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High-Confidence Computing 2024年第3期4卷 64-74页

作者： Chao Wang Xueqiao Xu Ke Xiao Yunhua He Guangcan Yang school of information science and technology North China University of TechnologyBeijing 100144North China University of TechnologyBeijing 100144

Recently,vehicles have experienced a rise in networking and informatization,leading to increased security *** the most widely used automotive bus network,the Controller Area Network(CAN)bus is vulnerable to attacks,as security was not considered in its original *** paper proposes SIDuBzip2,a traffic anomaly detection method for the CAN bus based on the bzip2 compression *** proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic,constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal *** method consists of three parts:the conversion of CAN ID values to characters,the calculation of similarity based on bzip2 compression,and the optimal solution of model *** experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks,including Denial of Service,replay,basic injection,mixed injection,and suppression *** addition,existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay,demonstrating the feasibility of the proposed method.

关键词： Automotive safety CAN bus Anomaly detection

Robust video question answering via contrastive cross-modality representation learning

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science China(information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG school of information science and technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center school of Computer science and information Engineering Hefei University of Technology Institutes of Physical science and information technology Anhui University school of Computer science and technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua science and technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang school of Computer science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm:Designed for Dispatch of Integrated Energy System in Coal Mine

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Tsinghua science and technology 2024年第2期29卷 368-385页

作者： Canyun Dai Xiaoyan Sun Hejuan Hu Yong Zhang Dunwei Gong school of information and Control Engineering China University of Mining and TechnologyXuzhou 221116China school of information science and technology Qingdao University of Science and TechnologyQingdao 266061China

The dispatch of integrated energy systems in coal mines(IES-CM)with mine-associated supplies is vital for efficient energy utilization and carbon emissions ***,IES-CM dispatch is highly challenging due to its feature as multi-objective and strong *** constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front,which greatly deteriorates dispatch *** tackle this problem,we transform the traditional dispatch model of IES-CM into two tasks:the main task with all constraints and the helper task with constraint *** we propose a constraint adaptive multi-tasking differential evolution algorithm(CA-MTDE)to optimize these two tasks *** helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible *** purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local ***,a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and ***,we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province,considering two IES-CM *** demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence,diversity,and distribution.

关键词： dispatch integrated energy system coal mine evolutionary multi-tasking constraint differential evolution

Quantum geometric tensor and the topological characterization of the extended Su-Schrieffer-Heeger model

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Chinese Physics B 2024年第3期33卷 260-265页

作者：曾相龙赖文喜魏祎雯马余全 school of science Beijing Information Science and Technology UniversityBeijing 100192China

We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger(SSH)model with long-range hopping *** computing the quantum geometry tensor,we derive exact expressions for the quantum metric and Berry curvature of the energy band electrons,and we obtain the phase diagram of the model marked by the first Chern ***,we also obtain the topological Euler number of the energy band based on the Gauss-Bonnet theorem on the topological characterization of the closed Bloch states manifold in the first Brillouin ***,some regions where the Berry curvature is identically zero in the first Brillouin zone result in the degeneracy of the quantum metric,which leads to ill-defined non-integer topological Euler ***,the non-integer"Euler number"provides valuable insights and an upper bound for the absolute values of the Chern numbers.

关键词： quantum geometric tensor topological Euler number Chern number Berry curvature quantum metric Su-Schrieffer-Heeger(SSH)model

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Energy consumption optimization for edge computing-supported cellular networks based on optimal transport theory

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science China(information sciences) 2024年第2期67卷 201-218页

作者： Xiangyu LV Xiaohu GE Yi ZHONG Qiang LI Yong XIAO school of Electronic information and Communications Huazhong University of Science and Technology

With advancements in mobile communication technologies, there have been multiple user device(UD) connections to cellular networks. Because of changes in the spatial distribution of UDs and application requirements, the traditional offloading mechanism based on the nearest distance will result in heavy loads for some base stations(BSs). Because there is a high-order relationship between the energy consumption of the BS processing tasks and the number of computing tasks, the traditional offloading mechanism will result in high energy consumption, and UD’s offloading decision must be dynamically adjusted. The offloading decision of UDs is optimized based on detailed information about various parameters associated with a network from the standpoint of distribution. This information includes details regarding the spatial distribution of UDs,application requirements, and the offloading period of computing tasks. Based on optimal transport theory,an energy consumption optimization algorithm is suggested to lower the total amount of energy consumed by the offloading process of UDs’ computing tasks by reasonably planning the offloading BSs of the UDs in the networks. The simulation results show that the proposed offloading mechanism based on energy consumption optimization reduces the total energy consumption of the offloading process by 28.09% when compared to the traditional offloading mechanism, and the traffic managed by each BS is balanced.

关键词： optimal transport theory edge computing energy consumption reduction dynamic voltage frequency scaling

Modifying Lyapunov exponent of chaotic map by self-cascading

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science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer science and technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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science China(information sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced information science and Network technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last ***, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

YOLOv5ST:A Lightweight and Fast Scene Text Detector

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Computers, Materials & Continua 2024年第4期79卷 909-926页

作者： Yiwei Liu Yingnan Zhao Yi Chen Zheng Hu Min Xia school of Computer and science Nanjing University of Information Science and TechnologyNanjing210044China school of Automation Nanjing University of Information Science and TechnologyNanjing210044China

Scene text detection is an important task in computer *** this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text *** primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded *** achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the *** results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,*** work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

关键词： Scene text detection YOLOv5 lightweight object detection

General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

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Chinese Physics B 2024年第5期33卷 255-262页

作者：康茁刘维琪齐锦贺晨 school of information science and technology Northwest UniversityXi'an 710127China

Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and *** practical security of an LLO CVQKD system,there are two main attack modes referred to as reference pulse attack and polarization attack ***,there is currently no general defense strategy against such attacks,and the security of the system needs further ***,we employ a deep learning framework called generative adversarial networks(GANs)to detect both *** first analyze the data in different cases,derive a feature vector as input to a GAN model,and then show the training and testing process of the GAN model for attack *** proposed model has two parts,a discriminator and a generator,both of which employ a convolutional neural network(CNN)to improve *** results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission *** only establishes a detection model by monitoring features of the pulse without adding additional devices.

关键词： CVQKD generative adversarial network attack classification

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