Computational Intelligence for Complex Structured Data

复杂结构化数据的计算智能

• 批准号: LP140100995
• 负责人: Prof Tamás (Tom) Gedeon
• 金额: $ 19.4万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-06-11 至 2020-06-10
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP140100995
• 关键词: Computational; Intelligence; Complex; Structured; Data

This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learning of the syntax and semantics of individual gestures and actions, nor the multi-gesture information fusion required for understanding, which could significantly enhance efficiency, for example, in sorting through named entities in an investigation. All of this is done naturally by most human beings, using biological neural networks.

该项目旨在使用计算智能技术来可靠地学习自适应自然人类指向和手势，以控制伪3D显示器上的界面。元素之间相互连接的高度复杂的数据很难在屏幕上可视化。大多数当前的工具都是在2D屏幕上使用点/点击/拖动操作的。捕捉适当的人类行为的物理技术已经存在，但还没有对个人手势和行动的语法和语义进行自适应学习，也没有理解所需的多手势信息融合，这可以大大提高效率，例如在调查中对命名实体进行排序。所有这些都是大多数人使用生物神经网络自然完成的。