Small and medium enterprises (SMEs) bring a significant contribution to each country’s economy, ensuring both a high employment rate and financial prosperity. Despite their essential role, these type of companies presents a higher vulnerability to default than large corporates. The default event implies that the SME could not properly reimburse the money owned to its suppliers. If their default could be forecast, experts could adopt measures in order to prevent it or its impactful consequences. For this reason, developing a credit scoring model which is able to predict which SMEs are endangered of default is crucial. There are plenty of credit scoring prediction models available in the literature. However, most of them are only relying on the financial status of one company. In this work, we are presenting a novel method for credit scoring prediction which does not only take into account the SMEs’ financial situation, but also their position and role within a transactional network. A transactional network is a graph, whereby the nodes are represented by SMEs and the edges show that between two nodes there should be at least one transaction. In our work, we highlight the limitations that traditional models face and provide an alternative to overcome them. Furthermore, our findings show that combining network features with financial features could lead to a more accurate prediction and increase the robustness of the model. For this reason, we believe that the transactional network carries significant insights and could be a meaningful addition to financial based credit scoring prediction models.

Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily. Within the different types of cash flow, Accounts Receivable (AR) classifies the balance of money that needs to be paid by the company's customers. In the most typical case, after receiving goods or services, the customer receives an invoice with the amount that is owed to the supplier. However, this often does not happen before the aforementioned date, meaning that the invoice is often paid late. Intervention requires resources and over-intervention could cause unwanted customer dissatisfaction. Knowing whether an invoice is going to be paid late can be vital information. Current methods of late payment prediction focus only on the history between the seller and the buyer and are unusable when this history is not present. Intuitively, one's business depends on the relationships and transactions that it has with its neighbors. Suggesting that neighbor behavior could be useful when predicting the cash flow of a company. Unfortunately, this type of information is not always given and needs to be data mining from non-relational data. This work presents a method for building a relational network of SMEs using entity resolution and improving the current state of the art of late payment prediction using features extracted from the graph.

Multimedia systems are typically optimized in a way that maximizes users’ satisfaction of using the systems/services. This user satisfaction is what is commonly referred to as Quality of Experience (QoE). For visual media, such as images and videos, the optimization of QoE has meant reducing the visibility of artifacts (e.g. noise or other disturbing factors) in the visual media. This is based on the assumption that the sole appearance of artifacts would disrupt the whole visual experience, in a world where media weremostly consumed passively, and in well defined contexts (e.g., TV broadcasts). Nowadays, the way users experience visual media has changed, thanks to the diffusion of mobile, interactive, immersive, and on-demand technology. Media are now consumed in many different contexts, for example, in the interactive and customizable contexts of social media, or in the immersive contexts of virtual and augmented reality. As consequence of these developments, a user’s visual QoE is no longer determined solely on the appearance of artifacts, but also by factors relevant to the viewing context. This thesis brings in new insights in modeling and automatically assessing users’ visual QoE in view of the developments above. The thesis starts with looking into subjective methodologies for QoE assessments, and continues with developing objective quality metrics that incorporate QoE influencing factors to improve state-of-the-art metrics. Developing reliable and accurate objective metrics to automatically assess users’ visual QoE requires subjective data that are reliable as well. This thesis argues that existing methodologies for collecting subjective data might not be reliable when used to evaluate QoE factors that are highly subjective, or that are new to the research community. Highly subjective quantities may yield different conclusions across experiments. As for new types of media, they often bring the uncertainty on how to evaluate them. Two studies are then presented in this regard. The first study considers the assessment of image aesthetic appeal, as one example of a highly subjective quantity. A large scale study was conducted to compare the use of different subjective methodologies to collect aesthetic appeal data, and some ways to measure the data reliably were proposed. The second study considers the assessment of point cloud quality, as one example of a new type of media (i.e. immersive media). The study explores quantitative and qualitative approaches to understand the way users judge point cloud images. Following the studies on subjective QoE assessments, two studies on objective QoE metrics are presented in this thesis. Despite existing efforts to model the influence of different factors on visual QoE, limited work have proposed to incorporate these factors into existing objective qualitymetrics to improve state-of-the-art. The first study on objective QoE metrics in this thesis investigates the influence of image content/semantic categories (i.e. scene and object categories) on visual QoE, and proposes to include semantic category features in objective image quality metrics. The proposed approach shows improvement from state-of-the-art in predicting image quality. The next study on objective quality metrics investigates new QoE influencing factors for point cloud images, and proposes to incorporate these into an objective quality metric for point cloud images. The results of the studies presented in this thesis show how existing subjective methodologies could yield reliable aesthetic appeal data, and explore point cloud QoE influencing factors. Moreover, the results show that incorporating new QoE influencing factors into objective image quality metrics could improve state-of-the-art performance in predicting users’ QoE. At the end of this thesis, some recommendations are given for future research following up the findings in this thesis.

A world without digital images is unthinkable in this era of information and communication technology. Billions of images are created, shared and ultimately enjoyed by users every day. However, digital images are sensitive to a wide variety of distortions during the delivery mechanism it goes through. Any of the distortions that can arise during the delivery might disrupt the perceptual quality of an image and can incite dissatisfaction from the user. Thus, it is important to optimize the delivery pipeline towards the arrangement of perceptually good results. For that the perceptual image quality needs to be estimated. Image quality assessment (IQA) is concerned with measuring the degradation in quality of images. The level of degradation needs to be measured in a way that is compliant to how humans would perceive it as humans are the final judges of the delivered quality of an image. General purpose image quality metrics are based on a two-step design. First, quality-aware features are extracted, describing artifact appearance to the Human Visual System. Secondly, these features are mapped to a perceived quality score. General purpose metrics require a large number of varied examples of pairs of distorted images and perceived quality scores to acquire an accurate distortion-agnostic quality prediction. Obtaining a distorted image is relatively simple. However, acquiring the quality assessment is a complex, laborious and expensive undertaking. Consequently, all of the widely known image quality datasets only accommodate at most several thousand examples which is in stark contrast to the mound of possible distortion types and distortion intensities there could be. This limitation hinders the creation of an accurate general-purpose image quality assessment metrics. It would be desirable to extend these datasets with more subjectively evaluated images, but given the cost of doing so, one may want to have a smart mechanism to select those that are most informative to improve the accuracy and robustness of quality metrics. Active learning is such a smart selection mechanism and could therefore be a possible solution. The main goal of this thesis is to explore whether active learning can be beneficial to improve the accuracy of image quality metrics.