Computing and Mathematical Sciences Papers

Permanent URI for this collectionhttps://researchcommons.waikato.ac.nz/handle/10289/6

This collection houses research from the School of Computing and Mathematical Sciences at the University of Waikato.

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Now showing 1 - 20 of 1653

Choosing the right time to learn evolving data streams
(IEEE, 2023) Bernardo, Alessio; Della Valle, Emanuele; Bifet, Albert
Continuous data generation over time presents new challenges for Machine Learning systems, which must develop real-time models due to memory and latency limitations. Streaming Machine Learning algorithms analyze data streams one sample at a time, progressively updating their models. However, is it necessary to utilize all the data for model updates? This paper introduces the Online Ensemble SPaced Learning (OE-SPL) strategy, an ensemble meta-strategy that combines online ensemble learning and the Spaced Learning heuristic to rapidly learn underlying concepts without using all samples. We evaluated OE-SPL on synthetic and real data streams containing various concept drifts, providing statistical evidence that OE-SPL achieves comparable performance to state-of-the-art ensemble models while recovering from multiple concept drift occurrences more efficiently, using less time and RAM-Hours.
Defining sets which intersect each Latin trade at least twice
(Elsevier, 2026) Bean, Richard; Cavenagh, Nicholas J.
A defining set of a Latin square is a partially filled-in Latin square which completes to no other Latin square of the same order. We introduce the concept of a k -strong defining set, in which if less than k entries are deleted, the property of being a defining set is retained. Equivalently, a k -strong defining set intersects every Latin trade in the Latin square at least k times. In the addition table for integers modulo n , when n is even we determine the minimum size of a k -strong defining set for any k . For odd n we give a construction for a minimally 2-strong defining set. We furthermore give computational results for Latin squares of small orders.
Social Capital is critical to perseverance at university during COVID-19: Pasifika students in Fiji, New Zealand and Solomon Islands universities
(University of Sydney, 2022) Dorovolomo, Jeremy; Laulaupea’alu, Siuta; Rafiq, Loriza Zinnie; Rodie, Patricia; Fito’o, Billy
This study investigated how university students in two Pacific Island contexts coped with the shift to remote learning caused by the COVID-19 pandemic. The researchers aimed to investigate the social capital of a group of Pasifika university students in New Zealand and the Solomon Islands based in Fiji and the Solomon Islands during COVID-19 and how they coped with the rapid shift to remote learning. Social capital refers to building relationships and networks to deal with collective issues in the present and future. The researchers used tokstori to collect data from Solomon Islands students and talanoa in New Zealand. Sixteen Solomon Islanders participated in tokstoris and ongoing Pasifika students’ talanoa and faikava (kava consumption) to share concerns and challenges to their academic studies and lives. Deductive in nature, the analysis found that communication with fellow students, often via digital media, and interaction with supportive staff helped their success. Furthermore, getting in touch with the family and following COVID-19 protective practices helped students adjust positively. Being able to ‘tap’ the social resources within these spaces helped build perseverance, resilience and strong social capital. The research findings show the importance of individuals, student communities and institutions consciously strengthening social networks among students during a crisis such as the COVID-19 pandemic.
Viability of automating the landing error scoring system using inertial measurement units
(Elsevier, 2026) Hamilton, Zane; Turner, Jessica; König, Jemma; Hébert-Losier, Kim
The Landing Error Scoring System (LESS) is an assessment tool used for identifying movement patterns linked with non-contact anterior cruciate ligament injuries during a double-leg jump-landing; however, the LESS is scored by experts using 2D video recordings, which limits large-scale screening. This study explores the viability of using inertial measurement unit (IMU) data to automate scoring of 17 LESS items, using conventional machine learning model architecture. Forty healthy participants completed six jumps each, and raw movement data from three IMU sensors placed on the sacrum and medial-inferior aspects of the tibias were processed and segmented into the key phases of the double-leg jump-landing. A total of 218 jumps were used to train supervised machine learning models using various subsets of the processed dataset, including extracted temporal and statistical features. Performance metrics were extracted, and the best performing models for each scoring item were evaluated against a majority-class baseline classifier (ZeroR). Results indicated moderate improvements over ZeroR, ranging from 0.3 % to 22.5 %, with some models demonstrating limited recognition of the minority class. These findings provide a foundation for future research in IMU-based LESS automation, and improving the accessibility of movement screening tools.
A multidimensional activity theory framework for human–computer interaction with Digital Twins
(Taylor & Francis, 2026) Adeel, Aleeza; Apperley, Mark
This article proposes a user-centered framework to address interaction and usability challenges in complex Digital Twin systems(DTs) through an adaptation of Activity Theory, termed Pyramidal Activity Theory (PAT).Despite the growing adoption of DTs across industries, designing effective user interactions remains challenging due to the diversity of application domains, where multiple models, stakeholders, phases, and operational scales are often distributed across heterogeneous software environments. These complexities can lead to fragmented workflows, communication barriers, and inconsistent interaction experiences. PAT adapts the framework to focus on interaction structures by explicitly incorporating users, tools (models), live Data, interfaces, and outcomes.The framework is demonstrated through two conceptual case studies.The first case study conceptually maps interactions in an advanced manufacturing environment, while the second demonstrates how PAT can inform the design of a prototype Digital Twin interface for industrial energy systems. PAT provides a structured mechanism for analyzing interaction pathways and translating socio-technical relationships into interface design considerations. Future work will include empirical evaluation and usability assessment.
Analyzing remote peering deployment and Its implications for internet routing
(IEEE, 2024) Mazzola, Fabricio; Setti, Augusto; Marcos, Pedro; Barcellos, Marinho
Internet eXchange Points (IXPs) have significantly transformed the structure and economics of the Internet by allowing many nearby networks to connect directly, avoiding the need for service providers. These large IXPs are so beneficial that they are not just used by nearby networks, but also by far away Autonomous Systems (AS). This is made possible by Remote Peering (RP), which typically involves the use of RP resellers to access remote IXPs. In this paper, we evaluate the effects of RP on four different routing aspects, using a representative group of IXPs located on three continents: (a) growth of RP deployment over one and a half years; (b) presence of route announcement mispractices (when networks prioritize the remote IXP over the local IXP), which are associated to routing anomalies; (c) reliability of RP interfaces and (d) adoption of RP-related BGP communities, i.e. to perform traffic engineering to remote peers. We make our data and results available to the community via a web portal.
Response-level identification of cloud API misconfigurations using large language models
(Scilight Press, 2026) Zahid, Farzana; Krishna, Akshay
Application Programming Interfaces (APIs) are a set of rules that enable communication, data exchange, and automated interactions between applications and services. With the rapid advancement of cloud computing, APIs have evolved from simple data-access interfaces into critical components for managing, configuring, and orchestrating cloud resources. Most modern cloud platforms rely on RESTful APIs for provisioning cloud resources, applying configurations, and maintaining services. As a result, APIs misconfigurations have become a critical cloud security threat that can lead to sensitive data exposure, unauthorized access, or operational disruptions. Identifying these misconfigurations is challenging because traditional rule-based and static analysis methods often fail to capture complex, context-dependent configuration issues and system behaviors. In this study, we investigate the use of Large Language Models (LLMs) to detect security misconfigurations directly from cloud API response data. By treating API responses as representations of a system’s configuration state, we assess whether LLMs can effectively identify potential security risks. We evaluate five LLMs using a unified zero-shot prompting approach and compare their performance with and without Retrieval-Augmented Generation (RAG) to understand the impact of external knowledge on misconfiguration detection. The study not only focuses on each model’s ability to identify configuration components and detect misconfigurations, but also evaluates their capability to accurately determine the number of misconfigurations and generate clear, actionable security explanations. Our preliminary results show that Meta Llama Instruct combined with RAG achieves the reliable performance for identifying security misconfigurations in cloud API responses. This study provides new insights into the practicality of LLM-driven API cloud security analysis and paves the way for future research.
Periodic boundary conditions and G2 cosmology
(IOP Publishing, 2023) Coley, Alan A.; Lim, Woei Chet
In the standard concordance cosmology the spatial curvature is assumed to be constant and zero (or at least very small). In particular, in numerical computations of the structure of the universe using N-body simulations, exact periodic boundary conditions are assumed which constrains the spatial curvature. In order to conﬁrm this qualitatively, we numerically evolve a special class of spatially inhomogeneous G2 models with both periodic initial data and non periodic initial data using zooming techniques. We consequently demonstrate that in these models periodic initial conditions do indeed suppress the growth of the spatial curvature as the models evolve away from their initial isotropic and spatially homogeneous state, thereby verifying that the spatial curvature is necessarily very small in standard cosmology.
Automatic detection of Android crypto ransomware using supervisor reduction
(Springer Nature, 2024) Chew, Christopher J.W.; Malik, Robi; Kumar, Vimal; Patros, Panos
This paper proposes a finite-state machine based approach to recognise crypto ransomware based on their behaviour. Malicious and benign Android applications are executed to capture the system calls they generate, which are then filtered and tokenised and converted to finite-state machines. The finite-state machines are simplified using supervisor reduction, which generalises the behavioural patterns and produces compact classification models. The classification models can be implemented in a lightweight monitoring system to detect malicious behaviour of running applications quickly. An extensive set of cross validation experiments is carried out to demonstrate the viability of the approach, which show that ransomware can be classified accurately with an F1 score of up to 93.8%.
Enriching cultural heritage communities: New tools and technologies
(Oxford University Press, 2024) Dix, Alan; Jones, Elizabeth; Cowgill, Rachel; Armstrong, Charlotte; Ridgewell, Rupert; Twidale, Michael B.; Downie, J. Stephen; Reagan, Maureen; Bashford, Christina; Bainbridge, David; Neads, Carys-Ann; Davies, Vince
This paper explores ways in which scholarly skill and expertise might be embodied in tools and sustainable practices that enable communities to create and manage their own digital archives. We focus particularly on tools and practices related to the recording and annotation of digitized materials. The paper is based on co-production practice in two very different kinds of community. Although the communities are different we find that tools designed for a specific community are valuable for others, thus offering the promise of general tools to support community-centred digitization and potentially also traditional archival practice.
A multimodal bracelet to acquire muscular activity and gyroscopic data to study sensor fusion for intent detection
(MDPI, 2024) Andreas, Daniel; Hou, Zhongshi; Tabak, Mohamad Obada; Dwivedi, Anany; Beckerle, Philipp
Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching (Formula presented.) on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.
Topological and dynamic characteristics in forced anisotropic magnetohydrodynamic turbulence
(AIP Publishing, 2026) Gao, Kai; Jiang, Bin; Li, Cheng; Yang, Yan; Zhou, Kangcheng; Matthaeus, William H.; Oughton, Sean; Wan, Minping
The invariants of the velocity gradient tensor in turbulence offer a compact description of local kinematics and flow topology. For incompressible magnetohydrodynamic turbulence, analysis of the second and third invariants (Q, R) of the velocity gradient tensor clarifies how coherent structures are organized and evolve. Extending the same analysis to the magnetic field gradient tensor provides additional information on the dynamics. In this study, pseudo-spectral simulation is used to obtain the velocity and magnetic field of the turbulent flow, and analysis of the flow field is conducted through joint probability density functions (PDFs) of the invariants. Furthermore, we explore the influence of the external mean magnetic field strength, B 0. The results show that when an external magnetic field is present, the Q–R joint PDF no longer maintains the familiar teardrop distribution for the velocity field, and the flow field structure tends to be two-dimensional with increasing B 0. For the fluctuation magnetic field, the Q–R joint PDF takes on a “cigar” shape that becomes more elongated as B 0 increases. Moreover, as the strength of the external mean magnetic field increases, the turbulence exhibits enhanced small-scale dissipation and localization, accompanied by a reduction in the effective dimensionality of the system toward a quasi-two-dimensional regime.
Anomaly detection for evolving maritime trajectories with continual learning
(Springer Nature, 2026) Julian, Jack; Koh, Yun Sing; Bifet, Albert
Anomaly detection in live trajectory data is a critical task for ensuring safety, security, and legality in global transport. Traditional anomaly detection methods often struggle with dynamic and evolving trajectory patterns, especially as systems must adapt to new scenarios over time due to increased traffic, geopolitical events, or global warming. We propose a continual learning approach to detect anomalous activity in moving vessels. Unlike conventional static models, our method leverages continual learning to enable the model to learn from new data continuously and recognise specific behaviours dependent on position and recent movements. We implement an adapter-based framework, Continual Learning for AIS Anomalies (CLAISA), that adapts to shifting behavioural environments in transportation, ensuring the system can identify novel and evolving patterns of anomalies, such as deviations from expected routes, irregular speed changes, or unusual local movements. Evaluations on synthetic maritime trajectory datasets spanning sparsely populated waters and heavily trafficked shipping lanes demonstrate that CLAISA achieves up to a decrease in error for trajectory forecasting and consistently outperforms benchmark methods in anomaly detection on synthetically generated datasets.
Integrating agentic artificial intelligence into pasture-based dairy systems: Applications, governance, and future directions
(Elsevier, 2026) Eastwood, Callum; Lim, Nick Jin Sean; Durie, Rachel; Dela Rue, Brian; Bifet, Albert; Reed, Charlotte
Digitalization and artificial intelligence (AI) provide opportunities for improved management of pasture-based dairy systems. Agentic AI, where autonomous systems can perceive and act independently of humans, are potentially transformative. This mini-review explores the potential use of agentic AI to address key challenges in pasture-based dairy systems. Applications include autonomous grazing animal health prediction, environmental modeling, and virtual assistants. Agentic AI can integrate multiple data sources to support real-time, farm-specific decisions. It also presents opportunities for employee training, enhanced advisory services, and digital twin modeling. However, deployment of agentic AI introduces governance, ethical, and socio-technical considerations. Issues of data ownership, transparency, explainability, and trust must be addressed. The experiential and tacit knowledge of farmers must be integrated into AI systems through hybrid intelligence (human and AI). Oversight models ranging from Human-in-the-Loop to Human-in-Command are necessary to ensure safe and responsible use. Future research should focus not only on technical AI development, but farmer-centered design, robust assurance frameworks, and inclusive and responsible innovation ecosystems that align technological progress with dairy sector, civil society, values, and needs.
An agentic system for LLM-driven public transportation analytics: A practical application and case study in Salvador-Brazil
(ACM, 2026) Borges, Lucas T.; Liu, Fei T.; Rios, Tatiane; Ferreira, Marcos V.; Carmo, Clovis; Coimbra, Danilo B.; Nery, Jorge; Souza, Matheus; Garcia, Noe O.; Bifet, Albert; Rios, Ricardo
Public transportation agencies gather vast and heterogeneous datasets, yet their decisions still depend largely on manual queries and fragmented analyses. To address this gap, we introduce SUNTInsight, an agentic system that combines large language models, machine learning, and visual analytics to strengthen human decision-making. Through a seamless workflow, free-form prompts are automatically converted into SQL queries, relevant data are retrieved and processed, and an LLM, supported by visual interfaces, interprets the results to produce actionable findings. Applied to a case study in Salvador-Brazil, SUNTInsight revealed spatial heterogeneity, identified high-demand segments, and recommended targeted operational strategies, such as short turns and headway control, instead of broad fleet expansions. The implementation follows the principle of least privilege and runs generated code in isolated execution, mitigating risk while keeping humans in control.
Q-Cowrie: An adaptive honeypot to analyse attackers’ behaviour
(Springer Nature, 2026) Var Naseri, Maryam; Welch, Ian; Haseeb, Junaid; Mansoori, Masood
Honeypots in computer security have been used as effective security solutions to lure attackers, capture their interactions with the honeypot systems and study their behaviour. Attackers interacting with honeypots may use Artificial Intelligence (AI)-based techniques to detect the presence of honeypots leading to evasion by the attackers. This paper discusses the application of Reinforcement Learning (RL) to address these issues by improving response generation in honeypots. We propose “Q-Cowrie”, a honeypot that is built upon customising a medium interaction server honeypot, that is, Cowrie, to increase the honeypot’s deception. RL capabilities have been integrated into the honeypot to support adaptive behaviour while interacting with attackers. Two experimental studies have been conducted in which Cowrie and Q-Cowrie honeypots were used, respectively. First, we deployed a Cowrie honeypot to capture cyber attacks and identify attackers’ goals and techniques. This allowed us to create a probabilistic model, that is, the Markov Decision Making Process (MDP), to understand the decision-making process of attackers in different situations. Learning from attackers’ unique patterns and applying RL techniques, Q-Cowrie was able to actively interact with attackers, making adaptive decisions.
On DR-semigroups satisfying the ample conditions
(Springer Nature, 2026) Stokes, Tim E.
A DR-semigroup S (also known as a reduced E-semiabundant or reduced E-Fountain semigroup) is here viewed as a semigroup equipped with two unary operations D, R satisfying finitely many equational laws. Examples include DRC-semigroups (hence Ehresmann semigroups), which also satisfy the congruence conditions. The ample conditions on DR-semigroups are studied here and are defined by the laws (Formula presented.) Two natural partial orders may be defined on a DR-semigroup and we show that the ample conditions hold if and only if the two orders are equal and the projections (elements of the form D(x)) commute with one-another. Restriction semigroups satisfy the ample conditions, but we give non-restriction examples using closure operators on sets, strongly order-preserving functions on a quasiordered set, and certain subsets of partial categories. Following the work of Stein, we show how to construct a certain partial algebra C(S) from any DR-semigroup, which is a category if S satisfies the congruence conditions, but is “almost" a category if the ample conditions hold. We then characterise the ample conditions in terms of the equality of two natural partial orders, and in terms of a converse of the condition on S ensuring that C(S) is a category. Our main result is an ESN-style theorem for DR-semigroups satisfying the ample conditions, based on the C(S) construction. We also obtain an embedding theorem, generalizing a result for restriction semigroups due to Lawson.
Building adaptive knowledge bases for evolving continual learning models
(Springer Nature, 2025) Julian, Jack; Koh, Yun Sing; Bifet, Albert
Continual learning addresses catastrophic forgetting and knowledge transfer when learning from task streams. Dynamic architectures have introduced task-specific components like adapters layered over fixed pre-trained backbones. However, identifying the task of a new input remains a core challenge, leading to task-agnostic and dynamic detection methods. Existing approaches often overlook the reuse of previously learned adapters, missing opportunities for efficient forward and backwards transfer. We propose Continual Adapter-Based Learning (CABLE), a reinforcement learning framework that computes gradient similarity between new examples and past tasks. This similarity score drives a policy that assigns existing adapters when beneficial, rewarding improved performance and reducing reliance on newly initialised parameters. CABLE adopts a dynamic adapter routing strategy without assuming prior task labels. Evaluations on image classification and time series forecasting show that CABLE mitigates catastrophic forgetting and promotes efficient knowledge transfer across tasks.
The age of DDoScovery: An empirical comparison of industry and academic DDoS assessments
(ACM, 2024) Hiesgen, Raphael; Nawrocki, Marcin; Barcellos, Marinho; Kopp, Daniel; Hohlfeld, Oliver; Chan, Echo; Dobbins, Roland; Doerr, Christian; Rossow, Christian; Thomas, Daniel R.; Jonker, Mattijs; Mok, Ricky; Luo, Xiapu; Kristoff, John; Schmidt, Thomas C.; Wählisch, Matthias; Claffy, K. C.
Motivated by the impressive but diffuse scope of DDoS research and reporting, we undertake a multistakeholder (joint industry-academic) analysis to seek convergence across the best available macroscopic views of the relative trends in two dominant classes of attacks - direct-path attacks and reflection-amplification attacks. We first analyze 24 industry reports to extract trends and (in)consistencies across observations by commercial stakeholders in 2022. We then analyze ten data sets spanning industry and academic sources, across four years (2019-2023), to find and explain discrepancies based on data sources, vantage points, methods, and parameters. Our method includes a new approach: we share an aggregated list of DDoS targets with industry players who return the results of joining this list with their proprietary data sources to reveal gaps in visibility of the academic data sources. We use academic data sources to explore an industry-reported relative drop in spoofed reflection-amplification attacks in 2021-2022. Our study illustrates the value, but also the challenge, in independent validation of security-related properties of Internet infrastructure. Finally, we reflect on opportunities to facilitate greater common understanding of the DDoS landscape. We hope our results inform not only future academic and industry pursuits but also emerging policy efforts to reduce systemic Internet security vulnerabilities.
BusEnv: A multi-agent reinforcement learning environment and benchmark for urban public transportation
(ACM, 2026) da Silva e Silva, Wesley; Rios, Ricardo A.; da Costa Fonseca, Rafael; Ponnambalam, Sabarikirishwaran; Cassé, Léa; dos Santos Ferreira, Marcos Vinícius; Bifet, Albert; Rios, Tatiane N.
Reinforcement learning (RL) offers a powerful paradigm for managing complex, dynamic transportation systems where autonomous agents must adapt to uncertain and rapidly changing environments. We present BusEnv, a benchmark environment grounded in real-world data from the Salvador Urban Transportation Network, encompassing approximately 700,000 passengers, 2,000 vehicles, 400 lines, and 3,000 stops, collected between March 2024 and March 2025 at sub-minute resolution. BusEnv simulates realistic bus operations with stochastic passenger demand, route-specific travel times, and traffic-dependent variability, enabling controlled experimentation under partially observable, high-dimensional conditions. The reward function integrates multiple objectives, such as passenger service quality, operational efficiency, maintenance adherence, and sustainability, allowing the assessment of how different RL algorithms balance these competing factors. We evaluate nine baseline methods implemented in MARLlib, analyzing their convergence, robustness, and environmental impact when deployed under independent-learning conditions. Results show that PPO-based approaches achieve the highest stability and lowest energy waste, linking algorithmic robustness to sustainability performance. By combining data realism with reproducibility and extensibility, BusEnv establishes a foundation for systematic research on learning-based transport management and provides a scalable testbed for future studies on cooperative, sustainability-aware reinforcement learning.

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