GUODONG LONG

Federated Learning for Open Banking

Publisher: Springer International Publishing

Date: 2020

DOI: 10.1007/978-3-030-63076-8_17

A Comprehensive Survey on Graph Neural Networks

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/TNNLS.2020.2978386

Exploring explicit and implicit graph learning for multivariate time series imputation

Publisher: Elsevier BV

Date: 2024

DOI: 10.1016/J.ENGAPPAI.2023.107217

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TNNLS.2022.3186103

Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TKDE.2022.3186016

Supervised Learning for Suicidal Ideation Detection in Online User Content

Publisher: Hindawi Limited

Date: 09-09-2018

DOI: 10.1155/2018/6157249

Abstract: Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal in iduals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter.

Salient Subsequence Learning for Time Series Clustering

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 09-2019

DOI: 10.1109/TPAMI.2018.2847699

Multi-center federated learning: clients clustering for better personalization

Publisher: Springer Science and Business Media LLC

Date: 09-06-2022

DOI: 10.1007/S11280-022-01046-X

Abstract: Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: ingxuts/multi-center-fed-learning ).

FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TBDATA.2022.3216747

Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 12-2019

DOI: 10.1109/TIE.2018.2873526

MHieR-encoder: Modelling the high-frequency changes across stocks

Publisher: Elsevier BV

Date: 07-2021

DOI: 10.1016/J.KNOSYS.2021.107092

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2021

DOI: 10.1109/TCSS.2020.3021467

Detecting Suicidal Ideation with Data Protection in Online Communities

Publisher: Springer International Publishing

Date: 2019

DOI: 10.1007/978-3-030-18590-9_17

UnifieR: A Unified Retriever for Large-Scale Retrieval

Publisher: ACM

Date: 04-08-2023

DOI: 10.1145/3580305.3599927

Decentralized Knowledge Acquisition for Mobile Internet Applications

Publisher: Springer Science and Business Media LLC

Date: 13-03-2020

DOI: 10.1007/S11280-019-00775-W

Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2022

DOI: 10.1109/TKDE.2020.3004939

Learning Graph Embedding with Adversarial Training Methods

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2020

DOI: 10.1109/TCYB.2019.2932096

University Of Technology Sydney

Location: Australia

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Linkage Projects - Grant ID: LP180100654

Start Date: 12-2019

End Date: 12-2023

Amount: $600,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP220100769

Start Date: 2022

End Date: 12-2025

Amount: $345,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP160100630

Start Date: 07-2016

End Date: 07-2020

Amount: $520,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP150100671

Start Date: 06-2016

End Date: 12-2019

Amount: $550,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP220100768

Start Date: 2022

End Date: 12-2025

Amount: $345,000.00

Funder: Australian Research Council