In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air compu...
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Malaria is one of the important public health issues of global concern. It is a kind of infectious disease caused by Plasmodium which can endanger human life and health. The examination of Plasmodium blood smear is th...
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The Earth surface is constantly moving and changing under the influence of internal and external dynamic geological processes and human *** a time scale,such changes may occur within a few seconds,such as earthquakes ...
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The Earth surface is constantly moving and changing under the influence of internal and external dynamic geological processes and human *** a time scale,such changes may occur within a few seconds,such as earthquakes and landslides,or last for several years or even longer,such as fault creep and frozen soil *** a spatial scale,such changes may manifest as local surface collapses or regional surface subsidence.
Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, cur...
In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This...
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Adversarial attacks have been viewed as the primary threat to the security of neural networks. Hence, extensive adversarial defense techniques have been proposed to protect the neural networks from adversarial attacks...
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ISBN:
(数字)9781450379991
ISBN:
(纸本)9781728180571
Adversarial attacks have been viewed as the primary threat to the security of neural networks. Hence, extensive adversarial defense techniques have been proposed to protect the neural networks from adversarial attacks, allowing for the application of neural networks to the security-sensitive tasks. Recently, the emerging devices, e.g., Resistive RAM (RRAM), attracted extensive attention for establishing the hardware platform for neural networks to tackle the inadequate computing capability of the traditional computing platform. Though the emerging devices exhibit the instinct instability issues due to the advanced manufacture technology, including hardware variations and defects, the error-resilience capability of neural networks enables the wide deployment of neural networks on the emerging devices. In this work, we find that the natural instability in emerging devices impairs the security of neural networks. Specifically, we design an enhanced adversarial attack, Variation-oriented ADvERsarial (VADER) attack which leverages the inherent hardware variations in RRAM chips to penetrate the protection of adversarial defenses and mislead the prediction of neural networks. We evaluated the effectiveness of VADER across various protected neural network models and the result shows that VADER achieves higher success attack rate over other adversarial attacks.
High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they ...
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Exploring the potential application of quantum computers in material design and drug discovery has attracted a lot of interest in the age of quantum computing. However, the quantum resource requirement for solving pra...
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The network load generators are widely used by network researchers to analyze link bandwidth, evaluate network performance and test device capabilities. Data center and IoT networks are quickly evolving and we desire ...
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Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output ...
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ISBN:
(数字)9798331527471
ISBN:
(纸本)9798331527488
Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.
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