The talking head generation aims to synthesize a speech video of the source identity from a driving video or audio or text data irrelevant to the source identity. It can not only be applied to games and virtual realit...
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With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens vario...
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Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to a...
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Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.
Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sh...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution.
Relation extraction as an important Natural Language processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to...
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Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fo...
Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fortresses. However, the presence of Byzantine attacks, such as data poisoning and backdoor attack, threatens the robustness of FL schemes. Currently, existing mainstream defense methods are susceptible to multiple adaptive attacks, some of which even violate the privacy principle of FL. Furthermore, these defense schemes become less robust when subjected to targeted poisoning attacks with highly non-IID data distributions. In this work, we propose FedNAT, a novel Byzantine-robust FL framework for whittling away these limitations mentioned above. Specifically, FedNAT first performs a privacy-respecting attention refinement on the activation layer outputs of the local uploads. Then, the server scores the local attentions by calculating their Wasserstein distances and clusters them through the k-median algorithm for global attention aggregation, thus rejecting poisoned local attentions for untargeted attacks. After this process, the global attention is transferred to local attention through the FedNAT loss function, which erases backdoors through the distillation concept. We conduct a comprehensive experimental evaluation to demonstrate that FedNAT significantly outperforms existing robust FL schemes in defending against Byzantine poisoning attacks under both IID and highly non-IID data proportions.
Image deblurring task is an ill-posed one, where exists infinite feasible solutions for blurry image. Modern deep learning approaches usually discard the learning of blur kernels and directly employ end-to-end supervi...
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Maximum likelihood estimation has been widely adopted along with the encoder-decoder framework for video captioning. However, it ignores the structure of sentences and restrains the diversity and distinction of genera...
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ISBN:
(纸本)9781665487900
Maximum likelihood estimation has been widely adopted along with the encoder-decoder framework for video captioning. However, it ignores the structure of sentences and restrains the diversity and distinction of generated captions. To address this issue, we propose a hard contrastive learning (HCL) method for video captioning. Specifically, built on the encoder-decoder framework, we introduce mismatched pairs to learn a reference distribution of video descriptions. The target model on the matched pairs is learned on top the reference model, which improves the distinctiveness of generated captions. In addition, we further boost the distinctiveness of the captions by developing a hard mining technique to select the hardest mismatched pairs within the contrastive learning framework. Finally, the relationships among multiple relevant captions for each video is consider to encourage the diversity of generated captions. The proposed method generates high quality captions which effectively capture the specialties in individual videos. Extensive experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, show that our approach outperforms state-of-the-art methods.
B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the ...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.
Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algor...
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