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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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Fixed-point quantum continuous search algorithm with optimal query complexity

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 80-91页

作者： Shan Jin Yuhan Huang Shaojun Wu Guanyu Zhou Chang-Ling Zou Luyan Sun Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610051China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230022China Center for Quantum Information Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing 100084China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Hefei National Laboratory Hefei 230088China

Continuous search problems(CSPs),which involve finding solutions within a continuous domain,frequently arise in fields such as optimization,physics,and *** discrete search problems,CSPs require navigating an uncountably infinite space,presenting unique computational *** this work,we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges,achieving a quadratic *** by the discrete search results,we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs,demonstrating the optimality of our *** addition,we demonstrate how to design the internal structure of the quantum search oracle for specific ***,we develop a general framework to apply this algorithm to a range of problem types,including optimization and eigenvalue problems involving continuous variables.

关键词： quantum search quantum algorithm quantum computation optimization spectral problems

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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A unified user behavior model for trajectory-based tasks with different types of path constraints

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science China(information sciences) 2023年第10期66卷 316-318页

作者： Hao ZHANG Jin HUANG Huawei TU Feng TIAN Guozhong DAI Hongan WANG Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Information Technology La Trobe University

An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicitly perform various trajectory-based interactions, such as navigating through menus [2], entering the boundary of a button,

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Hybrid Flow Shop with Setup Times Scheduling Problem

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computer Systems science & Engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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Impact of time delay and cooperation strategy on the stability of a predator-prey model with Holling type III functional response

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International Journal of Biomathematics 2023年第3期16卷 165-193页

作者： I.Benamara A.El Abdllaouit J.Mikramt Laboratory of Mathematics Computer Science&Applications-Security of Information Department of Mathematics Faculty of Sciences Mohammed V University in RabatMorocco

In this paper,we propose and analyze a delayed predator-prey model with Holling type III functional response taking into account cooperation behavior in *** time delay is introduced in the attack rate to represent the time necessary to trigger the *** analytical result is followed by an ecological *** investigate the effect of hunting cooperation on both the number and the level of positive steady *** observe that the level of the positive equilibrium decreases when increasing the hunting cooperation ***,we study the impact of the delay as well as the cooperation in hunting on the dynamics of the *** prove that the presence of delay in the attack rate induces stability switches around the coexisting equilibrium when predators *** addition,we consider the discrete delay as a bifurcation parameter and prove that the model undergoes a Hopf-bifurcation at the coexisting equilibrium when the delay crosses some critical *** simulations are presented to confirm our analytical findings.

关键词： Predator-prey system Holling type III discrete delay cooperative hunting Hopf-bifurcation switch of stability

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Learning Fine-Grained information with Capsule-Wise Attention for Salient Object Detection

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IEEE Transactions on Multimedia 2023年 1-14页

作者： Zhao, Sanyuan Wen, Zongzheng Qi, Qi Lam, Kin-Man Shen, Jianbing School of Computer Science & Technology Beijing Institute of Technology China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macau China

With the popularity of convolutional neural networks being used for salient object detection (SOD), the performance has been significantly improved. However, how to integrate crucial features for modeling salient objects needs further exploration. In this work, we propose an effective feature selection scheme to solve this task. Firstly, we provide a Simplified Atrous Spatial Pyramid Pooling (SASPP) module to lightweight the multi-scale features. Dealing with the SASSP features, we design a pixel-level local feature selection scheme named Multi-Scale Capsule-wise Attention (MSCA). It aggregates features from multi-scales by dynamic routing and helps the network to generate fine-grained prediction maps. In addition, we exploit holistic features by the Spatial-wise Attention and Channel-wise Attention (SA/CA) mechanisms, which adaptively extracts spatial or channel information. We also propose a Multi-crossed Layer Connections (MLC) structure in the upsampling stage, to fuse features from not only different levels but also different scales. The salient object prediction is performed in a coarse-to-fine manner. By conducting comprehensive experiments on five benchmark datasets, our method achieves the best performance when compared to existing state-of-the-art approaches. IEEE

关键词： Object detection

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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COVID-19 Patients’ Health Monitoring System using Fuzzy Ontology and Internet of Things

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 191-203页

作者： Hadjadj, Abdelhalim Halimi, Khaled Deptartment of Computer Science LabSTIC Laboratory University 08 Mai 1945 Guelma Algeria Department of Information LabSTIC Laboratory University 08 Mai 1945 Guelma Algeria

COVID-19 is a very dangerous pandemic attacking the respiratory organs of humans. It is characterized by its contagious speed, especially with its last versions. Effectiveness of confrontation resides in a strategy based on the speed of intervention, early detection, and appropriate and quick treatment. However, this strategy requires more effort and enormous human, material, and financial resources. The latter situation requires a much more efficient solution based on using new technologies. Through the integration of the internet of things in healthcare, the quality of the latter will be improved. This integration requires suitable architecture represent the foundation for the system that handles the data generated by this technology. To concretize this strategy, we propose an approach based on IoT architecture inspired by the organization of the Algerian health structure for fighting COVID-19. The architecture allows the organization to manage resources and ensure adequate resources. The approach also consists of a semantic web-based system to handle the heterogeneity of data sources and exchange it with different applications. The system is based on a proposed fuzzy ontology that helps to treat vague and imprecise data that characterize the medical domain. The fuzzy ontology is developed by reusing a standard IoT ontology for allowing the sharing and reuse of IoT data. It also uses the NEWS2 score system for defining membership functions. We conducted an experimental study, and the results of the proposed approach were compared with those obtained by physician assessment. The results show that the approach is effective, and even if COVID-19 disappears soon according to the World Health Organization indications, the proposed approach will still be valid for any other epidemic that may occur in the future or for any other disease. © 2023 Iraqi Journal for computer science and Mathematics. All rights reserved.

关键词： COVID-19

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Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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