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SSRN 2023年

作者： Cang, Haoze Zeng, Xiangyan Yan, Shuli School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

The effective prediction of fossil CO2 emissions provides a foundation for environmental protection authorities to make relevant management and decisions. CO2 emissions are influenced by various factors and exhibit a nonlinear and complex trend. First, the weighted conformable fractional accumulation (WCFA) method is proposed. The weight of old and new information can be regulated by adjusting the parameters. A nonlinear discrete grey Bernoulli multivariate convolution model is introduced to model the sequences processed by WCFA. Then, five parameters in the model need to be optimized. An improved marine predators algorithm is proposed through the tent map and an improved control parameter. The feasibility and predictive effectiveness of the model is demonstrated by comparing it with four improved grey multivariate convolution models. Finally, the total fossil CO2 emissions in China from 2022 to 2026 are predicted and analyzed. The results indicate a gradual decrease in fossil CO2 emissions over the next five years. Some reasonable suggestions are made based on the prediction results and sources of fossil CO2 emissions. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Comparisons of corneal biomechanical and tomographic parameters among thin normal cornea,forme fruste keratoconus,and mild keratoconus

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Eye and Vision 2021年第1期8卷 443-453页

作者： Lei Tian Di Zhang Lili Guo Xiao Qin Hui Zhang Haixia Zhang Ying Jie Lin Li Beijing Institute of Ophthalmology Beijing Tongren Eye CenterBeijing Ophthalmology&Visual Sciences Key LaboratoryBeijing Tongren HospitalCapital Medical UniversityBeijing 100730China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data‑Based Precision Medicine Beihang University&Capital Medical UniversityBeijing Tongren HospitalBeijing 100730China

Background:To compare the dynamic corneal response(DCR)and tomographic parameters of thin normal cornea(TNC)with thinnest corneal thickness(TCT)(≤500μm),forme fruste keratoconus(FFKC)and mild keratoconus(MKC)had their central corneal thickness(CCT)matched by Scheimpflug imaging(Pentacam)and corneal visualization Scheimpflug technology(Corvis ST).Methods:CCT were matched in 50 eyes with FFKC,50 eyes with MKC,and 53 TNC eyes with TCT≤500μ*** differences in DCR and tomographic parameters among the three groups were *** receiver operating characteristic(ROC)curve was used to analyze the diagnostic significance of these *** propagation(BP)neural network was used to establish the keratoconus diagnosis ***:Fifty CCT-matched FFKC eyes,50 MKC eyes and 50 TNC eyes were *** age and biomechanically corrected intraocular pressure(bIOP)did not differ significantly among the three groups(all P>0.05).The index of height asymmetry(IHA)and height decentration(IHD)differed significantly among the three groups(all P<0.05).IHD also had sufficient strength(area under the ROC curves(AUC)>0.80)to differentiate FFKC and MKC from TNC *** DCR parameters showed significant differences between the MKC and TNC groups,and the deflection amplitude of the first applanation(A1DA)showed a good potential to differentiate(AUC>0.70)FFKC and MKC from TNC *** model by BP neural network showed an accurate diagnostic efficiency of about 91%.Conclusions:The majority of the tomographic and DCR parameters differed among the three *** IHD and partial DCR parameters assessed by Corvis ST distinguished FFKC and MKC from TNC when controlled for CCT.

关键词： Thin normal cornea Forme fruste keratoconus Mild keratoconus Corneal biomechanical parameters

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A Survey of Route Recommendations: Methods, Applications, and Opportunities

arXiv

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arXiv 2024年

作者： Zhang, Shiming Luo, Zhipeng Yang, Li Teng, Fei Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens' travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of classic methods and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) Application-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Multi-agent Traffic Prediction via Denoised Endpoint Distrib...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yao Liu Ruoyu Wang Yuanjiang Cao Quan Z. Sheng Lina Yao Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatiotemporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and un-certainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

关键词： Uncertainty Navigation Soft sensors Robot kinematics Predictive models Transformers Road traffic Trajectory Complexity theory Spatiotemporal phenomena

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Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

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IEEE Transactions on Mobile computing 2025年

作者： Tang, Xiao Zhao, Kexin Shen, Chao Du, Qinghe Wang, Yichen Niyato, Dusit Han, Zhu Research & Development Institute Northwestern Polytechnical University Shenzhen518063 China National Key Laboratory of Wireless Communications Chengdu611731 China State Key Laboratory of Intelligent Game Taicang215400 China Northwestern Polytechinical University School of Electronics and Information Xi'an710072 China Xi'an Jiaotong University Faculty of Electronics and Information Engineering Xi'an710049 China Nanyang Technological University College of Computing and Data Science Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Has My Release Disobeyed Semantic Versioning? Static Detection Based on Semantic Differencing 22

Has My Release Disobeyed Semantic Versioning? Static Detecti...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software engineering

作者： Lyuye Zhang Chengwei Liu Zhengzi Xu Sen Chen Lingling Fan Bihuan Chen Yang Liu School of Computer Science and Engineering Nanyang Technological University Singapore College of Intelligence and Computing Tianjin University China College of Cyber Science Nankai University China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9781450394758

To enhance the compatibility in the version control of Java Third-party Libraries (TPLs), Maven adopts Semantic Versioning (SemVer) to standardize the underlying meaning of versions, but users could still confront abnormal execution and crash after upgrades even if compilation and linkage succeed. It is caused by semantic breaking (SemB) issues, such that APIs directly used by users have identical signatures but inconsistent semantics across upgrades. To strengthen compliance with SemVer rules, developers and users should be alerted of such issues. Unfortunately, it is challenging to detect them statically, because semantic changes in the internal methods of APIs are difficult to capture. Dynamic testing can confirmingly uncover some, but it is limited by inadequate coverage. To detect SemB issues over compatible upgrades (Patch and Minor) by SemVer rules, we conduct an empirical study on 180 SemB issues to understand the root causes, inspired by which, we propose Sembid (Semantic Breaking Issue Detector) to statically detect such issues of TPLs for developers and users. Since APIs are directly used by users, Sembid detects and reports SemB issues based on APIs. For a pair of APIs, Sembid walks through the call chains originating from the API to locate breaking changes by measuring semantic diff. Then, Sembid checks if the breaking changes can affect API’s output along call chains. The evaluation showed Sembid achieved recall and precision and outperformed other API checkers on SemB API detection. We also revealed Sembid detected over 3 times more SemB APIs with better coverage than unit tests, the commonly used solution. Furthermore, we carried out an empirical study on 1,629,589 APIs from 546 version pairs of top Java libraries and found there were 2 ∼ 4 times more SemB APIs than those with signature-based issues. Due to various version release strategies, of Patch version pairs and of Minor version pairs had at least one API affected by any breaking.

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Decay Regularized Stochastic Configuration Networks with Multi-Level data Processing for Uav Battery Rul Prediction

SSRN

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SSRN 2024年

作者： Liao, Zihao Li, Shaobo Li, Qian Li, Shengping State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Guizhou lnstitute of Technology Guizhou Guiyang550003 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Design and the Built Environment Curtin University Perth Australia

A precise and durable health management strategy for battery power systems is crucial to maintaining the reliable operation of Unmanned Aerial Vehicles(UAVs).This paper introduces an adaptive Decay Regularized Stochastic Configuration Networks(DRSCN) with Multi-level data processing for UAVs Battery Remaining Useful Life(RUL) Prediction. Firstly, a Multi source Signal Enhancement Analysis Framework (MSEAF) is effectively presented to criticalbattery health indicators from complex signals. Moreover, a significant innovation is enhancing the SCN model’s output layer, which introduces Decay Regularization to sparsify the layer’s weights, significantly reducing the risk of overfitting in later prediction stages. To further re fine and optimize DRSCN, a Convex Lens and Dual-Mechanism Enhanced Sand Cat Swarm Optimization (CLDM-SCSO) algorithm is employed for precise hyperparameter tuning, fur ther improving prediction accuracy. Finally, extensive control and ablation experiments on the NASA HIRF battery dataset validate the framework’s superior prediction accuracy and relia bility over existing methods, offering an efficient and reliable UAV battery health monitoring solution. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Lesion-Aware Open Set Medical Image Recognition with Domain Shift

Lesion-Aware Open Set Medical Image Recognition with Domain ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yiqian Xu Rui-Wei Zhao Rui Feng School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China

Medical image diagnosis in real clinical scenario faces the challenging open set recognition problem with domain shift. Based on the observation that it is often merely the local lesion areas in the whole image decide or cause the true seen or potential unseen disease labels, we propose a unified deep learning framework to address it by establishing a lesion-aware mask generator to guide the model towards focusing on the lesion areas for both seen and unseen diseases, followed by a multi-task feature learning module to extract the lesion image features; and an open set recognizer with improved capability to reject unseen classes. Last but not least, we propose tailored training strategies with mixed up data and mask augmentation for improved OSR and multi-clue cross-domain feature alignment for domain generalization. Through comparative experiments and ablation studies on the representative skin lesion benchmarks, we successfully validate the effectiveness of our proposed framework.

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