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Conference output Identifying Optimal Window Size Configurations for Big Data Time Series ForecastingJohn, David L; Binnewies, Sebastian; Stantic, Bela (2024 IEEE International Conference on Big Data (BigData), 2024)Optimal window sizing in time series forecasting emerges as a pivotal factor for enhancing predictive accuracy, particularly in the volatile cryptocurrency market. While traditional models often rely on static window sizes, resulting in compromised forecasting performance, this research explores optimal window configurations across various market volatilities. By employing a hybrid Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) model, the study systematically identifies the most effective window sizes for high, medium, and low volatility conditions. Results demonstrate that smaller windows are preferable in highly volatile environments to capture rapid market shifts, whereas larger windows are more suitable for stable conditions to incorporate a broader historical context. By identifying the predetermined optimal window sizes for each volatility segment, this study offers valuable insights for researchers aiming to enhance the adaptability and efficacy of predictive models. These results are especially useful for exploring dynamic window sizing techniques across various domains, particularly in fields where data volatility significantly impacts model performance.
Conference output Accurate determination of null-point between Overlapped Planar Spiral CoilsWilliams, KJ; Town, G; Deilami, S; Mitchell, S; Taghizadeh, F (2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024)Within the context of multi-coil inductive power transfer (IPT) systems, the presence of mutual induction between same side coils has been a considerable challenge since it directly impacts system stability and performance. In this paper, a mathematical model is presented to accurately estimate the location of a null point in mutual inductance between two planar spiral circular coils. The impact of coil design parameters such as vertical separation, fill factor and outer radius are investigated for impact on null point location. This model is then verified against finite element analysis simulation results. The identified location of the null point is within a maximum of 0.39% error. The findings of this work are directly applicable to the design of multi-coil inductive power transfer systems where decoupling between same-side coils is advantageous to achieving high efficiency and power capability.
Conference output On-chip Quantum Interference of Transverse Modes with Inverse Designed StructuresRoque, J; Peace, D; White, S; Polino, E; Das, S; Slussarenko, S; Tischler, N; Romero, J (Frontiers in Optics + Laser Science 2024 (FiO, LS), 2024)Transverse spatial modes have become an increasingly popular degree-of-freedom for encoding quantum information owing to the compact implementation when compared to others such as path. Here we present the demonstration of two photon Hong-Ou-Mandel interference between the different transverse modes in a multimode silicon photonic chip using a compact inverse designed beam splitter with up to 99.56 ± 0.64% interference visibility. We achieve high visibilities across both multiple copies of the same design and among different designs. This work demonstrates the potential of inverse designed devices for quantum information processing with transverse spatial modes.
Conference output Evaluating Energy Consumption Prediction Models of a Quadcopter Unmanned Aerial VehicleSarkar, A; Santoso, F; Shen, J; Du, B; Telikani, A; Yan, J (2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024)Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in various fields. A major concern with UAV operation is their limited power capacity which impacts mission planning, operational efficiency, and battery management, presenting significant research and engineering challenges. This paper evaluates the applications of multiple AI algorithms in predicting the energy consumption of low-cost quadcopter drones. One of the primary contributions involves developing four prediction models, including random forest, regression tree, support vector machine, artificial neural network, and adaptive Neuro-Fuzzy Inference System (ANFIS) on an open-source dataset of small quadcopter flights. This paper also performs a comparative study on the performance of the aforementioned algorithms in predicting the energy consumption of a UAV. This research enhances the field not only by leveraging established machine learning techniques but also by adopting and examining ANFIS, which has received limited prior research attention. By introducing and applying ANFIS, this study not only expands the existing knowledge but also offers a unique perspective, potentially paving the way for further research, especially in addressing uncertainty like weather conditions. According to our study, the power consumption of the UAV is notably influenced by the aircraft's altitude, wind speed, and velocity. The Random Forest model demonstrates superior accuracy in forecasting UAV power consumption compared to other models. We also provide an overview of the ongoing challenges and potential future endeavors.
Conference output Smart Verification of Unmanned Aerial Vehicle GPS Geolocation via Received Signal Strength IndicatorsSarkar, A; Santoso, F; Telikani, A; Shen, J; Du, B; Yan, J (2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024)The increased reliance on Unmanned Aerial Vehicles (UAVs) in various industries exalts the security requirements since it is critical to protect these systems from any cyber-attack. GPS spoofing presents an important challenge by deceiving UAVs through false GPS signals that would disrupt their operations, thereby endangering them. As a countermeasure, this study introduces a method of detecting GPS spoofing attacks that are aimed at UAV systems. This involves developing a robust methodology to detect the GPS spoofing attack based on the UAV's current reported location and Received Signal Strength (RSS) data at several base stations. In this study, we developed a smart verification algorithm using the K-Nearest Neighbors (KNN) algorithm to authenticate the reported locations of UAVs, based on RSS from various base stations antenna. We evaluated the performance of the algorithm using metrics such as accuracy, precision, and F1-score. The results indicate that the algorithm's effectiveness improves with an increase in the number of base stations used. Additionally, the paper will pinpoint the possible direction for UAV security and the adaptive countermeasures to improve the level of resilience against spoofing tactics, which are rapidly evolving.
Conference output An MILP Based Energy Optimization in a Multi Source EV Charging StationAli Kazmi, SN; Yang, F; Sanjari, MJ (2024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), 2024)The global rise in Electric Vehicle (EV) adoption to reduce the carbon footprint has intensified the need for efficient energy management strategies in grid-connected EV charging stations. This paper presents an optimal energy management approach using Mixed-Integer Linear Programming (MILP) to maximize the profit for the charging station owners (CSOs), a key factor in driving further investment in the charging infrastructure and accelerating widespread adoption of EVs. By leveraging MILP, the proposed framework enables charging station operators to optimize EV charging schedules, accounting for dynamic time-of-use (TOU) electricity rates, PV generation variability, and fluctuating EV demand. The objective is to maximize profit while ensuring sustainable and cost-effective operation. Simulation results highlight the effectiveness of this approach in improving profitability and reducing operational costs. The study underscores the potential of MILP to enhance the efficiency and sustainability of EV charging infrastructure, accelerating the shift towards smart and resilient urban mobility solutions.
Conference output Enhancing Histopathological Breast Cancer Classification with Thresholding Techniques and Transfer Learning ModelsKhan, RQ; Shakir, H; Khan, WQ (2024 International Visualization, Informatics and Technology Conference (IVIT), 2024)Breast Cancer is a disease that widely affects millions. A widely recognized approach for diagnosing breast cancer is through the analysis of histopathological images. In order to achieve notable results with these images require building complex ensemble models that are augmented through several techniques, making the process cumbersome. In this paper, the authors investigate famous thresholding techniques: Otsu Threshold, Adaptive Mean Threshold and Adaptive Gaussian Threshold, that are trained on basic transfer learning models: ResNet50, DenseNet201 and Inception v3 to improve diagnosis without creating complex architectures. The Adaptive Gaussian Threshold and Otsu Threshold showed promising results when trained on ResNet50 and Inception v3.
Conference output Design and Development of a Multi-Port EV Fast Charger with Dynamic Control StrategyNaz, MN; Lu, J; Arif, MT (2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024)Electric vehicles (EVs) demand is increasing due to their significant impacts on different aspects of society, the economy, and the environment. Unlike a normal-charger, a fast EV charger is critical for EV charging because it reduces the EV charging time significantly from several hours to just 30 minutes to 1 hour. To charge multiple EVs simultaneously and reduce the waiting time of other EVs, more power electronics converters are required which may also increase the infrastructure cost. To minimize the waiting time and infrastructure cost, and accommodate more EVs, a multi-port EV fast charger is designed in this paper. A dynamic mode control strategy is developed which not only controls the individual EV charging but also controls the charging of other EVs connected for charging in the meantime. In the developed dynamic control strategy, the charging current is controlled based on the state of charge (SoC) of each EV. When the SoC of any EV achieves its next predefined level, it triggers the mode synchronization and the level of charging current to all EVs connected to this multi-port charger adapts to change in real-time. Instead of a fixed charging current allocated to each EV, the charging current level for each EV dynamically changes even when one EV connected to a multi-port fast charger reaches to the next predefined SoC level. To design a multi-port offboard EV-fast charger, EVTECH & espresso charge standard specifications are used. The simulation is carried out using a Simulink environment to verify and evaluate the effectiveness of the developed dynamic mode control strategy for multi-port fast charger application.
Conference output Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic GraphsKandanaarachchi, Sevvandi; Sanderson, Conrad; Hyndman, Rob J (2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2024)Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limitations, such as high false positive rates as well as difficulties with handling variable-sized graphs and non-trivial temporal dynamics. To address this, we propose a technique where temporal dependencies are explicitly modelled via time series analysis of a large set of pertinent graph features, followed by using residuals to remove the dependencies. Extreme Value Theory is then used to robustly model and classify any remaining extremes, aiming to produce low false positives rates. Comparative evaluations on a multitude of graph instances show that the proposed approach obtains considerably better accuracy than TensorSplat and Laplacian Anomaly Detection.
Conference output Toxicity of carbon dots to sugarcane and human cellsZia, M; Watts, J; Sambasivam, P; Nguyen, T; Tonissen, KF; Chen, D; Ford, R; Bhuiyan, SA; Li, Q (45th Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2024), 2024)Sugarcane is Australia's second largest export crop and generates an annual revenue over $4 billion. A perennial crop grown along the eastern coast of Queensland and in northeast New South Wales, sugarcane cultivation is impacted by biotic and abiotic stresses. The use of nanomaterials to mitigate these stresses has attracted attention in recent years as they have the potential to provide more sustainable solutions. Carbon nanodots (C-dots) are carbogenic nanoparticles that are typically smaller than 10 nm, water dispersible, UV absorbing, highly fluorescent and biocompatible. C-dots have been identified as a potential nanocarrier and nutraceutical material for sugarcane farming. Therefore, evaluation of their toxicity towards sugarcane setts, human cell lines and other agriculture-relevant animals such as bees is imperative for assessing their suitability for agricultural applications. In this study, carbon dots (CD) were synthesized in-house, and different concentrations were applied to sugarcane setts at 10 min dipping times to evaluate their impact on germination, plant height, and biomass four weeks after planting (WAP). Cytotoxicity evaluation using normal human dermal fibroblast (NHDF) cells and MDA-MB-231 breast cancer cells exposed to CD showed no change in cell viability. Overall, the CDs did not exhibit phytotoxicity and cytotoxicity indicating their feasibility for use in sugarcane cultivation.
Conference output Vibratory Evaluation of Material Damping Performance of Cross-Laminated Timber PanelsFaircloth, A; Karampour, H; Brancheriau, L; Bailleres, H; So, S; Kumar, C (ACAM10: 10th Australasian Congress on Applied Mechanics, 2021)Serviceability is one of the governing parameters for the use of cross laminated timber (CLT) panels in floor systems for multi-storey buildings. The low comparative density of CLT products against steel and concrete systems can attract low damping and acoustical performance. Referring to the transient impulse response of a material, damping is a product of the boundary conditions (BCs) and material properties such as stiffness and density. There currently exists minimal understanding of the performance of CLT products in regards to damping as well as methods by which damping can be quantified and furthermore, the BCs under which these assessments should be conducted to ensure representative results to the intended application. The aim of this study was to investigate a vibratory assessment method to evaluate the damping performance of CLT panels for a range of conditions that may affect the damping response for a floor surface area equal to the tested CLT panel (3.0 x 3.0 m). 75 mm and 125 mm CLT panels were assessed for their damping response for a fully clamped (CCCC) BC to the influence of an impacting force with respect to a dropped weight of variable mass and drop height. The results of the damping response indicate a higher rate for impact tests at the boundary supports while the mid-span locations produced lower damping; while this was consistent across both panel types, the 125 mm CLT produced consistently higher damping rates compared with the 75 mm CLT, suggesting that the added material assists with damping. In addition to this the sound transmission difference between the surface to the underside of the panels were measured using appropriately positioned microphones and found a complimentary result to the damping where the transmission difference was lower for higher drop heights and varied with weight.
Conference output CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk PredictionNguyen, Tuan Dung; Huynh, Thanh Trung; Phan, Minh Hieu; Nguyen, Quoc Viet Hung; Nguyen, Phi Le (2024 Conference on Empirical Methods in Natural Language Processing, 2024)The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high generalizability. Inspired by how human experts leverage reasoning for medical diagnosis, we propose CARER, a novel health risk prediction framework, that enhances deep learning models with clinical rationales derived from medically proficient Large Language Models (LLMs). In addition, we provide a cross-view alignment loss which aligns the “local” view from the patient’s health status with the “global” view from the external LLM’s clinical reasoning to boost the mutual feature learning. Through extensive experiments on two predictive tasks using two popular EHR datasets, our CARER’s significantly exceeds the performance of state-of-the-art models by up to 11.2%, especially in improving data efficiency and generalizability. Our code is available at https://github.com/tuandung2812/CARER-EMNLP-2024
Conference output Robust Decoupling Control Based on Linear Extended State Observers for Grid-Forming InverterQu, Y; Li, H; Bai, F; Lu, J; Yang, F (2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024)Grid-forming inverters (GFMIs) based on virtual synchronous generators (VSGs) have emerged as a promising solution for providing inertia and enhancing renewable system stability. However, the inherent power coupling caused by the low reactance-to-resistance ratio in low-voltage networks negatively impacts the control performance of VSGs. Commonly used feedforward decoupling methods rely heavily on system parameters. Although these methods can effectively reduce power coupling under nominal systems, their decoupling capability is significantly limited when dynamic variations occur in the system. Therefore, a robust decoupling control strategy based on linear extended state observers (LESOs) is proposed in this paper. The proposed method achieves faster and more efficient decoupling and demonstrates strong robustness in the non-nominal system. Finally, the effectiveness and decoupling ability of the proposed strategy are verified by simulation results.
Conference output Ethnography Flying High: An Exploration of the Learning Journeys of Airline Cabin CrewLarrea, Maria F (9th Biennial ACSPRI Social Science Methodology Conference, 2024)This presentation focuses on the challenges and contributions of an ethnographic approach to the learning experiences of airline cabin crew members as they adapt to their dynamic work environments. Grounded in sociocultural theories of learning, particularly Lave and Wenger's (1991) situated learning concepts, this research aims to understand how cabin crew develop the necessary skills to operate in socially dynamic, safety-critical work contexts. I will explore how ethnographic methods can be applied to workplace learning in complex settings, contributing to ongoing discussions about the evolution of social science research.
Conducting ethnographic research in the highly regulated and fast-paced aviation industry posed significant challenges. First, getting permission to observe training sessions and live flight operations involved navigating organisational protocols and balancing safety concerns with the need for detailed observation. Once in the field, I had to adjust to the dynamic nature of the workplace, where crew members frequently changed flights, interacted with different colleagues and customers, and worked across various routes. Later, consistently tracking individual learning journeys required flexibility and perseverance in my data collection approach. For example, when participant observation was limited, interviews and casual conversations helped me follow their experiences and maintain our connections. During eight months of fieldwork, I could interpret social interactions, tacit knowledge, and context that shaped the learning experiences of new crew members transitioning from training to real-world flight operations.
Additionally, I carefully navigated my dual role as observer and participant. While immersed in the cabin crew's environment, I balanced engaging with participants and respecting their confidentiality, as well as the operational demands, especially during high-stress situations such as assessments and disrupted operations. Despite the challenges, this role adaptation enriched my insights into how cabin crew negotiate both explicit and tacit knowledge under real-world conditions. This immersion also allowed me to capture how learning is intertwined with everyday experiences, showing how ethnography can adapt to complex work environments.
This study contributes to social science research methodology, particularly vocational learning and professional development. The approach provides access to informal, context-specific learning often overlooked by more structured methods such as quantitative assessments, surveys, or retrospective interviews. It uncovers the social and cultural dimensions of learning that occur as cabin crew members participate in real-time challenges, interact with colleagues and customers, and engage in decision-making during flights. Moreover, the research advances our understanding of how professional identities are formed through situated learning in complex workplaces. By focusing on the negotiation between formal training and informal learning, the study highlights the value of ethnography for examining how individuals and teams develop expertise in high-pressure, safety-critical environments. These insights enrich the literature on workplace learning and contribute to broader methodological discussions about ethnography in modern work contexts.
Conference output Identifying students’ perceptions of industry placementsGratchev, Ivan; Howell, Simon; Shaeri, Saeed; Espinosa, Hugo; Busch, Andrew (34th Australasian Association for Engineering Education Conference, 2023)CONTEXT Griffith School of Engineering and Built Environment (EBE) and School of Computing, Mathematics and Engineering (SoCME) at Charles Sturt University run two distinct workintegrated learning (WIL) courses to prepare students for their industry careers. To maximize students’ experience, students are expected to possess certain knowledge and sets of skills before their industry placements, which will help them to successfully apply their theoretical knowledge in practice. However, as it is not possible to teach every single skill during the undergraduate years at university, there may be mismatches between the student’s knowledge level, competencies, and skills from one side, and the needs of the industry from the other side. PURPOSE OR GOAL This work seeks to investigate the students’ perceptions of the industry and evaluate students’ skills and readiness to undertake industry projects. The main research questions are as follows: What are the graduates’ skills and competencies currently required by the industry? Are students aware of the industry skill set needs and expectations, and have they developed such skills during the first few years of their undergraduate programs? APPROACH OR METHODOLOGY/METHODS This study has surveyed and interviewed both students and the industry partners to better understand students’ perceptions and experiences against the current industry perceptions and needs. Analysis of the data obtained from these surveys and student focus groups helped to identify areas for improvement at both universities. The results highlighted similarities/differences between students’ expectations and industry needs and will be used to develop recommendations on how to improve the work-integrated learning programs. ACTUAL OR ANTICIPATED OUTCOMES The student surveys have been conducted at both universities, with about seventy responses being collected so far. The analysis of these data indicates that students are generally aware of the skills and competencies that the industry expects from them; however, a few mismatches in terms of the importance of certain skills and/or competencies have also been identified. This study is currently in progress, and more data from the student and industry partner surveys and focus groups will be obtained in the next several weeks. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The results of student surveys showed that students have a good understanding of what is expected from them by the industry. However, there are still skills and competencies that students may not find currently important; however, they are highly desired by the industry.
Conference output A pilot study into developing animations for electrical and electronic engineering curriculumSo, Stephen; Schwerin, Belinda; Rowlands, David; Espinosa, Hugo; Tadj, Timothy; Busch, Andrew (34th Australasian Association for Engineering Education Conference, 2023)CONTEXT Many electrical and electronic engineering (EEE) programs comprise courses that cover a great deal of mathematical and physics-based fundamentals and link them to important engineering concepts. Compared with courses in other engineering disciplines, it is commonly observed that undergraduate EEE students often struggle with grasping the content since by nature they are mathematical, abstract, and intangible. Many respond more effectively to visuals rather than mathematical proofs and equations, but often the EEE instructor is limited to presenting static graphs and diagrams. Students are then expected to construct their own mental representations of the concepts, which is often a challenging task. PURPOSE OR GOAL The goal of this pilot study was to investigate the effectiveness of using narrated animations to help EEE students in two cores courses (Signals and Systems, Electric Circuits) understand concepts that are mathematical, abstract, and intangible by nature. The hypothesis was that “bringing these concepts to life” would enable students to better understand and comprehend them. APPROACH OR METHODOLOGY/METHODS In order to create animations that are both high quality and suited for technical content, the study used the manim CE (community edition) Python library, which provided a programmatic way of accurately rendering mathematical and graphical objects. The Signals and Systems course was used in this pilot study. The topics selected were convolution and Fourier series. The videos generated by manim CE were narrated together with inspirational background music and shown to second year students enrolled in the 2305ENG Signals and Systems, after they had covered the concepts in traditional lecture form. The students were then asked to complete a survey to gauge the effectiveness of the animations in helping them understand the concepts. ACTUAL OR ANTICIPATED OUTCOMES The students found that the animations were helpful to their learning and understanding. They also indicated that the videos clearly showed the transformation from one step to another, which is very important as this shows the value in using the animation to enhance the students learning and understanding. CONCLUSIONS/RECOMMENDATIONS/SUMMARY Overall, the students seemed to be positive with the structure, design, and targeting of the animations. They also felt that the animations complemented their learning. This was an encouraging result which indicates that the animations will be a benefit for theoretical courses and development of animations should be pursued.
Conference output Exploring first-year student perceptions of online work-experience modulesHowell, Simon; Hodge, Steven; Hall, Wayne (34th Australasian Association for Engineering Education Conference, 2023)CONTEXT The structure of the first year of the engineering degrees at Griffith University was recently revised, and essentially requires students to choose their major at the end of the first term of study. This need to select an engineering major early can be a disadvantage, with previous research noting that students who lacked information about their discipline area were more likely to leave the discipline. As the first year has a crucial role to play in enhancing student identity development and connecting students to their future disciplines, it follows that students must be given suitable guidance to allow them to understand their degree and the engineering majors within it. This paper describes using virtual work experience modules offered by Engineers Australia (EA) as part of an assessment task designed to support students in choosing an engineering major. PURPOSE OR GOAL As this was the first time to use the online work experience modules in an assessment task, the teaching team wished to understand if the online work experience modules could help first-year students better understand the engineering industry and their preferred major. Therefore, this paper focuses on exploring first-year student perceptions of the EA work experience modules. APPROACH OR METHODOLOGY/METHODS Students were invited to complete an online survey regarding their major choices and perceptions of the online work experience modules. The resulting survey data was analysed and common themes were identified for discussion. ACTUAL OUTCOMES While there is positive support for the modules in terms of helping students to understand the engineering industry and engineering majors, it appears the EA modules may not be suitable for use with first-year engineers without additional support. Although some students described finding the modules as interesting or engaging, the difficulty level may be too high for the average first-year student. CONCLUSIONS/RECOMMENDATIONS/SUMMARY There is nothing wrong with having challenging work experience modules designed to mirror the workplace. A potential improvement could be to have a wider range of modules, with some designed to provide an introductory experience to a major, and other more difficult modules for later year students. Future research into the design of suitable modules is recommended.
Conference output Blurring the line: Exploring the future of collaborative filmmaking with artificial intelligence and unreal engineCarter, Justin; Sun, Henry; Meissner, Nico (ASPERA Conference 2024: Filmmaking Intelligences, 2024)Virtual production and Artificial Intelligence have emerged as transformative forces in the entertainment industry for cinematic storytelling, blurring traditional boundaries between pre- and post-production. Unreal Engine is at the forefront of this revolution, a powerful real-time rendering platform that offers filmmakers unprecedented creative possibilities during production. This paradigm shift challenges traditional boundaries between creative and technical roles, paving the way for a more integrated approach to filmmaking.
This paper provides insight into our recent virtual production collaborations, integrating motion capture and highspeed robotic camera tracking technology with a dynamic team of performers, filmmakers, digital artists and researchers. Through a critical examination of these projects, we delve into the intricacies of the productions, including their technical design and reflect on our approach to explore the notion of ‘below the line’, which is typically associated with essential technical roles and tasks in filmmaking that operate separately from creative decision-making.
Our investigation underscores how Virtual Production and technology such as Unreal Engine disrupt entrenched filmmaking methodologies by seamlessly incorporating technical design alterations into the digital creative workflow during production. Additionally, we explore the burgeoning role of AI-driven tools in challenging both creative and technical tasks, ushering in novel avenues for collaborative innovation for filmmaking. The paper aims to provide insight from our experience on strategies and the challenges to virtual production approaches that attempt to ‘blur the line’ by fostering an agile, collaborative and integrated filmmaking approach. This discussion provides professional filmmakers with insight into production strategies that enhance the transformative influence of Unreal Engine and Artificial Intelligence on the collaborative landscape of filmmaking.
Conference output Entrepreneurial Opportunity and Institutional Void in Emerging Markets: Current State and Future DirectionsUddin, Md Reaz; Ekberg, Sara; Ge, Gloria (37th ANZAM Conference: Celebrating management research, its impact & future, 2024)Entrepreneurial opportunity to exploit institutional void in emerging markets is a growing field of research. Despite the growth of the field, studies on entrepreneurial opportunities are scattered which limits understanding of the actual advancement of the field. To synthesise scattered studies, 58 articles were selected systematically from leading academic journals over the period 1997 to 2023. The bibliographic coupling analysis allowed to identify themes of the literature which are (1) the intersection of strategies and institutions; (2) social organisations for community advancement; (3) the informal networks and alliances; and (4) purposive actions for change. The most frequently used theories are organisational institutionalism, network theory, resource-based view, and bricolage. The future directions show the paths for advancing the research field.
Conference output Beyond the Horizon: Exploring Cross-Market Security Discrepancies in Parallel Android AppsYang, S; Bai, G; Lin, R; Guo, J; Diao, W (2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), 2024)Multi-channel distribution of Android apps offers convenience to users, yet simultaneously introduces security concerns. Although apps published on Google Play and third-party markets share the same version code, differences in app content may still arise. Notably, a recent incident involving the third-party market version of Pinduoduo app containing malicious code highlights the intentionally-differentiated implementations of app functionalities by developers between Google Play and third-party markets. The case of Pinduoduo may be just the tip of the iceberg, underscoring the need for a comprehensive investigation of the disparities between Google Play and third-party market versions of apps.In this work, we systematically analyze the differences in security and privacy of cross-market apps that claim to share the same version code. Specifically, we propose three research questions that cover differences in app protection, security threats, and permission usage. To answer these questions, we constructed a dataset containing 17,218 app pairs (filtered from 236,731 apps) and permission mappings (27,046 SDK mappings, 1,656 ContentProvider mappings, and 309 Intent mappings) for API levels 16 - 33. This dataset enables us to perform a comprehensive differential analysis. Consequently, our investigation unveiled a series of captivating and insightful findings. Approximately 29.02% of apps show differences in one or all three aspects. For example, the third-party market versions of apps often request more permissions compared to their Google Play counterparts, particularly among apps in the game category. Our work can help developers and app store operators improve cross-market app consistency, enhancing the quality of the Android app ecosystem and user experience.