Searched for: author%3A%22Liem%2C+C.C.S.%22
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Altmeyer, P. (author), Giovan, Angela (author), Buszydlik, Aleksander (author), Dobiczek, Karol (author), van Deursen, A. (author), Liem, C.C.S. (author)
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and...
conference paper 2023
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Liem, C.C.S. (author), Demetriou, A.M. (author)
So far, the relationship between open science and software engineering expertise has largely focused on the open release of software engineering research insights and reproducible artifacts, in the form of open-access papers, open data, and open-source tools and libraries. In this position paper, we draw attention to another perspective:...
conference paper 2023
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Altmeyer, P. (author), Liem, C.C.S. (author), van Deursen, A. (author)
We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realistic and actionable changes can be used to provide AR: a set of...
conference paper 2023
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Bartlett, A.J. (author), Liem, C.C.S. (author), Panichella, A. (author)
Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using whitebox strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches...
conference paper 2023
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Sav, Andra Georgiana (author), Demetriou, A.M. (author), Liem, C.C.S. (author)
Machine Learning (ML) models influence all aspects of our lives. They also commonly are integrated in recommender systems, which facilitate users’ decision-making processes in various scenarios, such as e-commerce, social media, news and online learning. Training performed on large volumes of data is what ultimately drives such systems to...
journal article 2023
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Samiotis, I.P. (author), Lofi, C. (author), Alaka, Shaad (author), Liem, C.C.S. (author), Bozzon, A. (author)
In this demo we present Scriptoria, an online crowdsourcing system to tackle the complex transcription process of classical orchestral scores. The system’s requirements are based on experts’ feedback from classical orchestra members. The architecture enables an end- to-end transcription process (from PDF to MEI) using a scalable microtask design...
conference paper 2022
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Huang, H. (author), Liem, C.C.S. (author)
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies. Faced with similar challenges in dealing with an increasingly diversified audience, the...
conference paper 2022
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Kim, Jaehun (author), Liem, C.C.S. (author)
In the previous decade, Deep Learning (DL) has proven to be one of the most effective machine learning methods to tackle a wide range of Music Information Retrieval (MIR) tasks. It offers highly expressive learning capacity that can fit any music representation needed for MIR-relevant downstream tasks. However, it has been criticized for...
conference paper 2022
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Panichella, A. (author), Liem, C.C.S. (author)
Mutation testing is a well-established technique for assessing a test suite’s quality by injecting artificial faults into production code. In recent years, mutation testing has been extended to machine learning (ML) systems, and deep learning (DL) in particular; researchers have proposed approaches, tools, and statistically sound heuristics to...
conference paper 2021
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Yildiz, B. (author), Hung, H.S. (author), Krijthe, J.H. (author), Liem, C.C.S. (author), Loog, M. (author), Migut, M.A. (author), Oliehoek, F.A. (author), Panichella, A. (author), Pawełczak, Przemysław (author), Picek, S. (author), de Weerdt, M.M. (author), van Gemert, J.C. (author)
We present ReproducedPapers.org : an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest...
conference paper 2021
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Hiemstra, Annemarie M.F. (author), Cassel, Tatjana (author), Born, Marise Ph (author), Liem, C.C.S. (author)
In this article, we describe the implementation of algorithms based on machine learning for personnel selection procedures and how this data-driven approach corresponds to and differentiates from classical psychological assessment. We discuss if, and in what way, bias and discrimination occur when using algorithms based on machine learning...
journal article 2020
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Liem, C.C.S. (author), Panichella, A. (author)
The rise in popularity of machine learning (ML), and deep learning in particular, has both led to optimism about achievements of artificial intelligence, as well as concerns about possible weaknesses and vulnerabilities of ML pipelines. Within the software engineering community, this has led to a considerable body of work on ML testing...
conference paper 2020
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Escalante, Hugo Jair (author), Kaya, Heysem (author), Ali Salah, Albert (author), Escalera, Sergio (author), Güç;lütürk, Yağmur (author), Güçlü, Umut (author), Baro, Xavier (author), Sukma Wicaksana, Achmadnoer (author), Liem, C.C.S. (author)
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our...
journal article 2020
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König, Cornelius J. (author), Demetriou, A.M. (author), Glock, Philipp (author), Hiemstra, Annemarie M. F. (author), Iliescu, Dragos (author), Ionescu, Camelia (author), Langer, Markus (author), Liem, C.C.S. (author), Linnenbürger, Anja (author)
This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue...
journal article 2020
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Liem, C.C.S. (author), Mostert, C. (author)
Copyright restrictions prevent the widespread sharing of commercial music audio. Therefore, the availability of resharable pre-computed music audio features has become critical. In line with this, the AcousticBrainz platform<br/>offers a dynamically growing, open and community-contributed large-scale resource of locally computed low-level and...
conference paper 2020
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Samiotis, I.P. (author), Qiu, S. (author), Mauri, A. (author), Liem, C.C.S. (author), Lofi, C. (author), Bozzon, A. (author)
Human annotation is still an essential part of modern transcription workflows for digitizing music scores, either as a standalone approach where a single expert annotator transcribes a complete score, or for supporting an automated Optical Music Recognition (OMR) system. Research on human computation has shown the effectiveness of crowdsourcing...
conference paper 2020
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Weigl, David M. (author), Goebl, Werner (author), Hofmann, Alex (author), Crawford, Tim (author), Zubani, Federico (author), Liem, C.C.S. (author), Porter, Alastair (author)
The Web and other digital technologies have democratised music creation, reception, and analysis, putting music in the hands, ears, and minds of billions of users. Music digital libraries typically focus on an essential subset of this deluge - commercial and academic publications, and historical materials - but neglect to incorporate...
conference paper 2020
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Weigl, David M. (author), Goebl, Werner (author), Crawford, Tim (author), Gkiokas, Aggelos (author), Gutierrez, Nicolas F. (author), Porter, Alastair (author), Santos, Patricia (author), Karreman, Casper (author), Vroomen, Ingmar (author), Liem, C.C.S. (author), Sarasúa, Álvaro (author), Van Tilburg, Marcel (author)
The turn toward the digital has opened up previously difficult to access musical materials to wider musicological scholarship. Digital repositories provide access to publicly licensed score images, score encodings, textual resources, audiovisual recordings, and music metadata. While each repository reveals rich information for scholarly...
conference paper 2019
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Manolios, S. (author), Hanjalic, A. (author), Liem, C.C.S. (author)
The feld of recommender systems has a lot to gain from the feld of psychology. Indeed, many psychology researchers have investigated relations between models that describe humans and consumption preferences. One example of this is personality, which has been shown to be a valid construct to describe people. As a consequence, personality-based...
conference paper 2019
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Kim, Jaehun (author), Manolios, S. (author), Demetriou, A.M. (author), Liem, C.C.S. (author)
Prior research from the field of music psychology has suggested that there are factors common to music preference beyond individual genres. Specifically, research has shown that self-reported ratings of preference for individual musical genres can be reduced to 4 or 5 dimensions, which in turn have been shown to correlate to relevant...
conference paper 2019
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