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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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END-TO-END GLOBAL SEGMENTATION OF POLSAR IMAGES WITH data AUGMENTATION

END-TO-END GLOBAL SEGMENTATION OF POLSAR IMAGES WITH DATA AU...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Zehua Wang, Zezhong Qiu, Xiaolan Zhang, Zhe Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Deep neural networks have demonstrated impressive performance in PolSAR image classification. However, the lack of labelled data and the complexity of land boundaries hindering the model's ability to achieve high-quality classification results. In order to address these inherent challenges, this paper introduces an augmentation method designed to maximize the utility of available labelled data. Furthermore, we present an end-to-end PolSAR image classification network, which we refer to as PolSARMixer. Our experimental results indicate that our proposed methodology significantly streamlines the classification process for PolSAR images when compared to traditional methods. Moreover, our proposed network exhibits superior performance in terms of classification accuracy and efficiency. © The Institution of Engineering & Technology 2023.

关键词： Image classification

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DreamLayer: Simultaneous Multi-Layer Generation via Diffusion Model

arXiv

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arXiv 2025年

作者： Huang, Junjia Yan, Pengxiang Cai, Jinhang Liu, Jiyang Wang, Zhao Wang, Yitong Wu, Xinglong Li, Guanbin Sun Yat-sen University China ByteDance Intelligent Creation Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

Text-driven image generation using diffusion models has recently gained significant attention. To enable more flexible image manipulation and editing, recent research has expanded from single image generation to transparent layer generation and multi-layer compositions. However, existing approaches often fail to provide a thorough exploration of multi-layer structures, leading to inconsistent inter-layer interactions, such as occlusion relationships, spatial layout, and shadowing. In this paper, we introduce DreamLayer, a novel framework that enables coherent text-driven generation of multiple image layers, by explicitly modeling the relationship between transparent foreground and background layers. DreamLayer incorporates three key components, i.e., Context-Aware Cross-Attention (CACA) for global-local information exchange, Layer-Shared Self-Attention (LSSA) for establishing robust inter-layer connections, and Information Retained Harmonization (IRH) for refining fusion details at the latent level. By leveraging a coherent full-image context, DreamLayer builds inter-layer connections through attention mechanisms and applies a harmonization step to achieve seamless layer fusion. To facilitate research in multi-layer generation, we construct a high-quality, diverse multi-layer dataset including 400k samples. Extensive experiments and user studies demonstrate that DreamLayer generates more coherent and well-aligned layers, with broad applicability, including latent-space image editing and image-to-layer decomposition. Copyright © 2025, The Authors. All rights reserved.

关键词： Image fusion

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An Object Detection Algorithm with Multi-scale Context Information Based on YOLOv4 4

An Object Detection Algorithm with Multi-scale Context Infor...

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4th International Conference on Natural Language Processing, ICNLP 2022

作者： Ma, Sugang An, Wen Yang, Xiaobao Hou, Zhiqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Most current object detection algorithms have the issue of missing objects due to occlusion. As the great difference of scale between occlusion objects and their integrity is affected, how to reduce the missing rate of occlusion objects has become a challenging problem in object detection algorithms. Aiming at the problem, this paper proposes an object detection algorithm based on multi-scale context information on the basis of the YOLOv4 algorithm framework. Firstly, the object features are extracted through the CSPDarkNet53 feature extraction network. Then, the proposed attention-guided multi-scale context information module (AMCM) enhances the ability to perceive object context information and endows features with different influence factors through attention weighting to improve feature discrimination. The experimental results show that the detection accuracy and recall of the proposed algorithm reach 83.2% and 96.4% respectively on PASCAL VOC dataset, and 83.2% and 91% on KITTI dataset, which improves the detection performance of YOLOv4 algorithm. © 2022 IEEE.

关键词： Object detection

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A Knowledge-Enhanced and Topic-Guided Domain Adaptation Model for Aspect-Based Sentiment Analysis

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第2期15卷 709-721页

作者： Zeng, Yushi Wang, Guohua Ren, Haopeng Cai, Yi Leung, Ho-Fung Li, Qing Huang, Qingbao South China Univ Technol Sch Software Engn Guangzhou 510641 Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510641 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

Cross-domain aspect-based sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for supervised learning based methods. Most of current methods mainly focus on extracting domain-shared syntactic features to conduct the domain adaptation. Due to the language and syntax are diverse between domains, these methods lack generalization and even lead to syntactic transfer errors. External knowledge graphs have rich domain commonsense and share the relational structures between source and target domains. The domain-shared relational structure can effectively bridge the gap across domains and solve the problem of syntactic transfer errors. Moreover, not all the introduced external knowledge is equally important for the cross-domain aspect-based sentiment analysis. Motivated by these, we propose a knowledge-enhanced and topic-guided cross domain aspect-based sentiment analysis model with the domain-shared commonsense relational structure learning module and the topic-guided knowledge attention module. Extensive experiments are conducted and the experimental results evaluate the effectiveness of our proposed model.

关键词： Syntactics Feature extraction Task analysis Sentiment analysis Adaptation models Portable computers Knowledge graphs knowledge graph cross-domain

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Modeling multi-scale uncertainty with evidence integration for reliable polyp segmentation

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Neural Networks 2025年 189卷 107553页

作者： Kang, Xiaolu Ma, Zhuoqi Liu, Kang Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi'an710071 China

Polyp segmentation is critical in medical image analysis. Traditional methods, while capable of producing precise outputs in well-defined regions, often struggle with blurry or ambiguous areas in medical images, which can lead to errors in clinical decision-making. Additionally, these methods typically generate only a single deterministic segmentation result, failing to account for the inherent uncertainty in the segmentation process. This limitation undermines the reliability of segmentation models in clinical practice, as they lack the ability to provide insights into the confidence or certainty of their predictions, leaving clinicians skeptical of their utility. To address these challenges, we propose a novel multi-scale uncertainty modeling framework for polyp segmentation, grounded in evidence theory. Our approach leverages the Dirichlet distribution to classify pixels within polyp images while integrating uncertainty across different scales. We first employ an Uncertainty Region Enhancement Process (UREP) to refine uncertain regions and Integrated Balance Module (IBM) to dynamically balance the weights between different feature maps for generating semantic fusion feature maps. Subsequently, we utilize two feature extraction sub-networks to learn feature representations from original images and semantic fusion feature maps. We further develop a Multi-scale Evidence Integration Network (MEIN) to robustly model uncertainty through subjective logic, merging results from two sub-networks to ensure a comprehensive understanding of uncertainty and produce reliable segmentation results. In contrast to most existing methods, our approach not only generates segmentation results but also provides uncertainty estimates, offering clinicians valuable insights into the reliability of the predictions. Experimental results on five polyp segmentation datasets demonstrate that our proposed method remains competitive and generates effective uncertainty estimations compared to exi

关键词： Semantic Segmentation

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Deep Generative Modeling in Constraint Discovery and Anomaly Detection

SSRN

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SSRN 2024年

作者： Hu, Wentao Jiang, Dawei Wu, Sai Chen, Ke Chen, Gang Zhejiang Police College Hangzhou China The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University China

Experts in fraud detection and data security commonly encounter hurdles in the automatic extraction of constraints from relational databases, a vital step for the identification of anomalous data. The aim is to unearth intrinsic database constraints, so that any tuple infringements can be marked as anomalies. The crux of IceBerg is the KD-VAE, an innovative generative network. By integrating \underline{K}ernel \underline{D}ensity estimation with \underline{V}ariational \underline{A}uto\underline{E}ncoder, KD-VAE addresses the challenge of capturing complex data distributions, a critical aspect for effective anomaly detection. KD-VAE synergizes Kernel Density estimation with Variational Autoencoders, effectively addressing the intricacies of modeling complex data distributions, which is essential for proficient anomaly detection. The distinct combination of kernel density estimation with variational autoencoders in KD-VAE markedly improves the modeling of normal tuple distributions, thus facilitating enhanced anomaly detection. Anomalous data are pinpointed by evaluating the congruence of a tuple with the normal distributions, and anomalies are interpreted by contrasting detected aberrations against their normal analogs. Principal improvements encompass methodological refinements for generating interpretable rules, the addition of Jaccard and NMI to the evaluation metrics, and extensive empirical analyses over 13 real-world datasets. Furthermore, comparative analyses of temporal performance and visualizations for rule interpretation are introduced, yielding deeper insights into anomaly detection, and significantly surpassing our prior work's benchmarks. These progressions confirm IceBerg's exceptional capacity for modeling intricate, high-dimensional data distributions, establishing it as a formidable asset in the analysis of large-scale datasets. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Visual Paraphrase Generation with key Information Retained

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第6期19卷 1-19页

作者： Xie, Jiayuan Chen, Jiali Cai, Yi Huang, Qingbao Li, Qing South China Univ Technol Sch Software Engn Guangzhou Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Guangxi Univ Sch Elect Engn Nanning Guangxi Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Visual paraphrase generation task aims to rewrite a given image-related original sentence into a new paraphrase, where the paraphrase needs to have the same expressed meaning as the original sentence but have a difference in expression form. Existing studies mainly extract two semantic vectors to represent the entire image and the entire original sentence, respectively, for paraphrase generation. However, these semantic vectors for an image or a sentence may lead to the model failing to focus on some key objects in the original sentence, which may generate semantically inconsistent sentences by changing key object information. In this article, we propose an object-level paraphrase generation model, which generates paraphrases by adjusting the permutation of key objects and modifying their associated descriptions. To adjust the permutation of key objects, an object-sorting module aims to obtain new object sequences based on the key object information and original sentences. Then, a sequence generation module sequentially generates paraphrases based on the permutation of the newly object sequences. Each generation step focuses on different image features associated with different key objects to generate descriptions with differences. Furthermore, we use a semantic discriminator module to promote the generated paraphrase to be semantically close to the original sentence. Specifically, the loss function of the discriminator penalizes the excessive distance between the paraphrase and the original sentence. Extensive experiments on the MS COCO dataset show that the proposed model outperforms the baselines.

关键词： Multimodal visual paraphrase generation VisualBERT

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Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization

Where is My Spot? Few-shot Image Generation via Latent Subsp...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zheng, Chenxi Liu, Bangzhen Zhang, Huaidong Xu, Xuemiao He, Shengfeng South China Univ Technol Guangzhou Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798350301298

Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN, where the corresponding output image on that centroid can maximize the similarity with the given samples. Although the given samples are unseen for the conditional StyleGAN, we assume the neighboring latent subspace around the centroid belongs to the novel category, and therefore introduce two latent subspace optimization objectives. In the first one we use few-shot samples as positive anchors of the novel class, and adjust the StyleGAN to produce the corresponding results with the new class label condition. The second objective is to govern the generation process from the other way around, by altering the centroid and its surrounding latent subspace for a more precise generation of the novel class. These reciprocal optimization objectives inject a novel class into the StyleGAN latent subspace, and therefore new unseen samples can be easily produced by sampling images from it. Extensive experiments demonstrate superior few-shot generation performances compared with state-of-the-art methods, especially in terms of diversity and generation quality. Code is available at https://***/chansey0529/LSO.

关键词： Image and video synthesis and generation

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