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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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Advanced Operation and Control Schemes of Retired Batteries in DC Microgrid for Secondary Usage

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IEEE Transactions on Smart Grid 2025年

作者： Wang, Ning Jiang, Yongbin Wang, Yanbo Mou, Di Liu, Yonghui Hu, Weihao Chen, Zhe Aalborg University Department of Energy Technology Aalborg9220 Denmark Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology School of Electrical Engineering Chongqing400044 China Xi'an Jiaotong University School of Electrical Engineering Xi'an710049 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu610056 China

The utilization of repurposed second-life batteries from electric vehicles in DC microgrids presents a sustainable and cost-effective solution. However, efficiently integrating these batteries within DC microgrids poses two main challenges: (1) poor consistency among retired battery packs;(2) fluctuations in the DC bus voltage. Traditional approaches primarily focus on achieving the state of charge (SOC) balancing within retired battery packs, often neglecting the suppression of voltage fluctuations, thereby compromising system stability. To address these challenges, this paper proposes a virtual DC machine (VDCM)-based control strategy that enhances both the power quality of the DC microgrid and the consistency of retired battery packs. On the one hand, reconfigurable topology and SOC balancing strategies are developed to manage the equalization current, achieving SOC balancing within the retired battery packs. On the other hand, a VDCM strategy is designed to leverage the capacity characteristics of retired batteries to increase the inertia of the DC microgrid. Compared to conventional methods, experimental results show that the proposed strategy not only maintains SOC balancing during both the charging and discharging phases but also significantly reduces overshoot and settle times of dc voltage to 64.2% and 55.6%, respectively. © 2010-2012 IEEE.

关键词： state of charge

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Global Variability and Future Projections of Marine Heatwave Onset and Decline Rates

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Remote Sensing 2025年第8期17卷 1362-1362页

作者： Pan, Yongyan Sun, Wenjin Bao, Senliang Xie, Mingshen Jiang, Lei Ji, Jinlin Yu, Yang Dong, Changming State Key Laboratory of Climate System Prediction and Risk Management Nanjing210044 China International Geophysical Fluid Research Center Nanjing210044 China School of Marine Sciences Nanjing University of Information Science and Technology Nanjing210044 China Zhuhai519000 China College of Meteorology and Oceanography National University of Defense Technology Changsha410073 China Fujian Provincial Meteorological Observatory Fuzhou350007 China Key Laboratory of Straits Severe Weather China Meteorological Administration Fuzhou350007 China

Marine heatwaves (MHWs) can significantly impact marine ecosystems and socio-economic systems, and their severity may increase with global warming. Nevertheless, research on the onset and decline rates of MHWs remains limited, and their historical and future variations are not yet fully understood. This study, therefore, analyzes the spatiotemporal characteristics of MHW onset and decline rates by using historical and future sea surface temperature data from OISSTv2.1 and CMIP6. The results indicate that during the historical period from 1982 to 2014, MHW onset and decline rates were higher in eddy-active mid-latitude current systems and the western tropical region but lower in subtropical gyres. A remarkably high correlation (0.94) exists between the onset and decline rates;regions with higher onset rates also tend to have higher decline rates. Approximately 49.69% of the global ocean exhibits an increasing trend in MHW onset rates, with significant increases observed in the Eastern Equatorial Pacific. Meanwhile, 92.87% of oceanic regions exhibit an increase in decline rates. Looking ahead to the future (2015~2100), both the SSP245 and SSP585 scenarios display consistent spatial patterns of MHW onset and decline rates. The Kuroshio-Oyashio Extension, Gulf Stream, Antarctic Circumpolar Current, and Brazil-Malvinas Confluence regions exhibit relatively higher onset and decline rates. Under the SSP585 scenario, both the onset and decline rates of MHWs are higher than those under the SSP245 scenario. This indicates that as global warming intensifies, more extreme MHWs are likely to occur. This finding indicates that it is necessary to pay attention to the rate of global warming when mitigating its potential impacts. © 2025 by the authors.

关键词： Submarine geophysics

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On s-uniform property of compressing sequences derived from primitive sequences modulo odd prime powers

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science China(information sciences) 2017年第5期60卷 84-95页

作者： Yupeng JIANG Qun-Xiong ZHENG Dongdai LIN State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute

Let Z/（p;） be the integer residue ring modulo pewith p an odd prime and e 2. We consider the suniform property of compressing sequences derived from primitive sequences over Z/（p;）. We give necessary and sufficient conditions for two compressing sequences to be s-uniform with α provided that the compressing map is of the form φ(x;, x;,..., x;) = g(x;) + η(x;, x;,..., x;), where g(x;) is a permutation polynomial over Z/（p） and η is an（e-1）-variable polynomial over Z/（p）.

关键词： compressing map linear recurring sequence primitive sequence permutation polynomial s uniform

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A User-Centric Data Secure Creation Scheme in Cloud Computing

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Chinese Journal of Electronics 2016年第4期25卷 753-760页

作者： SU Mang LI Fenghua SHI Guozhen GENG Kui XIONG Jinbo School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Department of Information Security Beijing Electronic Science and Technology Institute State Key Laboratory of Integrated Services Network Xidian University

Due to the use of the cloud computing technology,the ownership is separated from the administration of the data in cloud and the shared data might be migrated between different clouds,which would bring new challenges to data secure creation,especially for the data privacy *** propose a User-centric data secure creation scheme(UCDSC) for the security requirements of resource owners in *** this scheme,a data owner first divides the users into different *** data owner encrypts data and defines different secure managing policies for the data according to *** encrypt the data in UCDSC,we present an algorithm based on Access control conditions proxy re-encryption(ACC-PRE),which is proved to be master secret secure and Chosen-ciphertext attack(CCA) secure in random oracle *** give the application protocols and make the comparisons between some existing approaches and UCDSC.

关键词： User-centric ACC-PRE Data creation Master secret secure CCA

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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Open Set RF Fingerprinting Identification: A Joint Prediction and Siamese Comparison Framework

arXiv

引用

arXiv 2025年

作者： Cai, Donghong Shan, Jiahao Gao, Ning He, Bingtao Chen, Yingyang Jin, Shi Fan, Pingzhi College of Cyber Security College of Information Science and Technology Jinan University Guangzhou510632 China The School of Cyber Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Mobile Communications Southwest Jiaotong University Chengdu611756 China

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed-set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample’s features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Authentication

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Adaptive Perception for Unified Visual Multi-modal Object Tracking

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Hu, Xiantao Zhong, Bineng Liang, Qihua Shi, Liangtao Mo, Zhiyi Tai, Ying Yang, Jian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Guilin541004 China Guangxi Normal University Security Guilin541004 China Guangxi Normal University Intelligent Technology Guilin541004 China Guangxi Normal University Ministry of Education Guilin541004 China Wuzhou University School of Data Science Wuzhou543002 China Wuzhou University Software Engineering Wuzhou543002 China Nanjing University State Key Laboratory for Novel Software Technology Suzhou215163 China Nanjing University of Science and Technology School of Computer Science Nanjing210094 China Nanjing University of Science and Technology Engineering Nanjing210094 China

Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex scenarios, making it challenging to fully perceive the advantages of multi-modal. As a result, a unified parameter model often underperforms in various multi-modal tracking tasks. To address this issue, we propose APTrack, a novel unified tracker designed for multi-modal adaptive perception. Unlike previous methods, APTrack explores a unified representation through an equal modeling strategy. This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks. Moreover, our tracker integrates an adaptive modality interaction (AMI) module that efficiently bridges cross-modality interactions by generating learnable tokens. Experiments conducted on five diverse multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022) demonstrate that APTrack not only surpasses existing state-of-the-art unified multi-modal trackers but also outperforms trackers designed for specific multi-modal tasks. © 2020 IEEE.

关键词： Multicarrier modulation

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Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

arXiv

引用

arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

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Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chen, Zhuolong Yin, Xiaoqing Yang, Fan Li, Xiaofan Zhao, Zixuan Li, Xueying Liu, Jianghu Zhao, Yubin Xu, Cheng-Zhong Zheng, Fangfang Lin, Yong Sun Yat-Sen University School of Microelectronics Science and Technology 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Department of Psychiatry 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Pediatrics Department 0000 China Jinan University School of Intelligent System Science and Engineering 0000 China University of Macau State Key Laboratory of IoTSC Department of Computer and Information Science Macau0000 China

Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical. © 2013 IEEE.

关键词： Contrastive Learning

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