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Optimal Linear Codes With Few Weights From Simplicial Complexes

IEEE Transactions on Information Theory 2025年第5期71卷 3531-3543页

作者： Chen, Bing Xu, Yunge Hu, Zhao Li, Nian Zeng, Xiangyong Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Wuhan430062 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430062 China

Recently, constructions of optimal linear codes from simplicial complexes have attracted much attention and some related nice works were presented. Let q be a prime power. In this paper, by using the simplicial complexes of Fmq with one single maximal element, we construct four families of linear codes over the ring Fq+uFq}( u2=0 ), which generalizes the results of Wu et al. (2020). The parameters and Lee weight distributions of these four families of codes are completely determined. Most notably, via the Gray map, we obtain several classes of optimal linear codes over Fq, including (near) Griesmer codes and distance-optimal codes. Moreover, it is shown that most of the Gray images are minimal or self-orthogonal codes which are useful in applications. © 1963-2012 IEEE.

关键词： Turbo codes

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T3 Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839.0页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics China National Engineering Laboratory for Big Data Analytics Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Peking University China Chinese University of HongKong Xi'an Jiaotong University China

network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46:6% cost, and supports 3:12× more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： network topology

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OACR2: Online Admission Control and Resource Reservation for 5G Slice networks With Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Fang Hao, Yijun Yang, Shusen Zhao, Peng China China Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security MOE KLINNS Lab China

network slicing architecture is expected to fulfill network applications with heterogeneous requirements through efficient slice admission control (SAC) policies. Existing SAC approaches entirely rely on current limited observations to make admission decisions, ignoring the potential impact of future demands. The short-sighted behaviors lead to poor service performance and infrastructure providers' (InPs') revenue in practice. In this paper, we propose OACR2, an online SAC approach based on deep reinforcement learning (DRL) that can exploit predictable future requests to make more precise admission control decisions for the long-term revenue, and reserve proper resources accordingly. Specifically, we design three novel schemes: (i) a requirement predictor based on long short-term memory (LSTM) and a novel input-output way to predict future unforeseen requests, (ii) a DRL admission controller based on the partially observable Markov decision process model to make precise admission decisions without accurate future request information, with the convergence strictly proved, and (iii) a decision defender to guarantee decision reliability. Extensive experiments on real-world traces demonstrate that compared to the No-wait, Wait-queue, and Wait-earliest time approaches, OACR2 improves InPs' revenue and acceptance ratio by up to 40.9% and 16.7%, respectively, without sacrificing online inference time (within 0.9 milliseconds). © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Disentangled Noisy Correspondence Learning

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IEEE Transactions on Image Processing 2025年 34卷 2602-2615页

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong Xi’an Jiaotong University School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China SGIT AI Laboratory Xi’an710048 China State Grid Corporation of China State Grid Shaanxi Electric Power Company Ltd. Xi’an710048 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc. Beijing100085 China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of modality-invariant information (MII) and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal inputs for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL’s efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. © 1992-2012 IEEE.

关键词： Information theory

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Poisoning Attacks and Defenses to Learned Bloom Filters for Malicious URL Detection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Fangming Wang, Pinghui Li, Rundong Cui, Xueyao Zhao, Junzhou Tao, Jing Zhang, Chen Guan, Xiaohong Zhejiang CreateLink Technology Co. Ltd Guangdong Guangzhou China Xi'an Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi'an710049 China Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing China

Approximate membership query (AMQ) structures represented by the Bloom Filter and its variants have been popularly researched in recent years. Researchers have recently combined machine learning with this type of structure to reduce space consumption and computation overhead further and make remarkable progress. However, with the booming performance in space or other metrics, researchers tend to ignore the security of the trained model. The machine learning model is vulnerable to poisoning attacks, and naturally, we infer that the learning-based filters also have the same deficiencies. Hence, in this paper, to confirm the inference mentioned above, experiments on the real-world datasets of URLs are conducted and prove that it is necessary to consider the security issue when using learning-based filters. We show that by data poisoning, the attacker can deflect learned Bloom Filters to make a false identification, which can lead to a significant loss in some cases. Aiming to solve this issue, we put forward a method named Defensive Learned Bloom Filter (DLBF) to diminish the influence of data poisoning and achieve a better performance compared to types of learned Bloom Filters. © 2004-2012 IEEE.

关键词： Wiener filtering

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

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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Pilot Backdoor Attack against Deep Reinforcement Learning Empowered intelligent Reflection Surface for Smart Radio

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IEEE Transactions on Wireless Communications 2025年

作者： Huang, Yunsong Wang, Hui-Ming Wang, Zhaowei Liu, Weicheng Xi’an Jiaotong University School of Information and Communications Engineering Xi’an710049 China Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China

intelligent reflection surface (IRS) has been used to assist communication by reflection and beamforming where a direct path is not available. Thus, IRS adjusts the wireless channel to enhance the data transmission efficiency with low power consumption. Recently, deep reinforcement learning (DRL) has been exploited in IRS coefficients optimization. IRS can be controlled by DRL to adapt their phase shift to the propagation environment and an expected reflection pattern can be obtained. However, due to the openness of wireless channel and the unexplainability of DRL, it is vulnerable to adversary attacks lauched through wireless channels. In this paper, we investigate a pilot contamination based backdoor attack against DRL based IRS beamforming, where an IRS controlled by an adversary attacker is used to contaminate the channel state information (CSI) during the training phase. The backdoor attack is covert because the adversary need not to know the legitimate pilot sequence and DRL performs well when the adversary IRS keeps inactive. We show that the backdoor attack can reduce the data rate significantly with the adversary IRS. At last, we propose a retraining method agaist the attack to recover the data rate. © 2025 IEEE. All rights reserved.

关键词： Deep reinforcement learning

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DAPEW: Towards robust collaborative filtering with graph contrastive learning

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Knowledge-Based Systems 2025年 318卷

作者： Zhu, Kuiyu Qin, Tao Liu, Haoxing Wang, Chenxu Wang, Pinghui School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Graph Contrastive Learning (GCL) has shown excellent performance in Collaborative Filtering (CF), one of the most widely used techniques in efficient recommender systems. However, existing GCL-based CF methods suffer from node degree disparity, feature oversmoothing, difficulty in distinguishing hard negative samples, and semantic loss. To address these problems, this paper proposes a novel graph contrastive learning method for robust CF, named Degree-Aware Propagation and Entropy-Weighted contrastive loss (DAPEW). DAPEW introduces a degree-aware propagation mechanism to dynamically adjust the influence of initial embeddings, adjacency matrix products, and degree matrix products on the final embeddings, which can effectively handle node degree disparity and alleviate feature oversmoothing. DAPEW also designs an entropy-weighted contrastive loss, which introduces entropy weights to better distinguish hard negative samples and enhance the model's discriminative ability and robustness. Experimental results show that DAPEW outperforms the existing GCL-based CF methods on several real-world datasets. Compared with existing GCL-based methods, DAPEW improves Recall@40 and NDCG@40 by 0.24%∼25.88% and 0.14%∼26.18% across four different datasets, respectively. © 2025 Elsevier B.V.

关键词： Contrastive Learning

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FDC-Swap: An efficient face swapping framework based on feature disentangling consistency

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Knowledge-Based Systems 2025年 318卷

作者： Tian, Jue Zhao, Chunya Liu, Yang Chen, Yanping School of Computer Xi'an University of Posts and Telecommunications Xi'an710121 China Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an710049 China

Face swapping images with high quality are critical for video production, privacy protection, and forgery detection, etc. In face swapping technology, one kernel element is the disentanglement of identity and attribute information. However, due to their deep entanglement in the latent space, effectively enhancing disentangling ability remains challenging. To tackle this challenge, this paper proposes the feature disentangling consistency: by executing the face swapping process (i.e., disentangling and recombining the identity and attribute information of two face images) in a pipeline twice, the generated twice-swapped images should be consistent with the original images. First, a new evaluation method (named FDC-Evaluation) is proposed to assess model performance according to disentangling consistency, which can be reflected in identity-attribute or image-level consistency. Meanwhile, to handle the limitation of missing ground truth, we introduce the FDC-Evaluation into original face swapping network, and propose a highly scalable and lightweight swapping framework (named FDC-Swap). Experiments demonstrate that the FDC-Evaluation overcomes traditional evaluation limitations, such as cognitive differences in identity-attribute features, identity assessment unfairness due to choices of identity encoders in training and evaluation, and operational complexity due to attribute feature diversity. Meanwhile, the FDC-Swap framework effectively enhances the performance of various existing face swapping networks. Code available at https://***/tzjoyzx/FDC_Swap. © 2025 Elsevier B.V.

关键词： Evaluation method Face swapping Feature disentangling consistency Generative adversarial networks Symmetric network structure

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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