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2010 Symposia

A Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

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Ubiquity Symposium: What is Computation

Table of Contents

 

  1. What is Computation, Editor's Introduction, by Peter J. Denning

  2. What is Computation, Opening Statement, by Peter J. Denning

  3. The Evolution of Computation, by Peter Wegner

  4. Computation is Symbol Manipulation, by John S. Conery

  5. Computation is Process, by Dennis J. Frailey

  6. Computing and Computation, by Paul S. Rosenbloom

  7. Computation and Information, by Ruzena Bajcsy

  8. Computation and Fundamental Physics, by Dave Bacon

  9. The Enduring Legacy of the Turing Machine, by Lance Fortnow

10. Computation and Computational Thinking, by Alfred V. Aho

11. What is the Right Computational Model for Continuous Scientific Problems?, by Joseph Traub

12. Computation, Uncertainty and Risk, by Jeffrey P. Buzen

13. Natural Computation, by Erol Gelenbe

14. Biological Computation, by Melanie Mitchell

15. What is Information?: Beyond the jungle of information theories, by Paolo Rocchi

16. What Have We Said About Computation?: Closing statement, by Peter J. Denning

 

Symposia

2018
  • When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium)

    While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.