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arXiv 2025年

作者： Hu, Zhao Qiu, Mingxiu Li, Nian Tang, Xiaohu Wu, Liwei Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Southwest Jiaotong University Chengdu610031 China Wuhan Maritime Communication Research Institute China

Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of t-weight linear codes over Fq are presented with the defining sets given by the intersection, difference and union of two certain sets, where t = 3, 4, 5, 6 and q is an odd prime power. By using Weil sums and Gauss sums, the parameters and weight distributions of these codes are determined completely. Moreover, three classes of optimal codes meeting the Griesmer bound are obtained, and computer experiments show that many (almost) optimal codes can be derived from our constructions. Copyright © 2025, The Authors. All rights reserved.

关键词： Weibull distribution

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Broadcast Authentication: An Efficient Identity Authentication Protocol With Provable security for VANETs

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IEEE Transactions on intelligent Transportation systems 2025年

作者： Wang, Jing Zhou, Yanwei Zhu, Yasi Qiao, Zirui Zhao, Chuanyuan Shaanxi Normal University School of Computer Science Xi’an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610017 China Xi’an University of Posts and Telecommunications School of Cyberspace Security Xi’an710121 China Xi’an Shiyou University School of Computer Xi’an710065 China

With the advancement of the Internet of Things (IOT), Vehicular Ad Hoc networks (VANETs) are integrated into intelligent transportation systems to facilitate vehicle communication. However, as the number of vehicles and application diversity increase, significant security concerns have emerged, including message authentication and vehicle privacy protection. Consequently, researchers have devised several identity authentication protocols to ensure legitimate communication between parties. Nevertheless, existing schemes fail to address real-time response identity authentication due to high-speed vehicle movement and the need for traversing multiple domains. This limitation poses challenges in traffic management and even threatens human life. To overcome these issues, we propose a novel identity authentication method called broadcast authentication and design a new protocol incorporating this approach. The confidentiality and unforgeability of our proposal are proven based on classic hardness assumptions. Our protocol enables vehicles to authenticate with multiple roadside units through a single request communication process, thereby avoiding delays in authentication while saving costs associated with communication and computation. Compared with existing schemes, our protocol demonstrates superior computing efficiency and performance. © 2000-2011 IEEE.

关键词： Privacy by design

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Enhanced Pyroelectric Response in Quenched BNT-Based Lead-Free Ferroelectrics for Uncooled Infrared Detection

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Advanced Functional Materials 2025年

作者： Shen, Meng Zhang, Xian Cen, Fangjie Zhang, Qingfeng Zhang, Haibo Zhang, Guangzu Jiang, Shenglin Chen, Yong Hu, Yongming Yao, Kui Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices Key Laboratory of Intelligent Sensing System and Security of Ministry of Education and School of Microelectronics Hubei University Wuhan430062 China School of Integrated Circuits Engineering Research Center for Functional Ceramics MOE and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Ministry of Education Key Laboratory of Green Preparation and Application for Functional Materials Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices Hubei Key Laboratory of Polymer Materials School of Materials Science & Engineering Hubei University Wuhan430062 China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die & Mould Technology Huazhong University of Science and Technology Wuhan430074 China Singapore138634 Singapore

Excellent pyroelectric performance in environmentally friendly lead-free ferroelectrics is highly demanded for uncooled infrared detector. However, the trade-off between room temperature pyroelectric coefficient (proom) and depolarization temperature (Td) remains a major challenge of lead-free ferroelectrics for their device applications. Herein, a superior proom of 13.8 × 10−4 C m−2 K−1 with high Td of 155 °C is achieved in quenched BNT-Fe/Mn-NBT sample. Oxygen vacancy engineering is proposed to solve the constraint between proom and Td in BNT-based ceramics. The local A/B site displacement and oxygen vacancies are constructed in the BNT-Fe/Mn-NBT perovskite structure by quenching treatment, resulting in larger lattice distortion and superior proom. Meanwhile, defect-induced inhomogeneous random field compensates the ferroelectric depolarization field and down-shifts the free energy well, maintaining high Td. The resulting pyroelectric infrared detector made from the high-performance quenched BNT-Fe/Mn-NBT generates the voltage responsivity of 5906 V W−1 and specific detection rate of 1.8 × 108 cm Hz1/2 W−1, which is comparable to the commercial RD-624 type PZT-based pyroelectric infrared detector. © 2025 Wiley-VCH GmbH.

关键词： Perovskite

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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A unified multi-granulation hierarchy structure: Construction, Selection, Fusion and Reduction

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Information Fusion 2025年 123卷

作者： Zhang, Xiaoxia Zhang, Jiangtao Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China

Granular computing is a new method that can simulate human brain thinking to solve complex problems, and it has gradually become an important theory for addressing uncertainty issues. The core of granular computing lies in its use of hierarchical, classification, or clustering methods to process uncertain information. However, the broad applicability of this approach has led to its blurred theoretical boundaries, with varying definitions of "granules" and computational paradigms across studies based on differing requirements. Currently, the absence of a unified granular computing model prevents the consistent handling of uncertain information under a standardized scale and paradigm. This paper attempts to fill this gap from a unified perspective by providing a multi-granulation hierarchical structure within a unified framework. Based on this, different studies can adaptively select appropriate granular layers according to their specific time constraints or precision requirements. Here, we adopt quantum genetic algorithms (QG) for optimal granular layer selection. After the granular layers are selected, a fusion algorithm is developed to combine them into a super layer, preserving essential information for subsequent attribute reduction. The attribute reduction process is guided by conditional entropy, which further refines the feature set, enhancing the model's efficiency and accuracy. Experimental results on twelve public datasets and four real datasets both demonstrate the effectiveness of the proposed method, showing superior performance in terms of classification accuracy and feature reduction compared to seven existing approaches. This study establishes a unified framework for various granular computing models, offering new insights into the integration of granular computing models and adaptive optimization techniques for real-world data analysis applications. © 2025 Elsevier B.V.

关键词： Granular computing

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension

arXiv

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arXiv 2025年

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing notarget and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric grounding

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Constructing Three-way Decision with Fuzzy Granular-ball Rough Sets Based on Uncertainty Invariance

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IEEE Transactions on Fuzzy systems 2025年

作者： Yang, Jie Liu, Zhuangzhuang Wang, Guoyin Zhang, Qinghua Xia, Shuyin Wu, Di Liu, Yanmin Zunyi Normal University School of Physics and Electronic Science Zunyi563002 China Jiangsu University of Science and Technology School of Computer Zhenjiang212100 China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China Zunyi Normal University School of Mathematics Zunyi563002 China

Granular-ball computing (GBC) proposed by Xia adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility. Moreover, GBC greatly improves the efficiency by replacing point input with granular-ball. However, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. In this paper, we introduce three-way decision (3WD) into GBC to construct a novel three-way decision with fuzzy granular-ball rough sets (3WD-FGBRS) from the perspective of uncertainty. This helps to construct reasonable multi-granularity spaces for handling complex decision problems with uncertainty. Firstly, 3WD-FGBRS is constructed in a data-driven method based on fuzziness, which avoids the subjective definition of certain risk parameters when calculating the threshold pairs. Based on 3WD-FGBRS, we further propose a sequential three-way decision with fuzzy granular-ball rough sets (S3WD-FGBRS) and analyze the fuzziness loss of multilevel decision result in S3WD-FGBRS. Then, the optimal granular-ball space selection mechanism of S3WD-FGBRS is introduced by combining fuzziness and granular-ball space distance. Finally, extensive comparative experiments are conducted with 3 state-of-the-art GB-based classifiers and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our models almost outperform other comparison methods in terms of effectiveness, efficiency and robustness. © 1993-2012 IEEE.

关键词： Granular computing

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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