Program synthesis aims to solve problems through coding by removing the need to write the programs yourself.
Given the grammar and problem specification, it aims to find a program that adheres to your problem specification.
This is done by iterating over many failing programs until a solution that adheres to the problem specification is found.
Conflict analysis automatically takes these failing solutions and learns new constraints to make the search more efficient.
Unfortunately, many conflict analysis techniques are heavily specialized.
They are difficult to apply to diverse problems or when trying to use a different search algorithm.
In this work, we present a modular framework in which these techniques can be implemented in a generalized way and applied independently to different problems and solvers, while having the ability to share generated constraints and parsed information.
We identify two distinct conflict types and show how to use semantics in conflict analysis effectively.
The framework is evaluated on two diverse domains; it prunes up to 96\% of the search space, where combining techniques can further improve its average effectiveness.
Domain applicability for the techniques has to be considered for optimal framework performance.
Compared to an enumeration solver, the framework shows marginal improvements on a real-world benchmark.
While the framework's overhead needs improvement, its modularity allows for comparison between solvers, problems, and conflict analysis techniques.

Cognitive processes have been used in recent years for context sensing and this has shown promising results. Multiple sets of features have shown good performance but no set of features has been determined the best for classifying gaze data. This paper looks at different feature sets and the heterogeneity of gaze signals from subjects and hardware to determine what impacts the performance of the classifiers and what returns the best results. These results are compared with deep learning classifiers using the same data set to determine which performs better.

For the different feature sets, saccade features show great positive influence on the accuracy (88\% accuracy) but fixation features show a significant lower ability to classify correctly (63% accuracy), a combination of some fixation and saccade features show the best results(95% accuracy). The way the data is split, has a huge impact on the performance, splitting the data on every activity gives an accuracy of 95%, while the splitting on subjects only reaches a maximum of 60% accuracy. Deep learning algorithms perform only slightly better at 97% accuracy but dropping down massively (38%) when splitting the data over subjects.

The main conclusions from this research revolve around feature selection and subject bias. Saccade features have the most impact on the classification of activity recognition using eye tracking data. Each subject performs each task in a significantly different way which drastically decreases performance when completely new subject data is tested on a trained classifier. Deep learning classifiers show similar results and back up the importance of the heterogeneity of the data. The evaluation of different types of hardware has not been accomplished in this research due to time constraints.