Large language models have achieved breakthroughs in many natural language processing tasks. One of their main appeals is the ability to tackle problems that lack sufficient training data to create a dedicated solution. Manga translation is one such task, a still budding and underdeveloped field, that at the same time deals with a problem even more complex than standard translation. One of its biggest challenges is successfully incorporating the visual modality in translation, to resolve ambiguities. Recently, a new type of LLMs -- multimodal LLMs -- has emerged and showed potential in understanding visual symbolism in narrative pieces like memes and comics. In this work, we investigate whether these models could be useful in the field of manga translation, ultimately helping manga authors and publishers make their works available to wider audiences. Specifically, we evaluate a number of methods based on GPT-4-Turbo for text only translation, image informed translation and volume-level translation. We perform both automatic and human evaluation. Moreover, we contribute new evaluation data -- the first parallel Japanese-Polish manga translation benchmark. Our findings show that our proposed methods are able to achieve state of the art results for English, and set a new standard for Polish. We conclude that while this is not a sufficient replacement for a professional human translator, it could help speed up the translation process or be used as a learning aid.

Schema matching is a critical data integration process, which aims at capturing relevance between elements of different datasets; when datasets are tabular, it translates to the process of discovering related columns among them. Accurately discovering column matches is integral for several applications, such as entity resolution, data cleaning and data augmentation. While there exists a multitude of schema matching methods in the literature, we identify three major issues: i) there is no comprehensive study of comparing them in terms of effectiveness and efficiency, due to not available implementations and lack of evaluation datasets, ii) existing methods might be impractical and even inapplicable in certain modern settings, and iii) the heterogeneity and complexity of data can impede capturing relevance among columns for existing methods, as certain assumptions might not be holding for the entirety of underlying datasets. In this thesis, we tackle these issues by reviewing existing schema matching techniques and proposing novel methods capable to address challenges imposed by modern settings.
Starting with Chapter 2, we present an extensive comparison study on existing schema matching methods, by introducing Valentine. Specifically, Valentine constitutes an open-source experimental suite, which encompasses several state-of-the-art schema matching solutions. To guide the evaluation process towards modern applications, we extract four relatedness scenarios from the dataset discovery literature. To tackle the lack of existing datasets with ground truth, we devise a principled fabrication process. Our findings lead to insights that can help to improve future research on the field of schema matching, while they affect the design choices we make for novel methods we present in the following chapters.
Next, in Chapter 3, we turn our focus on applying schema matching among datasets stored in different data silos, which cannot be collocated and each contains information about column matches. Towards this direction, we introduce SiMa, a matching method that leverages existing matches in each silo, to build a column match prediction model, powered by the employment of a Graph Neural Network (GNN). To do so, SiMa transforms columns and matches among them in each silo to a graph, while it performs targeted negative edge sampling and incremental training to enhance the learning process. In our experimental evaluation, we show the benefits of using SiMa over state-of-the-art techniques, both in terms of effectiveness and efficiency.
Finally, Chapter 4 discusses the problem of discovering join relationships among datasets in a repository. To ameliorate the shortcomings of previous methods, we propose OmniMatch, a self-supervised method that can effectively capture both equi- and fuzzy-joins among tabular data. At the core of the method is the exploitation of a comprehensive set of similarity signals among columns, which are then transformed into a similarity graph. This graph, in conjunction with automatically generated positive and negative column match examples, enable the employment of a Relational Graph Convolution Network (RGCN) towards training a generalizable join prediction model. We compare the effectiveness of OmniMatch with several other state-of-the-art matching and column representation methods, while we verify the usefulness of utilizing a wide-spectrum of similarity signals to capture joins.
We conclude the thesis by reviewing our main findings, reflecting on our contributions and discussing potential limitations of the methods and approaches presented. Moreover, based on the insights we gain from surveying and developing novel matching methods, we discuss challenges and future directions in the field.
@en

Music annotation and transcription of music sheets are traditionally performed by experts. Although these processes result in high quality data, the scope of each effort is relatively narrow resulting in highly specialised and specific datasets of annotated music compositions, which leads to a fragmentation in the design efforts for automated tools. In music traditions such as classical music, the shortcomings of current digitization work- flows become even clearer: due to the vast corpus and varying stylistic intricacies, experts tend to have specific knowledge and take up projects that concern very specific periods or composers, limiting our reach in regards to conserving classical music information as a whole.

On the other hand, crowdsourcing has been successfully utilized in other domains for annotating different modalities (text, image, video, audio), despite the unreliable pool of expertise on online platforms. Commercially successful projects have utilized the crowd, which provided annotations of adequate quality. These annotations were later used to fuel machine learning methods that rely on bulks of annotated data to perform automatic classification, regression, and detection tasks. However, due to the complexity of music as an artifact, there are still only a few examples where the crowd was integrated into any form outside of subjective annotation tasks (e.g., indicate the mood of the excerpt).

In this thesis, we tackle this research gap of integrating the crowd in the annotation processes of music compositions. We surveyed current practices on Optical Music Recognition to identify parts where the crowd could assist, alongside proposing hybrid annotation workflows for music compositions. We studied the capabilities of online participants with unknown musical expertise, quantified their musical abilities, and related them to their performance in music annotation tasks. With our goal being to identify ways to expand the preservation efforts for classical music through the assistance of the general public, we investigated potential online sources of music information and prospective participants outside the currently available crowdsourcing platforms. To that end, we studied how composers’ popularity manifests on community-driven platforms through the interactions of music enthusiasts online. We also conducted interviews and focus group discussions with experts and semi-experts, to understand their quality requirements on semantically-rich digital music scores, and identified transcription patterns that could inform our task designs. We finally delivered our system architecture, which combines computer vision and algorithmic scheduling, with microtasks designed to be performed by human annotators in parallel.

Our findings show that with the right methods to quantify the musical competence of a person, paired with careful design of the annotation tasks and interfaces, we can successfully integrate the crowd in the music annotation processes, to generate meaningful and useful information regarding classical music compositions and beyond. This thesis enables future research by showcasing the versatility of the crowd and providing task design methods to accommodate their lack of formal training in the field. It also provides experimental methods to reliably identify how different music composition elements affect the crowd’s performance, alongside proposing user interface elements that can mediate the complexity of the artifacts. Practices such as those presented in this thesis can lead to scaling up our digitization efforts, generating accurate and useful annotations through the crowd, even in such domain-specific and knowledge-intensive topics as classical music compositions.@en

Despite the low adoption rates of artificial intelligence (AI) in respiratory medicine, its potential to improve patient outcomes is substantial. To facilitate the integration of AI systems into the clinical setting, it is essential to prioritise the development of explainable AI (XAI) solutions that improve the understanding of the AI predictions. These XAI solutions empower clinicians to collaborate effectively with AI systems, thereby enhancing the overall outcomes for patients in respiratory medicine. Unfortunately, the lack of user-centric studies in this domain has made it challenging to identify the specific aspects of explainability that are most effective in improving the adoption of AI in the real-world environment. To address this gap, we conducted a mixed-methods study of clinicians in respiratory medicine to identify the most relevant and crucial aspects of XAI solutions. Our study focused on understanding how XAI can be effectively translated into clinical practice by leveraging the expertise of doctors in the field. Because of the lack of knowledge about XAI concepts among pulmonologists a different approach is taken to regular user-centric XAI research and no direct examples of state-of-the-art XAI solutions are used. Rather the expertise of doctors is used to make them implicitly identify their needs and intents. Our findings reveal that the successful adoption of XAI solutions in respiratory medicine requires tailored solutions that address communication barriers, promote patient-centric care, and overcome AI adoption challenges. The study highlights the significance of task-specific visualisations, comprehensive explanations, preferred granularity, and the ability to mimic human judgement in successful XAI solutions. Trust and collaboration between clinicians and AI systems are essential for effective adoption, wherein AI is perceived as a colleague rather than a replacement. This ensures that clinicians can easily understand and work with the model predictions, ultimately leading to improved patient outcomes. By aligning XAI design with the needs and intents of pulmonologists, we established the importance of Co-designing solutions with domain experts and embedding XAI within clinical workflows emerged as key strategies. Our research underscores the imperative of transparency, extended validation, and continuous alignment of AI technologies with medical values. By following these principles, XAI solutions can be developed to enhance the diagnosis and treatment of respiratory illnesses, ultimately improving patient outcomes in respiratory medicine.

Colorectal cancer is a widespread disease that significantly impacts the health of individuals worldwide. Understanding the needs and concerns of those affected by this disease is crucial for improving patient outcomes and enhancing the quality of care. Patient web forums have emerged as valuable platforms for individuals to openly share their experiences and thoughts related to colorectal cancer, providing unique insights into the social, physical and emotional aspects of their patient journey. These forums offer a more comprehensive and authentic portrayal of patient experiences compared to traditional patient data collection methods, such as questionnaires and interviews, which may not capture the full scope of patients experiences in the colorectal cancer carepath.

However, analyzing the vast amount of unstructured data within these patient web forums presents a significant challenge. Traditional manual analysis by human experts is time-consuming, labor-intensive, and limited in scalability, making it impractical to analyze the sheer volume of patient-generated content. This is where the application of natural language processing (NLP) techniques becomes crucial. NLP enables the automated processing and analysis of textual data, allowing for efficient extraction and interpretation of the large amounts of patient forum posts.

Nevertheless, relying solely on machine intelligence, such as topic modeling and natural language generation, for interpreting patient forum data carries inherent risks, including the potential for disseminating misleading information. While these machine-driven techniques offer efficient and scalable ways to analyze and generate insights from the large amount of diverse and unstructured patient forums, they may lack the necessary contextual understanding and domain expertise to ensure the accuracy, relevance, and ethical implications for interpreting colorectal cancer patient experiences.

To close this gap between human experts and machine intelligence, this thesis explores the potential of hybrid intelligence (HI) for topic interpretation in colorectal cancer research. The main research question is: ``How can topic modeling, GPT-3.5 language generation and human expertise be combined to explore the interpretation of patient web forums in colorectal cancer (CRC) research?"

To address the research question, three human studies were conducted. The first study employed NMF topic modeling to compare topic interpretations created independently by medical workers and GPT-3.5. This comparative analysis discovered unique observations that differentiate human-written and AI-generated interpretations on online patient stories. In the second study, it was investigated how medical researchers collaborate with GPT-3.5 to develop hybrid interpretations on patient experience topics generated by the BERTopic model. A Flask web application served as the interactive platform for combining their knowledge with the AI model. Finally, the third study made professional human evaluators assess the topic relevance of the interpretations generated by medical researchers and GPT-3.5 to determine whether the combination of GPT-3.5 and human expertise leads to improved topic interpretations compared to individual interpretations.

The proposed solution to the research problem is to explore a hybrid workflow that compares, combines and validates GPT-3.5 language generation and human expertise, aiming for enhanced interpretations of topics extracted from colorectal cancer patient forums. The three studies provide opportunities for researchers and medical professionals to integrate machine intelligence from topic models and GPT-3.5 in their field of work. The hybrid workflow has conclusively demonstrated that human experts were successfully able to compare and enhance the relevance of human and GPT-3.5 interpretations of colorectal cancer patient experience topics. This allowed human experts to efficiently reach a more comprehensive understanding of patient forum data, which is essential for improving patient health in colorectal cancer research.

The goal of this research is to model and understand the effects of tourism demand on air quality by performing data integration on multi-source data. This research is aimed at researchers and practitioners aiming to perform multidisciplinary research in the fields of data science and geoscience, presenting the methods and challenges that arise when performing such an analysis. A data processing pipeline explains the research from a data integration perspective involving the data retrieval and pre-processing tasks. This enables the construction of datasets for machine learning modelling and prediction of air pollutant levels based on tourism data. The study area of this research is Italy which is chosen based on its significant tourism industry and wide availability of data about tourism development. For this study, in situ air quality data sampled using Google Earth Engine (GEE) around accommodation, transportation and tourism attraction locations is modelled with tourist arrival numbers, nights spent and average length of stay. Long short-term memory (LSTM) multivariate time series modelling is performed afterwards to understand predictability of air quality on a national and regional level. To this end, this research looks into three different stages of the modelling process of tourism with air quality which are: (i) retrieving accommodation, transportation and tourism attraction locations using the RDF model, (ii) identifying which pollutants are correlated and Granger-caused by the different tourism demand features using sampled satellite air quality data of the identified tourism locations, (iii) understanding performance characteristics of LSTM time series models by training on tourism demand and air quality data. Correlation analysis indicates the potential to model the relation between tourism demand indicators and PM2.5 in overall cleaner regions in terms of this pollutant. In these regions, Granger-causality testing suggests a higher chance of predictability of PM2.5 time series using tourism demand data from the previous month. Training an LSTM model using the information of this lagged relationship suggests that regions with overall high PM2.5 levels are challenging to model showing high RMSE scores. Training an LSTM model for these regions also required more epochs compared to overall cleaner regions to model the effects of tourism demand on air quality.

Gene similarity has been an area of great interest in numerous fields for decades, as it can provide insights into the evolutionary relationships among different species. This knowledge is particularly useful for advancing biotechnologies, discovering new drugs and treatments for various issues and improving the characteristics in breed crops or animals. DNA sequencing enables gene annotation, which facilitates the identification of similarities between genes. Similar genes from different species are interesting candidates for studying gene functional similarity. Gene ontology (GO) provides a standardized vocabulary for gene annotation, which is considered to be the ground truth when describing their properties. Nevertheless, an interesting source for gathering additional information about genes can be the plethora of biomedical articles accessible online. These are the pillars on which is foundered the incentive of this paper - an endeavor to investigate how using graph theory could benefit scientists in transferring knowledge about gene functionalities between different plants. We present an overview of the methodology and the design of the system we used in order to convey to what extent using subgraphs similarity based on annotated data proves to yield results similar to those already established as ground truth for the model plant Arabidopsis Thaliana and its counterpart, Solanum Lycopersicum, as well as our conclusions and discussions regarding the quality of the datasets used throughout this research.

There are an estimated 253 million blind and visually impaired people in the world. To grant them access to text publications that contain images, experts are employed to write image descriptions. There is both a societal and a legislative pressure to supply image descriptions to all new and archived publications within a number of years, yet the number of available experts is limited. The image description task for images in textual context is complex, since a fitting description combines salient elements from both the image as well as the context into a description, which also differs depending on the publisher and published medium.

Because of this complex nature, current automated systems are unable to reliably produce desirable results. Instead, this thesis focuses on developing software to assist the experts in their general image description task in order to improve their efficiency. Specifically, we use existing, commercially available automated tools to generate alternative representations of the data. To analyse the system, we develop a user interface to present all of the available data and design an experiment with a small group of experts to investigate the system's applicability and perceived usefulness. We find that such a system has great potential to assist the experts, but that it might be desirable to focus on a solution aimed towards a smaller subset of publications, so that domain-specific information sources can be exploited to improve the information quality.

Over time Linked Data collections are continuously subject to change because of numerous reasons. Users could insert new observations, or they could rectify erroneous statements in these knowledge graphs. In order not to lose historically import information, this trend of evolving Linked Data collections increases the need to version these collections. Furthermore, retrieving prior versions and their in-between changes could provide Linked Data users relevant information. However, for some changes to these collections we should record both their transaction time and their valid time. To address these two problems of versioning Linked Data collections and having bi-temporal changes, we introduce the Bi-Temporal Versioning Approach for Knowledge graphs (Bi-VAKs): a prototypical bi-temporal change-based Version Control System for an arbitrary RDF dataset. Bi-VAKs registers both the transaction time and the valid time of a set of modified quads, and therefore, it allows for coupled historical and retrospective SPARQL queries. In addition, in order to enhance collaboration between its users Bi-VAKs also keeps track on provenance data; it supports diverged states; and provide a standard data access interface. However, since the standard RDF data model is atemporal, defining such a set of modifications (update) in RDF poses difficult challenges. Firstly, to indicate this transaction time and the valid time of an update Bi-VAKs divides a revision or version into a transaction revision and a valid revision. And hence it directly separates the metadata from the actual data. Secondly, to denote and retrieve the modified triples/quads within a update Bi-VAKs uses RDF-star and SPARQL-star. In order to connect these revisions we develop three reference strategies: the explicit, the implicit, and the combined reference strategy. These strategies let a transaction revision either refer explicitly to its corresponding valid revision(s) or implicitly by the same revision number and branch index. Based on these strategies we initiate some different approaches to query the updates from the revision-store. And, we propose some different methods to construct a (prior) version. However, to evaluate these different design decision we cannot use the existing uni-temporal benchmarks for our bi-temporal versioning approach. Therefore, we expand the BEAR benchmark to a bi-temporal benchmark (Bi-BEAR). By means of this benchmark we demonstrate that all three reference strategies have about the same storage size. We notice that the usage of a snapshot and retrieval of all updates worsen the version materialisation (VM), delta materialisation (DM), and version (VQ) query performance. In addition, the VM query look up time considerably decreases if only the matching updates are queried. And modified updates, branches, and more updates in the revision-store slightly lower the VM, DM, and VQ query performance. In addition, for the implicit and combined reference strategy the query time is rather the same, and sometimes even better if we sort the updates instead of aggregating them directly. Overall, the implicit reference strategy is performing best, and is quickly followed by the combined reference strategy.

Public technology has been shown to have a strong dependence on physical touch, which increases the transmission of diseases. Gesture recognition helps to reduce this transmission, as the dependence on physical touch is removed. Furthermore, the use of visible light for gesture recognition would reduce the power consumption of public technology, as less power would have to be supplied for a light source. In this paper, a dataset for gesture recognition using ambient light is presented, alongside the design process and challenges faced. The gestures were collected using three photodiodes in order for a machine learning algorithm to identify the patterns made by the shadows cast when the gestures are performed. The dataset consists of 10 gestures performed by a total of 50 people 5 times on each hand. This was collected under 5 different light intensity ranges. This dataset was then passed through a machine learning model to be trained and tested, resulting in a 86.8% (3.s.f) validation accuracy. There are many factors related to the light source that caused the accuracy of the algorithm not to be as high as expected, however, the highest accuracy was found in environments with light intensities of 100-1000 lux; a well-lit indoor room.

Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valued weights. Current BNNs use latent real-valued weights during training, where several training hyper-parameters are inherited from real-valued networks. The interpretation of several of these hyperparameters is based on the magnitude of the real-valued weights. For BNNs, however, the magnitude of binary weights is not meaningful, and thus it is unclear what these hyperparameters actually do. One example is weight-decay, which aims to keep the magnitude of real-valued weights small. Other examples are latent weight initialization, the learning rate, and learning rate decay, which influence the magnitude of the real-valued weights. The magnitude is interpretable for real-valued weights, but loses its meaning for binary weights.
In this paper we offer a new interpretation of these magnitude-based hyperparameters based on higher-order gradient filtering during network optimization. Our analysis makes it possible to understand how magnitude-based hyperparameters influence the training of binary networks which allows for new optimization filters specifically designed for binary neural networks that are independent of their real-valued interpretation. Moreover, our improved understanding reduces the number of hyperparameters, which in turn eases the hyperparameter tuning effort which may lead to better hyperparameter values for improved accuracy.

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 healthy individuals from pathological individuals when given only an electrocardiogram. Some of these methods make use of neural networks that do feature extraction and classification in an end-to-end fashion. Related research performs feature engineering on ECG data followed by clustering. The evaluation shows that the resulting clusters coincide with heart pathology annotations. This master thesis describes the search for a machine learning pipeline, where a combination of an input representation, autoencoder, and clustering algorithm produces clusters coinciding with heart pathology without being biased by either heart pathology annotations or feature engineering that is already known to be predictive of heart pathology. Although the preexisting methods yield state-of-the-art accuracies, they do not allow us to learn about the structure and patterns in the data and the supervised methods are very costly to create. There has been no research into feature extraction by autoencoder followed by unsupervised clustering. Doing this could expose patterns in the data that could lead to improved diagnostics of heart pathology, and to automatic methods that are cheaper to create by turning the problem of diagnosis into an unsupervised or semi-unsupervised problem. In order to find the combination of input representation, autoencoders, and clustering that is best suited for predicting heart pathology from ECG signals, experiments are done that give insight into how much the resulting clusters coincide with heart pathology. The experiments work by first feeding some representation of one-second ECG objects into the autoencoder, after which the resulting low-dimensional representations are fed into a clustering algorithm. The clustering algorithm gives every low-dimensional ECG object a cluster label. The cluster labels are mapped to heart pathology labels by making use of existing heart pathology labels. This heart pathology label can now be interpreted as a class prediction in the context of classification. This setup is created to quantitatively answer to what degree the resulting clusters coincide with heart pathology. In the end, the concatenated image plot representation objects fed into a convolutional autoencoder followed by SOM clustering are compared to existing research. The classification accuracy achieved by the autoencoder pipeline formed in this research is 0.76 ± 0.01. This means that the clusters formed by the existing research coincide much more with heart pathology labels than the clusters from this research. A qualitative visualization from the low dimensional representations after the autoencoder, however, shows that the setup from this research is better at identifying patient IDs than heart pathology. This means that the features extracted by the autoencoder are salient for identifying persons, but not for identifying heart pathology.

Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative impacts on the lives of these people. Therefore, eliminating bias and ensuring fairness within these models is crucial to the societal expectations these institutions would like to meet. While there are tools and research towards technical mitigation methods for bias and unfairness. There is a lack of focus on the process of implementing these tools and methods within the industry, to develop ML models with the consideration of fairness at an early stage. In particular, there is a lack of specification on what fairness goals and objectives the ML model should accomplish. Without having this clarity, industry stakeholders can apply the tools and algorithmic unfairness mitigation methods but if the fairness requirements are not defined, then a) one is still not sure whether the ML model is solving the correct fairness goals and b) one is still not sure what the trade-offs and feasibility within different fairness goals, and available resources may look like. To address this, we design a Multi-Level Fairness Framework (digital workflow) that aims towards supporting stakeholders within the industry to perform Requirements Engineering, specifically elicitation and modeling, for fairness in a Machine Learning Model.

Database entity type recognition is the practice of recognizing conceptual entity types for which given data sets contain data. In big data or data lake settings, it is not always known which conceptual entity types are represented in each data set, making it difficult to extract value from the data. Depending on the logical schemas, each conceptual entity type can also be represented in the data instances in multiple different ways. This phenomenon, called semantic heterogeneity, poses a challenge when attempting database entity type recognition. Narrowing down the problem space to a specific organization makes it easier to cope with such problems. Organizations know which entity types are used in the organization and require only those to be recognized. And while there is heterogeneity in representation, there is likely a common set of rules each logical schema adheres to which can be exploited to recognize semantic heterogeneity. Furthermore, experts at an organization can provide example data instances for each conceptual entity type of interest, which provide ground truth for the proposed database entity type recognition solution. The proposed solution makes data profiles of the example data instances, and then attempts to recognize entity types in previously unseen data instances using a rule-based approach. Rules are used to maximize the ease of explainability of results, as is often desired at a bank, and can easily be added to or removed from the solution to maximize adaptability. Experiments using the proposed solution show promising results, with up to 90 percent of entity types correctly recognized over a total of 170,000 entities.

OKademy is a start-up that wants to improve the healthcare in the Netherlands by improving the process by which graduated medical students are being matched to a hospital team. This is needed since there is a lack of surgery
assistants, while graduate students need to wait on average six months to start their work in a hospital. To match quickly and sufficiently, the hard-skills as well as soft-skills of the student need to be known. To achieve this, universities need to provide OKademy with each student’s soft-skills. However, this takes an excessive amount of effort for the universities. To let the universities benefit from this idea, OKademy wants to have a system which keeps track of the soft-skills and matches students in groups for assignments, thus being beneficial for universities and hospitals. To help Okademy achieve its goal we developed a system that can be used by universities to help the students keep track of their soft-skills. The same system can be used to form theoretically well-functioning groups. These groups should be generated using an optimization algorithm that approximates the best groups regarding the soft-skills of students. In this system, students, referred to as ‘members’, and instructors, referred to as ‘hosts’, can register. Members can be assigned into groups and hosts can create or modify groups and courses accordingly. Members can, after registration, fill in their top 10 soft-skills with a corresponding automatized grade. Based on these skills, they will be assigned to groups for a course created by a host. A course will go through multiple phases. When all groups have completed the assignment, a member is able to give feedback to five random soft-skills of their group members and recommend a new one if desired. Based on this feedback, their soft-skill grades will be adjusted. The possible combinations of groups can be extremely large, therefore, it is not feasible to blindly search for the best group formation. Our algorithm will use the idea of genetic algorithms to explore the search space and approximate the best group formation. The the final product consists of a web application in which the multi-objective group formation optimization algorithm is implemented. To work in a structured manner, we used the Scrum method. Meetings with our client and coach were held every week. The system has been tested by functionality tests, user tests and unit tests, ensuring a functional system. OKademy will use this system in collaboration with universities and hospitals to solve the problem.

Records from ledgers of Dutch companies all across the Netherlands are used in this study. Records can be submitted in the ledgers with various lags, because the data of many different bookkeepers is involved with different workflows. Bookkeepers can be punctual or late, therefore records can be submitted with various lags in the ledgers. This causes missing data, which results in a deformation of a time series that is constructed from these records. Using a technique called now­casting, a prediction can be made of how these series with no missing data would have looked like.
This study sheds light on how information of an incomplete time series from ledgers of Dutch companies can be used to now­cast on that series, without the use of external indicators. To better utilize the information available from the series, an addition to the Seasonal Auto Regressive Integrated Moving Average with eXogenous regressors (SARIMAX) model is proposed. The addition to the SARIMAX model is presented in two forms: the additive­ and multiplicative relation between indicator­ and target series. These are modeled with the goal to improve the information utilization and therefore improve the now­casting accuracy. Experiments have shown that this addition to the model does not give a direct improvement in accuracy compared to an ordinary SARIMAX model. Thereafter an iterative now­cast procedure is proposed to utilize information from highly lagged records. It has been shown that this gives a slight increase in accuracy for the overall now­cast.

Schema matching has been a researched topic for over 20 years. Therefore, many schema matching solutions have been proposed to treat various problems such as: creating unified knowledge bases or mediation schema, data translation, data discovery, data curation. Such a wide variety of schema matching algorithms requires a benchmarking system that can evaluate to what extent one solution is appropriate for a given problem. However, creating the benchmark requires open source algorithms, which are not widely available in the data management community. One solution to this problem is reproducing the algorithms, although there is a reproducibility crisis which proves that the majority of existing research can not be reproduced. These circumstances have determined the goal of this research: conducting a reproducibility study on the state-of-the-art schema matching algorithms. This study supports the schema matching development and emphasizes the issues regarding the ability to reproduce the algorithms or the results. Moreover, we implement the selected algorithms and benchmark them in an industry case study.

Delirium occurs in upto 80% of the ICU patients and is one of the most common source of morbidity and mortality in the ICU. Inattention is one of the core features of delirium and affects 97% - 100% of the delirium patients. This indicates that attention can be a good parameter for detecting delirium. In literature, it has been found that the use of visual attention tests have been successful in detecting delirium among the ICU patients. The VitalSky is a luminous ceiling used for providing light therapy to the ICU patients. Since, the VitalSky is already available in the ICU and allows to render visual contents, it can be used for detecting delirium in the ICU patients by performing visual attention tests. In this thesis, visual attention tests are developed for the VitalSky. Moreover, different interaction devices are explored that can be used by the ICU patients to respond to the tasks of the attention test and help in automation of the test. The technical feasibility and usability of the automated setup is also evaluated in this thesis. The validation of the performance of the attention tests and user preference of the response devices is assessed by conducting a pilot study on 10 healthy volunteers and collecting their subjective feedback.

Governments require companies to be able to explain where their data is coming from and going to. Our client helps these companies by creating maps of their data landscapes. This is the concept of data lineage. There are various issues that arise in the workflow of figuring out and building data lineage diagrams. Our contributions here are providing a model for what data lineage diagram instances are, and an interactive web application that can be used to visualize and edit these diagrams in an intuitive way. One of the core challenges of this project has been to combine the client's business perspective with our knowledge of computer science. Starting with our research by figuring out the client's use cases, analysing them for their feasibility within the constraints of the project. The team used both Scrum and an agile approach to develop a product that matches the client's expectations and needs throughout the project. The product was tested by adhering to the five metrics defined by the Consortium for IT Software Quality. The final product contains the desired functionality and allows building data lineage diagrams using company data from the client through external APIs. The client is eager to use the product and has provided additional opportunities for the team to work further on the product.

Open Relation Extraction (ORE) aims to find arbitrary relation tuples between entities in unstructured texts. Even though recent research efforts yield state-of-the-art results for the ORE task by utilizing neural network based models, these works are solely focused on the English language. Methods were proposed to tackle the ORE task for multiple languages, yet these works fail to exploit relation patterns that are consistent over languages. Moreover, they require additional data to train translators, hindering efficient extension to new languages. In this work, we introduce a Language-consistent Open Relation Extraction Model (LOREM). By adding a language-consistent component to the current state-of-the-art open relation extraction model, we enable exploitation of information from multiple languages. Since we remove all dependencies on language-specific knowledge and external NLP tools such as translators, it is relatively easy to extend our model to new languages. An extensive evaluation performed on 5 languages shows that LOREM outperforms state-of-the-art monolingual and cross-lingual open relation extractors. Moreover, experiments on low- and even no-resource languages indicate that LOREM generalizes to other languages than the languages that it is trained on.