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2020 5th International Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Jiang, Kaiwen Zhang, Degan Xu, Haixia Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin300410 China

With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D reconstruction and reduce the possibility of other algorithms falling into local optimal solutions, attempting to get better and faster performance, a new parallax calculation based on symmetric continuous optimization is proposed in this paper. The algorithm is proposed here will be tested respectively in the same data, in order to certificate the algorithm applied is better than traditional minimize El algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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End-to-End Delay Performance for Mobile Cyber-Physical Systems with Edge Computing 11

End-to-End Delay Performance for Mobile Cyber-Physical Syste...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Wu, Yan Yang, Qinghai Qiao, Feiyu Kwak, K.S. Leung, C.M. Pan, Guangchuan State Key Laboratory of ISN School of Telecommunications Engineering Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China Communication Engineering Inha University #100 Inha-Ro Nam-gu Incheon22212 Korea Republic of College of Computer Science and Software Engineering Shenzhen University 518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China

ISBN: (纸本)9781450398039

In this letter, we investigate the end-to-end (E2E) delay performance for mobile cyber-physical systems (M-CPS) with edge computing. The traditional delay analysis is based on flow conservation law, whereas the flow is not conserved in the M-CPS with edge computing due to the existence of edge computing node and remote controller. To tackle this problem, we introduce two scaling factors to ensure the flow conservation, then the E2E delay bound of M-CPS with edge computing is derived via stochastic network calculus. In addition, to cater for the delay demand for M-CPS stability, the stable-aware delay bound is obtained. Numerical results show the gap between the analytic bound and simulation can be narrower as the average signal-to-noise (SNR) growing. © 2022 ACM.

关键词： Cyber Physical System

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VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection

arXiv

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arXiv 2024年

作者： Song, Ziying Zhang, Guoxin Xie, Jun Liu, Lin Jia, Caiyan Xu, Shaoqing Wang, Zhepeng School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang050018 China Lenovo Research Beijing100085 China State Key Laboratory of Internet of Things for Smart City Department of Electrome chanical Engineering University of Macau 999078 China

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Cluster-guided Contrastive Class-imbalanced Graph Classification 39

Cluster-guided Contrastive Class-imbalanced Graph Classifica...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Shen, Jianhao Qiao, Ziyue Zhang, Ming College of Computer Science Sichuan University Chengdu China School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University Beijing China College of Mathematics Sichuan University Chengdu China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Huawei Hisilicon Shanghai China School of Computing and Information Technology Great Bay University Dongguan China

ISBN: (纸本)157735897X

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predictions biased towards the majority classes. On the other hand, existing class-imbalanced learning methods in vision may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To address these challenges, we propose a simple yet powerful approach called C3GNN that integrates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C3GNN clusters graphs from each majority class into multiple subclasses, with sizes comparable to the minority class, mitigating class imbalance. It also employs the Mixup technique to generate synthetic samples, enriching the semantic diversity of each subclass. Furthermore, supervised contrastive learning is used to hierarchically learn effective graph representations, enabling the model to thoroughly explore semantic substructures in majority classes while avoiding excessive focus on minority classes. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method against competitive baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bohai Zhao Kai Peng Victor C.M. Leung Yunni Xia College of Engineering Huaqiao University Quanzhou P.R.China State Key Lab. for Novel Software Technology Nanjing University Nanjing P.R.China School of Computer Science and Software Engineering Shenzhen University Shenzhen P.R.China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada School of computers Chongqing University Chongqing P.R.China

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches.

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An Efficient DeFi-based DataWarehousing Approach in Supply Chain Finance 5

An Efficient DeFi-based DataWarehousing Approach in Supply C...

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5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Rabieinejad, Elnaz Yazdinejad, Abbas Hasani, Tahereh Parizi, Reza M. Hammoudeh, Mohammad Cyber Science Lab School of Computer Science University of Guelph ON Canada Lang School of Business and Economics ON Canada College of Computing and Software Engineering Kennesaw State University GA United States Department of Computing & Mathematics Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9781450387347

In today's world, industries are looking to improve their productivity through effective logistics and supply chain management. The efficiency of the supply chain in dealing with the huge volumes of data plays a key role in the overall performance of businesses. Supply Chain Finance (SCF) aims to improve the robustness and efficiency of companies' supply lines. However, SCF suffers from problems like fraudulent transactions, information inconsistencies, and delays in financial transfers. Decentralized finance (Defi) is a new blockchain paradigm that tackles traditional finance problems. This paper proposes a Defi-based model on a hybrid private-public blockchain for SCF. Our model improves the efficiency of SCF by eliminating costs of using centralized financial institutions, reducing overload costs due to better estimation of the required amount of products, and also decreasing supply delays for the requested products. We also evaluated our model using the SWOT technique for adopting it in the supply chain. © 2021 ACM.

关键词： Smart contract

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Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition

arXiv

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arXiv 2024年

作者： Tang, Jielong Wang, Zhenxing Gong, Ziyang Yu, Jianxing Zhu, Xiangwei Yin, Jian School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Atmospheric Sciences Sun Yat-sen University Zhuhai China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China Pazhou Lab Guangzhou510330 China

Grounded Multimodal Named Entity Recognition (GMNER) is an emerging information extraction (IE) task, aiming to simultaneously extract entity spans, types, and corresponding visual regions of entities from given sentence-image pairs data. Recent unified methods employing machine reading comprehension or sequence generation-based frameworks show limitations in this difficult task. The former, utilizing human-designed type queries, struggles to differentiate ambiguous entities, such as Jordan (Person) and off-White x Jordan (Shoes). The latter, following the one-by-one decoding order, suffers from exposure bias issues. We maintain that these works misunderstand the relationships of multimodal entities. To tackle these, we propose a novel unified framework named Multi-grained Query-guided Set Prediction Network (MQSPN) to learn appropriate relationships at intra-entity and inter-entity levels. Specifically, MQSPN explicitly aligns textual entities with visual regions by employing a set of learnable queries to strengthen intra-entity connections. Based on distinct intra-entity modeling, MQSPN reformulates GMNER as a set prediction, guiding models to establish appropriate inter-entity relationships from a optimal global matching perspective. Additionally, we incorporate a query-guided Fusion Net (QFNet) as a glue network to boost better alignment of two-level relationships. Extensive experiments demonstrate that our approach achieves state-of-the-art performances in widely used benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Information retrieval

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Attacks and defences on intelligent connected vehicles:a survey

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Digital Communications and Networks 2020年第4期6卷 399-421页

作者： Mahdi Dibaei Xi Zheng Kun Jiang Robert Abbas Shigang Liu Yuexin Zhang Yang Xiang Shui Yu Department of Computing Macquarie UniversitySydneyNSWAustralia China State Key Lab of Automotive Safety and Energy Tsinghua UniversityBeijingChina School of Engineering Macquarie UniversitySydneyNSWAustralia School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVICAustralia School of Software University of Technology SydneyAustralia

Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication *** many efforts are made to improve the resilience to cyber attacks,there are still many unsolved *** paper first identifies some major security attacks on intelligent connected ***,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware *** challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

关键词： Intelligent vehicles Vehicular networks software vulnerabilities Deep learning 3GPP software defined security

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Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration

arXiv

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arXiv 2022年

作者： Zhou, Ziqi Qi, Lei Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance. ‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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