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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Youwang Chen Yunkai Deng Weiming Tian Radar Technology Research Institute Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronic Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed.

关键词： Adaptation models Accuracy Heuristic algorithms Delay effects Switches Numerical simulation Data processing Numerical models

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A Self-supervised SAR Image Despeckling Strategy Based on Parameter-sharing Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Liang Yin, Yifei Shi, Hao Sheng, Qingqing Li, Wei The Radar Research Laboratory Beijing Institute of Technology Beijing10081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China The Beijing Key Laboratory of Embedded Real-time Processing Technology Beijing100081 China The Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing10081 China The School of Information and Electronics Beijing Institute of Technology Beijing10081 China

Speckle noise is generated due to the SAR imaging mechanism, which brings difficulties in SAR image interpretation. Hence, despeckling is a helpful step in SAR pre-processing. Nowadays, deep learning has been proved to be a progressive method for SAR image despeckling. Most deep learning methods for despeckling are based on supervised learning, which needs original SAR images and speckle-free SAR images to train the network. However, the speckle-free SAR images are generally not available. So, this issue was tackled by adding multiplicative noise to optical images synthetically for simulating speckled image. Therefore, there are following challenges in SAR image despeckling: (1) lack of speckle-free SAR image;(2) difficulty in keeping details such as edges and textures in heterogeneous areas. To address these issues, we propose a self-supervised SAR despeckling strategy that can be trained without speckle-free images. Firstly, the feasibility of SAR image despeckling without speckle-free images is proved theoretically. Then, the sub-sampler based on the adjacent-syntropy criteria is proposed. The training image pairs are generated by the sub-sampler from real-word SAR image to estimate the noise distribution. Furthermore, to make full use of training pairs, the parameter sharing convolutional neural networks are adopted. Finally, according to the characteristics of SAR images, a multi-feature loss function is proposed. The proposed loss function is composed of despeckling term, regular term and perception term, to constrain the gap between the generated paired images. The ability of edge and texture feature preserving is improved simultaneously. Finally, qualitative and quantitative experiments are validated on real-world SAR images, showing better performances than several advanced SAR image despeckling methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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FedCross: Intertemporal Federated Learning Under Evolutionary Games

arXiv

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arXiv 2024年

作者： Lu, Jianfeng Zhang, Ying Jia, Riheng Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China School of Computer Science and Technology Zhejiang Normal University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Addressing this challenge involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose an intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. Specifically, FedCross comprises two distinct stages. In Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning

arXiv

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arXiv 2024年

作者： Hu, Gangqiang Lu, Jianfeng Han, Jianmin Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Zhejiang Normal University China School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Universityof Science and Technology China

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients' communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi- Markov model to estimate clients' communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss. © 2024, CC BY.

关键词： Federated learning

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A Dual-Population Algorithm Based on Self-Adaptive Epsilon Method for Constrained Multi-Objective Optimization

SSRN

引用

SSRN 2023年

作者： Song, Shiquan Zhang, Kai Zhang, Ling Wu, Ni School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

Balancing multiple objectives and various constraints is crucial for effectively solving constrained multi-objective optimization problems (CMOPs). Excessive focus on either convergence or feasibility may not result in favorable outcomes of the algorithm. To confront this challenge, this paper proposes a cooperative evolutionary algorithm named SaE-CMO, which aims to attain the constrained Pareto front by extracting favorable information from promising solutions in both feasible and infeasible regions. To achieve this, SaE-CMO employs a dual-population approach to enhance search progress, consisting of a main population, Population1, and an auxiliary population, Population2. These two populations complement each other to achieve optimal results. A newly proposed self-adaptive epsilon method is employed in both Population1 and Population2, using different comparison criteria to select next population from mating pools, respectively. Population2 can retain some solutions that are well-constrained but poorly converged, thereby preserving information about both the constrained and the unconstrained Pareto front. This property enables Population2 to assist Population1 in maintaining diversity in certain complex CMOPs. To verify the effectiveness of SaE-CMO, we conduct experiments on three benchmark test instances and four real-world CMOPs with some related state-of-art constrained multi-objective optimization algorithms, experimental results prove that the proposed algorithm outperforms the compared algorithms. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Wu, Shushu Liu, Fang Zhang, Kai School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China

ISBN: (纸本)9789811613531

For the sake of enhancing the accuracy of short text similarity calculation, a short text similarity calculation method on account of Jaccard and semantic mixture is proposed. Jaccard is a traditional similarity algorithm based on literal matching. It only considers word form, and its semantic calculation has certain limitations. The word vector can represent the semantic similarity by computing the cosine similarity of two terms in the vector space, and the semantic similarity is obtained by adding and averaging the word similarity of two sentences according to a certain method. The two methods are now weighted to compute the final text similarity. Experiments show that the algorithm improves the recall rate and F value of short text calculation to some extent. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Quality-Oriented Task Assignment for Heterogeneous Users in Mobile Crowdsensing

Quality-Oriented Task Assignment for Heterogeneous Users in ...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Kang Chen Peng Li Weiyi Huang Lei Nie Haizhou Bao Qin Liu School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China School of Cyber Science and Engineering Wuhan University China

Mobile crowdsensing (MCS) is a potential technology for large-scale data collection. This technology requires the platform to recruit users to complete tasks in specific areas. A vital issue in MCS is task assignment, and most existing task assignment efforts consider only a single user type, which is not reasonable in real scenarios. Task assignment becomes more complicated when the platform tries to assign tasks to heterogeneous users with a limited budget in the platform. In this paper, we consider a quality-oriented task assignment for heterogeneous users problem. Professional users have a high sensing quality with a high cost, and normal users have a low sensing quality with a low cost First, we model the sensing capabilities and the costs of different users and formulate the quality-oriented task assignment for heterogeneous users problem. Then, we design the integer linear programming form and prove that the problem is NP-hard. By verifying the submodularity of the objective function, we present a greedy algorithm. Considering the inefficiency of the algorithm, we design two genetic algorithms to improve the total sensing quality. Finally, we evaluate the proposed algorithms under different cost cases based on a real dataset The results show that our proposed algorithm performs well under different cost distribution scenarios.

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SMWT: A Universal and Lightweight SDN-Based Meter Table Watermarking Traceback Scheme

SMWT: A Universal and Lightweight SDN-Based Meter Table Wate...

引用

Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Heng He Sen Yang Xiaohu Zhou Jia Wang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Guilin Medical University Guilin China

The use of anonymous network has increased dramatically due to the development of network technology and people focus on the network security and privacy. However, anonymous networks are often used for illegal purposes, which has caused a significant increase in illegal transactions in the network. Therefore, it is necessary to investigate methods that can trace illegal transactions in anonymous networks. In this paper, we propose a universal and lightweight Software Defined Network (SDN)-based Meter table Watermarking Traceback scheme (SMWT), which is an active watermarking technology. SMWT uses the Bose-Chaudhuri-Hocquenghem (BCH) code as the encoding method and the flow rate as the carrier for the watermark to effectively trace flows of different communication protocols in various anonymous network environments. It utilises the SDN controller to install flow table entries and meter entries to the SDN switch, which can efficiently change the flow rate and embed the watermark in the flow in a stealthily manner. After the flow passes through the anonymous network, the SDN switch can extract the watermark using the flow rate and identify the two communicating parties. We have conducted experiments to evaluate SMWT in a real anonymous network Tor. Compared with other relevant existing schemes, SMWT demonstrated a higher detection rate of 96.7% and a lower false positive rate of 0.3%, as well as high flexibility and stealthiness, low deployment difficulty and controller load.

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Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing

Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Xiaobo Wei Peng Li Weiyi Huang Zhiyuan Liu Qin Liu School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China School of Cyber Science and Engineering Wuhan University China

Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework.

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