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Professor Xue Li

Professor

School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
xueli@eecs.uq.edu.au
+61 7 336 54044

Overview

Qualifications

  • Bachelor of Sciences, Chongqing
  • Master of Sciences, The University of Queensland
  • PhD, Queensland University of Technology

Publications

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Grants

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Supervision

  • Weighted Ensembles for Different machine learning model that support non-data-sharing / vertical partition

    Doctor Philosophy

  • Context-aware Representation Learning for Code Analysis

    Doctor Philosophy

  • Information Extraction from Large-scale Low-quality Data

    Doctor Philosophy

View all Supervision

Available Projects

  • Analytical Queries on Big Data

    Description:

    Traditional database queries are used to search for facts from structured database such as RDB (Relational Databases) to satisfy user search conditions. With big data currently available in many ways such as structured and unstructured multi-modalities, user queries should be constructed not only for searching facts, but also for searching patterns, emerging events, and outliers from available big data. This PhD research is to propose a new type of query language that can query on analytic results , to satisfy user requirements for informed decision support. In order to make such a language to be implementable on general big dataset, this PhD research will also define and design a framework that can answer declarative analytic queries by a data-driven approach to apply transparent machine learning algorithms in order to discover unexpected patterns, emerging trends, various correlations from big data. The challenges of this research will be on how to use an end-to-end black-box mechanism to provide big data analytic services to make big data available for general queries beyond classical data warehousing technologies.

    Background:

    In classical DSS systems based on data warehouses and OLAP operations, the queries such as Canned and Continuous Queries would not involve procedural operations that can reflect the dynamic parameters of queries. The operators such as Role-Up, Drill-Down, Slice/Dice, Cube, Pivoting etc, cannot reflect the context of the query objects in their business context. This PhD research will try to introduce more flexible analytical data manipulation operations based on machine learning algorithms that can provide end-to-end queries for strategic DSS with baselines.

  • Privacy Preservation for Sharing Distributed Big Data

    Description:

    Predictive data analytics usually involves Big Data that is distributed in different locations and owned by different organizations, such as the Taxation Office Data, Boarder-Control Customs Data, Crime-Stop Police Data, and Social Security Data. The organizations are legally responsible for the privacy preservation of their data which is of highly risk and sensitive. However, this should not prevent the sharing of those de-identified, privacy preserved data sets for the predictions of pending social-economic events, emerging trends, patterns of relationships, or correlations among entities. Currently, there are many algorithms that can preserve privacy for computing data from multiple owners, such as SMC (secure multi-party computation), Differential Privacy algorithms. However, the predictive tasks often require to use all original raw data for the learning. This would involve the individual organizations to conduct local learning tasks and contribute to global learning with their local models, instead of their sensitive data. Federated learning therefore coming to being as a promising and useful approach to learn from individual datasets and producing a general model for the required predicting tasks. This project is to research on the Federated Learning algorithms that can deal with large distributed, sensitive datasets and derive a computational model to predict some pre-defined tasks. The challenges of this project would be the following three issues in one solution, i.e., data shareability, data privacy, and computational utility.

    Key Terms: Federated Learning, Deep Learning, Distributed Database Technology, Privacy Preservation, Mathematical Modelling, Data Shareability, Computational Utility

  • The First Principle AI (FAI) Research

    Description:

    Artificial Intelligence (AI) applications are mostly based on the first-order thinking that is reasoning based on deduction, abduction, induction, or eduction. In this way, AI is limited and unable to discover the First Principles such as those in sciences and complex Math Equations, and laws in Physics and Chemistry. However, this should not prevent AI to be used together with the First Principles in those discovery projects. This research is to design an architecture of AI Application platform that can use First Principle in AI to speed up the human trial-and-error process of experiments, to use First Principle in a more intelligent way to converge an optimization process which has a large number of iterations faster and scalable for human's research problems.

View all Available Projects

Publications

Book

Book Chapter

  • Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction

    Subpaiboonkit, Sitthichoke, Li, Xue, Zhao, Xin and Zuccon, Guido (2022). Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction. Advanced Data Mining and Applications. (pp. 53-66) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-22064-7_5

  • E-nose pattern recognition and drift compensation methods

    Maskari, Sanad Al and Li, Xue (2018). E-nose pattern recognition and drift compensation methods. Electronic nose technologies and advances in machine olfaction. (pp. 38-57) edited by Yousif Albastaki and Fatema Albalooshi. Hershey, PA, United States: IGI Global. doi: 10.4018/978-1-5225-3862-2.ch003

  • Building Entity Graphs for the Web of Things Management

    Yao, Lina, Sheng, Quan Z., Ngu, Anne H.H., Li, Xue, Benatallah, Boualem and Wang, Xianzhi (2017). Building Entity Graphs for the Web of Things Management. Managing the Web of Things: Linking the Real World to the Web. (pp. 275-303) Cambridge, MA, United States: Elsevier . doi: 10.1016/B978-0-12-809764-9.00013-5

  • Cyberbullying prevalence - medium, motive and reaction

    Nahar, Vinita, Li, Xue and Pang, Chaoyi (2014). Cyberbullying prevalence - medium, motive and reaction. Handbook on bullying: Prevalence, psychological impacts and intervention strategies. (pp. 259-269) Hauppauge, NY United States: Nova Science Publishers.

  • Cyberbullying validation

    Nahar, Vinita, Li, Xue and Pang, Chaoyi (2014). Cyberbullying validation. Handbook on bullying: prevalence, psychological impacts and intervention strategies. (pp. 233-257) edited by Phoebe Triggs. New York, NY, United States: Nova Science Publishers.

  • Event management of RFID data streams: Fast moving consumer goods supply chains

    Mo, John P. T. and Li, Xue (2010). Event management of RFID data streams: Fast moving consumer goods supply chains. Unique radio innovation for the 21st Century: Building scalable and global RFID networks. (pp. 89-109) edited by Damith C. Ranasinghe, Quan Z. Sheng and Sherali Zeadally. Berlin, Heidelberg: Springer-Verlag. doi: 10.1007/978-3-642-03462-6

  • Database clustering methods

    Li, Xue (2009). Database clustering methods. Encyclopedia of Database Systems. (pp. 699-700) United States: Springer. doi: 10.1007/978-0-387-39940-9_550

  • Incremental learning for interactive e-mail filtering

    Ding-Yi Chen, Xue Li, Zhao Yang Dong and Xia Chen (2009). Incremental learning for interactive e-mail filtering. Agent technologies and web engineering: Applications and systems. (pp. 134-152) edited by David Rine and Ghazi Alkhatib. Hershey, PA, U.S.A.: IGI Global. doi: 10.4018/978-1-60566-618-1.ch008

  • K-Means and K-Medoids

    Li, Xue (2009). K-Means and K-Medoids. Encyclopedia of Database Systems. (pp. 1588-1589) edited by Ling Liu and M. Tamer Özsu. United States: Springer. doi: 10.1007/978-0-387-39940-9_545

  • Intelligent Business Portals

    Li, X. (2003). Intelligent Business Portals. Architectural Issues of Web-Enabled Electronic Business. (pp. 40-51) edited by N. Shi and V. Murthy. London: Idea Group Publishing.

Journal Article

Conference Publication

  • Fast Spectral Clustering of Multi-Relational Data

    Ling, Ping, Rong, Xiangsheng and Li, Xue (2022). Fast Spectral Clustering of Multi-Relational Data. 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, 23-25 September 2022. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICISCAE55891.2022.9927559

  • Content Adaptive Compressed Screen Content Video Quality Enhancement

    Liu, Yu, Ye, Mao, Gao, Yanbo, Li, Shuai, Zhao, Yu and Li, Xue (2022). Content Adaptive Compressed Screen Content Video Quality Enhancement. 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan, 18-22 July 2022. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICME52920.2022.9859602

  • Integrating Dependency Tree into Self-Attention for Sentence Representation

    Ma, Junhua, Li, Jiajun, Liu, Yuxuan, Zhou, Shangbo and Li, Xue (2022). Integrating Dependency Tree into Self-Attention for Sentence Representation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 May 2022. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICASSP43922.2022.9747221

  • Source-Style Transferred Mean Teacher for Source-data Free Object Detection

    Zhang, Dan, Ye, Mao, Xiong, Lin, Li, Shuaifeng and Li, Xue (2021). Source-Style Transferred Mean Teacher for Source-data Free Object Detection. MMAsia '21: ACM Multimedia Asia, Gold Coast, QLD Australia, 1 - 3 December 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3469877.3490584

  • Multi-hop reading on memory neural network with selective coverage for medication recommendation

    Wang, Yanda, Chen, Weitong, Pi, Dechang, Yue, Lin, Xu, Miao and Li, Xue (2021). Multi-hop reading on memory neural network with selective coverage for medication recommendation. ACM International Conference on Information & Knowledge Management, Virtual Event, 1-5 November 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3459637.3482278

  • Privacy-preserving gradient descent for distributed genome-wide analysis

    Zhang, Yanjun, Bai, Guangdong, Li, Xue, Curtis, Caitlin, Chen, Chen and Ko, Ryan K. L. (2021). Privacy-preserving gradient descent for distributed genome-wide analysis. ESORICS 2021 - 26th European Symposium on Research in Computer Security, Darmstadt, Germany, 4–8 October, 2021. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-88428-4_20

  • Alignment-Free Video Compression Artifact Reduction

    Luo, Dengyan, Ye, Mao, Chen, Shengjie and Li, Xue (2021). Alignment-Free Video Compression Artifact Reduction. IEEE International Conference on Visual Communications and Image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources, Munich, Germany, 5-8 December 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/VCIP53242.2021.9675384

  • Welcome from the ICBK 2021 Chairs

    Chen, Lei, Manjon, Baltasar Fernandez, Gong, Zhiguo, Li, Xue, Öǧüdücü, Sule Gündüz and Wu, Xindong (2021). Welcome from the ICBK 2021 Chairs. 12th IEEE International Conference on Big Knowledge, ICBK 2021, Auckland, New Zealand, 7-8 December 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICKG52313.2021.00005

  • Adaptive two-dimensional embedded image clustering

    Li, Zhihui, Yao, Lina, Wang, Sen, Kanhere, Salil, Li, Xue and Zhang, Huaxiang (2020). Adaptive two-dimensional embedded image clustering. The Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, NY, United States, 7-12 February 2020. Palo Alto, CA, United States: AAAI Press.

  • PrivColl: practical privacy-preserving collaborative machine learning

    Zhang, Yanjun, Bai, Guangdong, Li, Xue, Curtis, Caitlin, Chen, Chen and Ko, Ryan K. L. (2020). PrivColl: practical privacy-preserving collaborative machine learning. European Symposium on Research in Computer Security, Guildford, United Kingdom, 14-18 September 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-58951-6_20

  • Sequence-aware factorization machines for temporal predictive analytics

    Chen, Tong, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Peng, Wen-Chih, Li, Xue and Zhou, Xiaofang (2020). Sequence-aware factorization machines for temporal predictive analytics. 2020 IEEE 36th International Conference on Data Engineering, Dallas, Texas, United States, 20-24 April 2020. LOS ALAMITOS: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00125

  • Identification of functional connectivity features in depression subtypes using a data-driven approach

    Li, Xingjuan, Burnham, Samantha, Fripp, Jurgen, Li, Yu, Li, Xue, Fazlollahi, Amir and Bourgeat, Pierrick (2019). Identification of functional connectivity features in depression subtypes using a data-driven approach. International Workshop on Graph Learning in Medical Imaging (GLMI 2019), Shenzhen, China, 17-19 October 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-35817-4_12

  • DBRec: dual-bridging recommendation via discovering latent groups

    Ma, Jingwei, Wen, Jiahui, Zhong, Mingyang, Liu, Liangchen, Li, Chaojie, Chen, Weitong, Yang, Yin, Tu, Hongkui and Li, Xue (2019). DBRec: dual-bridging recommendation via discovering latent groups. CIKM '19, Beijing, China, 3-7 November 2019. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3357384.3357892

  • Personalised medicine in critical care using Bayesian reinforcement learning

    Utomo, Chandra Prasetyo, Kurniawati, Hanna, Li, Xue and Pokharel, Suresh (2019). Personalised medicine in critical care using Bayesian reinforcement learning. ADMA 2019: Advanced Data Mining and Applications, Dalian, China, 21–23 November, 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-35231-8_47

  • AIR: Attentional intention-aware recommender systems

    Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Yan, Rui, Nguyen, Quoc Viet Hung and Li, Xue (2019). AIR: Attentional intention-aware recommender systems. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00035

  • Causality discovery with domain knowledge for drug-drug interactions discovery

    Subpaiboonkit, Sitthichoke, Li, Xue, Zhao, Xin, Scells, Harrisen and Zuccon, Guido (2019). Causality discovery with domain knowledge for drug-drug interactions discovery. 15th International Conference on Advanced Data Mining and Applications, ADMA 2019, Dalian, China, 21–23 November 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-35231-8_46

  • Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks

    Zhang, Yanjun, Zhao, Xin, Li, Xue, Zhong, Mingyang, Curtis, Caitlin and Chen, Chen (2019). Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks. Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February 2019. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3289600.3290983

  • Exploiting centrality information with graph convolutions for network representation learning

    Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Peng, Wen-Chih and Li, Xue (2019). Exploiting centrality information with graph convolutions for network representation learning. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00059

  • Learning private neural language modeling with attentive aggregation

    Ji, Shaoxiong, Pan, Shirui, Long, Guodong, Li, Xue, Jiang, Jing and Huang, Zi (2019). Learning private neural language modeling with attentive aggregation. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IJCNN.2019.8852464

  • RecKGC: Integrating Recommendation with Knowledge Graph Completion

    Ma, Jingwei, Zhong, Mingyang, Wen, Jiahui, Chen, Weitong, Zhou, Xiaofang and Li, Xue (2019). RecKGC: Integrating Recommendation with Knowledge Graph Completion. 15th International Conference, ADMA 2019, Dalian, China, 21–23 November 2019. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-35231-8_18

  • A combined feature approach for speaker segmentation using convolution neural network

    Zhong, Jiang, Zhang, Pan and Li, Xue (2018). A combined feature approach for speaker segmentation using convolution neural network. 18th Pacific-Rim Conference on Multimedia, PCM 2017, Harbin, China, 28-29 September 2017. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-77383-4_54

  • Automated explanations of user-expected trends for aggregate queries

    Ibrahim, Ibrahim A., Li, Xue, Zhao, Xin, Maskari, Sanad Al, Albarrak, Abdullah M. and Zhang, Yanjun (2018). Automated explanations of user-expected trends for aggregate queries. Pacific-Asia Conference, PAKDD, Melbourne, VIC, Australia, 3-6 June 2018. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-93034-3_48

  • Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

    Chen, Tong, Li, Xue, Yin, Hongzhi and Zhang, Jun (2018). Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC, Australia, 3 June 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_4

  • Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit

    Chen, Weitong, Wang, Sen, Long, Guodong, Yao, Lina, Sheng, Quan Z. and Li, Xue (2018). Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit. 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, 17 - 20 November 2018. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM.2018.00111

  • EEG-based motion intention recognition via multi-task RNNs

    Chen, Weitong, Wang, Sen, Zhang, Xiang, Yao, Lina, Yue, Lin, Qian, Buyue and Li, Xue (2018). EEG-based motion intention recognition via multi-task RNNs. 2018 SIAM International Conference on Data Mining, SDM 2018, San Diego, CA, United States, 3-5 May 2018. Society for Industrial and Applied Mathematics Publications. doi: 10.1137/1.9781611975321.32

  • Feature extraction for smart sensing using multi-perspectives transformation

    Al-Maskari, Sanad, Ibrahim, Ibrahim A., Li, Xue, Abusham, Eimad and Almars, Abdulqader (2018). Feature extraction for smart sensing using multi-perspectives transformation. Australasian Database Conference, Gold Coast, QLD, Australia, 24-27 May 2018. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-92013-9_19

  • Interesting recommendations based on hierarchical visualizations of medical data

    Ibrahim, Ibrahim A., Almars, Abdulqader M., Pokharel, Suresh, Zhao, Xin and Li, Xue (2018). Interesting recommendations based on hierarchical visualizations of medical data. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC Australia, June 3 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-04503-6_6

  • Learning Concept Hierarchy from Short Texts Using Context Coherence

    Almars, Abdulqader, Li, Xue, Ibrahim, Ibrahim A. and Zhao, Xin (2018). Learning Concept Hierarchy from Short Texts Using Context Coherence. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, 12 - 15 November 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-02922-7_22

  • PME: projected metric embedding on heterogeneous networks for link prediction

    Chen, Hongxu, Wang, Hao, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Wang, Weiqing and Li, Xue (2018). PME: projected metric embedding on heterogeneous networks for link prediction. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3219986

  • Rumor detection via recurrent neural networks: a case study on adaptivity with varied data compositions

    Chen, Tong, Chen, Hongxu and Li, Xue (2018). Rumor detection via recurrent neural networks: a case study on adaptivity with varied data compositions. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC, Australia, 3 June 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_10

  • Similarity computing on electronic health records

    Pokharel, Suresh, Li, Xue, Zhao, Zin, Adhikari, Anoj and Li, Yu (2018). Similarity computing on electronic health records. Pacific Asia Conference on Information Systems, Yokohama, Japan, 26 - 30 June 2018. Pacific Asia Conference on Information Systems.

  • Streaming graph partitioning for large graphs with limited memory

    Li, Qi, Zhong, Jiang, Zheng, Linjiang and Li, Xue (2018). Streaming graph partitioning for large graphs with limited memory. 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017, Guangzhou, China, 12-15 December 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ISPA/IUCC.2017.00193

  • TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction

    Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wu, Lin, Wang, Hao, Zhou, Xiaofang and Li, Xue (2018). TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, 17-20 November 2018. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICDM.2018.00020

  • Treatment recommendation in critical care: a scalable and interpretable approach in partially observable health states

    Utomo, Chandra Prasetyo, Li, Xue and Chen, Weitong (2018). Treatment recommendation in critical care: a scalable and interpretable approach in partially observable health states. 39th International Conference on Information Systems, ICIS 2018, San Francisco, CA United States, 13-16 December 2018. Atlanta, GA United States: Association for Information Systems.

  • Vertical and sequential sentiment analysis of micro-blog topic

    Wan, Shuo, Li, Bohan, Zhang, Anman, Wang, Kai and Li, Xue (2018). Vertical and sequential sentiment analysis of micro-blog topic. 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, Nanjing, China, 16-18 November 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-05090-0_30

  • Advancing public health genomics

    Li, Xue, Zhao, Xin and Zhong, Mingyang (2017). Advancing public health genomics. 2016 International Workshop on Big Data and Information Security, IWBIS 2016, Jakarta, Indonesia, 18 - 19 October 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IWBIS.2016.7872883

  • Generating life course trajectory sequences with recurrent neural networks and application to early detection of social disadvantage

    Wu, Lin, Haynes, Michele, Smith, Andrew, Chen, Tong and Li, Xue (2017). Generating life course trajectory sequences with recurrent neural networks and application to early detection of social disadvantage. Advanced Data Mining and Applications 13th International Conference, Singapore, November 5–6, 2017. Heidelberg, Germany: Springer . doi: 10.1007/978-3-319-69179-4_16

  • Group recommender model based on preference interaction

    Zheng, Wei, Li, Bohan, Wang, Yanan, Yin, Hongzhi, Li, Xue, Guan, Donghai and Qin, Xiaolin (2017). Group recommender model based on preference interaction. 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore, Singapore, 5–6 November 2017. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-69179-4_10

  • PDD graph: bridging electronic medical records and biomedical knowledge graphs via entity linking

    Wang, Meng, Zhang, Jiaheng, Liu, Jun, Hu, Wei, Wang, Sen, Li, Xue and Liu, Wenqiang (2017). PDD graph: bridging electronic medical records and biomedical knowledge graphs via entity linking. The Semantic Web – ISWC 2017: 16th International Semantic Web Conference Vienna, Austria, October 21–25, 2017 Proceedings, Part II, Vienna, Austria, 21-25 October 2017. Cham, Switzerland: Springer Nature. doi: 10.1007/978-3-319-68204-4_23

  • Past, present, and future trend of GPU computing in deep learning on medical images

    Haryanto, Toto, Suhartanto, Hem and Lie, Xue (2017). Past, present, and future trend of GPU computing in deep learning on medical images. 9th International Conference on Advanced Computer Science and Information Systems (ICACSIS), Jakarta, Indonesia, 28-29 October 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICACSIS.2017.8355007

  • People opinion topic model: opinion based user clustering in social networks

    Chen, Hongxu, Yin, Hongzhi, Li, Xue, Wang, Meng, Chen, Weitong and Chen, Tong (2017). People opinion topic model: opinion based user clustering in social networks. International Conference on World Wide Web Companion, Perth, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051159

  • Predicting clinical outcomes of Alzheimer’s disease from complex brain networks

    Li, Xingjuan, Li, Yu and Li, Xue (2017). Predicting clinical outcomes of Alzheimer’s disease from complex brain networks. 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore,, November 5, 2017-November 6, 2017. CHAM: Springer Verlag. doi: 10.1007/978-3-319-69179-4_36

  • Recovering Missing Values from Corrupted Spatio-Temporal Sensory Data via Robust Low-Rank Tensor Completion

    Ruan, Wenjie, Xu, Peipei, Sheng, Quan Z., Falkner, Nickolas J. G., Li, Xue and Zhang, Wei Emma (2017). Recovering Missing Values from Corrupted Spatio-Temporal Sensory Data via Robust Low-Rank Tensor Completion. 22nd International Conference on Database Systems for Advanced Applications (DASFAA), Suzhou Peoples R China, Mar 27-30, 2017. CHAM: SPRINGER INTERNATIONAL PUBLISHING AG. doi: 10.1007/978-3-319-55753-3_38

  • Structured sentiment analysis

    Almars, Abdulqader, Li, Xue, Zhao, Xin, Ibrahim, Ibrahim A., Yuan, Weiwei and Li, Bohan (2017). Structured sentiment analysis. 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore, Singapore, 5–6 November 2017. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-69179-4_49

  • Uncovering locally discriminative structure for feature analysis

    Wang, Sen, Nie, Feiping, Chang, Xiaojun, Li, Xue, Sheng, Quan Z. and Yao, Lina (2017). Uncovering locally discriminative structure for feature analysis. 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016, Riva del Garda, Italy, 19 - 23 September 2016. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-46128-1_18

  • Opinion search engine

    Li, Xue (2016). Opinion search engine. 14th Australasian Data Mining Conference, AusDM 2016, Canberra, ACT, Australia, 6 - 8 December 2016. Brisbane, QLD, Australia: Australian Computer Society.

  • Classification based on compressive multivariate time series

    Utomo, Chandra, Li, Xue and Wang, Sen (2016). Classification based on compressive multivariate time series. 27th Australasian Database Conference on Databases Theory and Applications, ADC 2016, Sydney Australia, 28-29 September 2016. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-46922-5_16

  • Classification with quantification for air quality monitoring

    Al-Maskari, Sanad, Belisle, Eve, Li, Xue, Le Digabel, Sebastien, Nawahda, Amin and Zhong, Jiang (2016). Classification with quantification for air quality monitoring. 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, 19 - 22 April 2016. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-31753-3_46

  • Empowering truth discovery with multi-truth prediction

    Wang, Xianzhi, Sheng, Quan Z., Yao, Lina, Li, Xue, Fang, Xiu Susie, Xu, Xiaofei and Benatallah, Boualem (2016). Empowering truth discovery with multi-truth prediction. 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, United States, 24 - 28 October 2016. New York, NY, United States: ACM. doi: 10.1145/2983323.2983767

  • Forecasting seasonal time series using weighted gradient RBF network based autoregressive model

    Ruan, Wenjie, Sheng, Quan Z., Xu, Peipei, Tran, Nguyen Khoi, Falkner, Nickolas J. G., Li, Xue and Zhang, Wei Emma (2016). Forecasting seasonal time series using weighted gradient RBF network based autoregressive model. 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, United States, 24 - 28 October 2016. New York, NY, United States: ACM. doi: 10.1145/2983323.2983899

  • Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data

    Yao, Lina, Sheng, Quan Z., Li, Xue, Wang, Sen, Gu, Tao, Ruan, Wenjie and Zou, Wan (2016). Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data. 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, United States, 14-17 November 2015. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICDM.2015.102

  • Learning from less for better: semi-supervised activity recognition via shared structure discovery

    Yao, Lina, Nie, Feiping, Sheng, Quan Z., Gu, Tao, Li, Xue and Wang, Sen (2016). Learning from less for better: semi-supervised activity recognition via shared structure discovery. 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 12-16 September 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2971648.2971701

  • Outlier detection via minimum spanning tree

    Tang, Xin, Huang, Wei, Li, Xue, Li, Shengli and Liu, Yuewen (2016). Outlier detection via minimum spanning tree. Pacific Asia Conference on Information Systems, PACIS, Chiayi, Taiwan, 27 June - 1 July 2016. Pacific Asia Conference on Information Systems.

  • Preface

    Li, Jinyan, Li, Xue and Wang, Shuliang (2016). Preface. 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, Gold Coast, QLD, Australia, 12 - 15 December 2016. Berlin, Germany: Springer Verlag. doi: 10.1007/978-3-319-49586-6

  • Truth discovery via exploiting implications from multi-source data

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  • Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength

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  • RF-care: device-free posture recognition for elderly people using a passive RFID tag array

    Yao, Lina, Sheng, Quan Z., Ruan, Wenjie, Gu, Tao, Li, Xue, Falkner, Nickolas J.G. and Yang, Zhi (2015). RF-care: device-free posture recognition for elderly people using a passive RFID tag array. 12th International Conference on Mobile and Ubiquitous Systems, Coimbra, Portugal, 22-24 July 2015. ICST. doi: 10.4108/icst.mobiquitous.2015.260064

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    Ruan, Wenjie, Yao, Lina, Sheng, Quan Z., Falkner, Nickolas J.G., Li, Xue and Gu, Tao (2015). TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags. MOBIQUITOUS 2015, Coimbra, Portugal, 22-24 July 2015. Gent, Belgium: ICST. doi: 10.4108/eai.22-7-2015.2260072

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  • Data Centric Research at The University of Queensland

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    Chen, L., Li, X. and Han, J. (2013). MedRank: Discovering influential medical treatments from literature by information network analysis. 24th Australasian Database Conference, ADC 2013, Adelaide, Australia, 29 January 2013 - 1 February 2013. Sydney, NSW, Australia: Australian Computer Society.

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    Yan, Xin, Song, Dawei and Li, Xue (2006). Concept-based document readability in domain specific information retrieval. CIKM'06 Fifteenth Conference on Information and Knowledge Management, Arlington, Virginia, USA, 5-11 November 2006. USA: ACM. doi: 10.1145/1183614.1183692

  • Convergence analysis of a discrete-time single-unit gradient ICA algorithm

    Ye, Mao, Li, Xue, Yang, Chengfu and Gao, Zengan (2006). Convergence analysis of a discrete-time single-unit gradient ICA algorithm. Springer Verlag. doi: 10.1007/11759966_168

  • Document generality: its computation for ranking

    Yan, X, Li, X and Song, (2006). Document generality: its computation for ranking. Seventeenth Australiasian Database Conference (ADC2006), Hobart, Australia, 16-19 January, 2006. Tasmania, Australia: Australian Computer Society Inc..

  • Electricity price forecasting with effective feature preprocessing

    Zhao, Jun Hua, Dong, Zhao Yang and Li, Xue (2006). Electricity price forecasting with effective feature preprocessing. 2006 IEEE Power Engineering Society General Meeting, Montreal Canada, 18-22 June 2006. Piscataway, NJ United States: IEEE. doi: 10.1109/PES.2006.1709407

  • Finding frequent itemsets in high-speed data streams

    Sun, X., Orlowska, M E and Li, X (2006). Finding frequent itemsets in high-speed data streams. 2006 6th SIAM International Conference on Data Mining, Maryland, USA, 20-22 April, 2006. USA: Society for Industrial & Applied Mathematics (SIAM).

  • RSA-Grid: A grid computing based framework for power system reliability and security analysis

    Ali, M., Dong, Z. Y., Li, X. and Zhang, P. (2006). RSA-Grid: A grid computing based framework for power system reliability and security analysis. 2006 IEEE Power Engineering Society General Meeting, Montreal, Canada, 18-22 June 2006. Piscataway, NJ, United States: IEEE. doi: 10.1109/pes.2006.1709374

  • Recency-based collaborative filtering

    Ding, Y. H., Li, X and Orlowska, M E (2006). Recency-based collaborative filtering. 17th Australasian Database Conference (ADC2006), Tasmania, Australia, 16-19 January 2006. New South Wales: Australian Computer Society.

  • A general method for electricity market price spike analysis

    Zhao, J., Dong, Z. Y., Li, X. and Wong, K.P (2005). A general method for electricity market price spike analysis. IEEE Power Engineering Society General Meeting, San Francisco, USA, 12-16 July 2005. USA: IEEE.

  • Applications of grid computing in power systems

    Ali, M., Dong, Z. Y., Li, X. and Zhang, P. (2005). Applications of grid computing in power systems. Australasian Universities Power Engineering Conference 2005, Hobart, Australia, 25-28 September 2005. Hobart, Australia: AUPEC 2005.

  • Collaborative filtering on data streams

    Barajas, J. and Li, X. (2005). Collaborative filtering on data streams. 9th European Conference on Principles & Practice of Knowledge Discovery on Databases, Porto, Portugal, 3-7 October, 2005. Germany: Springer-Verlag. doi: 10.1007/11564126_42

  • Determining the fitness of a document model by using conflict instances

    Chen, D., Li, X., Dong, Z. Y. and Chen, X. (2005). Determining the fitness of a document model by using conflict instances. 16th Australasian Database Conference (ADC 2005), Newcastle, Australia, 31 January - 3 February 2005. Sydney, Australia: Australian Computer Society Inc..

  • Direct fingerprinting on multicasting compressed video

    Liu, Z., Li, X. and Dong, Z. Y. (2005). Direct fingerprinting on multicasting compressed video. 11th International conference on Multimedia Modelling, MMM05, Melbourne, Australia, 12-14 January, 2005. Piscataway NJ USA: IEEE Computer Society. doi: 10.1109/MMMC.2005.35

  • Document re-ranking by generality in bio-medical information retrieval

    Yan, X., Li, X. and Song, D. (2005). Document re-ranking by generality in bio-medical information retrieval. 6th International Conference on Web Information Systems Engineering, New York, USA, 20-22 November, 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11581062_28

  • Effectiveness of document representation for classification

    Chen, D., Li, X., Dong, Z. Y. and Chen, X. (2005). Effectiveness of document representation for classification. 7th International Conference on Data Warehousing and Knowledge Discovery, Copenhagen, Denmark, 22-26 August, 2005. Berlin Heidelberg, Germany: Springer. doi: 10.1007/11546849

  • Efficient spatial clustering algorithm using binary tree

    Ali, Mohsin, Li, Xue and Dong, Zhao Yang (2005). Efficient spatial clustering algorithm using binary tree. Sixth International Conference on Intelligent Data Engineering and Automated Learning - IDEAL '05, Brisbane, Australia, 6-8 July 2005. Berlin, Germany: Springer. doi: 10.1007/11508069_39

  • Finding temporal features of event-oriented patterns

    Sun, Xingzhi, Orlowska, Maria E. and Li, Xue (2005). Finding temporal features of event-oriented patterns. The 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 05), Hanoi, Vietnam, 18-20 May 2005. Berlin, Germany: Springer. doi: 10.1007/11430919_91

  • Hiding Classification Rules for Data Sharing with Privacy Preservation

    Natwichai, Juggapong, Li, Xue and Orlowska, Maria E. (2005). Hiding Classification Rules for Data Sharing with Privacy Preservation. 7th International Conference on Data Warehousing and Knowledge Discovery, Copenhagen, Denmark, 22-26 August, 2005. Germany: Springer. doi: 10.1007/11546849_46

  • Time weight collaborative filtering

    Ding, Yi and Li, Xue (2005). Time weight collaborative filtering. 14th ACM International Conference on Information and Knowledge Management (CIKM'05), Bremen, Germany, 31 October - 5 November 2005. New York, United States: Association for Computing Machinery. doi: 10.1145/1099554.1099689

  • A Correlation Analysis on LSA and HAL Semantic Space Models

    Yan, Xin, Li, Xue and Song, Daqei (2004). A Correlation Analysis on LSA and HAL Semantic Space Models. First International Symposium, CIS 2004, Shanghai, China, 16-18 December, 2004. Berlin: Springer-Verlag. doi: 10.1007/b104566

  • A lightweight encryption algorithm for mobile online multimedia devices

    Liu, Zeng, Li, Xue and Dong, Zhaoyang (2004). A lightweight encryption algorithm for mobile online multimedia devices. The Fifth International Conference on Web Information Systems Engineering (WISE 2004), Brisbane, Australia, 22-24 November 2004. Berlin, Germany: Springer-Verlag.

  • A new framework of privacy preserving data sharing

    Chen, X., Orlowska, M. E. and Li, X. (2004). A new framework of privacy preserving data sharing. The Fourth IEEE International Conference on Data Mining (ICDM '04), Brighton, U.K., 1-4 November 2004. Los Alamitos, CA, U.S.A.: IEEE Computer Society.

  • A sensor-based multimedia authentication system

    Liu, Zheng, Li, Xue and Dong, Zhaoyang (2004). A sensor-based multimedia authentication system. The 2004 IEEE International Conference on Multimedia and Expo (ICME 2004), Taipei, Taiwan, 27-30 June 2004. Los Alamitos, CA, U.S.A.: IEEE Communications Society. doi: 10.1109/ICME.2004.1394357

  • Enhancing security of frequency domain video encryption

    Liu, Z., Li, X. and Dong, Z. Y. (2004). Enhancing security of frequency domain video encryption. The Twelfth ACM International Conference on Multimedia, New York, 10-16 October, 2004. New York: The Association for Computing Machinery. doi: 10.1145/1027527.1027597

  • Finding negative event oriented patterns in long temporal sequences

    Sun, Xingzhi, Orlowska, Maria E.. and Li, Xue (2004). Finding negative event oriented patterns in long temporal sequences. The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004), Sydney, Autstralia, 26-28 May 2004. Berlin; Heidelberg, Germany: Springer-Verlag. doi: 10.1007/b97861

  • Interactive email filtering: Learning from misclassified examples

    Chen, D., Li, X., Dong, Z. Y. and Smith, P.A. (2004). Interactive email filtering: Learning from misclassified examples. The 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1-3 December, 2004. Los Alamitos, CA, U.S.A.: IEEE. doi: 10.1109/ICCIS.2004.1460736

  • Knowledge maintenance on data streams with concept drifting

    Natwichai, Juggapong and Li, Xue (2004). Knowledge maintenance on data streams with concept drifting. First International Symposium on Computational and Information Science (CIS 2004), Shanghai, China, 16-18 December 2004. Berlin; Heidelberg, Germany: Springer Berlin / Heidelberg. doi: 10.1007/b104566

  • Knowledge maintenance on data streams with concept drifting

    Natwichai, Juggapong and Li, Xue (2004). Knowledge maintenance on data streams with concept drifting. CIS 2004: Computational and Information Science , Shanghai, China, 16-18 December, 2004. Berlin, Germany: Springer Verlag. doi: 10.1007/978-3-540-30497-5_110

  • Motion vector encryption in multimedia streaming

    Liu, Zheng and Li, Xue (2004). Motion vector encryption in multimedia streaming. The Tenth International Conference on Multimedia Modelling, Brisbane, Australia, 5-7 January 2004. Los Alamitos, California, U.S.A.: IEEE Computer Society. doi: 10.1109/MULMM.2004.1264968

  • Multimedia authentication with sensor-based watermarking

    Liu, Z., Li, X. and Dong, Z. Y. (2004). Multimedia authentication with sensor-based watermarking. The Multimedia and Security Workshop 2004, Magdeburg, Germany, 20-21 September, 2004. New York: The Association for Computing Machinery. doi: 10.1145/1022431.1022458

  • On the development of a web electricity market simulator

    Sorbello, M., Dong, Z. Y. and Li, X. (2004). On the development of a web electricity market simulator. The Australasian Universities Power Engineering Conference 2004, Brisbane, 26-29 September, 2004. Brisbane: AUPEC.

  • PLD: A distillation algorithm for misclassified documents

    Chen, D. and Li, X. (2004). PLD: A distillation algorithm for misclassified documents. The Fifth International Conference on Web-Age Information Management (WAIM 2005), Dalien, China, 15-17 July 2004. Berlin, Germany: Springer-Verlag.

  • Reflective web interface agent

    Li, Xue (2004). Reflective web interface agent. APWeb 2004: Advanced Web Technologies and Applications, Hangzhou, China, 14-17 April, 2004. Berlin, Germany: Springer Verlag. doi: 10.1007/978-3-540-24655-8_15

  • Reflective web interface agent

    Li, Xue (2004). Reflective web interface agent. The Sixth Asia Pacific Web Conference (APWEB'04), Hangzhou, China, 14-17 April 2004. Berlin, Germany: Springer-Verlag. doi: 10.1007/b96838

  • How can managers adopt and use electronic communication media more effectively? An exploratory study

    Li, X., Huang, W., Jiang, F. and Xu, D.M. (2003). How can managers adopt and use electronic communication media more effectively? An exploratory study. The Seventh World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, Florida, 27-20 July, 2003. Orlando, Florida: The International Institute of Information Systems.

  • Intelligent encoding of concepts in web development retrieval

    Zakos, J., Verma, B., Li, X. and Kulkarni, S. (2003). Intelligent encoding of concepts in web development retrieval. Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), Xian, China, 27-30 September 2003. Los Alamitos, U.S.A.: IEEE Computer Society. doi: 10.1109/ICCIMA.2003.1238103

  • Introducing uncertainty into pattern discovery in temporal event sequences

    Sun, X., Orlowska, M. E. and Li, X. (2003). Introducing uncertainty into pattern discovery in temporal event sequences. Third IEEE International Conference on Data Mining 2003 (ICDM 2003), Melbourne, Australia, 19-22 November 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/icdm.2003.1250933

  • Real time web vehicle classifier

    Li, X., He, W., Dong, Z. Y., Verma, B., Yu, K., Koh, T. C. and Ng, C. W. (2003). Real time web vehicle classifier. The Seventh International Symposium on Digitial Processing and Communication Systems, The Gold Coast, 8-11 December, 2003. Wollongong: The University of Wollongong.

  • Weather depenent electricity market forecasting with neural networks, wavelet and data mining techniques

    Dong, Z. Y., Li, X., Xu, Z. and Teo, K. L. (2003). Weather depenent electricity market forecasting with neural networks, wavelet and data mining techniques. The Australasian Universities Power Engineering Conference, Christchurch, New Zealand, 28 September-1 October, 2003. New Zealand: AUPEC.

  • Web wrapper validation

    Pek, E., Li, X. and Liu, Y. (2003). Web wrapper validation. 5th Asia-Pacific Web Conference, Xian, China, 23-25 April 2003. Berlin, Germany: Springer Verlag. doi: 10.1007/3-540-36901-5_40

  • Automatic assessment of e-busineses

    Li, X. and Chen, P. (2002). Automatic assessment of e-busineses. International Conference on e-Business, Beijing, 23-26 May, 2003. Beijing: Beijing Institute of Technology Press.

  • Intelligent business portal: Availability vs. applicability

    Li, X. and Foo, I. P. (2002). Intelligent business portal: Availability vs. applicability. International Conference on e-Business, Beijing, 23-26 May, 2003. Beijing: Beijing Institute of Technology Press.

  • What web features and functions are used by Australian corporations in their websites? A conceptual framework and an empirical investigation

    Li, X., Huang, W., Gandha, G., Yao, X.G. and Huang, H.S. (2002). What web features and functions are used by Australian corporations in their websites? A conceptual framework and an empirical investigation. 2002 Information Resources Management Association International Conference, Seattle, 19-22 May, 2002. Hershey: Idea Group Publishing.

  • Building intelligent business portals

    Li, X. (2001). Building intelligent business portals. Asia Pacific Web Conference 2001, Changsha, China, 21-22 November, 2001. Beiging, China: Publishing House of Electronics Industry.

  • On the assessment of commercial website: An expert system approach

    Li, X. and Huang, W. (2001). On the assessment of commercial website: An expert system approach. Twelfth Australasian Conference on Information Systems, Coffs Harbour, NSW, 4-7 December, 2001. Coffs Harbour, NSW: Southern Cross University.

Edited Outputs

Other Outputs

PhD and MPhil Supervision

Current Supervision

  • Weighted Ensembles for Different machine learning model that support non-data-sharing / vertical partition

    Doctor Philosophy — Principal Advisor

  • Context-aware Representation Learning for Code Analysis

    Doctor Philosophy — Principal Advisor

  • Information Extraction from Large-scale Low-quality Data

    Doctor Philosophy — Principal Advisor

  • Open-domain Dialogue Generation

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Distribution-aware Automatic Summary Generalisation from Multi-modal Medical Data

    Doctor Philosophy — Principal Advisor

  • Information Extraction from Large-scale Low-quality Data

    Doctor Philosophy — Principal Advisor

  • Federated transfer learning on clinical multimodal data

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Data Mining on Many-to-Many Complex Relationships

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Graph Mining for Legal Case Retrieval

    Doctor Philosophy — Associate Advisor

    Other advisors:

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Analytical Queries on Big Data

    Description:

    Traditional database queries are used to search for facts from structured database such as RDB (Relational Databases) to satisfy user search conditions. With big data currently available in many ways such as structured and unstructured multi-modalities, user queries should be constructed not only for searching facts, but also for searching patterns, emerging events, and outliers from available big data. This PhD research is to propose a new type of query language that can query on analytic results , to satisfy user requirements for informed decision support. In order to make such a language to be implementable on general big dataset, this PhD research will also define and design a framework that can answer declarative analytic queries by a data-driven approach to apply transparent machine learning algorithms in order to discover unexpected patterns, emerging trends, various correlations from big data. The challenges of this research will be on how to use an end-to-end black-box mechanism to provide big data analytic services to make big data available for general queries beyond classical data warehousing technologies.

    Background:

    In classical DSS systems based on data warehouses and OLAP operations, the queries such as Canned and Continuous Queries would not involve procedural operations that can reflect the dynamic parameters of queries. The operators such as Role-Up, Drill-Down, Slice/Dice, Cube, Pivoting etc, cannot reflect the context of the query objects in their business context. This PhD research will try to introduce more flexible analytical data manipulation operations based on machine learning algorithms that can provide end-to-end queries for strategic DSS with baselines.

  • Privacy Preservation for Sharing Distributed Big Data

    Description:

    Predictive data analytics usually involves Big Data that is distributed in different locations and owned by different organizations, such as the Taxation Office Data, Boarder-Control Customs Data, Crime-Stop Police Data, and Social Security Data. The organizations are legally responsible for the privacy preservation of their data which is of highly risk and sensitive. However, this should not prevent the sharing of those de-identified, privacy preserved data sets for the predictions of pending social-economic events, emerging trends, patterns of relationships, or correlations among entities. Currently, there are many algorithms that can preserve privacy for computing data from multiple owners, such as SMC (secure multi-party computation), Differential Privacy algorithms. However, the predictive tasks often require to use all original raw data for the learning. This would involve the individual organizations to conduct local learning tasks and contribute to global learning with their local models, instead of their sensitive data. Federated learning therefore coming to being as a promising and useful approach to learn from individual datasets and producing a general model for the required predicting tasks. This project is to research on the Federated Learning algorithms that can deal with large distributed, sensitive datasets and derive a computational model to predict some pre-defined tasks. The challenges of this project would be the following three issues in one solution, i.e., data shareability, data privacy, and computational utility.

    Key Terms: Federated Learning, Deep Learning, Distributed Database Technology, Privacy Preservation, Mathematical Modelling, Data Shareability, Computational Utility

  • The First Principle AI (FAI) Research

    Description:

    Artificial Intelligence (AI) applications are mostly based on the first-order thinking that is reasoning based on deduction, abduction, induction, or eduction. In this way, AI is limited and unable to discover the First Principles such as those in sciences and complex Math Equations, and laws in Physics and Chemistry. However, this should not prevent AI to be used together with the First Principles in those discovery projects. This research is to design an architecture of AI Application platform that can use First Principle in AI to speed up the human trial-and-error process of experiments, to use First Principle in a more intelligent way to converge an optimization process which has a large number of iterations faster and scalable for human's research problems.

  • Rare Events Prediction based on Large Scale Multi-Source Data Fusion

    A Rare Event is a social-economic situation that happens unexpectedly and brings disastrous impact to society and human lives. For predicting predefined Rare Events (e.g., pandemics) we need a Knowledge Graph (KG) of the domain (e.g., public health), to understand and explain a “perfect storm” when the “ingredients” of the perfect storm are all in place with certain geographic-temporal conditions. However, there may be things that are emerging and forming up a trend, but we don’t know what it will lead to. For these kinds of situations, we use unsupervised learning approaches for trend detection.

    The aim of this project is to propose a solution that can be used to process a large volume of distributed data sources in order to detect the emergence of Rare Events and predict their geographic and temporal occurrences.

    People have the right to learn about new events that may cause concern or require urgent response. These emerging events may initially be small events or instances that appear in social media, social networks, and multilingual, global or local communities. Since the internet knows no borders, small incidents that occur in distant countries may eventually seriously affect the lives of local people, such as the COVID-19 pandemic. Through integration with large-scale real-time data stream representation learning, the results of this project provide new knowledge for academia and generate new knowledge for all people or organisations who embrace big data analysis and artificial intelligence technology for advancing social security.

    Current research on Rare Events prediction is mainly focused on natural disasters, such as earthquakes, extreme weather, flooding or bushfires; or on system performance, such as famines, financial market crashes, or patient mortality. The research gap is that the current methods are developed based on the environmental data monitoring or system performance indicators without integrating the data of social opinions and human behaviours that may significantly affect the developments of events.

    Most current approaches of predicting Rare Events are based on statistical modelling with small sample data sets. They do not take advantage of the recent substantial developments in deep learning and AI approaches. Both these developments are applicable to the data of complex networks formed based on multiple application domains [3]. In this project we consider the prediction of Rare Events that may significantly impact on human life, health, and wellbeing. For example, our system may be developed to predict “superbug” outbreaks in future years. To the best of our knowledge, our proposal is the first of its kind, i.e. using big data analysis and AI technology to predict Rare Events at a global scale.

    The outcomes are expected as (1) to develop a computational graph model of rare events prediction, (2) to collect and evaluate the data sets that are relevant to the rare event prediction model, (3) to develop the effective prediction algorithms based on the graph model of rare event prediction and (4) to provide a showcase, for the Rare Events prediction evaluation.

  • Building Knowledge Graph with Quality

    Description:

    In business intelligence, knowledge Graph is a representation of the domain knowledge for business decision support and event predictions. The learning of a knowledge graph requires the fusion of big data including the transactional data, text, image, and numerical data. There are many tools and algorithms used to build knowledge graphs for the application domains such as those in healthcare, medicine, IoT (Internet of Things), supply-chain, and sports management. However, due to the complexity of domain knowledge and the bottleneck in knowledge engineering, it is difficult to evaluate a knowledge graph against the given application domain for its quality, such as the issues regarding the completeness, consistency, and minimum representation of domain knowledge.

    In this project we will study the algorithms and theories that can be used to build a knowledge graph with the quality assessment and benchmarking. The effectiveness of a representation framework of its application domain will be studied in terms of graph embedding, canonical graph representation and the theory of information networks.

    Key Terms: Knowledge Graph, Graph Embedding, Information Network, Complex Networks, Graph-based Queries

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