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学者姓名：黄维
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Abstract ：
Dangerous driving behavior detection is one of the most important researches in Intelligent Transportation System (ITS), which can effectively reduce the probability and number of traffic accidents. Although some recent approaches combined with deep learning techniques have been proposed for detecting dangerous driving behaviors, the protection of user's privacy is neglected. Therefore, we propose a Federated Deep Attention Fusion model (FedDAF) to address the dual security issues in dangerous driving behavior detection, i.e., data security and traffic security. On the Client side, we design the Deep Attention Fusion Network for extracting and learning driving process features as well as fusing the environmental factors of the vehicle in driving. On the Server side, the Singular Spectrum Entropy Aggregation method is designed to aggregate Clients with high relevance and multiple information content, thereby realizing safety information sharing among Clients. Finally, the experimental results on real datasets show that the FedDAF method has the best performance on several evaluation metrics relative to the existing two categories of benchmark methods. © 2024 Elsevier B.V.
Keyword ：
Dangerous driving behavior detection Dangerous driving behavior detection Data fusion Data fusion Deep learning Deep learning Federated learning Federated learning Intelligent transportation system Intelligent transportation system
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| GB/T 7714 | Liu, J. , Yang, N. , Lee, Y. et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection [J]. | Information Fusion , 2024 , 112 . |
| MLA | Liu, J. et al. "FedDAF: Federated deep attention fusion for dangerous driving behavior detection" . | Information Fusion 112 (2024) . |
| APA | Liu, J. , Yang, N. , Lee, Y. , Huang, W. , Du, Y. , Li, T. et al. FedDAF: Federated deep attention fusion for dangerous driving behavior detection . | Information Fusion , 2024 , 112 . |
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Abstract ：
As deep learning has continuously made breakthroughs in computer vision, Image Forgery Localization (IFL) task has also started using deep learning frameworks. Currently, most deep learning-based IFL methods use binary cross entropy as the loss function during model training. However, the number of tampered pixels in image forgery is significantly smaller than the number of real pixels. This disparity makes it easier for the model to classify samples as real pixels during training, leading to a reduced F1 score. Therefore, in this paper, we have proposed a loss function for the IFL task: Forgery Loss. The Forgery Loss assigns weight to the classification loss of tampered pixels and edges, enhances tampered pixel constraints in the model, and amplifies the importance of difficult-to-classify samples. These enhancements facilitate the model to acquire more productive information. Consequently, the F1 score of the model is enhanced. Additionally, we designed an end-to-end, pixel-level detection network DSSE-Net. It comprises of a dual-stream codec network that extracts high-level and low-level features of images, and an edge attention stream. The edge attention stream have a Edge Attention Model which enhances the network's attention to the high frequency edges of the image and, in conjunction with the edge enhancement algorithm in Forgery Loss, improves the model's ability to detect tampered edges. Experiments demonstrate that Forgery Loss can effectively improve the F1 score, while the DSSE-Net accuracy outperforms the current SOTA algorithm.
Keyword ：
Attention mechanisms Attention mechanisms Dual stream network Dual stream network Forgery loss Forgery loss Image forgery localization Image forgery localization
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| GB/T 7714 | Zheng, Aokun , Huang, Tianqiang , Huang, Wei et al. DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (6) : 2323-2335 . |
| MLA | Zheng, Aokun et al. "DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 6 (2023) : 2323-2335 . |
| APA | Zheng, Aokun , Huang, Tianqiang , Huang, Wei , Huang, Liqing , Ye, Feng , Luo, Haifeng . DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (6) , 2323-2335 . |
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Abstract ：
合成了含对苯氧基联接链的羧酸盐Gemini表面活性剂,研究了其胶团化特性.结果表明,该羧酸盐Gemini表面活性剂具有很低的cmc值,给出了cmc-T(温度)以及lncmc-(m+1)(烷烃链长)的回归方程.计算了胶团化的热力学函数变化,证实胶团化过程来自熵驱动,并表现出焓/熵补偿现象,在所考察的系列中,以(m+1)=11的胶团最为稳定.
Keyword ：
合成 合成 对苯氧基联接链 对苯氧基联接链 羧酸盐Gemini表面活性剂 羧酸盐Gemini表面活性剂 胶团化 胶团化
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| GB/T 7714 | 杜恣毅 , 游毅 , 姜蓉 et al. 含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性 [J]. | 高等学校化学学报 , 2003 , 24 (11) : 2056-2059 . |
| MLA | 杜恣毅 et al. "含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性" . | 高等学校化学学报 24 . 11 (2003) : 2056-2059 . |
| APA | 杜恣毅 , 游毅 , 姜蓉 , 黄维 , 郑欧 , 黄长沧 et al. 含对苯氧基联接链的羧酸盐Gemini表面活性剂合成及胶团化特性 . | 高等学校化学学报 , 2003 , 24 (11) , 2056-2059 . |
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