There is a correlation between music and affect which researchers try to define and use in technology to improve healthcare and users' experience in music-related technology. However, since affect is a complex term there is not a specified way on how to represent different affective states in systems. A systematic literature review was performed to give an overview of affect representation schemes (ARS) used in music affect prediction. All the studies included in this survey were found in Scopus, Web of Science and IEEE Explorer and focus on automatic affect prediction in music. What is more, the literature written in a different language than English and not from the Computer Science field was excluded. Considering the time constraint of 10 weeks, an additional feasibility filtering was conducted. That is only the documents which mention common benchmark datasets for this task were included. The datasets were chosen after the research of related work and different literature reviews related to this topic. We were aware that it might include a bias correlated to dataset popularity. At the final stage of filtering, 113 records were chosen for data extraction from which 51 were used in the analysis due to time constraints and a single person conducting this research. After synthesising and analysing the extracted data we observed that the schemes can be distinguished between dimensional and categorical approaches and both are similarly popular. A rare but existing ARS are developed by combining both scheme types in a unique representation. What is more, there is no easily visible trend over time in the usage of certain schemes and only 66% of the reviewed studies support their choices with psychological theories. While conducting this research, we encountered limitations in terms of time and the research group, so we leave a gap for future improvements in this project.

In human-human interactions, the majority of information is conveyed through body language, specifically facial expressions. Consequently, researchers have been interested in improving human-computer interactions through developing systems with automatic understanding of body language and facial expressions.  This technology is especially useful due to its broad range of applications in fields such as healthcare, education, and safety & security. Vision-based automatic affect recognition (AAR) systems aim to predict a subject’s affective state based on visual input such as image or video. These systems analyze and classify subjects’ facial expressions and body language using affect representation schemes (ARS), most often classified as either categorical or dimensional. This paper explores the current state of ARS used in vision-based AAR through a systematic literature review following PRISMA guidelines. We selected 53 papers from WebOfScience according to our eligibility criteria which included computer science papers written in English proposing a vision-based AAR system targeting single subjects, and excluded studies dealing exclusively with micro-expressions or group affect recognition. Additionally, given the time limitation imposed on this research we excluded papers that were not readily accessible with our TU Delft license, used multimodal input, or did not use a dataset included in our  predefined list. For this exploration we specifically look at the schemes used, the popularity and trends of usage, motivations, and psychological basis. From the 53 reviewed papers, all of the papers target utilitarian emotions using at least one discrete ARS. The most commonly used schemes classify affective states into happiness, sadness, fear, anger, surprise, and disgust. While the majority of papers are lacking in providing explicit reasoning for their choices, most ARS are based grounded in psychological theories. Our results show an established norm within this area of research. However, they also evidence a lack of displayed critical thought in the selection of schemes. This oversight limits potential for future AAR research.

Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear, anger). Throughout the years many AAR reviews have been published. However, most of them do not include a detailed analysis of ARSs and the motivation behind them. This paper aims to fill this knowledge gap by performing a Systematic Literature Review (SLR) of Computer Science papers that propose a Physiological-signal-based AAR (PAAR) system. We explore how researchers discuss and choose an ARS and whether they base it on actual psychological theories. Eligible papers are retrieved from 4 databases: Web Of Science, IEEExplore, Scopus, and ACM Digital Library. Due to time limitations, the review is done rapidly and some additional search constraints are applied for feasibility. The most significant restrictions are: considering papers published since 2020 and performing experiments on specific benchmarking datasets. We take these constraints and their possible impact into consideration when interpreting the results. The presented review procedure can be stripped from the additional filters and reused for a full review. In total 115 papers are processed. The majority of papers introduce an EEG-based emotion recognition system. The analysis of the extracted information reveals that dimensional ARSs, in particular, Valence/Arousal model is the most popular. Moreover, authors often choose to reduce the dimensions to high/low values. Categorical ARSs are less frequent and usually are adopted from the dataset. Lastly, the authors do not provide extensive motivation for the choice of ARS and rarely refer to psychological theories.

In group recommendation, a key question is how preferences from individuals should be obtained and then aggregated into a group outcome. Collecting individual preferences can be done through implicit or explicit means, but there is insufficient research available on what option is optimal. For comparing different possible aggregation strategies, much of the existing research in the field departs from existing preference data (e.g.\ ratings), but considers synthetically created groups, rather than real groups. This study describes two experiments focusing on these issues. The first compared historical listening data with explicitly provided data and showed them to be similar. The second experiment compares different aggregation strategies in a more ecologically valid setting. More specifically, it considers playlist creation scenarios for groups, in which participants were asked to create their own, real-life groups. While the playlists as a whole did not show any significant differences in performance, inspecting the ratings of the tracks did show some differences. Finally, acoustic similarity was explored using the data obtained in the experiments. Some early results were observed, but more research is needed in this area.

In practice, many applications like traffic monitoring and smart grids rely on computing functions on privacy-sensitive data. In order to protect privacy-sensitive data and still keep the ability to compute any arbitrary function, multi-functional privacy-preserving data aggregation schemes have been created. These schemes, however, can be abused by malicious users, leading to incorrect results. Existing literature mostly provides aggregation schemes which are either multi-functional or support malicious user detection, but to the best of our knowledge, there is only one scheme that provides both. This scheme requires each user to send a number of ciphertexts linear in the size of the aggregation function's domain. Furthermore, that scheme is not collusion-resistant. In this thesis, we design a multi-functional privacy-preserving data aggregation scheme with malicious user detection. In contrast to existing schemes in literature, the amount of messages is independent of the size of the aggregation function's domain, it does not rely on a trusted authority and it is collusion-resistant as long as at least two users are honest-but-curious.

For traditional, manual real estate appraisals, the appraiser is required to provide a number of comparable properties (the 'Comps'). These comps act as a benchmark for the valuation as well as a provider of context in the final appraisal report. Traditionally, these comps are selected manually by an appraiser based on recent transactions within a ten mile radius. This manual selection is biased by the appraiser's market knowledge and the amount of transactions in the area. To replace this process, we developed an automated comparable selection service that does so based on objective characteristics, without restricting itself to a small spatial and/or temporal slice of the market.

Comparable selection does not have an objective ground truth, which complicates or even prohibits the use of many machine learning algorithms that could otherwise have been used. Additionally, the outcome of the service needs to be explainable to its users — it cannot be completely opaque. Finally, our service needed to integrate with a streaming data platform, with incremental new data that arrives continuously and needs to be incorporated into the service's model and output.

Our research phase focused on three aspects: determining the possible algorithms for selecting comparable properties given the constraints of explainability and a streaming environment, how to explain the output of the chosen algorithm to the user, and how to build a service around the chosen model that consumes a stream of input data and can generate a set of comparable properties ad-hoc.

Our process was based on agile methodology, with two-week sprints in which we gradually expanded our service into a fully functional proof of concept. Challenges were encountered while developing the logic to incrementally construct a database of real estate properties from the stream of data. These were resolved by switching from a document store to an RDF database, which better matched the flow of data coming in.

The final product consists of several microservices, each of which handles part of the problem domain and can be scaled out independently. A REST API and a web front-end are accessible to its users. The system was tested using both unit tests and end-to-end testing, whereas the model was refined by scoring output on closeness of features indicative of similarity.

As a future improvement, the current model used by the service is fairly simple and can most likely be improved upon once more data is available. Additionally, due to the lack of a ground truth it will be important to tune both comparable selection and explanation metrics in response to user testing.