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IEEE International Conference on Web services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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Virtual-Source and Virtual-Swarm-Based Particle Swarm Optimizer for Large-Scale Multi-Source Location via Robot Swarm

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, JunQi Lin, YuXuan Zhou, MengChu Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Multi-source location is a significant application in the field of robot swarm and is required to find all sources whose number and distribution are unknown in advance. With few parameters and fast search, Particle Swarm Optimizer (PSO) variants that have certain grouping capability have been applied to address multi-source location problems by dividing a swarm such that every source has robots to locate. However, they are difficult to predetermine the exact number of groups, require a big number of robots, and are easily trapped in the no-signal areas when the proportion of no-signal areas is high. This work proposes a Virtual-source and Virtual-swarm-based PSO (VVPSO) to divide a search area into multiple cells equally, each of which has a virtual source in its center. Then, instead of robots grouping, only one group of robots is employed to traverse all virtual sources, and search their corresponding cells to locate real sources by a new PSO called Real-virtual mapping PSO (RMPSO). RMPSO asymmetrically maps a robot into a particle swarm with multiple virtual particles to perform PSO, which greatly reduces the requirements for the number of robots. Experimental results show that VVPSO has great search scalability and can solve large-scale multi-source location problems than two state-of-the-art grouping methods and three representative multimodal PSO variants, even with only one robot. Hence, this work greatly advances the field of multi-source location by using mobile robot swarm. IEEE

关键词： Robots

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DCOM-GNN: A Deep Clustering Optimization Method for Graph

SSRN

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SSRN 2023年

作者： Yang, Haoran Wang, Junli Duan, Rui Yan, Chungang Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Deep clustering plays an important role in data analysis, and with the prevalence of graph data nowadays, various deep clustering models on graph are constantly proposed. However, due to the lack of more adequate clustering guidance, the discriminability of feature representation learned from these models for the clustering task is limited. Therefore, for the purpose of enabling the output of these models to be more cluster-oriented, we propose a Deep Clustering Optimization Method for Graph Neural Networks (DCOM-GNN), which can be attached to the original model architecture conveniently. For DCOM-GNN, it contains two components, one is the inter-cluster distance optimization module, whose role is to further adjust the distance between clusters of the original model output rationally. Another one is the intra-cluster distance optimization module, which aims to improve the cohesiveness of the original model output. Comprehensive experiments show that the performance of various deep clustering models on graph can be significantly improved after adding DCOM-GNN. © 2023, The Authors. All rights reserved.

关键词： Cluster analysis

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Graph Ensemble Neural Network

SSRN

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SSRN 2023年

作者： Duan, Rui Yan, Chungang Wang, Junli Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Ensemble methods have been shown to improve graph neural networks (GNNs). Existing ensemble methods on graphs determine a strong classifier by combining a set of trained base classifiers, i.e., combining the final outputs of base classifiers for prediction. However, these methods fail to promote many popular GNNs to perform well under heterophily (in graphs where many connected nodes havedifferent class labels), which limits their applicability. Furthermore, they ignore the hierarchical nature of GNNs, which results in no interaction between base classifiers when neighbors are aggregated (during training). Two issues arise from this: low applicability and shallow ensemble. We propose Graph Ensemble Neural Network (GEN) for addressing above issues, which is not a simple ensembleof GNNs, but instead integrates ensemble into GNNs to fuse a set of graphs. GEN deepens single ensemble into multiple ensembles during training and applies to homophily and heterophily graphs. In GEN, we design structure augmentation to generate some graphs for training and design feature augmentation for attenuating errors brought by the initial features. Different from existing graphensemble methods that execute only one ensemble, GEN executes multiple deep ensembles throughout the neighbor aggregation to fuse multiple graphs generated by structure augmentation. Extensive experiments show that GEN achieves new state-of-the-art performance on homophily and heterophily graphs for the semi- and full-supervised node classification. The source code of GEN is publicly available at https://***/graphNN/GEN1. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Convex Hull-based Algebraic Constraint for Visual Quadric SLAM

arXiv

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arXiv 2025年

作者： Yu, Xiaolong Zhao, Junqiao Song, Shuangfu Zhu, Zhongyang Yuan, Zihan Ye, Chen Feng, Tiantian School of Computer Science and Technology Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization. After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment. This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics. Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://***/tievtongji/convexhull-based-algebraic-constraint. © 2025, CC BY.

关键词： Semantic Segmentation

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Behavioral Unicity: On the Limits of Anonymized Social Behavior Metadata 1st

Behavioral Unicity: On the Limits of Anonymized Social Beh...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Tang, Hao Wang, Cheng Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200030 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9789819794119

Social behavior metadata has altered business models and human lifestyles. However, the inclusion of personal information in social behavior metadata poses risks of identity exposure for users. Under the requirements of modern data management laws, service providers now employ anonymization techniques when collecting user behavior metadata, which aims to break the link between personal identifiers and behavioral attributes for privacy protection. This paper conducts experiments on an open-source dataset of anonymized social behavior metadata and finds that even with privacy protection techniques such as data generalization, there remains a high risk of reidentification. Specifically, four social behavior metadata records are sufficient to reidentify users from the anonymized social behavior metadata, achieving a unicity of 90.10%. This indicates that the current widespread collection of social behavior metadata brings a high risk of privacy breaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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A Behavioral Authentication Method for Mobile Based on Browsing Behaviors

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1528-1541页

作者： Dongxiang Chen Zhijun Ding Chungang Yan Mimi Wang Department of Computer Science and Technology Tongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityShanghai 201804China IEEE

The passwords for unlocking the mobile devices are relatively simple,easier to be stolen,which causes serious potential security *** important research direction of identity authentication is to establish user behavior models to authenticate *** this paper,a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is *** architecture is suitable for users using the mobile terminal APP in the daily *** architecture includes data acquisition,data processing,feature extraction,and sub model *** can use this architecture for continuous authentication when the user uses APP at the mobile terminal.

关键词： Behavioral authentication browsing behaviors mobile device multiple factors

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Upper Bounds on Lifetime of Three Dimensional Extended UWB Sensor Networks*

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Chinese Journal of Electronics 2023年第2期19卷 307-312页

作者： Juan Xu Changjun Jiang Aihuang Guo Yongfa Hong Shu Li Cheng Wang School of Electronics and Information Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

The upper bounds on lifetime of three dimensional extended Time hopping impulse radio Ultrawide band (TH-IR UWB) sensor networks are derived using percolation theory arguments. The TH-IR UWB sensor network consists of $n$ sensor nodes distributed in a cube of edge length $n^{1/3}$ according to a Poisson point process of unit intensity. It is shown that for such a static three dimensional extended TH-IR UWB sensor network, the upper bound on the lifetime is of order $O(n^{-1})$ , while in the ideal case, the upper bound on the lifetime is longer than that of a static network by a factor of $n^{2/3}$ . Therefore sensor nodes moving randomly in the deployment area can improve the upper bound on network lifetime. The results also reveal that the upper bounds on network lifetime decrease with the number of nodes $n$ , thus extended TH-IR UWB sensor networks aren't prone to be employed in large-scale network.

关键词： Wireless communication Wireless sensor networks Upper bound Protocols

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC systems

Joint DNN Partition and Thread Allocation Optimization for E...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms Partitioning algorithms Resource management Optimization

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Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Hanqian Tu Dongming Xiang Wang Lin Guanjun Liu Department of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Department of Computer Science Key laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method.

关键词： Measurement Petri nets Semantics Stochastic processes Information leakage Explosions Complexity theory Cybernetics Business

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