Outputs (19)

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2022). Ego-graph replay based continual learning for misinformation engagement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN) (01-08). https://doi.org/10.1109/IJCNN55064.2022.9892557

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation detection systems to classify if a post is misinformation. Instead of pos... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., Mcconville, R., Hong, J., & Liu, W. (in press). Ego-graph replay based continual learning for misinformation engagement prediction. . https://doi.org/10.48550/arXiv.2207.12105

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinfor-mation detection systems to classify if a post is misinformation. Instead of po... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Social influence prediction with train and test time augmentation for graph neural networks (2021)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2021). Social influence prediction with train and test time augmentation for graph neural networks. In Proceedings of the International Joint Conference on Neural Networks 2021 (IJCNN 2021)https://doi.org/10.1109/IJCNN52387.2021.9533437

Data augmentation has been widely used in machine learning for natural language processing and computer vision tasks to improve model performance. However, little research has studied data augmentation on graph neural networks, particularly using aug... Read More about Social influence prediction with train and test time augmentation for graph neural networks.

Towards establishing a 'cooperation' measure for coupled movement in close-proximity human-robot interaction (2020)
Presentation / Conference
Camilleri, A., Hong, J., Dogramadzi, S., & Caleb-Solly, P. (2020, May). Towards establishing a 'cooperation' measure for coupled movement in close-proximity human-robot interaction. Presented at ICRA 2020

To achieve safe close-proximity human-robot interaction , particularly for physically assitive tasks, robot motion planning needs to recognize and adapt to the behaviour of humans in the long-term. Generally, motion prediction with probabilistic conf... Read More about Towards establishing a 'cooperation' measure for coupled movement in close-proximity human-robot interaction.

Social network influence ranking via embedding network interactions for user recommendation (2020)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2020). Social network influence ranking via embedding network interactions for user recommendation. In WWW '20: Companion Proceedings of the Web Conference 2020 (379-384). https://doi.org/10.1145/3366424.3383299

Within social networks user influence may be modelled based on user interactions. Further, it is typical to recommend users to others. What is the role of user influence in user recommendation In this paper, we first propose to use a node embedding a... Read More about Social network influence ranking via embedding network interactions for user recommendation.

Privacy preserving record linkage in the presence of missing values (2017)
Journal Article
Chi, Y., Hong, J., Jurek, A., Liu, W., & O'Reilly, D. (2017). Privacy preserving record linkage in the presence of missing values. Information Systems, 71, 199-210. https://doi.org/10.1016/j.is.2017.07.001

© 2017 The problem of record linkage is to identify records from two datasets, which refer to the same entities (e.g. patients). A particular issue of record linkage is the presence of missing values in records, which has not been fully addressed. An... Read More about Privacy preserving record linkage in the presence of missing values.

A novel ensemble learning approach to unsupervised record linkage (2017)
Journal Article
Jurek, A., Hong, J., Chi, Y., & Liu, W. (2017). A novel ensemble learning approach to unsupervised record linkage. Information Systems, 71, 40-54. https://doi.org/10.1016/j.is.2017.06.006

© 2017 Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of r... Read More about A novel ensemble learning approach to unsupervised record linkage.

The event calculus in probabilistic logic programming with annotated disjunctions (2017)
Presentation / Conference
McAreavey, K., Bauters, K., Liu, W., & Hong, J. (2017, May). The event calculus in probabilistic logic programming with annotated disjunctions. Paper presented at AAMAS 2017 - the 16th International Conference on Autonomous Agents and Multiagent Systems, Sao Paulo, Brazil

We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction. This is the first extension of the event calculus capable of h... Read More about The event calculus in probabilistic logic programming with annotated disjunctions.

Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments (2017)
Journal Article
Bauters, K., McAreavey, K., Liu, W., Hong, J., Godo, L., & Sierra, C. (2017). Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments. Journal of Artificial Intelligence Research, 58, 731-755. https://doi.org/10.1613/jair.5287

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents – or components – each one modelled based on... Read More about Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments.

Tweet for behavior change: Using social media for the dissemination of public health messages (2017)
Journal Article
Gough, A., Hunter, R. F., Ajao, O., Jurek, A., McKeown, G., Hong, J., …Kee, F. (2017). Tweet for behavior change: Using social media for the dissemination of public health messages. JMIR Public Health and Surveillance, 3(1), https://doi.org/10.2196/publichealth.6313

Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change. Objective: Th... Read More about Tweet for behavior change: Using social media for the dissemination of public health messages.