CORA
Cork Open Research Archive (CORA) is UCC’s Open Access institutional repository which enables UCC researchers to make their research outputs freely available and accessible.
UCC Research Communities
Recent Submissions
Exact Pollard-like internal water waves
(Springer Nature, 2021-01-06) Kluczek, Mateusz; Science Foundation Ireland
In this paper we construct a new solution which represents Pollard-like three-dimensional nonlinear geophysical internal water waves. The Pollard-like solution includes the effects of the rotation of Earth and describes the internal water wave which exists at all latitudes across Earth and propagates above the thermocline. The solution is provided in Lagrangian coordinates. In the process we derive the appropriate dispersion relation for the internal water waves in a stable stratification and discuss the particles paths. An analysis of the dispersion relation for the constructed model identifies one mode of the internal water waves.
AIQTrees: a drone imagery dataset for tree segmentation
(2024) Chai, Joseph; To, Alex; O’Sullivan, Barry; Nguyen, Hoang D.; Research Ireland; Science Foundation Ireland
The reliability of AI models typically depends on the data they are trained with, and accurate interpretations require large amounts of data. The scarcity of publicly available datasets is typically encountered for specific small-scale sustainability projects, making data accessibility a limiting factor for developing AI models for semantic segmentation tasks. In sustainability and forestry applications, the usage of UAVs is common due to their lightweight nature and the ability to provide a huge variety of data. In this paper, we present a new dataset of realistic and high-quality drone images taken around sites in Ireland. The images encompass temporal, spatial, and seasonal dimensions, which could alter the tree appearance or illumination conditions of the images and have to be taken into consideration. We also included a baseline benchmark for the semantic segmentation task along with the dataset. It can be accessed at: https://github.com/ReML-AI/AIQTrees.
Trustworthiness in multi-agent UAV systems: a scoping review
(2024) Le, Mai; Minghim, Rosane; O’Sullivan, Barry; Nguyen, Hoang D.; Research Ireland; Science Foundation Ireland
The integration of artificial intelligence (AI) into multi-unmanned aerial vehicle-assisted communication plays a pivotal role in sixth-generation wireless communication and beyond. Most AI techniques have primarily focused on AI-based applications and technical problems, rather than examining the accountability and trustworthiness of AI models, a crucial evaluation criteria for AI human beings. This work aims to provide a scoping review of the trustworthiness of AI in multi-agent UAV systems. Firstly, we present the background of multi-agent systems and methods to evaluate, enhance the trustworthiness of AI systems. Secondly, we review innovative techniques that address trustworthy requirements in terms of safety, robustness, privacy, accountability, explainability, and fairness, along with challenges in multi- agent UAV communications. Finally, we highlight several promising solutions and future research directions.
A lightweight and reliable framework toward real-time student engagement predictions in learning analytics
(2024) Hoang, Long; Shorten, George; O'Sullivan, Barry; Nguyen, Hoang D.; Research Ireland; Science Foundation Ireland
Learning analytics can enable the provision of meaningful feedback based on the collected data, help educators to make decisions with and about learners, and improve learner performance. Student engagement predictions are a key factor in generating feedback for real-time learning analytics applications, such as dashboards. However, most previous work has been based on a heavy deep learning model, which results in challenges for deployment in real-time applications (a resource efficiency requirement in reliable AI). This paper proposes a lightweight deep-learning framework for predicting student engagement in video to address this limitation. The proposed method uses customized MobileNetV2 as the backbone, with an input size of 32 by 32 by 3, to extract features from consecutive video frames. Multi-Scale attention – Residual (MUSER) is used to capture global information and contextual representation of the extracted features. Finally, LSTM examines the temporal variations in video frames and yields the prediction result. We use the DAISEE dataset, the most popular dataset in the learning analytics community, to evaluate the proposed framework. Experimental results demonstrate that the proposed method achieves good accuracy while significantly reducing the model size compared to other approaches.
Sharing gems: Report on the Performative Approaches to Language and Intercultural Learning (PAInt) Summer School and Drama in Education (DiE) days 2024
(Department of German, University College Cork, 2024-12-31) Bido, Giordana F. ; Salmaso, Nicolò
In this contribution, we report and reflect on the 2024 edition of the PAInt summer school and DiE days providing readers with some insights into our experience as participants. The event took place in Padua, Italy, and focused on inclusive practices in performative approaches to language and (inter)cultural learning, bringing together educators, researchers, and students to explore how drama fosters language competence, inclusion, and celebrates diversity.