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Caring, Commercial, and Conflicting Roles: Managing Tenancies in Social Housing
No full textA steady decline in social housing investment, and a subsequent shrinking in social housing stock, means that tenants in Australia’s social housing sector today face a high risk of homelessness if they are unable to sustain their tenancies. As a result of tight eligibility and targeting policies, tenants can also have high and complex support needs that make sustaining tenancies difficult without support. Not-for-profit community housing providers, which house a fast-growing proportion of social housing tenants in the state of Victoria, are required to remain financially viable while meeting a duty of care to their tenants. When tenants struggle to meet the obligations of their housing, providers may be caught between these competing demands. Little is known about the experiences of the sector’s frontline tenancy managers, who are expected to support the tenants in their care while protecting the organisation’s income. To address this knowledge gap, this thesis examines how tenancy managers at one housing provider conceptualise and navigate their role in responding to tenancies at risk of eviction, abandonment, or other exits from social housing that lead to negative outcomes for tenants.
Unison Housing is one of the largest community housing providers in Victoria, and it houses a cohort of tenants who experience severe and chronic disadvantage that puts them at high risk of tenancy breakdown and homelessness. This study collected data using in-depth interviews with 14 Unison tenancy managers and conducted an interpretative phenomenological analysis of the data to reach a deep understanding of their experiences managing and sustaining, or not sustaining, at-risk tenancies. It found that tenancy managers conceptualised their role as inhering a responsibility to provide care for tenants by supporting them to sustain their tenancies, but that they faced barriers to doing so. A theoretical framework built around feminist ethics of care was used to develop an account of how tenancies managers navigated these caring responsibilities as they responded to at-risk tenancies. Three further themes emerged from the findings: that the commercial imperative upon Unison and its tenancy managers was a barrier to what I have termed ‘tenancy-sustainment-as-care’; that tenancy breakdown could be understood as a breakdown in caring relations, and that tenancy sustainment was supported by collaborative practices of ‘caring with’ other actors involved in an at-risk tenancy, including colleagues, managers, support workers, and tenants themselves. These findings have clear ramifications for practice and policy in the social housing sector.</p
Adaptable Transfer Learning Models for Online Misinformation Detection
No full textThe spread of misinformation online significantly impacts public discourse and decision making across various domains. This thesis aims to develop deep learning models, particularly novel transfer learning techniques, for automatic detection of misinformation on social media platforms. Our first three studies focus on detecting rumours, which are unverified claims that circulate on social media. Briefly, we investigate (1) how we can adapt a rumour detection system from one language to another language without training data in the target language; (2) how we can improve rumour detection by incorporating not only content of the rumours but also the conversation structure of crowd responses and user relations; and (3) how we can develop rumour detection systems that are interpretable and provide insights into the factors contributing to the classification outcome. Our fourth
study looks at detecting state-sponsored trolls, malicious actors who aim to manipulate public opinion by spreading misleading or inflammatory content on social media. We explore few-shot learning techniques to develop troll detection systems that can effectively identify these users with limited training data.
Most existing models for rumour detection are tailored to a single language or specific event, raising the question of how to effectively transfer knowledge from one language or event to another. Our first study focuses on two important aspects of rumour detection: cross-domain transfer learning in the context of the COVID-19 infodemic and cross-lingual transfer learning. Firstly, we explore the application of rumour detection systems to new topics or events. We train our rumour detection system on a set of topics and events and then apply it to the specific case of COVID-19 pandemic. Secondly, we propose a crosslingual transfer learning framework that uses multilingual pretrained models (PLMs) as the backbone to adapt knowledge from a source language to a target language. We found that rumour detection systems can be applied to novel events or domains with some success, and including user responses is especially important.
Current systems to detect rumours on social media usually look at the text content or how quickly a post spreads. But it is still not clear how to use these signals all together effectively. To combine multiple signals, in our second study, we propose a hybrid model that fuses content features with language models alongside graph networks to encode the dynamic conversation structures of crowd responses and user relations.
While deep learning approaches greatly improved the performance of automatic rumour detection, the interpretability of these models remains a concern (i.e. how a model arrives at the classification decision is opaque to us). Our third study adapts causal mediation analysis, a method grounded in causal inference, to explain the decision-making processes of rumour detection systems. Our findings indicate that causal mediation analysis can uncover key comments that lead to a model decision which aligns with human judgements, demonstrating a novel paradigm to add a layer of interpretability and transparency to deep-learning based rumour detection systems.
In our last study, we focus on the task of detecting state-sponsored trolls, key operatives in social media influence campaigns. Current troll detection models, often trained on data from specific, known campaigns, struggle to adapt to newand unseen influence campaigns. To address this problem, we propose a meta-learning troll detection framework, which learns campaign-specific knowledge with minimal data and storing them in parameters that are not rewritten as the detection system is continually updated for new campaigns. Our detection system relies primarily on the textual content that a troll posts as core detection signal, but we further extend the framework for multimodal troll detection to incorporate images, broadening its capacity and effectiveness in combating misinformation.</p
Intelligent Traffic Flow Management Techniques for Dense Low Altitude Airspace and Urban Air Mobility
No full textUrban Air Mobility (UAM) promises to revolutionize urban transportation, offering an attractive solution to the challenges of traffic congestion and air pollution in rapidly growing cities. Central to this vision is the deployment of Unmanned Aircraft Systems (UAS), which, while promising reduced travel times and lower carbon emissions, face significant hurdles due to the current safety-related limitations associated to low-altitude airspace operations, especially over densely populated areas. Traditional Air Traffic Management (ATM) systems, primarily designed for manned aircraft, are under considerable strain from the increasing demand for UAS services and lack adequate provisions for low-altitude operations. These factors highlight the critical role of UAS Traffic Management Operators (UTMOs) in maintaining a balance between demand and capacity in congested airspaces. However, legacy ATM systems are ill-equipped to handle the unique challenges of UAS operations, necessitating a paradigm shift in traffic management approaches.
In response to the above challenges, this thesis explores the opportunities offered by the integration of Artificial Intelligence (AI) into Traffic Flow Management (TFM) systems. A thorough evaluation of AI algorithms was conducted to enhance Demand and Capacity Balancing (DCB) services, aiming to alleviate the workload on UTMOs and elevate operational efficiency and safety. AI algorithms promise a transformative impact on UAM by enabling more dynamic, adaptive traffic management strategies. Furthermore, the study delves into the critical aspect of AI explainability and trustworthiness, emphasizing the need for transparent and fair algorithms to foster user confidence in these automated systems. This aspect is crucial in ensuring equitable and unbiased decision-making processes in UAM operations.
Additionally, the interconnected nature of ATM Communications, Navigation, Surveillance, and Avionics (CNS+A) systems, while offering several opportunities for AI integration, also presented the range and impact of potential challenges. These include vulnerabilities to both cyber and physical security threats, with an expanded attack surface and additional propagation mechanisms in CNS+A systems, requiring new defensive measures both at system and component level. This study identifies potential vulnerabilities in the proposes CNS+A architecture and sets foundations for future research on possible AI-driven mitigation strategies to enhance ATM and UAS Traffic Management (UTM) systems.
The uncertainty inherent in UAM services, primarily due to fluctuating demand and capacity in urban low-altitude airspace, is the one of the key challenges tackled in this thesis. Technical solutions to enhance system safety and operational efficiency are explored, highlighting the importance of robust and flexible management systems in adapting to these uncertainties. The culmination of this research is the proposal and development of an adaptable TFM system framework, utilising hybrid AI algorithms tailored explicitly for UTM applications.
The practical application of this research is demonstrated in the design, implementation and testing of a prototype TFM system. This prototype underwent rigorous functional testing and performance assessments within a simulated UTM environment, offering valuable insights into the feasibility and effectiveness of the proposed solutions.
In summary, this paper provides a comprehensive analysis of integrating AI with Urban Air Mobility (UAM), addressing critical issues such as airspace resource scarcity, air traffic management system limitations, cybersecurity threats, and operational uncertainty. This research significantly contributes to the development of UAM by creating an adaptable AI-enhanced Traffic Flow Management (TFM) system, which paves the way for a more efficient, safer, and sustainable UAM framework. The findings and methods presented in this study have the potential to shape the future of UAM, facilitating machine-assisted automation in airspace operations and laying the groundwork for fully automated airspace management. The findings and methodologies outlined in this study have the potential to inform future developments in UAM, offering a blueprint for the challenges and opportunities of integrating highly automated aircraft and UASs into urban environments.</p
The Fun Model: How to implement fun in teams for better team function, relationships, and results: a quick-start guide for leaders, managers, and people passionate about teams.
No full text(Please note: all links in document are read only in Preview formatting but are hyperlinked when the "Fun Model" PDF is downloaded. All links are also available below in this Repository record.)In the ever-evolving landscape of higher education, where the pressures of performance, innovation, and collaboration are omnipresent, an often-overlooked element can make a great difference: fun.The concept of fun in professional settings might be met with scepticism, especially in academic environments where the stakes are high and the work is serious (Wright et al., 2021). However, integrating fun into higher education is not just a trivial pursuit; it’s a critical strategy for fostering creativity (Yang, 2020), enhancing teamwork (Michel et al., 2019), employees’ well-being (Renee Baptiste, 2009), and ultimately achieving success.To help achieve this, we have undertaken a three-year research project, supported by the Higher Education Research and Development Society of Australasia via a small grant to explore how we can test a Fun Model first penned by Hains-Wesson et al., (2023) and how it impacts teamwork. The research took the form of group-based autoethnography where we carved out dedicated time to pursue fun as a key element in team meetings, observing, reflecting, and critiquing fun. We used our experiences to create a Fun Quick Guide for leaders, managers and people passionate about teams, which we share here and for the wider community to consider introducing fun into their higher education work lives.</p
Compressing Atmosphere: Ceramic Encounters With Clay, Body and Site
No full textCompressing Atmosphere: Ceramic Encounters With Clay, Body and Site identifies compression, the decisive touch and resulting mark of the artist’s body on clay, as a potent starting point to consider practice-led research.
The sense of being in and attuning to the world is through encounters with atmosphere and with the assemblage of material bodies. Linking material and site enquiry, the research reveal how compression is both a method of making and a conceptual lens through which to investigate affective interpretations in ceramic sculpture and exhibition practice.
Compressive potential is understood through the concepts and language of autotheory and theories of affect, feminism, and new materialism. It is established that affect and autotheory is a theoretical framework from which to consider a creative condition of lived compression as experienced by the inhabitation of different sites of research.
By challenging foundational principles of ceramic hand building, this research has revealed ways of producing artwork that manifests a condition of maternal longing, call attention to climate crisis, and presents artefacts of Melbourne’s Covid-19 lockdown period. Atmosphere under compression is a creative condition for a ceramic art practice to perceive, synthesis and critique cultural, social and environmental events as embodied material experiences.</p
Optical Camera-Based Indoor and Vehicular Communication
No full textRecently, optical wireless communication (OWC) emerges as a complementary solution to conventional radio frequency (RF) communication, which can offer high data rate, secure communication and immunity to RF interference. With the development of lightemitting diode (LED)-based lighting infrastructure, as well as the wide availability of cameras on mobile phones and other devices (e.g., vehicles), Optical camera communication (OCC) has been proposed as a type of practical and cost effective OWC technology. In OCC systems, LEDs can serve as both illumination sources and communication transmitters, and ubiquitous cameras on different smart devices can be conveniently used as receivers without significant change to hardware. OCC is also a key component within the IEEE 802.15.7 standard, which focuses on short range wireless optical communication. The aspects such as modulation techniques, data rates, synchronization methods and error correction specific to OCC, are also included in this revised standard. It is worth noting that an accurate and reliable model is an essential piece of a physical layer communication analysis and simulation. However, current theoretical model for OCC is mostly based on general OWC channel model while ignoring the characteristic of image receivers. Therefore, in this research, a dedicated OCC model has been extensively investigated to solve this problem.
Firstly, a comprehensive OCC system model at the pixel level incorporating the imaging receiver and considering both line-of sight (LOS) and non-line-of-sight (NLOS) links has been established in Chapter 3. The characteristics of camera, such as camera distortion and receiver noise have been explored and incorporated into the designed camera model. In addition, we adopt stereo vision and P3P algorithms to verify the precision of our proposed model, especially whether the captured image can accurately represent the relationship of spatial position between the LEDs and the receiver camera. After that, an outdoor OCC based vehicular communication model has been established by incorporating solar radiation noise model for outdoor wireless channel in Chapter 5. Results show that solar radiation noise has great impact on the outdoor vehicular communication system, leading to an increase BER in the received signal. Consequently, the transmission distance of outdoor OCC systems is significantly reduced. In addition, the impacts of modulation scheme and modulation depth are also investigated.
In addition to the communication capability, the OCC can also provide the indoor localization function, which is also highly demanded recently, due to their widely applications in navigation, Internet of Things (IoT) and wireless communications. Conventionally, radio frequency (RF) based methods are widely used for the indoor localization, but they normally suffer from signal interference and multipath effects, which highly limits the achievable accuracy. Thus, OCC based indoor positioning approach has been proposed to overcome these limitations. There are several traditional positioning algorithms commonly used in OCC system, including received signal strength (RSS), time-of-arrival (ToA) and angle-of arrival (AOA). Besides, machine learning based image processing algorithm has also attracted intensive interests to solve this problem. In OCC system, the accuracy of the indoor positioning system is significantly influenced by the orientation of the receiver, as it affects how the transmitter is imaged within the field of view (FOV). However, current OCC based localization schemes mostly assume the receiver and transmitter planes are parallel, which ignore the critical tilt angle of the receiver in practical scenarios. Furthermore, previous OCC-based positioning techniques usually employed the central location of the LED image, ignoring the shape of the LED image, which can provide information about both orientation and distance. This is due to the difficulty of theoretically modelling and analyzing the shape information. These observations mentioned above have motivated the proposal of a novel indoor localization scheme, which can achieve accurate estimation for both position and orientation.
In Chapter 4, a novel OCC based indoor localization scheme has been proposed. Results show that our proposed method can realize an average Euclidean distance of 0.0571m and an average quaternion angle error of 0.5038°, thus accurate simultaneous position and orientation estimation has been achieved. Furthermore, the impacts of pretrained convolutional neural network (CNN) models and dataset size on the performance of the proposed method are also investigated. Results show that the GoogLeNet based CNN model with the dataset size of 40000 has the best performance in the localization system. In addition, the proposed scheme has been verified can effectively reduce the number of LEDs needed to be captured, thus can improve the working area of the localization system.</p
Understanding Key Factors Required for Developing a Circular Business Model for End-of-life Electric Vehicle Lithium Batteries in Australia
No full textThe transition to electric mobility in the road transportation sector has led to an increase in the
adoption of lithium batteries. Due to this adoption, electric vehicle (EV) lithium-ion battery
(LiB) waste is an emerging concern. Moreover, if disposed of to landfill, LiBs pose a threat to
the environment and on human health. Research has shown end of life (EOL) EV LiBs contain
residual capacity and valuable materials. To avail this value and mitigate the adverse disposal
to landfill, circular economy (CE) principles have been considered and proposed.
Consequently, circular business models (CBMs) act as a tool to implement CE principles. The
literature review revealed a lack of research on the development of CBMs in the Australian
context. Hence, this research aims to identify the key factors required to develop a CBM for
EOL EV LiBs in Australia.
To achieve this objective, a qualitative methodology was adopted following an inductive based
approach and semi – structured interviews were selected as a method of data collection. A total
of 19 interviews involving 22 participants were conducted and participants were categorised
based on stakeholder groups identified through the literature review. The interview data was
validated using data triangulation, and thereafter, a thematic analysis was used to develop six
major themes from the interview data. As a subset of these themes, several findings emerged
focusing on the scenario of EOL EV LiBs in Australia and identifying context-specific drivers
and barriers to develop a CBM. Of note, a major theme identified was that repurposing of EV
LiBs faces greater challenges than recycling in Australia. Additionally, the influence of federal
and state-based policies play an important role in the development of CBMs in Australia.
A noteworthy finding from this study highlights drivers and barriers compared to the literature
review vary due to dependence on overseas nations and presence of unknown variables due to
a smaller market scenario. Overall, the major considerations for stakeholders to adopt a
comprehensive view on the development of CBMs relies closely on the influence of
government regulations and collaboration between government, industry, and academic
institutions. As such, strategic interventions such as ‘stewardship schemes’ and ‘pilot
initiatives’, ‘vertical integration’, ‘leasing models’ and ‘co-locating pre-processing facilities’
were proposed using a process flow diagram which represent key factors required to develop a
CBM for EOL EV LiBs in Australia. These key factors enable stakeholders to gain a deeper
understanding of the Australian context and consequently implement CBMs that create value
adding opportunities.</p
Indonesian comics: shaping cultural identities and designing nation's incentive
No full textDescription not supplied by author.</p
InterActive Gut Experiences: Understanding the Design of Play to Support Gut Health Engagement and Reflection
No full textInteractive experiences powered by play are gaining prominence in Human-Computer Interaction (HCI) as they provide opportunities for users to engage with technology in a fun and meaningful way. Play-based approaches have been used to spark interest in science and to support the exploration of health topics, providing a safe space for individuals to reflect on their learnings, actions, and decisions. Such playful mediation using interactive technologies encourages personal discovery, fostering new relationships between the self and the larger ecosystem through rule-based and personal exploration.
This thesis represents one such active investigation into the design of interactive play experiences for gastrointestinal (gut) health engagement. Maintaining a healthy gut is crucial to human wellbeing and it is a complex topic to understand owing to its multi-factorial influences, including mode of birth, age, early microbial exposure, diet, lifestyle, and environment. Current approaches to supporting awareness of gut health factors are primarily passive forms of knowledge sharing, and they lack an interactive play-based approach focused on promoting adult engagement with this topic. This absence motivates this research.
The research for this thesis uses two case studies, the design of which draws on knowledge from the topics of gut health, play, and reflection and builds on existing works involving interactive technologies in the field of Human-Computer Interaction (HCI) and Human-Food Interaction (HFI). Following the Research through Design (RtD) methodology, the two case studies individually explore 1) structured play through a board game called Gooey Gut Trail (GGT) and 2) unstructured play through a smartphone game called Go-Go Biome (GGB). GGT uses real-world contexts and scenarios to bring attention to the factors that influence gut health, making the game relatable to players, while GGB is a smartphone game that promotes user engagement in real-world gut-friendly activities through playful exploration.
The design learnings from designing the two case studies and the empirical findings from testing the two design prototypes led to the development of a design framework called the HEAR Design Deck, a set of design cards to aid in the design of interactive play-based experiences for health engagement and reflection. The HEAR deck aims to guide interdisciplinary researchers in deconstructing and translating health science topics into play-based experiences. This framework has theoretical and practical implications that extend beyond the design of interactive gut experiences, making it generalisable to design for health engagement and reflection on many public health issues. This research has implications for the interaction design community in HCI, focused on designing for individual and community health initiatives through play.</p
High-speed Laser Directed Energy Deposition of Stellite 6 Coatings on Steel Substrates
No full textHigh-speed laser directed energy deposition (HS-L-DED) process, also known as the high-speed laser cladding (HSLC), and extreme high-speed laser application (EHLA), is gaining traction in coating applications and repair, driven by its high production efficiency and minimal disruption to substrate materials. Historical studies on single-track depositions, use of fibre optics with solid state lasers, and the development of dual-laser HS-L-DED system laid the groundwork for HS-L-DED. This leads to recent advancements in single-laser HS-L-DED system, showcasing deposition speed from 5 to 150 m/min. The combination of high process speed and thin material deposition results in refined microstructures, enhancing wear and corrosion resistance of targeted coating materials.
300M steel is a low-alloy ultra-high-strength steel, commonly used for manufacturing of aircraft landing gears. The component surface requires wear resisting coating which has been traditionally applied by electrolytic hard-chrome (EHC) plating. However, the increasing environmental concerns of electroplating processes are stimulating search for alternative coating and application process to reduce its environmental impact. Stellite® 6 is an established cobalt alloy, with a long history of successful use in hard-facing component surface through L-DED deposition. A further study is required to explore the key contributors to its deposit formation and microstructural evolution in HS-L-DED deposition of Stellite® 6 when deposition speed exceeds 10 m/min. The research emphasis in this thesis is on the effects of laser power, deposition speed, laser spot diameter, powder stream offset distance and track overlap on the deposition of Stellite® 6 powder on steel substrates. Process optimisation and the establishment of a process window are key objectives, considering both productivity and substrate integrity. The recommendations for depositing Stellite® 6 on 300M steel substrates through HS-L-DED include an optimal energy density level of 5 J/mm2, consisting of a 3 kW laser power, a 2.4 mm laser spot, a deposition speed of 15 m/min, with a linear powder mass delivery of 1.5 g/m, a 90% track overlap and a 0.8 mm powder stream offset distance from the substrate surface. The resulted thickness of a single-layer, multi-track deposit was measured at 495 ± 46 μm.
The microstructural evolution of as-deposited Stellite® 6 exhibits a mixture of columnar and equiaxed grains alternating across the overlaid track boundaries. This is attributed to the incorporations of semi-molten powder particles, as well as an accelerated columnar-equiaxed transition during solidification of the deposit. The increasing proportion of equiaxed grains, as a result of increasing deposition speed, directly correlates with the increase in microhardness, and the wear performance of deposited layers. The wear performance of the optimised HS-L-DED Stellite® 6 coating was evaluated against the conventional L-DED Stellite® 6 coating and EHC plating. The results highlight the potential of HS-L-DED for depositing high-quality Stellite® 6 coatings on steel substrates with improved deposition efficiency. The interfacial bond strength between HS-L-DED deposited Stellite® 6 and 300M steel surpasses the minimum shear strength requirement for EHC coating, even in the presence of pre-existing defects. It signals a potential improvement in wear resistance with a good metallurgical bond to the substrate.
In-situ monitoring with monochrome high-speed and welding camera was used to study the high-speed deposit formations in HS-L-DED. A novel observation, melt pool lag (MPL), is introduced. It is considered a result and representation of gradual heat accumulation within the deposition region. While the MPL was clearly observed in laser surface melting experiments, the irradiating pre-heated powder particles posed a challenge to directly observing the MPL phenomenon during HS-L-DED. The MPL development demonstrates a strong correlation with HS-L-DED deposited Stellite® 6 characteristics, which suggests
a potential in using the MPL measurement to regulate HS-L-DED depositions. Based on MPL measurements, an innovative approach was examined to align the powder stream spot with the delayed melt pool formation, improving the powder catchment efficiency during HS-L-DED.
In summary, this thesis contributes to the evolving field of HS-L-DED, offering insights into deposit formation, process optimization, and the potential for superior Stellite® 6 coatings with enhanced wear resistance and bond strength to steel substrates. The research findings deepen the understanding of microstructural evolution of Stellite® 6 alloy during rapid solidification. Further research is recommended to enhance the understanding of high-speed melt pool formation and its implications on deposit quality.</p