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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Pinghui Zhang, Yuanming Cheng, Kuankuan Zhao, Junzhou Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350317152

Graph data are ubiquitous in real-world systems such as social networks and protein-protein interaction networks. In many applications, nodes usually are associated with real-value attributes, e.g., age, income, and wealth. Recently, industry and research communities have attracted attention to mining and learning attribute graphs. In this paper, we study the problem of calculating the general neighborhood cardinality of each node v in the graph, i.e., the sum of non-negative attribute values of the nodes in the k -hop neighborhood of a node v. The naive solution is to run a k -step breadth-first-search (BFS) algorithm starting from each node and storing all visited nodes' attributes. Clearly, the time complexity of this solution is Oleft(|V| dmaxk), where |V| is the number of nodes and dmax is the maximum node degree in the graph. In real-world networks such as Twitter, dmax is over 3×106. Therefore, it is infeasible to compute the neighborhood cardinality of nodes exactly in such massive networks even if we set k=2. To solve this problem, we propose efficient methods to compute the neighborhood cardinality of graphs with non-negative node attributes and binary node attributes, respectively. Extensive experiments on large real-world networks show the efficiency and effectiveness of our methods. © 2024 IEEE.

关键词： Proteins

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Simulating learning methodology(SLeM):an approach to machine learning automation

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National Science Review 2024年第8期11卷 12-17页

作者： Zongben Xu Jun Shu Deyu Meng School of Mathematics and Statistics Xi'an Jiaotong University Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Peng Cheng Laboratory Pazhou Laboratory(Huangpu)

MACHINE LEARNING REQUIRES AUTOMATION Machine learning （ML） is a fundamental technology of artificial intelligence（AI） that focuses on searching the possibly existing mapping f:x→y to fit a given dataset D={（xi,y... 详细信息

关键词： automation learning approach machine methodology simulating slem

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Heterogeneous Dual-Dynamic Attention network for Modeling Mutual Interplay of Stocks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3595-3606页

作者： Liu, Haotian Zhou, Yadong Zhou, Yuxun Hu, Bowen Xi'an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi'an710049 China

Modern quantitative finance and portfolio-based investment hinge on the dependence structure among financial instruments (like stocks) for return prediction, risk management, and hedging. Several attempts have been made to leverage recent advancements in graph neural networks (GNNs) to achieve effective modeling to capture stock interactions. However, existing work suffers from the homogeneity and stationary of the predefined graph structure, and their reliance on hard-coded correlation-based relationships neglects the mutual intervention of different instruments. In this study, we propose the heterogeneous dual-dynamic attention network (HD2AT) to address these challenges in modeling the mutual interplay among stocks, across four key dimensions: heterogeneity, causality, structural dynamics, and lead-lag dynamics. More specifically, our method uncovers the causal interdependencies among instruments, dynamically inferring the heterogeneous network structure from real-time trading activities. Subsequently, we identify the cross-temporal propagation of interventions by integrating interactions at the node, time, and graph levels. Extensive experiments on real-world trading data demonstrate the superiority of the proposed framework over various baselines in terms of both return prediction and back-testing profitability. © 2020 IEEE.

关键词： Commerce

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Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1984-1999页

作者： Feiye Zhang Qingyu Yang Dou An Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory for Manufacturing Systems Engineering(SKLMSE) Xi’an Jiaotong UniversityXi’an 710049 MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an 710049China

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

关键词： Centralized training and decentralized execution demand side management multi-agent reinforcement learning privacy preserving

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CMW-Net: an adaptive robust algorithm for sample selection and label correction

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National Science Review 2023年第6期10卷 19-23页

作者： Jun Shu Xiang Yuan Deyu Meng School of Mathematics and Statistics Xi'an Jiaotong University Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM Deep neural networks (DNNs) have recently achieved impressive performance in various *** successis largely attributed to massive but carefully labeled data expected to properly and sufficiently simulate testing ***,the data collection and annotation processes are often expensiveand time consuming,making obtained training datasets in real-life applications error prone,especially with data biasfrom ideal testing distributions.

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Hierarchical Task Planning for Power Line Flow Regulation

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 29-40页

作者： Chenxi Wang Youtian Du Yanhao Huang Yuanlin Chang Zihao Guo the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong UniversityXi'an 713599China the State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China

The complexity and uncertainty in power systems cause great challenges to controlling power *** a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power ***,DRL has some inherent drawbacks in terms of data efficiency and *** paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow ***,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task *** addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist *** results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.

关键词： Knowledge graph power line flow regulation reinforcement learning task planning

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A Multi-scale Perception Enhancement network for Underwater Waste Detection 9

A Multi-scale Perception Enhancement Network for Underwater ...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Li, Yangke Zhang, Xinman MOE Key Lab for Intelligent Networks & Network Security School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350385991

Underwater waste detection plays an important role in aquatic ecosystems and environmental protection. In fact, waste deposits in aquatic environments not only cause water contamination, but also pose potential risks to human health. However, underwater images often face the problem of image degradation, which makes underwater waste detection tasks more challenging. Therefore, we propose an efficient multi-scale perception enhancement network (MPE-Net) for underwater waste detection, which can provide more discriminative information to improve detection performance. On the one hand, we introduce a multi-scale semantic enhancement module (MSEM) to capture richer contextual features in different receptive fields, and enhance critical semantic information by using frequency domain information. On the other hand, we design a multi-scale boundary enhancement module (MBEM) to extract boundary information at different scales, and emphasize important detail features by using hybrid boundary perception. We conduct comprehensive experiments on two common underwater waste image datasets. Relevant experimental results show that our method outperforms other representative deep learning models. © 2024 IEEE.

关键词： Health risks

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Pilot Backdoor Attack against Deep Reinforcement Learning Empowered intelligent Reflection Surface for Smart Radio

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4891-4903页

作者： Huang, Yunsong Wang, Hui-Ming Wang, Zhaowei Liu, Weicheng Xi’an Jiaotong University School of Information and Communications Engineering Xi’an710049 China Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China

intelligent reflection surface (IRS) has been used to assist communication by reflection and beamforming where a direct path is not available. Thus, IRS adjusts the wireless channel to enhance the data transmission efficiency with low power consumption. Recently, deep reinforcement learning (DRL) has been exploited in IRS coefficients optimization. IRS can be controlled by DRL to adapt their phase shift to the propagation environment and an expected reflection pattern can be obtained. However, due to the openness of wireless channel and the unexplainability of DRL, it is vulnerable to adversary attacks lauched through wireless channels. In this paper, we investigate a pilot contamination based backdoor attack against DRL based IRS beamforming, where an IRS controlled by an adversary attacker is used to contaminate the channel state information (CSI) during the training phase. The backdoor attack is covert because the adversary need not to know the legitimate pilot sequence and DRL performs well when the adversary IRS keeps inactive. We show that the backdoor attack can reduce the data rate significantly with the adversary IRS. At last, we propose a retraining method agaist the attack to recover the data rate. © 2025 IEEE. All rights reserved.

关键词： Deep reinforcement learning

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Design and Implementation of Routing Algorithms for Mobile Ad Hoc network Applying TDMA Strategy Based on Smart Antenna

Design and Implementation of Routing Algorithms for Mobile A...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Li, Fuqiang Yan, Zeyu Xiao, Haitao Mu, Pengcheng Yuan, Yongqiong Zhang, Weile 20th Institute Key Laboratory of Technology on Datalink Xi'an China Xi'an Jiaotong University Moe Key Lab for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350318012

This paper presents a routing algorithm for TDMA mobile ad hoc networks based on smart antennas. In this algorithm, a new proactive link state routing algorithm with QOS guarantee is proposed. The algorithm uses a new metric to determine the optimal path. Added assessments of the physical layer link quality and node load. This article also introduces simulation and experiments, including eight-node multi-hop simulation and three-node linear hopping experiments to show that our design choices are indeed quite effective. © 2023 IEEE.

关键词： Machine design

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A Revisit to Graph Neighborhood Cardinality Estimation

A Revisit to Graph Neighborhood Cardinality Estimation

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International Conference on Data Engineering

作者： Pinghui Wang Yuanming Zhang Kuankuan Cheng Junzhou Zhao MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph data are ubiquitous in real-world systems such as social networks and protein-protein interaction networks. In many applications, nodes usually are associated with real-value attributes, e.g., age, income, and wealth. Recently, industry and research communities have attracted attention to mining and learning attribute graphs. In this paper, we study the problem of calculating the general neighborhood cardinality of each node $v$ in the graph, i.e., the sum of non-negative attribute values of the nodes in the $k$ -hop neighborhood of a node $v$ . The naive solution is to run a $k$ -step breadth-first-search (BFS) algorithm starting from each node and storing all visited nodes' attributes. Clearly, the time complexity of this solution is $O\left(\vert V\vert d_{\max }^k\right)$ , where $\vert V\vert$ is the number of nodes and $d_{\max}$ is the maximum node degree in the graph. In real-world networks such as Twitter, $d_{\max}$ is over $3\timesnetworks0^{6}$ . Therefore, it is infeasible to compute the neighborhood cardinality of nodes exactly in such massive networks even if we set $k=2$ . To solve this problem, we propose efficient methods to compute the neighborhood cardinality of graphs with non-negative node attributes and binary node attributes, respectively. Extensive experiments on large real-world networks show the efficiency and effectiveness of our methods.

关键词： Proteins Industries Accuracy Social networking (online) Blogs Estimation Data engineering

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