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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

引用

IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

arXiv

引用

arXiv 2024年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 2024, CC BY.

关键词： Qubits

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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Quantum Recursive Programming with Quantum Case statements

arXiv

引用

arXiv 2023年

作者： Ying, Mingsheng Zhang, Zhicheng The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences The Department of Computer Science and Technology Tsinghua University China The Centre for Quantum Software and Information University of Technology Sydney Australia

We introduce a novel scheme of quantum recursive programming, in which large unitary transformations, i.e. quantum gates, can be recursively defined using quantum case statements, which are quantum counterparts of conditionals and case statements extensively used in classical programming. A simple programming language for supporting this kind of quantum recursion is defined, and its semantics is formally described. A series of examples are presented to show that some quantum algorithms can be elegantly written as quantum recursive programs. © 2023, CC BY.

关键词： Semantics

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Performance Analysis of IRS-Assisted Multi-Cell Data and Energy Integrated Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Bingxin Yang, Kun Wang, Kezhi Zhang, Guopeng Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China Nanjing University School of Intelligent Software and Engineering Suzhou Campus Suzhou215163 China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China

Intelligent reflecting surface (IRS) can significantly enhance the performance of data and energy integrated networks (DEIN) by adjusting its amplitude and/or phase. However, there is a lack of comprehensive performance analysis model for realistic DEIN where multiple cells exist rather than only one cell as assumed by most existing work. In this paper, we consider an IRS-assisted multi-cell DEIN. Specifically, in the downlink wireless energy transfer (WET) stage, the hybrid access point (HAP) in each cell broadcasts radio frequency (RF) energy signals to edge user equipments (UEs). Subsequently, during the uplink wireless information transfer (WIT) stage, the edge UEs employ the harvested energy to send their information to the HAP. We first represent the statistical characteristics of the signal-to-interference-plus-noise ratio (SINR) at the edge UE. Then, we derive the closed-form expressions for outage probability, ergodic rate and average symbol error probability of the edge UE in the typical cell. To gain more insights, we obtain the minimum required number of reflection elements and a sub-optimal solution for time allocation coefficients. Finally, extensive numerical results are provided to validate the correctness of the theoretical results. © 2002-2012 IEEE.

关键词： data and energy integrated network (DEIN) Intelligent reflecting surface (IRS) multi-cell performance analysis wireless energy transfer (WET)

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Minimizing Link Generation in Constraint Checking for Context Inconsistency Detection

Minimizing Link Generation in Constraint Checking for Contex...

引用

International Symposium on software Reliability Engineering (ISSRE)

作者： Chuyang Chen Huiyan Wang Lingyu Zhang Chang Xu Ping Yu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Adaptive applications rely on conditions about their environments (or contexts) to deliver smart services, e.g., location-aware services. Due to inherent noises in environmental sensing and interpretation, there is an increasing demand for guarding the consistency of contexts to avoid application misbehavior, and at the same time minimizing the guarding cost. Existing work has tried to reduce the cost by speeding up the kernel constraint checking module inside the consistency guarding process. Most efforts have been spent on reusing previous checking results or checking constraints in parallel, while leaving untouched one central problem of link generation, the step that consumes a substantially large part of the total time cost for explaining why constraints have been violated. In this paper, we propose a novel technique, MG, to automatically identify and remove redundant link generation, without harming any checking result. We show that MG is sound (always checking correctly) and complete (removing all redundancy). Our experiments with synthesized and real-world consistency constraints reported that compared with existing work, MG achieved significant efficiency improvements on the link generation (tens to hundreds times speedup), and could reduce the total constraint checking time up to 45.4%.

关键词： Costs Runtime Redundancy software reliability Sensors Time factors Kernel

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Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

arXiv

引用

arXiv 2022年

作者： Qian, Yi Chen, Ligeng Wang, Yuyang Mao, Bing State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

Function signature recovery is important for many binary analysis tasks such as control-flow integrity enforcement, clone detection, and bug finding. Existing works try to substitute learning-based methods with rule-based methods to reduce human *** made considerable efforts to enhance the system’s performance, which also bring the side effect of higher resource consumption. However, recovering the function signature is more about providing information for subsequent tasks, and both efficiency and performance are significant. In this paper, we first propose a method called Nimbus for efficient function signature recovery that furthest reduces the whole-process resource consumption without performance loss. Thanks to information bias and task relation (i.e., the relation between parameter count and parameter type recovery), we utilize selective inputs and introduce multi-task learning (MTL) structure for function signature recovery to reduce computational resource consumption, and fully leverage mutual information. Our experimental results show that, with only about the one-eighth processing time of the state-of-the-art method, we even achieve about 1% more prediction accuracy over all function signature recovery tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Overview of Blockchain Privacy Protection 8

Overview of Blockchain Privacy Protection

引用

8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Yang, Ce Chen, Juan Zeng, Bin Liao, Lei School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan YouDao Information Technology Co. Ltd. Changsha China Hunan Vocational College of Art Changsha China

ISBN: (纸本)9781665480697

In recent years, blockchain technology has attracted extensive attention in academia and industry by virtue of its unique advantages such as decentralization, tamper proof, openness and traceability. The combination of blockchain and data protection has always been the focus of scholars. This paper first analyzes the system structure and characteristics of the blockchain itself, and introduces the development process of the blockchain. Secondly, it analyzes the privacy threats faced by data protection in the blockchain, and introduces the relevant blockchain privacy protection technologies. Finally, it discusses how to combine blockchain and other technologies to enhance privacy protection. © 2022 IEEE.

关键词： Blockchain

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SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

arXiv

引用

arXiv 2025年

作者： Peng, Yubo Xiang, Luping Yang, Kun Jiang, Feibo Wang, Kezhi Wu, Dapeng Oliver State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Suzhou China School of Information Science and Engineering Hunan Normal University Changsha China Department of Computer Science Brunel University London United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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