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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB data

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2266-2276页

作者： Cheng, Long Song, Jiahe Zhao, Wenhao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy. © 2013 IEEE.

关键词： Inertial navigation systems

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MSMAE-Net: multi-semantic and multi-attention enhanced network for image forgery localization

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Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Liao, Jianjun Su, Lichao Lu, Menghan Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

The popularization of modern digital image technology has brought convenience to us, but it also poses many risks. The advancement of image editing software allows anyone to modify image content effortlessly. If these modified images are abused, they can severely impact societal safety and stability. To address these risks, we propose an end-to-end multi-scale collaborative enhancement image forgery localization network, termed MSMAE-Net. The method first employs a multi-branch feature extractor to initially capture both global and local information, with each branch optimized for different contextual information and features of the image. Next, to enhance the model’s ability to capture forged traces at various levels while maintaining the correlation between different spatial hierarchies, a boundary information aggregation module is designed. By constructing multiple branches with different receptive fields and collaborative learning with each other, a nested collaborative enhancement branch is designed to make the backbone network pay more attention to key features, so as to obtain better feature representation capability. Furthermore, to further fuse different semantic information, a novel strongly compatible semantic attention fusion module is proposed in this paper. Finally, an attention enhancement module is introduced in the localization of forgery regions to adaptively concentrate on key forged areas in the image and effectively capture the relationships between different pixels. In the experiments, we demonstrate that MSMAE-Net exhibits significant advantages in both localization accuracy and robustness in complex forgery scenarios when compared to other SOTA image forgery detection methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Photointerpretation

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A Frequency and Polarization Reconfigurable Transparent Water Antenna

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Progress in Electromagnetics Research C 2025年 152卷 177-186页

作者： Li, Lei Gao, Jing Nan, Jingchang School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing transparent liquid ground. Water enhances outstanding transparency, excellent aesthetics, and high optical stealth performance for a wider range of application scenarios. The entire structure is made with polyvinyl chloride material and distilled water, except for the feed structure. By filling different cavities with liquid water, five different operating states are obtained in 1.924–2.5 GHz (26.1%), 1.67–2.33 GHz (33%), 0.644–2.288 GHz (112.1%), 1.975–2.54 GHz (25%), and 1.748–2.108 GHz (18.7%), achieving frequency reconfigurability. The antenna can be flexibly switched between linear polarization (LP) and two right-handed circular polarization (RHCP) states. The results show that the 3 dB axial ratio (AR) bandwidth covers 1.93–2.08 GHz (7.5%) and 2.06–2.132 GHz (3.5%). The antenna achieves high optical transparency of 100% and a peak gain of 7.97 dBi. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

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HTOTP: Honey Time-Based One-Time Passwords

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4438-4453页

作者： Ding, Zixuan Wang, Ding Nankai University College of Cryptology and Cyber Science Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin300350 China Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Beijing100085 China

One-Time Passwords (OTPs) play a crucial role in Two-Factor Authentication (2FA) and Multi-Factor Authentication (MFA) by adding an additional layer of security. OTPs effectively reduce the risk of static passwords being intercepted and reused. Nevertheless, both academic schemes and industrial solutions face security threats stemming from server/device compromises and OTP factor forgery. Chain-based asymmetric OTP schemes are a promising approach to addressing the problem of server compromise but still face threats from device compromise and pre-generated chain leakage. We emphasize that since devices directly store the OTP seed, OTP authentication is essentially equivalent to verifying device possession. This means that in existing OTP schemes, OTP forgery and device compromise remain prevalent and difficult to overcome. In this work, we propose a brand new scheme to address OTP factor forgery and server/device compromises. For the first time, our scheme constructs a tightly coupled architecture between the password factor and the OTP factor. The OTP seed is derived from a password and a device-stored salt, preventing OTP seed extraction and OTP forgery even in the event of a device compromise. Through the integration of "honeywords" with the tightly coupled OTP architecture, the server stores decoy OTP seeds generated by decoy passwords, providing resistance against server compromises and partial password guessing from devices. We conduct a comprehensive evaluation of our OTP schemes. The computational overhead is correlated with the number of honeywords, and with the recommended set size of 20, the total verification overhead is approximately 0.24~ms . Additionally, we propose formal security properties and application metrics, and rigorously prove our scheme’s resistance against server/device compromise attacks and guessing attacks. Our scheme is the first to achieve comprehensive OTP security with low overhead. © 2005-2012 IEEE.

关键词： Authentication

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Auxiliary Tasks Benefit Skeleton-based Action Recognition

Auxiliary Tasks Benefit Skeleton-based Action Recognition

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Yuheng Chen, Haipeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9798350368741

Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or noisy skeleton data. State-of-the-art methods tend to learn human motion directly from these corrupted skeletons as if they were reliable. Unfortunately, this might lead to unsatisfactory results when key regions of the skeleton are occluded or disturbed. To tackle the problem, we propose a novel framework that integrates auxiliary tasks into a motion modeling network. These auxiliary tasks corrupt partial human skeletons with masking or noise and then force the network to recover the corrupted data, explicitly facilitating robust feature representation learning. We further propose supervising the auxiliary tasks with mutual information losses, mathematically ensuring feature consistency and spatial alignment between the recovered and original skeleton data. Empirically, our approach sets the new state-of-the-art performance on three benchmark datasets. © 2025 IEEE.

关键词： auxiliary task mutual information skeleton-based action recognition

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Orthogonal Projection-Based Fault Detection for Linear Discrete-Time Varying Systems

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IEEE Transactions on Automatic Control 2025年第5期70卷 3478-3485页

作者： Li, Linlin Ding, Steven X. Zhong, Maiying Peng, Kaixiang University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China Duisburg47057 Germany Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China

This article is devoted to one-class fault detection in linear discrete-time varying (LDTV) systems with uncertainties. Specifically, following the Hilbert Projection theorem, the residual generation problem is solved by means of an orthogonal projection of process data onto system subspaces in Hilbert space. The resulted Pythagorean equation enables establishing an adaptive threshold driven by the residual signal and using gap metric as the similarity measure. In this projection-based fault detection framework, the design and implementation of the residual generators and threshold setting can be uniformly addressed, which provides a systematic solution to the detection issues in linear discrete-time varying systems. Moreover, a comparison with the observer- and differential-algebraic equations-based fault detection schemes is studied, which highlights the improved fault detection capability of the projection-based method. © 1963-2012 IEEE.

关键词： Linear time-invariant system

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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

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Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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Logit Adjustment for Imbalanced Cognitive Diagnosis

Logit Adjustment for Imbalanced Cognitive Diagnosis

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Algorithms, Computing and Machine Learning (CACML), Asia Conference on

作者： Zhengtao Tan Jimin Bao Zhuangzhuang He Dacao Zhang Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Hefei China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Cognitive diagnosis is a critical task in intelligent education, aimed at inferring students’ mastery of knowledge concepts based on their response logs. Although existing cognitive diagnosis models achieve excellent performance, they underestimate the difficulty of easy exercises and overestimate the difficulty of hard exercises. We attribute this to the class imbalance in the response logs of easy and hard exercises. Moreover, the convergence speed varies from exercise to exercise during model training, which further challenges generalization. To address these problems, we propose an exercise’s correct rate-based logit adjustment approach for a wide range of cognitive diagnosis models. Specifically, we enforce logit adjustment in the loss during training to overcome the class imbalance in response logs. Then, we apply group distributionally robust optimization for generalization. Finally, extensive experiments demonstrate the effectiveness of our model, especially on easy and hard exercises.

关键词： Training Machine learning algorithms Accuracy Computational modeling Asia Machine learning Optimization Convergence

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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