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2023 China Automation Congress, CAC 2023

作者： Li, Xin Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350303759

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method. © 2023 IEEE.

关键词： Controllers

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Towards Task-Free Privacy-Preserving Data Collection

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China Communications 2022年第7期19卷 310-323页

作者： Zhibo Wang Wei Yuan Xiaoyi Pang Jingxin Li Huajie Shao School of Cyber Science and Engineering Wuhan University430072 China School of Cyber Science and Technology Zhejiang University310027 China State Key Laboratory of Integrated Services Networks Xidian University710071 China Department of Computer Science College of William and MaryWilliamsburgVA23186 USA

With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to *** privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local ***,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined *** this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream *** this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized *** the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except *** experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.

关键词： IoT privacy protection data collection adversarial learning deep learning

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TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles

arXiv

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arXiv 2023年

作者： Ma, Yifeng Wang, Suzhen Ding, Yu Ma, Bowen Lv, Tangjie Fan, Changjie Hu, Zhipeng Deng, Zhidong Yu, Xin Li, Lincheng Department of Computer Science and Technology BNRist THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University China Fuxi AI Lab Netease Zhejiang Hangzhou China The School of Computer Science The University of Queensland Brisbane Australia

Audio-driven talking head generation has drawn growing attention. To produce talking head videos with desired facial expressions, previous methods rely on extra reference videos to provide expression information, which may be difficult to find and hence limits their usage. In this work, we propose TalkCLIP, a framework that can generate talking heads where the expressions are specified by natural language, hence allowing for specifying expressions more conveniently. To model the mapping from text to expressions, we first construct a text-video paired talking head dataset where each video has diverse text descriptions that depict both coarse-grained emotions and fine-grained facial movements. Leveraging the proposed dataset, we introduce a CLIP-based style encoder that projects natural language-based descriptions to the representations of expressions. TalkCLIP can even infer expressions for descriptions unseen during training. TalkCLIP can also use text to modulate expression intensity and edit expressions. Extensive experiments demonstrate that TalkCLIP achieves the advanced capability of generating photo-realistic talking heads with vivid facial expressions guided by text descriptions. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Another Lattice Attack Against ECDSA with the wNAF to Recover More Bits per Signature 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Ma, Ziqiang Li, Shuaigang Lin, Jingqiang Cai, Quanwei Fan, Shuqin Zhang, Fan Luo, Bo School of Information Engineering Ningxia University Yinchuan China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China State Key Laboratory of Cryptology Beijing China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou310027 China Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

In the resource-constrained environment such as the Internet of Things, the windowed Non-Adjacent-Form (wNAF) representation is usually used to improve the calculation speed of the scalar multiplication of ECDSA. This paper presents a practical cache side channel attack on ECDSA implementations which use wNAF representation. Compared with existing works, our method exploits more information from the cache side channels, which is then efficiently used to construct lattice attacks in the ECDSA private key recovery. First, we additionally monitor the invert function which is related to the sign of the wNAF digits, and obtain a Double-Add-Invert chain through the Flush+Flush cache side channel. Then, we develop effective methods extracting 154.2 bits information of the ephemeral key per signature for 256-bit ECDSA from this chain, much more than the best known result which extracts 105.8 bits per signature. Finally, to efficiently use the extracted information, we convert the problem of recovering the private key to the Hidden Number Problem (HNP) and the Extended Hidden Number Problem (EHNP) respectively, which are solved by lattice reduction algorithms. We applied the attack on ECDSA with the secp256k1 curve in OpenSSL 1.1.0h. The experimental results show that only 3 signatures are enough to recover the private key. To the best of our knowledge, this work exploits the signs of the wNAF representation, along with the Double-Add chain against ECDSA, to recover the private key with the least number of signatures. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Side channel attack

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Device-to-Device Communications aided Integrated Sensing and Communication Networks: A Joint Design of Bandwidth and Power Allocations

Device-to-Device Communications aided Integrated Sensing and...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Dou, Chenglong Huang, Ning Wu, Yuan Qian, Liping Quek, Tony Q.S. University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519301 China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes. © 2024 IEEE.

关键词： Bandwidth

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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HRSE: Heterogeneous Reliable-Aware SFC Embedding by DRL with Greedy Rules in SAGIN

HRSE: Heterogeneous Reliable-Aware SFC Embedding by DRL with...

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International Conference on computer Supported Cooperative Work in Design

作者： Kaixin Qin Songtao Guo Guiyan Liu Yue Zeng College of Computer Science Chongqing University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai Department of Computer Science and Technology The State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Ensuring the quality of service (QoS) in the Space-Air-Ground Integrated Network (SAGIN) requires implementing appropriate service function chain (SFC) embedding strategies to provide reliable general services and AI services. However, the complicated topology, limited infrastructure resources, and the heterogeneity in hardware and software in SAGIN hinder existing reliable embedding solutions from delivering high QoS. To address the challenges, this paper takes into account the heterogeneous reliable-aware SFC embedding (HRSE) problem in SAGIN. Specifically, we formulate the HRSE problem as a nonlinear integer programming problem that is NP-hard. Subsequently, an algorithm called HRSFCEA, which consists of deep reinforcement learning (DRL) and greedy rules, is proposed to tackle the challenges. Initially, leveraging the autonomous decision-making capability of DRL, SFC embedding is executed to select backups efficiently. The proposed algorithm reduces frequent trial and error by modifying its actions. Next, the paper gives the algorithm analysis including the complexity and unreliable environment. Finally, simulation results based on real-world datasets demonstrate that the DRL scheme converges to high rewards with minimal differences across varying numbers of hidden layer neurons. The proposed algorithm can improve the acceptance rate by up to 82% while incurring no more than 5% additional cost compared to the state-of-art algorithms.

关键词： Service function chaining computer network reliability Software algorithms Quality of service Space-air-ground integrated networks Reliability engineering Deep reinforcement learning Software Software reliability Topology

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Action recognition through fusion of sEMG and skeletal data in feature level

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Journal of Ambient Intelligence and Humanized Computing 2022年第9期13卷 4125-4134页

作者： Wang, Xinming Ding, Weili Bian, Shuangshuang Liu, Honghai Department of Automation Institute of Electrical Engineering Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Yanshan University 438 West of Hebei Avenue Haigang District Qinhuangdao066004 China School of Mechanical Engineering and Automation State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen Nanshan District Shenzhen518055 China

Human action can be recognized through a unimodal way. However, the information obtained from a single mode is limited due to the fact that a single mode contains only one type of physical attribute. Therefore, it is motivational to improve the accuracy of actions through fusion of two different complementary modality, which are the surface electromyography (sEMG) and the skeletal data. In this paper, we propose a general framework of fusion of sEMG signals and skeletal data. Firstly, vector of locally aggregated descriptor (VLAD) was extracted from sEMG sequences and skeletal sequences, respectively. Secondly, features obtained from sEMG and skeletal data are mapped through different weighted kernels using multiple kernel learning. Finally, the classification results are obtained through the model of multiple kernel learning. A dataset of 18 types of human actions is collected via KinectV2 and Thalmic Myo armband to verify our ideas. The experimental results show that the accuracy of human action recognition are improved by combining skeletal data with sEMG signals. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Musculoskeletal system

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What limits limits?

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National science Review 2021年第1期8卷 11-14页

作者： Yong-Chun Liu Kun Huang Yun-Feng Xiao Lan Yang Cheng-Wei Qiu State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Frontier Science Center for Quantum Information Tsinghua University Department of Optics and Optical Engineering University of Science and Technology of China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Department of Electrical and Systems Engineering Washington University Department of Electrical and Computer Engineering National University of Singapore

There are fundamental rules and principles setting the limits of physical systems. It triggers an interesting thought—can we break the limits under specific circumstances? A realistic system can only provide limited functionalities because its performance is physically constrained by some fundamental principles.‘Breaking the limit’, which usually implies that the capability of a system could be enhanced significantly,

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Stage-Zero: A Dynamic Ensemble Method for Intrusion Detection in Industrial Control systems 10th

Stage-Zero: A Dynamic Ensemble Method for Intrusion Detectio...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Ma, Zhiming Zhou, Lin Hu, Xiaobo Li, Chao Chen, Siyuan Wang, Yong Department of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing China State Grid Digital Technology Holding Co. Ltd. Beijing China

ISBN: (纸本)9789819787487

Industrial control systems (ICS) have become increasingly vulnerable to security risks due to the proliferation of network applications. Intrusion detection systems (IDS) are employed to safeguard ICS from hostile attacks. While machine learning-based methods perform well in intrusion detection tasks, combining multiple machine learning classifiers can further increase the detection accuracy. Confidence calibration is a technique developed to enable a machine learning model to produce prediction results along with more accurate proximate correctness likelihood. This study proposes a dynamic ensemble method that utilizes confidence calibration and a zeroth layer neural net classifier to create a semi-stacked ensemble model, while weighted cross-entropy loss is employed to balanced training samples. We tested our method on two real ICS cybersecurity datasets and achieved accuracy that matches traditional aggregating ensembles like voting and stacking while significantly reducing inference computation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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