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Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler

Georgescu, Calin (author)

Kotlin is a programming language best known for its interoperability with Java, as well as the measurable improvements it offers over it. Since it became Android’s go-to language in 2019, the popularity and impact of Kotlin have risen greatly. Amidst this surge in popularity, the Kotlin developer team is working on a new version of the compiler...

master thesis 2023

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Preoperative assessment of the histopathological growth patterns of colorectal liver metastasis on CT using artificial intelligence

van Gurp, Samuel (author)

Background: Histopathological growth patterns (HGP) are a biomarker for predicting survival and systemic treatment effectiveness in colorectal liver metastasis (CRLM). Currently, HGP assessment in CRLM requires the resection specimen. Predicting the HGP from preoperative medical imaging could allow more personalised care and better outcomes....

master thesis 2023

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Blockchain-Based Verifiable and Privacy-Preserving Machine Learning Inference

Samardžić, Mariana (author)

The Machine Learning (ML) technology has taken the world by storm since it equipped the machines with previously unimaginable decision-making capabilities. However, building powerful ML models is not an easy task, but the demand for their utilization in different industries and areas of expertise is high. This was recognized by entities that...

master thesis 2023

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Machine Learning and Causal Inference for the estimation of the effect of tacrolimus on kidney rejections

Borges Carioca Moreno Rodrigues, Inês (author)

Tacrolimus is an immunosuppressive drug given to kidney transplant patients. A low concentration of this drug can lead to kidney rejection, but to our knowledge no research has been done to causally connect the two. This paper investigates the causal effect of tacrolimus concentration on kidney rejection occurrence using predictive analysis and...

master thesis 2023

document

Machine Learning Driven PV-Climate Classification

Triana de las Heras, Javier (author)

Technological advances, cost reduction, depletion of fossil fuels, environmental concerns, and growing energy demand are expanding photovoltaic solar energy (PV) in more latitudes and locations. A simple and effective procedure to assess the PV potential of a particular region is to analyse its climatic conditions. In general, climatic studies...

master thesis 2023

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Leveraging Related Datasets to Improve Model Performance on an Underrepresented Target Population

Ries, Maxmillan (author)

Training deep learning models for time-series prediction of a target population often requires a substantial amount of training data, which may not be readily available. This work addresses the challenge of leveraging multiple related sources of time series data in the same feature space to improve the prediction performance of a deep learning...

master thesis 2023

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Predictive Analysis of Anti-NMDA-Receptor Encephalitis: using a Random Forest Classifier on EEG Data

Lückerath, Femke (author)

During the initial phase of diagnosis, patients with anti-NDMA-receptor encephalitis (anti-NMDARE) often experience severe symptoms that significantly impact their quality of life. Anti-NDMARE is an autoimmune disorder affecting the brain, with electroencephalography (EEG) playing a vital role in diagnosis and treatment. Identifying EEG patterns...

master thesis 2023

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Performance of outlier detection on smartwatch data in single and multiple person environments: An analysis of the performance of different outlier detection methods on consumer-grade wearable data in environments with single and multiple subjects

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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Efficient Temporal Action Localization via Vision-Language Modelling: An Empirical Study on the STALE Model's Efficiency and Generalizability in Resource-constrained Environments

Wang, Yunhan (author)

Temporal Action Localization (TAL) aims to localize the start and end times of actions in untrimmed videos and classify the corresponding action types. TAL plays an important role in understanding video. Existing TAL approaches heavily rely on deep learning and require large-scale data and expensive training processes. Recent advances in...

bachelor thesis 2023

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Decoding EEG signals

Dai, Anthony (author), van de Weg, Joris (author)

In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a...

bachelor thesis 2023

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Annotation practices in affective computing: What are these algorithms actually trained on?

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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Estimating intentions to speak in social settings: Speaker intention estimation using accelerometer data and non-verbal vocal behaviour

Oudhuis, Waded (author)

Computers having the ability to estimate intentions to speak can improve human-computer interaction. While plenty of research has been done on next-speaker prediction, they differ from intentions to speak since these rely only on the person themselves. Previous research was done on inferring intentions to speak using accelerometer data with some...

bachelor thesis 2023

document

Effectiveness of propensity score methods with density estimation in identifying overlap for causal inference

Tjong, Jonathan (author)

For causal inference, sufficient overlap is needed. It is possible to use propensity scores with the positivity assumption to ensure overlap is present. However, positivity is not enough to properly identify the region of overlap. For this, propensity scores need to be used in combination with density estimation. This project aims to evaluate...

bachelor thesis 2023

document

Generalizability of Deep Domain Adaptation in case of Sample Selection Bias

Witting, Emiel (author)

Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain adaptation uses unlabeled target data to mitigate a shift or bias...

bachelor thesis 2023

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Non-Monotonicity in Empirical Learning Curves: Identifying non-monotonicity through slope approximations on discrete points

Socol, Codrin (author)

Learning curves are used to shape the performance of a Machine Learning (ML) model with respect to the size of the set used for training it. It was commonly thought that adding more training samples would increase the model's accuracy (i.e., they are monotone), but recent works show that may not always be the case. In other words, some learners...

bachelor thesis 2023

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Estimating Intention To Speak Using Non-Verbal Vocal Behavior

van Marken, Julie (author)

This research aims to answer the question whether non-verbal vocal behavior can be used to estimate intention to speak. To answer this question data from a dutch social networking event is used to gather intentions to speak. The intentions to speak are split up in two categories: successful and unsuccessful intentions. The unsuccessful...

bachelor thesis 2023

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Investigating Data Collection and Reporting Practices of Human Annotations in Societally Impactful Machine Learning Applications: A Systematic Review of Top-Cited IEEE Access Papers

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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Federated learning: A comparison of methods: How do different ML models compare to each other

Sīpols, Emīls (author)

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and...

bachelor thesis 2023

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Annotation Practices in Societally Impactful Machine Learning Applications: What are the recommender systems models actually trained on?

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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Finding Biomarkers for Schizophrenia: Can Machine Learning algorithms identify schizophrenia-related biomarkers within metagenomic data derived from the human gut microbiome?

Bastow, Timothy (author)

There is mounting evidence indicating a relation- ship between the gut microbiome composition and the development of mental diseases but the mech- anisms remain unclear. Shotgun sequenced data from 90 schizophrenic patients and 81 sex, age, weight, and location matched controls was used for three machine learning models: Logistic Re- gression,...

bachelor thesis 2023