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arXiv 2025年

作者： Li, Yachao Liang, Dong Ding, Tianyu Huang, Sheng-Jun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China Microsoft Washington United States

Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due to the empirical priors and illusions of these models. To address this issue, we introduce StructSR, a simple, effective, and plug-and-play method that enhances structural fidelity and suppresses spurious details for diffusion-based Real-ISR. StructSR operates without the need for additional fine-tuning, external model priors, or high-level semantic knowledge. At its core is the Structure-Aware Screening (SAS) mechanism, which identifies the image with the highest structural similarity to the low-resolution (LR) input in the early inference stage, allowing us to leverage it as a historical structure knowledge to suppress the generation of spurious details. By intervening in the diffusion inference process, StructSR seamlessly integrates with existing diffusion-based Real-ISR models. Our experimental results demonstrate that StructSR significantly improves the fidelity of structure and texture, improving the PSNR and SSIM metrics by an average of 5.27% and 9.36% on a synthetic dataset (DIV2KVal) and 4.13% and 8.64% on two real-world datasets (RealSR and DRealSR) when integrated with four state-of-the-art diffusion-based Real-ISR methods. Code - https://***/LYCEXE/StructSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Inference engines

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Semantics-Aware Avatar Locomotion Adaption for Indoor Cross-Scene AR Telepresence

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Li, Yi-Jun Yang, Hao-Zhong Shu, Wen-Tong Wang, Miao Tsinghua University Department of Computer Science and Technology Beijing100084 China Beihang University China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Zhongguancun Laboratory Beijing100094 China

Geographically dispersed users often rely on virtual avatars as intermediaries to facilitate interactive communication and collaboration. However, existing methods for augmented reality (AR) telepresence applications exhibit limitations, including restricted movement within confined sub-areas, lack of smooth transitions, and the necessity for manually establishing object mapping between dissimilar environments. We present a novel interactive AR framework for virtual avatar locomotion adaption while preserving semantic coherence across dissimilar indoor scenes. Initially, we conduct a preliminary user study to identify key attributes influencing preferred avatar movement. These attributes are quantified as features, and a dataset of user annotations on avatar movements is created. Based on the user interaction and scene configurations, we employ a deep reinforcement learning neural network to guide the avatar to the ideal position while maximizing semantic coherence. We validate our proposed framework through simulations and user studies by implementing an AR-based 3D telepresence prototype, demonstrating the efficacy of our framework in conveying user intentions across dissimilar environments, enabling natural and immersive 3D telepresence interactions. © 1995-2012 IEEE.

关键词： Reinforcement learning

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SAug: Structural Imbalance Aware Augmentation for Graph Neural Networks

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ACM Transactions on Intelligent Systems and technology 2025年第2期16卷

作者： Chen, Ke-Jia Mu, Wenhui Liu, Zulong Liu, Zheng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Novel Software Technology Nanjing University Nanjing China

Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification, and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of existing GML models, especially in learning tail nodes. This article proposes a selective graph augmentation method to solve this problem. Firstly, a Pagerank-based sampling strategy is designed to identify hub nodes and tail nodes in the graph. Secondly, a selective augmentation strategy is proposed, which drops the noise neighbors of hub nodes on one side, and discovers the latent neighbors and generates pseudo neighbors for tail nodes on the other side. Also, it can alleviate the structural imbalance between two types of nodes. Finally, a GNN model is retrained on the augmented graph. Extensive experiments demonstrate that the proposed method can significantly improve the backbone GNNs and achieve superior performance to its competitors of graph augmentation methods and hub/tail aware methods. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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Sparse and Dense: Learning Confusion Representation Network for 3D Action Recognition

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IEEE Internet of Things Journal 2025年

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Information Engineering Shandong Management University Jinan250100 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human Action Recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatiotemporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatiotemporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+ D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2025 IEEE. All rights reserved.

关键词： Joints (anatomy)

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SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment

arXiv

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arXiv 2025年

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc China National Key Laboratory for Novel Software Technology Nanjing University China

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and finetunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feedforward sub-layers including 6.5-8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 - 11.9× compared to the two-stage method. Copyright © 2025, The Authors. All rights reserved.

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv

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arXiv 2025年

作者： Xu, Junhao Zhang, Yanan Cai, Zhi Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth tradeoffs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://***/Xu2729/CoSDH. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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Corporate Fraud Detection in Rich-yet-Noisy Financial Graph

arXiv

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arXiv 2025年

作者： Wang, Shiqi Zhang, Zhibo Fang, Libing Nguyen, Cam-Tu Li, Wenzhon School of Artificial Intelligence Nanjing University Jiangsu Nanjing China School of Computer Science and Technology Nanjing University Jiangsu Nanjing China School of Management and Engineering Nanjing University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN);and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (KeGCNR), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of KeGCNR over a number of strong baselines in terms of fraud detection effectiveness and robustness. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

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A Trust-Based Approach to Estimating the Confidence of the software System in Open Environments

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Journal of computer science & technology 2009年第2期24卷 373-385页

作者：徐锋潘静陆文 State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

Emerging with open environments, the software paradigms, such as open resource coalition and Internetware, present several novel characteristics including user-centric, non-central control, and continual evolution. The goal of obtaining high confidence on such systems is more difficult to achieve. The general developer-oriented metrics and testing-based methods which are adopted in the traditional measurement for high confidence software seem to be infeasible in the new situation. Firstly, the software development is changed from the developer-centric to user-centric, while user＇s opinions are usually subjective, and cannot be generalized in one objective metric. Secondly, there is non-central control to guarantee the testing on components which formed the software system, and continual evolution makes it impossible to test on the whole software system. Therefore, this paper proposes a trust-based approach that consists of three sequential sub-stages： 1） describing metrics for confidence estimation from users; 2） estimating the confidence of the components based on the quantitative information from the trusted recommenders; 3） estimating the confidence of the whole software system based on the component confidences and their interactions, as well as attempts to make a step toward a reasonable and effective method for confidence estimation of the software system in open environments.

关键词： trust confidence open resource coalition Internetware software architecture

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Prune and Merge: Efficient Token Compression for Vision Transformer with Spatial Information Preserved

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IEEE Transactions on Multimedia 2025年

作者： Mao, Junzhu Shen, Yang Guo, Jinyang Yao, Yazhou Hua, Xiansheng Shen, Hengtao Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Beihang University State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beijing100191 China Terminus Group Beijing100027 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Token compression is essential for reducing the computational and memory requirements of transformer models, enabling their deployment in resource-constrained environments. In this work, we propose an efficient and hardware-compatible token compression method called Prune and Merge. Our approach integrates token pruning and merging operations within transformer models to achieve layer-wise token compression. By introducing trainable merge and reconstruct matrices and utilizing shortcut connections, we efficiently merge tokens while preserving important information and enabling the restoration of pruned tokens. Additionally, we introduce a novel gradient-weighted attention scoring mechanism that computes token importance scores during the training phase, eliminating the need for separate computations during inference and enhancing compression efficiency. We also leverage gradient information to capture the global impact of tokens and automatically identify optimal compression structures. Extensive experiments on the ImageNet-1k and ADE20K datasets validate the effectiveness of our approach, achieving significant speed-ups with minimal accuracy degradation compared to state-of-the-art methods. For instance, on DeiT-Small, we achieve a 1.64× speed-up with only a 0.2% drop in accuracy on ImageNet-1k. Moreover, by compressing segmenter models and comparing with existing methods, we demonstrate the superior performance of our approach in terms of efficiency and effectiveness. Code and models have been made available at https://***/NUST-Machine-Intelligence-laboratory/prune_and_merge. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Reconfigurable Intelligent Surface-Aided Covert Communications: A Multi-User Scenario

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IEEE Transactions on Vehicular technology 2025年

作者： Hu, Xiaoyan Zhao, Pengze Xiao, Han Zheng, Tong-Xing Wang, Wenjie Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering United Kingdom

This paper explores a multi-user covert communication scenario with the assistance of a reconfigurable intelligent surface (RIS). Except a covert user monitored by the warden, there are multiple public users existing in the service area. For the purpose of evaluating the efficacy of the covert communication system, a covert constraint is derived measuring the minimum detection error probability of the warden through the Kullback-Leibler (KL) divergence. Then an optimization problem is formulated to maximize the convert rate, while ensuring the constraints of covertness, transmit power and quality of service (QoS) for public users. A two-step iterative algorithm jointly designing the active and passive beamforming, is put forth to efficiently resolve this non-convex optimization problem. Additionally, the numerical results clearly demonstrate that the proposed RIS-assisted scheme can significantly enhance the covert communication performance on the premise of guaranteeing the public communication performance of the system. © 1967-2012 IEEE.

关键词： Convex optimization

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