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Award ID contains: 1757632

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  1. ; ; ; (, SIAM Conference on Applied and Computational Discrete Algorithms)
    Coronavirus Disease 2019 (Covid-19) is an ongoing outbreak and the latest threat to global health. It is imperative to understand the implications of social interaction on Covid-19 indicators in order to help formulate policies and guidelines by governments and local authorities. We present a case-study of curating state-level Covid-19 indicators such as Active Cases, Deaths, Hospitalization Rate, etc. for the United States. We also curate open source domestic US air travel data and present its impact on Covid-19 indicators. We perform a time-series analysis of the dataset using Independent Temporal Motif (ITeM) to find weekly trends in the data. We publish the dataset and the results for further exploration by the research community. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; (, IEEE Intl. Symp. on Circuits and Systems (ISCAS))
    null (Ed.)
    Full Text Available 
  3. ; (, Workshop on Evaluating Evaluation of AI Systems, AAAI Conference on Artificial Intelligence)
    There has been an explosion of challenge problems, algorithmic tests and datasets for evaluating AI systems. Yet no methodology exists to objectively measure either the collective difficulty of these problems or their similarity. This is an obstacle to creating more general AI systems. We pro- pose a theory for measuring the similarity between pair-wise problems. We evaluate this theory by utilizing a methodology based on a deep neural network to objectively measure these properties between test problems using foundational datasets. An implementation of these methods is then used to measure the similarity between well known datasets. Results show that the proposed measure successfully identifies the difficulty and similarity among problems. This can be used to ensure diversity in test suites used to evaluate AI systems. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; (, ACM Transactions on Cyber-Physical Systems)
    Full Text Available 
  5. ; ; ; ; ; ; ; ; (, IEEE Transactions on Industry Applications)
    Full Text Available