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11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Qi, Hui Wang, Yue Dai, Jinyu Li, Jinqing Di, Xiaoqiang School of Computer Science and Technology Changchun University of Science and Technology Changchun China Jilin Province Key Laboratory of Network and Information Security Changchun China

ISBN: (纸本)9783030322151

Neural network models have played an important role in text applications, such as document summaries and automatic short answer questions. In previous existing works, questions and answers are together used as input in recurrent neural networks (RNN) and convolutional neural networks (CNN), then output corresponding scores. This paper presents a method for measuring the score for short answer questions and answers. This paper makes scoring by establishing a hierarchical word-sentence model to represent questions and answers and using the attention mechanism to automatically determine the relative weight of questions and answers. Firstly, the model combines CNN and Bidirectional Long Short-Term Memory networks (BLSTM) to extract the semantic features of questions and answers. Secondly, it captures the representation vector of relevant questions and answers from the sentence-level features. Finally, all feature vectors are concatenated and input to the output layer to obtain the corresponding score. Experiment results show that the model in this paper is better than multiple baselines. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Recurrent neural networks

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The Privacy Data Protection Model Based on Random Projection technology 2nd

The Privacy Data Protection Model Based on Random Projection...

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2nd International Conference on Big Data and security, ICBDS 2020

作者： Shen, Wen Guo, Qian Zhu, Hui Tang, Kejian Zhan, Shaohui Hao, Zhiguo Global Energy Interconnection Research Institute Co. Ltd. Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Financial Technology Group Xiongan071000 China State Grid Jiangxi Electric Power Co. Ltd. Nanchang330000 China Xingtai Huancheng Power Supply Branch of State Grid Hebei Electric Power Co. Ltd. Xingtai054000 China

ISBN: (纸本)9789811631498

In general, as the technology developed, big data analysis provides opportunities to reduce the implementation time and budget. Compared with traditional methods, big data analysis has a greater advantage. It seeks to establish a privacy framework consistent with the factors and measures of mutual understanding in the context of random projection technology. Privacy concerns and perceived benefits have proven to greatly influence personal data protection. The success of stochastic projection techniques depends on voter privacy and personal data protection needs being met. It explores independent component analysis as a possible tool for breaking privacy with deterministic multiplicative perturbation-based models, for example: random orthogonal transformations and random rotations. An approach based on approximate random projection is then proposed that improves privacy protection while maintaining certain date statistical characteristics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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Burst image super-resolution based on dual branch fusion and adaptive frame selection

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Displays 2025年 89卷

作者： Duan, Lijuan Zuo, Wendi Gu, Ke Gong, Zhi School of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing University of Technology Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China Chongqing Research Institute of Beijing University of Technology China

The modern handheld camera is capable of rapidly capturing multiple images and subsequently merging them into a single image. The extant methodologies typically select the first frame as the reference frame, utilising the information from the remaining frames and the information from the reference frame to calculate the high-resolution image. However, for complex scenes and unstable shooting situations, this fixed frame selection is not the optimum solution. In this direction, we propose an adaptive frame selection method which, by calculating the frame channel weight information, selects the best frame image as the reference frame to perform the subsequent computation. Moreover, to enhance the visual quality of high-resolution images, we propose a dual-branch fusion module in the feature fusion phase for the sequence attributes of the input frame images. This allows the network to concentrate on the temporal global features of the input sequence and the spatial local detail features of the frame images. Subsequently, the feature map is obtained through residual computation utilising the adaptive selected frame image and the image features obtained through fusion. The image is then reconstructed through up-sampling to obtain a high-resolution image. The experimental results on the BurstSR and RealBSR datasets demonstrate that our approach not only outperforms existing techniques in terms of evaluation metrics but also exhibits superior visual effects. © 2025

关键词： Photointerpretation

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Data Anonymous Authentication for BIoMT with Proxy Group Signature

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Journal of Cyber security 2021年第4期3卷 207-216页

作者： Chaoyang Li Yalan Wang Gang Xu Xiubo Chen Xiangjun Xin Jian Li College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450001China Department of Computer Science University of SurreyGuildfordSurreyGU27XHUK School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical *** blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent *** paper first introduces a data anonymous authentication model to protect user privacy and medical data in ***,a proxy group signature(PGS)scheme has been proposed based on lattice *** scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer ***,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and *** efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

关键词： Blockchain-enabled Internet of Medical Things anonymous authentication proxy group signature medical data

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Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

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Android Malicious Code Detection Based on Secondary Pruning Optimization 12

Android Malicious Code Detection Based on Secondary Pruning ...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Yu, Qing Ma, Kuolang Wang, Zuohua Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China Tianjin Times Shengshi Technology Co. Ltd. No. 216 Hongqi Road Nankai District Tianjin300000 China

ISBN: (纸本)9781728148526

In this paper, we propose a detection algorithm for Android malicious code based on integrated multi-feature. By decompiling and processing APK files, the multi-class behavior features of Android application are extracted, and the classification is achieved through an integrated learning framework based on quadratic pruning optimization. The prototype system automatically detects the malicious code of the Android platform and analyzes the validity of the algorithm through experimental verification. © 2019 IEEE.

关键词： Android (operating system)

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Internet of Things Terminal Access security Based on Zero Trust

Internet of Things Terminal Access Security Based on Zero Tr...

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International Symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： RiXuan Qiu JunFeng Zhang Lu Chen Wei Li Nan Lin State Grid Jiangxi Electric Power Co. Ltd. Information & Telcommunication Branch Nanjing China Grid Digitizing Technology Department State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. Ltd Nanjing China State Grid Jiangxi Electric Power Co. Ltd Nanjing China

At present, production and daily life increasingly rely on the Internet of Things, and the network security problem of the Internet of Things is becoming increasingly prominent. Therefore, it is extremely important to ensure the network security of the Internet of Things through various technical means. The security of IoT terminal access behavior is an important part of IoT network security, so it is an important research object in the field of network security. In order to increase the security of IoT terminal access, a security evaluation model based on zero trust is proposed. After the simulation performance test of the model, it is found that the model shows excellent detection ability of malicious access behavior and system stability in different network environments. Under the premise that some network nodes are infected, the model proposed in the study still shows a significantly higher ratio of trusted nodes than other algorithms, The research results show that the model can improve the security level of the Internet of Things network to a certain extent.

关键词： Computational modeling Production network security Predictive models Stability analysis Behavioral sciences Zero Trust

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network security Situation Awareness technology Based on Multi-source Heterogeneous Data 2020

Network Security Situation Awareness Technology Based on Mul...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Daojuan Zhang Kexiang Qian Wenhui Wang Fuyang fang Chonghua Wang Xi Luo State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Beijing China Information Science Academy China Electronics Technology Group Corporation Beijing China Department of Security Technology China Industrial Control Systems Cyber Emergency Response Team Beijing China Cyber space Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781450387828

With the development of information technology, the scale of the network continues to expand, and the security issues continue to increase. The complexity of the network security situation is increasing and the importance of the network security situational awareness continues to increase. The data sources are wide, the type and the number are large in a large-scale network environment currently. This paper comprehensively studies the network security situational awareness technology based on multi-source heterogeneous data. In addition, this paper introduces the origin, concept and the model of the network situational awareness, as well as the characteristics of network security situational awareness based on multi-source heterogeneous data fusion. Finally, the key technologies in the security situational awareness such as the extraction of situational elements, multi-source data fusion, situation assessment, situation prediction and visual display are introduced.

关键词： Data Fusion

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Does Thermal Really Always Matter for RGB-T Salient Object Detection?

arXiv

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

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A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras

arXiv

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arXiv 2025年

作者： Xiao, Teng Hu, Qi Yan, Qingsong Liu, Wei Ye, Zhiwei Deng, Fei School of Computer Science Hubei University of Technology Wuhan China Hubei Key Laboratory of Green Intelligent Computing Power Network Wuhan China School of Surveying and Mapping Wuhan University Wuhan China Wuhan Tianjihang Information Technology Co. Ltd. Wuhan China

Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. This paper presents a Forward Distortion and Backward Warping network (FDBW-Net), a novel framework for wide-angle image rectification. It begins by using a forward distortion model to synthesize barrel-distorted images, reducing pixel redundancy and preventing blur. The network employs a pyramid context encoder with attention mechanisms to generate backward warping flows containing geometric details. Then, a multi-scale decoder is used to restore distorted features and output rectified images. FDBW-Net’s performance is validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net achieves SOTA performance in distortion rectification, boosting the adaptability of PTZ cameras for practical visual applications. © 2025, CC BY.

关键词： Nonlinear distortion

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