The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks poses a significant challenge to their deployment in safety-critical applications. While extensive research has addressed various attack scenario...
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Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of t...
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Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.
Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditi...
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With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ...
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作者：
Liu, HaozheWu, HaoqianXie, WeichengLiu, FengShen, Linlin1Computer Vision Institute
College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most...
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To address environmental issues related to heating, the Chinese government launched the "Clean Heating Plan in the Northern Region" as a national energy transition strategy, renovating heating systems for ov...
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Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the afford...
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Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.
Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the dis...
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Background and Objective: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheles...
Background and Objective: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plagued VB image registration. Firstly, the Convolutional Neural Networks (CNNs) in VB excel with grid-based image features make it challenging to extract features from non-uniformly located points located at tissue boundaries. Secondly, the underutilization of VB-provided uncertainty and the misfocus of the regions of interest (ROIs) lead to misleading generative likelihoods. Lastly, existing VB prior distributions struggle to balance the posterior-prior gap and reconstruction accuracy. Methods: To address these concerns, we extend the VB image registration model by incorporating non-uniformly spaced control points that specifically target displacements at object boundaries. We develop a network capable of concurrently extracting image and spatial features from non-uniformly spaced control points. The uncertainty of the Displacement Vector Field (DVF) is integrated to prioritize matching ROIs in two images and enhance generative likelihood. Additionally, Our VB model employs a factorized prior to regularize the posterior distribution. Theoretical analysis shows that the factorized prior reduces the KL divergence between the posterior and the prior. Results: Results on four public datasets demonstrate that our network outperforms state-of-the-art registration networks while providing valuable uncertainty information on registration outcomes. Conclusions: Experiments confirm that our VB image registration utilizing non-uniformly spaced control points effectively extracts features from boundary, and the uncertainty-based generative likelihood guides the DVF to match ROIs accurately across images. The factorized prior significantly improves reconstruction accuracy compared to existing priors.
Search result diversification ranking aims to generate rankings that comprehensively cover multiple subtopics, but existing methods often struggle to balance ranking diversity with relevance and face challenges in mod...
Search result diversification ranking aims to generate rankings that comprehensively cover multiple subtopics, but existing methods often struggle to balance ranking diversity with relevance and face challenges in modeling document interactions and dealing with limited high-quality training data. While GAN have proven highly successful in fields like computer vision, their application to search result diversification has been limited due to the discrete nature of ranking items and the complex interactions among documents. To address these challenges, we propose GSRDR-GAN, a novel approach that integrates multi-head self-attention with GAN. Our method consists of four key components designed to address the limitations of traditional approaches: the Selected Document State Retriever, the Subtopic Encoder with Multi-head Self-Attention, the Subtopic Decoder with Multi-head Self-Attention, and the Relevance Predictor. First, a self-attention-based feature extraction module is employed to enhance document representations, enabling the model to capture both global and local context effectively. Second, a GAN framework is introduced to improve generalization by generating diverse rankings, mitigating limited high-quality training data. Third, a carefully designed reward function optimizes the trade-off between ranking diversity and relevance, allowing the model to adaptively prioritize these competing objectives during training. Notably, the method improves the generator’s stability and the diversity of search results by reducing training variance, even without pre-trained models. Extensive experiments on the TREC Web Track dataset demonstrate that the proposed GSRDR-GAN method significantly enhances result diversity, achieving relative improvements of 1.7% in α -nDCG, 3.0% in ERR-IA, 3.3% in NRBP, and 0.9% in S-rec over strong baseline methods. Ablation studies and comparative analyses of different reward computation methods further validate the effectiveness of the propo
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