[Colloquium] Guest Speaker @ TTI-C (Ogihara -Thursday 1/19 @10:00am)

[Katherine Cumming]

TTI-C Guest Speaker
 
Presented by:  Toyota Technological Institute at Chicago
 
Speaker: Mitsunori Ogihara, University of Rochester
Speaker's home page: http://www.cs.rochester.edu/~ogihara/
 
Date: Thursday, January 19, 2006
Location: TTI-C Conference Room
Time:  10:00 am
 
Title:
 
Estimating Entropy of Network Traffic Data
 
Abstract::
 
Analysis of network traffic data is a subject of growing importance. It
offers many interesting challenges partly because network traffic data are
large in size and they quickly come and go. Space- and time-efficient
algorithms have been proposed to solve a variety of problems such as iceberg
queries and frequency moments. This talk is concerned with the problem of
estimating entropy. The use of entropy has been recently suggested in the
networking community to solve such problems as anomaly detection. Using a
result from communication complexity, it can be argued that both
approximation and randomness will be required for efficiently estimating the
entropy. Two algorithms for this problem will be presented. The first one
uses the idea of the celebrated frequency-moment-estimation algorithm of
Alon, Mathias, and Szegedy. The second one combines the first one with the
Elephant/Ant approach of Estan and Varghese. An empirical comparison of
these algorithms against some standard approaches has been made using some
real network data sets.
 
If time permits, the talk will cover another, fun topic: music information
retrieval. Many people listen to music through computers and portable
digital music players. The digital music collection of such a listener can
be very large and cover many genres and styles, and so it can be cumbersome
to classify, organize, and retrieve music in it. One of the ultimate goals
of music information retrieval is to develop efficient algorithms for these
tasks by learning from data. This talk will present some recent advances in
the area, including the use of wavelet coefficient histograms for genre
classification from audio data, detecting emotion aroused in listeners, and
semi-supervised learning of artist groups using features from audio and
lyrics data.
 
This is joint work with Ashwin Lall, Qi Li, Tao Li, Vyas Sekar, Jun Xu, and
Hui Zhang, 
 
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If you have questions, or would like to meet the speaker, please contact
Katherine at 773-834-1994 or kcumming at tti-c.org.   
For information on future TTI-C talks and events, please go to the TTI-C
Events page:  http://www.tti-c.org/events.html.  TTI-C (1427 East 60th
Street, Chicago, IL  60637)
 
 
 
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