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Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

Frontiers of computer Science 2022年第6期16卷 103-121页

作者： Qingqing GAN Joseph K.LIU Xiaoming WANG Xingliang YUAN Shi-Feng SUN Daxin HUANG Cong ZUO Jianfeng WANG Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhou510006China Department of Computer Science Jinan UniversityGuangzhou510632China Faculty of Information Technology Monash UniversityClayton3168Australia State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXi’an710071China

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable *** various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real *** a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search ***,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during *** address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search *** proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification ***,the proposed VSSE scheme can still provide a proof when the search result is ***,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

关键词： searchable symmetric encryption verifiability conjunctive keyword queries hash-based accumulator cloud storage

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UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

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CAAI Transactions on Intelligence technology 2024年第3期9卷 756-768页

作者： Wei Xie Lianghao Jin Shiqi Hua Hao Sun Bo Sun Zhigang Tu Jun Liu Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina National Language Resources Monitoring and Research Center for Network Media Central China Normal UniversityWuhanChina The First Affiliated Hospital of Dalian Medical University DalianChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingaporeSingapore

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality *** lesions are generally diffusely distributed,showing a variety of scales with irregular *** complex characteristics of lesions make SAH segmentation a challenging *** cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH ***,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale ***,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder ***,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed *** experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

关键词： image segmentation medical image processing

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Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering

arXiv

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arXiv 2025年

作者： Liang, Lili Sun, Guanglu School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Intelligent Information Processing and Application Harbin150080 China

Video Question Answering (VideoQA) is an important research direction in the field of artificial intelligence, enabling machines to understand video content and perform reasoning and answering based on natural language questions. Although methods based on static relationship reasoning have made certain progress, there are still deficiencies in the accuracy of static relationship recognition and representation, and they have not fully utilized the static relationship information in videos for in-depth reasoning and analysis. Therefore, this paper proposes a reasoning method for intra-type and inter-type message passing based on static relationships. This method constructs a dual graph for intra-type message passing reasoning and builds a heterogeneous graph based on static relationships for inter-type message passing reasoning. The intra-type message passing reasoning model captures the neighborhood information of targets and relationships related to the question in the dual graph, updating the dual graph to obtain intra-type clues for answering the question. The inter-type message passing reasoning model captures the neighborhood information of targets and relationships from different categories related to the question in the heterogeneous graph, updating the heterogeneous graph to obtain inter-type clues for answering the question. Finally, the answers are inferred by combining the intra-type and inter-type clues based on static relationships. Experimental results on the ANetQA and Next-QA datasets demonstrate the effectiveness of this method. © 2025, CC BY.

关键词： Question answering

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Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4314-4324页

作者： Yu, Jiejiang Li, Bingbing Pan, Xipeng Shi, Zhenwei Wang, Huadeng Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China Southern Medical University Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods. © 2013 IEEE.

关键词： Contrastive Learning

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Leveraging Hybrid Referring Expressions for Referring Video Object Segmentation

Leveraging Hybrid Referring Expressions for Referring Video ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan Li Qiong Wang College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Existing referring video object segmentation methods segment the target object referred by one referring expression or a simple group of multiple referring expressions by word concatenation in all video frames, which might still suffer from the issue of wrong object identification. To solve this problem, we propose a new transformer-based framework built upon hybrid referring expressions. To fully explore the multi-model fusion, three interaction methods are studied, and the early interaction shows the best alignment between discriminative video features and multi-language semantics. Moreover, two ways are explored to generate conditional queries in transformer-based methods, and the proposed word-group level method can better represent the target instance. Experiments on the large-scale Ref-YoutubeVOS benchmark show that the proposed model achieves 63.8% J & F, exceeding the state-of-the-art methods.

关键词： Semantics Object segmentation Benchmark testing Streaming media Transformers Object recognition

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The GECAM ground search system for gamma-ray transients

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 161-174页

作者： Ce Cai Yan-Qiu Zhang Shao-Lin Xiong Ping Wang Jian-Hui Li Xiao-Bo Li Cheng-Kui Li Yue Huang Shi-Jie Zheng Li-Ming Song Shuo Xiao Qi-Bin Yi Yi Zhao Sheng-Lun Xie Rui Qiao Yan-Qi Du Zhi-Wei Guo Wang-Chen Xue Chao Zheng Jia-Cong Liu Chen-Wei Wang Wen-Jun Tan Yue Wang Jin-Peng Zhang Chao-Yang Li Guo-Ying Zhao Xiao-Yun Zhao Xiao-Lu Zhang Zhen Zhang Wen-Xi Peng Xiang Ma Jing-Yan Shi Dong-Ya Guo Jin Wang Xin-Qiao Li Xiang-Yang Wen Zheng-Hua An Fan Zhang College of Physics and Hebei Key Laboratory of Photophysics Research and Application Hebei Normal UniversityShijiazhuang050024China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang550001China School of Physics and Optoelectronics Xiangtan UniversityXiangtan411105China School of Computer and Information Dezhou UniversityDezhou253023China Institute of Astrophysics Central China Normal UniversityWuhan430079China

In the era of time-domain,multi-messenger astronomy,the detection of transient events on the high-energy electromagnetic sky has become more important than *** gravitational wave high-energy electromagnetic counterpart all-sky monitor(GECAM)is a dedicated mission to monitor gamma-ray transients,launched in December,2020.A real-time on-board trigger and location software,using the traditional signal-to-noise ratio(SNR)method for blind search,is constrained to relatively bright signals due to the limitations in on-board computing resources and the need for real-time *** this work,we developed a ground-based pipeline for GECAM to search for various transients,especially for weak bursts missed by on-board *** pipeline includes both automatic and manual mode,offering options for blind search and targeted *** targeted search is specifically designed to search for interesting weak bursts,such as gravitational wave-associated gamma-ray bursts(GRBs).From the ground search of the data in the first year,GECAM has been triggered by 54 GRBs and other transients,including soft gamma-ray repeaters,X-ray binaries,solar flares,terrestrial gamma-ray *** report the properties of each type of triggers,such as trigger time and light *** this search pipeline and assuming a soft Band spectrum,the GRB detection sensitivity of GECAM is increased to about 1.1×10^(-8)erg cm^(-2)s^(-1)(10-1000 ke V,burst duration of 20 s).These results demonstrate that the GECAM ground search system(both blind search and targeted search)is a versatile pipeline to recover true astrophysical signals which were too weak to be found in the on-board search.

关键词： gamma-ray sources gravitational waves data analysis gamma-ray telescopes

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Real-time arbitrarily-shaped scene text spotting with weakly supervised points

Real-time arbitrarily-shaped scene text spotting with weakly...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： Network architecture

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Flexible Broadband Compact Antenna Using Hydrogel Material for Noninvasive Medical Diagnostic Systems 11

Flexible Broadband Compact Antenna Using Hydrogel Material f...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Wu, Jian Yang, Nan Sun, Jian Wu, Jin Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China Army Medical University College of Biomedical Engineering Chongqing400038 China Sun Yat-sen University State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and Technology School of Electronics and Information Technology Guangzhou510275 China

ISBN: (纸本)9798350326277

A flexible broadband compact antenna is presented in this paper. To make the antenna compact and flexible, a high-permittivity polyvinyl alcohol organohydrogel is prepared and used. A magnetic coupling feeding method is used to obtain a broadband design, along with lossy material and head loading. The measured impedance bandwidth is larger than 140%, which is useful for imaging and electromagnetic diagnostic systems. © 2023 IEEE.

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Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning 31

Unifying Dual-Space Embedding for Entity Alignment via Contr...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. In this paper, we propose a novel method named as UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we simultaneously learn graph structure embeddings in both Euclidean and hyperbolic spaces to maximize the consistency between embeddings in the two spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embeddings of similar neighboring entities become too close. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA methods. Our code is available at https://***/wonderCS1213/UniEA. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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