The advancement of informationtechnology has made cloud computingtechnology an innovative model for offering its consumers services on a rental basis at any time or location. Numerous firms converted to cloud techno...
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In this paper, the data-driven predictive maintenance technology is applied to the motion recognition of assembly line workers. Specifically, the motion data (acceleration, angular velocity and angle) of workers compl...
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Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale ...
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A Web Vulnerabilities Scanner scans the website’s web pages for comprehensive vulnerabilities. The recently constructed web application is a dynamic site that necessitates frequent changes for both clients and the de...
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Speaker Recognition Systems (SRSs) are becoming increasingly popular in various aspects of life due to advances in technology. However, these systems are vulnerable to cyber threats, particularly adversarial attacks. ...
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ISBN:
(纸本)9798350381993;9798350382006
Speaker Recognition Systems (SRSs) are becoming increasingly popular in various aspects of life due to advances in technology. However, these systems are vulnerable to cyber threats, particularly adversarial attacks. Traditional adversarial attack methods, such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), are designed for a white-box setting where attackers have complete knowledge of the inner workings of the target systems. This limits the practicality of these attacks. To overcome this limitation, we propose a new attack model that uses a neural network to generate adversarial examples directly, without the need for full knowledge of the recognition model in a target SRS. In addition, we have designed a novel loss function to balance the effectiveness and confidentiality of adversarial examples. Our new approach was evaluated against SincNet, a state-ofthe-art SRS. Experimental results show that our approach achieves outstanding performance, with the best attack success rate of 99.83% and the best Signal-to-Noise Ratio (SNR) value of 41.30.
Conventional elastic allocation methods of cloud computing resources mainly use OFDMA (Orthogonal Frequency Division Multi-plexing Access) multi location access technology to share resource allocation information, whi...
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ISBN:
(纸本)9798350328356;9798350328349
Conventional elastic allocation methods of cloud computing resources mainly use OFDMA (Orthogonal Frequency Division Multi-plexing Access) multi location access technology to share resource allocation information, which is vulnerable to the impact of the allocation adaptive rate, resulting in a low normalization rate of link resource allocation. Therefore, a new elastic allocation method of cloud computing resources needs to be designed based on real-time operation and maintenance. That is, based on real-time operation and maintenance, a cloud computing elastic resource allocation strategy is generated, and an efficient allocation and scheduling algorithm for cloud computing elastic resources is designed, thus realizing cloud computing elastic resource allocation. The experimental results show that the designed cloud computing elastic resource allocation method based on real-time operation and maintenance has good allocation effect, high link allocation normalization rate, effectiveness and certain application value, and has made certain contributions to improving the comprehensive utilization of cloud computing resources.
Support Vector Machine (SVM) has received much attention in machine learning due to its profound theoretical research and practical application results. Support Vector Regression (SVR) has become a powerful tool for s...
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E-commerce transactions are not a new concept anymore. E-commerce is a popular method of shopping, and many businesses utilize it to market and sell their goods. As a result, clients perceive an overabundance of infor...
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Data visualization is the use of graphical or visual representations to convey complex information, such as financial data. By incorporating data visualization techniques into mathematical finance courses, educators c...
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This paper investigates the performance of an Internet of Things (IoT) system utilizing a unmanned aerial vehicle (UAV) in an urban environment with the assistance of non-orthogonal multiple access (NOMA) based mobile...
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ISBN:
(纸本)9798400716713
This paper investigates the performance of an Internet of Things (IoT) system utilizing a unmanned aerial vehicle (UAV) in an urban environment with the assistance of non-orthogonal multiple access (NOMA) based mobile-edge computing (MEC). We consider two clusters of IoT device (ID) with limited resources that can harvest energy from a power beacon (PB) in order to offload their tasks to a UAV. We propose a four-phase protocol for energy harvesting (EH) and offloading task of IDs. To evaluate offloading performance, we derive the expression of successful computation probability (SCP) for the whole system. In addition, we present a formulation of the optimization problem that optimizes the SCP by optimizing the EH time and height of the UAV. The issue was resolved using particle swarm optimization (PSO). On the basis of the Monte Carlo simulation, a number of system parameters, including the number of IDs in each cluster, the EH time, and the height
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