ORCID Profile
0000-0001-5456-7035
Current Organisation
The Hong Kong Polytechnic University
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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 | Transport Engineering | Research, Science and Technology Policy | Business Information Systems | Global Information Systems | Data Structures |
ANZSRC Socio-Economic Objective (SEO)
Electronic Information Storage and Retrieval Services | Application Tools and System Utilities | Expanding Knowledge in the Information and Computing Sciences | Technological and Organisational Innovation | Road Passenger Movements (excl. Public Transport) | Expanding Knowledge in Technology
Related Links
Publications
LoG: a locally-global model for entity disambiguation
Publisher: Springer Science and Business Media LLC
Date: 22-10-2021
Diversified top- k route planning in road network
Publisher: Association for Computing Machinery (ACM)
Date: 07-2022
Abstract: Route planning is ubiquitous and has a profound impact on our daily life. However, the existing path algorithms tend to produce similar paths between similar OD (Origin-Destination) pairs because they optimize query results without considering their influence on the whole network, which further introduces congestions. Therefore, we investigate the problem of ersifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal. However, the current solutions all depend on the expensive graph traversal which is too slow to apply in practice. Therefore, we first propose an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration. After that, we e into the path relations and propose a path similarity computation method with constant complexity, and propose a pruning technique to improve efficiency. Finally, we provide the completeness and efficiency-oriented solutions to further accelerate the query answering. Evaluations on the real-life road networks demonstrate the effectiveness and efficiency of our algorithm over the state-of-the-art.
Temporal knowledge completion with context-aware embeddings
Publisher: Springer Science and Business Media LLC
Date: 03-2021
Multi-constraint shortest path using forest hop labeling
Publisher: Springer Science and Business Media LLC
Date: 11-08-2022
Dynamic Hub Labeling for Road Networks
Publisher: IEEE
Date: 04-2021
Distant supervision for neural relation extraction integrated with word attention and property features
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.NEUNET.2018.01.006
Abstract: Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each in idual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines.
Efficient Frequency-Based Randomization for Spatial Trajectories Under Differential Privacy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Efficient Constrained Shortest Path Query Answering with Forest Hop Labeling
Publisher: IEEE
Date: 04-2021
Path Query Processing Using Typical Snapshots in Dynamic Road Networks
Publisher: Springer International Publishing
Date: 2020
Value-wise ConvNet for Transformer models: An Infinite Time-aware Recommender System
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2003
Evidence Reasoning and Curriculum Learning for Document-level Relation Extraction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Short text understanding through lexical-semantic analysis
Publisher: IEEE
Date: 04-2015
Informed Multi-context Entity Alignment
Publisher: ACM
Date: 11-02-2022
An Efficient Approach for Spatial Trajectory Anonymization
Publisher: Springer International Publishing
Date: 2021
Efficient Batch Processing of Shortest Path Queries in Road Networks
Publisher: IEEE
Date: 06-2019
An efficient and effective approach for multi-fact extraction from text corpus
Publisher: Springer Science and Business Media LLC
Date: 14-12-2021
An Iterative Map-Trajectory Co-optimisation Framework Based on Map-Matching and Map Update
Publisher: Springer International Publishing
Date: 2019
A Survey and Quantitative Study on Map Inference Algorithms from GPS Trajectories
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
Publisher: ACM
Date: 17-10-2022
Summarizing Long-Form Document with Rich Discourse Information
Publisher: ACM
Date: 26-10-2021
Editorial of Special Issue of WISE 2019
Publisher: Springer Science and Business Media LLC
Date: 18-08-2020
Extracting Temporal Patterns from Large-Scale Text Corpus
Publisher: Springer International Publishing
Date: 2019
Batch Processing of Shortest Path Queries in Road Networks
Publisher: Springer International Publishing
Date: 2019
SIRCS: Slope-intercept-residual Compression by Correlation Sequencing for Multi-stream High Variation Data
Publisher: Springer International Publishing
Date: 2019
Diagnosis Ranking with Knowledge Graph Convolutional Networks
Publisher: Springer International Publishing
Date: 2021
TEA: Time-aware Entity Alignment in Knowledge Graphs
Publisher: ACM
Date: 30-04-2023
Typical Snapshots Selection for Shortest Path Query in Dynamic Road Networks
Publisher: Springer International Publishing
Date: 2020
A Survey on Map-Matching Algorithms
Publisher: Springer International Publishing
Date: 2020
SoulMate: Short-Text Author Linking through Multi-Aspect Temporal-Textual Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network with Spatial-Temporal Correlation Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Efficient Clue-Based Route Search on Road Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Identifying users' topical tasks in web search
Publisher: ACM
Date: 04-02-2013
A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Auto-regressive extractive summarization with replacement
Publisher: Springer Science and Business Media LLC
Date: 09-12-2022
DOI: 10.1007/S11280-022-01108-0
Abstract: Auto-regressive extractive summarization approaches determine sentence extraction probability conditioning on previous decisions by maintaining a partial summary representation. Despite its popularity, the framework has two main drawbacks: 1) the partial summary representation is irresolutely denoted by a weighted summation of all the processed sentences without any filtering, resulting in a noisy representation and degrading the effectiveness of extracting subsequent sentences 2) earlier sentences are biased towards a higher extraction probability due to the sequential nature of sequence tagging. To address these two problems, we propose the Auto-regressive Extractive Summarization with Replacement (AES-Rep), a novel auto-regressive extractive summarization model. In particular, the AES-Rep model consists of two main modules: the extraction decision module that determines whether a sentence should be extracted, and the replacement locater module that enables extracted deficient sentences to be replaced with latter sentences by comparing their expressiveness with respect to the main idea of the document. These modules update the partial summary with explicit actions using elaborated multidimensional guidance. We conduct extensive experiments on the benchmark CNN and DailyMail datasets. Experimental results show that AES-Rep can achieve better performance compared with various strong baselines in terms of multiple ROUGE metrics.
Trajectory-based spatiotemporal entity linking
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Preface
Publisher: Springer Science and Business Media LLC
Date: 07-2020
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects
Publisher: Elsevier BV
Date: 02-2021
Moving Object Linking Based on Historical Trace
Publisher: IEEE
Date: 04-2019
Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories
Publisher: IEEE
Date: 05-2022
Dependency-aware Self-training for Entity Alignment
Publisher: ACM
Date: 27-02-2023
A Pattern-Based Framework for Addressing Data Representational Inconsistency
Publisher: Springer International Publishing
Date: 2016
Exploiting Spatio-Temporal User Behaviors for User Linkage
Publisher: ACM
Date: 06-11-2017
Context-Aware Temporal Knowledge Graph Embedding
Publisher: Springer International Publishing
Date: 2019
Information Resilience: the nexus of responsible and agile approaches to information use
Publisher: Springer Science and Business Media LLC
Date: 16-01-2022
DOI: 10.1007/S00778-021-00720-2
Abstract: The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the erse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.
Efficient 2-Hop Labeling Maintenance in Dynamic Small-World Networks
Publisher: IEEE
Date: 04-2021
Entity Disambiguation Based on Parse Tree Neighbours on Graph Attention Network
Publisher: Springer International Publishing
Date: 2019
Operating systems support for process dynamic integrity measurement
Publisher: IEEE
Date: 09-2009
A Fine-grained and Noise-aware Method for Neural Relation Extraction
Publisher: ACM
Date: 03-11-2019
Trajectories know where map is wrong: an iterative framework for map-trajectory co-optimisation
Publisher: Springer Science and Business Media LLC
Date: 14-08-2019
Fast Query Decomposition for Batch Shortest Path Processing in Road Networks
Publisher: IEEE
Date: 04-2020
Minimal on-road time route scheduling on time-dependent graphs
Publisher: Association for Computing Machinery (ACM)
Date: 08-2017
Abstract: On time-dependent graphs, fastest path query is an important problem and has been well studied. It focuses on minimizing the total travel time (waiting time + on-road time) but does not allow waiting on any intermediate vertex if the FIFO property is applied. However, in practice, waiting on a vertex can reduce the time spent on the road (for ex le, resuming traveling after a traffic jam). In this paper, we study how to find a path with the minimal on-road time on time-dependent graphs by allowing waiting on some predefined parking vertices. The existing works are based on the following fact: the arrival time of a vertex v is determined by the arrival time of its in-neighbor u , which does not hold in our scenario since we also consider the waiting time on u if u allows waiting. Thus, determining the waiting time on each parking vertex to achieve the minimal on-road time becomes a big challenge, which further breaks FIFO property. To cope with this challenging problem, we propose two efficient algorithms using minimum on-road travel cost function to answer the query. The evaluations on multiple real-world time-dependent graphs show that the proposed algorithms are more accurate and efficient than the extensions of existing algorithms. In addition, the results further indicate, if the parking facilities are enabled in the route scheduling algorithms, the on-road time will reduce significantly compared to the fastest path algorithms.
Evidence-aware Document-level Relation Extraction
Publisher: ACM
Date: 17-10-2022
High-quality Task Division for Large-scale Entity Alignment
Publisher: ACM
Date: 17-10-2022
Decomposition-Based Approximation of Time Series Data with Max-Error Guarantees
Publisher: Springer International Publishing
Date: 2017
A survey and experimental study on privacy-preserving trajectory data publishing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2021
DOI: 10.36227/TECHRXIV.13655597
Abstract: Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an in idual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata. /
FHL-cube
Publisher: Association for Computing Machinery (ACM)
Date: 07-2022
Abstract: Multi-Constraint Shortest Path ( MCSP ) generalizes the classic shortest path from single to multiple criteria such that more personalized needs can be satisfied. However, MCSP query is essentially a high-dimensional skyline problem and thus time-consuming to answer. Although the current Forest Hop Labeling (FHL) index can answer MCSP efficiently, it takes a long time to construct and lacks the flexibility to handle arbitrary criteria combinations. In this paper, we propose a skyline-cube-based FHL index that can handle the flexible MCSP efficiently. Firstly, we analyze the relation between low and high-dimensional skyline paths theoretically and use a cube to organize them hierarchically. After that, we propose methods to derive the high-dimensional path from the lower ones, which can adapt to the flexible scenario naturally and reduce the expensive high dimensional path concatenation. Then we introduce efficient methods for both single and multi-hop cube concatenations and propose pruning methods to further alleviate the computation. Finally, we improve the FHL structure with lower height for faster construction and query. Experiments on real-life road networks demonstrate the superiority of our method over the state-of-the-art.
Route Reconstruction Using Low-Quality Bluetooth Readings
Publisher: ACM
Date: 03-11-2020
Discovering Correlations between Sparse Features in Distant Supervision for Relation Extraction
Publisher: ACM
Date: 30-01-2019
Understand Short Texts by Harvesting and Analyzing Semantic Knowledge
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Temporal knowledge extraction from large-scale text corpus
Publisher: Springer Science and Business Media LLC
Date: 02-09-2020
HD-GDD: high dimensional graph dominance drawing approach for reachability query
Publisher: Springer Science and Business Media LLC
Date: 03-09-2016
Online User Representation Learning Across Heterogeneous Social Networks
Publisher: ACM
Date: 18-07-2019
Efficient Clue-Based Route Search on Road Networks (Extended Abstract)
Publisher: IEEE
Date: 04-2018
Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory
Publisher: Springer Science and Business Media LLC
Date: 21-03-2018
Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis
Publisher: SAGE Publications
Date: 30-01-2020
Abstract: Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the s le mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the s le median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the s le mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the s le mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data.
Self-supervised Learning Approach for Extracting Citation Information on the Web
Publisher: Springer Berlin Heidelberg
Date: 2012
Few-Shot Human Activity Recognition on Noisy Wearable Sensor Data
Publisher: Springer International Publishing
Date: 2020
Stream Processing of Shortest Path Query in Dynamic Road Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Microblog Entity Linking with Social Temporal Context
Publisher: ACM
Date: 27-05-2015
A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2021
DOI: 10.36227/TECHRXIV.13655597.V1
Abstract: Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an in idual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata. /
Related Organisations
Related Funding Activities
Linkage Projects - Grant ID: LP180100018
Start Date: 08-2019
End Date: 12-2024
Amount: $330,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP200103650
Start Date: 2020
End Date: 12-2024
Amount: $493,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Early Career Researcher Award - Grant ID: DE210100160
Start Date: 2021
End Date: 01-2023
Amount: $423,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC200100022
Start Date: 07-2021
End Date: 07-2026
Amount: $4,883,406.00
Funder: Australian Research Council
View Funded Activity