18,308 research outputs found
A tutorial task and tertiary courseware model for collaborative learning communities
RAED provides a computerised infrastructure to support the development and administration of Vicarious Learning in collaborative learning communities spread across multiple universities and workplaces. The system is based on the OASIS middleware for Role-based Access Control. This paper describes the origins of the model and the approach to implementation and outlines some of its benefits to collaborative teachers and learners
CUACE/Dust ─ an integrated system of observation and modeling systems for operational dust forecasting in Asia
An integrated sand and dust storm (SDS) forecasting system – CUACE/Dust (Chinese Unified Atmospheric Chemistry Environment for Dust) has been developed, which consists of a comprehensive dust aerosol module with emission, dry/wet depositions and other atmospheric dynamic processes, and a data assimilation system (DAS) using observational data from the CMA (China Meteorological Administration) ground dust monitoring network and retrieved dust information from a Chinese geostationary satellite – FY-2C. This is the first time that a combination of surface network observations and satellite retrievals of the dust aerosol has been successfully used in the real time operational forecasts in East Asia through a DAS. During its application for the operational SDS forecasts in East Asia for spring 2006, this system captured the major 31 SDS episodes observed by both surface and satellite observations. Analysis shows that the seasonal mean threat score (TS) for 0–24 h forecast over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the DAS, a 41% enhancement. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against the surface PM<sub>10</sub> monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. This is a summary paper for a special issue of ACP featuring the development and results of the forecasting system
Learning from Multiple Sources for Video Summarisation
Many visual surveillance tasks, e.g.video summarisation, is conventionally
accomplished through analysing imagerybased features. Relying solely on visual
cues for public surveillance video understanding is unreliable, since visual
observations obtained from public space CCTV video data are often not
sufficiently trustworthy and events of interest can be subtle. On the other
hand, non-visual data sources such as weather reports and traffic sensory
signals are readily accessible but are not explored jointly to complement
visual data for video content analysis and summarisation. In this paper, we
present a novel unsupervised framework to learn jointly from both visual and
independently-drawn non-visual data sources for discovering meaningful latent
structure of surveillance video data. In particular, we investigate ways to
cope with discrepant dimension and representation whist associating these
heterogeneous data sources, and derive effective mechanism to tolerate with
missing and incomplete data from different sources. We show that the proposed
multi-source learning framework not only achieves better video content
clustering than state-of-the-art methods, but also is capable of accurately
inferring missing non-visual semantics from previously unseen videos. In
addition, a comprehensive user study is conducted to validate the quality of
video summarisation generated using the proposed multi-source model
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