Searched for: contributor%3A%22Liem%2C+C.C.S.+%28mentor%29%22
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Zagorac, Ivor (author)
Counterfactual explanations (CEs) are emerging as a crucial tool in Explainable AI (XAI) for understanding model decisions. This research investigates the impact of various factors on the quality of CEs generated for classification tasks. We explore how inter-class distance, data imbalance, balancing techniques, the presence of biased...
master thesis 2024
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Roelvink, Marijn (author)
Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human...
master thesis 2024
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ZHUANG, XUANYU (author)
In the task of music style transfer, the symbolic music representation based on Musical Instrument Digital Interface (MIDI) files has always been a popular research medium. By using such representation, some mature models for image style transfer can also be applied to this scenario, such as Cycle-consistent Generative Adversarial Networks ...
master thesis 2023
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Backer, Suzanne (author)
In the machine learning research community, significant importance is given to the optimization of techniques which are employed once a benchmark dataset is given. However, less importance is assigned to the quality of these datasets and to how these datasets are obtained. In this work, we look into annotation practices in the research area of...
bachelor thesis 2023
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Taşcılar, Doğa (author)
This research examines transparency between ICASSP conference papers and the dataset documentations related to the datasets' annotation practices. Top-cited 5 papers and 51 unique resources in total were considered. All of the selected papers utilized at least one dataset. For every paper, an extensive metadata search has done to reach the...
bachelor thesis 2023
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Ibrahim, Ahmed (author)
This systematic review investigates the practices and implications of human annotations in machine learning (ML) research. Analyzing a selection of 100 papers from the IEEE Access Journal, the study explores the data collection and reporting methods employed. The findings reveal a prevalent lack of standardization and formalization in the...
bachelor thesis 2023
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Sav, Andra (author)
Machine Learning models are nowadays infused into all aspects of our lives. Perhaps one of its most common applications regards recommender systems, as they facilitate users' decision-making processes in various scenarios (e.g., e-commerce, social media, news, online learning, etc.). Training performed on large volumes of data is what ultimately...
bachelor thesis 2023
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Andrasz, Aleksandra (author)
Depression diagnosis and treatment remain difficult tasks that could be improved with machine learning models. But those automatic systems should be reliable to apply in clinical psychology settings. Performing predictions in this field is most commonly done using supervised learning models, which rely on well-established annotations. Therefore...
bachelor thesis 2023
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Eckhardt, Thomas (author)
This paper presents a novel approach to synthetic data generation for OCR post-correction, utilizing specific background and font variations tailored to specific timeperiods. The goal is to use synthetic data to enhance text accuracy in digitized historical documents. The proposed three-step process involves generating synthetic images that...
master thesis 2023
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El Moussaoui, Chakir (author)
In today's society, the rapid progression of digitization has led to the automation of various facets of human existence. This transformation has been facilitated by the utilization of algorithms, which are instrumental in driving efficient and effective automated processes. These algorithms have also found widespread adoption in the public...
master thesis 2023
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Lugtenburg, Jochem (author)
Auto-tagging systems can enrich music audio by providing contextual information in the form of tag predictions. Such context is valuable to solve problems within the MIR field. The majority of re- cent auto-tagging research, however, only considers a fraction of tags from the full set of available annotations in the original datasets. Because of...
master thesis 2022
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Sarkar, Priya (author)
Mathematical fairness notions introduced in literature aim to make algorithmic decisions fair. However, their usage has been criticized in domains such as recidivism and lending for producing unfair decisions. Questions regarding fairness, which also have an important role in hiring are giving way to concerns about the increasing adoption of...
master thesis 2022
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Angela, Giovan (author)
Machine learning classifiers have become a household tool for banks, companies, and government institutes for automated decision-making. In order to help explain why a person was classified a certain way, a solution was proposed that could generate these counterfactual explanations. Several generators have been introduced and tested but include...
bachelor thesis 2022
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Buszydlik, Aleksander (author)
Algorithmic recourse aims to provide individuals affected by a negative classification outcome with actions which, if applied, would flip this outcome. Various approaches to the generation of recourse have been proposed in the literature; these are typically assessed on statistical measures such as the validity of generated explanations or their...
bachelor thesis 2022
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Dobiczek, Karol (author)
Employing counterfactual explanations in a recourse process gives a positive outcome to an individual, but it also shifts their corresponding data point. For systems where models are updated frequently, a change might be seen when recourse is applied, and after multiple rounds, severe shifts in both model and domain may occur. Algorithmic...
bachelor thesis 2022
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Salarian, Borna (author)
Working with trustworthy classifier models is important to the field of music information retrieval. However studies have shown some of the classifier models may not be as trustworthy as they appear. In this paper, we examine three of such classifiers available in the Essentia toolkit that have been evaluated using cross-validation, and measure...
bachelor thesis 2022
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Hulleman, Sjoerd (author)
Music Information Retrieval (MIR) is a field of research that focusses on extracting information from music related data. This includes the genre of music and the beats per minute (BPM) of a song. Pipelines that extract this information from music are called feature extractors. Essentia is a library for such feature extraction. Often, the audio...
bachelor thesis 2022
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in 't Veen, Leonard (author)
The GTZAN dataset, a collection of 1000 songsspanning 10 genres, proposed by Tzanetakis hasbeen around for 20 years. In this time hundredsof researches and applications have included thisdatabase. However, there seem to be some seri-ous limitations to this dataset. There are dupli-cates, mislabellings, low audio recordings and nar-row...
bachelor thesis 2022
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Čivas, Vykintas (author)
Beat detection is an important MIR research area. Due to its growing usage in multimedia applications, the need for systematic ways to evaluate beat detectors is growing too. This research tests RhythmExtractor2013, a pipeline offered by Essentia, an open-source music analysis library used in research and industry. The annotated test samples,...
bachelor thesis 2022
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Meuleman, Mathias (author)
The field of Optical Music Recognition has been making progress in the past decades to automate the process of transcribing music scores into computer-readable formats, but its results are still far from being generally applicable. Some research effort has focused on incorporating crowdsourcing techniques into this field to check and correct...
master thesis 2021
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