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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Hao Wang Huan Zhou Mingze Lit Liang Zhao Victor C.M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

This paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we formu-late the optimization problem with the goal of minimizing long-term content delivery delay. Second, we model the optimization problem as a Partially Observable Markov Decision Process, and propose a Federated Distributed Deep Deterministic Policy Gradient-based method (FD3PG) to solve the corresponding problem. In conclusion, simulation results demonstrate that the proposed FD3PG achieves not only a much lower content delivery delay but also a higher cache hit rate compared with other baselines in various scenarios.

关键词： Simulation Markov decision processes Conferences Channel allocation Deep reinforcement learning Delays Optimization

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Single frame memory-effect based bispectral analysis for high-resolution imaging through scattering media

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Chinese Optics Letters 2025年第3期 43-49页

作者：张攀刘远远詹其文 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Zhangjiang Laboratory

Imaging through scattering media remains a formidable challenge in optical *** the memory effect presents new opportunities for non-invasive imaging through the scattering medium by leveraging speckle *** speckle correlation imaging often utilizes a random phase as the initial phase,leading to challenges such as convergence to incorrect local minima and the inability to resolve ambiguities in object ***,a novel method for high-quality reconstruction of single-shot scattering imaging is *** employing the initial phase obtained from bispectral analysis in the subsequent phase retrieval algorithm,the convergence and accuracy of the reconstruction process are notably ***,a robust search technique based on an image clarity evaluation function successfully determines the optimal reconstruction *** study demonstrates that the proposed method can obtain high-quality reconstructed images compared with the existing scattering imaging *** innovative approach to non-invasive single-shot imaging through strongly scattering media shows potential for applications in scenarios involving moving objects or dynamic scattering imaging scenes.

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Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Y. Chen, B. Zhou, Y. Ye, C. Cao, Q. Huang, P. Kim, C. Zheng, Y. Oxenløwe, L.K. Yvind, K. Li, J. Li, J. Zhang, Y. Dong, C. Fu, S. Zhan, Q. Wang, X. Liu, J. Pu, M. DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Physics School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Zhangjiang Laboratory Shanghai China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China

We demonstrate bright soliton generation at room temperature within a grating-dressed AlGaAs-on-insulator microresonator and the simultaneous emission of phase-locked vortices, enabling the synthesis of self-torque pu... 详细信息

ISBN: (纸本)9781957171395

关键词： Microcavities

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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A Custom Loss Function for Machine Learning-based Resource Allocation Policies

A Custom Loss Function for Machine Learning-based Resource A...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Nidhi Simmons David E. Simmons Rashika Raina Michel Daoud Yacoub Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Dhali Holidings Ltd. Belfast UK Wireless Technology Laboratory School of Electrical and Computer Engineering University of Campinas Campinas Brazil

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

In this contribution, we present a new approach to minimize the outage probability of a machine learning-based (ML-based) resource allocation system by exercising a novel loss function. We demonstrate our approach on a single-user multi-resource greedy allocation system equipped with an ML model. Other resource allocation policies are apt for use though this is not the focus of our work. The ML model’s task is to assign resources to the user while minimizing outages. The critical issue is that this ML model has no access to any future channel state information but relies on its historical state to make future resource outage predictions. We first present outage probability expressions for this system. Following this, a custom loss function is presented that closely approximates the outage probability while also satisfying fundamental differentiable properties. Our experimental findings demonstrate that this loss function (i) is capable of effectively training our ML model in the presence of considerable biases in the training dataset, and (ii) significantly and invariably outperforms two commonly used loss functions, namely, the binary cross entropy (BCE) and mean squared error (MSE), in practical cases achieving improvements of two order of magnitude.

关键词： Training Machine learning Probability Predictive models Entropy Power system reliability Resource management

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Robust Monocular Camera-Based Localization in Industrial Environments

Robust Monocular Camera-Based Localization in Industrial Env...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yungu Won Yein Choi Hwangryol Ryu Youngmin Moon Kihoon Kwon Sung Soo Hwang Dept. of Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea Smart Engineering Research Laboratory Research Institute of Industrial Science & Technology Pohang Republic of Korea School of Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

This paper proposes a camera-based system for robust localization of workers or robots in large-scale factories and industrial sites. The system is designed with the convenience and efficiency of workers in mind, providing stable tracking capabilities. To achieve this, a monocular camera is attached to a safety helmet, enabling localization without restricting the worker's range of motion. Additionally, by combining Visual Simultaneous Localization and Mapping and Visual Place Recognition, the system tracks the location of workers or robots while minimizing tracking loss, ensuring consistent localization. Our methodology has enhanced the accuracy and reliability of position estimation by approximately 46.0%. By accurately identifying the location of workers or robots, the system aims to prevent accidents and ensure swift responses in emergency situations.

关键词： Location awareness Visualization Accuracy Head Tracking Service robots Estimation Cameras Production facilities Safety

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The connection between meridians and physiological functions:A quantum principle

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Nano Research 2023年第11期16卷 12817-12820页

作者： Xiang-Yu Kong Min Xiang Jiancheng Fang Dong Han Bo Song Lei Jiang CAS Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field InfrastructureHangzhou 310051China CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China School of Optical-Electrical Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China

In the long history of traditional Chinese medicine(TCM),meridians play essential roles as the critical network to regulate the normal physiological functions of the human *** are regarded to be the channels connecting the internal organs with the body surface and various parts of the *** there are many studies and doctrines trying to reveal the nature of meridians for their validation in TCM,the mechanism underlying the meridians remains ***,based on our macroscopic quantum state concept of ion channels(i.e.,sub-nanometer scale channels),we propose a quantum principle of *** acupoints and organ symptom are in a macroscopic coherence state of the ion channels in *** applying TCM treatments(e.g.,TCM massage,acupuncture,moxibustion,and electroacupuncture)on the acupoint,the corresponding organ symptom could be well regulated with help of quantum meridian state.

关键词： traditional Chinese medicine meridians macroscopic quantum state external stimulation

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Simultaneous Communication and Localization for Double-RIS Aided Multi-UE ISAC Systems 23

Simultaneous Communication and Localization for Double-RIS A...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Gan, Xu Huang, Chongwen Yang, Zhaohui Zhong, Caijun Chen, Xiaoming He, Jiguang Zhang, Zhaoyang Guo, Qinghua Yuen, Chau Debbah, Merouane College of Information Science and Electronic Engineering Zhejiang University China Xidian University State Key Laboratory of Integrated Service Networks China Technology Innovation Institute Abu Dhabi United Arab Emirates School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia School of Electrical and Electronics Engineering Nanyang Technological University Singapore Khalifa University of Science and Technology United Arab Emirates

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, a low-complexity simultaneous communication and localization algorithm is proposed, which employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization and provide an effective throughput improvement over 143%. © 2023 IEEE.

关键词： Bayesian learning Integrated sensing and communication (ISAC) reconfigurable intelligent surface (RIS) superimposed coding

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FAMOS: robust privacy-preserving authentication on payment apps via federated multi-modal contrastive learning 24

FAMOS: robust privacy-preserving authentication on payment a...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yifeng Cai Ziqi Zhang Jiaping Gui Bingyan Liu Xiaoke Zhao Ruoyu Li Zhe Li Ding Li Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education and School of Computer Science Peking University Department of Computer Science University of Illinois Urbana-Champaign School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science Beijing University of Posts and Telecommunications Ant Group

ISBN: (纸本)9781939133441

The rise of mobile payment apps necessitates robust user authentication to ensure legitimate user access. Traditional methods, like passwords and biometrics, are vulnerable once a device is compromised. To overcome these limitations, modern solutions utilize sensor data to achieve user-agnostic and scalable behavioral authentication. However, existing solutions face two problems when deployed to real-world applications. First, it is not robust to noisy background activities. Second, it faces the risks of privacy leakage as it relies on centralized training with users' sensor *** this paper, we introduce FAMOS, a novel authentication framework based on federated multi-modal contrastive learning. The intuition of FAMOS is to fuse multi-modal sensor data and cluster the representation of one user's data by the action category so that we can eliminate the influence of background noise and guarantee the user's privacy. Furthermore, we incorporate FAMOS with federated learning to enhance performance while protecting users' privacy. We comprehensively evaluate FAMOS using real-world datasets and devices. Experimental results show that FAMOS is efficient and accurate for real-world deployment. FAMOS has an F1-Score of 0.91 and an AUC of 0.97, which are 42.19% and 27.63% higher than the baselines, respectively.

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Intrusion Response System for In-Vehicle Networks: Uncertainty-Aware Deep Reinforcement Learning-based Approach

Intrusion Response System for In-Vehicle Networks: Uncertain...

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MILCOM, Military Communications Conference

作者： Han Jun Yoon David Soon Terrence J. Moore Seunghyun Yoon Hyuk Lim Dongseong Kim Frederica F Nelson Jin-Hee Cho Department of Computer Science Virginia Tech USA US DEVCOM Army Research Laboratory USA School of Energy Engineering Korea Institute of Energy Technology (KENTECH) Republic of Korea School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

Modern vehicles use the Controller Area Network (CAN) bus system to manage communication between electronic control units (ECUs). The CAN bus lacks authentication and authorization mechanisms, and cryptographic protections are rarely used. This creates vulnerabilities to attacks such as Denial of Service, spoofing, and fuzzing, which can disrupt ECU functionality. Injection attacks through external connections (e.g., telematic unit, head unit, OBD-II port) can cause ECU malfunctions, leading to abnormal vehicle behavior or catastrophic events like car crashes. To address these issues, we propose a deep reinforcement learning (DRL)-based intrusion response system (IRS). Upon detecting an attack with an intrusion detection system (IDS), our IRS responds with the optimal defense strategy, maximizing defense utility. Our goal is to minimize the attack's success while ensuring mission completion within deadlines. We define an action space representing defense strategies for detected intrusions. Our extensive experiments prove that our IRS significantly outperforms state-of-the-art and baseline counterparts in minimizing the attack's success by up to 60% and maximizing the mission performance by up to 70%. This work is the first to propose a DRL-based IRS for managing multiple attacks in in-vehicle networks under ML-based IDS alerts.

关键词： Military communication Authorization Intrusion detection Authentication Fuzzing Telematics Deep reinforcement learning Cryptography Automobiles Protection

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