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Time-varying networks based on activation and deactivation mechanisms

Chinese Physics B 2017年第10期26卷 527-532页

作者：王学文罗月娥张丽杰许新建 School of Physics and Electronic Sciences Guizhou Education University College of Sciences Shanghai University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education

A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.

关键词： time-varying networks activity-driven degree distribution

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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Trust-drive minimize cost within deadline algorithm for instance-intensive workflows in cloud computing environment

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Journal of Convergence Information Technology 2012年第13期7卷 412-419页

作者： Daoguo, Li Mukuai, Wang Rong, Zhao Hangzhou Dianzi University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Cloud computing is an emerging paradigm and it has been accepted and researched widely both in industry and academic fields. Instance-intensive workflows are workflows with a great number of concurrent instances;they are typical applications in cloud computing environment. However, cloud resources consist of unreliable servers;it becomes increasingly important to provide reliable scheduling strategy. Considering unreliable cloud resources and QoS requirements of instanceintensive workflows, we propose Trust-drive Minimize Cost Within Deadline (TD-MCWD) scheduling algorithm. On the one hand, the TD-MCWD algorithm staggers deadlines of the large number of concurrent instances of the same nature, expecting to get cheaper and more intensively competitive resources. On the other hand, considering the risk factors on the basis of resource reliability was estimated by trust model, the TD-MCWD algorithm tends to assign the task to the high reliable resource. Simulation experiment shows TD-MCWD algorithm can reduce the rate of delayed instance completion and the average execution cost of successful instance completion. Besides, TD-MCWD has a better load balancing and shows a better performance.

关键词： Quality of service

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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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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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Rate Control Algorithm for Hierarchical Video Coding

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Journal of Donghua University(English Edition) 2012年第3期29卷 222-226页

作者：陆千里岑锋许维胜朱芳来 The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Department of Control Science&Engineering Tongji University

For rate control (RC) of hierarchical structure coding, an independent rate-quantization (R-Q) model was proposed based on mean absolute differences (MADs) in different temporal levels (TLs). In the proposed R-Q model, a novel MAD model was developed according to the hierarchical structure. The experimental results demonstrate that the proposed algorithm provides better performance, in terms of average peak signal-to-noise ratio (PSNR) and quality smoothness, than the H.264 reference model, JM14.2, under various sequences.

关键词： rate-quantization (R-Q) model rate control(RC) video coding peak signal-to-noise ratio (PSNR) hierarchical structure

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Research on road topology based mobility prediction schemes

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High Technology Letters 2007年第2期13卷 140-144页

作者：陈林 Chen Hongzhong Jiang Changjun Key Lab of Embedded System and Service Computing of the Ministry of Education Department of Computer Science and Technology Tongji University Shanghai 201804 P.R.China

Geographic routing has been introduced in mobile ad hoc networks and sensor networks. But its per-formance suffers greatly from mobility-induced location errors that can cause Lost Link （LLNK） and LOOP problems. Thus various mobility prediction algorithms have been proposed to mitigate the errors, but sometimes their prediction errors are substantial. A novel mobility prediction technique that incorpo-rates both mobile positioning information and road topology knowledge was presented. Furthermore, the performance of the scheme was evaluated via simulations, along with two other schemes, namely, Linear Velocity Prediction （LVP） and Weighted Velocity Prediction （WVP） for comparison purpose. The results of simulation under Manhattan mobility model show that the proposed scheme could track the movement of a node well and hence provide noticeable improvement over LVP and MVP.

关键词： geographic muting mobility prediction road topology shortest path Manhattan mo-bility model

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Neighborhood Extended Dynamic Graph Neural Network 2022

Neighborhood Extended Dynamic Graph Neural Network

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14th International Conference on Machine Learning and computing, ICMLC 2022

作者： Yu, Da Wang, Junli Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781450395700

Representation learning on dynamic graphs has drawn much attention due to its ability to learn hidden relationships as well as capture temporal patterns in graphs. It can be applied to represent a broad spectrum of graph-based data like social networks and further use the learned representations to solve the downstream tasks including link prediction and edge classification. Although some approaches have been proposed for dynamic graphs in recent years, most of them paid little attention to the evolution of the entire graph topology, leading to the lack of global information of what happened in nodes' neighborhoods during their update. We propose NEDGNN, a novel Neighborhood Extended Dynamic Graph Neural Network on dynamic graphs represented as sequences of time-stamped events. We introduce a temporal attention propagation module to generate messages for n-hop neighbors through a self-attention mechanism, which can help disseminate information among nodes' n-hop neighbors. Besides, a FIFO message box module is also applied to gain some time efficiency. Due to the introduction of these modules, NEDGNN outperforms many state-of-the-art baselines in several tasks. We also perform a detailed ablation study to test the effectiveness and time cost of each module. © 2022 ACM.

关键词： Graph neural networks

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