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Adapting semantic similarity methods for case-based reasoning in the Cloud. (2022)
Conference Proceeding
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Adapting semantic similarity methods for case-based reasoning in the Cloud. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 125-139. Available from: https://doi.org/10.1007/978-3-031-14923-8_9

CLOOD is a cloud-based CBR framework based on a microservices architecture, which facilitates the design and deployment of case-based reasoning applications of various sizes. This paper presents advances to the similarity module of CLOOD through the... Read More about Adapting semantic similarity methods for case-based reasoning in the Cloud..

How close is too close? Role of feature attributions in discovering counterfactual explanations. (2022)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A cas... Read More about How close is too close? Role of feature attributions in discovering counterfactual explanations..