Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as tempo...
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Deepfake techniques have been evolving rapidly in recent days and pose a severe security threat. Detecting such generated fake videos is a challenging task. Existing deep learning based deep fake detection model strug...
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With the rise of digital infrastructure and Internet of Things (IoT), a substantial amount of data is continuously generated that needs to be processed efficiently. While modern artificial intelligence (AI) approaches...
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ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and det...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
Joint detection and decoding (JDD) achieves rates based on information theory but is too complex to implement for many channels with memory or nonlinearities. Successive interference cancellation (SIC) at the receiver...
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With the continuous growth of cloud computing and virtualization technology, network function virtualization (NFV) techniques have been significantly enhanced. NFV has many advantages such as simplified services, prov...
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With the continuous growth of cloud computing and virtualization technology, network function virtualization (NFV) techniques have been significantly enhanced. NFV has many advantages such as simplified services, providing more flexible services, and reducing network capital and operational costs. However, it also poses new challenges that need to be addressed. A challenging problem with NFV is resource management, since the resources required by each virtualized network function (VNF) change with dynamic traffic variations, requiring automatic scaling of VNF resources. Due to the resource consumption importance, it is essential to propose an efficient resource auto-scaling method in the NFV networks. Inadequate or excessive utilization of VNF resources can result in diminished performance of the entire service chain, thereby affecting network performance. Therefore, predicting VNF resource requirements is crucial for meeting traffic demands. VNF behavior in networks is complex and nonlinear, making it challenging to model. By incorporating machine learning methods into resource prediction models, network service performance can be improved by addressing this complexity. As a result, this paper introduces a new auto-scaling architecture and algorithm to tackle the predictive VNF problem. Within the proposed architecture, there is a predictive VNF auto-scaling engine that comprises two modules: a predictive task scheduler and a predictive VNF auto-scaler. Furthermore, a prediction engine with a VNF resource predictor module has been designed. In addition, the proposed algorithm called GPAS is presented in three phases, VNF resource prediction using genetic programming (GP) technique, task scheduling and decision-making, and auto-scaling execution. The GPAS method is simulated in the KSN framework, a network environment based on NFV/SDN. In the evaluation results, the GPAS method shows better performance in SLA violation rate, resource usage, and response time when co
One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagn...
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Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitoni...
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The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thu...
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