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QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR

IEEE Transactions on Vehicular Technology 2025年

作者： Li, Yang Wu, Yuan Xing, Huanlai Feng, Li Wang, Tian Jia, Weijia Southwest Jiaotong University School of Computer and Artificial Intelligent Chengdu611756 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform

关键词： Augmented reality

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Intrinsic Saliency Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Xiangyu Li, Wanyun He, Songcheng Li, Xiaoqiang Zhang, We Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Computer Engineering and Science Shanghai University Shanghai200433 China

Recent unsupervised video object segmentation (UVOS) methods predominantly adopt the motion-appearance paradigm, which leverages optical flow for motion cues and RGB image for appearance information to facilitate mask prediction. Mainstream motion-appearance approaches use either the two-encoder structure to separately encode motion and appearance features, or the single-encoder structure for joint encoding. However, these methods fail to properly balance the motion-appearance relationship. Consequently, even with complex fusion modules for motion-appearance integration, the extracted suboptimal features degrade the models’ overall performance. Moreover, the quality of optical flow varies across scenarios, making it insufficient to rely solely on optical flow to achieve high-quality segmentation results. To address these challenges, we propose the Intrinsic Saliency guided Trunk-Collateral Network (ISTC-Net), which better balances the motion-appearance relationship and incorporates model’s intrinsic saliency information to enhance segmentation performance. Specifically, considering that optical flow maps are derived from RGB images, they share both commonalities and differences. We propose a novel Trunk-Collateral structure. The shared trunk backbone captures the motion-appearance commonality, while the collateral branch learns the uniqueness of motion features. Furthermore, an Intrinsic Saliency guided Refinement Module (ISRM) is devised to efficiently leverage the model’s intrinsic saliency information to refine high-level features, and provide pixel-level guidance for motion-appearance fusion, thereby enhancing performance without additional input. Experimental results show that ISTC-Net achieved state-of-the-art performance on three UVOS datasets (89.2% J&F on DAVIS-16, 76% J on YouTube-Objects, 86.4% J on FBMS) and four standard video salient object detection (VSOD) benchmarks with the notable increase, demonstrating its effectiveness and superiority over previ

关键词： Image segmentation

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Construction of one special minimum storage regenerating code when α=2

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science China(information sciences) 2015年第6期58卷 116-125页

作者： LIANG SongTao LIANG WenJuan KAN HaiBin Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University College of Computer and Information Engineering Henan University

Regenerating codes is one class of erasure codes for distributed storage. A [n, k, d, α, β] regenerating code can recover the original message from any k out of n distributed storage nodes and repair any failed node from other arbitrary d nodes. α is the number of symbols stored in one node and β is the number of symbols downloaded by a newcomer node. Minimum storage regenerating(MSR) code is a regenerating code attaining the minimal storage requirement. In this paper, we address the design of a special MSR code where α = 2 and the first fragment of a node stores original symbol(we call it hybrid systematic MSR code or HS-MSR code). We point out that there exists no exact-repair construction when k 5. A simple exact linear construction is given when k = 2. Furthermore, by relaxing the condition of connecting any d nodes, we investigate the properties of quasi-cyclic regenerating code and propose a unified construction such that when k 3, repairing process can be achieved with less repair bandwidth by repair-by-transfer form.

关键词： array codes distributed storage network coding maximum-distance-separable(MDS) codes min-imum storage regenerating(MSR) codes

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D4A: An efficient and effective defense across agnostic adversarial attacks

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Neural Networks 2025年 183卷 106938页

作者： Li, Xianxian Gan, Zeming Bai, Yan Su, Linlin Li, De Wang, Jinyan Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Engineering and Technology University of Washington Tacoma TacomaWA United States

Recent studies show that Graph Neural Networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses in graph data. Previous defenses designed heuristic defense strategies for specific attacks or graph properties, and are no longer sufficiently robust across all these attacks. To address this problem, we discuss the abnormal behaviors of GNNs in structure perturbations from a posterior distribution perspective. We suggest that the structural vulnerability of GNNs stems from their dependence on local graph smoothing, which can also lead to unfitting — a first-found phenomenon specific to the graph domain. We demonstrate that abnormal behaviors, except for unfitting, can attribute to a posterior distribution shift. To intrinsically prevent the occurrence of abnormal behaviors, we first propose smooth-less message passing to enhance the tolerance with respect to structure perturbations, while significantly mitigating the unfitting. We also propose the distribution shift constraint to restrict other abnormal behaviors of our model. Our approach is evaluated on six different datasets across over four kinds of attacks and compared to 11 representative baselines. The experimental results show that our method improves the defense performance across various attacks, and provides a great trade-off between accuracy and adversarial robustness. © 2024 Elsevier Ltd

关键词： Generative adversarial networks

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Chinese Named Entity Recognition Augmented with Lexicon Memory

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Journal of computer science & Technology 2023年第5期38卷 1021-1035页

作者：周奕郑骁庆黄萱菁 School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200438China

Inspired by the concept of content-addressable retrieval from cognitive science,we propose a novel fragment-based Chinese named entity recognition(NER)model augmented with a lexicon-based memory in which both character-level and word-level features are combined to generate better feature representations for possible entity *** that the boundary information of entity names is particularly useful to locate and classify them into pre-defined categories,position-dependent features,such as prefix and suffix,are introduced and taken into account for NER tasks in the form of distributed *** lexicon-based memory is built to help generate such position-dependent features and deal with the problem of out-of-vocabulary *** results show that the proposed model,called LEMON,achieved state-of-the-art performance with an increase in the Fl-score up to 3.2%over the state-of-the-art models on four different widely-used NER datasets.

关键词： named entity recognition(NER) lexicon-based memory content-addressable retrieval position-dependent feature neural network

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Pre-trained models for natural language processing: A survey

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science China(Technological sciences) 2020年第10期63卷 1872-1897页

作者： QIU XiPeng SUN TianXiang XU YiGe SHAO YunFan DAI Ning HUANG XuanJing School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently, the emergence of pre-trained models(PTMs) has brought natural language processing(NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next,we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

关键词： deep learning neural network natural language processing pre-trained model distributed representation word embedding self-supervised learning language modelling

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Ordinal distribution regression for gait-based age estimation

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science China(information sciences) 2020年第2期63卷 21-34页

作者： Haiping ZHU Yuheng ZHANG Guohao LI Junping ZHANG Hongming SHAN Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Department of Biomedical Engineering Rensselaer Polytechnic Institute

computer vision researchers prefer to estimate age from face images because facial features provide useful information. However, estimating age from face images becomes challenging when people are distant from the camera or occluded. A person’s gait is a unique biometric feature that can be perceived efficiently even at a distance. Thus, gait can be used to predict age when face images are not ***, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue for age estimation. This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose gait-based age regression into a series of binary classifications to incorporate the ordinal age ***, an ordinal distribution loss is proposed to consider the inner relationships among these classifications by penalizing the distribution discrepancy between the estimated value and the ground truth. In addition,our neural network comprises a global and three local sub-networks, and thus, is capable of learning the global structure and local details from the head, body, and feet. Experimental results indicate that the proposed approach outperforms state-of-the-art gait-based age estimation methods on the OULP-Age dataset.

关键词： computer vision deep learning ordinal distribution regression global and local features gaitbased age estimation

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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UniAdapter: All-in-One Control for Flexible Video Generation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Cong Hu, Panwen Zhao, Haoyu Guo, Yuanfan Gu, Jiaxi Dong, Xiao Han, Jianhua Xu, Hang Liang, Xiaodan Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen China Fudan University School of Computer Science and Technology Shanghai China Huawei Noah'ark Lab Shanghai201206 China Sun Yat-Sen University School of Artificial Intelligence Zhuhai Campus Zhuhai519082 China Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Guangdong No. 66 Gongchang Road Guangming District Shenzhen518107 China Peng Cheng Laboratory and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Condition-based video generation aims to create video content based on given information that describes specific subjects. However, most existing works can only utilize a single condition to guide the denoising process, thereby limiting their applicability to specific scenarios. Although some works attempt to accommodate multiple conditions within one framework, they often require multiple encoders, leading to inefficiencies in integrating multi-condition features. In this work, we present a framework that, with the support of the proposed Unified Adapter (UniAdapter), enables simultaneous multi-condition control of video generation within a single model. To effectively merge these conditions, we propose a novel Probabilistic Multi-condition Concatenator (PMC) module, which employs a unified encoder to accommodate multiple conditions and concatenate condition features at the pixel level to achieve fine-grained control. Following the PMC module, we employ 2D down-sampling blocks to refine features for injection into the Video Diffusion Model (VDM). Moreover, our UniAdapter is designed to be model-agnostic and compatible with any U-Net-based VDM, offering a versatile solution for improving video generation quality. Experimental results on public benchmarks UCF-101 and MSR-VTT show that our method achieves superior results in both quantitative and qualitative evaluations. © 2025 IEEE.

关键词： Signal encoding

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