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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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Downscaling multilayer soil moisture using parameterized depth profiles associated with environmental factors

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Journal of Hydrology 2025年 661卷

作者： Zhang, Mo Ge, Yong Ma, Yuxin Jin, Yan Chen, Yingying Liu, Shaomin State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100101 China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory of Ecological Intelligent Monitoring and Comprehensive Treatment of Watershed School of Geography and Environment Jiangxi Normal University Nanchang330022 China New South Wales Department of Climate Change Energy the Environment and Water ParramattaNSW2150 Australia School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China State Key Laboratory of Earth Surface Processes and Hazards Risk Governance Beijing Normal University Beijing100875 China

High-resolution multilayer soil moisture is valuable for water resources management, drought monitoring, and crop optimization. However, integrating process-based models into statistical downscaling methods to estimate multilayer soil moisture can increase both prediction uncertainty and computational complexity of parameterization. Here, three parameterized equations (Peak, Trough, and Balance) were proposed to quantify soil moisture profiles associated with environmental factors, and downscale multilayer soil moisture. Global (0–40 cm) and local (0–10, 10–20, and 20–40 cm) fitting strategies represented the vertical extent of environmental influences. Three machine learning models were employed to downscale satellite soil moisture and predict depth parameters, which were then used to generate sub-surface soil moisture. The results indicated that the local fitting strategy consistently outperformed the global strategy across all depths, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 81.8 % and 80.4 %, and increasing the coefficient of determination (R2) by 22.5 %. Furthermore, local fitting revealed distinct nonlinear and linear patterns within the 0–20 cm and 20–40 cm intervals, respectively. The prediction accuracy of sub-surface soil moisture was improved compared to surface results, with MAE and RMSE reduced by 30.2 %–60.7 % and 34.9 %–56.7 %, respectively. The modified local fitting strategy further enhanced prediction performance, with the Peak–Trough–Balance combination recommended as the optimal configuration. Future studies should account for variations in soil and vegetation cover, refine interval division by incorporating soil-forming processes, and adopt advanced uncertainty quantification methods to enhance its adaptability and robustness. This study provides a reference for multilayer soil moisture downscaling in large-scale regions with depth variability and environmental heterogeneity. © 2025 Elsevier B.V.

关键词： Soil moisture

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Partial Deep Domain Adaptation for Non-Intrusive Load Monitoring 21

Partial Deep Domain Adaptation for Non-Intrusive Load Monito...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Yuying Yang, Yang Wang, Chen Liu, Runqin Jiang, Hongbo Liu, Wenping Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China Huazhong University of Science and Technology Wuhan China China Unicom Central South Research Institute Changsha China Hunan University Changsha China Hubei University of Economics Wuhan China

ISBN: (纸本)9798350329223

With the widespread deployment of smart meters and the burgeoning integration of the Ubiquitous Power Internet of Things (UPIoT), Non-Intrusive Load Monitoring (NILM) has emerged as an essential technology, providing granular insights into device-level energy consumption. However, NILM model generalization is impeded by user and appliance heterogeneity. Domain adaptation has been leveraged to enhance NILM models, but it typically necessitates consistent appliance categorization across households. Addressing this, our study presents Weighted Adaptation NILM (WeAd-NILM), a partial deep domain adaptation model designed for Partial Domain Adaptation (PDA) scenarios that accommodate diverse appliance categories, aligning with real-world load decomposition tasks. WeAd-NILM introduces a sample weighting mechanism tailored for PDA, mitigating negative transfer from outliers. Through distribution analysis and multi-objective regression, WeAd-NILM effectively performs load decomposition, fostering knowledge transfer. Verified on UK-DALE and REFIT datasets, WeAd-NILM demonstrates exceptional transferability and load decomposition accuracy. © 2023 IEEE.

关键词： Internet of things

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Design of Environment Monitoring system Based on Single-Chip Microcomputer

Design of Environment Monitoring System Based on Single-Chip...

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International Conference on intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Fan Wang Guojun Liu Wei Zhang Wen Wang School of Computer Science and Information Engineering Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education

This article proposes a real-time environment monitoring system based on single-chip microcomputer, which consists of the device and user. The user adopts WeChat small program, while the device includes STMicroelectronics (STM32) single-chip microcomputer (SCM) control module, temperature and humidity sensor module, illumination sensor module, smoke sensor module, Wireless Fidelity (WIFI) communication module, display module, alarm module, and CP2102 USB-TTL module. The device establishes the connection with the user through the WIFI communication module to achieve remote wireless transmission of data. This system can monitor physical environment information in real-time, not only improving comfort and office efficiency, but also achieving the purpose of preventing danger through preset alarms.

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Five-dimensional Floquet topological semimetals with emergent Yang monopoles and linked Weyl surfaces

arXiv

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arXiv 2024年

作者： Liu, Zheng-Rong Chen, Rui Zhou, Bin Department of Physics Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan430062 China

Recently, Floquet topological matter has attracted significant attention for its potential to reveal novel topological phases inaccessible in static systems. In this paper, we investigate the effect of a time-periodic driving on the five-dimensional (5D) normal insulators. We show that the time-periodic driving can induce a topological phase transition from a TP (time reversal combined with space inversion) symmetry-preserving normal insulator to a 5D Floquet topological semimetal with emergent Yang monopoles characterized by the second Chern number. Additionally, we show that this time-periodic driving can also lead to a topological phase transition from a TP symmetry-breaking normal insulator to a 5D Floquet topological semimetal with a Hopf link formed by Weyl surfaces. Besides, further increasing the strength of the time-periodic driving, the two 5D Floquet topological semimetal phases are transformed into the 5D Floquet Chern insulators. Our paper is helpful for future studies in higher dimensional Floquet systems. © 2024, CC BY.

关键词： Topology

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Current density in the anomalous Hall effect regime under weak scattering

arXiv

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arXiv 2024年

作者： Dai, Ning Zhou, Bin Department of Physics Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan430062 China

A finite equilibrium current density arises in the anomalous Hall effect (AHE) as a result of time-reversal symmetry breaking, affecting both the differential current density and total current. This study illustrates the equilibrium current density pattern in a ribbon-shaped system within the AHE regime, consisting of two sets of counterpropagating channels arranged in a zebra crossing pattern. While the middle channels are susceptible to scattering, the edge channels remain relatively robust. Despite this difference, all channels exhibit the same differential current density when subjected to a differential voltage across the two ends of the ribbon. When a differential voltage is applied to both sides of the ribbon, it results in a snaking pattern of differential current density forming across it. Furthermore, in a four-terminal device comprising an AHE ribbon and two normal leads, it is found that Hall conductance is independent of ribbon width within certain scattering strengths due to differences in robustness between middle and edge channels. These findings underscore the significant role played by current density in AHE transport. Copyright © 2024, The Authors. All rights reserved.

关键词： Hall effect devices

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Four-dimensional Floquet topological insulator with an emergent second Chern number

arXiv

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arXiv 2023年

作者： Liu, Zheng-Rong Chen, Rui Zhou, Bin Department of Physics Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei University Wuhan430062 China

Floquet topological insulators have been widely investigated in lower-dimensional systems. However, Floquet topological insulators induced by time-periodic driving in higher-dimensional systems remain unexplored. In this work, we study the effects of time-periodic driving in a four-dimensional (4D) normal insulator, focusing on topological phase transitions at the resonant quasienergy gap. We consider two types of time-periodic driving, including a time-periodic onsite potential and a time-periodic vector potential. We reveal that both types of time-periodic driving can transform the 4D normal insulator into a 4D Floquet topological insulator characterized by an emergent second Chern number. Moreover, it is found that the topological phase of the 4D system can be modulated by tuning the strength and frequency of the time-periodic driving. Our work will be helpful for the future investigation of Floquet topological insulators in higher dimensions. © 2023, CC BY.

关键词： Electric insulators

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AR-CNN: an attention ranking network for learning urban perception

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Science China(Information Sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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Poisoned LangChain: Jailbreak LLMs by LangChain

arXiv

引用

arXiv 2024年

作者： Wang, Ziqiu Liu, Jun Zhang, Shengkai Yang, Yang Wuhan University of Technology Wuhan China Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Hubei University Wuhan China

With the development of Natural Language Processing (NLP), Large Language Models (LLMs) are becoming increasingly popular. LLMs are integrating more into everyday life, raising public concerns about their security vulnerabilities. Consequently, the security of large language models is becoming critically important. Currently, the techniques for attacking and defending against LLMs are continuously evolving. One significant method type of attack is the jailbreak attack, which designed to evade model safety mechanisms and induce the generation of inappropriate content. Existing jailbreak attacks primarily rely on crafting inducement prompts for direct jailbreaks, which are less effective against large models with robust filtering and high comprehension abilities. Given the increasing demand for real-time capabilities in large language models, real-time updates and iterations of new knowledge have become essential. Retrieval-Augmented Generation (RAG), an advanced technique to compensate for the model’s lack of new knowledge, is gradually becoming mainstream. As RAG enables the model to utilize external knowledge bases, it provides a new avenue for jailbreak attacks. In this paper, we conduct the first work to propose the concept of indirect jailbreak and achieve Retrieval-Augmented Generation via LangChain. Building on this, we further design a novel method of indirect jailbreak attack, termed Poisoned-LangChain (PLC), which leverages a poisoned external knowledge base to interact with large language models, thereby causing the large models to generate malicious non-compliant *** tested this method on six different large language models across three major categories of jailbreak issues. The experiments demonstrate that PLC successfully implemented indirect jailbreak attacks under three different scenarios, achieving success rates of 88.56%, 79.04%, and 82.69% respectively. Experimental results and other resources: https://***/CAM-FSS/jailbreak-langchai

关键词： Knowledge based systems

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SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge Computing 8th

SCESP: An Edge Server Placement Method Based on Spectral Cl...

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

作者： Wang, Lijuan Guo, Yingya Yao, Jiangyuan Zhou, Siyu College of Computer and Data Science Fuzhou University Fuzhou350000 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350000 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China Tandon School of Engineering New York University New York10012 United States

ISBN: (纸本)9783031067600

With the rapid development of Internet of Things (IoT) and 5G, mobile edge computing is gaining popularity for its low computation latency, bandwidth costs and energy consumption. In mobile edge computing, the placement of edge servers is one of the most significant problems and attracts worldwide attention. However, two major problems of edge server placement: high access delay and unbalanced workload of edge servers, have not been completely solved yet. To better solve these two problems, this paper proposes a new Spectral-Clustering-based Edge Server Placement (SCESP) algorithm, which can effectively reduce the access delay and make the workload of each edge server more balanced. In the evaluation, we use the Shanghai Telecom’s base station dataset to test the performance of SCESP and extensive experiments demonstrate the superior performance of SCESP in reducing the access delay and balancing the edge server workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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