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Oblivious Encrypted keyword Search with Fine-Grained Access Control for Cloud Storage

IEEE Transactions on Information Forensics and Security 2025年 20卷 5521-5533页

作者： Tong, Qiuyun Deng, Junyi Li, Xinghua Miao, Yinbin Wang, Yunwei Deng, Robert H. Xidian University State Key Laboratory of Integrated Services Networks and the School of Cyber Engineering Xi’an710071 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi’an710071 China Ministry of Education Engineering Research Center of Big data Security Xi’an710071 China Singapore Management University School of Information Systems Singapore

With the rapid expansion of data volumes in cloud computing, more data owners are opting to outsource their data to cloud service providers to reduce local storage and management costs. However, data outsourcing deprives data owners of direct physical control over their data, increasing the risk of unauthorized access and exposure of sensitive information. To mitigate these risks, various privacy-preserving keyword search schemes with access control have been developed, but many are vulnerable to leakage-abuse attacks due to the exposure of access, search or volume patterns, which can lead to privacy breaches in outsourced data and queries. To solve this problem, we propose an oblivious encrypted keyword search scheme with fine-grained access control, called OEKA. It enables efficient oblivious keyword search over encrypted multi-maps by using the adapted XOR filter and distributed point function, ensuring protection of access, search and volume patterns. Moreover, OEKA enforces role-based access control by using polynomial-based access strategy and keyword-based private information retrieval, allowing access policies of retrieved objects to be detecting without revealing the objects themselves. A formal security analysis verifies the scheme’s robustness, and experimental results demonstrate its practical efficiency. 2005-2012 IEEE.

关键词： Authorization

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Out-of-Distribution Generalization on Graphs via Progressive Inference 39

Out-of-Distribution Generalization on Graphs via Progressive...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yiming Shi, Bin Peng, Zhen Liu, Huixiang Dong, Bo Chen, Chen School of Computer Science and Technology Xi'an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China School of Distance Education Xi'an Jiaotong University China University of Virginia CharlottesvilleVA United States

ISBN: (纸本)157735897X

The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap between the causal parts learned by existing methods and the ground-truth, leading to undesirable performance. In response to the above issues, this paper presents GPro, a model that learns graph causal invariance with progressive inference. Specifically, the complicated graph causal invariant learning is decomposed into multiple intermediate inference steps from easy to hard, and the perception of GPro is continuously strengthened through a progressive inference process to extract causal features that are stable to distribution shifts. We also enlarge the training distribution by creating counterfactual samples to enhance the capability of the GPro in capturing the causal invariant parts. Extensive experiments demonstrate that our proposed GPro outperforms the state-of-the-art methods by 4.91% on average. For datasets with more severe distribution shifts, the performance improvement can be up to 6.86%. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Image Fusion Method Based on NSCT and Adaptive Sparse Representation 4

Image Fusion Method Based on NSCT and Adaptive Sparse Repres...

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4th International Conference on Video and Image Processing, ICVIP 2020

作者： Dong, Zhangyu Wei, Xin Wang, Mengyang School of Computer and Information Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230009 China

ISBN: (纸本)9781450389075

ABSTRACT: Aiming at the imaging characteristics of multispectral and panchromatic image and the problem that the traditional image fusion methods cannot take into account the spectral quality and spatial resolution of the image at the same time, a novel fusion method based on the Non-subsampled Contourlet Transform (NSCT) domain and adaptive sparse representation (ASR) algorithm is presented in this paper. It combines the advantages of principal component substitution and multi-scale analysis, and uses the ASR model to encode the low-frequency components, which improves the shortcomings of the traditional SR-based approach applications. Firstly, NSCT transform is performed on thefirst principal component of multi-spectral image obtained by PCA transform and panchromatic image,and the low-pass and high-pass coefficients of the source image are obtained. Secondly, the Max-absolute' rule and the ASR model are used to fuse the separated high-pass and low-pass bands respectively. Finally, we obtained the final fused image by the corresponding inverse transformation reconstruction. The results show that the fusion algorithm proposed in this paper performs well in both spatial resolution and spectral performance;it not only improves the subjective visual effect, but also improves the objective evaluation index. © 2020 ACM.

关键词： Image fusion

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Di-Net: Decomposed Implicit Garment Transfer Network for Digital Clothed 3d Human

SSRN

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SSRN 2024年

作者： Zhong, Xiaojing Su, Yukun Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China Tencent Wechat Department China SenseTime Research

3D virtual try-on tasks aim to generate realistic try-on results for full- body garments, allowing them to be observed from arbitrary perspectives. Recent methods often represent the 3D human form with a fixed topol- ogy, limiting garment representation and leading to higher computational demands. Furthermore, the challenge of flexible transfer over various body parts remains. To address these problems, we introduce DI-Net(Decomposed Implicit Garment Transfer Network), where garment transfer is decomposed into 2D image layout transfer and 3D texture layout transfer. We propose a complementary warping module that leverages dense correspondence learn- ing and sparse flow learning to align the body appearance and garments with the target pose for image layout transfer. Subsequently, the transferred garments are lifted to the 3D human mesh by constructing a pixel-aligned implicit function and predicting the texture based on geometric priors. By applying different semantic masks for garment transfer, we efficiently control the texture of the generated mesh. Experimental results show the effective- ness and superiority of our method in the 3D virtual try-on task. © 2024, The Authors. All rights reserved.

关键词： Textures

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Predicting drug transcriptional response similarity using Signed Graph Convolutional Network

Predicting drug transcriptional response similarity using Si...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Ziyan Hong, Chengzhi Liu, Xuan Xiong, Zhankun Liu, Feng Zhang, Wen Wuhan University School of Computer Science Wuhan430072 China Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

ISBN: (纸本)9781665468190

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds. © 2022 IEEE.

关键词： Graph neural networks

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Mk-Nmf: A Novel Multiple Kernel-Based Non-Negative Matrix Factorization Model to Mini Synergistic Drug Combinations in Cell Lines

SSRN

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SSRN 2024年

作者： Li, Tianyi Han, Huirui Chen, Jiaqi Feng, Dehua Chen, Zhengxin Wang, Xuefeng Liu, Xinying Zhang, Ruijie Wang, Qibin Yu, Lei Li, Xia Li, Bing Wang, Limei Li, Jin College of Biomedical Information and Engineering Kidney Disease Research Institute Second Affiliated Hospital Hainan Engineering Research Center for Health Big Data Key Laboratory of Tropical Translational Medicine Ministry of Education Hainan Medical University Hainan Haikou571199 China

Drug synergism may occur when two or more drugs are used in combination. Synergistic drug pairs can enhance efficacy and reduce drug dosage and side effects. It is meaningful to use computational methods to mine specific synergistic drug combinations for clinical treatment. We proposed a multiple kernel-based non-negative matrix factorization, MK-NMF, specifically for mining specific synergistic drug pairs in cell lines. In this method, we treated the features of drug pair space and cell line space in the form of two kernel matrices. We incorporated feature kernel matrices into the matrix factorization process. MK-NMF achieved an area under the curve (AUC) of 0.884 and an area under the precision versus recall curve (AUPR) of 0.537 on the NCI ALMANAC dataset. Both measures were more than a 5% improvement over the previous matrix factorization model. MK-NMF had good robustness with the missing input data. Its performance was stable when the amount of input matrix data was not less than 40%. Literature and experimental verification confirmed some of our predictions. MK-NMF could assist medical professionals in rapidly screening synergistic drug combinations against specific cancer cell lines. © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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keyword-Oriented Multimodal Modeling for Euphemism Identification

arXiv

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arXiv 2025年

作者： Hu, Yuxue Li, Junsong Chen, Meixuan Su, Dongyu Wang, Tongguan Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

Euphemism identification deciphers the true meaning of euphemisms, such as linking "weed" (euphemism) to "marijuana" (target keyword) in illicit texts, aiding content moderation and combating underground markets. While existing methods are primarily text-based, the rise of social media highlights the need for multimodal analysis, incorporating text, images, and audio. However, the lack of multimodal datasets for euphemisms limits further research. To address this, we regard euphemisms and their corresponding target keywords as keywords and first introduce a keyword-oriented multimodal corpus of euphemisms (KOM-Euph), involving three datasets (Drug, Weapon, and Sexuality), including text, images, and speech. We further propose a keyword-oriented multimodal euphemism identification method (KOM-EI), which uses cross-modal feature alignment and dynamic fusion modules to explicitly utilize the visual and audio features of the keywords for efficient euphemism identification. Extensive experiments demonstrate that KOM-EI outperforms state-of-the-art models and large language models, and show the importance of our multimodal datasets 1 Copyright © 2025, The Authors. All rights reserved.

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation

Cross-Modality Comprehensive Feature Aggregation Network for...

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International Symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： Yale Yang Zihao Zhang Huifang Hou Wenwen Zhang Chunsheng Zhuang College of Information Science and Engineering Henan University of Technology Zhengzhou China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control and school of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences; then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance.

关键词： Deep learning Semantic segmentation Roads Design methodology Semantics Lighting Imaging Reliability Meteorology Intelligent control

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