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Pre-trained SAM as data augmentation for image segmentation

CAAI Transactions on Intelligence Technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

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Intelligent Automation & Soft Computing 2023年第7期37卷 1-16页

作者： Jixun Gao Bingyi Hu Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao School of Computer Henan University of EngineeringZhengzhou451191China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Computer Hubei University of Arts and ScienceXiangyang441053China School of Software Engineering Anyang Normal UniversityAnyang455000China School of Computer and Information Engineering Henan Normal UniversityXinxiang453003China The Publicity Department Zhengzhou University of TechnologyZhengzhou450044China

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service ***,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste *** the backup virtual machine under the above circumstances can effectively improve resource ***,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for *** deployment locations have different resource utilization and average service response *** want toﬁnd a balanced solution that minimizes the average service response time of the IoT application while maximizing resource *** this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment ***,the simulation results show that the TDA can signiﬁcantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical *** experimental results conﬁrm that the perfor-mance of TDA is better than that of other two methods.

关键词： Mobile edge computing overbooking resource utilization service response time task deployment algorithm

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Internet of Things (IoT) applications security trends and challenges

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Discover Internet of Things 2024年第1期4卷 1-22页

作者： Laghari, Asif Ali Li, Hang Khan, Abdullah Ayub Shoulin, Yin Karim, Shahid Khani, Muhammad Adnan Kaim Software College Shenyang Normal University Shenyang China Department of Computer Science Bahria University Karachi Campus Karachi75000 Pakistan School of Information Engineering Xi’an Eurasia University Shaanxi Xian710065 China Department of Computer Science Ilma University Karachi Pakistan

The idea behind layered design is the foundation of the Internet of Things. Each tier uses a variety of technologies for capacity, preparation, and information transmission. With regard to the risks and vulnerabilities related to IoT-enabled devices, as well as potential assurance mechanisms in light of equipment constraints and novel information transfer techniques, this study attempts to analyze the current architecture of the Internet of Things. After that, we talk about IoT architecture and applications. The following is a list of effective real-time IoT applications that are now in use: Self-driving cars, traffic management systems, smart grids, logistic management hierarchies, environment monitoring, building safety applications, and many more are examples of emerging technologies. Specifically, we highlight the latest improvements and challenges associated with IoT security. Finally, we discuss the state-of-the-art research and its conclusions, as well as the outstanding problems for future IoT security research. © The Author(s) 2024.

关键词： Cloud security

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Diverse Co-saliency Feature Learning for Text-Based Person Retrieval

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5465-5477页

作者： You, Shuai Chen, Cuiqun Feng, Yujian Liu, Hai Ji, Yimu Ye, Mang School of Internet of Things Nanjing China Anhui University School of Computer Science and Technology Hefei China South China Normal University school of computer Guangdong China NJUPT School of Computer Science Nanjing China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan430072 China

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning Network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly. © 2005-2012 IEEE.

关键词： Feature extraction

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Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive Control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism 3

Underwater Image Enhancement Method Based on CGAN and Parall...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Ma, Chi Yang, Le Hu, Hui Chen, Yuenai Bu, Anxu The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798350335972

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study. © 2023 IEEE.

关键词： Pixels

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Multi-Optimiser Training for GANs Based on Evolutionary Computation 13

Multi-Optimiser Training for GANs Based on Evolutionary Comp...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Yixia Xue, Yu Neri, Ferrante School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350308365

Generative adversarial networks (GANs) are widely recognized for their impressive ability to generate realistic data. Despite the popularity of GANs, training them poses challenges such as mode collapse and instability. To address these issues, many variants enhance GAN performance through improvements in network architecture, modifications to loss functions, and the inclusion of regularization techniques. However, a limited number of methods focuses on optimising GAN performance from the optimiser's perspective, despite the distinct roles different optimisers play in training. Existing GANs typically employ a single optimiser throughout, with Adam being the default choice for GAN training. Approaches using multiple optimisers have shown improved performance but are often task-specific. This paper introduces a novel, portable approach that lever-ages the strengths of multiple training methods, with an evolutionary supervisor coordinating the training of different sections of the network. Initially, real-number-encoded vectors representing the optimiser for each sub-parameter layer undergo pro-gressive enhancement using a standard evolutionary algorithm (SGA). Through the evolutionary process, optimal optimiser combinations are retained based on the performance of the trained GAN. The proposed method, SGA-GANs, is validated on the CIFAR10 dataset by integrating the steps into five benchmark GAN models: GAN, DCGAN, BEGAN, WGAN, and WGAN-GP. Experimental results demonstrate that SGA-GANs outperforms single optimiser training methods, achieving superior evaluation results and generating higher-quality images. Source code can be found at https://***/lizzhang-spec/SGA-GANs. © 2024 IEEE.

关键词： Generative adversarial networks

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Minimizing the cost of periodically replicated systems via model and quantitative analysis

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Frontiers of computer science 2024年第5期18卷 11-24页

作者： Chenhao ZHANG Liang WANG Limin XIAO Shixuan JIANG Meng HAN Jinquan WANG Bing WEI Guangjun QIN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China Smart City College Beijing Union UniversityBeijing 100101China

Geographically replicating objects across multiple data centers improves the performance and reliability of cloud storage *** consistent replicas comes with high synchronization costs,as it faces more expensive WAN transport prices and increased *** replication is the widely used technique to reduce the synchronization *** replication strategies in existing cloud storage systems are too static to handle traffic changes,which indicates that they are inflexible in the face of unforeseen loads,resulting in additional synchronization *** propose quantitative analysis models to quantify consistency and synchronization cost for periodically replicated systems,and derive the optimal synchronization period to achieve the best tradeoff between consistency and synchronization *** on this,we propose a dynamic periodic synchronization method,Sync-Opt,which allows systems to set the optimal synchronization period according to the variable load in clouds to minimize the synchronization *** results demonstrate the effectiveness of our *** with the policies widely used in modern cloud storage systems,the Sync-Opt strategy significantly reduces the synchronization cost.

关键词： periodic replication consistency maintenance synchronization cost synchronization strategy

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A Reputation Layered Coding Based Storage Strategy for Consortium Blockchain 22

A Reputation Layered Coding Based Storage Strategy for Conso...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ziqian Pan, Maolin Yu, Yang Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9798331509712

The improvement of consensus algorithms has greatly enhanced the performance of consortium blockchain, making it possible to be applied in large-scale network scenarios such as finance, healthcare and supply chain management. However, better performance often results in a heavier storage burden on the nodes. A storage strategy based on erasure code is a good solution, capable of reducing the storage consumption of each block to O(1). However, in large-scale consortium blockchain, this storage strategy encounters issues like poor performance and limited system dynamicity. To address the issues associated with the application of coding-based storage strategies in large-scale consortium blockchain, this paper proposes a storage strategy based on reputation-layered coding, named Level-Store. Level-Store first partitions the nodes into different storage units based on their reputations, then allocates the storage tasks of different block segments to different storage units for encoded storage. By calculating reasonable storage schemas for each storage unit, the data availability is ensured. Experiment shows that compared to existing coding-based storage strategies, Level-Store significantly improves system performance and dynamicity. It demonstrates notable advancements in key metrics such as space overhead, time consumption for storage and retrieval, the number of nodes and chunks affected by re-encoding, and the time consumption for re-encoding. Additionally, the system's security and fairness have also been enhanced. © 2024 IEEE.

关键词： Blockchain

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