Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc. In this paper, firstly, we propose Health Knowledge Graph Builder (HKGB), an end-to-end platform which could be used to construct disease-specific and extensible health knowledge graphs from multiple sources. Secondly, we analyze the capabilities and requirements of clinicians, design the tasks to involve the clinicians and implement a clinician-in-the-loop toolset to integrate the clinicians prior knowledge into the construction of health knowledge graphs. Thirdly, we design an extensible mechanism to add new diseases to an existing knowledge graph. Fourthly, we present a quantitative effort estimation algorithm to quantitatively evaluate the effort of clinicians during the construction, and use it to calculate the workloads such as 44.27 person days for knee osteoarthritis domain. Finally, we have developed several knowledge graph based tools to facilitate real applications.

This paper describes a self-directed, project-based learning scheme implemented in an introductory Signal Processing course at the University of New South Wales. The course was structured around a major laboratory project in which students were required to research course material, understand the relevant theory, and apply this in order to arrive at a solution. Lectures were delivered via prerecorded DVDs, allowing students to self-pace their absorption of new content and allowing teaching staff to concentrate on specific student issues during face-to-face classes. Evaluation of the course structure by the lecturer and instructors suggested that students gained a better conceptual understanding of signal processing theory than in previous years. Students were generally positive towards the process, but found it difficult to adjust to.

This paper presents our experience in constructing an electronic whiteboard-based computer laboratory for teaching digital signal processing (DSP) courses in Australian undergraduate and postgraduate programs. Student interaction with the electronic whiteboard-based tutorial class environment is also reported. Away from the laboratory, DSP lectures were presented using a tablet PC as a digital whiteboard. This supported high quality handwriting annotation of lecture slides, and overcame the limited flexibility present in the existing PowerPoint mode of lecture delivery. For selected self-paced tutorial questions, solutions were provided in electronic format comprising the lecturer's handwritten explanation on a blank slide, input using the tablet PC, combined with audio commentary. An evaluation of student opinions towards this multi-mode delivery of DSP education was illuminating, and the overall experience with these technological aids was that signal processing could be effectively and naturally taught with high student attention span.

The paper presents our experience in teaching a digital signal processing (DSP) course in an Australian postgraduate program entirely using virtual tele-lectures. A virtual teaching laboratory was designed for this purpose, allowing students to receive fully interactive, real time lectures delivered from a remote international location. We present the methodology and technology used to develop a complete set of tele-lectures and online tools for a course entitled 'Signal processing and applications'. An evaluation of student opinions towards the virtual teaching laboratory revealed that 90% of students rapidly became comfortable with the use of this new educational facility, among other results. The overall experience with the VTL was that signal processing can be effectively and naturally taught in this mode and that there are great potential benefits in connecting the signal processing research and educational community.