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作者： Yue Ma Chenghao Wei Hao Long Key Laboratory of Impression Evidence Examination and Identification Technology(Criminal Investigation Police University of China) Ministry of Public Security Big Data Institute College of Computer Science and Software EngineeringShenzhen University

Aiming at the issues that affect the gait recognition, such as outfit changes and carryon-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy.

关键词： A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour

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On Optimal Finite-length Binary Codes of Four Codewords for Binary Symmetric Channels

On Optimal Finite-length Binary Codes of Four Codewords for ...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Yanyan Dong Shenghao Yang School of Science and Engineering and the Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9784885523304

ISBN: (纸本)9781728128559

Finite-length binary codes of four codewords are studied for memoryless binary symmetric channels (BSCs) with the maximum likelihood decoding. For any block-length, best linear codes of four codewords have been explicitly characterized, but whether linear codes are better than nonlinear codes or not is unknown in general. In this paper, we show that for any block-length, there exists an optimal code of four codewords that is either linear or in a subset of nonlinear codes, called Class-I codes. Based on the analysis of Class-I codes, we derive sufficient conditions such that linear codes are optimal. For block-length less than or equal to 8, our analytical results show that linear codes are optimal. For block-length up to 300, numerical evaluations show that linear codes are optimal.

关键词： Sufficient conditions Binary codes Linear codes Maximum likelihood decoding Information theory

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Improved Biome-BGC model for simulating spatiotemporal dynamics of gross primary productivity in evergreen broadleaf forests of the karst region

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Environmental Modelling & Software 2025年 192卷

作者： Hengyuan Liu Qian Ding Yuechen Li Lei Fan Long Zhao Mingguo Ma Chongqing Engineering Research Center for Remote Sensing Big Data Application Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem School of Geographical Sciences Southwest University Chongqing 400715 China Key Laboratory of Monitoring Evaluation and Early Warning of Territorial Spatial Planning Implementation Ministry of Natural Resources Chongqing 401147 China

Accurately estimating gross primary productivity (GPP) is crucial for carbon cycle modeling, yet the karst landscape of southwest China poses challenges due to persistent cloud cover, rugged topography, and limited observations. This study enhances the Biome-BGC model for evergreen broadleaf forests (EBF) by optimizing key ecophysiological parameters and introducing a phase-specific bias correction method. A GPP dataset (1 km, daily; 2001–2019) was developed by integrating multi-source data. Validation with flux tower data confirmed significant improvements over the default model, with R increasing by 7 % and RMSE decreasing by 28 %. Compared to existing remote sensing products, the optimized model achieved a 75 % increase in R 2 and a 28 % reduction in RMSE. Trend analysis reveals seasonal fluctuations and an overall decline in GPP, with 35 % of the area projected to transition from a decreasing to an increasing trend in the future. This study provides enhanced GPP estimates for carbon cycle assessments.

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Towards artificial general intelligence via a multimodal foundation model

arXiv

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arXiv 2021年

作者： Fei, Nanyi Lu, Zhiwu Gao, Yizhao Yang, Guoxing Huo, Yuqi Wen, Jingyuan Lu, Haoyu Song, Ruihua Gao, Xin Xiang, Tao Sun, Hao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Information Renmin University of China Beijing China Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Electrical and Electronic Engineering University of Surrey Guildford United Kingdom

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI". © 2021, CC BY.

关键词： Semantics

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Extraction of transitional relations in healthcare processes from Chinese medical text based on deep learning 2019

Extraction of transitional relations in healthcare processes...

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4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： Ren, Fangwen Yuan, Song Gao, Feng School of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Institute of Big Data Science and Engineering Wuhan University of Science and Technology China

ISBN: (纸本)9781450362580

knowledge extraction in healthcare has become a recent research hotspot due to the rapid development of AI technology as well as the urgent demand in the medicine research and practice. Traditional methods focus on extracting terms and relations using pattern matching and machine learning, but they depend on the construction of rules and the accuracy of feature extraction. In contrast, deep learning methods have a stronger ability to process data with high dimensions. Moreover, current study neglects the fact that the key knowledge in medicine is the process of diagnosing and treating patients, i.e., the relations that causes the status change in a healthcare workflow. This paper first identifies the relations relevant for medical processes, and adopts the method of relationship extraction based on Bidirection Gated Recurrent Unit (bigRU) model and attention mechanism (bigRU-ATT), in order to retrieve these relations from Chinese medical text. The experimental results show that regarding Chinese medical entity relationship extraction, we can achieve better accuracy rate and recall rate than using convolutional neural network(CNN). © 2019 Association for Computing Machinery.

关键词： Extraction

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Adaptive hybrid projective models for aerial video stitching 6

Adaptive hybrid projective models for aerial video stitching

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6th International Conference on Systems and Informatics, ICSAI 2019

作者： Xi, Runping Han, Tianyi Zhang, Yaqing Han, Qianqian Natl. Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728152561

Aerial video stitching has been widely used in many fields such as large-scale videos surveillance, disaster monitoring and unmanned aerial vehicle (UAV) navigation. Ghosting and parallax are challenging problems when the video streams are captured by UAV. This paper presents a novel method based on hybrid projective model which combines local warp alignment and global warp projection for better registration accuracy. The geometric alignment model is used for global registration of images and the content-preserving alignment model is used for local refinement. Simultaneously, reference frame is updated adaptively according to the shift of scenery, which is used to eliminate the cumulative error appearing in long video streams. The proposed approach has been tested under multiple datasets in different scenarios, which shows good performance. © 2019 IEEE.

关键词： Security systems

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on Mobile Computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Fed-GAN: Federated Generative Adversarial Network with Privacy-Preserving for Cross-Device Scenarios

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Song Ding, Hongxin Zhao, Shuai Ren, Siqi Zeng, Shengke Xue, Mengqi Wang, Ruili Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang Gongshang University School of Computer Science and Technology China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Hangzhou China Xihua University School of Computer and Software Engineering Chengdu China Massey University School of Mathematical and Computational Science Auckland New Zealand

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies (Formula presented)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. © 2004-2012 IEEE.

关键词： Generative adversarial networks

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Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers

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Cleaner engineering and Technology 2025年 27卷

作者： Ma, Shuaiyin Cao, Yichun Liu, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Department of Management and Engineering Linköping University Linköping SE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu 90570 Finland

Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. © 2025 The Authors

关键词： Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN

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LCL-AKA: Lightweight Authentication and key Agreement Protocol for Power IoT

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IEEE Transactions on Smart Grid 2025年

作者： Liu, Zewei Hu, Chunqiang Ruan, Conghao Pu, Yuwen Hu, Pengfei Yu, Jiguo Cyber Physical Society Ministry of Education Key Laboratory of Dependable Service Computing Chongqing University China Chongqing University School of Big Data and Software Engineering Chongqing China China Southern Power Grid Joint Laboratory on Cyberspace Security China Shandong University School of Computer Science and Technology Shandong China Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan250353 China Shandong Laboratory of Computer Networks Jinan250014 China

The rapid adoption of power Internet of Things (PIoT) systems has made security a critical concern, particularly as existing certificateless authentication and key agreement (CL-AKA) protocols face three fundamental limitations that hinder their practical deployment: excessive computational overhead from multi-round interactions, inadequate protection against advanced cryptanalytic attacks, and inflexible architectures unsuitable for dynamic grid environments. To tackle the previously mentioned challenges, this paper proposes lightweight authentication and key agreement protocol (LCL-AKA) for PIoT. Firstly, this protocol efficiently completes the process of identity authentication and session key agreement within a single round of communication interaction, significantly reducing computational costs and improving communication efficiency. Security is rigorously validated through formal proofs under the eCK model and comprehensive analysis showing resistance to key compromise impersonation, unknown key-share attack. Ultimately, comparative experiments are executed to delve into the security and performance characteristics of the proposed protocol. Compared to benchmark protocols deployed in PIoT, the authentication delay of LCL-AKA is decreased by at least 14.88%;in addition, the energy consumption of LCL-AKA is decreased by at least 11.28%. © 2010-2012 IEEE.

关键词： certificateless authentication and key agreement communication efficiency PIoT security proofs

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