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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Response of the Yellow and East China seas low-trophic ecosystems to two typhoons at different translational speeds

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Journal of Oceanology and Limnology 2025年 1-21页

作者： Zhu, Lei Zhang, Jing Shi, Changcen Yang, Wei Zhang, Haiyan Wang, Yucheng Liu, Guangliang Bian, Changwei Zhao, Liang College of Marine and Environmental Science Tianjin University of Science and Technology Tianjin China Key Laboratory of Marine Resource Chemistry and Food Technology (TUST) Ministry of Education Tianjin China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya China Tianjin Key Laboratory for Marine Environmental Research and Service School of Marine Science and Technology Tianjin University Tianjin China Laoshan Laboratory Qingdao China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory Ocean University of China Qingdao China

Frequent typhoons can significantly change the temperature, nutrient availability, and phytoplankton biomass in marginal seas. The oceanic response to typhoons is usually influenced by the features of the typhoon, among which the translational speed is critically important. By using a highresolution coupled physical-biological model, we investigated the response of the Yellow and East China seas (YECS) to two typhoons at different translational speeds, Muifa in August 2011 and Bolaven in August 2012. The model well reproduced the spatial and temporal variations of temperature, chlorophyll-a concentration over the YECS. Results show that typhoons with slower translational speeds uplift more deep water, leading to a more significant oceanic response. Divergence and convergence caused nutrient fluxes in opposite directions in the surface and bottom layers. Moreover, the nutrient flux in the bottom layer was greater than that in the surface layer. These phenomena are closely related to the spatial distribution of nutrients. Further studies show that the degree of ocean response to typhoons is highly correlated with the initial conditions of physical and biological elements of the upper ocean before the typhoon, as well as with ocean structure. Pretyphoon initial conditions of oceanic physical and ecological elements, mixed layer depth, and potential energy anomalies can all alter the degree of typhoon-induced oceanic response. This study emphasizes the important roles of the translational speed of typhoons and the initial oceanic conditions in the oceanic response to typhoons.

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Effcient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Security and Safety

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Security and Safety 2022年第1期1卷 145-165页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua UniversityShanghai 201620China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer ScienceTongji UniversityShanghai 201804China Shanghai Network Financial Security Collaborative Innovation Center Tongji UniversityShanghai 201804China

Nowadays,large numbers of smart sensors(e.g.,road-side cameras)which com-municate with nearby base stations could launch distributed denial of services(DDoS)attack storms in intelligent transportation *** attacks disable the services provided by base *** in this paper,considering the uneven communication traffic ows and privacy preserving,we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the ***,in the federated learning,we need to consider the problem of poisoning attacks due to malicious *** poisoning attacks will lead to the intelligent transportation systems paralysis without security *** poisoning attacks mainly apply to the classi cation model with labeled *** this paper,we propose a reinforcement learning-based poisoningmethod speci cally for poisoning the prediction model with unlabeled ***,previous related defense strategies rely on validation datasets with labeled data in the ***,it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving,and our datasets are also ***,we give a validation dataset-free defense strategy based on Dempster-Shafer(D-S)evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS *** our experiments,we simulate 3000 points in combination with DARPA2000 dataset to carry out *** results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short ***,we compare our proposed defense algorithm with three popularly used defense *** results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intel-ligent transportation systems

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A Blockchain Based Automatic Access Scheme Design and Implement for Small Cell Base Station 21

A Blockchain Based Automatic Access Scheme Design and Implem...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Liang, Jingchao Wu, Jun Tan, Bin Ren, Haoqi Zhang, Zhifeng School of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Fudan University School of Computer Science Shanghai China Jinggangshan University Electronics and Information Engineering Ji'an China Tongji University Electronics and Information Engineering Shanghai China

ISBN: (纸本)9781665432061

5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the 'decentralization' of blockchain waiting to be solved. © 2021 IEEE.

关键词： Blockchain

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Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hypersepctral Imagery

Low-Rank Subspace Representation from Optimal Coded-Aperture...

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IEEE International Conference on Image Processing

作者： Jianchen Zhu Tong Zhang Shengjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach.

关键词： Image coding Encoding Apertures Clustering algorithms Imaging Three-dimensional displays Sensors

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Prescribed-time bipartite synchronization for directed higher-order networks

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Chaos, Solitons & Fractals 2025年 199卷

作者： Linlong Xu Xiwei Liu Department of Computer Science and Technology Ministry of Education Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

We investigate the prescribed-time complete and bipartite synchronization problems for directed higher-order networks (DHNs). At first, a hypergraph is employed to describe the network’s topological structure, where each hyperedge connects multiple nodes. Different from many existing studies, we introduce generalized coupling matrices and a weighted coupling function, where the elements of the coupling matrices can take positive or negative values, and the nodes in the coupling function are weighted differently. To address synchronization and control problems in DHNs, we then apply the rearranging variables’ order technique to link DHNs and multiweighted networks. Next, a time-varying regulatory function is introduced, and the coupling matrices are combined from a dimensional perspective to derive the prescribed-time bipartite synchronization criteria. Finally, some numerical simulations are provided.

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Cross-Image Siamese Graph Convolutional Network For Fine-Grained Image Retrieval in Diabetic Retinopathy

SSRN

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SSRN 2023年

作者： Chen, Fang Zhao, Weiling Zhou, Xiaobo School of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Center for Computational Systems Medicine

The categories of diabetic retinopathy (DR) are interrelated, and different ophthalmologists often give different results for the same fundus image. Automatic cross image retrieval of DR can provide an effective diagnostic solution for ophthalmologists and is of great significance in clinical practice. Cross-image (i.e. left and right fundus images for a patient) information is highly correlated and complementary and can be harnessed to improve various computer vision tasks such as image classification, object detection, image segmentation, and image retrieval. Previous studies did not explore the correlation between lesion areas in left and right fundus images of patients, limiting the effective diagnosis of DR. In this study, we proposed a cross-image siamese graph convolutional network(CIS-GCN) to retrieve fine-grained diabetic retinopathy fundus images. First, we constructed a global-specific structure to obtain the specific features of the left and right eyes. Then, we passed the specific features through the pathological localization network to obtain the location features of the lesion. Finally, a graph convolutional neural network was introduced to construct node sets for the left and right eyes, respectively, to represent relatively consistent regions in the fundus images of patients and learn their correlations. We tested our method using Diabetic Retinopathy Detection datasets and the results showed that our algorithm outperforms other state-of-the-art methods by 2.2% ~ 3.7% in image data retrieval. © 2023, The Authors. All rights reserved.

关键词： Object detection

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DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

arXiv

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arXiv 2022年

作者： Tian, Xuebo Zhao, Junqiao Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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DL-SLOT: Dynamic LiDAR SLAM and object tracking based on collaborative graph optimization

arXiv

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arXiv 2022年

作者： Tian, Xuebo Zhu, Zhongyang Zhao, Junqiao Tian, Gengxuan Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Ego-pose estimation and dynamic object tracking are two critical problems for autonomous driving systems. The solutions to these problems are generally based on their respective assumptions, i.e., the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption for object tracking. However, these assumptions are challenging to hold in dynamic road scenarios, where SLAM and object tracking become closely correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object tracking method, to simultaneously address these two coupled problems. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment into a unified optimization framework. First, we used object detection to identify all points belonging to potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using the filtered point cloud. Simultaneously, we proposed a sliding window-based object association method that accurately associates objects according to the historical trajectories of tracked objects. The ego-states and those of the stationary and dynamic objects are integrated into the sliding window-based collaborative graph optimization. The stationary objects are subsequently restored from the potentially dynamic object set. Finally, a global pose-graph is implemented to eliminate the accumulated error. Experiments on KITTI datasets demonstrate that our method achieves better accuracy than SLAM and object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually advantageous, dramatically improving the robustness and accuracy of SLAM and object tracking in dynamic road scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical radar

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