Computer Science - Conference Items
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Item AIQTrees: a drone imagery dataset for tree segmentation(2024) Chai, Joseph; To, Alex; O’Sullivan, Barry; Nguyen, Hoang D.; Research Ireland; Science Foundation IrelandThe 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.Item Trustworthiness in multi-agent UAV systems: a scoping review(2024) Le, Mai; Minghim, Rosane; O’Sullivan, Barry; Nguyen, Hoang D.; Research Ireland; Science Foundation IrelandThe 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.Item 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 IrelandLearning 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.Item Counterfactual explanation through constraint relaxation(Institute of Electrical and Electronics Engineers (IEEE), 2025-01-28) Gupta, Sharmi Dev; O'Sullivan, Barry; Quesada, Luis; Science Foundation IrelandInteractive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover feasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: “if the user is willing to drop some of their constraints, there exists a solution”. However, this form of explanation might not be very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to recover feasibility by helping them understand what changes can be applied to their existing constraints rather than removing them. We propose an iterative method based on conflict detection and maximal relaxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation. We have evaluated our approach using well known instances that occur in industrial applications and demonstrated the relevance of multi-point relaxations.Item Design of a physiologically based feedback loop using biosensors for interactive XR and spatial computing environments(IEEE, 2024) Ó Riain, Eoghan; McVeigh, Joseph G.; Fullen, Brona M.; Martin, Denis; Murphy, DavidThis work investigates the signal characteristics of a physiological response (acute stress) and determines the viability of developing a Virtual Reality (VR) integrated physiologically based real-time feedback loop. This work has possible applications in physiotherapy and patient rehabilitation for long-term conditions including long COVID, persistent pain, and chronic fatigue. Using real-time physiological data, this approach can offer an individualised and immersive therapeutic experience. By synchronizing VR experiences with physiological responses, clinicians can optimise treatment efficacy and facilitate targeted rehabilitation efforts. A design and early prototype were developed to include a feedback loop driven by an ensemble of biosignal signatures, correlating with stress responses, that adjusts dynamic components in the environment. The prototype shows the feasibility of developing a physiologically based XR environment suitable for virtual physiotherapy interventions.