检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Gao, Fan Xu, Wei Xiao, Yan Jiang, Wei Chen, Mu Liu, Zekai Zhang, Peng Yang, Tianyi Qian, Zhiyu Gao, Keqiang Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Pharmacy Nanjing Medical University Nanjing211166 China

The pathogenesis of Alzheimer's disease (AD) remains elusive. The development of new drugs is protracted, costly, and fraught with failure. There is a dearth of in vitro high-throughput assay platforms capable of observing the therapeutic effects of AD drugs at the level of cellular and network. Consequently, an in vitro multimodal spatiotemporal drug assessment platform has devised based on a neuronal network chip. It has a bi-directional distribution of electrode points of different sizes, which are more suitable for the detection of neuronal connections. The multi-level neurophysiological properties, including single neuron firing pattern, intercellular connection, transmission speed, and neural network communication, were detected in real time. The results demonstrated that the differences between experimental groups in electrophysiological properties could significantly distinguish the effects of AD representative drug treatment after different levels of neurological injury. The administration of the drug at the initial stage of β-amyloid oligomers toxicity was found to effectively impede the deterioration of neurons and network. Furthermore, the neurophysiological mechanism of drug interaction was elucidated, which may provide the guidance for the optimal timing and dosage of drug administration. It is anticipated that this will become a novel platform for the preclinical evaluation and screening of AD drugs. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave systems

arXiv

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arXiv 2023年

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Millimeter waves

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Towards Open-Set Document Layout Segmentation Through Class-Specific Prototype Learning

SSRN

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SSRN 2023年

作者： Sang, Yuman Yu, Shenbao Zeng, Yifeng Pan, Yinghui Yao, Zhangrui Department of Automation Xiamen University Xiamen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Open-set segmentation is an emerging and challenging topic, especially when it is combined with domain-specific tasks. Beyond recognizing known classes, the system needs to reject the unknowns. In this paper, we study the open-set document layout segmentation problem and propose a two-phase framework (OpenDLS) that accurately recognizes pixels in document images from known classes and rejects those from the unknowns. In the first phase, we propose a class-specific prototype learning strategy and design two loss functions for model training. In addition, considering the multifarious appearance of objects, a multi-scale feature fusion module is developed. In the second phase, we propose three rejection rules to detect the pixels that belong to unknown classes. To the best of our knowledge, this is the first work to investigate the open-set document layout segmentation task. Extensive experiments demonstrate the effectiveness of our framework in both known class classification and unknown class identification. © 2023, The Authors. All rights reserved.

关键词： Pixels

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Tensor-Based Multi-Scale Correlation Anomaly Detection for AIoT-Enabled Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Zeng, Jiuzhen Yang, Laurence T. Wang, Chao Deng, Xianjun Yang, Xiangli Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada University of South China School of Electrical Engineering Hengyang421001 China

Artificial Intelligence of Things (AIoT) is an innovative paradigm expected to enable various consumer applications that is transforming our lives. While enjoying benefits and services from these applications, we also face serious security issues due to malicious cyber attacks on the massive growth of AIoT consumer devices. Accurate anomaly detection is one of the critical tasks for the trustworthy AIoT removing those obstacles. However, limited by the vector-based data pattern and ill-considered factors for anomalous samples analysis, existing methods suffer from the low detection performance. In this paper, a multi-scale correlation tensor convolutional Gaussian mixture network (named as MTCGM) is presented for ameliorating this actuality. Specifically, MTCGM suggests to construct the multi-scale correlation tensor by stacking one self-correlation matrix and multiple surrounding-correlations of different scales, which well characterizes the network status of AIoT. Subsequently, a 3D-convolutional autoencoder (3DCA) is designed for capturing inter-feature correlations, and followed with a Gaussian mixture probability (GMP) network for the observations likelihood estimation. Moreover, low-dimensional space features, relative Euclidean distance and tensor cosine similarity (TCS) are adopted in MTCGM as the multi-factor to boost the likelihood estimation. Extensive experiments on public benchmark datasets verify the validity of MTCGM, and demonstrate its superiority over the state-of-the-art baselines even in presence of contaminated training samples and input noise. © 1975-2011 IEEE.

关键词： Tensors

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Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

arXiv

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arXiv 2023年

作者： Hu, Xinxin Chen, Haotian Chen, Hongchang Liu, Shuxin Li, Xing Zhang, Shibo Wang, Yahui Xue, Xiangyang National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China The Edward S. Rogers Sr Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Institute of Big Data Fudan University Shanghai China

With the rapid development of mobile networks, the people’s social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source real-world mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields . The CSGNN code and datasets are publicly available at https://***/xxhu94/CSGNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

引用

arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

引用

2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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A Bargaining Game-Based Peer-to-Peer Trading Optimization Method for Microgrid system on the Island 2

A Bargaining Game-Based Peer-to-Peer Trading Optimization Me...

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2nd IEEE China International Youth Conference on Electrical engineering, CIYCEE 2021

作者： Li, Hui Trivedi, Anupam Ren, Zhouyang Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing400044 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

ISBN: (纸本)9781665400640

This paper proposes a peer-to-peer trading optimization method for a microgrid (MG) system on the island. A bargaining game is applied to describe the trading and operating strategies for two MGs in the off-grid MG system under the regulation of the government. First, the peer-to-peer trading optimization models for both MGs are developed. The electricity price is negotiated by MGs in an interval limited by the government. The trading power between MGs is optimized coordinately to minimize the net cost of each MG. Second, the presented optimization models are linearized and transformed to solve the Pareto front of trading and operating schemes for both MGs. Finally, the effectiveness of the proposed method is verified in the case studies. A sensitivity analysis of pricing intervals is also implemented to reveal the influence of the government's regulation on the net cost of MG. © 2021 IEEE.

关键词： Sensitivity analysis

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Research on Key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

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