Young children in digital society: An Online Tool for service provision . This project aims to identify the practices enacted and shared amongst young children, their families and educators in digital society.The project is significant because in digital society families and educators face new demands ensuring technologies are used in the best interests of young children. Knowledge about practices in digital society informs adult decision-making using technologies with, by and for young children ....Young children in digital society: An Online Tool for service provision . This project aims to identify the practices enacted and shared amongst young children, their families and educators in digital society.The project is significant because in digital society families and educators face new demands ensuring technologies are used in the best interests of young children. Knowledge about practices in digital society informs adult decision-making using technologies with, by and for young children in the early years. The outcome is a new Online Tool for the Partner Organisations to share exemplar practices benefiting Australian children, their families and educators with new resources, materials and programs in areas including: digital media production, cyber-safety education, digital play and digital parenting.
Read moreRead less
A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons fo ....A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons for rejection at construction sites, resulting in significant costs, waste, and delays. Multimodal data sources will be used to provide a reliable workability estimate in real time, enabling construction teams to identify and rectify workability issues in transit while continuously monitoring the adjustments effects.Read moreRead less