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Proceedings of the 41st International Conference on Machine Learning

作者： Xiaofan Bai Chaoxiang He Xiaojing Ma Bin Benjamin Zhu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security Microsoft School of Computer Science and Technology Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF.

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Generation of tunable superchiral spot in metal-insulator-metal waveguide

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Chinese Optics Letters 2023年第1期21卷 125-130页

作者：庄涛胡海峰詹其文 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The chiral feature of an optical field can be evaluated by the parameter of g-factor enhancement,which is helpful to enhance chiroptic signals from a chiral *** this work,the superchiral spot has been theoretically proposed in metal-insulator-metal *** g-factor enhancement of the superchiral spot can be enhanced by 67-fold more than that of circularly polarized light,and the spot is confined in the deep wavelength scale along each spatial ***,the position of the superchiral spot can be tuned by manipulating the incident *** tunable superchiral spot may find applications in chiral imaging and sensing.

关键词： circular dichroism superchiral spot radially polarized beam metal-insulator-metal waveguide

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Trust-based Authentication Aided Blockchain for Distributed Learning in UAV Swarms: Challenges and Solutions

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IEEE Network 2024年

作者： He, Daojing Song, Yuxiao Dai, Minghui Chan, Sammy Chen, Lei Guizani, Mohsen School Of Computer Science And Technology Shenzhen China Donghua University School Of Computer Science And Technology Shanghai China City University Of Hong Kong Department Of Electrical Engineering Hong Kong Hong Kong GuangDong Engineering Technology Research Center Of Cryptographic Product And System Evaluation Shenzhen China United Arab Emirates

The remarkable characteristics of unmanned aerial vehicles (UAVs), such as high flexibility and real-time data acquisition, make them a promising platform for providing services and applications like data collection and aerial computing. Furthermore, by integrating UAVs with decentralized learning, emerging machine learning (ML)-assisted services (e.g., urban sensing and natural resource exploration) can be enhanced. However, the limited computation and communication resources of UAVs and the potential security risks pose challenges to the reliability and security for distributed learning in UAV swarms. This necessitates suitable learning policy design and continuous supervision throughout the process of decentralized learning. In this article, motivated by the merits of blockchain, we propose a trust-based authentication aided blockchain for distributed learning in UAV swarms, along with a corresponding layered architecture to ensure effectiveness and security. This architecture includes a perception layer, an aerial layer, and a blockchain layer. Moreover, the trust values of UAVs are derived from their behaviors during the decentralized learning process. These values, combined with smart contracts, enable dynamic and robust authentication for UAV swarms. Based on this framework, we present its advantages and exemplified applications. Moreover, the potential challenges related to security and efficiency when applying the proposed framework in wireless networks are also discussed. Additionally, we present a case study on trust-based authentication aided blockchain for distributed learning in UAV swarms. This includes a detailed procedure and solutions to the associated challenges, demonstrating its performance in terms of learning efficiency and security. We finally discuss some open research directions regarding the blockchain-assisted decentralized learning for UAV swarms. © 1986-2012 IEEE.

关键词： Blockchain

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Prediction of diabetes and hypertension using multi-layer perceptron neural networks

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 120-137页

作者： Hani Bani-Salameh Shadi MAlkhatib Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod Ahed J Alkhatib Department of Software Engineering The Hashemite University ZarqaJordan Department of Computer Information System The Hashemite University ZarqaJordan Department of Legal Medicine School of Medicine JUSTJordan

Background:Diabetes and hypertension are two of the commonest diseases in the *** they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in ***:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these ***:This work used two online datasets which consist of data collected from 768 *** applied neural network algorithms to predict if the individuals have those two diseases based on some *** prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and ***:A model based on the Multi-Layer Perceptron Neural Network(MLP)was *** inputs of the network were the factors for each disease,while the output was the prediction of the disease’s *** model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of ***,a tool was implemented to help diagnose the diseases and to understand the ***:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for *** results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and *** shows MLPs effectiveness in early disease prediction.

关键词： Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools

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CW-YOLO: joint learning for mask wearing detection in low-light conditions

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Frontiers of computer Science 2023年第6期17卷 191-193页

作者： Mingqiang GUO Hongting SHENG Zhizheng ZHANG Ying HUANG Xueye CHEN Cunjin WANG Jiaming ZHANG School of Computer Science China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China Wuhan Zondy Cyber Technology Co.Ltd. Wuhan 430074China Shenzhen Data Management Center of Planning and Natural Resources Shenzhen 518000China Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources) Shenzhen 518000China College of Engineering Boston UniversityBoston 02215USA

1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and ***,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy *** unfavorable conditions lead to a series of image degradations that seriously hamper machine vision *** camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs.

关键词： hardware lighting weather

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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Cost-Effective Network Packet Manipulation and Error-Detection Device

Cost-Effective Network Packet Manipulation and Error-Detecti...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alexander F. K. Janus Steffen Moser Frank Slomka Faculty of Engineering Computer Science and Psychology Institute of Embedded System/Real-Time Systems School of Advanced Professional Studies Ulm University Ulm Germany

Real-time multimedia communication applications like video conferencing and screen sharing tools are an integral part of our daily communications. These time-critical services are handled via the Internet, a packet-switched and essentially best-effort network, not explicitly designed for robust real-time communication. From a video conferencing service provider's perspective, network issues cause a lot of user complaints due to various reasons: lossy WLAN channels, poorly configured home routers, or crowded carrier-grade network address translation devices. All of these issues have in common that they are often time-variant and non-deterministic. Furthermore, the end-to-end transmission in these highly inhomogeneous networks often suffers from a mixture of more than one error source. To enable a detailed and impartial analysis of multimedia service quality, it is necessary to analyze packets in defective online scenarios and introduce a broadly similar error pattern in an otherwise stable lab network. In this paper, we present SEDER, a hardware-software design of a layer two transparent Ethernet bridge that can be used for two purposes: error detection and error recreation. Implemented on low-cost hardware, the bridge can be easily deployed anywhere to allow debugging, even at a user's site. It is possible to parameterize our bridge to imitate almost all possible network effects, including complex combinations of more than one effect. In addition, we present two case studies, firstly emphasizing the capabilities of SEDER and secondly showing the need to use SEDER to harden open source Video Conferencing Tools against errors.

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 24

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent systems

作者： Xiaoqiang Wu Qingling Zhu Qiuzhen Lin Weineng Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400704864

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method.

关键词： evolutionary algorithm evolutionary reinforcement learning reinforcement learning

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Structure design and analysis of a rigid-soft robotic hand with large load capacity 3

Structure design and analysis of a rigid-soft robotic hand w...

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3rd International Conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Wu, Peichen Lan, Fuming Hong, Wen Chen, Xiaoping Shuai, Wei Cui, Guowei Gao, Yang Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Shanghai Institute of Aerospace System Engineering Shanghai201100 China Intelligent Manufacturing Institute of HFUT HeFei230041 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

In recent years, soft grippers have gained significant attention due to their adaptability and safety. However, when performing horizontal object grasping, their limited structural stiffness prevents them from withstanding the torque generated by friction between the gripper and the object, leading to a loss of contact and limiting their horizontal load capacity. To address this limitation while preserving the adaptability of soft grippers, this paper proposes a gripper structure called the rigid-soft robotic hand, which incorporates lateral rigid connecting rods outside the soft joints. The paper analyzes the bending capacity of the rigid-soft joint using kinematic methods and investigates the influence of link design parameters on maximum bending angle. Subsequently, a finite element analysis is conducted to study the finger structure with the added connecting rods, aiming to optimize the link design parameters. Experimental results demonstrate the grasping capabilities of the rigid-soft gripper in both vertical and horizontal orientations. The findings show that the rigid-soft gripper significantly improves the load-carrying capacity during horizontal object grasping while maintaining the adaptability of the soft gripper. © The Authors.

关键词： Robotic arms

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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