Cybersecurity governance is increasingly critical in a digital economy, with board directors playing a central role in shaping organisational resilience. Directors are pivotal in setting cybersecurity strategies and carrying fiduciary obligations that extend to digital risk oversight. This study examines the cybersecurity literacy and governance practices of Australian board directors through a qualitative interview study with 13 participants. Findings reveal a substantial gap in directors’ knowledge and confidence, undermining effective oversight and informed decision-making. This deficit limits their ability to interrogate risk reports, challenge assumptions, and steer investment in line with organisational resilience goals. In response, we propose a Board Cyber Governance Model that integrates targeted education, strategic interventions, and structured board–CISO engagement to improve governance capability. By situating cyber governance at the intersection of executive decision-making, risk perception, and digital security, this work contributes to human-computer interaction by highlighting socio-organisational challenges and offering actionable insights for stronger board-level engagement.

Governments worldwide are increasingly embracing digital transformation initiatives to enhance service delivery, engage citizens, and achieve better outcomes. However, obtaining continuous feedback on these initiatives poses a substantial challenge. This paper investigates the feasibility of leveraging mobile app reviews as a valuable source of citizen feedback on government digital services. We analyse 100,146 app reviews from 129 government mobile apps in Australia and identify several functional and usability issues. These include issues such as app instability, complexity, integration problems, navigation difficulties, inaccuracies, and challenges with ID verification and authentication processes. Furthermore, we uncover several factors that influence user satisfaction, including accuracy and reliability, convenience, dependability, user-centric design, and overall user-friendliness. These findings demonstrate a strong correlation between user feedback and the government's digital transformation strategy, underscoring the viability of mobile app reviews as a cost-effective avenue for collecting citizen feedback.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2020.2974741]

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leveragecrowdsourcedsocial media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time, and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach.

We propose a new social-sensor cloud services trust model. We propose to represent social media data streams, i.e., images' meta-data and related posted information, as social-sensor cloud services. Images' meta-data and the related posted information are abstracted as the functional and non-functional aspects of the social-sensor cloud services. The trustworthiness of a social-sensor cloud service is measured based on the users' stance based trust model. We use the textual features of the social-sensor cloud services, i.e., comments and meta-data, e.g., spatio-temporal information to gather the trust-rate of the service. Analytical results are presented to show the performance of the proposed model with real datasets.

We propose a new framework for social-sensor cloud services selection based on spatio-textual correlation between user's query and service. The proposed research defines a formal social-sensor cloud service model that abstracts the functional and non-functional aspects of social-sensor data on the cloud in terms of spatio-temporal, textual and quality of service parameters. Proposed framework is a 4-stage filtering algorithm, to select social-sensor cloud services based on user query and quality of service demands. 4-stage filtering is based on spatial correlation, textual correlation, visual features and quality of service parameters. Analytical results are presented to show the performance of the proposed approach.