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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]
  • Modeling and Optimization Analysis of Ancient Building Construction Rule
           Components Based on Deep Learning

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      Abstract: Chinese culture is broad and profound, and successive dynasties have left many cultural treasures. Ancient architecture is a significant treasure, and it is also the core content of the inheritance of Chinese culture. Every Chinese ancient building has its own characteristics, and the creative components of each ancient building are an important part of ancient buildings. As a new learning mode of current scientific inquiry, the deep learning model includes high-level and high-stage cognitive processing ability and innovative thinking ability. Under the background of the above modeling and optimization analysis of ancient building construction rule components and the development of deep learning mode, this paper proposes the modeling and optimization analysis of ancient building construction rule components about deep studying. The results of the experiment are as follows: (1) about the concept of the deep learning technology model and the vacancy problems existing in the current situation of the design framework and optimization of ancient building construction rule components, the research direction of the experiment is determined, and through the investigation and analysis of the modeling and optimization of ancient building construction rule components based on the deep learning model, the technical guarantee is provided for the research of this paper; (2) the convolution neural network algorithm, inversion model algorithm, loss function algorithm, and optimization algorithm are used to calculate, evaluate, and analyze the research problems, and the investigation contents are identified and analyzed through experimental research. It can not only analyze the root of the research problems but also improve the specific modeling optimization problems of ancient buildings, to reduce the unnecessary loss of time and resources.
      PubDate: Wed, 21 Sep 2022 10:20:00 +000
       
  • An Automated Data Desensitisation System Based on the Middle Platform

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      Abstract: Built on top of a big data platform, the Middle Platform develops data through abstraction, sharing, and reuse capabilities to provide data products and data services for upper-level business development. While fully analysing and mining the intrinsic value of data, privacy and sensitive information in the data must also be protected, so the Middle Platform needs a data desensitisation system to ensure the safe and open use of data. In order to solve the problems of high usage costs, low efficiency, and lack of standardised results of desensitisation that exist in conventional data desensitisation systems, an automated desensitisation system with data assets, access control, and desensitisation strategies as the main modules is established using an adaptive method of generating dynamic desensitisation rules, combined with a security monitoring mechanism of sensitivity classification and two-level permissions. The system optimises the configuration structure to obtain stable and reliable desensitisation results and efficiently respond to diverse business needs. Users are able to get rid of complex rule management and focus on the data usage itself.
      PubDate: Wed, 21 Sep 2022 10:05:00 +000
       
  • A Study on Urban Spatial System Planning of Qingdao City Park Based on
           Intelligent Monitoring Sensors

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      Abstract: Since the release of the 13th Five-Year Plan, the construction plan of the smart city and Internet of Things (IoT) has once again pushed the sensors into the limelight. In the current situation, whether in China or around the world, the construction of smart communities has become an irreversible trend. In this environment, the sensor as a smart city “bridge” will certainly usher in an industrial explosion. “Smart city” is a new concept and model for urban development. Academics have also explored the concept and theoretical model of smart cities from different perspectives in different forms. With the gradual establishment of the national smart city system, the Internet of Things and other technologies are being widely used to serve other aspects of human social life, such as urban smart communities, intelligent transportation, and intelligent home security systems. The application of technology is gradually penetrating into the life of our human society. In the context of building a smart city, scholars have also brought an unprecedented broader idea. By seeking a reasonable balance between human and urban nature, the concept of “park city,” which aims to create a good ecological habitat where people and nature can coexist and develop in harmony, has emerged as China’s urbanization process accelerates and the conflict between people and land becomes more and more prominent. As an important factor to promote the harmonious development of humans and nature, the planning and construction of urban parks is an important means to meet the needs of people for a better life and to improve the ecological environment of cities; therefore, the planning and design of urban parks under the concept of “park city” is getting more and more attention. Qingdao, as a typical coastal city, has a mosaic of green areas and urban construction land, so urban ecological parks with the carrier of nonconstruction land exist in large numbers in the city and become a key link in the practice of the “park city” concept. However, while urban ecological parks provide ecological and social services to citizens, their special land use has also led to a series of problems. At present, there is not enough basis for the planning and management of such special parks in Qingdao, and it is not possible to guide the planning and construction of such parks with universal standards, so how to effectively protect and reasonably utilize them to promote the construction of “park city” is an urgent problem to be solved. Based on the above background, this study takes Qingdao urban ecological park as the research object and summarizes the typical problems in the process of planning, construction, and use of this kind of park through relevant data research and extensive studies. Based on this, we propose three aspects, namely, the location and layout of parks at a macro level, the construction of the park system at meso-level, and the design optimization of parks at a micro level. The specific research content includes the following aspects: the first part defines the background, purpose, and significance of the topic and the research object and summarizes and reviews the existing research results at home and abroad, and then proposes the overall idea and framework of the research. The second part analyzes the problems of Qingdao urban ecological parks with the concept of “park city.” Based on the connotation and characteristics of the “park city” concept, the impact of the concept on urban parks is analyzed, and the significance of the “park city” concept on the planning and construction of urban ecological parks is clarified based on the special characteristics of urban ecological parks. Finally, we provide the basis for the proposed strategy by combining case studies at different levels. Finally, we propose a better spatial planning for the construction of a park city in Qingdao using smart detection sensors and other means of building a smart city.
      PubDate: Wed, 21 Sep 2022 09:20:00 +000
       
  • Modeling and Analysis Method of National Fitness Big Data for Basketball
           Projects Based on a Multivariate Statistical Model

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      Abstract: How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance.
      PubDate: Wed, 21 Sep 2022 09:20:00 +000
       
  • A Novel Cooperative Relaying-Based Vertical Handover Technique for
           Unmanned Aerial Vehicles

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      Abstract: The real-time monitoring and autonomous decision making through unmanned aerial vehicles (UAVs) are the potential applications of future networks. Vertical handover in future networks is a mechanism to switch communication between different network access technologies like Wireless Local Area Network (WLAN), Worldwide Interoperability for Wireless Microwave Access (WiMAX), Third-Generation (3G), Fourth-Generation (4G), and Fifth-Generation (5G) mobile technologies. These technologies have significant importance in providing fast, reliable, and timely communication. However, during a vertical handover, an inadequate delay and packet loss can cause considerable disruption in maintaining communication sessions and results in intolerable end-to-end delay, disconnectivity, and poor packet delivery ratio. The proposed work addresses the vertical handover method in UAVs communication by designing a relay-based vertical handover technique. The relay UAVs is an assistant node, requiring an organized and intelligent deployment that assists in vertical handover and communication by minimizing the average packet loss and average delay from source to destination. Moreover, a multicriteria handover parameter triggering is used for seamless and more extended network coverage. Extensive simulations using S-shaped and U-shaped trajectories are designed and simulated for relay-based vertical handover performance evaluation. The results obtained show that our proposed relay-based handover method offers seamless connectivity and high-performance experienced during the vertical handover process. The extensive comparison with state-of-the-art techniques proves that the proposed method is better in terms of 18% handover success rate, 21% end-to-end delay, and 29% packet loss.
      PubDate: Wed, 21 Sep 2022 09:05:00 +000
       
  • Design and Analysis of Machine Learning Based Technique for Malware
           Identification and Classification of Portable Document Format Files

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      Abstract: Modern day antivirus software, which is available commercially, is incapable of providing the protection from the malicious portable document format (PDF) files and thus considered as a threat to system security. In order to mitigate the same to some extent, a new PDF malware classification system based on machine learning (ML) is introduced in this paper. The novelty of this system is that it will be inspecting the given PDF file both statistically and dynamically, which in turn will increase the accuracy of finding the correct nature of the document. This method is nonsignature-based and hence can possibly distinguish obscure and zero-day malware. The experiment is carried out for this system by deploying five different classifier algorithms to find out the best fit for the system. The best fit approach is analyzed by calculating the true positive rate (TPR), precision, false positive rate (FPR), false negative rate (FNR), and F1-score for each of these classifier algorithms. Comparison of this work is carried out with previously existing PDF classification systems. A malicious attack on to the proposed system is also implemented, which will in turn obfuscate the malicious code inside the PDF file by making it hidden during the parsing phase by the PDF parser. It has been inferred that the proposed approach achieved F1-measure of 0.986 by using the random forest (RF) classifier in comparison to state-of-the-art where F1-measure was 0.978. Thus, our approach is quite effective in the identification of the malwares when embedded in the PDF file in comparison to the existing systems.
      PubDate: Wed, 21 Sep 2022 08:50:01 +000
       
  • Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles

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      Abstract: PassPoint is a graphical authentication technique that is based on the selection of five points in an image. A detected vulnerability lies in the possible existence of a pattern in the points that make up the password. The objective of this work is to detect nonrandom graphical passwords in the PassPoint scenario. A spatial randomness test based on the average of Delaunay triangles’ perimeter is proposed, given the ineffectiveness of the classic tests in this scenario, which only consists of five points. A state-of-the-art of various applications of Voronoi polygons and Delaunay triangulations are presented to detect clustered and regular patterns. The distributions of the averages of the triangles’ perimeters in the PassPoint scenario for various sizes of images are disclosed, which were unknown. The test’s decision criterion was constructed from one of the best distributions to which the data were adjusted. Type I and type II errors were estimated, and it was concluded that the proposed test could detect clustered and regular graphical passwords in PassPoint, therefore being more effective in detecting clustering than regularity.
      PubDate: Wed, 21 Sep 2022 08:35:00 +000
       
  • Analysis of Legal Issues of the Crime of Endangering Public Safety Based
           on Data Mining Algorithm

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      Abstract: In order to improve the analysis effect of legal issues of the crime of endangering public safety, this paper analyzes the legal issues of the crime of endangering public safety based on the actual mining algorithm and uses numerical methods to qualitatively study the evolution behavior across phase intervals. Moreover, this paper improves the algorithm based on the actual needs of legal mining. When solving the eigenvalues of the given Hamiltonian’s duration equation, the zero-energy solution can be found in the case of open boundaries. In addition, this paper applies the improved algorithm proposed in this paper to the mining of criminal law legal issues of crimes against public safety, assigns a dynamic IP to each process, and builds a model based on this. Through the above simulation research, it can be seen that the legal problem analysis model of the crime of endangering public safety based on the data mining algorithm proposed in this paper has a good legal data mining effect.
      PubDate: Wed, 21 Sep 2022 07:20:00 +000
       
  • Data Collection and Analysis of Physical Education Teaching Practice Based
           on Multisensor Perception

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      Abstract: Because our country is constantly updating training standards, the first mechanism for assessing physical fitness is far from current needs. A good assessment process can help to stimulate student learning, identify strengths and weaknesses, and better improve sports knowledge. In this paper, based on the multisensor perception of physical education and teaching practice data collection and analysis, it is found that the original sensor data often have some defects, but the Kalman filter can be processed, which can make the data more accurate. After comparing the data, it can be found that each group of data basically has an error of 0.02–0.9. After processing, the data better reflect the changes in the measurement. With the reform of the professional evolution of boxers, higher requirements have been placed on athletes. The sensor can be continuously tested. According to the experiment, the basic probability of different sensors on the test paper can be found that the fused sensor data are 1/2, while the single sensor data are 1/6, and the data of a single sensor are much lower than the confidence of fused sensors, effectively improving the comprehensive ability of boxing.
      PubDate: Wed, 21 Sep 2022 05:50:00 +000
       
  • Secure Internet of Things Gateway Technology Based on Multicommunication
           Methods

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      Abstract: In order to explore how the Internet of Things implements a secure Internet of Things gateway technology, the author proposes a research on a secure Internet of Things gateway technology based on multicommunication methods. This method recommends key technical problems and solutions based on information represented by multiple communication methods and explores how the Internet of Things can realize the research of Internet of Things gateway technology. Research has shown that the security IoT gateway based on multiple communication methods is about 40% more efficient than the traditional method. By studying some exploratory guidance and suggestions for the development of the Internet of Things, it is found that there are still many problems to be solved before realizing the real Internet of Things environment.
      PubDate: Wed, 21 Sep 2022 05:05:00 +000
       
  • Ethereum Ponzi Scheme Detection Based on PD-SECR

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      Abstract: Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.
      PubDate: Wed, 21 Sep 2022 01:50:00 +000
       
  • Dynamic Task Offloading for NOMA-Enabled Mobile Edge Computing with
           Heterogeneous Networks

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      Abstract: With the rapid development of the Internet of Things (IoT), more and more computation-intensive tasks are generated by IoT devices. Due to their own limitations, IoT devices cannot process all tasks locally, and some tasks need to be offloaded to edge servers for processing. In addition, nonorthogonal multiple access (NOMA) technology allows multiple IoT devices to share the same frequency resource. IoT devices can use NOMA technology to transmit data to increase the data transmission rate. In this article, we study the problem of NOMA-enabled dynamic task offloading in heterogeneous networks. We formulate a stochastic optimization problem to minimize system energy consumption. Using stochastic optimization techniques, we transform this problem into a deterministic optimization problem and decompose it into five sub-problems to solve. At the same time, we propose a NOMA-enabled dynamic task offloading (NDTO) algorithm. Then, we mathematically analyze the performance of the NDTO algorithm. We conduct a series of parameter analysis experiments and comparative experiments, and the results verify the performance of the NDTO algorithm.
      PubDate: Tue, 20 Sep 2022 12:05:00 +000
       
  • Analysis of English Education Quality Evaluation and Internationalization
           Integration Based on Deep Learning

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      Abstract: English education is one of the most active research directions in the field of natural language processing. With the gradual implementation of deep learning in various fields, more and more industries have begun to use deep learning to carry out more efficient work. In the field of education, it is also urgent to adopt a more intelligent set of algorithms to relieve the pressure of teachers to correct test papers, and also to increase the fairness of non-subjective evaluations in the process of scoring. Teachers conduct teaching evaluation when the concept of teaching evaluation is not clear; there are defects in learning evaluation goals; there are many problems in the relationship between ability evaluation and knowledge evaluation; in the process of English teaching evaluation, the phenomenon of using summative evaluation instead of procedural evaluation is very serious. Therefore, this subject uses deep learning to study the problem of text line positioning and recognition. At the same time, this subject also builds a text scoring network based on RNN and STLM as a quantitative evaluation index for text line detection and recognition algorithms. We will examine students later. Whether problem-based learning theory can be used to promote deep learning among students to determine whether students’ systematic use of PBL in teaching can promote the use of deep learning in college English courses. Finally, comparing the effects of deep learning and shallow learning, it is concluded that the evaluation of deep English teaching can provide students with more learning opportunities, access to more learning-related materials, and questions are more transparent and are free, which is easy. It is speculated that the purpose of this problem is to facilitate the use of deep learning methods to find meaning types.
      PubDate: Tue, 20 Sep 2022 11:35:01 +000
       
  • Cultural Product Appearance Design Based on Improved Multiobjective
           Optimization Algorithm

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      Abstract: Cultural creative products are important carriers of cultural heritage and play a very important role in the inheritance, innovation, and dissemination of culture. At present, cultural and creative products emerge in an endless stream with a variety of patterns. No matter in function or form, most of them still follow the traditional design method, and the product style is relatively single, the identification is not strong, and the heat is not enough. Therefore, in the design of cultural and creative products, it is far from meeting the requirements of the flexible and changeable market to only use culture to conform to the consideration of product form. Instead, it should also take into account the personalized and diversified needs of users for products and carry out in-depth practical research on their functionality and imagery. Therefore, this study proposes an improved multiobjective optimization algorithm, combined with the VGG (visual geometry group) model, to study and design the appearance characteristics, color collocation, and design aesthetic feeling of cultural and creative products. This study hopes that through the continuous attempts of cultural and creative product design, people can feel the splendid cultural connotation civilization again and provide new ideas and good for the advanced theme design of traditional Chinese culture.
      PubDate: Tue, 20 Sep 2022 11:20:00 +000
       
  • Bio-Optimization of Deep Learning Network Architectures

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      Abstract: Deep learning is reaching new heights as a result of its cutting-edge performance in a variety of fields, including computer vision, natural language processing, time series analysis, and healthcare. Deep learning is implemented using batch and stochastic gradient descent methods, as well as a few optimizers; however, this led to subpar model performance. However, there is now a lot of effort being done to improve deep learning’s performance using gradient optimization methods. The suggested work analyses convolutional neural networks (CNN) and deep neural networks (DNN) using several cutting-edge optimizers to enhance the performance of architectures. This work uses specific optimizers (SGD, RMSprop, Adam, Adadelta, etc.) to enhance the performance of designs using different types of datasets for result matching. A thorough report on the optimizers’ performance across a variety of architectures and datasets finishes the study effort. This research will be helpful to researchers in developing their framework and appropriate architecture optimizers. The proposed work involves eight new optimizers using four CNN and DNN architectures. The experimental results exploit breakthrough results for improving the efficiency of CNN and DNN architectures using various datasets.
      PubDate: Tue, 20 Sep 2022 11:20:00 +000
       
  • Influence Analysis of Hotel and Tourism Economic Development Based on
           Computational Intelligence

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      Abstract: As the main infrastructure of the tourism industry, the hotel industry is an important part of the tourism industry and one of the important symbols of the development level of the tourism industry. In recent years, with the prosperity and development of the hotel industry, it can not only effectively solve many social work problems but also contribute to the further development of the region’s economy. In this paper, the convergence model and fitness function of the GSO-MCM algorithm are evaluated, and the optimal adaptive threshold of the particle swarm is given. The impact of computational intelligence on the development of hotel and tourism economy is analyzed, and the accuracy of computational intelligence method, data mining method, and fuzzy statistical method are evaluated and compared. The computational intelligence method shows better performance. The domestic tourism revenue is predicted using computational intelligence and compared with the actual value, which shows a good prediction effect. The hotel industry, an important tourism infrastructure, is an important symbol of the level of development of the tourism industry. In recent years, the rise of the hotel industry has not only effectively addressed many of the company’s labor problems but also made great contributions to the region's economic growth. With this in mind, we are conducting pilot studies on data collection.
      PubDate: Tue, 20 Sep 2022 02:35:00 +000
       
  • A New Certificateless Signcryption Scheme for Securing Internet of
           Vehicles in the 5G Era

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      Abstract: The application of digital signature technology to the Internet of vehicles (IoV) is affected by its network and communication environment. In the 5G era, the influx of a large number of intelligent devices into the mobile Internet requires a low transmission delay and power consumption as well as high-security requirements. To the best of our knowledge, a well-designed solution in which signcryption technology is used has not been proposed in the IoV research area. Motivated by the fact, a certificateless signcryption scheme based on the elliptic curve digital signature algorithm, in which pseudonym and timestamp mechanism are also considered, has been designed in this paper. We prove that the scheme proposed by us can be reduced to solving the difficulty of the computational Diffie–Hellman problem with a standard model, showing that the scheme meets requirements on both security and efficiency, which provides a comparative analysis with the state-of-the-art schemes in terms of security analysis, computational cost, and communication cost, demonstrating that the scheme proposed by us is suitable to be deployed in the IoV environment, which is of the characteristics of high-speed vehicle movement.
      PubDate: Mon, 19 Sep 2022 11:05:00 +000
       
  • Research on the Effective Fusion of Traditional Art and Old Street Culture
           Construction Based on Fuzzy Algorithm

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      Abstract: With the rapid development of modern science and technology, people gradually lose the importance of traditional art and old street culture, and it plays a vital role in improving the knowledge and cultural level of the people. This paper aims to effectively integrate traditional art and old street culture construction by studying fuzzy algorithms. In the era of rapid technological development, traditional culture and art must keep pace with the times and combine traditional art and old street culture through more scientific algorithms through fuzzy algorithm to solve the effective integration of traditional art and old street culture in today's society. Based on the fuzzy algorithm and visualization technology, this paper checks the meaning of traditional art and collective research by analyzing the fuzzy algorithm, uses experiments to verify the theory, refines cultural symbols, and proposes activation methods to integrate traditional art and old street culture as a whole. Through the defuzzification algorithm and the hierarchical evaluation fuzzy algorithm, people's subjective evaluation of the integration of traditional art and old street culture is calculated, and the algorithm is optimized from the above aspects, which substantially contributes to the effective integration of traditional art and old street culture. This case study can also provide new ideas for the protection of historical and cultural blocks in many cities and the integration of traditional art, remedy the dying traditional art and old street culture, and bring a gluttonous feast to the construction of traditional art and old street culture.
      PubDate: Mon, 19 Sep 2022 08:20:00 +000
       
  • Material Analysis and Application Based on Intelligent Computing in the
           Context of Contemporary Watercolor Painting

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      Abstract: Scholars have researched the current situation and development trend of contemporary watercolor materials in terms of material intervention, material expression language, spiritual connotation in material “materiality,” and practical application of materials, which are of profound significance to the consolidation of the theoretical system of watercolor painting in China. However, how to better analyze and apply watercolor materials on the basis of intelligent computing, as well as the material analysis and application based on intelligent computing in the context of contemporary watercolor painting, still has a lack of research at present. Therefore, this study considers the analysis and application of materials based on intelligent computing in the context of contemporary watercolor painting, in an attempt to seek ways to integrate with Chinese watercolor painting and its own national art.
      PubDate: Sat, 17 Sep 2022 11:35:00 +000
       
  • A Trusted and Privacy-Preserved Dispersed Computing Scheme for the
           Internet of Mobile Things

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      Abstract: Existing computing paradigms of the Internet of Mobile Things (IoMT) and social networks cannot effectively serve users due to their limited computing ability, dynamic mobile networks, weak connectivity, and high energy consumption and investment costs. Dispersed computing (DCOMP) is a promising way to solve the above issues. However, DCOMP is emergent, and few studies apply DCOMP to IoMT and social networks. Moreover, security problems also arise when introducing DCOMP to IoMT and social networks. In this paper, we propose a novel reference architecture to realize DCOMP in IoMT or social networks. We also propose two models—a trusted application discovery and acquisition model and a security domain-based computing offloading model—to enhance the security of the proposed architecture. The key idea of the first model is to use blockchain to construct a trusted application storage and acquisition system. This system guarantees that the tasks offloaded to dispersed devices are trusted. In the second model, we design two algorithms to offload tasks to appropriate dispersed devices to protect users’ privacy as much as possible. The experimental results prove the effectiveness of the proposed scheme.
      PubDate: Fri, 16 Sep 2022 12:20:00 +000
       
  • SaaS Service Combinatorial Trustworthiness Measurement Method Based on
           Markov Theory and Cosine Similarity

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      Abstract: With the rapid progress of information technology, cloud computing and cloud services are widely accepted and applied to all aspects of social life. In the cloud computing environment, SaaS (Software-as-a-Service) services have become the main form of software services. For SaaS services, evolutionary and iterative development methods have become the main methods of software system construction. For systems with high trustworthiness, the independent trustworthiness of each SaaS service has a great impact on the overall status. However, SaaS services with high independent trustworthiness do not always build highly trusted software systems. The combinatorial trustworthiness between SaaS services is as important as the independent trustworthiness of each SaaS service. This paper takes combinatorial trustworthiness between SaaS services as the research object. Combinatorial trustworthiness measurement method based on Markov and cosine similarity theory is proposed. The feasibility and effectiveness of the proposed method are verified through simulation experiments. Applicable scenarios, advantages, and disadvantages of the proposed method are shown through the comparison of different measurement methods. The proposed method provides theoretical and technical support for users to select SaaS services suitable for their application scenarios, build cloud service systems, and monitor the operation status of cloud service systems.
      PubDate: Fri, 16 Sep 2022 12:20:00 +000
       
  • Efficient Personalized Recommendation Based on Federated Learning with
           Similarity Ciphertext Calculation

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      Abstract: With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.
      PubDate: Fri, 16 Sep 2022 10:20:01 +000
       
  • Related-Key Differential Attacks on Reduced-Round LBlock

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      Abstract: LBlock, as one of the typical lightweight encryption schemes, is a 32-round block cipher with 64 bit block and 80 bit master key. It can be widely applied in the IoT environment because of its friendly software and hardware implementations. Since it came out, it has encountered many attacks. In this paper, we evaluate LBlock’s ability against related-key differential attack more accurately based on SMT method. On the one hand, we propose tighter lower bounds on the minimal number of active S-boxes for up to 19 rounds of LBlock, which are 8 more rounds than previous ones. Then, we propose the upper bounds of total probabilities for up to 19 rounds of LBlock for the first time. On the other hand, with a suitable 17-round related-key differential distinguisher, we propose attacks on 22- and 23-round LBlock. Each of these attacks has lower time complexity and data complexity than previous ones for the same rounds of LBlock.
      PubDate: Fri, 16 Sep 2022 10:20:01 +000
       
  • Prediction of Higher Education Cost and Analysis of Sharing Ability Based
           on Artificial Neural Network

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      Abstract: In order to explore how to realize the cost prediction and sharing ability of higher education, this paper proposes an analysis of the cost prediction and sharing ability of higher education based on artificial neural network. This method explores how to realize the cost prediction of higher education through the key technical problems and solutions of information recommendation based on artificial neural network. The research shows that the ability of college education cost prediction and sharing based on artificial neural network is 61% higher than that of traditional cost prediction and sharing. With the help of BP neural network algorithm, the accuracy of cost prediction can be maintained above 90%. It is proved that the artificial neural network algorithm can effectively improve the cost forecasting, financial system, and cost management system of colleges and universities.
      PubDate: Fri, 16 Sep 2022 10:05:00 +000
       
  • A Hybrid Model for Commercial Brand Marketing Prediction Based on Multiple
           Features with Image Processing

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      Abstract: Recently, deep learning has been employed in automatic feature extraction and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing, and artificial intelligence. Compared with the traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. With the development of the Internet and e-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability, and easy-to-obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Based on deep learning technology, this paper studies the prediction model of click-through rate (CTR) for advertising and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multilayer network structure of the deep network and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models and predict whether an advertisement is clicked or not by given advertisement information and user information. By establishing a reasonable advertising click-through rate prediction model, it can help the platform estimate future revenue so as to make cooperative decisions with advertisers. For advertisers, it is necessary to evaluate the price by predicting the click-through rate and estimate the bidding price of their own advertisements.
      PubDate: Thu, 15 Sep 2022 09:05:00 +000
       
  • Adaptive Control Strategy of Multiemergency Power Supply Network System
           Connected to New Energy

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      Abstract: In order to improve the adaptive control effect of the multiemergency power supply networking system connected to new energy (NE), this paper studies the adaptive control strategy (ACS) of the multiemergency power supply networking system connected to NE by combining intelligent algorithms and determines the appropriate installation node of the distributed power supply according to the voltage stability index. Moreover, this paper proposes a method to optimize the distribution network feeder reorganization and distributed power configuration problems based on the fireworks algorithm to reduce the network loss and improve the voltage distribution. In addition, distribution network reorganization and distributed power configuration optimization can more effectively minimize network losses and improve voltage distribution. Through the experimental research, it can be seen that the ACS of the multiemergency power supply network system connected to NE proposed in this paper has the established effect.
      PubDate: Thu, 15 Sep 2022 06:05:00 +000
       
  • Blockchain-Based Electronic Medical Records System with Smart Contract and
           Consensus Algorithm in Cloud Environment

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      Abstract: The blockchain is a peer-to-peer distributed ledger technology that works on the precept of “write-once-read-only.” In a blockchain, pieces of information are arranged in the form of blocks, and these blocks are linked together using the hash value of previous blocks. The blocks in a blockchain mechanism are appended only, which means that once information is stored in a block and it cannot be changed; no one tampers the block’s content. The traditional electronic medical records (EMRs) based system stores the patients’ information in a local database or server, which provides centralization of information, and traditional EMRs are more centric on the health providers. So, security and sharing of patients’ information are difficult tasks in the traditional EMR system. The blockchain mechanism has the potential to resolve these existing problems. Due to the appended-only-ledger principle and decentralization of blocks between the network participants, blockchain technology is suited to the EMR system. In this article, first, we discuss all the existing EMR systems and discuss their drawbacks. Keeping all the drawbacks in our mind, we propose a blockchain-based medical record system that utilizes clouding technology for storage purposes. Furthermore, we have designed a smart contract and consensus algorithm for our proposed EMR. Our system only uses a permissioned blockchain model so that only verified and authenticated users can generate their data and participate in the data-sharing system.
      PubDate: Thu, 15 Sep 2022 01:50:00 +000
       
  • A SYN Flood Attack Detection Method Based on Hierarchical Multihead
           Self-Attention Mechanism

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      Abstract: Existing SYN flood attack detection methods have obvious problems such as poor feature selectivity, weak generalization ability, easy overfitting, and low accuracy during training. In the paper, we present a SYN flood attack detection method based on the Hierarchical Multihad Self-Attention (HMHSA) mechanism. First, we use one-hot encoding and normalization to preprocess traffic data. Then the preprocessed traffic data is transmitted to the Feature-based Multihead Self-Attention (FBMHA) layer for feature selection. Finally, we use data slices to determine the features of the preprocessed traffic data under time series by passing the preprocessed traffic data into the Slice-based Multihead Self-Attention (SBMHA) layer. We tested the proposed method on different datasets. The experimental results show that compared with other works, our method presents better in feature selection and higher detection accuracy (even up to 99.97%).
      PubDate: Wed, 14 Sep 2022 12:20:01 +000
       
  • Support Personalized Weighted Local Differential Privacy Skyline Query

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      Abstract: The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.
      PubDate: Wed, 14 Sep 2022 12:20:00 +000
       
  • An In-Place Simplification on Mixed Boolean-Arithmetic Expressions

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      Abstract: Mixed Boolean-arithmetic (MBA) expression, which involves both bitwise operations (e.g., NOT, AND, and OR) and arithmetic operations (e.g., ,, and ), is a software obfuscation scheme. On the other side, multiple methods have been proposed to simplify MBA expressions. Among them, table-based solutions are the most powerful simplification research. However, a fundamental limitation of the table-based solutions is that the space complexity of the transformation table drastically explodes with the number of variables in the MBA expression. In this study, we propose a novel method to simplify MBA expressions without any precomputed requirements. First, a bitwise expression can be transformed into a unified form, and we provide a mathematical proof to guarantee the correctness of this transformation. Then, the arithmetic reduction is smoothly performed to further simplify the expression and produce a concise result. We implement the proposed scheme as an open-source tool, named MBA-Flatten, and evaluate it on two comprehensive benchmarks. The evaluation results show that MBA-Flatten is a general and effective MBA simplification method. Furthermore, MBA-Flatten can assist malware analysis and boost SMT solvers’ performance on solving MBA equations.
      PubDate: Wed, 14 Sep 2022 12:20:00 +000
       
 
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