Conference Papers
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7616
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Item Customization Meets 2-Hop Labeling: Efficient Routing in Road Networks(VLDB Endowment, 2025-01-01) Farhan M; Koehler H; Wang Q; Wang J; Laupichler M; Sanders P; Palpanas T; Tatbul NEfficient route planning is crucial for modern navigation systems, yet traditional methods face challenges in scenarios with unknown or frequently changing traffic dynamics. This paper introduces a general labeling framework based on the 2-hop cover property, enabling robust, metric-independent preprocessing. Using this framework, we propose Customizable Tree Labeling (CTL), a tree-based method combining three key components: metric-independent preprocessing with tree hierarchies, metric customization for dynamic updates, and efficient query algorithms for fast route computation. To allow trade-offs between customization time, labeling size, and query performance, we further develop a parameterized customization technique by dynamically combining tree labels and shortcut graphs. Our key contributions include the introduction of a customizable labeling framework, a novel tree hierarchy for compact and scalable representation, and a hybrid query algorithm that integrates labels and shortcuts for fast and accurate route computation. We conduct extensive experiments on ten large-scale real-world road networks and a case study on the traffic assignment problem. Our algorithms achieve query response times significantly faster than the state-of-the-art methods, while maintaining competitive customization times and labeling size, making it well-suited for real-time and dynamic routing applications.Item CSMSA: Cross-Space Multiscale Adaptive Link Prediction for ceRNA-Mediated Multimolecular Disease Regulatory Networks(Association for Computing Machinery, 2025-12-10) Long J; Li J; Qu G; Liu K; Liu BRegulatory interactions associated with diseases are pivotal for elucidating the molecular mechanisms that drive disease progression and promoting precision medicine. Nevertheless, existing research algorithms often overlook the potential dynamic synergistic-competitive mechanisms between different ceRNA regulatory networks and lack cross-space learning capabilities across multiple heterogeneous graph structures, making it difficult to comprehensively capture the multidimensional molecular regulatory biological mechanisms in disease data with different structural densities. Therefore, we propose the cross-space multiscale adaptive learning framework (CSMSA) that integrates a heterogeneous five-layer ceRNA regulatory network and introduces an adaptive cross-space learning mechanism to dynamically capture complementary and specific interactions and effectively learn the intrinsic biological regulatory mechanisms. Moreover, the CSMSA framework employs a multi-scale feature fusion strategy that hierarchically learns node embeddings by integrating local structural information and global topological features from heterogeneous graphs to enhance predictive performance and robustness across complex datasets of varying sizes. Comprehensive evaluations on three independent datasets show that CSMSA surpasses existing methods in the multimolecular disease prediction task (Max AUC = 0.9880, Max AUPR = 0.9829), thereby providing a reliable new paradigm for probing disease regulatory links.Item Smart Glasses for CVI: Co-Designing Extended Reality Solutions to Support Environmental Perception by People with Cerebral Visual Impairment(Association for Computing Machinery, 2025-10-22) Gamage B; Mcdowell N; Kovacic D; Holloway L; Do TT; Lowery AJ; Price N; Marriott K; Kane S; Shinohara KCerebral Visual Impairment (CVI) is the set to be the leading cause of vision impairment, yet remains underrepresented in assistive technology research. Unlike ocular conditions, CVI affects higher-order visual processing - impacting object recognition, facial perception, and attention in complex environments. This paper presents a co-design study with two adults with CVI investigating how smart glasses, i.e. head-mounted extended reality displays, can support understanding and interaction with the immediate environment. Guided by the Double Diamond design framework, we conducted a two-week diary study, two ideation workshops, and ten iterative development sessions using the Apple Vision Pro. Our findings demonstrate that smart glasses can meaningfully address key challenges in locating objects, reading text, recognising people, engaging in conversations, and managing sensory stress. With the rapid advancement of smart glasses and increasing recognition of CVI as a distinct form of vision impairment, this research addresses a timely and under-explored intersection of technology and need.Item MIC: Medical Image Classification Using Chest X-ray (COVID-19 & Pneumonia) Dataset with the Help of CNN and Customized CNN(Association for Computing Machinery, 2025-06-06) Fahad N; Ahmed R; Jahan F; Jamal Sadib R; Morol MK; Jubair MAAThe COVID-19 pandemic has had a detrimental impact on the health and welfare of the world's population. An important strategy in the fight against COVID-19 is the effective screening of infected patients, with one of the primary screening methods involving radiological imaging with the use of chest X-rays. Which is why this study introduces a customized convolutional neural network (CCNN) for medical image classification. This study used a dataset of 6432 images named Chest X-ray (COVID-19 & Pneumonia), and images were preprocessed using techniques, including resizing, normalizing, and augmentation, to improve model training and performance. The proposed CCNN was compared with a convolutional neural network (CNN) and other models that used the same dataset. This research found that the Convolutional Neural Network (CCNN) achieved 95.62% validation accuracy and 0.1270 validation loss. This outperformed earlier models and studies using the same dataset. This result indicates that our models learn effectively from training data and adapt efficiently to new, unseen data. In essence, the current CCNN model achieves better medical image classification performance, which is why this CCNN model efficiently classifies medical images. Future research may extend the model's application to other medical imaging datasets and develop real-time offline medical image classification websites or apps.Item How does a teacher sustain collective mathematizing among non-dominant students?(East China Normal University Press and World Scientific Publishing Co Pte Ltd, 2024-10-01) Tupouniua JG; Hunter JIn this paper, we describe a teacher’s attempt to sustain collective mathematizing among non- dominant students in a classroom that emphasizes collective success. Taking a collectivist stance, we conceptualize the featured classroom as one in which the students function as a single learning organism. We analyze three roles that the teacher played within a lesson focused on students’ engagement with repeating patterns. Specifically, we discuss the affordances of the three roles with respect to sustaining three characteristics of a classroom that functions as a single learning organism.Item Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing(Elsevier B.V., 2021-04-14) Lam N; Docherty PD; Murray R; Chase JG; Morenga LTMost parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases, least squares estimation can yield poor results. Outlying data is often manually removed to avoid inaccurate outcomes, but this process is complex, tedious and operator dependent. This research presents an adaptation of the Levenberg-Marquardt (L-M) parameter identification method that effectively ignores least-square contributions from outlying data. The adapted method (aL-M) is capable of ignoring outlier data in accordance with the coefficient of variation of the residuals and was thus, capable of operator independent omission of outlier data using the 3 standard deviation rule. The aL-M was compared to the original Levenberg-Marquardt (L-M) method in C-peptide, insulin and glucose data. In total three cases were tested: L-M in the full dataset, L-M in the same data where the points that were suspected to be affected by incomplete mixing at the depot site were removed, and the aL-M in the full data set. There were strong correlations between the aL-M and the reduced dataset from [0.85, 0.71] for the clinically valuable glucose parameters. In contrast, the unreduced data yielded poor residuals and poor correlations with the aL-M [0.44, 0.33]. The aL-M approach provided strong justification for consistent removal of data that was deemed to be affected by mixing.Item Growth and milk production of dairy heifers born to two-year-old or mixed-age dams(New Zealand Society of Animal Production, 2019-07-02) Handcock RC; Lopez-Villalobos N; Back PJ; Hickson RE; McNaughton LRKeeping replacement heifers that were the progeny of primiparous cows mated by artificial insemination, enhances rates of genetic gain. Heifers that were the progeny of primiparous cows were lighter at birth and grew at a slower rate to first calving compared with heifers born to multiparous dams. Heifers that were heavier before first calving produced more milk than did lighter heifers. This study aimed to determine if there were liveweight (LWT) or milk-production disadvantages for heifers born from primiparous compared with multiparous dams. Data comprised of LWT records from 189,936 New Zealand dairy heifers. Dams were allocated to four groups according to their age: two (2yo; n=13,717), three (3yo; n=39,258), four to eight (4-8yo; n=120,859) and nine years old or greater (≥9yo; n=16,102). Heifers born to 2yo dams were lighter (P<0.01) from three to 21 months of age than heifers born to 3yo and 4-8yo dams. The progeny of 2yo and 3yo dams produced similar milksolids yields during their first lactation (304.9±1.6 and 304.1±1.5 kg, respectively), but more (P<0.01) than that of 4-8yo dams (302.4±1.5) and ≥9yo dams (P<0.001; 297.8±1.6 kg). Heifers born to 2yo dams were lighter but produced more milk than heifers from older dams.Item Mining Meaningful Keys and Foreign Keys with High Precision and Recall(VLDB Endowment, 2025-01-01) Koehler H; Link S; Palpanas T; Tatbul NWe demonstrate a next-generation Entity/Relationship (E/R) Profiler that mines meaningful key/foreign key relationships from a given data repository. Core novelties include a strict hierarchy of key variants ranging from candidate keys to SQL unique constraints that represent different ways to identify incomplete entities, a measure of orthogonality that separates accidental from meaningful keys, and algorithms for mining approximate keys for all these variants under different thresholds of arity, completeness, dirtiness, and orthogonality. We showcase the high precision and recall achieved by our tool and how it facilitates the users’ understanding which entity and referential integrity constraints govern their data.Item The materialised temporality of dust: developing a biodesign methodology to spatialise time and temporalise spaceNevin A; RAMIREZ-FIGUEROA C; ORME CThe paper uses the material and conceptual figure of dust and matter out of place to amplify more-than-human perspectives of time, to trace the changing orientations and ethos of a site. Dust contains a complex mixture of inorganic and organic material, made up of an exuberance of microbial life such as Penicillium, Aspergillus, Cladosporium, and around twenty other fungal sources. We are interested in dust as a material and metaphorical device to situate and critique temporality, and the way we narrate and investigate the past and future, from a non-human, microbial point of view. Dust implies residual matter, a contradiction to order often associated with dirt. It indicates something that needs to be removed, rearranged, something that is “out of place”, an element that does not fit. Dust also indicates time and space, signals movement and life: dust hosts a medley of nonhumans particles and microbial communities that engage in their own worldmaking practices. The paper brings together methods of ‘un-cleaning’ with archival research, and spatial methods of 3D scanning, modelling and mapping, as an opportunity to decentre human hubris and explore the ways in which non-humans have and continue to inhabit “our” spaces. keywords: Dust, Temporality, Nonhumans, Site reading, Speculation, biodesignItem Mātauranga Moana: uplifting Māori and Pacific values of conceptualisation over western co-design constructs(Design Research Society, 2023-11-29) Withers S; Stokes G; Jones D; Borekci N; Clemente V; Corazzo J; Lotz N; Nielsen LM; Noel L-AThis paper offers a critical examination of the problematic use of western co-design methodologies when applied to indigenous and diasporic communities. By centring place-based, relational design approaches to enable cultural conventions from our position in Aotearoa New Zealand, we argue the use of co-design constructs risks overlaying neo-liberal ideologies on top of our resilient indigenous Māori and Pacific knowledge systems, values, ethics, and collective approaches towards design conceptualisation. As design researchers located in te moana-nui-a-Kiwa our discussion is underpinned by our Māori whakapapa, Sāmoan gafa, and relationship to Te Tiriti o Waitangi. We present our kōrero through a case study relationship with a local healthcare service, aiming to increase access for Māori and Pacific tamariki through design actions. Our collaboration was developed within the format of a tertiary course involving Māori and Pacific tauira enrolled in Design and Fine Arts degrees at Ngā Pae Māhutonga School of Design, Te Kunenga ki Pūrehuroa Massey University of New Zealand. Unlike traditional university design courses that aim to achieve a specific measurable outcome, we focussed on fostering whakawhānaungatanga and evidencing this through activated learning of the cultural conventions of wānanga and talanoa towards weaving together our values through critically reflective practice. Our case study relationship demonstrates the importance of relational place-based knowledge systems and their conditions for enabling reflexivity towards tino rangatiratanga and ola manuia within Māori and Pacific communities; further highlighting the systemic barriers that practices of co-design can seed when attempting to serve our communities in Aotearoa.