• Adaptive background modeling for land and water composition scenes 

      Zhao, Jing (Jane); Pang, Shaoning; Hartill, B.; Sarrafzadeh, Hossein (International Conference on Image Analysis and Processing (ICIAP), 2015-09)
      In the context of maritime boat ramps surveillance, this paper proposes an Adaptive Background Modeling method for Land and Water composition scenes (ABM-lw) to interpret the traffic of boats passing across boat ramps. We ...
    • Analysis and configuration of boundary difference calculations 

      Dacey, Simon; Pang, Shaoning; Song, Lei; Zhu, Lei (ICONIP, 2014-11)
      In the field of land management, stakeholders (people) everywhere have many disputes over the location of boundaries between private land and public land. We find that the stakeholders disagree with each other over boundaries. ...
    • The global cyber security workforce : an ongoing human capital crisis 

      Fourie, Leon; Pang, Shaoning; Kingston, Tamsin; Hettema, Hinne; Watters, Paul; Sarrafzadeh, Hossein (Global Business and Technology Association, 2014-07)
      Cyber threats pose substantial risk to government, businesses and individuals. There is an alarming shortage of trained professionals and academic programs to train and produce these professionals. Many countries including ...
    • An intelligent agent based land encroachment detection approach 

      Dacey, Simon; Song, Lei; Pang, Shaoning (Asia Pacific Neural Network Assembly (APNNA), 2013)
      Land management and planning is essential to assist the economic growth, sustainable resource use and environmental protection of a city. This paper describes a novel approach to automatic encroachment detection to assist ...
    • Smart task orderings for active online multitask learning 

      Pang, Paul; An, Jianbei; Zhao, Jing; Li, Xiaosong; Ban, Tao; Inoue, Daisuke; Sarrafzadeh, Hossein (Society for Industrial and Applied Mathematics, Activity Group on Data Mining and Analytics, 2014-04-26)
      This paper promotes active oMTL (i.e., Online Multitask Learning with task selection) by proposing two smart task ordering approaches: QR-decomposition Ordering and Minimal-loss Ordering, in which the optimal sequence of ...

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