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arXiv 2024年

作者： He, Wenjie Huang, Chunfeng Guan, Rui Chen, Ye Zhang, Zhenrong Wei, Kejin Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China

Secure quantum remote sensing (SQRS) uses quantum states to gather information about distant objects or environments while ensuring secure data transmission against eavesdropping. It has potential applications in various fields, including environmental monitoring, military surveillance, and disaster response, where both data accuracy and transmission security are critical. Recent experiments have demonstrated the feasibility of SQRS using entanglement states. Here, we experimentally demonstrate an SQRS that can estimate a phase without requiring entanglement, offering the practical advantage that single-qubit states are easier to prepare. We successfully estimate the preset phase information at a remote site over a fiber distance of 50 km, which serves as a key step toward long-distance applications. © 2024, CC BY-NC-ND.

关键词： Optical fibers

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Refrence-Frame-Independent Quantum key Distribution over 250 km of Optical Fiber

arXiv

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arXiv 2024年

作者： Liu, Xin Luo, Di Luo, Zhicheng Li, Shizhuo Zhang, Zhenrong Wei, Kejin Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China

The reference-frame-independent quantum key distribution (RFI QKD) protocol enables QKD systems to function effectively despite slowly varying reference frames, offering a distinct advantage in practical scenarios, particularly in mobile platforms. In this study, we successfully distribute secure key bits over a 250 km optical fiber distance by developing an RFI QKD system with a repetition rate of 150 MHz. Benefiting from high repetition rate, we achieve a finite-key secret key rate of 49.65 bit/s at a distance of 200 km, which is more than three times higher than state-of-the-art systems. Our work dramatically extends the transmission distance and enhances the secret key rate of RFI QKD, significantly promoting its practical application. © 2024, CC BY-NC-ND.

关键词： Optical fibers

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Practical asynchronous measurement-device-independent quantum key distribution with advantage distillation

arXiv

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arXiv 2024年

作者： Luo, Di Liu, Xin Qin, Kaibiao Zhang, Zhenrong Wei, Kejin Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China

The advantage distillation (AD) method has proven effective in improving the performance of quantum key distribution (QKD). In this paper, we introduce the AD method into a recently proposed asynchronous measurement-device-independent (AMDI) QKD protocol, taking finite-key effects into account. Simulation results show that the AD method significantly enhances AMDIQKD, e.g., extending the transmission distance by 16 km with a total pulse count of N = 7.24×1013, and enables AMDI-QKD, previously unable to generate keys, to generate keys with a misalignment error rate of 10%. As the AD method can be directly integrated into the current system through refined post-processing, our results facilitate the practical implementation of AMDI-QKD in various applications, particularly in scenarios with high channel losses and misalignment errors. © 2024, CC BY-NC-SA.

关键词： Quantum cryptography

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP-PP页

作者： Wang, Muyu Zhao, Sanyuan Dong, Xingping Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China Beijing Institute of Technology School of Computer Science Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://***/muyuWang/HHNeRF. © 1995-2012 IEEE.

关键词： Rendering (computer graphics)

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Memory-guided two-stream spatio-temporal coding network based for video anomaly detection 23

Memory-guided two-stream spatio-temporal coding network base...

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Proceedings of the 2023 International Conference on information Education and Artificial Intelligence

作者： Bonan Li Hong Zhang Xingang Wang Rui Cao Jinyan Zhou China Radio and Television Shandong Network Co Ltd China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (纸本)9798400716157

In the paper, we propose a memory-guided dual-stream spatio-temporal encoder network (MSTAE) based on the U-Net network as the backbone, the spatial stream uses the time displacement module to obtain the spatial features of the video, and the temporal stream is aggregated across frames to obtain the temporal features of the video, meanwhile, the coordinate attention module is introduced to improve the U-Net network and enhance the dynamic entity representation capability. In order to reduce the prediction error, the memory module is used to record the prototype patterns of normal data to reduce the problem of small error between the prediction anomaly and its true value due to the excessive generalisation ability of the deep network. We conducted extensive experiments on three publicly available standard datasets (Ped2, Avenue and ShanghaiTech datasets). The experiments demonstrate that the research model outperforms state-of-the-art methods.

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

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Advanced information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm.

关键词： Patents Vocabulary Technological innovation Accuracy Semantics Predictive models Writing Prediction algorithms Data mining Robotics and automation

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Improvement in the Transferability of Target Adversarial Examples Based on Data Augmentation

Improvement in the Transferability of Target Adversarial Exa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kun Wang Xing Jin Fuqiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Despite the high performance of deep neural networks in image classification, they are very sensitive to those adversarial examples with small perturbations, which is likely to cause classification errors. Currently, there are various adversarial attack methods developed, but the adversarial samples generated using most methods are still disadvantaged by the poor target ransferability of cross-model in black-box scenarios. To address this issue, a new method called Adaptive Variation Method (AVM) is proposed in this paper to improve the transferability of targeted adversarial examples in black-box scenarios. AVM relies on Adjustment of diversified inputs(ADI) method to achieve data enhancement on the input image and Maximum absolute difference (MD) method to calculate the loss for adversarial perturbation. Specifically, the Resized-Diverse-Inputs (RDI) method is improved in this paper to propose a new diversified input method, namely the Adjustment of diversified inputs (ADI) method. By means of repeated scaling and random filling performed on the input image, the image becomes relatively rough, with its input diversity and random flexibility improved. In addition, the Maximum absolute difference (MD) method is proposed in this paper to enhance the advantage of adversarial samples by minimizing the maximum absolute difference between the adversarially perturbed global image and local image. According to the experimental results, this method improves the success rate in the presence of transfer-based targeted attacks.

关键词： Deep learning Perturbation methods Data enhancement Closed box Data augmentation Robustness Filling Cybernetics Overfitting Image classification

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuecheng Wen Xiaotong Li Haiqing Lv Menglu Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab’s self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic.

关键词： Image coding Accuracy Computational modeling Transforms Telecommunication traffic Gray-scale Data models Mobile handsets Cryptography Convolutional neural networks

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Context-Guided and Syntactic Augmented Dual Graph Convolutional network for Aspect-Based Sentiment Analysis

Context-Guided and Syntactic Augmented Dual Graph Convolutio...

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

作者： Jia Yi Xiaoming Wu Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Predicting the sentiment polarity of aspect terms in sentences is the goal of Aspect-Based Sentiment Analysis(ABSA) task. Graph Convolutional network(GCN) is used in majority of the ABSA task due to its ability to effectively capture the dependencies among words or entities within sentences. However, it may not work as expected when some sentences have no obvious syntactic structure. To alleviate this issue, we propose a Context-guided and Syntactic Augmented Dual Graph Convolutional network(CSADGCN) model for the ABSA task. Specifically, we propose a context-guided attention mechanism that captures both global and local information by combining self-attention and aspect-level attention, even though some sentences have no obvious syntactic structure. In addition,we augment the GCN with multiple linguistic features and utilize a biaffine attention module to capture the relationship between words. On three datasets, extensive experimental modifications reveal that our CSADGCN model performs better than the most recent baseline approach.

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An Efficient Token Mixer Model for Sheet Metal Defect Detection

An Efficient Token Mixer Model for Sheet Metal Defect Detect...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huijuan Hao Sijian Zhu Changle Yi Yu Chen Hongge Zhao Yue Feng Rong Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Defects such as scratches, patches, and cracks frequently occur during sheet metal production. However, the low detection accuracy and slow processing speed of industrial defect detection models significantly impede enterprise production efficiency. The aforementioned issues primarily manifest in three aspects. Firstly, the model complexity and computational overhead are substantial. Secondly, detecting small local defects poses a significant challenge. Thirdly, extracting global features, such as elongated scratches, proves to be difficult. To address these challenges, this paper introduces a novel network architecture called SATRNet. Firstly, within the model backbone, the STR module is devised. Through incorporation of the sparse self-attention method and the CNNs parallel vision Transformer model in the shallow layers, this module significantly enhances the model's capability to extract global features. Secondly, the SCATR module is designed in this paper. By substituting self-attention with the designed SCA soft attention as the token mixer, the module aims to enhance detection accuracy while reducing the number of parameters, thereby fundamentally addressing the problem of model complexity. Finally, this paper presents the GCD bottleneck convolution module. This module combines shallow and deep features, enabling the fusion of more information beneficial for detection, thereby achieving improved efficacy in capturing minute targets. Experiments demonstrate that SATRNet surpasses existing advanced models in detection accuracy on public datasets.

关键词： Accuracy Computational modeling Metals Production network architecture Feature extraction Transformers Complexity theory Mixers Defect detection

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