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SECP-AKE: Secure and efficient certificateless-password-based authenticated key exchange protocol for smart healthcare systems

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Journal of systems Architecture 2025年 167卷

作者： Zhiqiang Zhao Xuexian Hu Yining Liu Jianghong Wei Yuanjun Xia Yangfan Liang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou 450000 Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325027 Zhejiang China School of Computer and Information Security Guilin University of Electronic Technology Guilin 541004 Guangxi China College of Information Science and Engineering the Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems the Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province and the Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 Zhejiang China

Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes still suffer from incomplete security properties, imperfect authentication mechanisms, and inadequate privacy preservation. Therefore, this paper presents SECP-AKE, a secure and efficient certificateless-password-based authenticated key exchange protocol for IoT-based smart healthcare, which enables batch authentication, resists physical attacks, and provides strong anonymity. Specifically, using certificateless cryptography, the SECP-AKE protocol enables batch authentication of authorized users and devices while also resolving the key escrow problem. In particular, the SECP-AKE protocol incorporates Physical Unclonable Functions (PUFs) to resist physical attacks, thus enhancing device security and ensuring reliable medical service delivery. Additionally, the design of a pseudonym update mechanism can achieve user unlinkability, thereby providing enhanced privacy preservation. The results from both formal verification using SVO logic and informal security analyses demonstrate that the SECP-AKE protocol is secure and offers more comprehensive security properties. Meanwhile, the use of a well-known automated security verification tool Scyther further evaluates the protocol’s security reliability. Ultimately, comparative experiments on communication overhead and computational overhead demonstrate that the SECP-AKE protocol is efficient and feasible compared to state-of-the-art existing works.

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Multi-Granularity Distribution Alignment for Cross-Domain Crowd Counting

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IEEE Transactions on Image Processing 2025年

作者： Zhong, Xian Qiu, Lingyue Zhu, Huilin Yuan, Jingling He, Shengfeng Wang, Zheng Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572025 China Nanyang Technological University Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering 639798 Singapore Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan430070 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Singapore Management University School of Computing and Information Systems 188065 Singapore Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China

Unsupervised domain adaptation enables the transfer of knowledge from a labeled source domain to an unlabeled target domain, and its application in crowd counting is gaining momentum. Current methods typically align distributions across domains to address inter-domain disparities at a global level. However, these methods often struggle with significant intra-domain gaps caused by domain-agnostic factors such as density, surveillance angles, and scale, leading to inaccurate alignment and unnecessary computational burdens, especially in large-scale training scenarios. To address these challenges, we propose the Multi-Granularity Optimal Transport (MGOT) distribution alignment framework, which aligns domain-agnostic factors across domains at different granularities. The motivation behind multi-granularity is to capture fine-grained domain-agnostic variations within domains. Our method proceeds in three phases: first, clustering coarse-grained features based on intra-domain similarity;second, aligning the granular clusters using an optimal transport framework and constructing a mapping from cluster centers to finer patch levels between domains;and third, re-weighting the aligned distribution for model refinement in domain adaptation. Extensive experiments across twelve cross-domain benchmarks show that our method outperforms existing state-of-the-art methods in adaptive crowd counting. © 1992-2012 IEEE.

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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Mining Frequent Patterns for ECG Multi-label Data by FP-Growth Algorithm Based on Spark 7

Mining Frequent Patterns for ECG Multi-label Data by FP-Grow...

引用

7th International Conference on Information, Communication and Networks, ICICN 2019

作者： Wang, Di Ge, Jing Wu, Lu Song, Xiaofeng Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Medical Artificial Intelligence Jinan China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China State Key Laboratory of High-end Server and Storage Technology Inspur Electronic Information Industry Co. Ltd Jinan China

ISBN: (纸本)9781728104263

Chinese Cardiovascular Disease Database (CCDD), which contains 12-Lead electrocardiogram (ECG) data and detailed features with diagnosis, include more than 270 categories of ECG-type data. Each record can correspond to multiple diagnosis results. However, most of the current methods are for single-label data, few people study multi-label ECG data, especially the correlation in ECG type labels. As one of the top ten classic algorithms for data mining, association rule mining algorithm can be used to discover interesting associations or related information between item sets in a large amount of data. The association rule mining algorithm on a single machine is difficult to adapt to big data analysis and processing tasks because of its space-time complexity and I/O cost. This paper makes full use of the powerful data processing capabilities of Spark's cluster system, studies the frequent patterns for ECG big data. © 2019 IEEE.

关键词： Electrocardiograms

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A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional Chinese medicines

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Science Bulletin 2021年第9期66卷 884-888,M0003页

作者： Yifei Dai Weijie Qiang Yu Gui Xue Tan Tianli Pei Kequan Lin Siwei Cai Liang Sun Guochen Ning Jianxun Wang Hongyan Guo Yimin Sun Jing Cheng Lan Xie Xun Lan Dong Wang State Key Laboratory of Southwestern Chinese Medicine Resources School of Basic Medical SciencesChengdu University of Traditional Chinese MedicineChengdu 611137China Department of Basic Medical Sciences School of MedicineTsinghua UniversityBeijing 100084China Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100193China School of Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Electronic and Computer Engineering College of EngineeringDrexel UniversityPhiladelphia 19104USA Dongli District Jinqiao Street Community Health Service Center Tianjin 300300China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing 100084China School of Life Sciences Beijing University of Chinese MedicineBeijing 100029China National Engineering Research Center for Beijing Biochip Technology Beijing 102206China State Key Laboratory of Membrane Biology School of MedicineTsinghua UniversityBeijing 100084China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310003China Medical Systems Biology Research Center School of MedicineTsinghua UniversityBeijing 100084China

The application of traditional Chinese medicine(TCM)has made great contributions to the fight against the epidemic of coronavirus disease-2019(COVID-19).Despite the remarkable therapeutic effects of TCM,the molecular mechanisms of TCM formulae inhibiting COVID-19 are still not fully ***,we cφm-bined the automated high throughput sequencing-based high throughput screening(HTS^(2))assay with bioinformatics and computer-aided drug design(CADD)to investigate the molecular mechanisms of TCM-mediated therapeutic effects on COVID-19-related cytokine storm(Fig.1a).

关键词：细胞因子风暴六君子汤理中汤诊疗方案黄连解毒汤中药提取物肺炎分子机制

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Towards provably efficient quantum algorithms for large-scale machine-learning models

arXiv

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arXiv 2023年

作者： Liu, Junyu Liu, Minzhao Liu, Jin-Peng Ye, Ziyu Wang, Yunfei Alexeev, Yuri Eisert, Jens Jiang, Liang Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Chicago Quantum Exchange ChicagoIL60637 United States Kadanoff Center for Theoretical Physics The University of Chicago ChicagoIL60637 United States qBraid Co. ChicagoIL60615 United States SeQure ChicagoIL60615 United States Department of Physics The University of Chicago ChicagoIL60637 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Simons Institute for the Theory of Computing University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Martin A. Fisher School of Physics Brandeis University WalthamMA02453 United States Dahlem Center for Complex Quantum Systems Free University Berlin Berlin14195 Germany

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 × polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that faulttolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Differential equations

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Cooperative bi-path metric for few-shot learning

arXiv

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arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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ACG-Engine: An Inference Accelerator for Content Generative Neural Networks

ACG-Engine: An Inference Accelerator for Content Generative ...

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IEEE International Conference on computer-Aided Design

作者： Haobo Xu Ying Wang Yujie Wang Jiajun Li Bosheng Liu Yinhe Han University of Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences

The technological breakthrough in Generative Adversarial Networks (GAN) has propelled the advancement of content generative applications such as AI-based paintings, style transfer, and music composition. However, in contrast to previous deep learning models for prediction and categorization, generative networks generally rely on instance normalization (IN) layer for better feature distribution, which performs significantly better than batch normalization(BN) in image style-transfer, image to image translation, etc. Unlike batch or group normalization that can be fused into convolutional layers and ignored during the network inference stage, an instance normalization layer induces intensive computation and memory access. However, prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations, which could become a performance bottleneck on edge devices with insufficient computational power. To address this problem, we propose an inference accelerator for content generation (ACG-Engine) aimed to support the fundamental operations of generative networks, including convolution layers, deconvolution layers, specifically instance normalization layer. We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness, so that it can be efficiently mapped to the classic 2D processing element array. Owing to the proposed optimization techniques, ACG-Engine achieves 4.56X speedup and improve power efficiency up to 29X compared to prior baseline acceleration scheme in generative network acceleration. In addition, ACG-Engine can achieve performance comparable to the classic CNN-specific accelerators with negligible power consumption and area overhead.

关键词： Performance evaluation Deep learning Deconvolution Convolution Neural networks Generative adversarial networks Acceleration

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Dual-constrained deep semi-supervised coupled factorization network with enriched prior

arXiv

引用

arXiv 2020年

作者： Zhang, Yan Zhang, Zhao Wang, Yang Zhang, Zheng Zhang, Li Yan, Shuicheng Wang, Meng School of Computer Science and Technology Soochow University Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei University of Technology China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Pengcheng Laboratory Shenzhen China Sea Ai Labs Singapore Singapore

Nonnegative matrix factorization is usually power-ful for learning the parts-based "shallow" representation, however it fails to discover deep hidden information within both the basis concept and representation spaces. In this paper, we therefore pro-pose a new Dual-constrained Deep Semi-Supervised Coupled Fac-torization Network (DS2CF-Net) for learning hierarchical repre-sentations. DS2CF-Net is formulated as the joint partial-label and structure-constrained deep factorization network using multi-layers of linear transformations, which coupled updates the basis concepts and new representations in each layer. An error correction mechanism with feature fusion strategy is also integrated between consecutive layers to improve the representation ability of features. To improve the discriminating abilities of both representation and coefficients in feature space, we clearly consider how to enrich the prior knowledge by the coefficients-based label prediction, and incorporate the enriched prior knowledge as the additional label and structure constraints. To be specific, the label constraint enables the intra-class samples to have the same coordinate in the feature space, while the structure constraint forces the coefficients in each layer to be block-diagonal so that the enriched prior knowledge are more accurate. Besides, we integrate the adaptive dual-graph learning to retain the locality structures of both the data manifold and feature manifold in each layer. Finally, a fine-tuning process is performed to refine the structure-constrained matrix and data weight matrix in each layer using the predicted labels for more accurate representations. Extensive simulations on public databases show that our method can obtain state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Error correction

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