The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function i...
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Object detection algorithms must first identify all the objects inside an image before machine vision can properly categorize and localize them. Many methods have been proposed to handle this problem, with most of the...
Object detection algorithms must first identify all the objects inside an image before machine vision can properly categorize and localize them. Many methods have been proposed to handle this problem, with most of the motivation coming from computervision and deep learning methods. However, prevailing technologies have never effectively recognized tiny, dense things and often failed to detect objects that have undergone random geometric alterations. We analyze the current state of the art in object identification and propose a deformable convolutional network with adjustable depths to address these concerns. The results of our research suggest that they are better than the current best practices, blend deep convolutional networks with flexible convolutional structures to account for geometric variations, and get multi-scaled features. Next, we perform the remaining phases of object identification and region regress by up-sampling the fusion of multi-scaled elements. Experimental validation of our proposed framework demonstrates a considerable improvement in accuracy relative to time spent recognizing small target objects with geometric distortion.
Aiming to address the data security issues in data sharing, current approaches such as RBAC, ABAC, and blockchain-based data sharing platforms suffer from problems like role redundancy, role abuse, complex authorizati...
Aiming to address the data security issues in data sharing, current approaches such as RBAC, ABAC, and blockchain-based data sharing platforms suffer from problems like role redundancy, role abuse, complex authorization, and data user privacy leakage. In response, a role-based trusted transfer access control model based on trust and attributes is proposed. This model is built upon Attribute-Based Access control (ABAC) while integrating Role-Based Access control (RBAC). Users obtain roles based on their attributes and trust levels, and roles determine access permissions based on trust level and defined policies, and then determine the transferability of roles, at the same time, the transfer of roles is limited by trust levels. Encryption techniques and off-chain storage systems are utilized to protect data privacy and integrity, thereby alleviating the storage pressure on the blockchain. Finally, the performance and effectiveness of the proposed model were evaluated through experimental analysis, demonstrating its capability to achieve trustworthy role transfer and enhance the security and privacy of on-chain sharing schemes.
In the domain of software engineering, software fault prediction is a prominent research area. To enhance the quality of software, software fault prediction is introduced. The primary intention of software fault predi...
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An enterprise honeypot is developed to protect virtual machines (VMs) in Cloud Infrastructure (ICI). A Honeyed honeypot with Snort is integrated to identify hidden security flaws and to prevent internal intrusions or ...
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As an important part of the information age, network security has been concerned. Industrial control system is an indispensable part of modern production process, but it is also facing the threat from cyber-attacks. I...
As an important part of the information age, network security has been concerned. Industrial control system is an indispensable part of modern production process, but it is also facing the threat from cyber-attacks. Intrusion detection system is an indispensable part of the industrial control system security defense system. It detects the attack data by monitoring the real-time status of network traffic. This paper offers an intrusion detection approach based on parallel CNN-LSTM with self-attention mechanism in accordance with the features of industrial control system network traffic, using the UNSW-NB15 dataset as the research object. After experimental analysis, the accuracy, recall and F1 value are 98.66%, 95.88% and 95.91% and The FPR and FAR are 2.28% and 2.23%. Compared with other intrusion detection models, the proposed method has better performance.
Visual tracking is a challenge in computervision. Visual tracking has various practical application scenarios, and is used in intelligent video surveillance system, analysis and research of driver's abnormal beha...
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The automated wrinkle fingerprint recognition techniques rely on some principles from the domain of pattern recognition. There are two prevalent techniques for wrinkle fingerprint recognition. The first method is base...
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In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution charac...
In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution characteristics in the clustering process. Finally, this paper proposes a new security access control scheme. The system uses local abnormal factors to judge non-safe data and eliminates abnormal data according to the judgment results. The system sorts and outputs the factors that may have abnormal data points in the current time window. The Java object-oriented programming language is used. The network attack will be divided into fuzzy measures of DoS attack and non-DoS attack, and finally, the results of various attacks will be displayed. Simulation results show that this paper's entire access control scheme is highly feasible. The proposed algorithm has a high recognition rate.
Due to the integration of technology in the form of Artificial Intelligence and Internet of Things into the cold chain, the fourth industrial revolution, often known as “Industry 4.0,” evolves as a new technological...
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ISBN:
(数字)9798350396157
ISBN:
(纸本)9798350396164
Due to the integration of technology in the form of Artificial Intelligence and Internet of Things into the cold chain, the fourth industrial revolution, often known as “Industry 4.0,” evolves as a new technological paradigm. According to the Industry 4.0 vision, the smart cold chain emphasizes worldwide networks of autonomously communicating information along with efficient control systems. Autonomous operation of the smart cold network is made possible by using the cyber-physical system. Since the cooperation between suppliers, manufacturers, producers, and processors, as well as customers, is crucial to improve transparency of all processes from the point of origin to the point of consumption, it is imperative to look at the influence of Industry 4.0 on the entire cold chain. This research study analyzes the application of technology in cold chain and suggests the use of various technologies in the form of solutions to resolve various issues existing in the cold chain.
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