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Improving the Transferability of Adversarial Samples through Automatically Learning Augmentation Strategies from Data

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International Journal of Network Security 2023年第6期25卷 983-991页

作者： Xu, Ru-Zhi Lyu, Chang-Ran School of Control and Computer Engineering North China Electric Power University Beijing102206 China

The attackers cause the deep neural network (DNN) to misclassify the original image by adding perceptible perturbations, which brings security risks to deploying deep neural networks. Since the existing transfer-based attack algorithms overfit to the source model resulting in poor transferability of black-box attacks and data augmentation is one of the main methods to avoid overfitting the source model. We propose an auto-augmented transferable black-box attack method. Firstly, we set up a search space for data augmentation strategies, and then we use reinforcement learning to search for the best augmentation strategy automatically. We use the strategies to augment images that are used to compute gradients. Finally, we employ the fast gradient sign algorithm to generate adversarial examples. Extensive experiments on ImageNet show the superiority of our method to stateof-the-art baselines in attacking different undefended and defended models. © 2023, Femto Technique Co. All rights reserved.

关键词： Deep neural networks

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Bilinear Mapping and Blockchain-based Privacy-Preserving and Data Sharing Scheme for Smart Grid

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International Journal of Network Security 2023年第1期25卷 151-160页

作者： Zhang, Xiaoxu School of Control and Computer Engineering North China Electric Power University Beijing102206 China

With the popularity of the smart grid, there are many problems, such as data security, high cost, and low efficiency in the query and access control of users’ electricity data. To solve the above problems, this paper proposes a scheme of privacy-preserving and data sharing based on bilinear mapping and blockchain for an intelligent pricing system of a smart grid. Then, the secure encryption, signature, and authentication mechanism ensure the integrity and confidentiality of the message. An ASV-FLD algorithm is designed based on dichotomy to improve the speed of signature verification and fast recognition. First, the ID-based encryption scheme is used for data aggregation, which can significantly improve the calculation efficiency. Second, an ID-based proxy re-encryption scheme is used for data sharing. Finally, the simulation results show that the scheme has the characteristics of low storage cost, high efficiency of data calculation, flexible access control, and can meet the growing business needs of users. © 2022,International Journal of Network Security. All Rights Reserved.

关键词： Blockchain

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Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

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Big Data Mining and Analytics 2023年第2期6卷 201-217页

作者： Xuehong Wu Junwen Duan Yi Pan Min Li Computer Science and Engineering Central South UniversityChangsha 410083China School of Computer Science Hunan First Normal UniversityChangsha 410006China School of Computer Science and Engineering Central South UniversityChangsha 410083China Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical *** this end,we offer an in-depth review of MKG in this *** research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG ***,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for *** addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major ***,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

关键词： medical knowledge graph knowledge graph construction knowledge reasoning intelligent medical applications intelligent healthcare

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Temperature-triggered liquid metal actuators for fluid manipulation by leveraging phase transition control

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International Journal of Smart and Nano Materials 2024年第4期15卷 730-742页

作者： Hongda Lu Jiayi Yang Mengqing Zhao Qingtian Zhang Jialu Wang Xiangbo Zhou Yipu Guo Liping Gong Zexin Chen Shi-Yang Tang Weihua Li School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongAustralia School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijingP.R.China School of Mechanical and Manufacturing Engineering University of New South WalesSydneyAustralia School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Small-scale pumps for controlling microfluidics have promising applications in drug delivery and chemical *** metal(LM)demonstrates excellent flow pumping performance due to its simple structure and the electrocapillary effect under an electric ***,LM droplets risk escaping from constrained structures,which can lead to pump *** regulation is also a critical parameter in optimizing chemical reactions in fluidic systems,however,integrating it into a compact system remains ***,we develop a temperature-triggered gallium-based actuator(TTGA)by introducing a gallium(Ga)droplet wetted on a copper(Cu)plate as the core element for flow *** Cu plate prevents the Ga droplet from escaping the chamber and significantly increases the flow *** leveraging the electrochemical method to inhibit the supercooling effect of Ga,the TTGA enables activation/deactivation for flow actuation at different *** investigate the impact of electrode position,solution concentration,and applied voltage on TTGA’s pumping *** dynamically tuning the Ga droplet’s temperature to control phase transition,TTGA allows for accurate flow actuation ***,placing Ga and eutectic Ga-indium(EGaIn)droplets in different channels enables the expected flow divergence for fluids with different *** development of TTGA presents new opportunities in microfluidics and biomedical treatment.

关键词： Liquid metal actuators temperature sensation phase transition

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph 25

Exploiting Machine Learning and LSTM for Human Activity Reco...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5554-5570页

作者： Li, Shike Li, Jianbin Liang, Yin Zhang, Hengyang Wu, Sixing Wang, Shen Cheng, Long North China Electric Power University School of Control and Computer Engineering Beijing102206 China University College Dublin School of Computer Science DublinD04 V1W8 Ireland

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This article proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons. © 2016 IEEE.

关键词： Blockchain

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Dust-Mamba: An Efficient Dust Storm Detection Network with Multiple Data Sources 39

Dust-Mamba: An Efficient Dust Storm Detection Network with M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Cong Lin, Zhonghao Zhang, Jinglin Chen, Shengyong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shangdong University China School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)157735897X

Accurate detection of dust storms is challenging due to complex meteorological interactions. With the development of deep learning, deep neural networks have been increasingly applied to dust storm detection, offering better learning and generalization capabilities compared to traditional physical modeling. However, existing methods face some limitations, leading to performance bottlenecks in dust storm detection. From the task perspective, existing research focuses on occurrence detection while neglecting intensity detection. From the data perspective, existing research fails to explore the utilization of multi-source data. From the model perspective, most models are built on convolutional neural networks, which have an inherent limitation in capturing long-range dependencies. To address the challenges mentioned, this study proposes Dust-Mamba. To the best of our knowledge, this study is the first attempt to accomplish both the occurrence and intensity detection of dust storms with advanced deep learning technology. In Dust-Mamba, multi-source data is introduced to provide a comprehensive perspective, Mamba and attention are applied to boost feature selection while maintaining long-range modeling capability. Additionally, this study proposes Structure Sharing Transfer Learning Strategies for intensity detection, which further enhances the performance of Dust-Mamba with minimal time cost. As shown by experiments, Dust-Mamba achieves Dice scores of 0.963 for occurrence detection and 0.560 for intensity detection, surpassing several baseline models. In conclusion, this study offers valuable baselines for dust storm detection, with significant reference value and promising application potential. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

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Chinese Physics B 2024年第5期33卷 292-304页

作者：田婧希金松昌张晓强杨绍武史殿习 School of Computer Science National University of Defense TechnologyChangsha 410003China Intelligent Game and Decision Laboratory (IGDL) Beijing 100091China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface *** safeguard this sensitive data,image encryption technology is *** this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption *** encryption algorithm tailored for handling the multi-band attributes of remote sensing images is *** algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple ***,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing *** results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

关键词： hyperchaotic system elliptic curve cryptosystem(ECC) 3D synchronous scrambled diffusion remote sensing image unmanned aerial vehicle(UAV)

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Crowd E-Home—A Crowd-Based Collaborative Decoration Platform

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International Journal of Crowd Science 2022年第3期6卷 135-141页

作者： Ge, Rui Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

With the rapid development of Chinese urbanization and industrialization level, the Chinese real estate transaction area continues to grow, and the country's decoration industry is expanding. Due to the rapid expansion, the decoration industry has failed to establish a good industry order, and the current decoration industry has many problems, such as opaque information and low industrial efficiency. Traditional and Internet decoration companies have failed to solve these problems effectively. The purpose of this study is to build a new collaborative decoration platform to solve the shortcomings of the decoration industry and promote its healthy development. A crowd-based collaborative decoration platform called Crowd E-home is proposed. Based on the Building Information Modeling (BIM) of houses, the platform constructs collaborative intelligent digital models, which have the characteristics of evolution, coordination, multifaceted, and initiative. These models act as intermediate coordinators to complete the collaborative division of labor. This platform plays a role in matching multiparty transactions among consumers, designers, construction teams, material suppliers, and supervisors to solve the problem of the huge coordination costs of traditional decoration companies. It also breaks the space-time limit of consumer selection during the decoration process, improves the decision-making power of consumers, and meets consumer needs for personalized services. The collaborative concept of the platform and the application of BIM optimize the existing decoration industry chain, which transforms the traditional chain model into a star-shaped decoration closed-loop ecosystem. © The author(s) 2022.

关键词： Decision making

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A Personality Detection Method Based on Semantic and Psycholinguistic of Social Texts 7

A Personality Detection Method Based on Semantic and Psychol...

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7th International Conference on computer Information Science and Application Technology, CISAT 2024

作者： Wang, Chundong Yang, Yuhan Lin, Hao Han, Lu Tianjin University of Technology School of Computer Science and Engineering National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350375107

At the intersection of personality psychology, computer science, and linguistics, more and more researchers are paying attention to personality detection based on content analysis of texts on social media. However, existing social text-based personality detection methods rely heavily on pre-trained language models, overlook multi-dimensional sentiment in social texts, and lack of effective feature selection of psycholinguistic features. To address these limitations, we propose a method for Myers-Briggs type indicator (MBTI) personality detection. Our approach extracts and selects high-dimensional psycholinguistic feature, utilizes an optimal intermediate layer output from pre-trained language models for semantic representation, and fuses these features to enhance detection accuracy. The experiment results indicate that our proposed method improves the state-of-art results for MBTI personality traits by 1.65% on average on Kaggle MBTI dataset. © 2024 IEEE.

关键词： Psychoacoustic

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