Reviewerly/exHarmony huggingface dataset:
Data Analysis Method of English Education Based on Improved Deep Learning Algorithm

In the reform of college foreign language education, it is often one of the focuses of discussion whether the college curriculum can make students fully prepared to cope with the challenges of learning, future life and career. This is mainly because some students still have low learning efficiency at the stage of higher education, rely on mechanical memory and recitation, and lack high-level thinking ability, especially the ability to solve practical problems. Deep learning algorithm is the latest research trend of English education data analysis. It has been widely used in computer vision, speech recognition and text processing. Deep learning algorithm is an artificial intelligence (AI) technology, which uses neural networks to achieve advanced cognition. It can be divided into two types: 1. Deep convolution network 2. Deep recursive network in this paper, we will mainly focus on using deep convolution network to analyze English data, and provide a method to improve the accuracy of its results by using its improved deep learning algorithm.
Applying 5PKC-Based Skeleton Partition Strategy into Spatio-Temporal Graph Convolution Networks for Fitness Action Recognition

With the rise of health awareness, people's demand for fitness has gradually increased. However, improper exercise may easily cause damage to the body. It would be possible to avoid wrong actions if automatic action recognition can detect and judge the human motion of exercises. Therefore, we aim to grasp the user's fitness status through human action recognition. However, most human action recognition mostly uses CNN-based models to process images, which may introduce unnecessary noise other than the human body from the background. To address this problem, we use the Spatio-Temporal Graph Convolutional Network (ST-GCN) as the backbone and take skeleton data as input to learn skeleton relationships. To further improve the accuracy, we propose a novel partition strategy based on Five Primary Kinetic Chains (5PKC) to explore the skeleton partition status and then enrich the skeleton relationships. Finally, the proposed method with 9 ST-GCN blocks that integrated the proposed partition strategy achieved 99.5% of accuracy which outperforms the model using 9 ST-GCN blocks with 84.5%.
Clustering Analysis of English Scores Based on K-means Clustering Algorithm

The integration of Internet information technology and education has brought tremendous changes to the whole education system. At the same time, English curriculum standards require teachers to make full and reasonable use of modern educational technology in daily teaching according to the actual situation of students and teaching content. This paper introduces a k-means algorithm. The k-mean algorithm based on LAGSA and the traditional K-mean algorithm were simulated and compared with the measured data. Finally, the clustering results show that the Load-clustering algorithm based on LAGSA has less iteration times, fast convergence speed, good clustering accuracy, strong anti-noise performance and good robustness. Experimental results show that the improved algorithm can not only improve the efficiency of curve clustering, but also refine the mining of English score curves of different students, reflecting the rules and characteristics.
Design and Implementation of Online Teaching System Based on J2EE

In order to solve the problems of online teaching system failing to meet the requirements of online distance education for processing massive teaching data and limited network bandwidth, the existing system is optimized based on J2EE (Java 2 Platform, Enterprise Edition) by adopting mature cloud computing and load balancing algorithm. Create standard reusable module components and build a hierarchical structure that can automatically process programming to meet the needs of teachers and students for online learning systems. After the ideological and political course teaching test, the system operation meets the design requirements.
A Metaheuristic Approach for Inspection and Reconnaissance of Organized Areas

In this paper, we present a metaheuristic approach for path planning for area surveillance and inspection using unmanned aerial vehicles. The focus is on organized areas, such as city streets or storage zones. We exploit the row-like spatial organization of these scenarios and formulate the problem as a Distance-Constrained Rural Postman Problem. We represent the area of interest as a graph, where edges correspond to surveillance targets, such as city streets or rows of field storage areas, and vertices to their entry and exit points. The subtour length constraints represent the limited flight time of unmanned aerial vehicles. The goal is then to traverse every target edge exactly once, resulting in paths inspecting the area of interest entirely. We propose a Greedy Randomized Adaptive Search Procedure metaheuristic as a solution to this problem. Furthermore, we show that the same problem formulation and metaheuristic can be used for deep inspection of specific locations identified for further inspection in the previous step.
VSCM: Blockchain-Based COVID-19 Vaccine Supply Chain Management

The unprecedented impact of COVID-19 has hastened the world to get the vaccine, and the most challenging job is to immunize the nation effectively and efficiently. In our proposed model, we devise a systematic technique based on blockchain technology which can be used for assuring the transparent tracking of COVID-19 vaccination, real-time vaccine parameters (temperature, humidity, expiry date, etc.), and side effects. Thus, the proposed model will avail us a technique through which identity theft, impersonation, black marketing, illegal storage, etc., can be curtailed down. Prototype is implemented in Ethereum test network, considering most of the above-claimed solutions and tracking conditions of COVID-19 vaccine supply chain management. The obtained results for each chain can be checked and validated in terms of gas values indicating all the aspects of the proposed system such as safety rules for registration, vaccine tracking, and administration are maintained.
Quality Assessment of the Experimental Data of Wood Structure Using Kanpur Theorem

When an object is scanned using an X-ray scanner, it’s difficult to assess data generated contains errors or not. A methodology based on the application of Kanpur theorem-1 has been worked out. An experiment was performed on samples of Palash and Rosewood using CT-Mini micro-CT scanner and corresponding data set was obtained. The data set was actually ln[Iin/Iout], also called the projection data, should be theoretically equal to radon transform of object function ‘f’ i.e. ʃf(x, y)dl. The data set was used in a program of image reconstruction and an image was reconstructed. It was called the ‘original-image’. This ‘original image’ was used as an object itself. Now, using MATLAB, the radon transform of this ‘original image’, i.e., ʃf(x, y)dl was done, and therefore, a new dataset got generated. Further, the Fourier transform of the new dataset was taken, five filters, namely, Hann, Hamming, Cosine, Shepp-Logan, and Ram-Lac were applied, and two-dimensional inverse Fourier transform was taken to generate five reconstructed images. For each image, Nmax was calculated, and a plot of 1/Nmax (which is proportional to error) versus second derivative of filter function at origin, i.e., W″(0) was drawn with the help of MATLAB. If the reconstructed image only had inherent errors, then the plots would have points very close to the fitted line and occurring at fixed spacing from it. Hence, with this process, one can judge the quality of image and conclude if the experiment performed is free of errors or not.
Design and Application of Japanese MOOC Teaching Resources System Based on User Collaborative Filtering Recommendation Algorithm

The core of personalized recommendation is an algorithm, which searches for a group of users with similar interests to the target user as “nearest neighbors”, and realizes recommendation by finding “nearest neighbors”. According to the actual needs of MOOC teaching in Japanese major, a teaching resource system is built, and based on the user collaborative filtering recommendation algorithm, the teaching resources of interest are recommended to users to meet the needs of MOOC teaching in the mobile information age. The core work completed in this paper mainly includes two aspects: First, a mathematical model is constructed according to the user-based collaborative filtering recommendation algorithm process. Second, based on the use case diagram method, the functional requirements analysis is carried out for the student users, the teacher users and the system administrators, and the key problems of the system development are solved.
Computer Graphic and Photographic Images Classification—A Transfer Learning Approach

Multimedia tools and applications have led to the tremendous development in information and communication technology. Images play a vital role in information communication in many application domains. Due to the photorealistic nature, computer graphic images are highly challenging to differentiate them between real time photographic images. Accurate classification of photographic and computer graphic images is highly imperative in image forensics. Computer graphic images are used as the fake source of proof in criminal investigations and other illegal activities. In this work, the classification of computer graphics and photo realistic images is performed using transfer learning approach. The popular CNN model, GoogleNet, is used for classifying the images based on the features. The model performance was evaluated on Columbia dataset and photo realistic dataset. The model has achieved a classification accuracy of 90% in 80:20 training ratio and 87.65% in 70:30 training ratio.
A Hybrid and Multi-objective Approach for Data Leak and Tamper Detection in Healthcare Cloud Data

The emerging technology cloud computing has become a strong pillar beneath the digital healthcare systems. Storing and maintaining the medical records in cloud has been proven beneficiary for smooth and efficient running of the digital healthcare systems. Almost every large private and public healthcare organizations have evolved their system into a cloud-based e-healthcare system. In this digital healthcare environment, the electronic health records are the most important instrument and commonly face security threats and attacks. The data leak due to a malicious insider inside a digital healthcare system pile even more weight on the security concerns. Encryption techniques and watermarking approaches separately have been used to enhance the security of cloud data, but alone they are not strong enough to detect the insider attack or to identify any deformation of medical reports. To find the solution of this we have proposed one hybrid approach by combining the encryption technique and watermarking approach while aiming toward multiple objectives of identifying the malicious insider and the tampering detection in the medical images stored in any healthcare cloud.
Financial Evaluation Model Based on Data Mining Algorithm

Enterprise financial evaluation refers to the evaluation and summary of the enterprise’s financial status, operating results and cash flow, so as to provide relevant financial information for the enterprise’s information users. The financial evaluation model based on data mining algorithm is a method to calculate the company value. The model is used to determine the value of the company and its shares. The valuation formula is based on the number of shares in circulation and their price per share. The model also considers other factors, such as dividends paid by the company, which will affect its overall value. In addition, it also considers the amount invested in the company through the purchase of shares or stock options given to employees and executives.
Design and Research of Chinese Painting Authenticity Identification System Based on Image Recognition

The design of the system is based on image recognition technology. The main function of the system is to identify the authenticity of Chinese paintings through image analysis. The system can be used for the authentication and identification of ancient paintings in museums, galleries or private collections. It will also help collectors obtain more information about their works by comparing them with other works at different times and places. The project is designed as a multi-layer method, using statistical pattern recognition, neural network, fuzzy logic and other methods to identify real Chinese paintings according to their images. The most common way to verify ancient Chinese paintings is to compare them with other similar works and then determine whether they are true. However, there are some problems in this traditional way: first, it is difficult to find similar works that have been lost over time; Secondly, due to changes in materials and technology, there may be some differences between the original and its copies. In order to solve these problems, we need a new authentication system based on image recognition technology, which can accurately identify the originality of artworks from images.
The Design of College Chinese Module Teaching System Under Multimedia Environment

This study aims to design a new teaching system for College Chinese. The main goal is to develop a module based teaching system, which can be used in distance education with multimedia environment. Methods and tools are mainly based on cognitive science, human-computer interaction and communication theory. Use multimedia teaching materials to provide effective learning experience, facilitate students to master the basic knowledge of Chinese, enhance students’ enthusiasm through interactive activities, improve students’ oral level through audio-visual demonstration, and use advanced technology to improve students’ fluency. The purpose of this study is to design a college Chinese module teaching system based on Multimedia environment. The main objective is to design a modular teaching system of College Chinese that can be used in a multimedia environment, and to investigate and evaluate the effectiveness of different learning strategies such as self-study, cooperative learning, computer tutoring, etc. Investigate and evaluate the effectiveness of different types of learning materials (such as audio-visual materials, reading materials, writing materials, etc.). Analyze students’ grades by using various evaluation methods.
Computer Vision with the Internet of Things (IoT)

The most common and dangerous natural catastrophes are floods. Floods kill and devastate far too many people and businesses throughout the world. There needs to be a more effective reaction to flooding. Camera pictures and wireless sensor data from Internet of things networks have been an excellent resource for flood management research throughout the last decade. Computer vision and Internet of things sensor methodologies utilized in the literature are highlighted in this research to monitor real-time surges, simulate floods, and anticipate the water level. Ideas for further study can also be found in the publication. According to a new study, computer vision and Internet of things sensors can better monitor and manage coastal lagoons. There has not been enough research done in this area. There will be many gadgets creating and exchanging information flows to represent actual life on the Internet of things. Many things need to work together to link the real world to the virtual world, store and analyze sensor data, monitor and operate connected devices, or construct a history that can predict what will happen on the internet of things software platform. Even if there are numerous weak dependencies, it is feasible to draw out a perfect design. Two things should be clarified before researchers begin their work.
Research on Visualization of Student Management System Based on Distributed Storage Technology

The rapid development of network technology, the rapid expansion of mobile terminals, the massive growth of industry data even the growing student data of colleges will need to be organized systematically. At the same time, the current student management in colleges is lack of uniform, targeted and effective information management methods, the paper aims to help managers adopt efficient management strategies and optimize Management process by improving management through the use of big data technology and innovative ideas so as to build a data-based, informatized and networked management model for student management.
Design and Implementation of Teaching Assistant System Based on Intelligent Recommendation Algorithm

Language teaching is different from the training process of applied majors, which requires a lot of listening and speaking exercises to consolidate the knowledge learned. In order to improve the effect of online English teaching, with the help of the development of computer simulation technology and multimedia technology, based on intelligent recommendation algorithm and combined with virtual reality technology, a three-dimensional model of modular simulation teaching scene (MPS) was established. Unity3D engine was used to complete the development of eight training scenes in MPS simulation teaching platform. The test results show that the developed virtual teaching platform can meet the needs of English teaching and runs smoothly.
Trustworthiness of Customer Reviews of E-Commerce Portal Using Supervised and Semi-supervised Machine Learning Techniques

Moving toward digitization and in a situation where we are largely affected by the COVID-19 pandemic, the use of online portals for our daily needs is very common now. Hence, buying a good and reasonable product is always in need and here comes the importance of available reviews given by the users or customers. Users buy and give their feedback in the form of reviews. These reviews help others to know about the product before buying related the pros and cons of products. These create an environment where anyone can influence one’s decision by writing biased reviews either to promote a product or to demote. As the reviews are playing a significant role in taking decisions, this raises the question of whether a particular review can be trusted or not? In this direction, we attempted to determine the trustworthiness of a review utilizing the concept of sentiment analysis along with other parameters. In this paper, we have extracted many features to create a model to check the authenticity using supervised and semi-supervised machine learning techniques. Experiments suggest that the approach can be effective in checking the trustworthiness and gives a promising result to handle large data.
Design of an Intelligent Educational Virtual Classroom Platform Based on the Mobile Terminal APP of the Internet of Things

While traditional education and curriculum models are under increasing pressure, the development of intelligent terminal technology based on the Internet of Things provides practical technical support for new curriculum models. This is the need for social education development and the trend of platform curriculum development. The purpose of this article is to study the intelligent education virtual classroom platform (CP) based on the mobile terminal APP of the Internet of Things. This article introduces the basic technology and scientific principles involved in the development of an intelligent education virtual CP based on the Internet of Things mobile terminal APP, introduces the system development process, and introduces the database and web server technology used in the background. Experimental research shows that after the use of virtualization, the response time is significantly reduced by about 20% when the number of concurrent users is large, which effectively improves the response speed of the intelligent education virtual classroom system. Prove the effectiveness and correctness of the system.
Prediction Models for Car Theft Detection Using CCTV Cameras and Machine Learning: A Systematic Review of the Literature

Car theft is a constant problem in parking lots and places where cars are left unattended. Car theft detection is a time-consuming task due to the human resources that are required. Therefore, the task of checking closed circuit television (CCTV) cameras can be automated using machine learning techniques. The implementation of such a system would mean an optimization of the current technology. Even if a CCTV camera is installed, it requires human labor to supervise the area, which is a repetitive and time-consuming task. A machine learning algorithm could simplify the task and attend to many cameras without decreasing the attention on each one. In this context, a systematic review of the literature on machine learning was conducted based on four research questions using the PRISMA methodology. The research method may help to find the current methods used in similar applications and possible ways to implement the proposed automatic solution. This scientific study retrieved 384 articles from Web of Science, Scopus, and IEEE databases. The number of studies used to answer the research questions was 58. Finally, analyzing the most frequent models and metrics, Convolutional Neural Networks and Accuracy were the most referenced, with 30 and 42 mentions, respectively.
Comparison and Research of Roof Waterproof Materials Based on Computer Algorithm

Comparison of roof waterproof materials based on computer algorithm: use computer algorithm to compare various roof waterproof materials. This is the most common way to compare different roof materials. The results obtained from this method are very accurate and reliable because it uses advanced technology for these comparisons. How to compare and study roof waterproof materials based on computer algorithm? The comparison process of different types of roofs involves a series of steps, including: before online purchase, you can compare the prices, functions, specifications and other details of different products. This comparison can help you find out which product is better than others. It also provides information about how much you need to spend to get the product you need.
Corpus Translator's Style in the Era of Big Data Under Data Mining Algorithm

The research of corpus translation style in the era of big data under data mining algorithm is to use big data to analyze the characteristics and changes of translation style. This study aims to achieve the following goals: to analyze how translation styles have changed in different periods from ancient times to the present; Study how translation style changes according to various factors such as age, gender and major; Investigate whether there are any differences between the translations of professional translators and non professional translators.. In this article, we will introduce two problems in dealing with big data: (1) big data analysis and (2) translation quality evaluation. We will discuss how to solve these problems by using machine learning algorithms such as neural networks and deep learning models.