Reviewerly/exHarmony huggingface dataset:
On the Application of Privacy Computing in the Field of National Cyberspace Security

Privacy computing technology means the collection of technologies that realize data analysis and calculation under the premise of protecting the data itself from external leakage, in order to realize the transformation and release of data value. Privacy computing is applied in different fields such as finance and medical care to assist the technological innovation of the industry. With the continuous virtualization of the cyber world, national cyberspace security has become the focus of development, and the country needs to mine data and establish a market for data elements, and the security of data has become a top priority. In this study, we analyze the present situation of the development of privacy computing technology, including the development status of secure multi-party computing technology, federal learning technology, and trusted execution environment technology, and explore the feasibility of applying privacy computing technology to the national cyberspace security field based on the application of privacy computing technology in several fields, which ensures the reliability of data in the process of data element market circulation, under the Data Security Act With the strong support of the Data Security Act, it is the future development trend to study the integration of a multi-functional new privacy computing technology with the national spatial security field, which is also a strong guarantee to promote people’s privacy security and national stability.
Overview of Automatic Modulation Recognition Methods

Blind detection and identification processing such as communication signal detection, modulation identification, and individual radiation source identification under non-cooperative reception conditions is an important basis for radio spectrum monitoring and battlefield communication reconnaissance and confrontation. However, due to adverse factors such as strong interference and fast-changing electromagnetic environment during non-cooperative reception, complex and diverse communication signal systems, and inability to fully grasp signal prior information, blind detection and identification of communication signals are extremely challenging. This article comprehensively sorts out the modulation recognition algorithm. The DL-based AMR algorithm is systematically reviewed from the three levels of the data set, network model, and final performance. Finally, the existing problems, potential research directions, and conclusions of AMR are outlined.
Comparative Analysis of Handwritten Text Recognition using CNN and SVM

Handwritten Character Recognition (HCR) is an active area of research in the recognition domain. Many handwritten character recognition systems have been put forth in recent years for real-world applications that require high identification accuracy and dependability. Many of these organizations, like the banking and healthcare sectors, demand extremely accurate HCR. HCR systems are also used in newly developing fields, such as the creation of electronic libraries and multimedia databases, where handwriting data entry is necessary. The goal of the study is to evaluate the models of a potent Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) for the most accurate recognition of handwritten characters. Due to its receptive field, CNN can instantly learn local features. Each layer's output serves as the following layer's input. The Support Vector Machine (SVM)'s primary function is to represent a multi-dimensional dataset in a space where data items from various classes are divided by a hyperplane. The SVM also seeks to reduce generalization errors resulting from unobserved data. The topic of this paper is understanding Kannada numerals. The results show that CNN is the most optimal machine learning technique to classify handwritten text with an accuracy of 98.85 percent whereas SVM shows only 98.62 percent. CNN model outperforms the SVM model by 0.23 percent.
Analysis and Research of Athletes’ Physical Fitness Test Index Based on Data Mining Technology

The physical fitness test index is an index to measure the physical fitness of athletes. It is used to evaluate the performance of athletes in various events. The purpose of this study is to analyze and study the physical fitness test indicators of athletes based on data mining technology. Research methods This study adopts a combination of qualitative and quantitative methods. Researchers used primary and secondary sources in this study. Research method This research method is a combination of qualitative and quantitative methods. This study uses various sources, such as literature review, primary and secondary data collection, interviews, observations, etc. Build a fast association classification model of physical fitness indicators based on improved data mining algorithm to find out the association between physical fitness indicators. The physical fitness evaluation model can efficiently analyze the health level of athletes' physical fitness, providing a new research perspective for physical education and evaluation of physical fitness level. This article takes the analysis of athletes' physical fitness test data as the research object, and studies the principles and methods of data mining technology to solve the problems in the management and analysis process of athletes' physical fitness test indicators. This paper introduces the basic principles, methods, and processes of data mining technology, with emphasis on the principles, methods, and typical algorithms of association rule mining and neural network mining. An algorithm suitable for analyzing tennis players' physical fitness data was proposed and implemented, and applied to the database, discovering unconventional rules.
A Hybrid Approach of Semantic Weight Based Re-Propagation For Convolutional Neural Networks in Content Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a homogeneous search technology based on visual features, and information has been leaked to potential users of pharmaceutical testing, education, and research. However, the CBIR collects images related to acquisition properties, as a result of the inability to predict the similarity of images using the medical part, and the difference between the user’s search destination and object space. In the existing methods, effective points of interest are searched for, and L2 normalization is applied to the spatial arrangement of the original and global features and the RGB channels in the index image by effectively detecting their points of interest. The principal component analysis is used for redundant feature sets and feature sets to provide significant effects. The existing system has certain limitations, such as information loss during the hard task completion coding process and ignores the spatial relationship among the patches in image representation. To overcome the existing method issues, the integrated feature of the images is preferred. In this work, Semantic Weight Based Feed Forward Recurrent Back Propagation (SWFFRBP) is used to retrieve the images, and then based on the semantic weight the correctness of retrieval of images is calculated. The semantic weight determines the accuracy of retrieved images. After that, the recurrent backpropagation calculates the weight error derivatives arising from this entire episode of processing in the network. The existing features compared with the proposed features were Bag of Features (BoF), SURF, and HOG. The retrieval methods taken to compare with proposed retrieval techniques are RFRM and KNN. Thus, medical image retrieval for diagnosis using SWFFRBP, accuracy, False positive rate, relevance, feedback, precision, and recall parameters were tested and proved to be efficient than other methods.
Information Fusion for Seveirty Detection using Machine Learning in Data Mining Healthcare

Improved patient care and less mental strain on healthcare providers are two benefits of using algorithms for machine learning to healthcare data. These algorithms may be used to spot irregularities in vital signs, which might speed up medical assistance or provide light on a disease's progression. While there is a wealth of literature comparing the unsupervised and supervised performances of anomaly detection algorithms on popular public datasets, this same level of conceptual comparison is lacking when it comes to physiological data. Knowing one's heart rate may provide valuable insight on one's health and level of physical activity, making it an underutilised data source. Specifically, we used and compared five machine learning methods, two of which were unsupervised and the other three supervised, to identify outliers in heart rate data. The algorithms were tested using physiological data from human subjects' hearts. Results demonstrated that both outlier factor and regression trees algorithms were effective in detecting heart rate anomalies, with both models successfully generalising from their simulation heart rate data training to real-world heart rate data. In addition, the findings lend credence to the idea that, in the absence of real labelled data, simulated data can be used to configure methodologies to a certain degree of performance, indicating that this kind of training could be particularly useful in the initial rollout of a system with no preexisting data.
Comparative Study Analysis of MachineLearning Algorithms for Anomaly Detection in Blockchain

Anomaly detection is one of the challenging problems encountered by the modern network security industry. In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Despite being an effective tool, the blockchain is not impervious to cyberattacks. For instance, a successful 51% attack on Ethereum Classic exposed security flaws in the technology. Attacks can be viewed from a statistical standpoint as an aberrant finding that strongly deviates from the norm. Machine learning is a science whose objective is to discover insights, trends, and anomalies in massive data sets; as a result, it can be used to detect blockchain attacks. In this work, we define a federated learning-based anomaly detection system that is trained using aggregate data gathered from observing blockchain activity on the end device itself. Experiments on the whole historical logs of the Ethereum Classic network demonstrate our model’s ability to accurately identify assaults that have been made public while also automatically signing digital transactions for further protection. Therefore, it is necessary to create an anomaly detection system that can monitor networks for any dangerous actions and produce findings for the management authority in the end device itself. Several classification techniques and machine learning algorithms have been taken into consideration in our suggested article to categorize the accurate model.
Protection and Utilization of Personal Information in the Context of Big Data

With the innovation of technology, the amount of data has doubled and people have entered the era of big data. As one of the important resources, the economic and social value of data is increasing, but in the process of sharing, processing, circulation, and use of data, how to achieve a balance between the protection and utilization has become a key issue affecting national security today. Firstly, this paper presents the significance of this research based on the analysis of high-profile cases in recent years; Secondly, the current situation at national and international will be analyzed in two directions; national legislation and technical research; Thirdly, a summary of current problems. Finally, solutions are proposed on how to achieve the protection and use of personal information.
The Smart Development of Human Thinking Prediction Using Complex Fuzzy Systems

The use of complex fuzzy systems to predict human thinking is an area of active research. These systems are based on fuzzy logic, which is an approach to computing based on approximation and imprecision. Fuzzy logic has been used in a variety of areas, such as control systems, image processing, decision support systems, and even robotics. The idea behind fuzzy logic is to use a combination of fuzzy rules, fuzzy sets, and fuzzy inference to approximate the decisions that humans make in complex situations. This means that the system can take into account the uncertainty of the situation and make a decision based on available data. The system can also be trained to recognize patterns in the data and make predictions about future decisions. In order to predict human thinking, a complex fuzzy system needs to be able to take into account a variety of factors, such as situational context, emotions, and values. For example, if a person is deciding whether to buy a car, the system would need to consider factors such as price, reliability, and environmental impact. The system would also need to consider the person's preferences, such as their preferred color or style.
Application Analysis and Development Strategy of Cloud Computing Technology in Computer Data Processing

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing.
An Analysis System Based on Improved ID3 Algorithm for Sports Effect Evaluation Methods

Sport data is an important part of large data resources, and the collection and analysis of sport data can be effective in understanding the effects of sport on the human body and the effects of sport. The purpose of this paper is to study the analysis system of sports effect evaluation methods based on the improved ID3 algorithm. Starting from the study of data mining algorithms, the adaptation effects of different sports are studied and a sports effect evaluation dataset is constructed. An improved ID3 algorithm is proposed for studying the effects of sports on the body and provides a simple and easy to use system for analysing the effects of sports. According to the results of the study and analysis, it was found that the effects of different types of sports on the body of adolescents varied greatly.
The Optimal Resource Allocation Model for Task Management in Commercial Cloud Server Environment

The resource allocation in commercial cloud server environment is an important part of any cloud computing infrastructure. The resources that are allocated to each cloud server are determined by the cloud provider, and they vary depending on the type of service that the provider offers. The resources that are allocated to each cloud server include computing power, memory, storage, and bandwidth. It is important to note that the resources that are allocated to each cloud server are limited and must be allocated in a manner that maximizes the efficiency of the system. One of the most important aspects of resource allocation in a commercial cloud server environment is the ability to scale the resources. This allows for the addition of more resources to the cloud server when additional computing power or storage capacity is needed. The cloud provider must make sure that the resources allocated to each cloud server are able to be monitored and managed. This is important because it allows the cloud provider to make sure that the resources are being used efficiently and that the cloud server is able to meet the needs of the applications that are running on the server.
Deep Learning &amp; Computer Vision Integrated Smart Voting System

In this paper, an online voting method for elections in India is initially suggested. The suggested model has higher security as the voter’s raised secure password must be validated prior to the recording of the vote in the major database owned by our nation’s Election Commission. The model’s additional feature allows the voter to verify that the right candidate or party received their vote. In this arrangement, a voter has the option to cast a ballot from a place other than the one designated for them or from their favorite site. Vote counting will be made in an automated fashion under the proposed approach, saving a significant amount of period and allowing our nation’s Commission set for Election to declare the results in a much fast succession. Alongside the password-based authentication, we have also utilized the face-based authentication along with the successful implementation of the OpenCV in addition to the password validation. We describe a model for a web voting entity for India in this application using the above said constituents. When contrasted with the conventional voting system, this version of voting systems seems to be considerably much secure and efficient. The delays and frauds occurring during counting of votes can be easily prevented.
Key Technologies of a Service Platform Based on Blockchain Technology

Blockchain has the characteristics of decentralization, stability, security, and tamper-proof. With the further improvement and application of blockchain technology, blockchain will be integrated with traditional industries such as finance, medical care, and logistics. This article aims to study the key technology of a service platform based on blockchain technology. Taking supply chain finance as an example, it analyzes the architecture and characteristics of blockchain, the principle of blockchain technology, and the combination of blockchain and supply chain financial service platforms. On the basis of meaning, a supply chain financial service system based on blockchain technology was designed, and the performance of the system was tested. The test results show that the system login success rate is 99.92%, the system response time is 1.91s on average, the CPU and memory usage rates are less than the target value, and the system performance can meet the expected target. In addition, the actual login success rate of the system tested in this article is 99.92%, and the actual system business success rate is 99.86%. The supply chain financial service system designed in this article based on blockchain technology has good performance.
Development and Application of Constructive English MOOC System Based on RBF Algorithm

With the development of information technology, networking and intelligence have become indispensable parts of people’s daily life, and learning is increasingly inseparable from the Internet. As a new discipline, RBF system development is more in line with the trend of the times. This paper analyzes the current situation of English teaching in China and the students’ ability to accept and use knowledge, and studies the principle of RBF algorithm, designs and develops an English MOOC system, and then tests the operation effect of the system. The test results show that the Constructive English MOOC system based on RBF algorithm can run smoothly, with the system running time of 3-7 seconds, and the maximum running efficiency of 98%. This shows that the constructive English MOOC system can meet the needs of users. Teachers are the disseminators of knowledge in traditional English classroom teaching, whereas students just passively receive knowledge. Owing to time constraints and a lack of instructional methodologies, teaching activities must be augmented with computer and multimedia technology. Rich instructional strategies can help students develop their abilities to conduct independent research. It is advantageous to construct this English learning platform in order to boost students’ capacity to use information technology. It can increase students’ interest and awareness of English learning by making them the major body of teaching and learning. This English learning platform performs a good and positive auxiliary role for students to learn English and acts as a superior means of English education by using the radial basis function (RBF) algorithm in conjunction with constructive English teaching methods. In a nutshell, English teaching research has both research value and positive significance.
Random Logistic Vector Analysis Based Opinion Mining For Identifying Best Product Using User Reviews in Ecommerce Applications

Social Media (SM) has emerged as a new communication channel between consumers and enterprises to generate a large volume of unstructured text data about products. Many web users post their opinions on several products through the blog, review sites and social networking sites-based text of the attitude. Customer feedback plays a very important role in the daily movements of products. Opinions of others are also taken into account when making decisions to select the best products. Event though, it reads reviews of all the customers, it has difficulty in making decisions based on the information about whether or not to purchase the product. Keeping track of the customer's opinion, manufacturers are also finding it difficult to manage the products which lead to economic collapse. To address this problem, the proposed Random Logistic Vector (RLV) algorithm is used to analyze the product quality and life of the products based on reviews. The first process is data collection based on customer content-based reviews about products from Ecommerce applications. Then, collected data are trained into preprocessing to remove unwanted data and noise. Secondly, preprocessed data are trained into feature extraction to select the best features of the lexicon-based sentiment words, adverbs, adjectives word based on consumer reviews about products from the dataset. Finally, feature extraction data are trained into the proposed Random Logistic Vector (RLV) algorithm is done to identify the polarity or subjectivity orientation that indicates the customer opinion text expressed by the user or client in terms of value. Random Logistic Vector (RLV) algorithm which is used to classify the data to help select the best products and analyze the product quality. It will also lead to the economic growth of productive enterprises.