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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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21st IEEE International Symposium on Parallel and Distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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FreqBlender: enhancing DeepFake detection by blending frequency knowledge 24

FreqBlender: enhancing DeepFake detection by blending freque...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Hanzhe Li Jiaran Zhou Yuezun Li Baoyuan Wu Bin Li Junyu Dong School of Computer Science and Technology Ocean University of China School of Data Science The Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9798331314385

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces FreqBlender, a new method that can generate pseudo-fake faces by blending frequency knowledge. Concretely, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods.

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Text information Mining in Cyberspace: An information Extraction Method Based on T5 and keyBERT 9

Text Information Mining in Cyberspace: An Information Extrac...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Kuan Li, Yumei Qi, Yuanhua Qi, Ning Zhai, Mengran Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Institute of Economy and Informatization Development Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (纸本)9798350391367

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value. © 2024 IEEE.

关键词： Spatio-temporal data

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Positive-Guided Knowledge Distillation for Document-Level Relation Extraction with Noisy Labeled Data 12th

Positive-Guided Knowledge Distillation for Document-Level R...

引用

12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Zeng, Daojian Zhu, Jianling Jiang, Lincheng Dai, Jianhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Institute of AI and Targeted International Communication Hunan Normal University Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China

ISBN: (纸本)9783031446924

Since one entity may have multiple mentions and relations between entities may stretch across multiple sentences in a document, the annotation of document-level relation extraction datasets becomes a challenging task. Many studies have identified that these datasets contain a large number of noisy labels, hindering performance improvement for the document-level relation extraction task. The previous and most straightforward method is denoising noisy labeled data from a data annotation perspective. However, this time-consuming approach is not suitable for large-scale datasets. In this paper, we propose a novel Positive-Guided Knowledge Distillation (PGKD) model to address the noisy labeled data problem for document-level relation extraction. We design a new teacher-student architecture. The teacher model trained with only positive samples can partially supervise the student model. The positive-guided knowledge distillation algorithm transfers the clean positive-class patterns from the teacher model to the student model. In this way, the student model trained with all samples can efficiently prevent the interference of false negative samples. Extensive experiments on Mix-DocRED demonstrate that PGKD achieves state-of-the-art effectiveness for document-level relation extraction with noisy labeled data. Moreover, PGKD also surpasses other baselines even on the well-annotated Re-DocRED. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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RMSLRS: Real-Time Multi-terminal Sign Language Recognition System 13th

RMSLRS: Real-Time Multi-terminal Sign Language Recognition S...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on information and Communication Technologies, WICT 2022

作者： Zhao, Yilin Zhang, Biao Ma, Kun Business School of Jinan University University of Jinan Jinan250022 China School of Computer and Information Hefei University of Technology Hefei230009 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9783031274985

Sign language is commonly used by deaf or speech-impaired people to convey meaning. Sign language recognition (SLR) aims to help users learn and use sign language by recognizing the signs from given videos. Although sign language has gained social acceptance, few sign language recognition systems have been developed with educational purposes. Current SLR system has a single form of expression and lacks real-time interaction and feedback with an expensive cost. And videos captured in real scenes are complex, which has impact on the performance of models. To this end, this paper has proposed a novel real-time multi-terminal sign language recognition system (RMSLRS). Specifically, a lightweight sign language recognition model based on MediaPipe Holistic is proposed to perform sign language inference by sensing multi-dimensional information such as pose, facial expression, and hand tracking, achieving near real-time performance on mobile and desktop devices. Then, a novel pre-processing module is proposed to reduce the adverse effects of background noise in videos through the YOLO model and OpenCv. Furthermore, a novel technical architecture for front-end and back-end separation and multi-terminal deployment is designed, including WeChat applet, desktop application and website. Finally, this system has been deployed with gratifying success in practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions 9

MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Chen, Zili Peng, Yilong Yang, Yaokai Jiao, Jiadong Wang, Laigang Wang, Aiguo Liu, Jianjun Lin, Wei Guo, Yan School of Tourism Henan Normal University Xinxiang China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou China Zhengzhou Tobacco Research Institute of Cntc Zhengzhou China Henan Provincial Tobacco Company of Cntc Zhengzhou China College of Computer and Information Engineering Henan Normal University Xinxiang China Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning Xinxiang China Ministry of Agriculture and Rural Affairs Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Zhengzhou China

ISBN: (纸本)9798350376548

For tobacco leaf disease spots with varying scales and shapes causing difficulties in segmentation and low accuracy, a novel MD-Unet algorithm is proposed for precise segmentation of tobacco leaf disease spots, aiding in scientific prevention and control of tobacco diseases. Innovative design includes Multi-scale Convolution Module (MC) and Dense Residual Dilated Convolution Module (MVCR) to enhance feature extraction of multiscale disease spots and perception of global contextual information. Utilizing Residual Unit Fusion Framework (RUFF) in the upsampling section effectively compensates for spatial details loss and strengthens network representation capability. Additionally, integration of Channel Spatial Attention Mechanism (CBAM) and Attention Gate (AG) further enhances the network's focusing ability on key information and fine-grained feature extraction capability. The MD-Unet algorithm achieves significant improvements in spot CPA, Recall, IoU, F1 metrics with 92.75%, 90.94%,84.93%,91.81% respectively, and an overall Dice score of 94.67%. Compared to mainstream methods like Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, DoubleU-Net, it shows remarkable enhancements, providing theoretical basis and technical support for precise segmentation of tobacco leaf disease spots and other plant diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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3D Face Alignment and Face Reconstruction Based on Image Sequence 14

3D Face Alignment and Face Reconstruction Based on Image Seq...

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14th International Conference on Digital Image processing, ICDIP 2022

作者： Wei, Yao Qiao, Biao Wang, Huabin Zhang, Mengxin Liu, Shijun Tao, Liang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Computer Science and Information Engineering Bengbu University Bengbu China

ISBN: (纸本)9781510657564

Existing 3D face alignment and face reconstruction methods mainly focus on the accuracy of the model. When the existing methods are applied to dynamic videos, the stability and accuracy are significantly reduced. To overcome this problem, we propose a novel regression framework that strikes a balance between accuracy and stability. First, on the basis of lightweight backbone, encoder-decoder structure is used to jointly learn expression details and detailed 3D face from video images to recover shape details and their relationship to facial expression, and dynamic regression of a small number of 3D face parameters, effectively improve the speed and accuracy. Secondly, in order to further improve the stability of face landmarks in video, a jitter loss function of multi-frame image joint learning is proposed to strengthen the correlation between frames and face landmarks in video, and reduce the difference amplitude of face landmarks between adjacent frames to reduce the jitter of face landmarks. Experiments on several challenging datasets verify the effectiveness of our method. © 2022 SPIE.

关键词： Jitter

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Alignment-Free RGB-T Salient Object Detection: A Large-Scale Dataset and Progressive Correlation Network 39

Alignment-Free RGB-T Salient Object Detection: A Large-Scale...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Kunpeng Chen, Keke Li, Chenglong Tu, Zhengzheng Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University China

ISBN: (纸本)157735897X

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on two unaligned three weakly aligned three aligned datasets demonstrate the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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DI-MVS: Learning Efficient Multi-View Stereo With Depth-Aware Iterations

DI-MVS: Learning Efficient Multi-View Stereo With Depth-Awar...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jianfei Jiang Mingwei Cao Jun Yi Chenglong Li Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University School of Computer Science and Information Engineering Hefei University of Technology

Learning-based Multi-View Stereo (MVS) methods aim to reconstruct 3D scenes from a set of 2D calibrated images. However, existing learning-based MVS methods often overlook depth maps that include the geometric shapes of the scene when constructing the cost volume. This can result in suboptimal reconstructions, particularly in low-texture or repetitive-texture regions where valuable geometric information is absent. To address this issue, we develop DI-MVS, a coarse-to-fine framework that effectively incorporates context-guided depth geometry into the cost volume using a depth-aware iterator. First, we employ the proposed depth-aware cost completion module to update the cost volume, followed by 2D ConvGRUs to iteratively optimize depth maps efficiently. Second, we propose a hybrid loss strategy that combines two loss functions’ strengths to improve depth estimation’s robustness. Extensive experiments demonstrate that DI-MVS outperforms state-of-the-art methods on the DTU dataset and the Tanks & Temples benchmark. The source code is available at: https://***/JianfeiJ/DI-MVS.

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Hand Detection Based on YOLOv3 6th

Hand Detection Based on YOLOv3

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Yu, Xipu Ma, Linjuan Zhang, Fuquan International Curriculum Center The High School Affiliated To Renmin University of China Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Hand detection is paid more attention to in the field of augmented reality, which can enhance user experience of human–computer interaction. It is more and more popular to use deep neural networks to research hand detection. A new method of hand detection based on YOLOv3 Model is proposed in our work. Our target is to contribute to a real-time hand detection system to make it more accuracy and convenient for the experience of the user and can apply in all aspects of our life. 11 K hands dataset is used to perfrom the experiments and the result is sound. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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