Memory training exercises are known to have a positive effect on improving the memorability of humans. A memory training task can be as simple as trying to remember a password of increasing complexity or a number of varying length.
Memorability concept has been mostly investigated on the images using Machine Learning . However, there is no measure available in the literature that can assess the difficulty of remembering a given password (word) or number. In this project, we will conduct a controlled experiment on sets of students in order to come up with a new difficulty measure that quantifies the memorability of a string (whether it is - a word or a number). As an alternative way forward we might rather use images ranked according to their memorability using Machine Learning as a way for ranking memorability .
We will also devise an adaptive method for varying the difficulty of the tests in an online manner based upon the ability of the subject to pass or fail in a test . The approach is based on asymmetric version of the Search Point Location (SPL)  which is different from the main stream of work which falls under the class of symmetric SPL.
The outcomes of the project has potential for being published in a conference or journal venue.
- Insight into advanced techniques of machine learning
- Working on a real world application
- Collaboration with researchers in the topic of machine learning, specifically deep learning
- Possibility to implement and research a novel approach
- Michael Riegler
- Asieh Abolpour Mofrad, OsloMet, email@example.com
- Anis Yazidi, OsloMet, firstname.lastname@example.org
- Hugo Lewi Hammer, OsloMet, email@example.com
 Isola, P., Xiao, J., Torralba, A., Oliva, A. What makes an image memorable? IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. Pages 145-152.
 Asieh Abolpour Mofrad, Anis Yazidi, and Hugo Lewi Hammer. "Solving Stochastic Point Location Problem in a Dynamic Environment with Weak Estimation." Proceedings of the International Conference on Research in Adaptive and Convergent Systems. ACM, 2017.