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 1655

Detection of human and machine-authored fake news in Urdu
(Association for Computational Linguistics, 2025) Ali, Muhammad Zain; Wang, Yuxia; Pfahringer, Bernhard; Smith, Tony C.
The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like Chat-GPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues have also become less effective. Moreover, current detectors primarily focus on binary classification and English texts, often overlooking the distinction between machine-generated true vs. fake news and the detection in low-resource languages. To this end, we updated the detection schema to include machine-generated news focusing on Urdu. We further propose a conjoint detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings.
Supporting interactive system testing with interaction sequences
(ACM, 2017) Turner, Jessica; Bowen, Judy; Reeves, Steve
In software engineering testing is an important part of the development process. In interactive systems human input introduces the possibility of human error, which increases the testing requirements. In safety-critical systems user or system error can result in injury or death to a user. We propose using interaction sequences as a way of supporting interactive system testing to help address these issues.
Towards formalisation of 3D user interaction: Making spatial interaction verifiable
(ACM, 2025) Turner, Jessica; Weyers, Benjamin
Interactive systems which incorporate 3D interaction techniques, such as virtual or augmented reality, lack appropriate methods for formalisation to verify and validate their properties. In safety-critical contexts, such as virtual environments used for training users in medical settings, ensuring systems behave as expected is crucial to enabling positive user experiences. In this paper we propose a formalisation that will allow us to express 3D virtual environments, end users and their interactions. This work represents a first step towards creating formalisation methods for virtual reality applications, enabling model checking techniques to ensure systems behave as expected, leading to improved reliability, safety and better user experience in virtual environments.
How positioning wearable haptic interfaces on limbs influences virtual embodiment
(IEEE, 2024) Dwivedi, Anany; Yu, Shihan; Hao, Chenxu; Salvietti, Gionata; Prattichizzo, Domenico; Beckerle, Philipp
With increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy.
On the impact of different light wavelengths in decoding human intention in lightmyography controlled prosthetic hands
(IEEE, 2025) Guan, Bonnie; Godoy, Ricardo V.; Dwivedi, Anany; Liarokapis, Minas
Lightmyography is a method of human interfacing proposed for the control of prosthetic systems in which light is reflected on the skin and captured using photosensors. While sharing conceptual similarities with forcemyography, lightmyography differentiates itself in the way it interacts with the skin due to skin optics. Different wavelengths of light interact with the skin in different depths and degrees, a principle utilized in photoplethysmography to estimate heart rate and blood oxygen levels. Therefore, lightmyography was previously designed with two wavelengths of light, green and near-infrared. This study seeks to examine the impact of using different wavelengths of light in the decoding of hand postures using different machine learning methods. Four light configurations were tested: using only green light, using only infrared light, using both green and infrared light blinking alternately each 250 ms, and finally using both lights blinking at 125 ms. Data was decoded using four machine learning algorithms, namely linear discriminant analysis, random forest, convolutional neural networks, and a transformer-based model. Statistical testing showed no significant effect from light configuration in the machine learning decoding of hand posture performance. The dataset collected in this study has been made available for further investigation.
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.

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