Reviewerly/exHarmony huggingface dataset:
Use of electronic teaching media based on the flip builder application to increase student's reading interest

This study aims to increase student interest in reading at SMK Pasundan 1 Serang City Class XI Multimedia 1, the use of electronic teaching media based on the flip builder application is made using the flip builder application. This study used qualitative descriptive research by observing students' reading interest levels and using data collection procedures in the form of observation, interviews, and documentation to check the validity of the data using data triangulation with the stages of source technique, time technique, and data technique. Based on the results of observations in this study, the use of electronic teaching media based on the flip builder application to increase students' reading interest, it can be concluded that electronic teaching materials using applications based on the flip builder application can be said to have increased in terms of students' reading interest.
Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment

With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and aided disease diagnosis scheme. We design some efficient basic operation algorithms for encrypted data. Then, we design an efficient and privacy preserving SVM model training protocol using the basic operation algorithms. We propose a secure maximum finding algorithm and secure comparison algorithm. Then, we design an efficient online-aided disease diagnosis scheme based on the BFV cryptosystem and blinding technique. Detailed security analysis proves that our scheme can protect the privacy of each participant. The experimental results illustrate that our proposed scheme significantly reduces the computation overhead compared with the previous similar works. Our proposed scheme completes most of the operations of aided disease diagnosis by the cloud servers and the client only needs to complete a small amount of encryption and decryption operations. The overall computation overhead is 0.175 s, and the efficiency of online aided disease diagnosis is improved by 85.4%. At the same time, our proposed scheme provides multiclass diagnosis results, which can better assist doctors in their treatment.
Se-xDeepFEFM: Combining Low-Order Feature Refinement and Interaction Intensity Evaluation for Click-Through Rate Prediction

Click-through rate (CTR) prediction can provide considerable economic and social benefits. Few studies have considered the importance of low-order features, usually employing a simple feature interaction method. To address these issues, we propose a novel model called Senet and extreme deep field-embedded factorization machine (Se-xDFEFM) for more effective CTR prediction. We first embed the squeeze-excitation network (Senet) module into Se-xDFEFM to complete low-order feature refinement, which can better filter noisy information. Then, we implement our field-embedded factorization machine (FEFM) to learn the symmetric matrix embeddings for each field pair, along with the single-vector embeddings for each feature, which builds a firm foundation for the subsequent feature interaction. Finally, we design a compressed interaction network (CIN) to realize feature construction with definite order through a vector-wise interaction. We use a deep neural network (DNN) with the CIN to simultaneously implement effective but complementary explicit and implicit feature interactions. Experimental results demonstrate that the Se-xDFEFM model outperforms other state-of-the-art baselines. Our model is effective and robust for CTR prediction. Importantly, our model variants also achieve competitive recommendation performance, demonstrating their scalability.
Foreign direct investment and economic growth nexus in ECOWAS: The leveraging effect of anti-corruption

Purpose ― This paper sought to investigate the interactive effect of corruption and FDI on economic growth in the Economic Community of West African States (ECOWAS) region empirically. Methods ― With panel data spanning 2000–2019 across 15 ECOWAS countries, this paper estimates its results by employing the system-GMM estimator, which combines a system of regressions in difference and in levels to resolve the problem of endogeneity. Findings ― Results reveal that while FDI independently spurs economic growth, control of corruption has no direct effect on growth in the region. The interactive effects reveal the complementarity between FDI and control of corruption in promoting economic growth in the ECOWAS region. The growth effect of FDI is larger and stronger given an improvement in the control of corruption across the 1st, 5th, 10th, and 25th percentiles. Implication ― To improve investor confidence, bolster FDI inflows and optimize its beneficial impacts on economic growth, this paper calls for measures to increase transparency and stronger political commitment to strictly investigate, prosecute and punish corruption in the ECOWAS region. Originality/value ― Although foreign direct investment (FDI) to host countries have been shown in the literature to be a crucial driver of economic growth, little is known about how anti-corruption measures affect the FDI-growth relationship. This paper contributes to policy by providing empirical evidence to bridge this gap.
APPLICATION OF TRANSPORT SECURITY SYSTEM SYMBOLOGY FOR EMERGENCY MASS NOTIFICATION SYSTEMS

The visualisation of resources is becoming an increasingly important factor in the operations of crisis management teams. The efficiency of crisis management processes depends on the possibility of data exchange between crisis management teams and people within the range of expected and actual threats. An important part of the information provided to people is the description of the condition of the transport system. The authors of this work carried out comparative analyses of visualisation standards in terms of a selected transport system for six selected countries and organisations. Based on the survey results, they managed to assess the functionality and usability of the visualisation of the Polish transport system. This research was based on a modified model of assessment of software functionality and usability. Methods of determining the functionality and usability of a transport system visualisation can help crisis management teams assess the suitability of IT tools for use in mass notification systems. The literature on this subject lacks a scientific analysis of the Polish method of the visualisation of the transport system. Therefore, this study will clearly fill this research gap.
Blockchain-based Communication Mechanism for Unmanned Aircraft

With the maturity of unmanned aircraft technology, unmanned aircraft have a wide range of applications in areas such as public safety. However, unmanned aircraft are frequently involved in accidents due to network intrusion, and it is urgent to ensure the safety of unmanned aircraft networks. This paper first introduces the current situation and challenges of unmanned aircraft and blockchain, and proposes a communication mechanism for unmanned aircraft based on blockchain technology to achieve unmanned aircraft data security and privacy protection, multi-party data sharing and data storage security to ensure unmanned aircraft network security.
Design of Intelligent Control Arm Strength Training Device for Students

The design belongs to the technical field of sports training equipment, especially for the arm strength training system, including a support seat, a support rod, a holding rod and a body. The two sides of the support seat are connected with knobs through screw thread, the inner side of the support seat is clamped and connected with a support rod, and the bottom side of the support rod is fixedly connected with No. 1 fixed base, One side of the No.1 fixed base is rotationally connected with a screw rod through a rotating shaft, the upper end of the screw rod is connected with a rod groove through screw thread, the upper end of the rod groove is clamped and connected with a pull rope, the upper end of the rope is fixedly connected with a handle, and a variety of strength training structures are arranged in the training device, It can carry out a variety of arm strength training exercises, and can carry out complete and comprehensive training on arm strength, wrist strength, biceps and triceps and other muscle groups, with good training effect and comprehensive training.
Yolov5-based fall detection algorithm for homebound people

According to the statistics from the official website of the National Bureau of Statistics, the population over 60 years old in China is close to 260 million, accounting for about 18.7% of the total population. As society moves towards aging, the safety problem of the elder people alone is gradually highlighted, and the accidental fall of the elderly at home has become a problem that cannot be ignored, and developing fall detection technology to mitigate the danger of falls of the elderly is imperative. So, this paper proposes a fall detection method which is based on deep learning. Specifically, the method is based on the Yolov3 method, and to enhance the accuracy and speed, the Yolov3 algorithm is updated to Yolov5s. The final experimental results also confirm that the above method has achieved the purpose.
Design and Implementation of Insurance Product Recommendation System

In this paper, a hybrid collaborative filtering recommendation algorithm with Latent Dirichlet Allocation (LDA) and Alternating Least Squares (ALS) is proposed, which can effectively alleviate the cold start problem in traditional recommendation system and improve the accuracy of recommendation. At the same time, real-time recommendation according to the real-time behavior of users is realized in the real-time recommendation system, which effectively improves effectiveness of the recommendation results. In the system construction, user historical data is collected and analyzed by big data tools. Based on the Spark framework, the offline and real-time recommendation of insurance products are effectively combined and realized, with personalized recommendation as an important function. The designed system can improve the purchase rate and experience of users.
Unit Link Application Quality Analysis Using ISO 25010 . Method (Case Study of PT Asuransi Jiwasraya Persero)

Abstract : PT Asuransi Jiwasraya Persero is a state-owned insurance company that is the largest local insurance company in Indonesia. PT Asuransi Jiwasraya has many products for individuals and groups. Jiwasraya's product management uses the Unit Link application, which is a web-based application that is used to facilitate the management of product transactions at Jiwasraya. Research on the analysis of Unit Link application quality aims to determine the quality of the application, using ISO 25010 which is a standard for software with eight characteristics, including functional suitability, performance efficiency, compatibility, usability, portability, and security. Determination of weights using the Analytical Hierarchy Process (AHP) method with the testing methods used are blackbox testing, questionnaires, observation and stress testing. The unit link application test got a score of 4.196 with a good category and the research results can help to determine the quality of the Unit Link application.&#x0D; Keywords: ISO 25010, Quality, Application, User Satisfaction, Unit Link
Decision support system in predicting pneumonia transmission in toddlers based on life patterns

The increasingly widespread spread of pneumonia during the Covid-19 pandemic that attacks children, especially toddlers, has caused the under-five mortality rate (IMR) to increase. Pneumonia is the most worrying thing for parents because it is an acute respiratory disease that attacks the lungs due to the lack of special health information for toddlers that parents get about lifestyle during the Covid-19 pandemic. With the health information from the government for toddlers through the media, especially in improving a healthy lifestyle, it is expected to minimize the transmission of pneumonia. The problem that occurs in the transmission of pneumonia among the general public is air pollution by the presence of bacteria, viruses, and fungi or parasites and the most common types of bacteria attack Streptococcus Pneumonia and Haemophilus Influenzae Virus type b (Hib). The weakening of the condition of children with this disease due to lack of adequate oxygen intake so that children will feel pain in the esophagusarea when breathing. To be able to predict the transmission of pneumonia in toddlers based on lifestyle, the Fuzzy Multiple Attribute Decision Making (Fuzzy MADM) method through a Decision Support System (DSS) is used.
Teaching Activity in the Context of Digital Education

The article updates professional skills of a teacher in the context of digital transformation of education. The author actualizes the need for changes in the traditional functions of a schoolteacher. The new functional features include motivation management (pedagogical coaching), designing individual academic road maps (pedagogical design), creating lesson scripts (pedagogical directing), providing pedagogical assistance (personal tutoring), and encouraging positive involvement (pedagogical animation). The article establishes a logical connection between these new professional functions in the teacher's activity and the key principles of digital education. It also offers some new prospect areas in professional teacher training, e.g., theory and methodology of teaching and upbringing in the context of digital education.
Methodological measures to promote publishing projects in university libraries: from theory to practice

Today, in the era of digitalization, large arrays of educational and scientific literature data are presented in the libraries of educational institutions. The availability of sources, including through open access resources, on the one hand, facilitates the work of users, on the other hand, presents difficulties in the selection and structuring of resources. In the course of the study, we analyzed the websites of libraries of pedagogical universities and considered the main types of measures to promote educational resources, identified further vectors of development in the context of digital transformation. The object of the study is to identify the features of the promotion of publishing projects. The subject of the study is methodological activities carried out within the walls of libraries. The article describes in detail the types of methodological measures offered by publishers in offline and online formats. The joint work of libraries and publishing houses to popularize electronic resources is an important component on which the quality of the educational process depends.Main conclusions: methodological support of publishers is very important for the promotion of educational resources, which cannot be limited to instructions or other methodological materials. Field workshops and online events are designed to increase interest in electronic resources and structure work with large amounts of data. Professional development of library specialists in modern conditions also depends on the support of publishers.
Analysis of High Performance Computing System for Agricultural Big Data

With the development of the information age, people pay more and more attention to the big data industry. In many industries, big data will be used for corresponding processing and analysis. However, at present, in the agricultural industry, there are still few researches on agricultural data processing by using big data. Therefore, in the agricultural data processing, the use of high-performance computing system to process its massive data can play a certain role in promoting the transformation and upgrading of agriculture, and it can play a better practical significance and use value in promoting agricultural development.
Classification of Pictures Using Different Algorithms

This study can be seen as an attempt of handwritten text recognition and analysis, because the dataset can be extended to a dataset including alphabets with or without the colour, and when the model can successfully learn the patterns, it can be applied to real-life applications such as scanning a handwritten document and save its text into a digital document.
A Self-Adaption Recognition Method for English Speech on Cloud Platform Based on Wavelet Neural Network

Although there are various recognition methods for digital English speech (DES) on cloud platform network, the accuracy of them is low and the average time consumption is too long. Due to above shortages, this study put forward a self-adaption recognition method based on wavelet neural network (WNN). At first, the study used zero-crossing rate as feature of voiceless and voiced sound of the DES and used WNN to carry out conversion for the English speech information. Then, the study obtained wavelet-scaling function and parameterized wavelet function via zero-pole model. The parameterized wavelet function was used as the feature vector of the recognition. Wavelet function base of DES feature was generated via stretching and translating transformation. The transformed wavelet function was used to build WNN model. Moreover, error entropy function of the recognition was calculated by introducing momentum factor and partial derivative of the error entropy function to adjust parameter of the built WNN model. Thus, the study achieved the self-adaption recognition of DES. Simulation results show that the proposed method has high recognition accuracy and short recognition time.
The development and application research of computer artificial intelligence technology

In recent years, with the rapid development of modern science and technology in China, artificial intelligence technology is widely used in many fields. From the essential level analysis, artificial intelligence mainly through the simulation of human brain thinking to execute user commands. From the application requirements of modern society, the embodiment and development of intelligent functions in the field of artificial intelligence need to rely on the basic computer network and related technologies. The auxiliary and interworking applications of the two have a very key role in achieving better application effects of computer network technology. This paper mainly introduces artificial intelligence and computer technology, further analyzes and explores the specific application of artificial intelligence in computer technology, and expects to provide some useful reference for relevant personnel
A Questions Answering System on Hadith Knowledge Graph

Several works have presented the Hadith on different digital platforms, ranging from websites to mobile apps. These works were successful in presenting the text of the Hadith to users, but this does not help them to answer any particular questions about religious matters. Therefore, in this work we propose a question-answering system that was built on a Hadith knowledge graph. To interpret the user questions correctly, we used the Levenshtein distance function, and for storing the Hadith in graph format we used Neo4J as the graph database. Our main findings were: (i) a knowledge graph is suitable for representing the Hadith and also for doing the reasoning task, and (ii) our proposed approach achieved 95% for top-1 accuracy.
Research on cloud computing task scheduling based on calculus mathematical equation

Due to the heterogeneity, distribution, autonomy and diversity of services in cloud computing environment, higher requirements are put forward for cloud platform scheduling mechanism, so the research on cloud architecture and its scheduling mechanism has attracted more and more attention from the industry. A cloud computing task scheduling algorithm based on calculus mathematical equation is proposed. Through the double boundary convergence control of the partial differential classification mathematical model, the partial differential classification data model is integrated into the data set, and the fuzzy control of the data is completed through the increment and decrement support vector. The membership function is used to transform the multi-QoS(quality of service) objective constraint problem into a single objective constraint solving problem. Compared with traditional methods, the method proposed in this paper can effectively reduce the deadline baseline violation rate of user task scheduling, and reduce its average task execution time and average task execution cost on the premise of meeting the user task multi-QoS target constraints.
Channel attention convolutional recurrent neural network on street view symbol recognition

Character recognition has always played an important role in the area of image processing. In our paper, we focus on the recognition problem of street view symbol, and propose a novel network architecture called Channel-Attention-based Convolutional Recurrent Neural Network (CACRNN) for character detection and character recognition. Different from CRNN, our method is based on convolutional neural network (CNN) structure with the help of attention guidance, which is a new way of combining CNN, recurrent neural network, and SE block. CRNN can be learned directly from sequence labels without detailed annotations, and the recognized objects are not limited in length. In the network, CNN plays the role of text detection, and recurrent neural network have a significant effect on text recognition. Among them, we use the long short-term memory (LSTM) network as the recurrent neural network. In addition, we introduce SE block and propose an effective attention layer, so that the model is able to dynamically pay attention to certain parts that contribute to the existing task. Then our model can determine the most relevant aspects, and achieve better effect of filtering useless information and removing noise. Finally, the entire network is optimized using the CTC loss function. Through experiments, we found that the recognition accuracy of CACRNN processing characters is higher than other methods.
Analysis of Studies on Coding Education: A Meta Synthesis Study

In this research, 39 studies carried out between 2015 and 2021 were examined by metasynthesis method. In this context, current trends in published research on coding education in Turkey have been tried to be determined. Studies on coding education in Turkey were examined according to the years they were published, the subject and application areas of the studies, research methods, study groups, data collection tools, data analysis techniques, results and suggestions. In order to examine the publications included in the research, a data collection form was developed by the researchers and analyzed within the framework of the determined categories. As a result of the findings, it was seen that more studies were carried out on coding education in 2019-2020, studies were carried out to determine opinions on robotics and coding education, studies were carried out in the field of educational science, case study and experimental design were preferred, mostly secondary school students were preferred as the study group, and the data were collected mostly by interview technique. It has been revealed that content analysis is frequently used in analysis. When the studies examined, it was seen that mostly results were obtained to explain the current situation and suggestions for applications were included. In line with the results of the research, longitudinal or experimental studies can be made for coding education.