Griffith Research Online

Subspace is widely used to represent objects under different viewpoints, illuminations, identities, and more. Due to the growing amount and dimensionality of visual contents, fast search in a large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, clustering, video retrieval, and visual recognition. This can be facilitated by approximate nearest subspace (ANS) search which requires effective subspace representation. All existing methods for this problem represent a subspace by a point in the Euclidean or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods is not guaranteed because the subspace representation step can be very time consuming when coping with high-dimensional data. Moreover, the subspace to point transforming process may cause subspace structural information loss which influences the search accuracy. In this paper, we present a new approach for hashing-based ANS search which can directly binarize a subspace without transforming it into a vector. The proposed method learns the binary codes for subspaces following a similarity preserving criterion, and simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. Experiments on face and action recognition and video retrieval applications show that our method outperforms several state-of-the-art methods in both efficiency and accuracy. Moreover, we also compare our method with vector-based hashing methods. Results also show the superiority of our subspace matrix based search scheme.

Griffith University’s Climate Action Beacon conducted the third of five planned Climate Action Surveys in September-December 2023. The survey aimed to discover what Australians think, feel, and do about climate change and related environmental and climatic events, conditions, and issues. This report gives details of the background to the survey, as well as its methods, major findings, and potential implications. Comparisons are made with findings from the corresponding 2021 and 2022 surveys and with other recent survey research.

This study sheds light on the determinants of consumers’ adoption of artificial intelligence-driven drone food delivery service (AI-driven DFDS) using a mixed-methods approach. Interviews with hospitality industry professionals revealed several drivers and inhibitors of AI-driven DFDS adoption. Using these findings, we developed a theoretical model AI-driven DFDS adoption based on the premise of the behavioral reasoning theory and innovation resistance theory. The model was tested using data collected from 1240 consumers. The results suggest that drones’ relative advantage, perceived ubiquity, social influence, and green image positively influence attitudes and adoption. Risk, usage, and experience barriers have an adverse influence on attitudes and adoption. Consumers’ openness to new technology has a positive influence on ‘reasons for’ using AI-driven DFDS. The research makes an important theoretical contribution to research on the adoption of AI-driven DFDS. The study also provides important practical implications for marketers and industry professionals.

Purpose: The International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), acknowledged that plus-like retinopathy of prematurity (ROP) vascular changes occurs along a spectrum. Historically, clinician-experts demonstrate variable agreement for plus diagnosis. We developed a 9-photograph reference image set for grading plus-like changes and compared intergrader agreement of the set with standard grading with no plus, preplus, and plus disease. Design: Retinal photographic grading and expert consensus opinion. Participants: The development set included 34 international ICROP3 committee members. The validation set included 30 ophthalmologists with ROP expertise (15 ICROP3 committee members and 15 non-ICROP3 members) Methods: Nine ROP fundus images (P1 through P9) representing increasing degrees of zone I vascular tortuosity and dilation, based on the 34 ICROP3 committee members’ gradings and consensus image reviews, were used to establish standard photographs for the plus (P) score. Study participants graded 150 fundus photographs 2 ways, separated by a 1-week washout period: (1) no plus, preplus, or plus disease and (2) choosing the closest P score image. Main Outcome Measures: Intergrader agreement measured by intraclass correlation coefficient. Results: Intergrader agreement was higher using the P score (intraclass correlation coefficient, 0.75; 95% confidence interval, 0.71–0.79) than no plus, preplus, or plus disease (intraclass correlation coefficient, 0.67; 95% confidence interval, 0.62–0.72). Mean ± standard deviation P scores for images with mode gradings of no plus, preplus, and plus disease were 2.5 ± 0.7, 4.8 ± 0.8, and 7.4 ± 0.8, respectively. Conclusions: Intergrader agreement of plus-like vascular change in ROP using the P score is high. We now incorporate this 9-image reference set into ICROP3 for use in clinician daily practice alongside zone, stage, and plus assessment. P score is not yet meant to replace plus diagnosis for treatment decisions, but its use at our institutions has permitted better comparison between examinations for progression and regression, communication between examiners, and documentation of vascular change without fundus imaging. P score also could provide more detailed ROP classification for clinical trials, consistent with the spectrum of plus-like change that is now formally part of the International Classification of Retinopathy of Prematurity. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

To date the Australian and Queensland governments have invested in different policy and program mechanisms including incentives, extension and education, market-based instruments, regulation (Great Barrier Reef Protection Amendment Bill 2009 (Queensland Government)) at a management practice level or gully and streambank remediation level, and conservation management land purchases. More recently, greater focus has been placed on wetland restoration or the application of treatment systems including treatment (constructed) wetlands and bioreactors to reduce land-based anthropogenic pollutant runoff from entering the Great Barrier Reef (GBR). Globally, wetlands have been restored and treatment systems have been constructed to help improve water quality from diffuse pollutants such as sediments, nutrients and pesticides from agriculture. In Australia and the GBR specifically, there is a very limited number of studies that have captured all the measured costs and that have been monitored over a number of years. Understanding the measured costs and cost drivers of wetland management and restoration actions is critical for informing new programs and projects seeking to achieve reductions in land-based pollutants.