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  Subjects -> ELECTRONICS (Total: 207 journals)
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Security and Communication Networks
Journal Prestige (SJR): 0.285
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-0114 - ISSN (Online) 1939-0122
Published by Hindawi Homepage  [339 journals]
  • Analysis of Risk Assessment of Overseas Infrastructure Projects
           Integrating BP-ANN Algorithm

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      Abstract: In order to improve the risk assessment effect of infrastructure projects, this paper combines the BP-ANN algorithm to conduct risk assessment and analysis of overseas infrastructure projects to avoid the problem of slow learning and speed up network learning. Moreover, this paper normalizes the input value, and redefines the concept of risk on the basis of previous research. In addition, this paper sets up to record the basic information and risk-related information of the project. In order to verify the effectiveness of the algorithm, this paper predicts the training data, test data, and verification data, respectively, to verify the effectiveness of the algorithm, and collects existing overseas infrastructure project cases as samples to perform the simulation experiments. The experimental study shows that the risk assessment model of overseas infrastructure projects that integrates the BP-ANN algorithm proposed in this paper has a good risk analysis effect.
      PubDate: Mon, 08 Aug 2022 22:50:02 +000
       
  • Value Assessment for a Theory-Oriented Flipped Classroom of Physical
           Education Based on Multi-Source Data Analysis

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      Abstract: In order to overcome the problem of students’ boredom in physical education theory classes in general, and to give full play to the role of assessment in guiding, diagnosing, motivating and proving, this paper analyses the multi-source data generated during the teaching process, which can predict students’ subsequent learning status. Based on the collection and processing of data from multiple sources, this study takes the PE course as an example and predicts the course performance based on multiple dimensions; and carries out empirical analysis with the actual course performance through numerical correlation, ranking correlation and the bottom band. The analysis was compared with actual course grades through numerical correlation, ranking correlation and bottom band warning coverage of students. The results of the study show that the earliest grade prediction using taught courses has the highest student warning coverage; the highest grade prediction based on unit tests has the highest numerical relevance and ranking relevance; and the highest grade prediction based on unit tests has the highest student warning coverage.
      PubDate: Mon, 08 Aug 2022 22:50:02 +000
       
  • Algorithm Model Design of the Aging Transformation Scheme of Computer
           

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      Abstract: The study is to improve the living environment of the elderly in rural areas, improve the quality of life and well-being of the elderly, and ensure the living environment of the elderly while promoting rural revitalization and development under the policy background of rural revitalization, this study optimizes the aging transformation scheme by integrating computer intelligent auxiliary technology, and analyzes and compares the algorithm model design of the aging transformation scheme through the algorithms of the traditional transformation scheme and computer intelligent auxiliary technology. The results show that compared with the transformation scheme of the traditional algorithm, the transformation scheme after using computer intelligent auxiliary technology has higher accuracy and coupling for the aging transformation scheme, and has a better effect on improving rural development, beautiful rural construction, and improving the life of rural residents.
      PubDate: Mon, 08 Aug 2022 22:50:02 +000
       
  • An Authentication and Key Agreement Scheme Based on Roadside Unit Cache
           for VANET

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      Abstract: Vehicular Ad Hoc Network (VANET) is a wireless Mobile Ad Hoc Network that is used for communication between vehicles, vehicles and fixed access points, and vehicles and pedestrians. However, because of the use of open wireless channels, VANET is more vulnerable. Therefore, VANET security is critical for safe driving and user privacy protection. Authentication and key agreement are crucial for ensuring security. Numerous authentication schemes have been proposed between vehicles and roadside units (RSUs). Many solutions are authentication and key negotiation between the vehicle and a single RSU. The vehicle passing through a region needs to complete authentication and key agreement with multiple RSUs separately, which brings a great burden to the vehicle. In order to simplify the authentication process of vehicles and multiple RSUs and improve the efficiency of authentication and key agreement, an efficient authentication and key agreement scheme based on RSU cache is proposed when the vehicle moves from one RSU to another RSU region. In the proposed scheme, RSUs are divided into regions, and each region has a RSU cluster head. When the vehicle enters a certain region and authenticates with a RSU successfully, the RSU submits the authentication information to the RSU cluster head. The RSU cluster head shares the authentication information with other RSUs in the region using the shared key. Other RSUs record the authentication information in the cache. When the vehicle communicates with other RSUs, the authentication is not necessary; the session key can be negotiated by simply exchanging information. After using the cache, the calculation and communication cost of the authentication and key negotiation between the vehicle and other RSU can be significantly saved, the calculation cost is reduced by 37%, and the traffic is reduced by 35%. The random oracle model is used to prove the security of the scheme. The results revealed that the authentication overhead of the proposed scheme is considerably lower than those of other schemes. Compared with the related schemes, the computational cost of the proposed scheme is reduced by 34% on average; the communication cost is close to other related schemes. Moreover, the security analysis shows that the proposed scheme provides better security compared to other related schemes.
      PubDate: Mon, 08 Aug 2022 22:50:01 +000
       
  • An Intelligent Assessment Method of English Teaching Ability Based on
           Improved Machine Learning Algorithm

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      Abstract: In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality.
      PubDate: Mon, 08 Aug 2022 22:50:01 +000
       
  • Water Quality Substance Detection System Based on Internet of Things

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      Abstract: According to the sources of pollution in different river regions, the characteristics of rivers and lakes, and on the basis of investigation and analysis of the actual situation of water environment monitoring in different river regions, this paper carries out a research on the optimization of water quality detection and gives the definition of a super network as a super network based on a hypergraph structure. Moreover, according to the simulation results of topological parameters, this paper analyzes the structural characteristics of the super network and combines the Internet of Things technology to construct a water quality material detection system. In addition, this paper evaluates the water quality detection effect of the system proposed in this paper through a control experiment. According to the quality inspection results, it can be seen that the accuracy rate of the water quality material detection system based on the Internet of Things proposed in this paper is more than 92%, which verifies that the system model proposed in this paper has a good effect.
      PubDate: Mon, 08 Aug 2022 22:50:01 +000
       
  • Key-Value Data Collection with Distribution Estimation under Local
           Differential Privacy

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      Abstract: Local differential privacy (LDP) is a promising privacy-preserving technology from users’ perspective, as users perturb their private information locally before reporting to the aggregator. We study the problem of collecting heterogeneous data, that is, key-value pairs under LDP, which is widely involved in real-world applications. Although previous LDP work on key-value data collection achieves a good utility on frequency estimation of key and distribution estimation of value, they have three downfalls: (1) existing work perturbs numerical value in a discrete manner that does not exploit the ordinal nature of the numerical domain and lead to poor accuracy, (2) they do not lead to improved privacy budget composition and consume more privacy budget than necessary to achieve the given privacy level, and (3) the frequency estimation of the key is not the most accurate due to the lack of consistency requirement. In this paper, we propose a novel mechanism to collect key-value data under LDP leveraging the numerical nature of the domain and result in better utility. Due to our correlated perturbation, the mechanism consumes less privacy budget than previous work while keeping the privacy level. We also adopt consistency as the postprocessing, which is applied to the estimated key frequency to further improve the accuracy. Comprehensive experiments demonstrate that our approach consistently outperforms the state-of-the-art mechanisms under the same LDP guarantee.
      PubDate: Mon, 08 Aug 2022 22:50:01 +000
       
  • Optimization of Economic Development Path in Western Region under
           Background of “the Belt and Road Initiative” Based on Intelligent
           Internet of Things

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      Abstract: The Internet of things is a network that can realize the information interaction between all independent inanimate objects. It is a network that allows people to experience how to talk with objects. Its emergence is a milestone of world information technology. With the development of the Internet of things, more and more research is carried out on the basis of the Internet of things, and the economic development of the western region should seize the opportunity of the Internet of things and find a suitable development path. This paper mainly studies the optimization of the economic development path of the western region under the background of the “the Belt and Road” to help the western region develop rapidly. This paper puts forward the interactive development path of industrial clusters and new urbanization. Through the simulation data, the total GDP and per capita GDP of the western region in the next few years are analyzed and summarized. The data show that under this method, the total GDP and per capita GDP of the western region will increase by more than 30% every year in the next five years, and will continue to rise.
      PubDate: Mon, 08 Aug 2022 13:50:02 +000
       
  • Quality Evaluation and Satisfaction Analysis of Online Learning of College
           Students Based on Artificial Intelligence

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      Abstract: In order to better study the quality and satisfaction of online learning of college students, this paper analyzes and researches online learning of college students based on relevant theories of artificial intelligence. Through the traditional machine learning method to evaluate the quality of online learning, the deep learning theory is applied to the satisfaction analysis of college students'’ online learning. The results show that different statistical indexes have different influences on traditional machine learning, but they all show a gradually decreasing trend. The main reason for the different degrees of influence is that the emphasis of different statistical indexes is different, and the order from large to small is MAE > RMSE > MAPE > TIC. Statistical indicators can better describe the first stage of test data, while the corresponding quality indicators can better characterize the second stage of test data. It indicates that statistical and quality indexes should be considered comprehensively to analyze the test data accurately. The increase of evaluation indexes based on traditional machine learning can improve the evaluation indexes of online learning quality of college students. And the improvement of statistical indicators and evaluation factors can promote the accuracy of online learning quality evaluation of college students. Based on the theory of artificial intelligence, the quality and satisfaction of online learning of college students are analyzed and evaluated by using the traditional machine learning method and deep learning method, respectively. Relevant research can provide a research basis for artificial intelligence in online learning methods of college students.
      PubDate: Mon, 08 Aug 2022 13:50:01 +000
       
  • On English Courses and Teaching Strategies in Compound
           Application-Oriented Talents Training Based on the Education Ideas of CDIO
           in the Construction of Smart Cities

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      Abstract: To improve the level of English education in China and promote the cultivation of compound application-oriented talents in colleges and universities, this research integrates the concept of conceive design implement operate (CDIO) into college English education. First, the CDIO educational concept is introduced in detail. Second, the moth-flame optimization (MFO) algorithm and the support vector machine (SVM) model by the MFO algorithm are presented, and they are applied to teaching evaluation, and then the teaching plan is proposed by the CDIO concept. The teaching evaluation model is applied to the English teaching of students in class A with ordinary learning levels in a university in Hengyang City. The implementation method of the teaching plan is explained in detail by taking “Unit 2 Charlie Chaplin” in the textbook “New Horizons College English” as an example. Finally, the accuracy of the MFO-SVM model for teaching evaluation is verified by simulation analysis, and the corresponding teaching strategies are summarized through teaching evaluation and customization of teaching plans. The experimental results reveal that the MFO-SVM model has an accuracy of 96.43% in teaching evaluation, while the extreme learning machine (ELM), SVM, and back propagation neural network (BPNN) models have an accuracy of 92.27%, 90.45%, and 86.36%, respectively in the evaluation results of the teaching quality. The summarized teaching strategies are mainly divided into four parts, namely, autonomous learning, project-based teaching, dynamic generation and information feedback, and production teaching. This research has a certain reference for the current reform of English education in China and the cultivation of compound application-oriented talents.
      PubDate: Mon, 08 Aug 2022 13:50:01 +000
       
  • QoE-Aware Video Delivery in Multimedia IoT Network with Multiple
           Eavesdroppers

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      Abstract: How to deal with the increasing video traffic and diverse service demands while ensuring the security of transmission is an open issue in the multimedia Internet of Things (IoT). This paper addresses this issue and studies a secure delivery scheme under a multicast scenario in the presence of multiple eavesdroppers where small base stations (SBSs) can send videos to users cooperatively. Aiming at potential eavesdroppers, a channel model including artificial noise is introduced to reduce the harm of illegal data acquisition. A network quality of experience (QoE) optimization problem is first formulated to account for video quality and delivery delay. In order to solve the nonconvex problem, the successive convex approximation (SCA) technique is applied to optimize multicast group beamforming, reduce the possibility of multicast video eavesdropping, and select video quality where a heuristic scheme is proposed to maximize the network QoE. The effectiveness of the proposed scheme is finally validated by extensive simulations in terms of algorithm convergence performance and network QoE-enhanced performance.
      PubDate: Mon, 08 Aug 2022 13:50:01 +000
       
  • Integrated Energy Security Defense Monitoring Software Based on Cloud
           Computing

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      Abstract: In order to solve the problem of outlier detection of integrated energy security defense monitoring software, an automatic detection algorithm of virtual machine power anomaly in a cloud computing environment is proposed. The method is implemented through three main steps: data preprocessing, pattern recognition, and prediction of virtual machine power anomaly detection model. It is found through experiments that with the increase of node number, the convergent iterations of the model are less and RMSE is lower, but the increase of node number of the hidden layer will lead to a longer model running time. When the number of nodes reaches 100, the test results of the validation set are significantly improved, and the loss function of the validation set is minimal when the number of nodes is less than 30 iterations. Finally, the hidden layer of the model consists of 100 LSTM units, followed by a dense output layer with 1 neuron, and 0.2 loss, retrospection, and foresight equal to 1. Adam optimizer was used to train LSTM and stop it in advance after 50 iteration steps. Its parameters remained default, with a learning rate of 0.001 and attenuation of 0.9. It can be seen that this model can well predict the virtual machine power consumption data and effectively solve the problem of outlier detection of integrated energy security defense monitoring software.
      PubDate: Mon, 08 Aug 2022 13:50:01 +000
       
  • FSEE: A Forward Secure End-to-End Encrypted Message Transmission System
           for IoT

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      Abstract: Leakage of long-term secrets is a major concern when cryptographic schemes are implemented on devices with weak protection capability, especially for resource-constrained IoT devices. Forward secrecy is a means to minimize the damage when such an event takes place. For pub-/sub-based IoT systems, several end-to-end (from publisher to subscriber) encrypted message transmission schemes have been proposed to tackle the confidentiality problems brought by malicious message brokers. But none of them provide forward secrecy. This article presents FSEE, a forward secure end-to-end encrypted message transmission system for pub-/sub-based IoT. To support FSEE, we design a novel group key exchange protocol BA-GKE, which relies on a semi-trusted key exchange server to provide forward secrecy and support asynchronous communication between group members. We prove its forward secrecy by ProVerif. The core idea of FSEE is to establish a forward secure symmetric key per device using BA-GKE asynchronously, and this device-specific key is shared with the device and its authorized subscribers for encrypting messages securely. By adding a semi-trusted key exchange server to realize BA-GKE in the current IoT architecture, FSEE does not need to change the existing message broker and could be deployed incrementally. The experimental results show that FSEE has comparable performance to existing prominent research and provides higher security.
      PubDate: Mon, 08 Aug 2022 13:50:01 +000
       
  • Patient Family Binding and Authentication Scheme with Privacy Protection
           for E-Health System

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      Abstract: The emergence of the E-health system has brought convenience to many chronically ill patients and elderly people with limited mobility. With the help of the E-health system, patients can upload their physiological data timely and get a diagnosis at home, which is more convenient and efficient as they do not have to line up in hospitals. In order to ensure this convenience while protecting patients’ privacy, many schemes have been proposed which can help patient and medical server authenticate each other. However, considering these patients’ inconvenience, sometimes family members need to participate in the patient’s treatment process. So, the E-health system needs to provide a secure communication platform for the family members. At present, most of the authentication schemes for the E-health system only focus on the secure communication between the patient and the medical server, while ignoring the participation of family members. Moreover, in the E-health system, the permissions of family members and patient should be different, and the medical server needs to distinguish their permissions efficiently. In order to overcome these problems, we propose a patient family binding and authentication privacy protection scheme for the E-health system. In the scheme proposed by us, the medical server can efficiently assign different permissions to the family member and patient. And our scheme can allow patient to authorize their family members freely, and the increase in the number of family members will not impose additional burden on the server. At the same time, the authentication between the family member and the medical server does not require the participation of the patient. In addition, by comparing with other related schemes, we prove that our scheme has suitable efficiency and security performance in the E-health system.
      PubDate: Mon, 08 Aug 2022 13:50:00 +000
       
  • Multimedia Computer-Aided Teaching Platform Based on Particle Swarm
           Optimization Algorithm

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      Abstract: This paper mainly expounds the research hotspots and trends of computer-aided teaching platforms in view of particle swarm optimization algorithm and introduces current situation of computer-aided teaching platforms. This paper analyzes the realization of the core business system of the computer help education platform. And it puts forward the optimization idea of multimedia compute help education platform in view of particle swarm optimization algorithm. The experimental results illustrate Za = 1.42 when students in two classes use the computer-aided teaching platform before optimization. It shows that, at the 0.05 obvious level, there is no important objection in the scores of the two groups of students in the test. When the students in the experimental class used the optimized computer-aided teaching platform, Zb = 3.67, indicating that, at the 0.01 significant level, the performance of the students in the control class was significantly lower than that of the students in the experimental class.
      PubDate: Mon, 08 Aug 2022 13:50:00 +000
       
  • GPBFT: A Practical Byzantine Fault-Tolerant Consensus Algorithm Based on
           Dual Administrator Short Group Signatures

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      Abstract: The practical Byzantine fault-tolerant consensus algorithm reduces the operational complexity of Byzantine protocols from an exponential level to a polynomial level, which makes it possible to apply Byzantine protocols in distributed systems. However, it still has some problems, such as high communication overhead, low security, poor scalability, and difficulty in tracking. In this article, we propose a Byzantine fault-tolerant consensus algorithm based on dual administrator short group signatures (GPBFT). Firstly, the certification authority chooses the master node and group administrators based on the credit value. The group administrators organize the nodes into a group, and the members generate the signatures by applying the short group signatures scheme, in which any group member can represent the group during the GroupSign phase. Additionally, the GPBFT algorithm adds the Trace phase. According to member and client authentication information, the group administrator can track the true identity of the malicious node, identify the malicious node, and revoke it. The experimental results show that compared with the PBFT algorithm, the GPBFT algorithm can reduce the network communication overhead, reduce the consensus delay, and greatly improve the security and stability of the system. The algorithm can effectively manage member nodes and enable the tracking of identified malicious nodes while maintaining anonymity in terms of node tracking.
      PubDate: Fri, 05 Aug 2022 15:35:00 +000
       
  • The Construction of Smart Tourism City and Digital Marketing of Cultural
           Tourism Industry under Network Propaganda Strategy

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      Abstract: With the emergence of new tourism trends such as popularized and individualized tourism, the traditional development model can no longer meet the development requirements of the new era. Therefore, the construction of tourism informatization is imperative. This work aims to enhance the promotion of tourism resources in Zhejiang Province and explore the effective promotion forms and strategies of tourism resources in Zhejiang network media. The construction of smart tourism city (STC) is taken as the research object. First, the evaluation index system and evaluation model of the construction level of STC are constructed. Besides, an empirical evaluation is conducted with the pilot project of smart tourism city construction determined by the National Tourism Administration as a case. Then, the concept of strength, weakness, opportunity, and threat (SWOT) is used to analyze the advantages, disadvantages, opportunities, and threats of Zhoushan Town’s tourism development. Finally, the model proposed here is tested. The results show that the comprehensive level of STC in 18 cities is quite different. The current average level of STC in China is 0.2791. Except for the support level of smart tourism environment that is lower than Suzhou, the rest levels of Beijing are in the first place, and the comprehensive level of STC construction is in the first place. The comprehensive level of STC construction in Suzhou ranks second, with an average level of 0.1521. Nevertheless, there is a big gap between Suzhou with Beijing. The overall evaluation satisfaction of Zhoushan Town’s tourism is in a moderate state. The analysis results of the SWOT intelligent model demonstrate that Zhoushan Town tourism should choose a growth marketing strategy. The research reported here provides a particular reference for realizing the seamless connection between the intelligent cultural tourism industry and consumers.
      PubDate: Fri, 05 Aug 2022 15:05:00 +000
       
  • Research on the Evaluation and Optimization of Innovation Ability of
           Private Enterprises Based on DEA Model

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      Abstract: As private businesses are an integral part of China’s market economy, it is essential to foster their expansion in a positive manner. Even though some private enterprises have made significant strides in their ability to innovate independently, the majority of them have not yet established an effective endogenous mechanism of independent innovation, and their independent innovation capability and level are insufficient to meet the requirements of economic and social development in the new era. Therefore, it is essential to evaluate the innovation capacity of private businesses. Therefore, the first section of this paper examines the research on measuring enterprise productivity. Second, this paper employs a combination of endogenous dynamics to develop an endogenous dynamics mechanism that encourages innovation in private companies. In addition to employing the DEA model, this paper proposes an evaluation optimization mechanism for assessing the innovation capability of private enterprises in a Chinese province from 2015 to 2021. It concludes that the innovation capability of private enterprises in this province is generally low and that factors such as the number of technical employees, the R&D investment funds of private enterprises, and the number of granted patents have a significant impact.
      PubDate: Fri, 05 Aug 2022 14:50:00 +000
       
  • Application of User Experience Gene Extraction Model Based on Industrial
           Design

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      Abstract: Product design DNA is a new concept produced by applying the idea of genetic engineering to the field of industrial design, involving multiple knowledge fields, aiming to give products a unique shape and style image to build a brand. This paper systematically summarizes the current situation and progress of product design DNA research at home and abroad, focusing on the expression structure, application research progress, and research on key technologies. The law of DNA generation and derivation; explore the user’s cognitive mechanism for product design DNA; realize the connection between product design DNA reasoning and production. The designed user experience gene extraction based on industrial design provides new ideas for product design and has strong guiding significance.
      PubDate: Thu, 04 Aug 2022 15:35:01 +000
       
  • Big Data Storage Index Mechanism Based on Spatiotemporal Information Cloud
           Platform

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      Abstract: In order to study the storage and management mechanism of raster data and vector data for different purposes in data services, a research method of big data storage and indexing mechanism based on spatiotemporal information cloud platform is proposed. This paper discusses the application of big data storage index in virtualization platform, cloud management software, and storage management, so that Hadoop cluster can use the dynamic expansion ability of cloud platform to obtain better expansion ability. High performance statistical applications for geographical conditions are constructed. A high performance geostatistical analysis system Hadoop-Geostatistics is designed and implemented. A variety of spatial statistics index calculation, flow, and MapReduce algorithm was realized. The experimental results show that in the cluster environment, the time consumption is basically the same as that of the single index calculation, while in the single computer environment, when the comprehensive index is calculated in parallel for 10,000 statistical objects, the system performance drops rapidly and reaches an early inflection point. In the comprehensive statistical concurrent calculation, when the time consumption reaches 5 × 10^7, the amount of calculation data is as high as 7000, which increases linearly. The experimental data show that the designed spatiotemporal information cloud platform model can store spatial big data, and the storage method is very accurate. By establishing a spatiotemporal information cloud platform, cloud computing technology can provide higher spatial information services.
      PubDate: Thu, 04 Aug 2022 15:35:01 +000
       
  • Reversible Data Hiding Based on Multichannel Difference Value Ordering for
           Color Images

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      Abstract: Considering the correlation existing among different color channels of the image, we propose a novel reversible data hiding (RDH) scheme based on multichannel difference value ordering. By applying the technique of pixel value ordering (PVO), we establish a certain relationship by sorting the differences among adjacent pixels of different color channels. Combined with the optimized pairwise prediction-error expansion by analyzing the distribution of pixel difference, our scheme can adaptively select pixel channel to achieve reversible embedding of information. Through simulation experiments, the feasibility of this RDH scheme in image distortion and computational complexity is verified. When the threshold T is set to 3, the average PSNR of the test images embedded with 5000 bits of additional information in each color channel can reach 63.41, 64.38, and 63.96, respectively. When the embedding capacity is 20000 bits, the PSNR of the color image can reach 58.90 dB. Considering the embedding capacity and image distortion comprehensively from the simulation data, our proposed scheme has a better performance than the previous PVO-RDH schemes which only consider a single channel.
      PubDate: Thu, 04 Aug 2022 15:20:01 +000
       
  • Multitarget Tracking Algorithm in Intelligent Analysis of Football
           Movement Training Stance

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      Abstract: In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.
      PubDate: Thu, 04 Aug 2022 15:20:01 +000
       
  • XAI-Based Reinforcement Learning Approach for Text Summarization of Social
           IoT-Based Content

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      Abstract: The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.
      PubDate: Thu, 04 Aug 2022 15:20:00 +000
       
  • Problems and Countermeasures of China’s International Trade in
           Agricultural Products under the Belt and Road Strategy Based on Big Data
           Analysis Technology in the Internet of Things Era

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      Abstract: In order to study the analysis of China’s trade development under big data technology, through the analysis of the import and export of trade, the expansion of international trade exports of agricultural products, the adjustment of agricultural structure, the enrichment of import and export markets, the security of agricultural products, and other issues through the Internet of things technology and traditional technology, as well as the comparison of the overall effect, comprehensive performance, and coupling data, it can be seen that the agricultural trade under Internet of things technology is more convenient and faster. The application of network technology makes things better connected with people, changes the previous way of life, speeds up the development process of international trade, and realizes the real intelligent era.
      PubDate: Wed, 03 Aug 2022 15:20:00 +000
       
  • Data Mining Technology for Equipment Machinery and Information Network
           Data Resources

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      Abstract: In order to solve the problem of aviation equipment system maintenance, it is very difficult to judge the faulty finished product according to the fault phenomenon, the author proposes a data mining-based prediction model for aviation equipment failure finished products. The model takes historical fault record data as input, clusters a large number of fault descriptions through text clustering to obtain fault phenomenon clusters, and establishes a many-to-many relationship between “fault phenomenon” and “fault finished product.” A probability distribution algorithm for faulty finished products is proposed, and by matching new fault phenomena and fault phenomenon clusters, the probability distribution of faulty finished products is calculated. The experimental results show that after calling the model to complete the clustering of the fault information database, 18966 fault phenomenon clusters are obtained, and each fault phenomenon cluster contains 2.9 fault records on average, the many-to-many relationship between the fault phenomenon and the faulty finished product of the fault information database is successfully constructed. The model can effectively predict the probability distribution of products that may fail according to the fault description, and the prediction accuracy can be improved with the increase of the amount of data to meet the actual security needs.
      PubDate: Wed, 03 Aug 2022 13:05:00 +000
       
  • Evaluation and Application of College English Mixed Flipping Classroom
           Teaching Quality Based on the Fuzzy Judgment Model

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      Abstract: With the development of big data technology, there are more and more evaluation models for college English teaching quality, which can better promote the improvement of college English teaching quality. Based on the fuzzy decision model, this paper describes the algorithm establishment process of the model in detail. Then, the fuzzy evaluation model between teachers and students according to different grades is established. Finally, aiming at the problems in college English teaching, this paper obtained the corresponding countermeasures for the college English teaching platform based on fuzzy judgment model. The results indicate the model improves the quality of college English teaching in different grades and promotes the development of college English. In addition, the model platform can also well predict the measures to improve the quality. In short, this paper provides some theoretical and experimental support for the quality of college English hybrid flipping classroom.
      PubDate: Wed, 03 Aug 2022 12:35:00 +000
       
  • Application of Data Encryption Technology in Computer Network Information
           Security

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      Abstract: In order to solve the partial optimization problems of the RSA algorithm in computer network security, a method of RSA algorithm optimization based on data encryption was proposed. In the research, the application of data encryption in network information security system was mainly investigated, using RSA as the representative algorithm in the public key cryptosystem. The network information security model on the basis of data encryption was built on the public key cryptosystem in the research. Through the introduction of the RSA algorithm and the corresponding optimization scheme, the experiments for comparison were set up. Through the experiments, the feasibility of the optimization scheme was verified. Experimental results show that the efficiency of the RSA algorithm was about 1.0% to 2% higher than that of the traditional algorithm after a reasonable selection of parameters and the use of an optimized algorithm (also known as the combination algorithm), which improved the efficiency of RSA algorithm to a certain extent and achieved the purpose of improving RSA algorithm. It was proved that the method could effectively improve the budget efficiency of the RSA algorithm and solve the optimization problem of the RSA algorithm in computer network security.
      PubDate: Tue, 02 Aug 2022 14:35:00 +000
       
  • An Improved Machine Translation Model and its Application in Japanese
           Multi-Context Translation

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      Abstract: In order to further improve the application of machine translation model in Japanese translation, analytic analysis method is adopted to optimize the original machine translation model. The improved machine translation model is used to analyze and describe Japanese translation. Finally, the optimized machine translation model is used to analyze Japanese multicontext. The relevant indexes and parameters were extracted and verified, and finally the model was verified by relevant experiments. The results show that the vector variation graph with different parameters can be divided into slow decline stage, stable change stage, and fast decline stage according to the increase of iteration number and the influence of corresponding change trend. In addition, it can be seen from the value of PE curve that the influence of parameter pe is the least, while the influence of corresponding re parameter is the greatest. The multicontext index of Japanese has the greatest influence on Japanese fluency and the least influence on Japanese keywords, and the trend of influence is parabolic. The application curve of the optimized machine translation model to Japanese in multiple contexts shows that different parameters have different effects on Japanese, which should be represented by the positive parameter V. Finally, the accuracy of the model is verified by experimental data. The above research can provide support for the application of machine learning in different fields and also provide research ideas for the multicontext translation of Japanese.
      PubDate: Tue, 02 Aug 2022 14:35:00 +000
       
  • X-Ray Small Target Security Inspection Based on TB-YOLOv5

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      Abstract: Security inspection is extremely important for the safety of public places. In this research, we are trying to propose a novel algorithm and investigated theoretically in the X-ray dataset, which can optimize the relative low detection accuracy and the latent omission detection of smaller objects when using You Only Look Once version 5 (YOLOv5). For one side, the transform detection network is selected to be added at the bottom layer of backbone structure to avoid the loss of useful information during sequential calculation. On another side, we attempt to adjust the existing PANet structural elements of the model, including their connections and other related parameters to improve the detection performance. We integrate an efficient BiFPN with the CA mechanism, which can enhance feature extraction, and named it attention-BiFPN. Experimental consequences demonstrate that the detection accuracy of the proposed model, which we name “TB-YOLOv5,” has obvious advantages in check performance compared with the mainstream one-stage object detection models. Meanwhile, compared with YOLOv5, the data results display an improvement of up to 14.9%, and the average precision at 0.5 IOU even reached 23.4% higher in the region of small object detection. Our purpose was to explore the potential of changing a popular detection algorithm such as YOLO to address specific tasks and provide insights on how specialized adjustments can influence the detection of small objects. Our work can supply an effective method of enhancing the performance of X-ray security inspection and show promising potential for deep learning in related fields.
      PubDate: Tue, 02 Aug 2022 14:20:01 +000
       
  • An Improved Image Spam Classification Model Based on Deep Learning
           Techniques

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      Abstract: Image Spam is a type of spam that has embedded text in an image. Classification of Image Spam is done using various machine learning approaches based on a broad set of features extracted from the image. For its remarkable results, the convolutional neural networks (CNN) are widely used in image classification as well as feature extraction tasks. In this research, we analyze image spam using a CNN model based on deep learning techniques. The proposed model is fine-tuned and optimized for both feature extraction as well as for classification tasks. We also compared our proposed model to different “Improved” and “Challenge” image spam datasets, which were developed for increasing the difficulty of the classification task. Our model significantly improves the accuracy of the classification task as compared to other approaches on the same datasets.
      PubDate: Tue, 02 Aug 2022 14:20:01 +000
       
 
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