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2014 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).

To organize a symposium, please read our guidelines.


Ubiquity Symposium: The Multicore Transformation

Table of Contents

  1. Opening Statement, by Walter Tichy

  2. Waiting for Godot—The RIGHT Language Abstractions for Parallel Programming Should be Here Soon,  by Wolfram Schulte

  3. Auto-Tuning Parallel Software, by Thomas Fahringer

  4. Engineering Parallel Algorithms, by Peter Sanders

  5. GPUs: High-performance accelerators for parallel applications, by Mark Silberstein

  6. Multicore Processors and Database Systems, by Kenneth Ross

  7. The Future of Synchronization on Multicores, by Maurice Herlihy

  8. Making Effective Use of Multicore Systems: A software perspective, by Keith Cooper

  9. Closing Statement, by Walter Tichy

Ubiquity Symposium: MOOCs and Technology to Advance Learning and Learning Research

Table of Contents

  1.MOOCs and Technology to Advance Learning and Learning Research Opening Statement,  by Candace Thille

Section 1: Technical and Scientific Innovations in MOOCs

  2. Assessment in Digital At-scale Learning Environments, by Piotr Mitros, Anant Agarwal, and Vik Paruchuri

  3. Offering Verified Credentials in Massive Open Online Courses, by Andrew Maas,Chris Heather,Chuong(Tom) Do, Relly Brandman, Daphne Koller,and Andrew Ng

  4. Data-driven Learner Modeling to Understand and Improve Online Learning, by Kenneth R. Koedinger, Elizabeth A. McLaughlin, and John C. Stamper

Section 2: The impact of MOOCs on Residential Institutions, Courses and Computer Science Education.

  5. MOOCs on and off the Farm, by John Mitchell

  6. Limitations of MOOCs for Computing Education: addressing our needs, by Mark Guzdial

Section 3: The MOOC Phenomenon and Higher Education

  7. Can MOOCs Help Reduce College Tuition?, by Stephen Ruth

  8. The MOOC Spring, by Frederick Siff

  9. MOOCs: Symptom, not cause of disruption, by Lewis Perelman

  10. The MOOC and the Genre Moment, by Michael Feldstein

Ubiquity Symposium: The Technological Singularity

Table of Contents

  1. Opening Statement by Espen Andersen

  2. The Singularity and the State of the Art in Artificial Intelligence by Ernest Davis

  3. Human Enhancement—The Way Ahead by Kevin Warwick

  4. Exponential Technology and The Singularity by Peter Cochrane

  5. Computers versus Humanity: Do we compete? by Liah Greenfeld and Mark Simes

  6. What About an Unintelligent Singularity? by Peter J. Denning

  7. Closing Statement: Reflections on A Singularity Symposium by Espen Andersen




  • 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.