ORCID Profile
0000-0002-4149-839X
Current Organisations
Charité Universitätsmedizin Berlin
,
CSIRO
,
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Systems | Database Management | Interorganisational Information Systems and Web Services | Recommender systems | Data mining and knowledge discovery | Data engineering and data science | Pattern Recognition and Data Mining | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) | Electronic Information Storage and Retrieval Services | Expanding Knowledge in the Information and Computing Sciences |
Related Links
Publications
ALDA: An Adaptive Layout Design Assistant for Diverse Posters throughout the Design Process
Publisher: ACM
Date: 26-10-2023
Learning to Recommend With Multiple Cascading Behaviors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Novel artificial bee colony algorithms for QoS-aware service selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Generalizable sample-efficient siamese autoencoder for tinnitus diagnosis in listeners with subjective tinnitus
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Graph Neural Network for Robust Public Transit Demand Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
New insights into the genetic etiology of Alzheimer’s disease and related dementias
Publisher: Springer Science and Business Media LLC
Date: 04-2022
DOI: 10.1038/S41588-022-01024-Z
Abstract: Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.
Cure-GNN: A Robust Curvature-Enhanced Graph Neural Network Against Adversarial Attacks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
Publisher: ACM
Date: 27-02-2023
Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
RLTIR: Activity-Based Interactive Person Identification via Reinforcement Learning Tree
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2022
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Uncertainty Estimation With Neural Processes for Meta-Continual Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
When Object Detection Meets Knowledge Distillation: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Unraveling Metric Vector Spaces With Factorization for Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender
Publisher: ACM
Date: 14-09-2023
DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial Embeddings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity
Publisher: Association for Computing Machinery (ACM)
Date: 13-04-2023
DOI: 10.1145/3574157
Abstract: Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation ( WBAIR ) is to recommend auxiliary items based on the primary items in the basket. Such a task exists in many real-life scenarios. Unlike the associations between items that can be transmitted in both directions, primary and auxiliary relationships are unidirectional. Then, the suitable matching patterns between primary and auxiliary items cannot be explored by traditional directionless methods. Therefore, we design the Matc4Rec algorithm to integrate the primary and auxiliary factors, and finally recommend items that not only match the interests of users but also satisfy the primary and auxiliary relationships between items. Specifically, we capture the pattern from three aspects: matchability within-basket , matchability between baskets , and ubiquity . By exploiting this pattern, the designed algorithm not only achieves good results on real-world datasets but also improves the interpretability of recommendations. As a result, we can know which commodities are suitable as auxiliary items. The experiment results demonstrate that our algorithm can also alleviate the cold start problem.
Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Intent-Aware Interactive Internet of Things for Enhanced Collaborative Ambient Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
EAGCN: An Efficient Adaptive Graph Convolutional Network for Item Recommendation in Social Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Diversity-Boosted Generalization-Specialization Balancing for Zero-Shot Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Motor Imagery Classification via Temporal Attention Cues of Graph Embedded EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
NP-SSL: A Modular and Extensible Self-supervised Learning Library with Neural Processes
Publisher: ACM
Date: 21-10-2023
Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Publisher: ACM
Date: 21-10-2023
Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2026
Inferring User Profile Attributes From Multidimensional Mobile Phone Sensory Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information
Publisher: Association for Computing Machinery (ACM)
Date: 05-02-2018
DOI: 10.1145/3134438
Abstract: Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi-dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.
Preventing Sensitive Information Leakage From Mobile Sensor Signals via Integrative Transformation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Zero-Shot Camouflaged Object Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
DeepKey
Publisher: Association for Computing Machinery (ACM)
Date: 31-05-2020
DOI: 10.1145/3393619
Abstract: Biometric authentication involves various technologies to identify in iduals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.
A Learning-Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases
Publisher: Association for Computing Machinery (ACM)
Date: 05-02-2018
DOI: 10.1145/3155806
Abstract: Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications as they provide facts and relationships that can be automatically understood by machines. Curated knowledge bases usually use Resource Description Framework (RDF) as the data representation model. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has problems like network instability and latency, which affect the query efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL queries to vector representation that are fed into machine-learning algorithms. A time-aware smoothing-based method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive experiments performed on real-world queries showcase the effectiveness of our approach, which outperforms the state-of-the-art work in terms of the overall querying speed.
A Graph-Based Hierarchical Attention Model for Movement Intention Detection from EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Guided image-to-image translation by discriminator-generator communication
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation
Publisher: ACM
Date: 27-02-2023
Unveiling correlations via mining human-thing interactions in the web of things
Publisher: Association for Computing Machinery (ACM)
Date: 30-06-2017
DOI: 10.1145/3035967
Abstract: With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T , a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal, and spatial information. We model this various information using two graphs, namely a spatio-temporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.
Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease
Publisher: Springer Science and Business Media LLC
Date: 03-07-2202
DOI: 10.1038/NG.803
Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.WATRES.2022.119349
Abstract: Membrane Capacitive Deionization (MCDI) is a promising electrochemical technique for water desalination. Previous studies have confirrmed the effectiveness of MCDI in removing contaminants from brackish groundwaters, especially in remote areas where electricity is scarce. However, as with other water treatment technologies, performance deterioration of the MCDI system still occurs, hindering the stability of long-term operation. Herein, a machine learning (ML) modelling framework and various ML models were developed to (i) investigate the performance deterioration due particularly to insufficient charging/discharging of the electrode caused by accumulation of ions and electrode scaling and (ii) optimise MCDI operating parameters such that the impacts of these deleterious effects on unit performance were minimized. The ML models developed in this work exhibited a prediction accuracy of cycle time with average mean absolute percentage error (MAPE) values of 16.82% and 16.09% after 30-fold cross validation for Random Forest (RF) and Multilayer Perceptron (MLP) models respectively. The pre-trained ML model predicted different declining trends of water production for two different operating conditions and provided corresponding recommendations on frequencies of chemical cleaning. A case study on the adjustment of operating parameters using the results suggested by the optimization ML model was conducted. The model validation results showed that the overall water production and water recovery of the system using the cycle-based optimized process control parameters (SCN 1) exceeds the MCDI system performance under three fixed parameter settings that were used at each stage of SCN 1 by 1.78% to 4.48% and 2.95% to 9.46%, respectively. Permutation-based and Shapley additive explanation (SHAP) coefficients were also employed for variable importance (VIMP) analysis to uncover the "black-box" nature of the ML models and to better understand the various features' contributions to overall MCDI system performance.
Causal disentangled variational auto-encoder for preference understanding in recommendation
Publisher: ACM
Date: 18-07-2023
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease
Publisher: Springer Science and Business Media LLC
Date: 06-09-2009
DOI: 10.1038/NG.440
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Publisher: ACM
Date: 18-07-2023
A semisupervised recurrent convolutional attention model for human activity recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Agglomerative Neural Networks for Multiview Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease
Publisher: Public Library of Science (PLoS)
Date: 15-11-2010
On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Related Organisations
Charité Universitätsmedizin Berlin Campus Benjamin Franklin
Location: Germany
Related Funding Activities
Discovery Projects - Grant ID: DP230100233
Start Date: 05-2023
End Date: 04-2026
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP190100140
Start Date: 2021
End Date: 12-2023
Amount: $330,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Early Career Researcher Award - Grant ID: DE160100509
Start Date: 2016
End Date: 03-2020
Amount: $375,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP210101723
Start Date: 04-2021
End Date: 03-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Start Date: 05-2018
End Date: 01-2022
Amount: $348,026.00
Funder: Australian Research Council
View Funded Activity