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arXiv 2023年

作者： Tao, Yunbo Liu, Daizong Zhou, Pan Xie, Yulai Du, Wei Hu, Wei Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Predictor-Based Constrained Fixed-Time Sliding Mode Control of Multi-Uav Formation Flight

SSRN

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SSRN 2024年

作者： Khodaverdian, Maria Hajshirmohamadi, Shahram Hakobyan, Aleksandr Ijaz, Salman Center for Scientific Innovation and Education Aerial Robotics Laboratory of National Polytechnic University of Armenia Yerevan Armenia Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Key Laboratory of More Electric Aircraft and Control System Lab University of Nottingham Ningbo China

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicles formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational ***, a constrained performance index is constructed based on the prediction of the fixed-time sliding surface, the reaching law, and the control input. Secondly, optimal control input is calculated by solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop system stability in the presence of external disturbances and ***, the comparative evaluation with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, a test is conducted to demonstrate the algorithm's potential for hardware implementation by employing Hardware-in-the-Loop (HIL) test. © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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A Novel Machine Learning Approach for Fast and Efficient Detection of Mango Leaf Diseases

A Novel Machine Learning Approach for Fast and Efficient Det...

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： S M Masfequier Rahman Swapno Sm Nuruzzaman Nobel Md Babul Islam R. Haque V. P. Meena F. Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine Learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99 %. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Training Plant diseases Accuracy Plantations Machine learning Safety Reliability

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API Usage Pattern Search Based on Model Checking 7th

API Usage Pattern Search Based on Model Checking

引用

7th International Symposium on Dependable Software engineering: Theories, Tools, and Applications, SETTA 2021

作者： Ding, Xue-er Niu, Jun Wang, Jia Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030912642

Reusing existing class libraries can improve the productivity of software development. API usage patterns are useful resources for programmers in reusing class libraries. Existing approaches often exploit API graphs to model semantic relations between API elements of class libraries, and traversal graphs to capture API usage patterns. However, those approaches cannot describe the number of parameters and the number of overload methods, and may not search for various API usage patterns. In this paper, we propose an automatic and complete approach to searching for API usage patterns by model checking technique. We introduce a novel kind of API transition system model to completely describe the relations between all API elements of existing class libraries. We obtain API usage constraints from queries, namely the numbers and types of input and output objects and class types transformed based on APIs replacement model, and give the logical characterizations of the constraints by suitable CTL* (computation tree logic) formulas. Then, we can obtain suitable API usage patterns by model checking the logical formulas in related API transition system models based on model checker NuSMV. The experiments indicate that, in contrast to existing approaches, our approach can automatically and effectively search for various API usage patterns. © 2021, Springer Nature Switzerland AG.

关键词： Model checking

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A Continuous Verification Mechanism for Clients in Federated Unlearning to Defend the Right to be Forgotten

A Continuous Verification Mechanism for Clients in Federated...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shanshan Chen Jun Liu Yuying Li Yamin Li Yang Yang Gaoyang Liu Chen Wang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In Federated Learning (FL), the regulatory need for the "right to be forgotten" requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***.

关键词： Training Privacy Distributed processing Accuracy Federated learning Training data Feature extraction Data models Servers Protection

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： Image reconstruction Computed tomography Planning Radiation therapy Diffusion models Biomedical imaging Image restoration Training Reconstruction algorithms Image quality

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A Summary of Research on Combustion Stability of Coal-fired Power Station Boilers

A Summary of Research on Combustion Stability of Coal-fired ...

引用

2021 China Automation Congress, CAC 2021

作者： Cai, Guoyuan Niu, Yuguang Liu, Xuefei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665426473

The research on the combustion stability of coal-fired power plant boilers is related to the safe operation and economic benefits of the boiler. The combustion stability judgement plays an important role for the assessment of flexibility retrofit with coal-fired power plants participating in deep peak-regulating operation. Therefore, the study of combustion stability judgment has always been a hot research direction. This paper briefly introduces the development history of boiler flame combustion stability research, and focuses on two popular research directions in this field in recent years, namely flame image combustion stability detection method and multiinformation fusion combustion Stability testing methods, briefly summarize the development trends of these two research directions and their respective advantages. Then, the current research status and problems in this field are put forward three aspects of thinking and suggestions. Finally, several problems that need to be solved in the future development of combustion stability research are summarized. © 2021 IEEE

关键词： Coal fired power plant

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A New SOC Distribution Synergy Method for Capacity Configuration of Flywheel Energy Storage in Primary Frequency Regulation

SSRN

引用

SSRN 2024年

作者： Hong, Feng Wei, Kuanchang Ji, Weiming Hao, Junhong Fang, Fang Liu, Jizhen The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Energy storage systems, coupled with power sources, are applied as an important means of frequency modulation support for large-scale grid connection of new energy. Flywheel energy storage systems (FESS) are considered short-term energy storage solutions due to their capacity for rapid and efficient energy storage and release. However, the power output characteristic of flywheels is potentially correlated to the state of charge (SOC) in the process of continuous operation while the SOC distribution would be attributed to the output power and duration of the flywheel. This study theoretically developed analytical correlations between SOC distribution and flywheel dynamic characteristics on statistical performance based on a cross-entropy method under wide range sampling scenarios of SOC. The results demonstrate that various operation states of FESS have a disparity effect on the statistical performance of modules. A SOC management and capacity configuration method considering continuous operation states is proposed, and the results illustrate that the capital expenditure of capacity configuration cuts approximately 8.2% attaining the SOC stability under the homogenous frequency modulation scenario. Therefore, capacity configurations of FESS can effectively compensate for the capital loss caused by detrimental SOC so that the cross-entropy correlation method conducted in series has the capacity advantages. © 2024, The Authors. All rights reserved.

关键词： Frequency modulation

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