arXiv subjects

Senuri Wijenayake

Publications and source records attributed to Senuri Wijenayake.

4 recordsLinked to original sources

arXiv2026

Advancing Inclusivity in Cybersecurity Education: Integrating Intersectionality to Enhance Student Engagement in Australian Higher Education Curriculums Strategies, Barriers, and Future Directions

Australian women, gender-diverse individuals, and culturally and linguistically diverse (CALD) communities are often more susceptible to phishing and other forms of cybercrimes due to factors such as language barriers, limited access to cybersecurity education, and social isolation. These communities encounter substantial obstacles both entering and progressing in the cybersecurity field. In Australia, the Higher Education sector still leans heavily on a largely uniform cybersecurity curriculum, focusing heavily on technical proficiency, overlooking the vital impact of intersectionality and user-centered thinking for boosting student engagement and learning. Without gender inclusivity and proper consideration of intersectionality forms such as CALD, the workforce is deprived of the varied perspectives necessary to tackle today's intricate cybersecurity issues. In this study, we conducted semi-structured interviews with 15 experienced academics teaching and coordinating cyber security programs from a diverse range of Australian universities, covering all states, to explore their perspectives on: i) current strategies for addressing the women, gender-diverse and CALD perspective in cyber security education in the Australian HE sector; ii) barriers to incorporate women, gender-diverse and CALD perspective in cybersecurity curriculums in higher education; iii) future work and support that is needed. Our research highlights a lack of systematic methods for integrating intersectional perspectives into cybersecurity curriculums. In particular, we identified four key barriers and four areas where support and future efforts are needed to address this issue. Our findings offer vital insights that can substantially guide curriculum development in cybersecurity education.

cs.CY↗

arXiv2026

From Passersby to Placemaking: Designing Autonomous Vehicle-Pedestrian Encounters for an Urban Shared Space

Autonomous vehicles (AVs) tend to disrupt the atmosphere and pedestrian experience in urban shared spaces, undermining the focus of these spaces on people and placemaking. We investigate how external human-machine interfaces (eHMIs) supporting AV-pedestrian interaction can be extended to consider the characteristics of an urban shared space. Inspired by urban HCI, we devised three place-based eHMI designs that (i) enhance a conventional intent eHMI and (ii) exhibit content and physical integration with the space. In an evaluation study, 25 participants experienced the eHMIs in an immersive simulation of the space via virtual reality and shared their impressions through think-aloud, interviews, and questionnaires. Results showed that the place-based eHMIs had a notable effect on influencing the perception of AV interaction, including aspects like visual aesthetics and sense of reassurance, and on fostering a sense of place, such as social interactivity and the intentionality to coexist. In measuring qualities of pedestrian experience, we found that perceived safety significantly correlated with user experience and affect, including the attractiveness of eHMIs and feelings of pleasantness. The paper opens the avenue for exploring how eHMIs may contribute to the placemaking goals of pedestrian-centric spaces and improve the experience of people encountering AVs within these environments.

cs.HC↗

arXiv2025

Advancing Interdisciplinary Approaches to Online Safety Research

The growing prevalence of negative experiences in online spaces demands urgent attention from the human-computer interaction (HCI) community. However, research on online safety remains fragmented across different HCI subfields, with limited communication and collaboration between disciplines. This siloed approach risks creating ineffective responses, including design solutions that fail to meet the diverse needs of users, and policy efforts that overlook critical usability concerns. This workshop aims to foster interdisciplinary dialogue on online safety by bringing together researchers from within and beyond HCI - including but not limited to Social Computing, Digital Design, Internet Policy, Cybersecurity, Ethics, and Social Sciences. By uniting researchers, policymakers, industry practitioners, and community advocates we aim to identify shared challenges in online safety research, highlight gaps in current knowledge, and establish common research priorities. The workshop will support the development of interdisciplinary research plans and establish collaborative environments - both within and beyond Australia - to action them.

cs.HC↗

arXiv2018

A Decision Tree Approach to Predicting Recidivism in Domestic Violence

Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.

cs.LG↗