Searched for: contributor%3A%22Lan%2C+G.+%28graduation+committee%29%22
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Harthoorn, Jip (author)
Advancements in the precision and accuracy of consumer-grade wearables, such as a Fitbit, have enabled the identification and therefore authentication of individuals based on their emitted heart frequencies using these wrist-worn devices. With this type of authentication, a password is essentially sent out every second. This makes it a perfect...
bachelor thesis 2023
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van der Voort, Niels (author)
Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also...
bachelor thesis 2023
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Wubben, Luuk (author)
Outlier detection is an essential part of modern systems. It is used to detect anomalies in behaviour or performance of systems or subjects, such as fall detection in smartwatches or voltage irregularity detection in batteries. This provides early indications of something of potential problems.<br/><br/>A part of outlier detection that is not...
bachelor thesis 2023
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Chiriţă, Matei (author)
The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research...
bachelor thesis 2023
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FANG, Siyuan (author)
In recent years, with the rapid expansion of IoT (Internet of Things) devices, more and more research and commercial projects have focused on various application areas of IoT. Signify, as a leading player in the smart home industry, has been deeply involved in this field for many years, particularly focusing on smart lighting for smart homes and...
master thesis 2023
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Reda, Yuji (author)
Badnets are a type of backdoor attack that aims at manipulating the behavior of Convolutional Neural Networks. The training is modified such that when certain triggers appear in the inputs the CNN is going to behave accordingly. In this paper, we apply this type of backdoor attack to a regression task on gaze estimation. We examine different...
bachelor thesis 2023
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Koning, Robbert (author)
Recent years have seen an increasing interest in stablecoins from major corporate and governmental parties. The European Central Bank is investigating the possibility of introducing its own Central Bank Digital Currency. The desired features of such a currency are under discussion. One such feature is offline spending: the ability to use the...
master thesis 2023
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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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Psathas, Steffano (author)
A machine learning classifier can be tricked us- ing adversarial attacks, attacks that alter images slightly to make the target model misclassify the image. To create adversarial attacks on black-box classifiers, a substitute model can be created us- ing model stealing. The research question this re- port address is the topic of using model...
bachelor thesis 2022
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Vigilanza Lorenzo, Pietro (author)
Machine Learning (ML) models are vulnerable to adversarial samples — human imperceptible changes to regular input to elicit wrong output on a given model. Plenty of adversarial attacks assume an attacker has access to the underlying model or access to the data used to train the model. Instead, in this paper we focus on the effects the data...
bachelor thesis 2022
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Dwivedi, Kanish (author)
Adversarial training and its variants have become the standard defense against adversarial attacks - perturbed inputs designed to fool the model. Boosting techniques such as Adaboost have been successful for binary classification problems, however, there is limited research in the application of them for providing adversarial robustness. In this...
bachelor thesis 2022
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van Veen, Floris (author)
Model extraction attacks are attacks which generate a substitute model of a targeted victim neural network. It is possible to perform these attacks without a preexisting dataset, but doing so requires a very high number of queries to be sent to the victim model. This is otfen in the realm of several million queries. The more difficult the...
bachelor thesis 2022
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Jansen, Joost (author)
In recent years, there has been a great deal of studies about the optimisation of generating adversarial examples for Deep Neural Networks (DNNs) in a black-box environment. The use of gradient-based techniques to get the adversarial images in a minimal amount of input-output correspondence with the attacked model has been extensively studied....
bachelor thesis 2022
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Koops, Reinier (author)
Natural Language Interfaces for Databases (NLIDBs) offer a way for users to reason about data. It does not require the user to know the data structure, its relations, or familiarity with a query language like SQL. It only requires the use of Natural Language. This thesis focuses on a subset of NLIDBs, namely those with 'plain English' sentences...
master thesis 2022
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