Solving propositional satisfiability (SAT) and constraint programming (CP) instances has been a fundamental part of a wide range of modern applications. For this reason a lot of research went into improving the efficiency of modern SAT and CP solvers. Recently much of this research has gone into exploring the possibilities of integrating machine learning approaches with these solvers. However, with hybrid solvers, which combine both SAT and CP, dominating recent benchmarks it is surprising that no research has been done yet to apply those machine learning approach to improve hybrid solvers.This research proposes using a machine learning technique called unsatisfiable core learning to improve the performance of the Lazy Clause Generation solver Chuffed. The approach developed fort his study uses a Graph Convolutional Network model, which is trained on a dataset containing unsatisfiable instances. This machine learning model is then used for predicting unsatisfiable cores on CP instances and the predictions are used to initialise the activity score of the Variable State Independent Sum heuristic which is incorporated in Chuffed. The resulting approach managed to consistently solve a set of Multi-mode Resource-Constrained Project Scheduling instances 2.5% faster on average.These results indicate that, while this technique was originally developed for SAT, it can also be used to improve hybrid SAT/CP solvers.

As of this moment there is a lack of data about rainfall in cities. To collect such data, IBM has started the Sense Umbrella Connection and Desensitisation project. For this project an umbrella was equipped with a piezoelectric sensor and a Bluetooth device to record the rain that falls on the surface of this umbrella. One downside of this umbrella is that, besides rain, it will also record other sounds which include recognisable human speech. Because IBM values privacy, one of the tasks was to make sure that no recording containing recognisable human speech would be uploaded to a server.

This bachelor project focuses on creating a mobile application to connect to the umbrella via Bluetooth and save the audio recordings of rain together with GPS data. After saving this data it would originally be analysed for presence of rain and it was also required to remove all human speech before it was sent from the phone to the server. Over the course of the project it became clear that because of a scarce set of sample data and the limited availability of audio processing libraries on Android, it would be difficult to process audio on Android devices. This is why the decision was made to upload all data, and the processing and analysing was moved to an external device supporting Java.
The raw data will now be uploaded to a server which will make a database entry which includes GPS data, and saves the audio file. In the end an app has been developed that is able to gather data from the umbrella and send it to an external server. Additionally an implementation has been made to analyse the audio data gathered to classify which parts may contain rain.

This project focused on developing an Android app and not on other operating systems due to time constraints.