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Enabling efficient and accurate semantic search over encrypted cloud data

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Information sciences 2025年 719卷

作者： Zixin Tang Haihui Fan Xiaoyan Gu Jiang Zhou Hui Ma Athanasios V. Vasilakos Bo Li Institute of Information Engineering Chinese Academy of Sciences Beijing 100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing 100049 China Key Lab of Cyberspace Security Defense Beijing 100085 China Department of Networks and Communications College of Computer Science and Information Technology IAU P.O. Box 1982 Dammam 31441 Saudi Arabia Center for AI Research (CAIR) University of Agder (UiA) Grimstad Norway

The privacy and security of cloud data have drawn much attention, leading to more data owners outsourcing encrypted data. However, the common practice of encryption can reduce data searchability. Semantic searchable encryption aims to support flexible queries over encrypted data and achieve efficient search while ensuring that search results match the user's search intent. Although semantic searchable encryption schemes have made progress, they still have limitations in properly balancing accuracy, efficiency, and security. In this paper, we propose a novel C ontext- E nhanced S emantic S earchable E ncryption ( CESSE ) scheme to achieve accurate and highly efficient secure semantic search over encrypted cloud data. To achieve it, we first adopt a context-enhanced pre-trained model component to mine the relevance between queries and documents by contrastive learning and obtain context-enhanced vector representations to improve search accuracy. Then, to ensure privacy protection, we utilize an optimized asymmetric scalar-product-preserving encryption (optimized ASPE) algorithm to encrypt vectors before outsourcing to the cloud. Additionally, we construct the approximate nearest neighbor (ANN) index to accelerate vector searching. At last, we give a formal definition of security and theoretically prove the safety of our scheme under a more practical threat model. Extensive experiments demonstrate that the CESSE outperforms state-of-the-art baselines with better accuracy and efficiency.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (ai) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based ai-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

Improving the multi-class classification of Alzheimer's disease with machine learning-based techniques: An EEG-fNIRS hybridization study

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057565-e057565页

作者： Thi Kieu Khanh Ho Minhee Kim YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jae Gwan Kim Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: As an incurable neurodegenerative disorder, Alzheimer's Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) reveals an ideal technique-derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi-class classification tasks. METHODS: In this study, the hybrid EEG-fNIRS was employed in constructing the classification machine learning (ml)-based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG-fNIRS setup was utilized to record data from 41 subjects during the Oddball - a cognitive ability test and 1-back - a memory ability test. Second, while fNIRS features were computed and extracted, the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built for the multi-class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods. RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject

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Forensic Analysis of Synthetically Generated Western Blot Images

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arXiv 2021年

作者： Mandelli, Sara Cozzolino, Davide Cannas, Edoardo Daniele Cardenuto, João Phillipe Moreira, Daniel Bestagini, Paolo Scheirer, Walter Rocha, Anderson Verdoliva, Luisa Tubaro, Stefano Delp, Edward J. The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy The Dipartimento di Ingegneria Elettrica e Tecnologie Dell'Informazione University Federico Ii of Naples Naples80125 Italy The Dipartimento di Ingegneria Industriale University Federico Ii of Naples Naples80125 Italy The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Artificial Intelligence Lab RECOD.ai Institute of Computing University of Campinas Campinas13083-970 Brazil The School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images. In this paper, we focus on the detection of synthetically generated western blot images. These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors. To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods. We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western blot images at training stage. The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images. We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (mlP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

Compact CMOS spectral sensor for the visible spectrum

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Photonics Research 2019年第9期7卷 961-966页

作者： YIBO ZHU XIN LEI KEN XINGZE WANG ZONGFU YU Coherent AI LLC Redwood CityCalifornia94065USA School of Physics Huazhong University of Science and TechnologyWuhan430079China Department of Electrical and Computer Engineering University of Wisconsin–MadisonMadisonWisconsin53705USA

A compact single-shot complementary metal-oxide semiconductor(CMOS) spectral sensor for the visible range(wavelength 400–700 nm) is presented. The sensor consists of two-dimensional silicon nitride-based photonic crystal(PC) slabs atop CMOS photodetectors. The PC slabs are fabricated using one-step lithography and amenable to monolithic integration into CMOS image sensors. Featuring a small footprint of 300 μm× 350 μm,the sensor can successfully measure the spectra over the 400–700 wavelength range with a best resolution of1 nm. The footprint of the sensor may be further reduced to enable hyperspectral imaging with high spatial resolution.

关键词： PC red Compact CMOS spectral sensor for the visible spectrum

Dependent Task Offloading for Multiple Jobs in Edge Computing

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Dependent Task Offloading for Multiple Jobs in Edge Computin...

International Conference on computer Communications and Networks (ICCCN)

作者： Zhiqing Tang Jiong Lou Fuming Zhang Weijia Jia Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of IoT for Smart City University of Macau Macau China BNU-UIC Joint AI Resrach Institute Beijing Normal University & UIC (Zhuhai) Guangdong China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

The dependent task offloading problem for one single job in edge computing (EC) has drawn attention widely. Unlike most existing approaches that only focus on a single job, we aim to solve the dependent task offloading problem for multiple jobs, which is more general in the real world. To solve this problem, we propose a deep reinforcement learning (DRL) based multi-job dependent task offloading algorithm. Specifically, 1) we model edge nodes, jobs, and tasks in a resource-limited EC scenario, where the dependent tasks of multiple jobs are offloaded to the nodes to be processed. Then we model the task offloading decision as a Markov decision process (MDP) problem to minimize the transmission cost and computation cost. 2) To represent the state space of MDP and to accelerate decision-making in EC, we propose a DRL-based algorithm with the aid of graph convolutional network (GCN) to extract the dependency information of different tasks and then improve the action selection process. 3) We conduct experiments with real-world trace, demonstrating our algorithm outperforms the baseline algorithms 13.78% on average in regarding to offloading cost.

关键词： Task analysis Computational modeling Decision making Delays Edge computing Machine learning Mobile handsets

The FFSVC 2020 evaluation plan

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arXiv 2020年

作者： Qin, Xiaoyi Li, Ming Bu, Hui Das, Rohan Kumar Rao, Wei Narayanan, Shrikanth Li, Haizhou Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical & Computer Engineering National University of Singapore Singapore Signal Analysis and Interpretation Lab University of Southern California Los Angeles United States AI Shell Foundation Beijing China

Speaker verification is a key technology in speech processing and biometric authentication, which has broad impact on our daily lives, e.g. security, customer service, mobile devices, smart speakers. Recently, speech based human computer interaction has become more and more popular in far-field smart home and smart city applications, e.g. mobile devices, smart speakers, smart TVs, automobiles. Due to the usage of deep learning methods, the performances of speaker verification in telephone channel and close-talking microphone channel have been enhanced dramatically. However, there are still some open research questions that can be further explored for speaker verification in the far-field and complex environments, including but not limited to • Far-field text-dependent speaker verification for wake up control • Far-field text-independent speaker verification with complex environments • Far-field speaker verification with cross-channel enrollment and test • Far-field speaker verification with single multi-channel microphone array • Far-field speaker verification with multiple distributed microphone arrays • Far-field speaker verification with front-end speech enhancement methods • Far-field speaker verification with end-to-end modeling using data augmentation • Far-field speaker verification with front-end and backend joint modeling • Far-field speaker verification with transfer learning and domain adaptation. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

Capsule-Transformer for Neural Machine Translation

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arXiv 2020年

作者： Duan, Sufeng Cao, Juncheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Transformer hugely benefits from its key design of the multi-head self-attention network (SAN), which extracts information from various perspectives through transforming the given input into different subspaces. However, its simple linear transformation aggregation strategy may still potentially fail to fully capture deeper contextualized information. In this paper, we thus propose the capsule-Transformer, which extends the linear transformation into a more general capsule routing algorithm by taking SAN as a special case of capsule network. So that the resulted capsule-Transformer is capable of obtaining a better attention distribution representation of the input sequence via information aggregation among different heads and words. Specifically, we see groups of attention weights in SAN as low layer capsules. By applying the iterative capsule routing algorithm they can be further aggregated into high layer capsules which contain deeper contextualized information. Experimental results on the widely-used machine translation datasets show our proposed capsule-Transformer outperforms strong Transformer baseline significantly. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods