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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Qian Liu, Wenting Lu, Hao Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing211100 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China

ISBN: (纸本)9781665410205

In the past few years, learned video compression has received increasing attention. However, most current learned methods rely on the bilinear warping operations in motion compensation, which is equivalent to a low-pass filtering operation and brings the distortion of reconstruction. To address this problem, we propose a spatial-temporal motion compensation for learned video compression (STMC-LVC). STMC-LVC uses a spatial-temporal motion compensation network (STMC-Net) to fully consider the spatial-temporal correlation between successive frames, performing accurate motion compensation. Specifically, STMC-Net mainly consists of the initial feature prediction module and the fusion module. The initial feature prediction module predicts reference features based on DCN to provide sufficient information for motion compensation. The fusion module obtains temporal attention information by calculating the similarity between features, performs spatial attention operations, and finally performs spatial-temporal fusion, further improving the robustness of motion compensation. In addition, STMC-LVC uses a conditional coding framework. We use a concatenation of the current feature and predicted feature as context to explore spatial-temporal correlations in feature space. Experimental results show that our method effectively improves video compression. Specifically, our model achieves an average of 33.86% and 58.70% bitrate savings than x265 (veryslow) on PSNR and MS-SSIM, respectively. © 2024 IEEE.

关键词： Low pass filters

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Unveiling Spectrum-Structure Correlation in Vibrational Spectroscopy: Task-Driven Deep Learning Classification Balancing Global Fusion and Local Extraction

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Analytical Chemistry 2025年第21期97卷 10988-10998页

作者： Shi, Guoyang Guo, Haoyu Gao, Tianchu Wu, Haoping Luo, Si-Heng Wang, Sanlei Lei, Yong Zhang, Yun Xie, Yangfan Ren, Bin Tian, Zhong-Qun Shao, Gui-Fang Liu, Guo-Kun State Key Laboratory of Marine Environmental Science Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies Center for Marine Environmental Chemistry & Toxicology College of the Environment and Ecology Xiamen University Xiamen361102 China Key Laboratory of Big Data Intelligent Analysis and Decision Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen361102 China State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Conservation Technology Department Palace Museum Beijing100009 China

Spectrum-structure correlation is crucial to identify and quantify chemicals, in which classification of mixtures and identification of functional groups are two central tasks. Deep learning-driven algorithms have made significant strides to these two tasks. However, many of these algorithms are merely adaptations of models originally designed for computer vision applications. As a result, the models often suffer from either low accuracy or limited generality when applied to spectral data due to the overlooked inherent limitations in feature richness and volume of spectral data. Here, in light of the distinctive difference in the attention of global and local information in spectral data between these two tasks, we developed contrapuntally two CNN-based algorithms, incorporating multiscale convolution and attention mechanism, to address the unique requirements of each task. It was found that the lightweight CNN-Peak algorithm is favored for the classification of a mixture, a type of single-label task, in which the feature fusion of global information is more important. Meanwhile, the more complex ResNet-ResPeak algorithm is ideally suited for the identification of functional groups, a type of multilabel task, in which the feature extraction of local information takes precedence. The task-oriented, conceptual design of deep learning algorithms not only enhances the efficacy and accuracy of spectrum-structure correlation analysis but also feeds back to achieve a more rigorous experimental design and implementation, forming a closed loop of AI for science. © 2025 American Chemical Society.

关键词： Deep learning

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Secure and Lightweight Signcryption Scheme With Group Equality Test for Heterogeneous WBAN

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IEEE Internet of Things Journal 2025年

作者： Zhang, Qijia Zhang, Jianhong Yang, Nan Tian, Youliang Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang050025 China North China University of Technology College of Information Sciences and Technology Beiing100144 China

Wireless body area network (WBAN) integrates body sensors to collect and upload various health indicators, which facilitates timely remote healthcare. To realize the real-time monitoring of the patient’s health status while ensuring the security of sensitive data, signcryption schemes with equality test provide a feasible solution. Nevertheless, most of the existing schemes suffer from heavy computational burdens and are vulnerable to some inherent threats, such as offline message recovery attack and known session temporary key attack. Additionally, some of them cannot provide the essential security properties. To cope with these problems, this paper constructs a secure and lightweight signcryption scheme with group equality test, which is suitable for the heterogeneous environment. To formally define the resistance to offline message recovery attack and known session temporary key attack, we first propose two corresponding security models. Then, we introduce a group mechanism to the equality test to support the aforementioned security properties. Besides, we adopt the online/offline construction to avert the time-consuming operations in the real-time data processing phase. Furthermore, our scheme supports message aggregation to reduce the computational overhead of handling multiple messages. Formal security proof shows that our scheme satisfies confidentiality and unforgeability. Compared to several recent schemes, the experimental results demonstrate that our scheme enjoys the best performance in terms of both computational overhead and communication cost. With implementation, our scheme is proved to be feasible for lightweight devices in real-world applications. © 2014 IEEE.

关键词： Electronic health record

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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Ddnet: A Knowledge Distillation Defense Network Based on Multi-Level Adaptive Weight Calculation for Adversarial Defense

SSRN

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SSRN 2023年

作者： Jin, Yang Zhi, Li Li, Zhang ShuaiWei, Liu Yi, Wang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University China

Deep neural networks (DNNs) have demonstrated exceptional performance in various computer vision tasks. However, they are highly vulnerable to adversarial examples, leading to a significant decline in their performance. This paper introduces a novel multi-level adaptive weight calculation module to assign significant weight coefficients to feature channels and corresponding spatial channels obtained at different scales. This module enhances the network’s ability to extract more informative and crucial features, thereby improving the quality of reconstructed images. Additionally, we propose and implement a distillation defense network (DDNet) to effectively remove adversarial perturbations from adversarial examples. DDNet leverages features extracted from clean examples in a teacher model to transfer knowledge to a student model, enabling it to accurately eliminate adversarial perturbations and align the feature distribution of adversarial examples with that of clean examples. Furthermore, we design defense contrastive regularization (DCR) based on contrastive learning techniques to further enhance the defense effect and classification accuracy. Extensive experiments validate the effectiveness of the proposed defense model, demonstrating high defense accuracy and generalization capability. © 2023, The Authors. All rights reserved.

关键词： Distillation

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data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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An Efficient Blockchain-Based Certificateless Anonymous Authentication Scheme for VANETs

An Efficient Blockchain-Based Certificateless Anonymous Auth...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Bi, Changbing Tian, Youliang Li, Ta Wu, Shihong Zhou, Hua Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou University Guizhou Province Key Laboratory of Cryptography and Block Chain Technology Guiyang550025 China Institute of Cryptography & Data Security Guizhou University Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9798350327380

Vehicle ad-hoc networks (VANETs) play a critical role in providing security and privacy protection for intelligent transportation systems. However, the limited network bandwidth and computing capacity within VANETs have resulted in existing authentication schemes having excessive computational overhead, which cannot meet the low-latency communication requirements of VANETs. To tackle these problems, we propose an efficient blockchain-based certificateless anonymous authentication scheme for VANETs. Firstly, we propose a novel signature scheme without bilinear pairing to reduce the computational overhead in VANETs. Secondly, we utilize blockchain to facilitate trusted information sharing. Thirdly, we provide privacy-preserving through the use of anonymous keys, and enable tracing of vehicle identities in the event of malicious behavior. Finally, security analysis and simulation experiments demonstrate that our proposed scheme has lower communication and computational overhead, while satisfying various security requirements for VANETs, such as anonymity, unlinkability, and traceability. © 2023 IEEE.

关键词： Authentication

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Dual Branch PnP Based Network for Monocular 6D Pose Estimation

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Intelligent Automation & Soft Computing 2023年第6期36卷 3243-3256页

作者： Jia-Yu Liang Hong-Bo Zhang Qing Lei Ji-Xiang Du Tian-Liang Lin Department of Computer Science and Technology Huaqiao UniversityXiamen361000China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen361000China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen361000China College of Mechanical Engineering and Automation Xiamen361000China

Monocular 6D pose estimation is a functional task in the field of com-puter vision and *** recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based ***,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)*** is still a difference in the distance from the expected estimation *** obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate ***,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression ***,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation ***,the proposed method is verified on the public LM,LM-O and YCB-Video *** ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,*** AUC of ADD(-S)value on YCB-Video is *** experimental results show that the performance of the proposed method is superior to that of similar methods.

关键词： 6D pose monocular RGB edge enhancement dual-branch PnP 2D-3D correspondence

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Energy-optimal DNN model placement in UAV-enabled edge computing networks

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Digital Communications and Networks 2024年第4期10卷 827-836页

作者： Jianhang Tang Guoquan Wu Mohammad Mussadiq Jalalzai Lin Wang Bing Zhang Yi Zhou State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China School of Engineering The University of British Columbia OkanaganKelownaV1V 1V7Canada College of General Aviation and Flight Nanjing University of Aeronautics and AstronauticsNanjing210016China

Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication *** the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical *** neural networks(DNNs)are becoming a driving force to extract valuable information from sensing ***,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of *** this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks *** is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge *** Lyapunov optimization technique is leveraged for the proposed DNN model placement *** on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues ***,extensive simulations are provided to depict the effectiveness of the AOP *** numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.

关键词： UAV-Enabled edge computing DNN model Placement 6G networks Inference tasks

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An alert-situation text data augmentation method based on MLM

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High technology Letters 2024年第4期30卷 389-396页

作者： DING Weijie MAO Tingyun CHEN Lili ZHOU Mingwei YUAN Ying HU Wentao Department of Computer and Information Security Zhejiang Police CollegeHangzhou 310053P.R.China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityHangzhou 310053P.R.China Zhejiang Dahua Technology Co. LtdHangzhou 310053P.R.China

The performance of deep learning models is heavily reliant on the quality and quantity of train-ing *** training data will lead to ***,in the task of alert-situation text classification,it is usually difficult to obtain a large amount of training *** paper proposes a text data augmentation method based on masked language model(MLM),aiming to enhance the generalization capability of deep learning models by expanding the training *** method em-ploys a Mask strategy to randomly conceal words in the text,effectively leveraging contextual infor-mation to predict and replace masked words based on MLM,thereby generating new training *** Mask strategies of character level,word level and N-gram are designed,and the performance of each Mask strategy under different Mask ratios is analyzed and *** experimental results show that the performance of the word-level Mask strategy is better than the traditional data augmen-tation method.

关键词： deep learning text data augmentation masked language model(MLM) alert-sit-uation text classification

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