Cynthia C. S. Liem, Andrew M. Demetriou (2023), Treat societally impactful scientific insights as open-source software artifacts, L. O'Conner (Eds.), In Proceedings of the 2023 IEEE/ACM 45th International Conference on Software Engineering p.150-156, IEEE.

Ioannis Petros Samiotis, Christoph Lofi, Shaad Alaka, Cynthia C. S. Liem, Alessandro Bozzon (2022), Scriptoria: A Crowd-powered Music Transcription System, F. Laforest, R. Troncy, L. Médini, I. Herman (Eds.), In WWW 2022 - Companion Proceedings of the Web Conference 2022 p.256-259, Association for Computing Machinery (ACM).

Han-Yin Huang, Cynthia C.S. Liem (2022), Social Inclusion in Curated Contexts: Insights from Museum Practices, In Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 p.300-309, Association for Computing Machinery (ACM).

Jaehun Kim, C.C.S. Liem (2022), The power of deep without going deep? A study of HDPGMM music representation learning, In Proceedings of the 23rd International Society for Music Information Retrieval Conference p.116 - 124.

Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C.S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemysław Pawełczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert (2021), ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility, Bertrand Kerautret, Miguel Colom, Adrien Krähenbühl, Daniel Lopresti, Pascal Monasse, Hugues Talbot (Eds.), In Reproducible Research in Pattern Recognition - 3rd International Workshop, RRPR 2021, Revised Selected Papers p.3-11, Springer.

A. Panichella, C.C.S. Liem (2021), What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection, In 43rd International Conference on Software Engineering - New Ideas and Emerging Results p.66-70, ACM/IEEE.

C.C.S. Liem, C. Mostert (2020), Can't trust the feeling?: How open data reveals unexpected behavior of high-level music descriptors, In Proceedings of the 21st International Society for Music Information Retrieval Conference p.240-247.

Annemarie M.F. Hiemstra, Tatjana Cassel, Marise Ph Born, Cynthia C.S. Liem (2020), De (On)mogelijkheden van machine learning voor het verminderen van bias en discriminatie bij personeelsbeslissingen, In Gedrag en Organisatie Volume 33 p.279-299.

I.P. Samiotis, S. Qiu, A. Mauri, C.C.S. Liem, C. Lofi, A. Bozzon (2020), Microtask crowdsourcing for music score Transcriptions: an experiment with error detection, In Proceedings of the 21st International Society for Music Information Retrieval Conference.

Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yağmur Güç;lütürk, Umut Güçlü, Xavier Baro, Achmadnoer Sukma Wicaksana, Cynthia C.S. Liem, More Authors (2020), Modeling, Recognizing, and Explaining Apparent Personality from Videos, In IEEE Transactions on Affective Computing Volume 13 p.894-911.