Reviewerly/exHarmony huggingface dataset:
Technology acceptance model among nurses and other healthcare providers during the 2019 Coronavirus pandemic: a comparative cross-sectional study

Aim: The study aimed to explore differences in usefulness and the ease of use of electronic health records (EHRs) as perceived by nurses and other healthcare providers (HCPs) during the 2019 coronavirus (Covid-19) pandemic in Jordan.It also aimed to investigate the association of gender, marital status, age, educational level, experience, and experience of using EHRs in predicting the technology acceptance model (TAM) of EHRs among the same selected group.Design: A comparative cross-sectional study.Methods: A convenience sample of 90 nurses and 96 HCPs completed the technology acceptance model survey.The response rate for nurses was 60%, compared to 64% for HCPs.The study was conducted from May to July 2020.Results: The study found that, generally, both nurses and other HCPs perceived high levels of usefulness and ease of use of EHRs during the Covid-19 pandemic.Other HCPs perceived higher levels of usefulness (86%) and ease of use (76.5%) of EHRs than nurses (79.5% and 73.5%, respectively) during the Covid-19 pandemic.Statistical differences were noted between both groups (p < 0.05).Marital status and experience in using EHRs were found to be associated with TAM among nurses.There were no associations of TAM among other HCPs during the Covid-19 pandemic.Conclusion: Due to the nature of nurses' work, the implementation and acceleration of the use of EHRs during the Covid-19 pandemic could be expected to encounter certain obstacles.
Retracted: A Method for Detection of Rare Industrial Fake Film Based on Deep Learning

A Method for Detection of Rare Industrial Fake Film Based on Deep Learning" [1] due to concerns that the peer review process has been compromised.
Expert System for Detecting Diseases of Potatoes of Granola Varieties Using Certainty Factor Method

The low productivity of potatoes is caused by many factors, including the very low quality of the seeds used, poor storage, climate, capital, limited farmer knowledge, and attacks by plant-disturbing organisms, especially diseases. Not only that, many farmers are still unfamiliar with the various diseases that can attack potato plants, or their knowledge about potato plant diseases is incomplete. This study aims to design and develop an expert system web-based application technology using the Certainty Factor (CF) method to detect potato disease symptoms. The CF method defines a measure of the capacity of a fact or provision to express the level of an expert's belief in a matter experienced by the concept of belief or trust and distrust or uncertainty contained in the certainty factor. The results showed that the CF method could function optimally in detecting potato plant diseases which can help farmers based on the symptoms that appear with an accuracy value of 94%.
Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset

Sentiment analysis aims to categorize opinions using an annotated corpus to train the model. However, building a high-quality, fully annotated corpus takes a lot of effort, time, and expense. The semi-supervised learning technique efficiently adds training data automatically from unlabeled data. The labeling process, which requires human expertise and requires time, can be helped by an SSL approach. This study aims to develop an SSL-Model for sentiment analysis and to compare the learning capabilities of Naive Bayes (NB) and Random Forest (RF) in the SSL. Our model attempts to annotate opinion documents in Indonesian. We use an ensemble multi-classifier that works on unigrams, bigrams, and trigrams vectors. Our model test uses a marketplace dataset containing rating comments scrapping from Shopee for smartphone products in the Indonesian Language. The research started with data preparation, vectorization using TF-IDF, feature extraction, modeling using Random Forest (RF) and Naïve Bayes (NB), and evaluation using Accuracy and F1-score. The performance of the NB model outperformed previous research, increasing by 5,5%. The conclusion is that SSL performance highly depends on the number of training data and the compatibility of the features or patterns in the document with machine learning. On our marketplace dataset, better to use Random Forest.
Designing Next-Gen Enterprise Software Systems with Cloud-Native Architecture

The emergence of cloud computing platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure (Azure) has changed the way enterprise software systems are designed, deployed, and managed. These platforms provide different types of services including
Designing Enterprise Platforms with Event-Driven Architecture Patterns

Enterprise software systems use messages to share data between systems. Users of enterprise systems use different types of client applications such as web, mobile, and terminal applications to exchange data with the system. Sharing of data always happens between two or more systems. There are two types of communication patterns we can identify within enterprise software system:
Building Enterprise Software Systems with Hybrid Integration platforms

In today’s highly competitive world of business, technology is a key differentiator that can make or break an organization. Some technology experts express this idea by saying “every company becomes a software company in the near future.” It is evident that today’s consumer-driven market urges enterprises to bring technology to their ecosystems to attract young generation as well as older generation due to the popularity of smartphones, tablet computers, and intuitive applications. The digital experience offered by these applications teases consumers to purchase products and services from the comfort of their homes. All these aspects converge to building enterprise software systems that are capable of serving these demands. The need for having systems to cater to different requirements becomes so important that enterprise architects tend to onboard various third-party systems into the enterprise ecosystem in quick succession. As an example, an enterprise who used to sell clothes through their brick-and-mortar showrooms has to build an online retail shopping website so that customers can easily make their purchases without physically coming into the store. This requires enterprise teams to bring in CRM software, billing software, shipping and handling software, and many other on-premise and cloud-based systems. These systems cannot work in isolation and need to integrate with one another to build a complete solution to the customers. On top of the integration, we need to expose the enterprise’s products and services through multiple channels such as web applications, mobile applications, and terminal applications. That is where the need for hybrid integration platforms comes into the picture, which allows us to integrate systems and expose these integrated services via common interfaces such as APIs. Finally, all these consumer-facing applications that offer products and services need to be secured with modern Identity and Access Management (IAM) capabilities to make sure that the user gets the best possible experience.
Topic Modeling on Podcast Short-Text Metadata

Podcasts have emerged as a massively consumed online content, notably due to wider accessibility of production means and scaled distribution through large streaming platforms. Categorization systems and information access technologies typically use topics as the primary way to organize or navigate podcast collections. However, annotating podcasts with topics is still quite problematic because the assigned editorial genres are broad, heterogeneous or misleading, or because of data challenges (e.g. short metadata text, noisy transcripts). Here, we assess the feasibility to discover relevant topics from podcast metadata, titles and descriptions, using topic modeling techniques for short text. We also propose a new strategy to leverage named entities (NEs), often present in podcast metadata, in a Non-negative Matrix Factorization (NMF) topic modeling framework. Our experiments on two existing datasets from Spotify and iTunes and Deezer, a new dataset from an online service providing a catalog of podcasts, show that our proposed document representation, NEiCE, leads to improved topic coherence over the baselines. We release the code for experimental reproducibility of the results.
Analisis Sensitivitas Dalam Metode Analytic Hierarchy Process dan Pengaruhnya Terhadap Urutan Prioritas Pada Pemilihan Smartphone Android

Smartphones nowadays have become a basic need for everyone because they provide many benefits and conveniences for users. People always want to have a smartphone with good quality. However, due to the lack of information along with the many types of smartphones in circulation, it often makes it difficult for users to choose a smartphone that suits their needs. To overcome these problems, it is necessary to have a method that can provide recommendations for appropriate decision-making for users. This study aims to apply the Analytic Hierarchy Process (AHP) method and sensitivity analysis in determining the priority order of smartphone selection by comparing one smartphone to another. The criteria for consideration are Facilities, Price, Battery, and RAM with alternative choices in the form of Xiaomi, Oppo, and Vivo brand smartphones. Data collection in this study was carried out by distributing questionnaires to 100 students of the Mathematics Study Program. The data were processed using the AHP method and sensitivity analysis. AHP is used to produce a more consistent ranking order of each alternative, while the sensitivity test is carried out to measure the stability of the calculation results if there is a change in decision-making. From the results of the analysis with AHP, it was found that Xiaomi was the first priority of the respondent's choice, followed by Vivo, and the last priority was Oppo with an inconsistency level of 0.02. Meanwhile, sensitivity testing shows that RAM is the most influential criterion for changing the order of alternative priorities, where Xiaomi remains the first priority, followed by Oppo, and Vivo is the last priority.&#x0D;
Interpretable analysis of remote sensing image recognition of vehicles in the complex environment

Deep learning technology has yielded good results in remote sensing image recognition of vehicles, but most existing recognition network models have poor interpretability, which limits its wide application. In order to achieve effective detection and recognition of vehicles in the complex environment, in this paper, the YOLOv4 is adopted to realize remote sensing images for vehicle target recognition. In addition, the optimized interpretation method with LIME is used to interpret the recognition results, improving the credibility of the recognition results.
Research on recommendation method based on tensor similarity

Traditional recommendation methods model users as vectors in a way that focuses only on single-sided user preferences. In order to compensate for the limitations of this modelling approach, a tensor modelling method is proposed that models the user as a rectangle. Firstly, a recommendation model based on a fusion of collaborative filtering and sequential recommender algorithms is constructed, which integrates the Transformer model and Pooling layer to model the user tensor; secondly, the similarity between the user tensor and the target item is calculated by combining the distance between the user tensor and the target item and the bias. The model is experimentally validated on the MovieLens datasets, and the results show that the model is able to focus on multiple user preferences and outperforms the baseline method in terms of accuracy of recommendation results.
UAV route planning algorithm based on conflict resolution mechanism

Aiming at the problems of long time-consuming and low accuracy of UAV route planning from start to end in the current algorithm, a UAV route planning algorithm based on conflict resolution mechanism is proposed. Determine the constraints and avoidance conditions of UAV turning angle, calculate the UAV navigation node distance, introduce the conflict resolution mechanism, plan the UAV multi route trajectory, smooth the UAV route, and realize the UAV route planning. The experimental results show that when the proposed algorithm is applied to the actual UAV route planning, it can shorten the UAV flight time, ensure the equal time consumption of each flight, and improve the UAV flight efficiency and route planning accuracy on the premise of ensuring that the UAV is not affected by each threat point.
Improvement of magnetic properties through the synthesis of ceramic materials with various weight ratios of BaTiO, BiFeO3, and BaFe12O19 with sol-gel method

Electronic devices designed with multiferroic materials comprising both electrical and magnetic properties are needed for significant memory storage. Several studies have been carried out on multiferroic materials based on BaTiO3, BiFeO3, and BaFe12O19. However, none have obtained optimum multiferroic properties because they still show inadequate magnetic properties, especially energy values. Therefore, this study aims to enhance the mechanical properties of ceramics synthesized by the sol-gel method. The XRD and permagraph tests with metallographic observations using Scanning Electron Microscopy (SEM) were used to enhance the ceramic compounds by mixing powder based on BaTiO3, BiFeO3, and BaFe12O19. Furthermore, the ratio of the weight composition of the materials varied by 1:1:1; 1:2:2; 2:1:1; 1:2:1 and 2:1:2. The result showed that the best magnetic properties were obtained at the weight composition ratio of 1:2:2 with a magnetic energy value of 48.0897x104 GkA/m. Based on SEM analysis, the sample with this weight ratio was dominated by the BiFeO3 and BaFe12O19 phases with high magnetic properties.
Online Voting and Information Management

The main motivation for this project is to be very focused on processing votes and their candidates, voters, etc. The project also displays all available voting data, including vote counts, voters, and non-voters. The project is divided into his two parts:Admin Panel and Voter Panel. The main purpose of voting (in scenarios involving citizens of a particular country) is to find leaders of their choice. Problems associated with this include, but are not limited to, voter driving during elections, unsafe or inaccessible polling places, inadequate voting materials, and inexperienced staff. They will be trained on how to vote online before Election Day. This project is designed for a threat-free, user-centric online voting system. Elections can take place in her two ways: paper elections and automated elections .Automated voting is known as electronic voting. An online voting system can be called Internet voting because it takes place over the Internet. Electronic you to manually mark and count electronic cards containing all candidate symbols electronic. There are many different types of electronic voting systems today, including punch cards, marksens, and digital pen tally brings a new voting system that allows voting systems. Electronic voting makes it easier for voters to vote by providing choices on a voting display located on an electronic machine. A direct-record electronic voting machine provides a display that can be activated when a voter touches the display, consisting of mechanical and electro-optical buttons, software that accepts votes, and an image or icon on the display. E-voting is connected to a central voting system, accepting all votes directly and receiving updated records. A central voting system uses a constituency counting method that counts all votes from ballots at polling stations..
Factors Influencing the Foreign Language Learning Quality: A Study at a Cooperative Education Unit (SPK) School in Surabaya

This research examines the correlation between the Native-Speaking Foreign Language Teachers' Professional Competencies, Teaching Methods, and Attitude Towards Learning in the Foreign Language Learning Quality in a Cooperation Education Unit or Satuan Pendidikan Kerjasama (SPK) in Surabaya. This quantitative research involved 144 students from the fourth, fifth, and sixth grades as the respondents or research participants. The data of this research were analyzed using the Partial Least Square (PLS) on SmartPLS 3.0 application. The results show that the Native-Speaking Foreign Language Teachers' Professional Competencies significantly influenced Teaching Methods. Furthermore, Teaching methods significantly influenced the Attitude Towards Learning, while the Attitude Towards Learning significantly influenced Foreign Language Learning Quality. Teaching Methods have also proven to be a mediator in the influence of Attitude Towards Learning (VAF=0,1965 or 19,65%). Based on the research findings, schools and teachers need to emphasize the professional competencies of native-speaking foreign language teachers and teaching methods to improve the quality of foreign language learning.
The Context, Input, Process, and Product Evaluation Model on English Language Teaching at Public Health Study Program

Evaluation of English teaching is carried out to determine the achievement of program objectives and provide recommendations for program improvement. This study aims to evaluate the implementation of English Language Teaching to identify its effectiveness in achieving the objectives of the Public Health Study Program curriculum. In this study, the CIPP (Context, Input, Process, and Product) evaluation model was applied. This research was conducted at the Public Health Study Program, Faculty of Sport and Public Health, Mandalika University of Education. The population in this study was the first semester students of Public Health study program in the amount of 133 students. In determining the number of samples, there were 46 students chosen as samples by using simple random sampling technique, and 1 English lecturer and the Head of Public Health study program as the participants in this study. This study was an evaluative study utilizing a mixed-method research design. The data gained from the questionnaire were analysed by using descriptive quantitative whereas, the data obtained from interview and class observation were analysed qualitatively. Based on the findings of the study, it was found that for context dimension got a Percentage of Effectiveness of 78.52; input dimension got a Percentage of Effectiveness of 59.47; process dimension got a Percentage of Effectiveness of 76.74, and product dimension got a Percentage of Effectiveness of 51.86. Based on the result of the study, it was concluded that the implementation of teaching English in Public Health study program has not been effective. It is recommended that language skills and language components are integrated since those elements can support communication ability stated in the curriculum of Public Health study program.
The effect of body shaming on students who blame themselves

Teenagers often become victims and perpetrators of body shaming carried out by those closest to them, such as family, community, and friends, by joking. Ignorant adolescents will not respond to these comments, but adolescents who cannot accept comments from their environment tend to blame themselves. This study aims to determine the effect of body shaming on self-blaming in students of SMP Negeri 27 Banjarmasin. This study used an observational method with a cross-sectional design and random sampling and obtained 239 samples. The analysis technique uses simple linear regression. The instruments used in this study were the body shaming scale by Vargas and the self-blaming scale by Coleman. The independent variable in this study is body shaming, and the dependent variable is self-blame. The results showed that there was an effect of body shaming on self-blaming in students of SMP Negeri 27 Banjarmasin. This study concluded that body shaming by students of SMP Negeri 27 Banjarmasin was classified as very high and resulted in students who were victims of body shaming experiencing moderate self-blaming. There is a significant influence between body shaming and self-blaming on students.
Parenting information for parents: Efforts to prepare children to experience menarche in prepubertal young girls

The role of parents is very important as an effort to provide information to their children, especially young women about the fulfillment of education, especially as women. The purpose of this study was to determine the effectiveness of parenting information to prepare young women for prepubertal menarche. The research method used is a mixed methods model, with a concurrent model design that combines qualitative and quantitative data. This research involved 66 research subjects who were selected purposively from 360 female students at State Junior High School 8 Pagaralam. The research instrument used was the menarche scale and interview guidelines. The collected data were analyzed using multiple regression. The results of the menarche scale analysis and interviews before providing information services about menarche, the percentage of children who are not ready to face menarche is 81% thus there are still children who are afraid, embarrassed, and even anxious about facing menarche. After providing parenting information to parents as an effort to prepare children to experience menarche, prepubertal young women experienced a decrease in the number of children to 18%. Thus parenting information plays a very important role in increasing the knowledge of parents and children about menarche.
THE USE OF SIGIL SOFTWARE IN THE DEVELOPMENT OF GUIDED DISCOVERY LEARNING-BASED E-MODULES FOR TEACHERS OF CHEMICAL MGMP PADANG PANJANG

Sigil is an e-module creation software that can be used by high school teachers to help provide various teaching materials. However, the teacher's ability to use the Sigil software is still low where the teacher is still not able to apply the teaching materials in this e-module. E-modules can be used in online learning. One of the learning models that can be used is the Guided Discovery Learning (GDL) learning model. For this reason, society service for chemistry teachers who are members of the Padang Panjang Chemistry MGMP has been carried out in the form of a GDL-based e-module development workshop using the Sigil application. After the activity, it was found that the teacher understood and was able to create a GDL-based e-module using Sigil.
Evaluating distributed-learning algorithms on real-world healthcare data

Abstract With the application of intelligent data analysis in healthcare, the need to protect patients’ data increases as well. The centralised approach, where data from several institutions are aggregated into a single server renders good knowledge discovery opportunities but is a huge data breach liability. Distributed learning can help reduce this risk as data is exploited inside each institution and only derived models are shared among peers, but there is no clear assessment of the performance gap between approaches. In this work, we used real clinical data from nine Portuguese hospitalar obstetrics departments, to compare the predictive performance of a) local, b) distributed and c) globally centralised models, focusing on predicting delivery and patient-related outcomes. Our evidence suggests that distributed learning is a viable tool to ap- ply machine learning without a significant decrease in performance while promoting a more robust privacy-concerning methodology.