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Robbemond, Vincent (author)
Advances in artificial intelligence and machine learning have led to a steep rise in the adoption of AI to augment or support human decision-making across domains.<br/>There has been an increasing body of work addressing the benefits of model interpretability and explanations to help end-users or other stakeholders decipher the inner workings of...
master thesis 2023
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Bharos, Abri (author)
Powerful predictive AI systems have demonstrated great potential in augmenting human decision-making. Recent empirical work has argued that the vision for optimal human-AI collaboration requires ‘appropriate reliance’ of humans on AI systems. However, accurately estimating the trustworthiness of AI advice at the instance level is quite...
master thesis 2022
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Lee, Quentin (author)
The state-of-the-art shows the potential of chatbots and other Machine Learning (ML) models to perform many tasks of high quality. Especially chatbots are already used by many companies to assist their customer service. However, chatbots will likely never be able to perform all tasks perfectly. Therefore, it is still the question whether such a...
master thesis 2022
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van Schijndel, Jessie (author)
The workflow of a data science practitioner includes gathering information from different sources and applying machine learning (ML) models. Such dispersed information can be combined through a process known as Data Integration (DI), which defines relations between entities and attributes. When all information is combined in one source suited...
master thesis 2022
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Wang, Wang Hao (author)
Current speed of data growth has exponentially increased over the past decade, highlighting the need of modern organizations for data discovery systems. Several (automated) schema matching approaches have been proposed to find related data, exploiting different parts of schema information (e.g. data type, data distribution, column name, etc.)....
master thesis 2022
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Chen, Dina (author)
Recent works explain the DNN models that perform image classification tasks following the "attribution, human-in-the-loop, extraction" workflow. However, little work has looked into such an approach for explaining DNN models for language or multimodal tasks. To address this gap, we propose a framework that explains and assesses the model...
master thesis 2022
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Mokiem, Riaas (author)
Dataset discovery techniques originally required datasets to have the same domain which made them unsuitable to be used on a larger scale. To avoid this requirement, newer techniques use additional information, aside from the datasets being processed, to better understand the data. They might rely on a knowledge base that describes the meaning...
master thesis 2022
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van den Bos, Sjoerd (author)
Electrocardiography is the craft of producing electrocardiograms. These graphs give physicians insight into the potential pathology of the heart. In order to come to a diagnosis, physicians use electrocardiograms in combination with follow-up physical examinations. There has been extensive research into automated methods that can differentiate...
master thesis 2022
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Lammerts, Philippe (author)
Hate speech detection on social media platforms remains a challenging task. Manual moderation by humans is the most reliable but infeasible, and machine learning models for detecting hate speech are scalable but unreliable as they often perform poorly on unseen data. Therefore, human-AI collaborative systems, in which we combine the strengths of...
master thesis 2022
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Jongerius, Ricardo (author)
Online dating has become the most popular method of finding potential romantic partners. At the core of these platforms, there is a reciprocal recommender system which recommends users to other users on the platform. Breeze is an example of such a dating app, serving its users potential romantic partners every day in the hopes of sending them on...
master thesis 2022
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Biswas, Shreyan (author)
Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. <br/>Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches:...
master thesis 2022
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Chen, Xinyue (author)
While the performance of traditional confidence-based rejectors is heavily dependent on the calibration of the pretrained model, this study proposes the concept of feature-based rejectors and the whole pipeline where such rejector can be used in. Multiple design and development decisions along the implementation are discussed in the paper -...
master thesis 2022
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MA, YAO (author)
Crowd-powered conversational systems (CPCS) solicit the wisdom of crowds to quickly respond to on-demand users' needs. The very factors that make this a viable solution ---such as the availability of diverse crowd workers on-demand--- also lead to great challenges. The ever-changing pool of online workers powering conversations with individual...
master thesis 2022
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Palakodeti, Anitej (author)
Generating synthetic images has wide applications in several fields such as creating datasets for machine learning or using these images to investigate the behaviour of machine learning models. An essential requirement when generating images is to control aspects such as the entities or objects in the image. Controlling this helps in creating...
master thesis 2022
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Kuiper, Adrian (author)
Commonsense knowledge is the key of human intelligence in generalizing their knowledge to deal with complex tasks. Over the past years, a lot of research has been done in both natural language processing (NLP) and computer vision (CV) on leveraging commonsense knowledge to improve AI models. However, no systematic comparisons of existing work...
bachelor thesis 2022
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Rachwani, Sanjay (author)
Commonsense knowledge (CK) in artificial intelligence (AI), is an expanding field of research. Because CK is intrinsically implicit, current datadriven machine learning models are still far from competent compared to humans in commonsense reasoning tasks. To minimize the gap between machine learning models with the goal of artificial general...
bachelor thesis 2022
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Renţea, Ilinca (author)
Commonsense knowledge is information that all humans own and use to interpret common situations and react to them accordingly. This kind of information is necessary for the training of artificial intelligence models to reach a performance as close as possible to human performance. Researchers have developed methods that use crowdsourcing to...
bachelor thesis 2022
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Ezard, François (author)
Common sense knowledge (CSK) comes naturally to humans, but is very hard for computers to comprehend. However it is critical for machines to behave intelligently, and as such collecting CSK has become a prevalent field of research. Whilst a lot of research has been done to develop CSK acquisition methods, not much work has been done to survey...
bachelor thesis 2022
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Jeong, Yoon Hwan (author)
In order to combat increasing spam calls, many applications are developed and downloaded to block those calls. Some studies about performance of the applications were previously conducted, however, the actual processes the applications go through to intercept and block the spam calls are not well studied. This research presents a method to...
bachelor thesis 2022
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Busropan, Colin (author)
Spam calls are a widespread problem as people receive about 14 spam calls per month on average. In response, tens of applications are available in Google’s Play Store that aim to block these calls. While these apps have hundreds of millions of installations, there’s a lack of research into these applications. Research has been done on the user...
bachelor thesis 2022
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