Abeer AlJarrah

Publications:

1. Abeer AlJarrah and Mohamed Shehab. Closer Look Inside Mobile Hybrid Apps Configurations: Statistics & Implications”. IEEE Future of Information and Communication Conference (FICC) 2019.
2. Abeer AlJarrah , Michael Thomas and Mohamed Shehab. Investigating Temporal Access in A Flipped Classroom: Procrastination Persists". International Journal of Educational Technology in Higher Education 2017

CE-18.09

Virtual Reality shows strong potential to be used as diverse medical treatments. A VR, called Labyrinth, is a virtual environment that challenges and stimulates the hippocampus region of the brain (associated with Long Term Memory). Adaptive training with Labyrinth is intended to stimulate hippocampal plasticity and enhance accuracy and capacity for detailed memory. Labyrinth uses Unity Engine for the procedural generation, quick prototyping and Virtual Reality (VR) capabilities.

CE 18.07

Automatic music transcription systems take music recordings as input, and produce notated scores or MIDI files as output. With the tremendous number of audio recordings today, there are not enough music experts to transcribe all unnotated recordings of improvised jazz solos, folk music from oral traditions, etc. In this project, the author created an automatic music transcription system called Tranz, using deep learning techniques. Tranz was designed to transcribe solo piano music. It uses a variable-Q transform to preprocess the input audio feature extraction.

CE 18.08

Rich and comprehensive knowledge graphs (KG) of the Web, such as, Google KG, NELL, and Diffbot KG, are becoming increasingly prevalent and powerful as the underlying AI technology is rapidly progressing. In this work, we leverage this ongoing advancement for the task of answering questions posed from any domain and any type (factoid and non-factoid). We present a framework for knowledge graph based question answering systems, KGQA, and experiment with two instances of this framework that employ Diffbot and Google KG.