Reviewerly/exHarmony huggingface dataset:
Enterprise Transformation Projects

This article presents the ETP-HSC and a corresponding enterprise transformation framework, where the focus is on the application of Enterprise Architecture (EA) to support Security (EA4S) and it is the central topic. The EA4S is a Polymathic-holistic approach, which adopts a clear EA as the main ETP constraint for the implementation of a secured Information and Communications System’s (ICS). EA and all other ICS related architecture disciplines, are inspired from the term Architecture that comes from civil engineering, and in this domain, secured building objects are achieved by implementing robust building and urbanistic architectures, like the famous case of Hausmann’s Architecture and Urbanistic plan for the (re)Architecture of Paris in France, in which its primary objective was to assert Paris’ security. Therefore, the author considers that EAS’ first step is to build an enterprise security concept. But for enterprises that have been archaically built and do not have the needed resources to implement a Haussmannian security concept, therefore an iterative ETP-HSC implementation process can transform the enterprise’s security. ETP-HSC’s feasibility and integrity can be supported by an integrated Applied Holistic Mathematical Model (AHMM) for EAS (AHMM4EAS), and the author’s various research works on the applications of holistic security concepts, ETPs, Artificial Intelligence (AI), Cloud Services (CS), and AHMM. The ETP-SRC is based on a multi-disciplinary proprietary-mixed research method.
Analysis of food lovers’ reviews using long short-term memory method

To determine a restaurant that sells delicious food is by searching reviews of that restaurant, namely reviews from food lovers about the food and restaurant such as taste, service, ambience, etc. Food lovers’ reviews are assessments and views from food lovers about the quality of the food and restaurant and to find out whether the reviews are positive or negative, it is necessary to read them. Analysis of food lovers’ reviews is a sentiment analysis system that will analyse reviews in Bahasa automatically. In this study, a review’s analysis system was built to classify reviews into positive and negative sentiment classes. The method used to classify the review sentiment is Long Short-Term Memory method and Word2vec to create a word embedding. This system was built using review data from restaurants in Zomato that sell Indonesian food with a total of 7000 data divided into 5600 training data and 1400 test data. The output of this system is the sentiment classes and category of the restaurant. The result of the classification accuracy of 1400 test data is 91% to recall value of 91.57% and a specificity value of 87.13%.
Predicting the difficulty of EFL reading comprehension tests based on linguistic indices

Abstract Estimating the difficulty of reading tests is critical in second language education and assessment. This study was aimed at examining various text features that might influence the difficulty level of a high-stakes reading comprehension test and predict test takers’ scores. To this end, the responses provided by 17,900 test takers on the reading comprehension subsection of a major high-stakes test, the Iranian National University Entrance Exam for the Master’s Program were examined. Overall, 63 reading passages in different versions of the test from 2017 to 2019 were studied with a focus on 16 indices that might help explain the reading difficulty and test takers’ scores. The results showed that the content word overlap index and the Flesch-Kincaid Reading Ease formula had significant correlations with the observed difficulty and could therefore be considered better predictors of test difficulty compared to other variables. The findings suggest the use of various indices to estimate the reading difficulty before administering tests to ensure the equivalency and validity of tests.
Research on the Paradigm Transformation of Education Management under the Background of Education Modernization

This study aims to explore the impact of educational modernization on the paradigm shift of educational management, and analyze how the field of educational management responds to the constantly changing educational environment and needs. The modernization of education, as an important trend in the global education field, has raised doubts and challenges to the traditional paradigm of education management. Research has found that educational modernization is driving the evolution of educational management paradigms, making them more interdisciplinary, flexible, and adaptable. The emerging paradigm of education management emphasizes leadership, innovation, and sustainability in the field of education, with a greater focus on student participation, improvement of education quality, and global competitiveness compared to traditional models. Through case studies, demonstrate the strategies and best practices adopted by different countries and institutions in addressing the challenges of educational modernization. This study provides important insights for scholars, educational policy makers, and educational institutions in the field of educational management on how to adapt to the challenges of educational modernization, in order to promote continuous improvement and innovation in the education system.
Bert-based graph unlinked embedding for sentiment analysis

Abstract Numerous graph neural network (GNN) models have been used for sentiment analysis in recent years. Nevertheless, addressing the issue of over-smoothing in GNNs for node representation and finding more effective ways to learn both global and local information within the graph structure, while improving model efficiency for scalability to large text sentiment corpora, remains a challenge. To tackle these issues, we propose a novel Bert-based unlinked graph embedding (BUGE) model for sentiment analysis. Initially, the model constructs a comprehensive text sentiment heterogeneous graph that more effectively captures global co-occurrence information between words. Next, by using specific sampling strategies, it efficiently preserves both global and local information within the graph structure, enabling nodes to receive more feature information. During the representation learning process, BUGE relies solely on attention mechanisms, without using graph convolutions or aggregation operators, thus avoiding the over-smoothing problem associated with node aggregation. This enhances model training efficiency and reduces memory storage requirements. Extensive experimental results and evaluations demonstrate that the adopted Bert-based unlinked graph embedding method is highly effective for sentiment analysis, especially when applied to large text sentiment corpora.
The Effect of Media Edutainment Education on Hand washing Accuracy in Grades 4 – 6 Students

Children in school age are a period of acquiring the basics of knowledge. Various diseases that attack school-age children such as diarrhea, ARI, hepatitis, typhoid and bird flu, are generally related to daily behavior. To avoid sickness in youngsters, it is vital to carry out health education on hand washing. Health education in delivered through entertaining and engaging media prevent disease in children, it is necessary to carry out health education on hand washing. Health education is provided in the form of interesting and not boring media, like edutainment. The purpose of this study is to ascertain the impactelementary school pupils in grades 4 – 6. This research method uses pre-experimental design with One group pre post test design. This research was conducted at SDN Banjarsawah 1. The population was 30 respondents. The sampling technique used Total Sampling with 30 sampleof 30 respondents. The research instrument used an observation sheet. Analysis of the data used in this study using the Wilcoxon test.According to the findings, up to 25 respondents (83,3%) were determined to fall into the incorrect category of hand washing before receining edutainment media education. As many as 28 respondents (93,3%) were categorizet as practicing proper hand washing after receiving edutainment media education. The results of the analysis test showed that there was an effect of edutainment media education on the accuracy of hand washing with r-value = 0.000 &lt;α = 0.05. It can be concluded that there is an effect of edutainment media education on the accuracy of hand washing in school children.Edutainment media can increase the accuracy of hand washing in children, so that further research can be carried out in other fields of study with the abilities possessed by various ages and characters.
Incidence of Seizures with Fat Embolism Syndrome and Impact on Outcome: An Analysis of National Inpatient Sample Database (P2.9-079)

May 6, 2019April 9, 2019Free AccessIncidence of Seizures with Fat Embolism Syndrome and Impact on Outcome: An Analysis of National Inpatient Sample Database (P2.9-079)Ephrem Teklemariam, Ankur Bhargava, John Gaughan, Christa Schorr, and Tapan KaviAuthors Info & AffiliationsApril 9, 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.P2.9-079 Letters to the Editor
A Systematic Review of Communication Augmented by Human Intelligence and Artificial Intelligence

Artificial intelligence (AI) appears to have altered the entire process of processing and disseminating the information to larger audiences. The present study is intended to investigate the similitude, variance, and inferences of AI-driven communication in relation to human communication. This study explores AI-driven vs. human communication through a literature review, emphasizing human communication's unique ability to foster connections, empathy, trust, and authenticity. AI offers speed but lacks depth. Ethical concerns include privacy and misinformation. It highlights the importance of an ethical approach when using AI in communication.
Artificial Intelligence as a Promising Technology

Artificial intelligence is a global technological phenomenon with a wide range of possibilities to be applied in almost all areas of human life. It simplifies and makes tasks more manageable, thereby destroying many jobs across the globe. However, the current literature on the subject highlights various challenges and new areas of its application. The primary purpose of the research is to explore, through several types of literature, the current issues on artificial intelligence, prospects, and challenges in the daily lives of human beings. The study will present the options for AI applications to make life convenient and identify how and where the technology could be applied in addition to its current application in different areas of human life. In addition, it will add to the body of growing literature on the subject, which scholars and students will find helpful. The study shows that multiple technologies (AI with others could be integrated) provide synergy and convenience to get a competitive edge in the industry.
The Impact of 4IR on Africa's Industrialization

The fourth industrial revolution (4IR) has the potential to transform African economies and industries. However, from different disciplines, the impact of 4IR on the Africa's industrialisation is highly misunderstood. This is evident to various disciplines from built environment. With that being elucidated, this chatper is investigating the possible challenges that and the opportunities that are presented through crystalising 4IR in Africa's industrialisation from urban planners' perspectives. This chapter is examining how the landscape of urban planning can effectively provide the amalgamation of 4IR technologies into African industries; furthermore, the study investigates how the 4IR technologies can provide sustainable industrialization in African cities spaces. With that being explicated, the study adopts qualitative research approach because it allows the flexibility to deep dive and explore the planner's perspective on the impact of 4IR on Africa's industrialisation.
Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients.The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case-control cohorts (17 491 patients) selected from 149 596 T2DM patients' EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA).The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model.The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model's potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.
PBSI Students’ Competency Using Letters and Punctuation in Writing Research Proposal about Language and Literature Learning based Environment

This article aims to describe the results of research on competency in Ejaan Yang Disempurnakan (EYD) in research proposals for students of Indonesian language and literature education study program (PBSI) class of 2020. Specifically, the objectives are focused on two, namely competency in using letters and competency in using signs. read. This research method is descriptive, involving 80 research subjects. Data analysis was carried out quantitatively by calculating the average and percentage of competency for each student. The results obtained from this research illustrate that students’ competence in using letter spelling is sufficient. Meanwhile, students’ competence in using qualified punctuation marks is lacking. This proves that PBSI students class of 2020 still do not have the skills to apply spelling properly. Suggestions from the results of this research are that supervisors are expected to focus more on checking and reviewing the use of spelling in student research proposals. Apart from that, students need intensive guidance regarding the use of correct spelling in accordance with the spelling rules that apply in Indonesian.
Algebraic reconfiguration of LSTM network for automated video data stream analytics using applied machine learning

Recurrent neural networks (RNNs) are a powerful tool for processing sequential data. However, the standard LSTM architecture, despite its effectiveness in capturing long-range dependencies, can still encounter some problems when dealing with particularly complex sequences. In this paper, we present a mathematical modification of LSTM that extends the basic advantages of the long short-term memory model and will help to model complex dependencies in data more accurately.
An educational model of the Deutsch algorithm for secondary school

Abstract In this paper, we present the outline of an educational path to introduce a crucial historical turnpoint of quantum information research—namely the Deutsch algorithm—to secondary school students. We discuss a basic elementarization strategy allowing students to single out and focus on the individual features of quantum mechanics involved in the different steps of the algorithm information processing phase, which can potentially be useful for the educational reconstruction of other algorithms and protocols. The sequence includes the experimental realization on the optical bench of an analogue of the Deutsch algorithm, working with classical coherent light. The educational path was tested both in curricular and out-of-school settings, and preliminary results will be discussed.
Comprehensive Analysis of Solar Panel Performance and Correlations with Meteorological Parameters

To mitigate the adverse effects of fossil fuel-based energy, mankind is in constant search of clean and cost-effective sources of energy, such as solar energy. The economic viability of a power plant to harness solar energy mostly depends on the efficiency of solar panels. Investigations over the years show that the solar panel efficiency significantly depends on the different meteorological parameters. Therefore, there is an imminent need for a correlation explaining the relations between the efficiency and different meteorological parameters. In this study, an effort has been made to analyze the effects of various meteorological parameters on the efficiency and subsequently propose a correlation between them. Initial investigations reveal that the optimal tilt angle for the maximum power output is 26°. The study demonstrates that efficiency is directly proportional to solar intensity and wind speed while being inversely proportional to temperature, humidity, and dew point temperature. Regression analysis of a data set comprising 100 data sets establishes a strong correlation between efficiency and five meteorological parameters: temperature, humidity, wind speed, solar intensity, and dew factor. The calculated efficiencies using the developed correlation deviate from the experimental values, with absolute errors ranging from 0.08 to 1.20%. The findings provided valuable insights for optimizing solar power plant performance by understanding the relationship between efficiency and meteorological parameters.
Gaussian-Based Parametric Bijections for Automatic Projection Filters

The automatic projection filter is a recently developed numerical method for projection filtering that leverages sparse-grid integration and automatic differentiation. However, its accuracy is highly sensitive to the accuracy of the cumulant-generating function computed via the sparse-grid integration, which in turn is also sensitive to the choice of the bijection from the canonical hypercube to the state space. In this paper, we propose two new adaptive parametric bijections for the automatic projection filter. The first bijection relies on the minimization of Kullback–Leibler divergence, whereas the second method employs the sparse-grid Gauss–Hermite quadrature. The two new bijections allow the sparse-grid nodes to adaptively move within the high-density region of the state space, resulting in a substantially improved approximation while using only a small number of quadrature nodes. The practical applicability of the methodology is illustrated in three simulated nonlinear filtering problems.
Graph Neural Network for Smartphone Recommendation System: A Sentiment Analysis Approach for Smartphone Rating

The increasing demand for mobile phones has resulted in abundant online reviews, making it challenging for consumers to make informed purchasing decisions.In this study, we propose Graph Neural Network (GNN) models to classify mobile phone ratings using Term Frequency-Inverse Document Frequency (TF-IDF) features. We collected a dataset of over 13,000 mobile phone evaluations from the Flipkart website. The proposed method includes data purification, balancing, feature extraction from the TF-IDF, and model prediction using deep learning models. The proposed approach utilized other models such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM to compare other classifiers. The experiments’ outcomes demonstrate that the suggested model performs better than conventional deep learning methods regarding accuracy and efficiency. The GNN model achieved the best 99.0% accuracy rate. The proposed approach can help consumers make informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.
Learned Kernel-based Interpolation for Efficient RGBW Remosaicing

RGBW remosaicing is an interpolation technique that converts RGBW images captured using RGBW color filtering arrays into Bayer images. Although recent learning-based approaches using convolutional neural networks have shown substantial performance improvements, most algorithms require high computational and memory complexities, which limit their practical applicability. In this work, we propose an efficient and effective RGBW remosaicing algorithm based on learned kernel-based interpolation. First, the proposed algorithm extracts deep feature maps from input RGBW images. Then, we develop a learned kernel-based interpolation module composed of local and non-local interpolation blocks that generates two intermediate Bayer images. Specifically, the local interpolation block learns local filters to recover a Bayer image, whereas the non-local interpolation block recovers a Bayer image by estimating the non-local filters of dynamic shapes. Finally, a reconstructed Bayer image is obtained by combining the complementary information from the intermediate Bayer images using a spatially weighted fusion block. Experimental results demonstrate that the proposed algorithm achieves comparable or even better performance than state-of-the-art algorithms while providing the lowest computational and memory complexities.
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation

Visual Question Answering on 3D Point Cloud (VQA-3D) is an emerging yet challenging field that aims at answering various types of textual questions given an entire point cloud scene. To tackle this problem, we propose the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLEVR3D</b> , a large-scale VQA-3D dataset consisting of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">171K</b> questions from <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8,771</b> 3D scenes. Specifically, we develop a question engine leveraging 3D scene graph structures to generate diverse reasoning questions, covering the questions of objects' attributes ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , size, color, and material) and their spatial relationships. Through such a manner, we initially generated 44K questions from 1,333 real-world scenes. Moreover, a more challenging setup is proposed to remove the confounding bias and adjust the context from a common-sense layout. Such a setup requires the network to achieve comprehensive visual understanding when the 3D scene is different from the general co-occurrence context ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , chairs always exist with tables). To this end, we further introduce the compositional scene manipulation strategy and generate 127K questions from 7,438 augmented 3D scenes, which can improve VQA-3D models for real-world comprehension. Built upon the proposed dataset, we baseline several VQA-3D models, where experimental results verify that the CLEVR3D can significantly boost other 3D scene understanding tasks. Our code and dataset are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yanx27/CLEVR3D</uri> .