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Author details

Wei Ni

Publications

59

Citations

712

Publications by Year

20182023

Co-Authors:

David AbbottMehran AbolhasanMing AiJingpeng BaiSiguo Bi

Show All Co-Authors (136)

Wei Ni

Affiliation

CSIRO, Sydney, Australia

Publication Topics

Unmanned Aerial Vehicles,
Deep Reinforcement Learning,
Time Slot,
Mobile Edge Computing,
Base Station,
Internet Of Things,
Channel Gain,
Energy Consumption,
Internet Of Things Devices,
Global Model,
Neural Network,
Resource Allocation

Biography

Wei Ni (Senior Member, IEEE) received the B.E. and Ph.D. degrees in electronics engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. He was a Senior Researcher at Nokia Devices Research and Development from January 2008 to March 2009, and the Deputy Project Manager at Alcatel-Lucent Bell Labs from January 2005 to December 2007. He is currently the Principal Research Scientist and the Group Leader of the Cyber-Physical Systems (CPS) Program, Cybernetics Group, Data61 Business Unit, CSIRO. He is also an Adjunct Professor with the University of Technology Sydney and an Honorary Professor at Macquarie University, Sydney. His research interests include optimization, game theory, and graph theory.(Based on document published on 8 November 2021).

Publications

59

Citations

712

Publications by Year

20182023

Co-Authors:

David Abbott
Mehran Abolhasan
Ming Ai
Jingpeng Bai
Siguo Bi

Show All Co-Authors (136)

Author's Published Works

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Showing 1-25 of 28 results

Conferences (13)

Journals (13)

Early Access Articles (2)

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Junli Wang(28)
Chungang Yan(15)
Changjun Jiang(15)
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Hongda Qi(5)
National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, China(5)
Key Laboratory of Embedded System and Service Computing, Ministry of Education, and the National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, China(4)
Department of Computer Science & Engineering, University of Tongji, Shanghai, China(4)
Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China(3)
Ministry of Education, Key Laboratory of Embedded System and Service Computing (Tongji University), Shanghai, China(2)
2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)(2)
IEEE Transactions on Big Data(2)
IEEE Transactions on Neural Networks and Learning Systems(2)
IEEE Transactions on Pattern Analysis and Machine Intelligence(2)
2005 First International Conference on Semantics, Knowledge and Grid(1)
IEEE(28)
Media(1)
Melbourne, Australia(2)
Beijing, China(1)
Guangzhou, China(1)
Guilin, China(1)
Hangzhou, China(1)
Convolutional Neural Network(11)
Graph Neural Networks(10)
Attention Mechanism(8)
Neural Network(7)
Graph Convolutional Network(6)

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Leveraging Pixel Difference Feature for Deepfake Detection

Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Early Access Article |

The rise of Deepfake technology poses a formidable threat to the credibility of both judicial evidence and intellectual property safeguards. Current methods lack the ability to integrate the texture information of facial features into CNNs, despite the fact that fake contents are subtle and pixel-level. Due to the fixed grid kernel structure, CNNs are limited in their ability to describe detailed ...Show More

Leveraging Pixel Difference Feature for Deepfake Detection

Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Early Access Article |

Graph Multi-Convolution and Attention Pooling for Graph Classification

Yuhua Xu;Junli Wang;Mingjian Guang;Changjun Jiang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

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Many studies have achieved excellent performance in analyzing graph-structured data. However, learning graph-level representations for graph classification is still a challenging task. Existing graph classification methods usually pay less attention to the fusion of node features and ignore the effects of different-hop neighborhoods on nodes in the graph convolution process. Moreover, they discard...Show More

Graph Multi-Convolution and Attention Pooling for Graph Classification

Yuhua Xu;Junli Wang;Mingjian Guang;Changjun Jiang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

Data-Free Knowledge Filtering and Distillation in Federated Learning

Zihao Lu;Junli Wang;Changjun Jiang

IEEE Transactions on Big Data

Year: 2024 | Early Access Article |

Cited by: Papers (2)

In federated learning (FL), multiple parties collaborate to train a global model by aggregating their local models while keeping private training sets isolated. One problem hindering effective model aggregation is data heterogeneity. Federated ensemble distillation tackles this problem by using fused local-model knowledge to train the global model rather than directly averaging model parameters. H...Show More

Data-Free Knowledge Filtering and Distillation in Federated Learning

Zihao Lu;Junli Wang;Changjun Jiang

IEEE Transactions on Big Data

Year: 2024 | Early Access Article |

Adaptive Knowledge Recomposition for Personalized Federated Learning via Discrete Wavelet Transform

Zihao Lu;Junli Wang;Changjun Jiang

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 21 | Journal Article |

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Heterogeneous data silos hinder the application of deep learning in the Internet of Things. As a dealing scheme, personalized federated learning (pFL) distributedly customizes multiple local models for these silos. Most pFL methods directly use a global model to assist the local model optimization ignoring the performance drop caused by irrelevant or misleading global-model knowledge. To address t...Show More

Adaptive Knowledge Recomposition for Personalized Federated Learning via Discrete Wavelet Transform

Zihao Lu;Junli Wang;Changjun Jiang

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 21 | Journal Article |

DRL-Based VNF Cooperative Scheduling Framework With Priority-Weighted Delay

Jiamin Yao;Junli Wang;Cheng Wang;Chungang Yan

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

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Effective Service Function Chains (SFCs) mapping and Virtual Network Functions (VNFs) scheduling are crucial to ensure high-quality service provision for Internet of Things (IoT) tasks. Meeting the varying demands of multiple SFCs poses a significant challenge, particularly when working with the limited resources available in edge computing networks. Most existing working focuses on uniformly mapp...Show More

DRL-Based VNF Cooperative Scheduling Framework With Priority-Weighted Delay

Jiamin Yao;Junli Wang;Cheng Wang;Chungang Yan

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

Graph Convolutional Networks With Adaptive Neighborhood Awareness

Mingjian Guang;Chungang Yan;Yuhua Xu;Junli Wang;Changjun Jiang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

Cited by: Papers (1)

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Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, neighborhood awareness remains essential and challenging for GCNs. Existing methods usually perform neighborhood-aware steps only from the node or hop level, which leads to a lack of capability to learn the neigh...Show More

Graph Convolutional Networks With Adaptive Neighborhood Awareness

Mingjian Guang;Chungang Yan;Yuhua Xu;Junli Wang;Changjun Jiang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

The Probabilistic Liveness Decision Method of Unbounded Petri Nets Based on Machine Learning

Hongda Qi;Junli Wang;Chungang Yan;Changjun Jiang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 2 | Journal Article |

Cited by: Papers (3)

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The liveness of Petri nets (PNs) means that every event can occur in any state, establishing a close relationship with the deadlock-free property of existing systems. Due to the problem of state space explosion and the infinite state space of unbounded PNs (UPNs), the time complexity and space complexity of the liveness decision are difficult to give accurate measures; at least, they are both NP-h...Show More

The Probabilistic Liveness Decision Method of Unbounded Petri Nets Based on Machine Learning

Hongda Qi;Junli Wang;Chungang Yan;Changjun Jiang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 2 | Journal Article |

PHAED: A Speaker-Aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-Turn Dialogue Generation

Zihao Wang;Ming Jiang;Junli Wang

IEEE Transactions on Big Data

Year: 2024 | Volume: 10, Issue: 1 | Journal Article |

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This article presents a novel open-domain dialogue generation model emphasizing the differentiation of speakers in multi-turn conversations. Differing from prior work that treats the conversation history as a long text, we argue that capturing relative social relations among utterances (i.e., generated by either the same speaker or different persons) benefits the machine capturing fine-grained con...Show More

PHAED: A Speaker-Aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-Turn Dialogue Generation

Zihao Wang;Ming Jiang;Junli Wang

IEEE Transactions on Big Data

Year: 2024 | Volume: 10, Issue: 1 | Journal Article |

Stable QoE-Aware Multi-SFCs Cooperative Routing Mechanism Based on Deep Reinforcement Learning

Jiamin Yao;Chungang Yan;Junli Wang;Changjun Jiang

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 1 | Journal Article |

Cited by: Papers (3)

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The explosive development of the Internet of Things (IOT) has stimulated the sudden and dynamic demand pattern of potential Service Function Chain (SFC) traffic. The virtual network built based on the requirements of Multiple SFCs (Multi-SFCs), sharing the resource, should provide better Quality of Experience (QoE) for Multi-SFCs according to the real-time network status. However, simultaneous Mul...Show More

Stable QoE-Aware Multi-SFCs Cooperative Routing Mechanism Based on Deep Reinforcement Learning

Jiamin Yao;Chungang Yan;Junli Wang;Changjun Jiang

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 1 | Journal Article |

Probabilistic Reachability Prediction of Unbounded Petri Nets: A Machine Learning Method

Hongda Qi;Mingjian Guang;Junli Wang;Chungang Yan;Changjun Jiang

IEEE Transactions on Automation Science and Engineering

Year: 2024 | Volume: 21, Issue: 3 | Journal Article |

Cited by: Papers (5)

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Unbounded Petri nets (UPNs) can describe and analyze discrete event systems with infinite states (DESIS). Due to the infinite state space and the combination explosion problem, the reachability analysis of UPNs is an NP-Hard problem. The existing reachability analysis methods cannot achieve an accurate result at reasonable costs (computational time and space) due to the finite reachability tree wi...Show More

Probabilistic Reachability Prediction of Unbounded Petri Nets: A Machine Learning Method

Hongda Qi;Mingjian Guang;Junli Wang;Chungang Yan;Changjun Jiang

IEEE Transactions on Automation Science and Engineering

Year: 2024 | Volume: 21, Issue: 3 | Journal Article |

A Multichannel Convolutional Decoding Network for Graph Classification

Mingjian Guang;Chungang Yan;Yuhua Xu;Junli Wang;Changjun Jiang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 10 | Journal Article |

Cited by: Papers (6)

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Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in decoding, resulting in the loss of global information or ignoring some local information of large graphs. And the commonly used cross-entropy lo...Show More

A Multichannel Convolutional Decoding Network for Graph Classification

Mingjian Guang;Chungang Yan;Yuhua Xu;Junli Wang;Changjun Jiang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 10 | Journal Article |

An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

Zheyuan Wang;Junli Wang;Chungang Yan

2023 6th International Conference on Information Systems and Computer Networks (ISCON)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Software defined network (SDN) enables efficient and green traffic management by separating the control and data planes. However, the existing itemized scheduling approach is prone to waste of network resources and energy consumption due to the different sensitivity of traffic to time delay. To address these problems, we design a Slime Mould Algorithm based Energy-efficient Traffic Scheduling Meth...Show More

An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

Zheyuan Wang;Junli Wang;Chungang Yan

2023 6th International Conference on Information Systems and Computer Networks (ISCON)

Year: 2023 | Conference Paper |

Multistructure Graph Classification Method With Attention-Based Pooling

Yuhua Xu;Junli Wang;Mingjian Guang;Chungang Yan;Changjun Jiang

IEEE Transactions on Computational Social Systems

Year: 2023 | Volume: 10, Issue: 2 | Journal Article |

Cited by: Papers (13)

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Graph neural networks (GNNs) have achieved effective performance in many graph-related tasks involving recommendation systems, social networks, and bioinformatics. Recent studies have proposed several graph pooling operators to obtain graph-level representations from node representations. Nevertheless, they usually adopt a single strategy to evaluate the importance of nodes, which may generate nod...Show More

Multistructure Graph Classification Method With Attention-Based Pooling

Yuhua Xu;Junli Wang;Mingjian Guang;Chungang Yan;Changjun Jiang

IEEE Transactions on Computational Social Systems

Year: 2023 | Volume: 10, Issue: 2 | Journal Article |

Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning

Yu Ke;Junli Wang;Chungang Yan;Jiamin Yao

2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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With the expansion of network scale, traditional routing strategies in Software-Defined Networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes...Show More

Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning

Yu Ke;Junli Wang;Chungang Yan;Jiamin Yao

2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2022 | Conference Paper |

A Dual-stage Attention Based SDN Traffic Prediction Method

Chenjing Gao;Junli Wang;Chungang Yan

2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2022 | Conference Paper |

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Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stag...Show More

A Dual-stage Attention Based SDN Traffic Prediction Method

Chenjing Gao;Junli Wang;Chungang Yan

2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2022 | Conference Paper |

LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

Yifan Xu;Minghao Zhu;Junli Wang;Chungang Yan

2022 IEEE International Conference on Big Data (Big Data)

Year: 2022 | Conference Paper |

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Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XM...Show More

LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

Yifan Xu;Minghao Zhu;Junli Wang;Chungang Yan

2022 IEEE International Conference on Big Data (Big Data)

Year: 2022 | Conference Paper |

GNN-EA: Graph Neural Network with Evolutionary Algorithm

Yun Huang;Chaobo Zhang;Junli Wang

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Recently, Graph Neural Networks (GNNs) have shown great promise in addressing various tasks with non-Euclidean data. Encouraged by the successful application on discovering convolutional and recurrent neural networks, Neural Architecture Search (NAS) is extended to alleviate the complexity of designing appropriate task-specific GNNs. Unfortunately, existing graph NAS methods are usually susceptibl...Show More

GNN-EA: Graph Neural Network with Evolutionary Algorithm

Yun Huang;Chaobo Zhang;Junli Wang

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2022 | Conference Paper |

Word Order is Considerable: Contextual Position-aware Graph Neural Network for Text Classification

Yingying Tan;Junli Wang

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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Text Classification is a crucial and classical research branch in Natural Language Processing. Recently, Graph Neural Networks (GNNs) have been explored for this task since GNNs are proficient in handling non-Euclidean data with more complex relations. Despite the success, these graph-based methods have some limitations to model the word order information in the document. In this paper, to address...Show More

Word Order is Considerable: Contextual Position-aware Graph Neural Network for Text Classification

Yingying Tan;Junli Wang

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Net Learning

Junli Wang;Hongda Qi;Mingjian Guang;Chaobo Zhang;Chungang Yan;Changjun Jiang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2022 | Volume: 33, Issue: 12 | Journal Article |

Cited by: Papers (7)

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Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behaviora...Show More

Net Learning

Junli Wang;Hongda Qi;Mingjian Guang;Chaobo Zhang;Chungang Yan;Changjun Jiang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2022 | Volume: 33, Issue: 12 | Journal Article |

Multi-View Metrics Enhanced Heterogeneous Graph Neural Network for Extractive Summarization

Chaobo Zhang;Junli Wang;Hongda Qi;Chungang Yan;Changjun Jiang

2021 China Automation Congress (CAC)

Year: 2021 | Conference Paper |

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Currently, many works regarded extractive summarization as a binary classification problem. However, the metric of the summary classification is always too singular and cannot fully utilize sentence features. To address this issue, we propose a multi-view metrics enhanced heterogeneous graph neural network for extractive summarization (MHGS). Firstly, the text is modeled as a heterogeneous graph, ...Show More

Multi-View Metrics Enhanced Heterogeneous Graph Neural Network for Extractive Summarization

Chaobo Zhang;Junli Wang;Hongda Qi;Chungang Yan;Changjun Jiang

2021 China Automation Congress (CAC)

Year: 2021 | Conference Paper |

Benchmark Datasets for Stochastic Petri Net Learning

Mingjian Guang;Chungang Yan;Junli Wang;Hongda Qi;Changjun Jiang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Cited by: Papers (9)

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The existing Stochastic Petri Net (SPN) analysis methods are based on a series of steps, including generating the reachable graph, solving the state equation, etc. Unfortunately, these methods cannot perform performance analysis when the state equation has no unique solution. The end-to-end deep learning methods can build a mapping relationship from SPN to performance indicators, which avoids solv...Show More

Benchmark Datasets for Stochastic Petri Net Learning

Mingjian Guang;Chungang Yan;Junli Wang;Hongda Qi;Changjun Jiang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Face Image Inpainting With Evolutionary Generators

Chong Han;Junli Wang

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Cited by: Papers (21)

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Recently, deep learning has become a mainstream method of image inpainting. It can not only restore the image texture, obtain high-level abstract features of images, but also restore semantic images such as human face images. Among these methods, generative adversarial networks (GANs) using autoencoder as the generator have become the promising model for image inpainting. These models implement th...Show More

Face Image Inpainting With Evolutionary Generators

Chong Han;Junli Wang

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Text Generation Service Model Based on Truth-Guided SeqGAN

Yuxi Wu;Junli Wang

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (8)

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The Generative Adversarial Networks (GAN) has been successfully applied to the generation of text content such as poetry and speech, and it is a hot topic in the field of text generation. However, GAN has been facing the problem of training and convergence. For the generation model, this paper redefines on the loss function. The truth-guided method has been added to make the generated text closer ...Show More

Text Generation Service Model Based on Truth-Guided SeqGAN

Yuxi Wu;Junli Wang

Year: 2020 | Volume: 8 | Journal Article |

Optimal Web Service Selection based on Multi-Objective Genetic Algorithm

Junli Wang;Yubing Hou

2008 International Symposium on Computational Intelligence and Design

Year: 2008 | Conference Paper |

Cited by: Papers (25)

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Considering that there are three aspects of constrains in the service selection process, such as control structure within a composition plan, relationship between concrete services, and tradeoff among multiple QoS indexes, a QoS based optimal Web services selection method by multi-objective genetic algorithm is presented. First we design a chromosome coding method to represent a feasible service s...Show More

Optimal Web Service Selection based on Multi-Objective Genetic Algorithm

Junli Wang;Yubing Hou

2008 International Symposium on Computational Intelligence and Design

Year: 2008 | Volume: 1 | Conference Paper |

AI Planning for Web Service Automatic Composition Using Petri Nets

Zhijun Ding;Junli Wang;Hong Song

2007 11th International Conference on Computer Supported Cooperative Work in Design

Year: 2007 | Conference Paper |

Cited by: Papers (4)

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This paper presents an AI planning method for Web service automatic composition using Predicate/Transition (Pr/T) net model. First, based on Web service description of inputs, outputs, preconditions and effects in OWL-S specification, a Pr/T net model is constructed for a service composition plan. Then, this plan is effectively solved by a reachability algorithm of the Pr/T net and a corresponding...Show More

AI Planning for Web Service Automatic Composition Using Petri Nets

Zhijun Ding;Junli Wang;Hong Song

2007 11th International Conference on Computer Supported Cooperative Work in Design

Year: 2007 | Conference Paper |

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