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Efficient Routing Protection Algorithm in Large-Scale Networks

computers, Materials & Continua 2021年第2期66卷 1733-1744页

作者： Haijun Geng Han Zhang Yangyang Zhang School of Software Engineering Shanxi UniversityTaiyuan030006China Institute of Big Data Science and Industry Shanxi UniversityTaiyuan030006China School of Cyber Space and Technology Beihang UniversityBeijing100191China College of Engineering Northeastern University Boston02115USA

With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become ***,a large number of relevant studies have shown that network failures occur on the Internet inevitably and *** current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network *** the reconvergence process,the packets may be lost because of inconsistent routing information,which reduces the network’s availability greatly and affects the Internet service provider’s(ISP’s)service quality and reputation ***,improving network availability has become an urgent *** such,the Internet engineering Task Force suggests the use of downstream path criterion(DC)to address all single-link failure ***,existing methods for implementing DC schemes are time consuming,require a large amount of router CPU resources,and may deteriorate router ***,the computation overhead introduced by existing DC schemes is significant,especially in large-scale ***,this study proposes an efficient intra-domain routing protection algorithm(ERPA)in large-scale *** analysis indicates that the time complexity of ERPA is less than that of constructing a shortest path *** results show that ERPA can reduce the computation overhead significantly compared with the existing algorithms while offering the same network availability as DC.

关键词： Large-scale network shortest path tree time complexity network failure real-time and mission-critical applications

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IIoT Mobile Business data Placement Strategy Based on Bayesian Optimization Algorithm 24

IIoT Mobile Business Data Placement Strategy Based on Bayesi...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shen, Zhijie Liu, Bowen Xu, Xiaolong Qi, Lianyong Dai, Fei Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350319934

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies. © 2022 IEEE.

关键词： Edge computing

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus Image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yindong Zhang Jie Chen Li Wang Miaohong Chen Guoming Zhang Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Shenzhen Eye Hospital China

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation.

关键词：

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A Survey of data Partitioning and Sampling Methods to Support big data Analysis

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big data Mining and Analytics 2020年第2期3卷 85-101页

作者： Mohammad Sultan Mahmud Joshua Zhexue Huang Salman Salloum Tamer Z.Emara Kuanishbay Sadatdiynov National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityUniversityShenzhen 518060China Big Data Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China.

computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and *** cluster computing,data partitioning and sampling are two fundamental strategies to speed up the computation of big data and increase *** this paper,we present a comprehensive survey of the methods and techniques of data partitioning and sampling with respect to big data processing and *** start with an overview of the mainstream big data frameworks on Hadoop *** basic methods of data partitioning are then discussed including three classical horizontal partitioning schemes:range,hash,and random *** partitioning on Hadoop clusters is also discussed with a summary of new strategies for big data partitioning,including the new Random Sample Partition(RSP)distributed *** classical methods of data sampling are then investigated,including simple random sampling,stratified sampling,and reservoir *** common methods of big data sampling on computing clusters are also discussed:record-level sampling and blocklevel ***-level sampling is not as efficient as block-level sampling on big distributed *** the other hand,block-level sampling on data blocks generated with the classical data partitioning methods does not necessarily produce good representative samples for approximate computing of big *** this survey,we also summarize the prevailing strategies and related work on sampling-based approximation on Hadoop *** believe that data partitioning and sampling should be considered together to build approximate cluster computing frameworks that are reliable in both the computational and statistical respects.

关键词： big data analysis data partitioning data sampling distributed and parallel computing approximate computing

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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data Poisoning Attack to X-armed Bandits 21

Data Poisoning Attack to X-armed Bandits

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21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022

作者： Luo, Zhi Li, Youqi Chen, Lixing Xu, Zichuan Zhou, Pan Huazhong University of Science and Technology School of Cyber Science and Engineering China Beijing Institute of Technology School of Cyberspace Science and Technology School of Computer Science China Shanghai Jiao Tong University Institute of Cyber Science and Technology China Dalian University of Technology School of Software China Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China

ISBN: (纸本)9781665494250

X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine learning tasks, specially in big data-driven personalized recommendation. However, bandit algorithms are vulnerable to adversarial attacks. Existing works mainly focus on attacking multi-armed bandits in discrete setting;nevertheless, the attacks against X-armed bandits in continuous setting have not been well explored. In this paper, we aim to bridge this gap and investigate the robustness problem for the X-armed bandits. Specifically, we consider data poisoning attack and propose an attack algorithm named Confidence Poisoning Attack algorithm, which could hijack the clean tree-based X-armed bandits algorithm, i.e., high confidence tree (HCT) and make it choose the nodes including the arm targeted by the attacker very frequently with a sub-linear attack cost, i.e., O(Tα)(0 © 2022 IEEE.

关键词： Stochastic systems

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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification

arXiv

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arXiv 2024年

作者： Yan, Jiexuan Huang, Sheng Mu, NanKun Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP’s embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over baselines. The code is available at https://***/jiexuanyan/CPRFL. © 2024, CC BY-NC-SA.

关键词： Semantics

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