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Measuring the urban sprawl based on economic-dominated perspective:the case of 31 municipalities and provincial capitals

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地球空间信息科学学报（英文版） 2024年第4期27卷 1272-1289页

作者： Qiqi Zhu Meizhi Zeng Pengfei Jia Mingqiang Guo Xun Liang Qingfeng Guan School of Geography and Information Engineering China University of GeosciencesWuhanChina National Engineering Research Center of GIS China University of GeosciencesWuhanChina National Engineering Research Center of GIS China University of GeosciencesWuhanChina School of Geography and Information Engineering China University of GeosciencesWuhanChina School of Geography and Information Engineering China University of GeosciencesWuhanChina National Engineering Research Center of GIS China University of GeosciencesWuhanChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhenChina

Accurate measurement of urban sprawl is vital for urban planning and *** planning-induced internal structure complexity affects the extent of urban *** addition,urban sprawl is closely linked to economic *** study attempts to explore the impact of urban sprawl from an economic-dominated *** a City-Ring road-County(CRC)scale framework based on top-down administrative divisions for urban sprawl measurement is proposed:1)the single-index measurement based on economic activity is applied to calculate urban sprawl;2)the spatiotemporal pattern of urban sprawl is investigated through a case study in 31 economy-dominated provincial capital cities across China from 2005 to 2015;3)the impact of economy and land on urban sprawl is explored using correlation *** results indicate that the degree of urban sprawl at the city scale shows an"inverted U-shaped"curve from 2005 to 2015,which represents that the phenomenon of urban sprawl was most severe in *** finds that urban sprawl was more severe in the east and central regions relative to the provincial capitals in the western region,with the situation being most severe in the northeast *** that have been transformed from suburban to urban built-up areas need to be given priority attention by the local government,including population movement,land layout,and fiscal policy,to meet the criteria of the urbanization *** correlation analysis,we also found that urban sprawl was influenced by the industry structure and the form of built-up *** outcome of the study suggests that the data scale is sufficiently small in granularity to provide geographic boundaries for systematic analysis of urban sprawl in multiple administrative ***,the study helps provide a reference for differential planning policy formulation by governments at diverse economic levels.

关键词： China urban sprawl urban planning economy land finance 31 municipalities and provincial capitals

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Sim-to-real: Quadruped Robot Control with Deep Reinforcement Learning and Parallel Training

Sim-to-real: Quadruped Robot Control with Deep Reinforcement...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Jiang, Han Chen, Teng Cao, Jingxuan Bi, Jian Lu, Guanglin Zhang, Guoteng Rong, Xuewen Li, Yibin Center for Robotics School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781665481090

In recent years, deep reinforcement learning methods provide a new realization idea for the motion control of the quadruped robot, which makes the neural network controller have the characteristics of strong adaptability and high stability. In this paper, we propose an end-to-end neural network framework that, contains an estimator network for estimating the robot's ontology states in addition to the critic and actor networks. These states serve as an important observation data for the critic and actor networks. In addition, in order to solve the disadvantage of high cost of neural network training time, we exploit the mechanism of parallel training and deploy the entire training process in GPU, which improves the speed of network convergence. Finally, we reconstruct the forward network on the CPU through the Eigen library, and transfer the model to the real robot, successfully implementing sim-to-real. We demonstrate our model on SDUQuad-48, and experiments show that the learned policy can achieve various dynamic motions with strong robustness. © 2022 IEEE.

关键词： Reinforcement learning

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Impulsive Control of Nonlinear Multiagent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1889-1898页

作者： Han, Fang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China

In this article, we present a hybrid control approach that integrates an adaptive fuzzy mechanism with an event-triggered impulse strategy to address consensus control challenges in nonlinear multiagent systems (MASs) with uncertain information, both in leader and leaderless scenarios. First, a fuzzy logic-based adaptive control mechanism is proposed to handle system nonlinearity and uncertainty. This mechanism dynamically adjusts fuzzy control rules in real time, enabling the system to adapt to changing conditions and enhancing its robustness against nonlinear disturbances. Second, to alleviate communication overhead and reduce control frequency, an event-triggered impulse strategy is implemented. This strategy activates impulse control only under specific conditions, effectively minimizing control actions while maintaining system consensus. Using Lyapunov stability theory, sufficient conditions for achieving system consensus are established, demonstrating the global consensus and robustness of the MASs under certain criteria. Finally, numerical simulations and experimental evaluations validate the proposed method's effectiveness and its practical advantages. © 1993-2012 IEEE.

关键词： Multi agent systems

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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A Real-time Smooth Lifting Path Planning for Tower Crane Based on TD3 with Discrete-Continuous Hybrid Action Space 22

A Real-time Smooth Lifting Path Planning for Tower Crane Bas...

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14th International Conference on computer Modeling and Simulation, ICCMS 2022

作者： Yin, Zhiyuan Wang, Kai Ma, Xin Center for Robotics School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781450396547

Smoothness and rapidity are two important performance indexes for crane lifting path planning. Traditionally, cranes are modeled as multi-freedom robots and use robot path planning algorithms to plan path in continuous space. However, the paths planned by these methods are not smooth enough to operate. In addition, presently, most of proposed lifting path planning algorithms focus on static environments, requiring accurate environmental information to build maps, which is too computation expensive to meet the requirement of real-time path planning. In this paper, we propose a deep reinforcement learning-based lifting path planning algorithm for hybrid action spaces. The network structure is developed based on TD3. A new reward function is designed and hindsight experience replay is used to solve the reward sparsity problem in long distance path planning. The planning path is smooth and can achieve real-time path planning in unknown environments. The result of simulation experiments demonstrates the effectiveness of the proposed approach. © 2022 ACM.

关键词： Motion planning

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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

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science China(Information sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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Vision-based Moving Target Tracking of Certain Target for Quadruped Robots

Vision-based Moving Target Tracking of Certain Target for Qu...

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Kexin Chen, Teng Chen, Xin Zhang, Guoteng Li, Yibin Rong, Xuewen Center for Robotics School of Control Science and Engineering Shandong University China

Due to the high flexibility of quadruped robots compared with some traditional robots, it has become an important branch in the field of mobile robot research. Target detection and tracking technology is important for the environment perception and autonomous motion tasks of quadruped robots. To achieve effective tracking of a specific target, the premise is that the target is accurately detected. In this article, a target detection algorithm based on deep learning for quadruped robot person tracking is proposed. After the targets are detected, a simple but accurate selection strategy is used to extract the certain target to be tracked from other targets. The visual distance and angle information from the target is used to realize the autonomous tracking. In the part of experiment, we conducted experiments based on the changeable outdoor environment, which included interference factors such as light changes, people crossing, obstruction by obstacles. During the experiments, the SDUQuad-48 quadruped robot can simultaneously realize the stable tracking of a specific moving target based on our algorithm with a speed of 0.8 m/s. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Target tracking

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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SLARD: A Chinese Superior Legal Article Retrieval Dataset 31

SLARD: A Chinese Superior Legal Article Retrieval Dataset

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Zhe Ren, Pengjie Sun, Fuhui Wang, Xiaoyan Li, Yunjun Zhao, Siwen Yang, Tengyi School of Information Science and Engineering Shandong University China School of Computer Science and Technology Shandong University China Information Technology Service Center of People's Court

ISBN: (纸本)9798891761964

Retrieving superior legal articles involves identifying relevant legal articles that hold higher legal effectiveness. This process is crucial in legislative work because superior legal articles form the legal basis for drafting new laws. However, most existing legal information retrieval research focuses on retrieving legal documents, with limited research on retrieving superior legal articles. This gap restricts the digitization of legislative work. To advance research in this area, we propose SLARD: A Chinese Superior Legal Article Retrieval Dataset, which filters 2,627 queries and 9,184 candidates from over 4.3 million effective Chinese regulations, covering 32 categories, such as environment, agriculture, and water resources. Each query is manually annotated, and the candidates include superior articles at both the provincial and national levels. We conducted detailed experiments and analyses on the dataset and found that existing retrieval methods struggle to achieve ideal results. The best method achieved a R@1 of only 0.4719. Additionally, we found that existing large language models (LLMs) lack prior knowledge of the content of superior legal articles. This indicates the necessity for further exploration and research in this field. © 2025 Association for Computational Linguistics.

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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