Skip to main content

Research Repository

Advanced Search

Professor Nirmalie Wiratunga's Outputs (113)

Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.). Proceedings of the 2024 SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822Aachen: CEUR-WS [online], pages 11-18. Available from: https://ceur-ws.org/Vol-3822/short1.pdf

Evidence-based medicine (EBM) is a foundational element in medical research, playing a crucial role in shaping healthcare policies and clinical decision-making. However, the rigorous processes required for EBM, particularly during the abstract screen... Read More about Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models..

SCaLe-QA: Sri Lankan case law embeddings for legal QA. (2024)
Presentation / Conference Contribution
JAYAWARDENA, L., WIRATUNGA, N., ABEYRATNE, R., MARTIN, K., NKISI-ORJI, I. and WEERASINGHE, R. 2024. SCaLe-QU: Sri Lankan case law embeddings for legal QA. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 47-55. Available from: https://ceur-ws.org/Vol-3822/short6.pdf

SCaLe-QA is a foundational system developed for Sri Lankan Legal Question Answering (LQA) by leveraging domain-specific embeddings derived from Supreme Court cases. The system is tailored to capture the unique linguistic and structural characteristic... Read More about SCaLe-QA: Sri Lankan case law embeddings for legal QA..

Dual-task dialogue understanding. (2024)
Presentation / Conference Contribution
ANWAR, S., WIRATUNGA, N. and SNAITH, M. 2024. Dual-task dialogue understanding. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 40-46. Available from: https://ceur-ws.org/Vol-3822/short5.pdf

In dialogue systems, utterances do not occur in isolation. One conversation might involve interactions between several speakers. It's crucial to determine the intentions behind utterances in multi-party conversations when more than two interlocutors... Read More about Dual-task dialogue understanding..

Towards improving open-box hallucination detection in large language models (LLMs). (2024)
Presentation / Conference Contribution
SURESH, M., ALJUNDI, R., NKISI-ORJI, I. and WIRATUNGA, N. 2024. Towards improving open-box hallucination detection in large language models (LLMs). In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 1-10. Available from: https://ceur-ws.org/Vol-3822/paper1.pdf

Due to the increasing availability of Large Language Models (LLMs) through both proprietary and open-sourced releases of models, the adoption of LLMs across applications has drastically increased making them commonplace in day-to-day lives. Yet, the... Read More about Towards improving open-box hallucination detection in large language models (LLMs)..

Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening. (2024)
Journal Article
OFORI-BOATENG, R., TRUJILLO-ESCOBAR, T.G., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening. Artificial intelligence in medicine [online], 157, article number 102989. Available from: https://doi.org/10.1016/j.artmed.2024.102989

Systematic Review (SR) are foundational to influencing policies and decision-making in healthcare and beyond. SRs thoroughly synthesise primary research on a specific topic while maintaining reproducibility and transparency. However, the rigorous nat... Read More about Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening..

Building personalised XAI experiences through iSee: a case-based reasoning-driven platform. (2024)
Presentation / Conference Contribution
CARO-MARTÍNEZ, M., LIRET, A., DÍAZ-AGUDO, B., RECIO-GARCÍA, J.A., DARIAS, J., WIRATUNGA, N., WIJEKOON, A., MARTIN, K., NKISI-ORJI, I., CORSAR, D., PALIHAWADANA, C., PIRIE, C., BRIDGE, D., PRADEEP, P. and FLEISCH, B. 2024. Building personalised XAI experiences through iSee: a case-based reasoning-driven platform. In Longo, L., Liu, W. and Montavon, G. (eds.) xAI-2024: LB/D/DC: joint proceedings of the xAI 2024 late-breaking work, demos and doctoral consortium, co-located with the 2nd World conference on eXplainable artificial intelligence (xAI 2024), 17-19 July 2024, Valletta, Malta. Aachen: CEUR-WS [online], 3793, pages 313-320. Available from: https://ceur-ws.org/Vol-3793/paper_40.pdf

Nowadays, eXplainable Artificial Intelligence (XAI) is well-known as an important field in Computer Science due to the necessity of understanding the increasing complexity of Artificial Intelligence (AI) systems or algorithms. This is the reason why... Read More about Building personalised XAI experiences through iSee: a case-based reasoning-driven platform..

iSee: a case-based reasoning platform for the design of explanation experiences. (2024)
Journal Article
CARO-MARTÍNEZ, M., RECIO-GARCÍA, J.A., DÍAZ-AGUDO, B., DARIAS, J.M., WIRATUNGA, N., MARTIN, K., WIJEKOON, A., NKISI-ORJI, I., CORSAR, D., PRADEEP, P., BRIDGE, D. and LIRET, A. 2024. iSee: a case-based reasoning platform for the design of explanation experiences. Knowledge-based systems [online], 302, article number 112305. Available from: https://doi.org/10.1016/j.knosys.2024.112305

Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation app... Read More about iSee: a case-based reasoning platform for the design of explanation experiences..

Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRANTUGA, N. and MORENO-GARCIA, C.F. 2024. Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. In Finkelstein, J., Moskovitch, R. and Parimbelli, E. (eds.) Proceedings of the 22nd Artificial intelligence in medicine international conference 2024 (AIME 2024), 9-12 July 2024, Salt Lake City, UT, USA. Lecture notes in computer science, 14844. Cham: Springer [online], part I, pages 261-272. Available from: https://doi.org/10.1007/978-3-031-66538-7_26

Evidence-based medicine (EBM) represents a cornerstone in medical research, guiding policy and decision-making. However, the robust steps involved in EBM, particularly in the abstract screening stage, present significant challenges to researchers. Nu... Read More about Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models..

Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review. (2024)
Journal Article
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review. Artificial intelligence review [online], 57(8), article number 200. Available from: https://doi.org/10.1007/s10462-024-10844-w

Systematic reviews (SRs) constitute a critical foundation for evidence-based decision-making and policy formulation across various disciplines, particularly in healthcare and beyond. However, the inherently rigorous and structured nature of the SR pr... Read More about Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review..

A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C. 2024. A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews. In Duh, K., Gomez, H. and Bethard, S. (eds.) Proceedings of the 2024 North American Chapter of the Association for Computational Linguistics conference (NAACL 2024): human language technologies, 16-21 June 2024, Mexico City, Mexico. Stroudsburg, PA: ACL [online], volume 1: long papers, pages 3725-3736. Available from: https://doi.org/10.18653/v1/2024.naacl-long.206

Systematic Reviews (SRs) are foundational in healthcare for synthesising evidence to inform clinical practices. Traditionally skewed towards English-language databases, SRs often exclude significant research in other languages, leading to potential b... Read More about A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews..

CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. (2024)
Presentation / Conference Contribution
WIRATUNGA, N., ABEYRATNE, R., JAYAWARDENA, L., MARTIN, K., MASSIE, S., NKISI-ORJI, I., WEERASINGHE, R., LIRET, A. and FLEISCH, B. 2024. CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. In Recio-Garcia, J.A., Orozco-del-Castillo, M.G. and Bridge, D (eds.) Case-based reasoning research and development: proceedings of the 32nd International conference of case-based reasoning research and development 2024 (ICCBR 2024), 1-4 July 2024, Merida, Mexico. Lecture notes in computer science, 14775. Cham: Springer [online], pages 445-460. Available from: https://doi.org/10.1007/978-3-031-63646-2_29

Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require e... Read More about CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering..

Context-aware data-to-text generation. (2024)
Thesis
UPADHYAY, A. 2024. Context-aware data-to-text generation. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571408

Data-to-Text Generation (D2T) is the subfield of Artificial Intelligence (AI) and Natural Language Processing (NLP) that aims to build systems capable of summarising nonlinguistic structured data into textual reports. D2T systems extract important in... Read More about Context-aware data-to-text generation..

Mitigating gradient inversion attacks in federated learning with frequency transformation. (2024)
Presentation / Conference Contribution
PALIHAWADANA, C., WIRATUNGA, N., KALUTARAGE, H. and WIJEKOON, A. 2024. Mitigating gradient inversion attacks in federated learning with frequency transformation. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 750-760. Available from: https://doi.org/10.1007/978-3-031-54129-2_44

Centralised machine learning approaches have raised concerns regarding the privacy of client data. To address this issue, privacy-preserving techniques such as Federated Learning (FL) have emerged, where only updated gradients are communicated instea... Read More about Mitigating gradient inversion attacks in federated learning with frequency transformation..

Clinical dialogue transcription error correction with self-supervision. (2023)
Presentation / Conference Contribution
NANAYAKKARA, G., WIRATUNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2023. Clinical dialogue transcription error correction with self-supervision. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 33-46. Available from: https://doi.org/10.1007/978-3-031-47994-6_3

A clinical dialogue is a conversation between a clinician and a patient to share medical information, which is critical in clinical decision-making. The reliance on manual note-taking is highly inefficient and leads to transcription errors when digit... Read More about Clinical dialogue transcription error correction with self-supervision..

Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. (2023)
Presentation / Conference Contribution
SALIMI, P., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2023. Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. In Gal, K., Nowé, A., Nalepa, G.J., Fairstein, R. and Rădulescu, R. (eds.) ECAI 2023: proceedings of the 26th European conference on artificial intelligence (ECAI 2023), 30 September - 4 October 2023, Kraków, Poland. Frontiers in artificial intelligence and applications, 372. Amsterdam: IOS Press [online], pages 2057-2064. Available from: https://doi.org/10.3233/FAIA230499

Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. However, many cf-XAI methods neglect the feasibility of those changes. In this paper, we introduce a novel... Read More about Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method..

Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023). (2023)
Presentation / Conference Contribution
IBRAHIM, Z., WU, H. and WIRATUNGA, N. (eds.) 2023. Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023), co-located with the 32nd International joint conference on artificial intelligence (IJCAI 2023), 20 August 2023, Macao, China. CEUR workshop proceedings, 3479. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-3479/

This workshop is centred around novel AI methodologies that aim to solve some of the grand challenges associated with medical data. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI 2023), this year's works... Read More about Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023)..

Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. (2023)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., JAYNE, C., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2023. Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. Porcedia computer science [online], 222: selected papers from the 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023), co-located with the 2023 International joint conference on neural networks (IJCNN), 18-23 June 2023, Gold Coast, Australia, pages 114-126. Available from: https://doi.org/10.1016/j.procs.2023.08.149

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screenin... Read More about Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation..

CBR driven interactive explainable AI. (2023)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., MARTIN, K., CORSAR, D., NKISI-ORJI, I., PALIHAWADANA, C., BRIDGE, D., PRADEEP, P., AGUDO, B.D. and CARO-MARTÍNEZ, M. 2023. CBR driven interactive explainable AI. In MASSIE, S. and CHAKRABORTI, S. (eds.) 2023. Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023, (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages169-184. Available from: https://doi.org/10.1007/978-3-031-40177-0_11

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requi... Read More about CBR driven interactive explainable AI..

Failure-driven transformational case reuse of explanation strategies in CloodCBR. (2023)
Presentation / Conference Contribution
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and CORSAR, D. 2023. Failure-driven transformational case reuse of explanation strategies in CloodCBR. In Massie, S. and Chakraborti, S. (eds.) Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023 (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages 279-293. Available from: https://doi.org/10.1007/978-3-031-40177-0_18

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves trans... Read More about Failure-driven transformational case reuse of explanation strategies in CloodCBR..

A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction. (2023)
Journal Article
WIJEKOON, A. and WIRATUNGA, N. 2023. A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction. Knowledge-based systems [online], 278, article 110830. Available from: https://doi.org/10.1016/j.knosys.2023.110830

Counterfactual explanations highlight actionable knowledge which helps to understand how a machine learning model outcome could be altered to a more favourable outcome. Understanding actionable corrections in source code analysis can be critical to p... Read More about A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction..