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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： ling Lin Qian Yu Wen Ji Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Electronics and Information Engineering College Yili Normal University Yining Xinjiang China School of Data and Computer Science Shandong Womens University China

Along with the prosperity of the Mobile Internet, a large amount of stream data has emerged. Stream data cannot be completely stored in memory because of its massive volume and continuous arrival. Moreover, it should be accessed only once and handled in time due to the high cost of multiple accesses. Therefore, the intrinsic nature of stream data calls facilitates the development of a summary in the main memory to enable fast incremental learning and to allow working in limited time and memory. Sampling techniques are one of the commonly used methods for constructing data stream summaries. Given that the traditional random sampling algorithm deviates from the real data distribution and does not consider the true distribution of the stream data attributes, we propose a novel sampling algorithm based on feature-selected and -preserved algorithm. We first use matrix approximation to select important features in stream data. Then, the feature-preserved sampling algorithm is used to generate high-quality representative samples over a sliding window. The sampling quality of our algorithm could guarantee a high degree of consistency between the distribution of attribute values in the population (the entire data) and that in the sample. Experiments on real datasets show that the proposed algorithm can select a representative sample with high efficiency.

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Fingerprint-based access to personally controlled health records in emergency situations

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Science China(information Sciences) 2018年第5期61卷 338-340页

作者： Shaopeng GUAN Yongyu WANG Jian SHEN School of Information and Electronic Engineering Shandong Institute of Business and Technology Haian High-Tech Research Institute of Nanjing University School of Computer and Software Nanjing University of Information Science and Technology

Dear editor,Medical records are the original diagnosis and treatment calendar of the patient in medical institutions,including the patient’s personal information,diagnosis results,treatment methods,prescriptions,and medical *** records not only form the basis for the treatment of patients,but also provide a reference for future ***,medical records are handwritten by *** the application of information and network technology in the medical

关键词： Fingerprint-based access to personally controlled health records in emergency situations

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using computer Simulation technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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A Game Theory Approach for Multi-agent System Resources Allocation against Outside Threats

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Journal of Risk Analysis and Crisis Response 2019年第3期9卷 134-144页

作者： Wu, Cheng-Kuang Hu, Xingwei School of Computer Science and Software Engineering Zhaoqing University Zhaoqing Road Guangdong Province Zhaoqing City 526021 China Information Technology Department International Monetary Fund 700 19th St NW Washington 20431 DC United States

This study proposes an integrated model for the deployment of multiagent resources for resisting outside threats. The proposed two-stage model applies the divide-and-conquer strategy to solve the resources allocation problem. First, the interactive actions between an external attack and a response agent are modeled as a non-cooperative game, after which the external threat value is derived from the Nash equilibrium. Second, the threat values of all response agents are utilized to compute each agent’s Shapley value. Then an acceptable resource allocation of agents based on their expected marginal contribution creates a minimum set of resource deployment costs. The experimental results show that our approach is feasible as a means to mobilize search and rescue resources from a non-affected district and to improve relief efforts against earthquake damage. The Shapley value allocation approach proposed in this study;the percentage of resources allocation of districts is closer to death rate of each district than the proportional division of resources. © 2019 The Authors. Published by Atlantis Press SARL.

关键词： external threat value Multi-agent system Nash equilibrium resources allocation Shapley value

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A Rear-end Collision Warning Algorithm based on Vehicular Communication*

A Rear-end Collision Warning Algorithm based on Vehicular Co...

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International Conference on Intelligent Transportation

作者： Dawei Cao Zhiying Qin Ruiling Liu Yang Yang Dexing Zhong School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China University Shenzhen Research School Shenzhen China State Key Lab. for Novel Software Technology Nanjing University Nanjing P.R. China

ISBN: (纸本)9781538670255

With the combination of vehicle information and communication technology, there is a new opportunity to solve the problem of rear-end collision in the field of transportation. Based on 802.11p, vehicles equipped with the WAVE (Wireless access in Vehicular Environment) standard can communicate each other in real time. When an accident occurs, vehicles within range can receive warning information quickly through inter-vehicle communication. The drivers will have enough time to calmly brake to stop and also reduce the risk of secondary collision. In order to prevent secondary collisions, we propose an algorithm that includes vehicle accident identification and rear-end collision warning. If it is calculated that the vehicle will collide, the system will notify the driver to take brake. The algorithm is based on the historical path of the vehicle and is primarily used for curved road conditions. The results show that the algorithm has good performance and the false alarm rate does not exceed 1%.

关键词： Accidents Roads Global Positioning System Brakes Automobiles Real-time systems

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Multi-scale dynamic graph convolutional network for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Wan, Sheng Gong, Chen Zhong, Ping Du, Bo Zhang, Lefei Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on ATR National University of Defense Technology Changsha410073 China State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430079 China

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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An efficient quantum blind digital signature scheme

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Science China(information Sciences) 2017年第8期60卷 226-239页

作者： Hong LAI Mingxing LUO Josef PIEPRZYK Zhiguo QU Shudong LI Mehmet A.ORGUN College of Computer and Information Science Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences School of Computer and Software Nanjing University of Information Science and Technology College of Mathematics and Information Science Shandong Technology and Business University School of Computer Science National University of Defense Technology Department of Computing Macquarie University Faculty of Information Technology Macau University of Science and Technology

Recently, many quantum digital signature(QDS) schemes have been proposed to authenticate the integration of a message. However, these quantum signature schemes just consider the situation for bit messages,and the signing-verifying of one-bit modality. So, their signature efficiency is very low. In this paper, we propose a scheme based on an application of Fibonacci-, Lucas-and Fibonacci-Lucas matrix coding to quantum digital signatures based on a recently proposed quantum key distribution(QKD) system. Our scheme can sign a large number of digital messages every time. Moreover, these special matrices provide a method to verify the integration of information received by the participants, to authenticate the identity of the participants, and to improve the efficiency for signing-verifying. Therefore, our signature scheme is more practical than the existing schemes.

关键词： blind quantum digital signature Fibonacci- Lucas-and Fibonacci-Lucas matrix coding digital messages signing-verifying modality

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Dynamic Multimodal Optimization Using Brain Storm Optimization Algorithms 13th

Dynamic Multimodal Optimization Using Brain Storm Optimizati...

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Cheng, Shi Lu, Hui Song, Wu Chen, Junfeng Shi, Yuhui School of Computer Science Shaanxi Normal University Xi’an710119 China School of Electronic and Information Engineering Beihang University Beijing100191 China School of Information Engineering Hainan Tropical Ocean University Sanya572000 China College of IOT Engineering Hohai University Changzhou213022 China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789811328251

Dynamic multimodal optimization (DMO) problem is introduced and solved with brain storm optimization (BSO) algorithms in this paper. A dynamic multimodal optimization problem is defined as an optimization problem with multiple global optima and characteristics of global optima are changed during the search process. The effectiveness of BSO algorithm is validated on a test problem which was constructed based on the dynamic optimization and multimodal optimization. Results show that BSO algorithm is an efficient and robust optimization method for solving dynamic multimodal optimization problems. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Blockchain-enabled authentication handover with efficient privacy protection in SDN-based 5G networks

arXiv

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arXiv 2019年

作者： Yazdinejad, Abbas Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond Faculty of Computer Engineering University of Isfahan Iran Department of Software Engineering and Game Development Kennesaw State University GA30060 United States Cyber Science Lab School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

5G mobile networks provide additional benefits in terms of lower latency, higher data rates, and more coverage, in comparison to 4G networks, and they are also coming close to standardization. For example, 5G has a new level of data transfer and processing speed that assures users are not disconnected when they move from one cell to another;thus, supporting faster connection. However, it comes with its own technical challenges relating to resource management, authentication handover and user privacy protection. In 5G, the frequent displacement of the users among the cells as a result of repeated authentication handovers often lead to a delay, contradicting the 5G objectives. In this paper, we propose a new authentication approach that utilizes blockchain and software defined networking (SDN) techniques to remove the re-authentication in repeated handover among heterogeneous cells. The proposed approach is designed to assure the low delay, appropriate for the 5G network in which users can be replaced with the least delay among heterogeneous cells using their public and private keys provided by the devised blockchain component while protecting their privacy. In our comparison between Proof-of-Work (POW)-based and network-based models, the delay of our authentication handover was shown to be less than 1ms. Also, our approach demonstrated less signaling overhead and energy consumption compared to peer models. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A feedforward artificial neural network model for classification and detection of type 2 diabetes

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Journal of Physics: Conference Series 2021年第1期1734卷

作者： Enoch A. Frimpong Ariyo Oluwasanmi Edward Y. Baagyere Qin Zhiguang School of Information and Software Engineering. University of Electronic Science and Technology of China Chengdu China No.4 Section 2 North Jianshe Road 610054 Chengdu P. R. China Department of Computer Science University for Development Studies Box TL 1350 Tamale Ghana

Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics; however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset.

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