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Global asymptotical stability in a rational difference equation

Applied Mathematics(A Journal of Chinese Universities) 2021年第1期36卷 51-59页

作者： LI Xian-yi LI Wei Institute of Nonlinear Analysis and Department of Big Data Science Zhejiang University of Science and TechnologyHangzhou 310023China

In this paper we prove a global attractivity result for the unique positive equilibrium point of a difference equation,which improves and generalizes some known ones in the existing ***,our results completely solve an... 详细信息

关键词： global asymptotic stability global attractivity Open problem and conjecture Schwartzian derivative,rational difference equation

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Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

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Digital Communications and Networks 2022年第5期8卷 644-653页

作者： Linjie Wang Youliang Tian Jinbo Xiong School of Data Science Tongren UniversityTongren554300China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou UniversityGuiyang550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in ***,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing *** this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data ***,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion *** results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

关键词： Outsourcing computation Ethereum Smart contract Public verification Homomorphic encryption

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MAML^(2):meta reinforcement learning via meta-learning for task categories

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Frontiers of Computer science 2023年第4期17卷 57-67页

作者： Qiming FU Zhechao WANG Nengwei FANG Bin XING Xiao ZHANG Jianping CHEN School of Electronics and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Chongqing Industrial Big Data Innovation Center Co.Ltd. Chongqing 404100China School of Medical Informatics Xuzhou Medical UniversityXuzhou 221004China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou 215009China

Meta-learning has been widely applied to solving few-shot reinforcement learning problems,where we hope to obtain an agent that can learn quickly in a new ***,these algorithms often ignore some isolated tasks in pursuit of the average performance,which may result in negative adaptation in these isolated tasks,and they usually need sufficient learning in a stationary task *** this paper,our algorithm presents a hierarchical framework of double meta-learning,and the whole framework includes classification,meta-learning,and ***,in the classification process,we classify tasks into several task subsets,considered as some categories of tasks,by learned parameters of each task,which can separate out some isolated tasks ***,in the meta-learning process,we learn category parameters in all subsets via ***,based on the gradient of each category parameter in each subset,we use meta-learning again to learn a new metaparameter related to the whole task set,which can be used as an initial parameter for the new ***,in the re-adaption process,we adapt the parameter of the new task with two steps,by the meta-parameter and the appropriate category parameter ***,we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task ***,our algorithm presents a more rapid adaptation process within ***,we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.

关键词： meta-learning reinforcement learning few-shot learning negative adaptation

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A Digital Currency Scheme Based on Blind Signatures Using Chaotic Hash Functions and RSA Algorithm 5

A Digital Currency Scheme Based on Blind Signatures Using Ch...

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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Zhang, Chunfu Bai, Jing Liang, Yanchun Tavares, Adriano Wang, Lidong Jiang, Jie Zhuhai College of Science and Technology School of Big Data Zhuhai519041 China University of Minho Department of Industrial Electronics Guimaraes4800-058 Portugal Zhuhai College of Science and Technology School of Computer Science Zhuhai519041 China

ISBN: (纸本)9798350352719

Blind signature is a cryptographic technique where a signer signs a message without knowing its content, thereby protecting the privacy of the message. It is widely used in digital currencies, electronic voting systems, anonymous credentials, and other areas. In response to the shortcomings of some existing blind signature schemes in terms of security and efficiency, this article proposes a digital currency scheme based on chaotic hash function and RSA algorithm blind signatures. The chaotic hash function is used to convert digital currencies, improving the shortcomings of previous algorithms. The proposed scheme has anonymous and non-traceable characteristics, can resist various attacks, and has a relatively short processing time, with higher computational efficiency and security performance. © 2024 IEEE.

关键词： Authentication Protocol

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Computer-Aided Diagnosis of COVID-19 Radiography using deep transfer deep learning model TLDeNet

Computer-Aided Diagnosis of COVID-19 Radiography using deep ...

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2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022

作者： Yao, Yutong Zuo, Xingjie Data Science and Big Data Technology Xi'An Jiaotong-liverpool University Suzhou China School of Science Xi'An Jiaotong-liverpool University Suzhou China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

Computer-Aided Diagnosis (CAD) is applied in the medical analysis of X-ray images widely. Due to the COVID-19 pandemic, the speed of COVID-19 detection is slow, and the workforce is scarce. Therefore, we have an idea to use CAD to diagnose COVID-19 and effectively respond to the pandemic. Recent studies show that convolutional neural network (CNN) is an appropriate technique for medical image classification. However, CNN is more suitable for datasets with many images, such as ImageNet. Medical image classification relies on doctors to label medical images, so obtaining large-scale medical image data sets is a time-consuming, costly, and unrealistic task. The method of data augmentation for a limited medical dataset can be used to increase the number of images. However, this technology will produce many repeated images, which will easily lead to the overfitting problem of CNN. In the case of a limited number of radiological images, transfer learning is a practical and effective method which can help us overcome the overfitting problem of ordinary CNN by transferring the pre-Trained models on large datasets to our tasks. The proposed model is DenseNet based deep transfer learning model (TLDeNet) to identify the patients into three classes: COVID-19, Normal or Pneumonia. We then analyzed and assessed the performance of our model on COVID-19 X-ray testing images by performing extensive experiments. It is finally demonstrated that the proposed model is superior to other deep transfer learning models according to comparative analyses. The Grad-Cam method is finally applied to interpret the convolutional neural network, revealing that our proposed model focuses on the similar region of the X-ray images as doctors. © 2022 IEEE.

关键词： COVID-19

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Enhancing EHR Implementation with Process Mining

Enhancing EHR Implementation with Process Mining

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2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Asare, Esther Wang, Lili Fang, Xianwen College of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China

Hospitals have increased the adoption of Hospital Information Systems to optimize processes for the efficient and effective delivery of services to customers (i.e., patients). However, there are still challenges in adopting information systems in healthcare, despite technological advancements and the enormous business benefits. These challenges have led to resistance of some healthcare providers to use these systems. Moreover, investigative and diagnostic measures are not exhaustively carried out to improve the processes of healthcare centers and the implemented information systems used in executing the processes. Fortunately, process mining techniques help optimize business processes, but they are primarily utilized post-implementation/post-go-live. This paper demonstrates the role process mining can play in adopting an EMR/EHR by improving the existing EHR Implementation Lifecycle by proposing an Enhanced Model. We suggest using process mining at suitable phases of the EHR Implementation lifecycle, not post-implementation/post-go-live only. Therefore, we propose an Enhanced EHR Implementation Lifecycle that supports process mining as part of a testing protocol adopted for EMRs/EHRs usability in conformance to the organization's workflow in other implementation phases. An experiment is performed with event logs from an open-source EHR for the feasibility of the proposed Enhanced EHR/EMR implementation model. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Research progress of quantum artificial intelligence in smart city

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Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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High-Performance Real-Time data Processing: Managing data Using Debezium, Postgres, Kafka, and Redis

High-Performance Real-Time Data Processing: Managing Data Us...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Sayar, Alperen Arslan, Suayip Cakar, Tuna Ertugrul, Seyit Akcay, Ahmet Data Science Tam Finans A.Ş. Information Technologies İstanbul Turkey Massachusetts Institute of Technology Big Data and Data Governance Massachusetts United States Mef University Computer Engineering İstanbul Turkey Information Technologies Tam Finans A.Ş İstanbul Turkey

ISBN: (纸本)9798350306590

This research focuses on monitoring and transferring logs of operations performed on a relational database, specifically PostgreSQL, in real-time using an event-driven approach. The logs generated from database operations are transferred using Apache Kafka, an open-source message queuing system, and Debezium running on Kafka, to Redis, a non-relational (No-SQL) key-value database. Time-consuming query operations and read operations are performed on Redis, which operates on memory (in-memory), instead of on the primary database, PostgreSQL. This approach has significantly improved query execution performance, data processing time, and backend service performance. The study showcases the practical application of an event-driven approach using Debezium, Kafka, Redis, and relational databases for real-time data processing and querying. © 2023 IEEE.

关键词： Open systems

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Archive-based multiple feature construction methods using adaptive Genetic Programming

Archive-based multiple feature construction methods using ad...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Jia, Kaixuan Gao, Xiaoying Niu, Jiaxin Ma, Jianbin College of Information Science and Technology Hebei Agricultural University Baoding China Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, insufficient information caused by constructing a single feature or constructing only a few features can affect the classification performance of feature construction. In addition, GP may fall into premature convergence, which also affects classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. Experiments on ten datasets show that our proposed archive-based multiple feature construction method without using adaptive GP can significantly improve the classification performance compared with traditional single feature construction method, and the classification performance can be maintained or further improved by adding the adaptive strategy. The comparisons with three state-of-the-art techniques show that our proposed methods can significantly achieve better classification performance. © 2024 held by the owner/author(s).

关键词： Genetic programming

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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Computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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