检索结果-内蒙古大学图书馆

Attribute-Based Encryption With Payable Outsourced Decryption Using Blockchain and Responsive Zero Knowledge Proof

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cai, Dongliang Chen, Borui Zhang, Liang Li, Kexin Kan, Haibin Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of Blockchain Shanghai200433 China Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Hong Kong Yiwu Research Institute of Fudan University Yiwu322000 China

Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results;however, they lack an effective mechanism to achieve exemptibility which enables the honest DCS to escape from wrong claims. And it is impractical to assume that the DCS will provide free services. In this paper, we propose a blockchain-based payable outsourced decryption ABE scheme that achieves both verifiability and exemptibility without adding redundant information to ABE ciphertext. We use zero-knowledge proof to verify outsourced results on blockchain and introduce an optional single-round challenge game under optimistic assumption to address the high cost of proof generation. Moreover, our system achieves fairness and decentralized outsourcing to protect the interests of all parties. Finally, we implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, the gas usage in attribute numbers from 5 to 60 is 11× to 140× in the happy case and 4× to 55× in the challenge case lower than the scheme of Ge et al. (TDSC'23). Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Huadeng Liu, Zhipeng Lan, Rushi Liu, Zhenbing Luo, Xiaonan Pan, Xipeng Li, Bingbing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://***/antifen/mitosis-nuclei-segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

ASMNet:Action and Style-Conditioned Motion Generative Network for 3D Human Motion Generation

学校读者我要写书评

暂无评论

Cyborg and Bionic Systems 2024年第1期5卷 699-708页

作者： Zongying Li Yong Wang Xin Du Can Wang Reinhard Koch Mengyuan Liu School of Artificial Intelligence Chongqing University of TechnologyChongqingChina Multimedia Information Processing Laboratory at the Department of Computer Science Kiel UniversityKielGermany Advanced Institute of Information Technology(AIIT) Peking UniversityHangzhouChina Hangzhou Linxrobot Co.Ltd. HangzhouChina National Key Laboratory of General Artificial Intelligence Peking UniversityShenzhen Graduate SchoolShenzhenChina

Extensive research has explored human motion generation,but the generated sequences are influenced by different motion *** instance,the act of walking with joy and sorrow evokes distinct effects on a character’s *** to the difficulties in motion capture with styles,the available data for style research are also *** address the problems,we propose ASMNet,an action and style-conditioned motion generative *** network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic *** extract motion features from human motion sequences,we design a spatial temporal ***,we use the adaptive instance normalization layer to inject style into the target *** results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative *** code is available at https://***/ZongYingLi/***.

关键词： action conditioned motion generative network style conditioned D human motion generation spatial temporal extractor style research motion capture human motion generationbut

BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological images

学校读者我要写书评

暂无评论

Genomics, Proteomics & Bioinformatics 2021年第6期19卷 1032-1042页

作者： Zixiao Lu Xiaohui Zhan Yi Wu Jun Cheng Wei Shao Dong Ni Zhi Han Jie Zhang Qianjin Feng Kun Huang Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical EngineeringSouthern Medical UniversityGuangzhou 510515China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical EngineeringHealth Science CenterShenzhen UniversityShenzhen 518060China Department of Medicine Indiana University School of MedicineIndianapolisIN 46202USA Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA Department of Biostatistics and Health Data Science Indiana University School of MedicineIndianapolisIN 46202USA Regenstrief Institute IndianapolisIN 46202USA

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and *** stroma from epithelial tissues is critically important for spatial characterization of the tumor ***,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological *** CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)*** achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art *** this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression *** subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue *** Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these *** all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid *** can be accessed at https://***/Serian1992/ImgBio.

关键词： Whole-slide tissue image Computational pathology Deep learning Integrative genomics Breast cancer

Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

SAT-Net: Structure-Aware Transformer-Based Attention Fusion Network for Low-Quality Retinal Fundus images Enhancement

学校读者我要写书评

暂无评论

IEEE Transactions on Multimedia 2025年

作者： Wen, Yang Luo, Bin Shi, Wuzhen Ji, Jianhua Cao, Wenming Yang, Xiaokang Sheng, Bin Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shanghai Jiao Tong University AI Institute Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

In ophthalmology diagnosis, high-fidelity fundus images are essential for disease diagnosis and intervention. However, many real-world clinical conditions may degrade the quality of the acquired images and thus affect clinical diagnostic accuracy. Traditional convolutional neural network-based retinal fundus image enhancement methods cannot always capture long-range dependencies, which reduces the overall visual quality of images, especially for real retinal fundus images. Furthermore, existing enhancement methods often fail to fully utilize low-resolution structural detail information, which potentially leads to inaccurate pivotal fundus vessel topology or capillary details. In this paper, we propose a novel Structure-Aware Transformer-based attention fusion Network (SAT-Net) for low-quality retinal fundus image enhancement. First, we introduce a Transformer-based attention fusion module which incorporates window-based self-attention and channel self-attention to capture global spatial dependencies and emphasize important feature channels simultaneously. This fusion significantly improves the overall perceptual quality of the image by enhancing both the local details and the uniformity of the non-vessel background regions. Second, we introduce a cross-quality knowledge distillation technique, which bridges the quality gap between high-quality and low-quality fundus images. By designing a high-performing teacher network to guide a lightweight student network, the student network enables to capture detailed features from low-quality fundus images, further preserving critical diagnostic information and fine topology structures. Moreover, we design a structure-aware multi-scale loss function by using a trainable subnetwork to obtain the edge structure from different scales to better constrain pivotal fundus vessel structure and capillary details. Comprehensive quantitative and qualitative experiments on both synthetic and real fundus image datasets robustly validate th

关键词： Students

FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

学校读者我要写书评

暂无评论

29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Dai, Yimian Pan, Peiwen Qian, Yulei Li, Yuxuan Li, Xiang Yang, Jian Wang, Huan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security China The department of intelligence science School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Marine Radar Institute Nanjing China VCIP CS Nankai University China The NKIARI Futian Shenzhen China

Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the "Pick of the Bunch" principle. At its core lies our Selective Rank-Aware Attention (SeRank) module, employing a non-linear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our Large Selective Feature Fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network’s discernment is further refined by our Dilated Difference Convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://***/GrokCV/SeRankDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Report-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide image

学校读者我要写书评

暂无评论

Report-Guided Cross-Modal Representation Learning for Predic...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qi Qiao Jun Shi Zhiguo Jiang Wei Wang Haibo Wu Yushan Zheng School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Image Processing Center School of Astronautics Beihang University Beijing China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Hefei China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL.

关键词： Representation learning Training Codes Supervised learning Semantics Medical treatment Benchmark testing Bioinformatics image reconstruction Biomedical imaging