery of public services. This study intends to investigate the factors that influence the end-users' acceptance of smart-government services.This paper identifies key determinants of the end-users’ acceptance of smart government services in the UAE to develop a theoretical model which is tested empirically, using the partial least squares structural equation modelling. This study examines the relationship among the factors that influence the adoption of smart government applications, along with the moderation effects of gender, age, and experience of the end-users on this linkage. The paper reveals that performance expectancy is the strongest factor influencing adoption of smart government, followed by trust in government, effort expectancy, and social influence. The Multi Group Analysis is used to test the moderation effects of the gender, age, and smart service use experience of the end-users on the relationship between the factors that influence smart-government service adoption.
CQA Transformer Models in the Home Improvement Domain

To find answers for subjective questions about many topics through Q\&amp;A forums, questioners and answerers can cooperatively help themselves by sharing their doubts or answers based on their background and life experiences. These experiences can help machines redirect questioners to find better answers based on community question-answering models. This work proposes a comparative analysis of the pairwise community answer retrieval models in the home improvement domain considering different kinds of user question context information. Community Question-Answering (CQA) models must rank candidate answers in decreasing order of relevance for a user question. Our contribution consists of transformer-based language models using different kinds of user information to accurate the model generalisation. To train our model, we propose a proper CQA dataset in the home improvement domain that consists of information extracted from community forums, including question context information. We evaluate our approach by comparing the performance of each baseline model based on rank-aware evaluation measures.
Sentiment Analysis: A Comparative Analysis

Sentiment analysis has become essential in terms of making decisions when dealing with the online world. Sentiment analysis of microblogging data detects the user's feelings from plain text, images, videos, emoticons, and plenty of other things by text analysis. The product review and online rating system is an essential component of the online business industry. As a result, businesses are concerned about their overall online image, and user reviews have become extremely relevant. Users leave reviews on the company's website or microblogging sites about products, services, or brands. Microblogging sites like Twitter, Tumblr are an excellent source of global everyday news. They also provide feedback on a variety of products available to consumers. Several people are tweeting about a variety of current events and activities occurring all over the world. This paper presents a comparison of different AI approaches on the poorly categorized data and implemented various techniques. An efficient bidirectional Long Short-Term Memory (BiLSTM)-based approach for sentiment analysis this poorly categorized Twitter data outperforms the other artificial intelligence techniques. The word embedding technique is used with the Word2Vec model with BiLSTM. Experimental results show that it enhanced the accuracy up to nearly 85%.
Multi-scale Context Network for Image Inpainting

Although the existing image inpainting methods can generate seemingly reasonable contents. However, these methods may produce image artifacts. This is because the image details are lost in the coding process, and the relationship between distant pixels cannot be modeled. To solve these problems, we propose a multi-scale context network (MSCN), which attempts to extract different levels of feature information and fuse them to reconstruct a complete image. MSCN uses encoder-decoder structure and consists of cascaded residual blocks. Then, we designed a multi-scale dilated convolution (MSDC) module to obtain richer contextual features by increasing the receptive field. Finally, the encoder features are fused into the decoder by the improved attention (IA) module. A large number of experiments on CelebA-HQ, FFHQ, and PairStreet datasets show that this method can effectively generate realistic and visually credible results.
Coded Distributed Computation with Limited Resources

A central issue of distributed computing systems is how to optimally allocate computing and storage resources and design data shuffling strategies such that the total execution time for computing and data shuffling is minimized. This is extremely critical when the computation, storage and communication resources are limited. In this paper, we study the resource allocation and coding scheme for the MapReduce-type framework with limited resources. In particular, we focus on the coded distributed computing (CDC) approach proposed by Li et al.. We first extend the asymmetric CDC (ACDC) scheme proposed by Yu et al. to the cascade case where each output function is computed by multiple servers. Then we demonstrate that whether CDC or ACDC is better depends on system parameters (e.g., number of computing servers) and task parameters (e.g., number of input files), implying that neither CDC nor ACDC is optimal. By merging the ideas of CDC and ACDC, we propose a hybrid scheme and show that it can strictly outperform CDC and ACDC. Furthermore, we derive an information-theoretic converse showing that for the MapReduce task using a type of weakly symmetric Reduce assignment, which includes the Reduce assignments of CDC and ACDC as special cases, the hybrid scheme with a corresponding resource allocation strategy is optimal, i.e., achieves the minimum execution time, for arbitrary amount of computing servers and storage memories.
Asynchrony as a Value of Professional Education in the Digital World

The contribution investigates the issue of asynchronous learning in the context of digitalization of higher professional education. Some advantages and disadvantages of various modes of educational work are considered, the results of the analysis of existing research in this area, own observation and diagnostic work in the format of non-standardized interviewing of students, teachers and representatives of the management of an educational organization are presented in this contribution. The study was conducted in 2020 on the basis of the Samara State University of Economics, the sample was 65 people. Presenting the research results, the authors indicate a problem of heterogeneous and ambiguous attitude to asynchrony on the part of management personnel, on the one hand, and the acceptance, desire to use advantages of asynchrony as a leading feature of digital learning on the part of the main participants of the educational process (students and teachers), on the other hand.
Open source text visualization tools: A comparative analysis

Text visualization is usefulfor researchersfrom many fields like social media analysts, information graphics, digital libraries, linguistics, marketing etc. who often use tools to obtain visualization of their textual data. Though there are a lot of open source tools for this purpose, especially for creating word clouds, most of the free open source tools employ straightforward frequency based approach and consideration ofword significance is user's responsibi lity. Users from interdisciplinary researches are not equipped with adequate knowledge or free tools to summarize text corpus into single text file or identify significant words. The main idea behind this paper is to provide a summary of all those popular open source visualization tools available for text visualization. We provide a comparative analysis of these tools with their pros and cons. Experimental results are provided for visual comparison to serve as quick reference for a user to help in selection of tool.
Topic Modeling for Short Texts: A Novel Modeling Method

Topic modeling is one of the major concerns in the short texts area, and mining these texts could uncover meaningful insights. However, the extreme short texts’ sparsity and imbalance bring new challenges to conventional topic models. In this paper, we combine a new ranking method with hierarchical representation for short text. Words ranking proves to be inexorable in generating value from disorganized short texts; thus, a novel ranking paradigm is developed and is referred to as the ordered biterm topic model (OBTM). OBTM models the semantic connection between every two words, regardless of whether they show up in similar short content, reinforcing the capacity to reveal the genuine semantic examples behind the corpus. The intense contextual information maintained in the space of word-to-word assures the word sensation in recognizing relevant topics with reliable quality. Then, the paradigm is associated with a hierarchical representation that captures the relations connecting the created topics. OBTM learns topics at the corpus level. This makes the inference more effective and robust, referring to hidden semantics patterns. Experiments on real-world collection reveal that OBTM can discover more relevant and coherent topics. It achieves high performance in various tasks and outperforming the state-of-the-art baselines.
Application of Improved Ant Colony Algorithm in Aerobat Structure Optimization

Abstract This paper analysis the characteristics of two kinds of ant colony algorithm, some kind of aircraft structure dynamic optimization design model is established, based on the analysis of the model, two kinds of ant colony algorithm are combined with optimization calculation method respectively, the two kinds of ant colony algorithm is studied in the aircraft structure optimization design, the calculation results indicate that improved ant colony algorithm is scientific and feasible, it can effectively solve the optimization problems of aerobat and other structures.
Review on Study and Usage of MERN Stack for Web Development

Abstract: Building, creating, and maintaining websites is known as web development. Abandoning the traditional method of web development (HTML, CSS, JavaScript), MERN stack includes four key technologies namely: MongoDB, Express.JS, React.JS and Node.JS. Clearly, it is a JavaScript Stack and it allows smooth development and faster deployment. The main contribution of this work lies in the deep understanding of each technology component on the stack. Keywords: ReactJS, NodeJS, MongoDB, ExpressJS, MERN
Representation of categories through prototypes formed based on coordinated activity of units in convolutional neural networks

Various techniques focus on understanding the way an image or a category concept is represented within convolutional neural networks. It is common to assume that one latent neuron can be a detector for a category or its dominant features. The analysis of the collective activity of neurons in hidden layers shows that representation of most of the categories is complex and distributed across as many as 93% of units participating in the encoding. To account for the complexity, this study suggests an approach that represents category by constructing a prototype formed as a covariance matrix of layer neurons’ activation. The approach makes it possible to take into account the coordinated response of the population of artificial neurons, as well as to preserve the complexity of the distributed activation pattern at different stages of processing, from low-level image statistics to the level of abstract and semantic features.
TikTok pays $92m to settle facial ID lawsuit

The hugely popular TikTok video-sharing site has been forced to pay its customers $92 million, to settle a lawsuit that accused it of misusing facial recognition data.
Fusion-based Spatiotemporal Convolutions with Constant Temporal Snapshots for Sign Language Recognition

Non-intrusive sign language recognition has started to redefine the wellness of millions of hearing impaired people around the world. In this study we propose two models, named ContiNet and F-ContiNet. ContiNet is a 3D convolutional network with a constant number of temporal snapshots, and F-ContiNet is an evolution of ContiNet where the fusion idea is applied so that the spatial and temporal information from the early layers will have greater direct impact on the final recognition. We have conducted extensive experiments on a 500-class dataset and its 100-class subset, using both raw videos and mask videos produced by the skeletal information obtained from OpenPose. For all the models we have tested, those using mask videos performed significantly better and learned faster than those using raw videos, and F-ContiNet performed the best, with the best per-class top-1 accuracy of 96.6% over the 500-class dataset. More importantly, it confirms that keeping a constant temporal dimension in a deep network is a viable approach to SLR, especially when the temporal information from all the available snapshots can be fully exploited by a time-sensitive construct such as ConvLSTM.
Center for Advancement and Synthesis of Open Environmental Data and Sciences (NSF)

Federal Grants & ContractsVolume 45, Issue 4 p. 3-3 Grants Alerts Center for Advancement and Synthesis of Open Environmental Data and Sciences (NSF) First published: 15 January 2021 https://doi.org/10.1002/fgc.31507Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue421 January 2021Pages 3-3 RelatedInformation
Plagiarism Awareness and Academic Writing Ability: The Relationship with the EFL Students' Plagiarism Practice

Plagiarism is considered as the writing difficulty and has been a topic among researchers in the past ten years. Many studies explored plagiarism perceptions and the reasons behind plagiarism. Some studies also attempted to find an association between the elements that might contribute to plagiarism such as writing skills, language level, studying behavior, etc. However, most of the studies does not include the plagiarism rate or similarity index as one of the research variables. To fill this gap, this study aimed to find out whether there is any relationship between plagiarism awareness and academic writing ability towards the students’ actual plagiarism practice. The 30 respondents are students of the English Department in Mulawarman University who has written an academic paper namely a Mini research proposal. This study design is quantitative-correlational design with a Multiple regression data analysis technique. The study three main findings are; (1) there is no significant relationship between plagiarism awareness and plagiarism rate; (2) there is a significant negative relationship between academic writing ability and plagiarism rate; and (3) there is a simultaneous relationship between plagiarism awareness and academic writing ability with plagiarism rate. Additionally, academic writing ability has more influence on plagiarism rate than plagiarism awareness.
Blockchain Applications of Manufacturing Systems: A Survey

Abstract In recent years, future manufacturing systems following a vision of Industry 4.0 has been accelerated in order to enable more personalized, smart, and sustainable production. However, unsolved security challenges from their enlarged accessibility and connectivity have delayed realizing such systems. To address the emerging issues, the blockchain technology can be adopted to utilize its inherent generic security capabilities. However, research regarding the use of blockchain in manufacturing systems has only begun in the past few years while their research findings have been disseminated through a wide range of different outlets. This paper presents a literature review covering how manufacturing systems can adopt the blockchain technology while maintaining their production capacity, flexibility, and cost-efficiency. In addition to the survey, five categories of manufacturing systems striving to achieve the goal of Industry 4.0 are defined to provide a system model-based analysis. Also, a technology roadmap — that visualizes the chronological history of the technology — has been adopted to present the research trend and identify opportunities for future studies.
Digital Educational Ecosystem: Transformation and Development Trends of Online Education

The authors’ research focuses on the transformation of the online education market in the current context and the creation of the concept of a digital educational ecosystem. The relevance of the topic can be substantiated by reasons that are associated with the overall digitalisation of the economy and the COVID-19 pandemic. Determination of the reasons for the transformation of the conventional hierarchical education system, the rapid development of the online education market and, as a consequence, the development of the concept of a digital educational ecosystem as a present-day approach to arranging for the educational environment in the context of the society’s transition to a new technological set-up, networkisation of the world educational space and active implementation of digital technologies in the educational process is defined by the authors as the key goal of the research. Based on the empirical data analysis, a conclusion is made about the accelerated rates of development of the online education market, the predicted value of the growth rate of online education in the future in the short term. The author’s vision of the digital educational ecosystem as a network infrastructure, which shapes a single technological educational platform with digital technologies, creates conditions for effective interaction between the stakeholders through the provision of tailored educational services based on taking into account the interests of consumers, is formulated.
Optimal approximations made easy

The fundamental result of Li, Long, and Srinivasan [14] on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics, and data analysis. The goal of this paper is to give a modular, self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
Analyzing the Cloud Performance Using Different User Subscription Times

Cloud providers face the challenge of managing large amounts of heterogeneous resources in real time. It is usually very costly to conduct experiments with real cloud systems. Therefore, tools to analyze and evaluate cloud scenarios and experimental studies are very useful for them. In this paper, we model cloud systems and the user interactions with the cloud provider using the UML2Cloud profile. In general, users request virtual machines according to their needs, but they can also subscribe to the cloud provider and wait to be notified when the requested resources are not available. In this case, users indicate a maximum subscription time, so once this time elapses without being notified, users leave the system unattended. Thus, we present an exhaustive experimental study to measure how the user subscription times affect the overall system responsiveness. To this end, four different cloud configurations are analyzed, and the workloads for these studies are produced by using three distribution functions for the user arrivals, namely, a uniform, a normal, and a cyclic normal distribution. Furthermore, we also analyze the cloud performance with a workload obtained from a real trace. The purpose of this study is to find out the inflection point for the waiting time of the users, from which the cloud responsiveness and its performance do not improve. The obtained information is, therefore, useful for the cloud provider to improve the configuration of the cloud.
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records (Preprint)

<sec> <title>BACKGROUND</title> Predicting the risk of glycated hemoglobin (HbA&lt;sub&gt;1c&lt;/sub&gt;) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes. </sec> <sec> <title>OBJECTIVE</title> Our study investigated the performance of predictive models to forecast HbA&lt;sub&gt;1c&lt;/sub&gt; elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models. </sec> <sec> <title>METHODS</title> This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (&amp;lt;5.7%) and elevated (≥5.7%) levels of HbA&lt;sub&gt;1c&lt;/sub&gt;. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records. </sec> <sec> <title>RESULTS</title> The machine learning models achieved promising results for predicting current HbA&lt;sub&gt;1c&lt;/sub&gt; elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. </sec> <sec> <title>CONCLUSIONS</title> This study shows that machine learning models can provide promising results for the task of predicting current HbA&lt;sub&gt;1c&lt;/sub&gt; levels (≥5.7% or less). Using patients’ longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies. </sec>