Real-time processing of log data can give valuable insights in the behaviour of systems and processes. The Rabobank is a large bank and has several of these systems and processes, one of such is the QR device registration process. In order to monitor this process, an application was built which detects abnormal behaviour. The application reads in log data, parses it and then analyses it. Analysing is done by fitting distributions of the duration of each step in the process, and checking for anomalies in new incoming data. Finally, a dashboard was made in which the analysis and the distributions are visualised. More information is also available in the dashboard, like a Markov chain of the process and several key performance indicators.

Bitcoin’s underlying consensus algorithm, Proof of Work, is of inefficient nature. Due to the sheer size that Bitcoin has grown to over the recent years, power consumption has increased so much that the Bitcoin network has been estimated to consume more power than the whole country of Ireland. This paper investigates several alternatives to the Proof of Work consensus algorithm, with a focus on energy efficiency. We found permissioned and permissionless consensus algorithms that offer solutions that consume significantly less energy than Proof of Work.

Online dating has become the most popular method of finding potential romantic partners. At the core of these platforms, there is a reciprocal recommender system which recommends users to other users on the platform. Breeze is an example of such a dating app, serving its users potential romantic partners every day in the hopes of sending them on a date. Current approaches to matchmaking depend exclusively on interaction data and static features such as age, height and location. There is more information present on the user profiles, however, in the form of bios and answers to open questions. Given the state-of-the-art performance of pre-trained language models in a multitude of general language understanding tasks, these free-text profile sections present a source of untapped potential. Our work combines the potential of these free-text profile sections and the state-of-the-art performance of recent language models to create a new approach for matchmaking, or reciprocal recommender systems in general. In this work, the goal is to find out how textual data from user profiles can be leveraged to make good suggestions in a reciprocal recommender system, and how language models can be used for this. Furthermore, this work also explores whether the cold-start problem can be alleviated using this approach. We also perform user research to find out how Breeze users make their decisions on suggestions served by the platform. We introduce LoveBERT, a model to serve text-based recommendations, harnessing the power of several language models fine-tuned on different free-text user profile sections. Our results show that while LoveBERT is better at predicting unidirectional likes than traditional recommender system approaches, it does not outperform them when considering bidirectional matches. Furthermore, we show that while LoveBERT is not able to circumvent the cold-start problem, it is more robust, losing less performance than traditional techniques. Lastly, we show that especially the relation between different profile sections is an effective predictor for matches.