Conference Papers
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7616
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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 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 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.Item A Framework to Assess Multilingual Vulnerabilities of LLMs(Association for Computing Machinery, 2025-05-23) Tang L; Bogahawatta N; Ginige Y; Xu J; Sun S; Ranathunga S; Seneviratne SLarge Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to prevent them from answering illegal questions, imbalances in training data and human evaluation resources can make these models more susceptible to attacks in low-resource languages (LRL). This paper proposes a framework to automatically assess the multilingual vulnerabilities of commonly used LLMs. Using our framework, we evaluated six LLMs across eight languages representing varying levels of resource availability. We validated the assessments generated by our automated framework through human evaluation in two languages, demonstrating that the framework's results align with human judgments in most cases. Our findings reveal vulnerabilities in LRL; however, these may pose minimal risk as they often stem from the model's poor performance, resulting in incoherent responses.Item Learning to Bound for Maximum Common Subgraph Algorithms(Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany, 2025-08-08) Kothalawala BW; Koehler H; Wang Q; Garcia de la Banda MIdentifying the maximum common subgraph between two graphs is a computationally challenging NP-hard problem. While the McSplit algorithm represents a state-of-the-art approach within a branch-and-bound (BnB) framework, several extensions have been proposed to enhance its vertex pair selection strategy, often utilizing reinforcement learning techniques. Nonetheless, the quality of the upper bound remains a critical factor in accelerating the search process by effectively pruning unpromising branches. This research introduces a novel, more restrictive upper bound derived from a detailed analysis of the McSplit algorithm's generated partitions. To enhance the effectiveness of this bound, we propose a reinforcement learning approach that strategically directs computational effort towards the most promising regions within the search space.Item Multi-lingual mathematical word problem generation using long short term memory networks with enhanced input features(European Language Resources Association (ELRA), 2020-01-01) Liyanage V; Ranathunga SA Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP generation should be carefully handled. When it comes to multi-lingual MWP generation, language specific morphological and syntactic features become additional constraints. Standard template-based MWP generation techniques are incapable of identifying these language specific constraints, particularly in morphologically rich yet low resource languages such as Sinhala and Tamil. This paper presents the use of a Long Short Term Memory (LSTM) network that is capable of generating elementary level MWPs, while satisfying the aforementioned constraints. Our approach feeds a combination of character embeddings, word embeddings, and Part of Speech (POS) tag embeddings to the LSTM, in which attention is provided for numerical values and units. We trained our model for three languages, English, Sinhala and Tamil using separate MWP datasets. Irrespective of the language and the type of the MWP, our model could generate accurate single sentenced and multi sentenced problems. Accuracy reported in terms of average BLEU score for English, Sinhala and Tamil languages were 22.97%, 24.49% and 20.74%, respectively.Item Concentration of 12 Oligosaccharides in the Milk of New Zealand Breastfeeding Women(MDPI (Basel, Switzerland), 2023-03-23) Jia LL; Brough L; Weber JL; Smith C; Mackay S; Jalili-Moghaddam S; Gibbs MHuman milk oligosaccharides (HMOs) are the third most abundant component in breast milk. HMOs benefit infant gut health, modulate immune responses, and promote brain development. The profile and concentration of HMOs vary considerably among breastfeeding women, and are reported to be associated with genetic, maternal, and environmental factors as well as feeding practices. One reason for the diversity in HMO concentration is the secretor gene, which determines the presence of an enzyme responsible for the synthesis of 2′-FL and LNFP-I. To date, there is no report about HMO concentration or profile in the New Zealand population. Our objective was to investigate 12 HMO concentrations in a small sample of New Zealand women. Sixty-eight breastfeeding mothers (mean age 32 years, 77% Caucasian) of singleton infants (median age [Q1, Q3] 108 [70, 166] days) were included, with 65% exclusively breastfeeding and 54% who had two or more children. Concentrations of 12 HMOs were measured by UHPLC with fluorescence detection. Overall, 68% of mothers were secretors, which was defined by the presence of 2′-FL in the milk. HMO profiles varied widely; total HMO concentration varied 4.2-fold between women; and individual HMOs varied from 4.8-fold to >100-fold. The median of total HMO concentration (Q1, Q3) of the secretors and non-secretors were 6774.9 (6395.4, 8245.6) mg/L and 7128.0 (6093.1, 7880.1) mg/L respectively. Significant differences in concentration of 2′-FL, 3-FL, A-Tet, LNFP-I, LNFP-II, LNFPV, and LNnT between secretors and non-secretors were found by Mann–Whitney tests. However, there was no significant difference in concentrations of LNFP-III, LNnFP, 3′-SL, 6′-SL, LNT, or total HMOs between the secretors and the non-secretors. HMO concentrations vary broadly between breastfeeding women. A longitudinal cohort of a larger sample size is required to fully investigate HMO profiles at different lactation stages of New Zealand women and to further explore the influence of maternal and environmental factors on HMO concentration.Item Efficacy of needle and endoscopic lavage on the recuperation of microspheres from the adult equine metacarpo−/metatarsophalangeal joint and digital flexor tendon sheath(Wiley Periodicals LLC on behalf of American College of Veterinary Surgeons, 2025-06-25) Beggan CP; Panizzi L; Oliver LJObjectives: To measure microsphere recovery following needle-through-and-through lavage (NTAT) of the metacarpo−/metatarsophalangeal joint (fetlock) and digital flexor tendon sheath (DFTS) compared to endoscopic lavage (EL). Study design: Ex vivo experimental study. Animals: Adult equine cadavers immediately following euthanasia (n = 10). Methods: Colored 15 μm microspheres (2 million) were injected into fetlock joints and DFTS. Synovial structures were assigned to NTAT or EL groups. Each lavage was performed using 5 L of 0.9% NaCl, sequentially collecting egress fluid for microsphere quantification. Recovery was compared using a full-factorial general linear model. Results: There was a significant effect of the liter of egress fluid and microsphere recovery in both fetlocks (p <.01) and DFTS (p <.01), with most microspheres recovered in the first 2 L (79%–83%) for both techniques. More microspheres were recovered in the first liter using NTAT than EL (p <.01) in both fetlocks (659 883 ± 20 820 vs. 567 601 ± 24 452) and DFTS (644 341 ± 17 460 vs. 550 637 ± 38 022). No difference in total recovered microspheres was observed between NTAT lavage of fetlock (981 600 ± 46 839) and DFTS (957 419 ± 45 729) across 5 L (p =.88). Conclusion: Needle-through-and-through lavage was more effective than EL at recovering microspheres in the first liter from cadaveric equine fetlock joints and DFTS. Both techniques demonstrated comparable efficacy between fetlock and DFTS in microsphere recovery following increased lavage volumes. Clinical significance: Needle-through-and-through lavage (NTAT) is a viable alternative for suspected synovial contamination when EL is delayed or not feasible. This study does not evaluate NTAT's efficacy for treating established sepsis or removing pannus/foreign bodies.Item Word embedding evaluation for Sinhala(European Language Resources Association, 2020-01-01) Lakmal D; Ranathunga S; Peramuna S; Herath I; Calzolari N; Béchet F; Blache P; Choukri K; Cieri C; Declerck T; Goggi S; Isahara H; Maegaard B; Mariani J; Mazo H; Moreno A; Odijk J; Piperidis SThis paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.