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作者： Xu, Xiaolong Cao, Hao Geng, Qingfan Liu, Xihua Dai, Fei Wang, Chuanjian School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China College of Information Science and Technology Shihezi University Xinjiang China

Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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A blockchain-based computation offloading method for edge computing in 5G networks

A blockchain-based computation offloading method for edge co...

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作者： Xu, Xiaolong Chen, Yi Zhang, Xuyun Liu, Qingxiang Liu, Xihua Qi, Lianyong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand School of Information Science and Engineering Qufu Normal University Qufu China

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth-generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain-based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain-based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA-III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

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computer Systems Science & engineering 2023年第10期47卷 187-204页

作者： Rui Wen Jie Zhou Zhongliang Shen Xiaorui Zhang Sunil Kumar Jha Department of Electromechanical Engineering Zhejiang Business Technology InstituteNingbo315012China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Department of Physics Faculty of ScienceAdani UniversityAhmedabadIndia

Convolution Neural networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of ***,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 *** reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 *** solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class ***,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation ***,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation *** experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference *** COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

关键词： Covid-19 diagnose convolutional neural networks XGBoost COVID triplet-center loss early and incubation COVID-19 patients

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An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

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computers, Materials & Continua 2020年第10期65卷 743-760页

作者： Peipeng Yu Zhihua Xia Jianwei Fei Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareJiangsu Engineering Center of Network MonitoringJiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Coronaviruses are a well-known family of viruses that can infect humans or ***,the new coronavirus(COVID-19)has spread *** countries in the world are working hard to control the coronavirus ***,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of *** a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer *** the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical *** to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical *** research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

关键词： Artificial intelligence COVID-19 medical applications clinical diagnosis

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A New Sequential Image Prediction Method Based on LSTM and DCGAN

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computers, Materials & Continua 2020年第7期64卷 217-231页

作者： Wei Fang Feihong Zhang Yewen Ding Jack Sheng School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023China Department of Economics FinanceInsurance and Risk Management University of Central ArkansasConway72035USA

Image recognition technology is an important field of artificial *** with the development of machine learning technology in recent years,it has great researches value and commercial *** a matter of fact,a single recognition function can no longer meet people’s needs,and accurate image prediction is the trend that people *** paper is based on Long Short-Term Memory(LSTM)and Deep Convolution Generative Adversarial networks(DCGAN),studies and implements a prediction model by using radar image *** adopt a stack cascading strategy in designing network connection which can control of parameter convergence *** new method enables effective learning of image features and makes predictive models to have greater generalization *** demonstrate that our network model is more robust and efficient in terms of timing prediction than 3DCNN and traditional *** sequential image prediction model architecture proposed in this paper is theoretically applicable to all sequential images.

关键词： Image prediction LSTM DCGAN

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A novel quantum visual secret sharing scheme

arXiv

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arXiv 2023年

作者： Liu, Wenjie Xu, Yinsong Zhang, Maojun Chen, Junxiu Yang, Ching-Nung Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Division Department of Computer Science and Information Engineering National Dong Hwa University Taiwan97441 Taiwan

Inspired by Naor et al.’s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes, and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with IBM Q is conducted to demonstrate the practical feasibility. © 2023, CC BY.

关键词： Pixels

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A Robust Document Image Watermarking Scheme using Deep Neural network

arXiv

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arXiv 2022年

作者： Ge, Sulong Xia, Zhihua Fei, Jianwei Sun, Xingming Weng, Jian Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China College of Cyber Security Jinan University Guangzhou510632 China

Watermarking is an important copyright protection technology which generally embeds the identity information into the carrier imperceptibly. Then the identity can be extracted to prove the copyright from the watermarked carrier even after suffering various attacks. Most of the existing watermarking technologies take the nature images as carriers. Different from the natural images, document images are not so rich in color and texture, and thus have less redundant information to carry watermarks. This paper proposes an end-to-end document image watermarking scheme using the deep neural network. Specifically, an encoder and a decoder are designed to embed and extract the watermark. A noise layer is added to simulate the various attacks that could be encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian noise, Resize, and JPEG Compression. A text-sensitive loss function is designed to limit the embedding modification on characters. An embedding strength adjustment strategy is proposed to improve the quality of watermarked image with little loss of extraction accuracy. Experimental results show that the proposed document image watermarking technology outperforms three state-of-the-arts in terms of the robustness and image quality. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A WiFi-aware method for mobile data offloading with deadline constraints

A WiFi-aware method for mobile data offloading with deadline...

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作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

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Access Control Policy Based on Friend Circle

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computers, Materials & Continua 2020年第3期62卷 1143-1159页

作者： Qin Liu Tinghuai Ma Fan Xing Yuan Tian Abdullah Al-Dhelaan Mohammed Al-Dhelaan School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China CICAEET Jiangsu Engineering Centre of Network MonitoringNanjing University of Information Science and TechnologyNanjing210044China Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia

Nowadays,the scale of the user’s personal social network(personal network,a network of the user and their friends,where the user we call“center user”)is becoming larger and more *** is difficult to find a suitable way to manage them *** order to solve this problem,we propose an access control model for social network to protect the privacy of the central users,which achieves the access control accurately and *** on the hybrid friend circle detection algorithm,we consider the aspects of direct judgment,indirect trust judgment and malicious users,a set of multi-angle control method which could be adapted to the social network environment is ***,we propose the solution to the possible conflict of rights in the right control,and assign the rights reasonably in the case of guaranteeing the privacy of the users.

关键词： Social network privacy protection circle of friends access control

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Non-Exchangeable Error Compensation for Strapdown Inertial Navigation System in High Dynamic Environment

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computers, Materials & Continua 2020年第8期64卷 973-986页

作者： Qi Wang Changsong Yang Shao’en Wu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncie47306USA

Strapdown non-exchangeable error compensation technology in high dynamic environment is one of the key technologies of strapdown inertial navigation *** platform is used in strapdown inertial navigation system instead of physical platform in traditional platform inertial navigation system,which improves reliability and reduces cost and volume of *** maximum error source of attitude matrix solution is the non-exchangeable error of rotation due to the non-exchangeable of finite rotation of rigid *** rotation non-exchangeable error reaches the maximum in coning motion,although it can be reduced by shortening the correction period and increasing the real-time *** equivalent rotation vector method is used to modify the attitude to reduce the coning error in this *** experiments show that the equivalent rotation vector method can effectively suppress the non-exchangeable error and improve the accuracy of attitude calculation.

关键词： Strapdown error compensation coning motion equivalent rotation vector simulation experiments

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