Searched for: contributor%3A%22Tielman%2C+M.L.+%28graduation+committee%29%22
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Mo, Tianyu (author)
ChatGPT, a cutting-edge technology based on LLM, demonstrated great potential in search tasks. While the importance and potential of ChatGPT are growing, the gap in the understanding of how users interact and engage in ChatGPT search remains open. Past research has extensively examined traditional information search, but there is a need for...
master thesis 2023
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Li, Shikuan (author)
With the growing importance of teamwork in higher education, effective communication and goal congruence have become vital in improving the effectiveness of student teamwork. This study aims todesign and implement an innovative system that combines a goal-setting chatbot and an effort visualizer to facilitate effective collaboration in student...
master thesis 2023
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Marcu, Alexandra (author)
Intelligent agents are increasingly required to engage in collaboration with humans in the context of human-agent teams (HATs) to achieve shared goals. Interdependence is a fundamental concept in teamwork. It enables humans and robots to leverage their capabilities and collaboratively work towards a shared goal, fostering the development of...
bachelor thesis 2023
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Kuiper, Lucie (author)
Artificial Intelligence (AI) is increasingly helping people with all kinds of tasks, due to its promising capabilities. In some tasks, an AI system by itself will take over tasks, but in other tasks, an AI system making decisions on its own would be undesired due to ethical and legal reasons. In those cases, AI can still be of help by forming...
master thesis 2022
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Cromjongh, Robin (author)
Police officers are exposed to many potentially traumatising and stressful situations, but they do not always find the right mental health help they need. In this thesis, conversational agent Robyn is developed to help officers keep an eye on their mental well-being and find help when needed. An evaluation approach is also presented. The...
master thesis 2022
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Langendam, Thijmen (author)
Wave Function Collapse (WFC) is a powerful generative algorithm, able to create locally-similar output based on a single example input. One of the inherent limitations of the original WFC is that it often requires users to understand its inner workings, and possibly make their own ad-hoc mods to achieve satisfactory results. Besides distracting...
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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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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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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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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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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Schijlen, Fiske (author)
Side-channel attacks (SCA) can obtain secret information related to the private key used during encryption executed on some device by exploiting leakage in power traces produced by the device. In recent years, researchers found that a neural network (NN) can be employed to execute a powerful profiled SCA, even on targets protected with...
master thesis 2022
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Grundmann, Sharon (author)
Around the world, child helplines through their services provide a safe and confidential space for children to be heard and empowered. The Dutch Kindertelefoon is one of such helplines providing counselling services to children via call and chat all year round. In this thesis, we explore the design of a conversational agent for training...
master thesis 2022
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Chen, Siyu (author)
Suicide prevention is an important global topic, since suicide is being a worldwide serious health issue for decades. In the meantime, people start to pay more attention to mental health issues and Artificial Intelligence is developed and applied in many different fields. Recently, there are many studies been done on using Conversational Agents...
master thesis 2021
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Tĩtu, Andrei (author)
Federated learning (FL) is a new paradigm that allows several parties to train a model together without sharing their proprietary data. This paper investigates vertical federated learning, which addresses scenarios in which collaborating organizations own data from the same set of users but with differing features. The survey provides an...
bachelor thesis 2021
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Soos, Márton (author)
Federated Learning (FL)[1] is a type of distributed machine learning that allows the owners of the training data to preserve their privacy while still be- ing able to collectively train a model. FL is a new area in research and several chal- lenges reagarding privacy and communication cost still need to be overcome. Gradient leakage[1], for...
bachelor thesis 2021
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Culea, Horia (author)
Federated learning is a machine learning technique proposed by Google AI in 2016, as a solution to the GDPR regulations that made the classical Centralized Training, not only unfeasible, but also illegal, in some cases. In spite of its potential, FL has not gained much trust in the community, especially because of its susceptibility to data...
bachelor thesis 2021
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Mînea, Robert (author)
Privacy in today's world is a very important topic and all the more important when sizeable amounts of data are needed in Neural Network processing models. Federated Learning is a technique which aims to decentralize the training process in order to allow the clients to maintain their privacy, while also contributing to a broader learning...
bachelor thesis 2021
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Filip, Eduard (author)
Federated Learning starts to give a new perspective regarding the applicability of machine learning in real-life scenarios. Its main goal is to train the model while keeping the participants' data in their devices, thus guaranteeing the privacy of their data. One of the main architectures is the Horizontal Federated Learning, which is the most...
bachelor thesis 2021
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Koffas, Stefanos (author)
Deep learning has made tremendous success in the past decade. As a result, it is becoming widely deployed in various safety and security-critical applications like autonomous driving, malware detection, fingerprint identification, and financial fraud detection. It was recently shown that deep neural networks are susceptible to multiple attacks...
master thesis 2021
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