This thesis investigates the potential of solving large-scale linear systems of equations (LSEs) in the Tensor Train (TT) format. First, we study when this approach can outperform traditional solvers like Conjugate Gradient (CG) and Gaussian Elimination (GE). In turn, we examine the time complexity of the TT-solve technique when applied to ill-conditioned, sparse, and symmetric positive definite (SPD) matrices. This enables us to assess the scenarios where TT-solve may offer significant advantages.
During this exploration, we have also identified and proposed solutions for a handful of research gaps that could further optimize the TT-solve process. These include challenges related to poorly factorizing matrix sizes, as well as the need for a deeper understanding of the trade-offs between TT-matrix (TTM) rank and maximal mode size during the decomposition process. Additionally, the study examined the potential for altering the coefficient matrix through techniques like variable reordering and partial Gaussian elimination (PG) to achieve lower TTM-ranks without affecting the solution of the LSE.

A network, is defined as a collection of nodes interconnected by links. When this topology changes through time, we call it a temporal network. A specific class of networks, with only two types of nodes with no connections between one kind, is the bipartite network. An example is a telecommunications network, where nodes represent telecommunication base station and various mobile services like web-browsing, streaming etc. A link may exist only between a base station and a service. Moreover, each link is associated with a time-evolving weight, which represents the volume of the traffic between the corresponding base station and service over time. This weight associated with each link is also called the activity weight, with the link considered active only when the weight is non-zero. Predicting such a temporal weighted network in the future is crucial for telecommunications engineers, allowing for e.g., better traffic management. Prediction of the unweighted temporal network one step ahead, at time $t+1$, based on the network observed in the past between $[t-L-1; t]$, has been studied recently in contact networks. However, the prediction of weighted temporal networks, or equivalently, predicting the activity weight of each link, in the future has not been explored yet. Moreover, we also aim to uncover the mechanisms that enable the prediction of a weighted temporal network. We achieve this by devising several strategies that help us select the most relevant links within the network, whose activity weights in the past serve as the input for the interpretable, statistical learning algorithm, LASSO Regression, to predict the activity of a given target link at time $t+1$. The focus of the strategies is to capture a relationship of activity weights between the selected and target links. These selected links range from most active links (amount of timesteps the link weight is non-zero), those with largest activity weights or most similar to the target link using several metrics. In this thesis we apply this general methodology to two bipartite networks sourced from real world data and evaluate the performance of different strategies. Through the learned LASSO coefficients and prediction accuracy, we discover that past activity weight of a link is the best predictor for it's future weights. In terms of predicting power, most is coming from the past weights of the link we want to predict and one or two neighbouring links. Most of the selected links have minimal impact on the prediction accuracy. While different strategies of link selection excel in specific conditions, their improvement over the random link selection, is relatively low. The proposed method could be further applied to predict other weighted temporal networks with different properties to understand whether and how the the performance of link selection strategies depends on properties of the network to be predicted.

This research is a case study investigating the effect of participation in the development process of a network-based scientist-journalist recommender system on the mental model of digital innovation of a team of communication professionals from Delft University of Technology and Naturalis Biodiversity Center.

Communication professionals at research institutes are tasked with connecting scientists and journalists. The recommender system supports this process by recommending scientist-journalist connections based on data from previous collaborations. A scientist collaboration network, a journalist collaboration network and a scientist-journalist collaboration network are combined into a multilayer network. A recommender system is designed based on centrality metrics in the scientist and journalist collaboration networks and distance metrics in the multilayer network. In contrast to traditional link prediction problems - which aim to predict what links are most likely to form in the network - the problem in this thesis is how to recommend the most likely link for a single node, i.e. the most likely scientist links for a given journalist or most likely journalist links for a given scientist. A novel evaluation method is created to evaluate the performance of the recommender system.

The development of this system is used as a vessel to research how participation in a digital development process affects the mental model of digital innovation. This research contributes to addressing the lack of understanding of how to develop a mental model that facilitates innovation in the context of digital transformation. Three themes were identified in their mental model change: The extent to which innovation requires involvement, the complexity of innovation processes and what outcomes can realistically be expected of a digital innovation process. The team went from a model of digital innovation as 'a mysterious black box' - something external, where they could hand in a list of requirements and walk away with a digital tool - to a 'super puppy' that can do remarkable things, but has to be trained and interacted with to get a desired effect.

In an era where cyber threats evolve with alarming speed and sophistication, the role of Security Operation Centers (SOCs) has become increasingly pivotal in safeguarding digital infrastructures. SOCs serve as the frontline defence against malicious entities, where they continuously monitor and analyze network traffic, as well as the activity of users and systems for potential threats. The rapid growth of advanced cyber-attacks has amplified the reliance on Intrusion Detection Systems (IDS) to generate alerts for anomalous activities, and on SOC analysts to analyze those alerts. However, these systems often yield an overwhelming number of alerts, many of which are false positives, leading to alert fatigue among analysts. The scarcity of effective visualization tools, coupled with the analysts' dependence on manual investigation and correlation of events aggravates this issue, resulting in extended alert analysis times. Moreover, the number of attack scenarios keeps increasing daily, making it difficult to understand the possible next actions of an attacker and apply preventive measures.

This thesis introduces an innovative approach to aid SOC analysts in managing the large influx of alerts, mitigating alert fatigue, and enhancing the efficiency of threat identification and response. We present an attack prediction tool with alert visualization capabilities that produces real-time attack graphs, summarizing the alerts associated with a specific host. Our method utilizes a Suffix-based Probabilistic Deterministic Finite Automaton (SPDFA) to predict future attacker actions, promoting a proactive defence strategy, and achieving an accuracy of 33.71 %. We validate the practicality and relevance of our contributions through interviews with six security experts, confirming the utility of our methods in a live SOC context. Furthermore, we demonstrate the applicability of our approach by testing it with three datasets collected in the real world. Our work stands apart by simultaneously addressing alert correlation, attack visualization, and predictive modelling of attacker behaviour.

This report details the software part of the development process of the eNose technology. The technology is posed by Momo Medical. Momo Medical is a start-up company located in Delft, it provides and develops non-intrusive monitoring systems in the nursing sector. The project is the next step in an already existing product: BedSense. BedSense enables nurses to check for among others decubritus, whether the patient is out of bed, and even if the patient has passed away. The finished project will be able to detect solid stool and hence, when integrated into the system of BedSense, will greatly assist the nurses.

The eNose technology is able to detect solid bowel movement using its gas sensing abilities. It consists of gas sensors that detect the relevant gasses that are related to feces. These sensor values are then fed into an algorithm that is able to interpret them and detect defecation. Besides it includes a communication system that handles the internal and external communication. Finally, the technology supports Over The Air (OTA) updates which allows to update the firmware of the devices remotely.

The final prototype functions accurately in certain restrooms and can be regarded as a proof of concept.However more work and data is needed in order to make the eNose work in various environments, with possible integration of machine learning analysis, as it has showed great potential.

The project is executed in two groups, hardware and software. This report contains only the software part of the process. It includes the development of the detection algorithm. And the developmentof a communication and OTA programming system.