Water supply companies around the globe are struggling to meet the needs of an ever-increasing population, while climate change contributes to more drought. At the same time, up to 30% of the total amount of treated drinking water in the water supply system is lost due to leaks. An important strategy to reduce leak losses is using hydraulic modeling to localize leaks in a expert-driven manner. In this paper, we present a hybrid leak localization approach combining both hydraulic modeling and machine learning-based classification. A Gaussian Naive Bayes classifier is trained to localize leaks based on simulated pressures and historical pressure measurements. The simulated pressures are obtained using a hydraulic model of the water supply system. In our methodology, learned parameters of the classifier are inferred directly from processing the simulated and measured pressures, without the need for explicit training. We demonstrate the effectiveness of our leak localization approach by using real leak experiments, achieved by opening hydrants at different locations in an operational water supply system. State-of-the-art results are achieved, similar to an approach where explicit training is still needed.

Abstract Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount of time to complete, especially when dealing with high-dimensional feature sets. Alternatively, filter methods are considerably faster, but suffer from several other disadvantages, such as (i) requiring a threshold value, (ii) many filter methods not taking into account intercorrelation between features, and (iii) ignoring feature interactions with the model. To this end, we present powershap, a novel wrapper feature selection method, which leverages statistical hypothesis testing and power calculations in combination with Shapley values for quick and intuitive feature selection. Powershap is built on the core assumption that an informative feature will have a larger impact on the prediction compared to a known random feature. Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time. As such, powershap provides a competitive and quick algorithm that can be used by various models in different domains. Furthermore, powershap is implemented as a plug-and-play and open-source sklearn component, enabling easy integration in conventional data science pipelines. User experience is even further enhanced by also providing an automatic mode that automatically tunes the hyper-parameters of the powershap algorithm, allowing to use the algorithm without any configuration needed.

As a result of legislation imposed by the European Parliament, in order to protect inhabitants from being exposed to a too high financial risk when investing in a variety of financial markets and products, Financial Service Providers (FSPs) are obliged to test the knowledge and experience of potential investors. This is oftemtimes done by means of questionnaires. However, these questionnaires differ in style and structure from one FSP to the other. The goal of this research is to manage in a more cost-effective manner (aligned with the needs and competencies of the individual financial investor in terms of products and services) the management of the private equity and to facilitate the fine-tuned personalised financial advisory services needed. This is achieved by means of a knowledge-based approach, integrating the available information of the investor (e.g. personal profile in terms of financial knowledge and experience) and for an extendable amount of financial service providers w ith their financial products and demonstrated by a number of exemplary use case scenarios.

In education, learners no longer perform learning activities in a well-defined and static environment like a physical classroom. Digital learning environments promote learners anytime, anywhere and anyhow learning. As such, the context in which learners undertake these learning activities can be very diverse. To optimize learning and the environment in which it occurs, learning analytics measure data about learners and their context. Unfortunately, current state of the art standards and systems are limited in capturing the context of the learner. In this paper we present a Learning Record Store (LRS), compatible with the Experience API, that is able to capture the learners' context, more concretely his location and used device. We use ontologies to model the xAPI and context information. The data is stored in a RDF triple store to give access to different services. The services will show the advantages of capturing context information. We tested our system by sending statements from 100 learners completing 20 questions to the LRS.

Real-Time Travel Information (RTTI) for rail commuters is still used inefficiently today and is rarely combined with other knowledge to come to a truly personalised and situation-aware multimodal travelling assistance. It is up to the travellers themselves to look for important info about their trip through static schedules or dedicated non-personalised applications. In a highly dynamic context such as that of public transportation, it would make life easier if one was able to consult the right information at the right time (removing superfluous information), for a variety of multimodal public transportation options, taking into account the context of the person travelling. In this paper we present the LimeDS framework, allowing application developers to rapidly define data workflows from a variety of data sources, deploy these workflows in a scalable and resilient manner and expose results to client applications as REST endpoints. A Proof-of-Concept (PoC) shows how our proposed framework can be used to tie together different open transportation data sources in order to create highly dynamic multimodal travel assistance applications by semantically enriching the data into knowledge, checking for ontological consistency and reason over the resulting knowledge.