Dr Jinglan Zhang

Service and professional activities Within QUT: Co-leader, Environment and Natural Systems, QUT Centre for Data Science (2020 - present) Student Advisor (Computer Science) (2018 - present) Course coordinator, Bachelor of Information Technology (Honours)  (2010-2015) Workplace health and safety officer/representative/warden (2007 - present) Outside QUT: International journal editor

• Mobile Information Systems (2015 – 2020)

International journal reviewer

• Computer-aided design (Elsevier) (2005 – 2006)
• Journal of Research and Practice in Information Technology (2002)

Conference program committee

• Knowledge Engineering and Ontology Development (KEOD) (2012-2017)
• International Conferences on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2013 – 2017)

Conference reviewer

• International Multi-Media Modelling Conference (MMM) (2004 - 2006)

International conference organizing committee

1. Served as the secretary of the 2nd International Symposium on Autonomous Minirobots for Research and Edutainment (AMIRE 2003) and helped the local chair to successfully organize this conference.
2. Served as a member of the Local Organizing Committee of MMM2004.
3. Served as a member of the steering committee of South East Queensland Chapter of ACM SIGGRAPH.
4. Served as a member of the Local Organizing Committee of eScience2010

Research Projects

1. Human Data Interaction (Big Data Visual Analytics): We will research on integration of the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. This research will use the datasets that Queensland government and the QUT  ecoAcoustic research group  have collected over multiple years. Other big datasets, such as Amazon’s product review dataset, could also be used. Research question: What are good ways to support human interaction with big data?
2. Data for environment and nature: Sensor networks bring ecologists and pattern recognition researchers together to make some applications possible. These applications include assessing risks from potential bird collisions, un-obtrusive observations (where the presence of humans changes some animal behaviours), and the studies of spatial and temporal variation in biological processes.  While huge amount of data has been collected, processing and mining those data is challenging. One approach to this is to apply existing high performance computing, data management and analysis solutions to specific scientific challenges and solve the new problems that will arise. A systematic investigation on the ways for effective data analysis and pattern recognition will be conducted. This project will investigate algorithms for animal species recognition using machine learning and image processing techniques. We also investigate semi-automated analysis and citizen science approaches.  The QUT  ecoAcoustic research group is one of the leading groups in the world in this area.   We are looking for more research students in this area. We need multiple students, and each student can work on a specific aspect of the project e.g. computer-human interaction, information retrieval, machine learning, signal processing and pattern recognition etc. Research question: How to utilize information technology to aid ecological research?
3. Designing technologies for preventing homelessness: This project is about preventing homelessness for  women before it begins. This aim is to intervene prior to the crisis occurring, that is, we aim to intervene when housing stress is occurring or likely to occur (nb. housing stress is defined here as meaning having difficulty maintaining safe and appropriate housing). According to the health continuum of prevention, three stages of harm prevention exist: primary (prevent the harm from occurring), secondary (attempt to stop existing harm early), and tertiary (attempt to reduce the impact of established harm). While there are some excellent resources available, these resources tend to focus on women in the secondary or tertiary points of intervention (at the ‘tipping point’ or already in crisis, respectively). Other resources such as helplines or access to emergency housing are also targeted to women already experiencing a crisis. We aim to help women at the primary stage.  This project focuses on designing and developing a digital solution that will enable early responders (that is, a professional source of support who is not directly associated with homeless services – like a real estate agent or a lawyer) to have conversations with pre-crisis (primary prevention-stage) women to help them access the support needed. The digital solution could be anything: – it could be a web interface, an app, a chatbot, etc. Research Question: How might we empower women experiencing a change of circumstance and prevent homelessness?
4. Information accessibility for people with disability: The web has become the primary mechanism for information delivery. However, for people with intellectual disabilities there can be significant barriers in accessing this type of information. This project aims to address the web usability issue by developing new technological solutions that allow people with intellectual disabilities to independently seek information. The research involves collaborative investigation between QUT researchers, a disability service provider and people with intellectual disabilities to understand their aspirations and current practices in accessing the web. We will bring together technical and client-centred knowledge to develop innovative information access techniques, tested in a  web app, mobile app or conventional PC app or IoT solution. We need multiple students, and each student can work on a specific aspect of the project e.g. computer-human interaction, information retrieval, user-centred and participatory design, or inclusive web design. Research question: How can we make information accessible to people with diverse needs?   older adults is another user group.
5. Data for Good (analytics for humanity) is a general area I am interested in.  Another project along this direction is physical activity analysis for the wellbeing of youth people.
6. Information Retrieval (IR):  Information storage and retrieval is the art and science of retrieving useful information from a collection of items that serves the user’s purpose.  The main trick is to retrieve what is useful while leaving behind what is not. The more important trick is to push the most relevant and important information onto the top 10 in the results list (the first page in web search).   We will focus on search engine technologies, including textual documents and audio/image documents.  We will also research on the evaluation of the search technologies e.g.  precision/recall metrics, human and machine based evaluation etc. Search efficiency (how fast) and effectiveness (how well) are the main goals.

• Gan, H., Zhang, J., Towsey, M., Truskinger, A., Stark, D., Van Rensburg, B., Li, Y. & Roe, P. (2021). A novel frog chorusing recognition method with acoustic indices and machine learning. Future Generation Computer Systems, 125, 485–495. https://eprints.qut.edu.au/212075
• Alzubaidi, L., Fadhel, M., Oleiwi, S., Al-Shamma, O. & Zhang, J. (2020). DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools and Applications, 79(21-22), 15655–15677. https://eprints.qut.edu.au/131069
• Dema, T., Brereton, M., Cappadonna, J., Roe, P., Truskinger, A. & Zhang, J. (2017). Collaborative exploration and sensemaking of big environmental sound data. Computer Supported Cooperative Work, 26(4 - 6), 693–731. https://eprints.qut.edu.au/113943
• Xie, J., Towsey, M., Truskinger, A., Eichinski, P., Zhang, J. & Roe, P. (2015). Acoustic classification of Australian anurans using syllable features. Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015), 1–6. https://eprints.qut.edu.au/89673
• Towsey, M., Zhang, L., Cottman-Fields, M., Wimmer, J., Zhang, J. & Roe, P. (2014). Visualization of long-duration acoustic recordings of the environment. Procedia Computer Science, 29, 703–712. https://eprints.qut.edu.au/74420
• Towsey, M., Wimmer, J., Zhang, J., Huang, K., Cottman-Fields, M., Truskinger, A., Roe, P., Duan, S. & Dong, X. (2013). Managing and analysing big audio data for environmental monitoring. Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering, 997–1004. https://eprints.qut.edu.au/69096
• Yang, H., Zhang, J. & Roe, P. (2011). Using reputation management in participatory sensing for data classification. Procedia Computer Science, 5, 190–197. https://eprints.qut.edu.au/42443
• Li, Z., Liu, Y., Walker, R., Hayward, R. & Zhang, J. (2010). Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved hough transform. Machine Vision and Applications, 21(5), 677–686. https://eprints.qut.edu.au/29121
• Mason, R., Roe, P., Towsey, M., Zhang, J., Gibson, J. & Gage, S. (2008). Towards an acoustic environmental observatory. Proceedings of the IEEE Fourth International Conference on eScience, 2008, 135–142. https://eprints.qut.edu.au/29068
• Cai, J., Ee, M., Pham, B., Roe, P. & Zhang, J. (2007). Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition. Proceedings of 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 293–298. https://eprints.qut.edu.au/11227

Title

New Information Access Technologies for People with Intellectual Disability

Primary fund type

CAT 1 - Australian Competitive Grant

Project ID

LP160100800

Start year

2016

Keywords

Title

Framing authentic assessment of service learning within an information technology curriculum

Primary fund type

CAT 1 - Australian Competitive Grant

Project ID

SD15-5151

Start year

2015

Keywords

Assessment And Feedback Practices; Information Technology; Personal Development; Service Learning

• Animal call recognition with acoustic indices and machine learning
  PhD, Principal Supervisor
  Other supervisors: Professor Paul Roe, Professor Yuefeng Li
• Feature Selection for Specialized Classification using Granule Mining
  PhD, Principal Supervisor
  Other supervisors: Professor Yuefeng Li
• Analysis and Visualisation of Long-Duration Environmental Audio Using Autoencoders
  PhD, Principal Supervisor
  Other supervisors: Professor Paul Roe, Professor Margot Brereton

• Sensor-Based Prediction of Physical Activity and its Impacts Using Machine Learning (2018)
• Acoustic Classification of Australian Frogs for Ecosystem Survey (2017)
• Classification and Ranking of Environmental Recordings to Facilitate Efficient Bird Surveys (2017)
• Aspect-Based Opinion Mining from Customer Reviews (2016)
• Content-based Birdcall Retrieval from Environmental Audio (2016)
• Automated Species Recognition in Environmental Recordings (2014)
• Aerial Image Analysis Using Spiking Neural Networks with Application to Power Line Corridor Monitoring (2011)
• Ontology Based Image Annotation (2010)

• Detection of Rain in Acoustic Recordings of the Environment Using Machine Learning Techniques (2015)
• Reputation modeling in citizen science for environmental acoustic data analysis (2012)