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SSRN 2022年

作者： Wang, Xiaoliang Huang, Zhiyu Xiong, Neal Naixue Zeng, Peng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Northeastern State University United States

With the increase of electric vehicles, the demand of electric vehicles for charging piles is increasing, but the number of charging piles is far less than that of electric vehicles, especially in rural areas and underdeveloped areas. We can effectively solve the problem of electric vehicle power supply demand by sharing private charging piles by using the characteristics of V2V technology. Ordinary online matching charging piles need to upload data to the server. In the process of matching, the data may be attacked, which may expose users' privacy information, and privacy protection is also a concern of most users now, users often worry that the leakage of location information will lead to their privacy invasion. To solve this problem, this paper proposes an anonymous matching scheme for shared charging piles. The system uses homomorphic encryption technology to encrypt the privacy information of users, and completes the matching between electric vehicles and charging piles on the premise of protecting users' privacy. In the proxy server, the data is encrypted, and in the cloud server, the data is the result of interference, so the privacy of users can be effectively protected. We also set up a new set of matching rules. © 2022, The Authors. All rights reserved.

关键词： Piles

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TRACI: A Data-centric Approach for Multi-Domain Generalization on Graphs 39

TRACI: A Data-centric Approach for Multi-Domain Generalizati...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Yusheng Wang, Changhu Luo, Xiao Luo, Junyu Ju, Wei Xiao, Zhiping Zhang, Ming State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University Beijing China Department of Statistics University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

ISBN: (纸本)157735897X

Graph neural networks (GNNs) have gained superior performance in graph-based prediction tasks with a variety of applications such as social analysis and drug discovery. Despite the remarkable progress, their performance often degrades on test graphs with distribution shifts. Existing domain adaptation methods rely on unlabeled test graphs during optimization, limiting their applicability to graphs in the wild. Towards this end, this paper studies the problem of multi-domain generalization on graphs, which utilizes multiple source graphs to learn a GNN with high performance on unseen target graphs. We propose a new approach named Topological Adversarial Learning with Prototypical Mixup (TRACI) to solve the problem. The fundamental principle behind our TRACI is to produce virtual adversarial and mixed graph samples from a data-centric view. In particular, TRACI enhances GNN generalization by employing a gradient-ascent strategy that considers both label prediction entropy and graph topology to craft challenging adversarial samples. Additionally, it generates domain-agnostic node representations by characterizing class-graph pair prototypes through latent distributions and applying multi-sample prototypical Mixup for distribution alignment across graphs. We further provide theoretical analysis showing that TRACI reduces the model's excess risk. Extensive experiments on various benchmark datasets demonstrate that TRACI outperforms state-of-the-art baselines, validating its effectiveness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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LGFormer: Local-to-Global Transformer for Hyperspectral Image Classification

LGFormer: Local-to-Global Transformer for Hyperspectral Imag...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiaqi Yang Bo Du Chen Wu State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan P. R. China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan P. R. China

Recently, many transformer-based approaches have emerged in the field of hyperspectral image (HSI) classification. However, existing transformer-based works either model the information of spectral vectors using a transformer or introduce a vision transformer (ViT) for feature expression of the spatial patch after principal component analysis (PCA), neglecting the spectral-spatial correlation of HSI. Besides, local details and global distributions are always challenging to simultaneously extract in these methods. To address the above issues, a local-to-global transformer (LGFormer) is proposed in this paper. In detail, the proposed approach directly extracts inherent features on the originally spectral-spatial patch, which not only takes the spatial distribution and spectral continuity into account but also preserves the intrinsically spectral-spatial correlation of the HSI cube. Moreover, a local-to-global self-attention (LGSA) including 3-D convolutional neural network (CNN) and ViT is designed in the presented LGFormer. With the above HSI-tailored structure, both fine- and coarse-grained features can be captured by progressively spectral-spatial feature learning. Experimental results on benchmark HSI datasets demonstrate that the proposed LGFormer can outperform other methods in terms of higher accuracy and finer classification maps.

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BPMN++: Comprehensive Business Process Modeling for Industrial Internet Application

BPMN++: Comprehensive Business Process Modeling for Industri...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hangyu Cheng Guosheng Kang Jianxun Liu Yiping Wen Buqing Cao Zhen Wang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process models. However, BPMN cannot support the intuitive modeling for some typical tasks in complex business scenarios of the Industrial Internet context, increasing the communication cost between modelers and business personnel. The current solutions try to extend the attributes of BPMN elements with annotations, but a large number of annotations makes business process models complex and cumbersome. This paper proposes to extend new business process modeling elements for Industrial Internet application scenarios based on BPMN extension mechanism, generating an extended business process modeling language, namely BPMN++. And an extended process modeling tool is implemented, which is available online. A case study over a real-world Industrial Internet process validates the usefulness of this extension mechanism and the well understandability of business process models.

关键词： Industries Process modeling Annotations Internet Personnel Task analysis Optimization

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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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ACL-Net: Adaptive and Collaborative Learning Network for Multi-Site Prostate MRI Segmentation

ACL-Net: Adaptive and Collaborative Learning Network for Mul...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Ma, Zibo Zhang, Bo Zhang, Zheng Wang, Wendong Mi, Yue Huang, Haiwen Wu, Jingyun Beijing University of Posts and Telecommunications Beijing100876 China Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School State Key Laboratory of Networking and Switching Technology China Beijing University of Posts and Telecommunications School of Modern Post China Peking University First Hospital Beijing100034 China Peking University First Hospital Department of Urology China Peking University First Hospital Department of Radiology China

ISBN: (纸本)9781665480451

High-performance deep learning models require large amounts of data with high quality annotations for model training, while the labeling work usually takes a lot of time for the experts. Meanwhile, the inter-observer variability al-ways exist between annotations from different experts and the distribution shift between the data acquired from different medical institutions. To address these challenges, we propose an end-to-end domain adaptive collaborative learning network for multi-institutional prostate MRI segmentation. Specifically, we introduce an unpaired image translation module to match the image domains between different institutions, which can alleviate the heterogeneity between 1.5T and 3T prostate MR images during model training. Moreover, we design a self-taught strategy to transfer domain-aware knowledge to jointly learn generic and unique representations. Furthermore, we evaluate our approach in scenarios with limited or without annotations, experimental results show that our approach has better adaptation performance than traditional supervised learning approaches, and has the potential to extend to unsupervised domain adaptation scenario. We also evaluate our approach with prostate MRI segmentation benchmark datasets, experimental results show that our approach outperforms several state-of-the-art methods. © 2022 IEEE.

关键词： Deep learning

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Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

arXiv

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images

arXiv

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arXiv 2023年

作者： Zheng, Mingwu Zhang, Haiyu Yang, Hongyu Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely Neu-Face, to learn accurate and physically-meaningful underlying 3D representations by neural rendering techniques. It naturally incorporates the neural BRDFs into physically based rendering, capturing sophisticated facial geometry and appearance clues in a collaborative manner. Specifically, we introduce an approximated BRDF integration and a simple yet new low-rank prior, which effectively lower the ambiguities and boost the performance of the facial BRDFs. Extensive experiments demonstrate the superiority of Neu-Face in human face rendering, along with a decent generalization ability to common objects. Code is released at NeuFace. Copyright © 2023, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

arXiv

引用

arXiv 2023年

作者： Xiao, Zhiqing Wang, Haobo Jin, Ying Feng, Lei Chen, Gang Huang, Fei Zhao, Junbo College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University China CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group China

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces;(ii)-we further develop a fine-grained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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