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2022 International Conference on Geographic Information and Remote Sensing Technology, GIRST 2022

作者： Dai, Xuemei Chen, Shuisen Jiang, Hao Nainarpandian, Chandrasekar Guangzhou Institute of Geography Guangdong Academy of Sciences Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou China Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China Centre for Geotechnology Manonmaniam Sundaranar University Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9781510662186

Lodging is a major yield-limiting factor in rice. Accurate assessment of rice lodging is essential for yield damage estimation, agricultural insurance claims settlement and subsequent management decisions. This study aims to explore the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data. Based on this, an extraction method of lodging rice distribution using multi-source remote sensing data was proposed. The results showed that: (a) The lodging area of rice could be effectively extracted from dual-polarization Sentinel-1 image data with an accuracy of 87.87%;(b) The vegetation indices (NDVI, DVI and LSWI) extracted by Sentinel-2 are sensitive to lodging rice. Based on a certain threshold, lodging rice can be effectively extracted with an accuracy of 87.5%. Our findings demonstrate the potential of Sentinel data for near real-time detection of the rice lodging. © 2023 SPIE.

关键词： Polarization

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AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

arXiv

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arXiv 2023年

作者： Yu, Yang Liu, Qi Zhang, Kai Zhang, Yuren Song, Chao Hou, Min Yuan, Yuqing Ye, Zhihao Zhang, Zaixi Yu, Sanshi Lei Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China OPPO Research Institute China Hefei University of Technology China

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

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IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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Performance Analysis for STAR-RIS-assisted-UAV-empowered Networks under Nakagami-m Fading

Performance Analysis for STAR-RIS-assisted-UAV-empowered Net...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Yuxi Wang Yaxuan Liu Yiyang Ni Demin Kong Jie Peng Yueze Lian Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Nanjing China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Reconfigurable intelligent surface (RIS) promises to improve the received signal-to-interference-plus-noise ratio by manipulating the phase shift of the incident wave. However, the traditional RIS can only reflect the signal by one side and the other half space is blocked. Fortunately, the emerging simultaneous transmitting and reflecting RIS (STAR-RIS) can solve this problem. STAR-RIS supports to reflect and transmit the signal at the same time and then realize the 360-degree coverage. In this paper, we analyze the performance of STAR-RIS assisted unmanned aerial vehicle (UAV) empowered wireless communication systems, including the outage probability and the ergodic achievable rate. The general fading channels, i.e., the Nakagami-m fading channels are considered. We first derived the closed-form expression of the outage probability and the ergodic achievable rate, and then explore a series of insights. The numerical results verify the correctness of our analytical expressions by Monte Carlo simulations.

关键词： Wireless communication Closed-form solutions Protocols Rayleigh channels Reconfigurable intelligent surfaces Probability Autonomous aerial vehicles

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Discrete Jaya algorithm to solve the police resource deployment problem in emergency traffic accident 10

Discrete Jaya algorithm to solve the police resource deploym...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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Immobilized ZnO based nanostructures and their environmental applications

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Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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research on Indifference Management of Differentiated Computer Room

Research on Indifference Management of Differentiated Comput...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Pengju Tang Yanxia Gao Yujun Yang Meixiu Tang College of Computer and Artificial Intelligence (Software College) Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Huaihua University Library Huaihua University Huaihua China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

University computer rooms generally prefer batch purchasing strategy to simplify the management process such as system deployment and software update. However, with the growth of the life of the equipment, the failure of electronic products is inevitable, especially once the motherboard problem occurs, it is often difficult to find a replacement that exactly matches the original motherboard. Even if the replacement is found, its price is far beyond the market average level. After several years of operation, even the motherboard purchased in the same batch may also lead to the coexistence of a variety of models due to various reasons. In this situation, it becomes impractical to carry out unified network cloning. To solve this problem, we propose an innovative solution: deploying a local backup system combined with a server-controlled startup mode. The core of this solution is that through the local backup system, we can effectively deal with the challenge of different motherboard models, and ensure that each machine can have its own system snapshot. At the same time, the server-controlled boot mode further enhances the flexibility and efficiency of management, so that even in the hardware diversity of the environment, it can also achieve batch system recovery. The introduction of local backup system not only solves the problem of differentiated computer room management, but also significantly improves the convenience and stability of computer room operation and maintenance.

关键词： Biological system modeling Ecosystems Cloning System recovery Stability analysis Software Hardware Maintenance Information technology Consumer electronics

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Convolutional Attention Fusion for RGBT Tracking

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IEEE Signal Processing Letters 2025年 32卷 2149-2153页

作者： Zhang, Dawei Wang, Xuan Xiao, Xin Chen, Hao Zheng, Zhonglong Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Big Data AI Research Institute of China Telecom Corporation Limited Jinhua Branch China

RGBT target tracking accomplishes the tracking task by fusing visible and thermal infrared information. The development of Convolutional Neural Networks (CNNs) and Transformer has greatly advanced this field. Most existing transformer-based trackers focus on global modeling while neglecting the utilization of local information. In this paper, we propose a novel Convolutional Attention Fusion Module (CAFM) for RGBT target tracking. To be specific, this module continuously slides local windows on the image like convolution, and captures context features in each window like attention. Additionally, local position embedding is added to the window and works in conjunction with global position embedding to enhance the model's understanding of spatial information. Therefore, our CAFM enhances the extraction of local features by restricting the attention area and promotes multimodal fusion through cross-attention. We extend OSTrack to RGBT tracking and integrate the proposed CAFM into it. Experimental results show that our method performs well on the LasHeR, RGBT210, and RGBT234 datasets, and is superior to other advanced trackers. © 1994-2012 IEEE.

关键词： Aircraft detection

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Power Internet of Things Architecture and Access CTechnology for Massive Heterogeneous Terminals 10

Power Internet of Things Architecture and Access CTechnology...

引用

10th China International Conference on Electricity Distribution, CICED 2022

作者： Guo, Pengtian Guo, Xiaoyan Wang, Xiaohui Chen, Yong Xiao, Kai Li, Daoxing Department of Artificial Intelligence Application China Electric Power Research Institute Co. Ltd. Beijing China Key Laboratory of Energy Big Data Simulation of Tianjin Enterprise Information and Communication Company State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781665452687

ISBN: (纸本)9781665452687

Power Internet of things is the development trend of today's era and the demand for national strategic development. With the development of the power Internet of things, the power Internet of things platform is facing the pressure of massive heterogeneous terminal access. Firstly, based on the current construction of power Internet of things, this paper puts forward an architecture composed of terminal, edge, pipe, and cloud. Then, the terminal management and control technology based on software definition is proposed to realize the flexible and efficient management and control of the power IoT terminal;A massive heterogeneous terminal access technology based on load balancing and multi-protocol analysis technology is proposed to realize the rapid distribution of traffic and the adaptive processing of service requests, improve the processing efficiency of the platform, and realize the decoupling of business logic and message transmission. Finally, the application prospect of the above technology in the fields of distribution network operation state perception, operation, and distribution business connection and improvement, power supply scheme optimization and auxiliary decision-making is given. © 2022 IEEE.

关键词： Internet of things

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