Intelligible Machine Learning and Knowledge Discovery Boosted by Visual Means

Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity, link prediction, community detection, crowd-generated data, and others. The role of the interpretable method in web search and mining activities is also very significant to enhance clustering, classification, data summarization, knowledge acquisition, opinion and sentiment mining, web traffic analysis, and web recommender systems. Deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts motivated the surge of efforts to make Machine Learning (ML) models more intelligible and understandable. The prominence of visual methods in getting appealing explanations of ML models motivated the growth of deep visualization, and visual knowledge discovery. This tutorial covers the state-of-the-art research, development, and applications in the area of Intelligible Knowledge Discovery, and Machine Learning boosted by Visual Means

Critical Hypersonic Aerothermodynamic Phenomena

The challenges in understanding hypersonic flight are discussed and critical hypersonic aerothermodynamics issues are reviewed. The ability of current analytical methods, numerical methods, ground testing capabilities, and flight testing approaches to predict hypersonic flow are evaluated. The areas where aerothermodynamic shortcomings restrict our ability to design and analyze hypersonic vehicles are discussed, and prospects for future capabilities are reviewed. Considerable work still needs to be done before our understanding of hypersonic flow will allow for the accurate prediction of vehicle flight characteristics throughout the flight envelope from launch to orbital insertion