Recently, the Deep Learning (DL) research community has focused on developing efficient and highly performing Neural Networks (NN). Meanwhile, eXplainable AI (XAI) making Machine (ML) methods interpretable transparent, seeking explainability. This work is a preliminary study applicability of Architecture Search (NAS) (a sub-field DL looking for automatic design NN structures) in XAI. We propose...

The United States Department of Defense (DoD) is engaged in a mission to unify its software systems towards a “net-centric” vision— where commanders gain advantage by rapidly producing, consuming, and sharing information using service oriented architectures (SOAs). In this paper, we study the cyber survivability of mission-critical net-centric systems, focusing on Ballistic-Missile-Defense (BMD...

The power of computational neural networks (CNN) for microbiological growth prediction was evaluated. The training set consisted of growth responses data from a combination of three strains of Salmonella in a laboratory medium as affected by pH level, sodium chloride concentration and storage temperature. The architecture of CNN was designed to contain three input parameters in the input layer ...

Machine comprehension is a critical problem that lies on the frontier of natural language processing. The Stanford Question Answering Dataset (SQuAD), offers a set of questions and their answers created by humans through crowdsourcing. We implemented an end-to-end neural architecture for the task based on MatchLSTM and Pointer Net, inspired by previous work done by Wang and Jiang in Machine Com...