ASIAN'06,
Asian Computing Science Conference, Dec. 6 – 8, 2006, Tokyo, Japan.
The theme of this year's ASIAN Computing Sciennce Conference is "Secure
Software." The conference aims at discovering and promoting new ways
to apply theoretical and practical techniques to secure software analysis,
design, development, and operation. Papers are invited on all aspects of theory,
practice, applications, and experiences related to this theme. Moreover, papers
focused on lessons learned from and/or education for the development and operation
of secure software are particularly welcome.
Kernel methods for pattern analysis and kernel matrix
evaluation, by Prof. Ho Tu Bao, Japan Advanced Institute of Science
and Technology, Japan.
Friday, Aug. 4, 2006, 11:00, CSIM #209.
Kernel methods are an emerging trend in machine learning, pattern recognition,
data mining and others. In this talk, I will first address some key concepts
of kernel methods then our research results in this field. The main focus
is our
Sequential and Parallel Algorithms for Some Problems
on Trees, by Raymond Greenlaw, Armstrong Atlantic State University,
USA.
Thursday, Mar. 16, 2006, 09:00, Milton Bender auditorium.
A node (edge) ranking of a tree is a labeling of the nodes (respectively,
edges) using natural numbers such that on the path between any two nodes (respectively,
edges) with the same label there is an intermediate node (respectively, edge)
with a higher label. A node (edge) ranking is optimal if the highest label
used is as small as possible. These problems have applications in scheduling
the manufacture of complex multi-part products. A Prufer code of a labeled
free tree with n nodes is a sequence of length n-2 constructed by the following
sequential process: for i ranging from 1 to n-2 insert the label of the neighbor
of the smallest remaining leaf into the ith position of the sequence, and
then delete the leaf. Prufer codes provide an alternative to the usual representation
of trees. We'll discuss algorithms for these problems from both sequential
and parallel perspectives.
Seminar by Massive Software, by Stephen Regelous
and Artaya Boonsong.
Wednesday, Feb. 22, 2006, 13:30, Milton Bender auditorium.
This is the company that played a leading role in producing computer-generated
animation for movies such as the Lord of the Rings trilogy, King
Kong, Chronicles of Narnia, to name a few. From a tiny start-up
in 2002 to a world-famous name in less than four years —it’s Peter
Jackson’s favorite software company— Massive’s namesake
product Massive (currently in version 2.0) “designs and builds custom
agents with a 'brain' which determines a set of actions and reactions”.
AI-based animation of artificial life as Massive calls it.
Intelligent User Interfaces for Product Search, by Dr.
Pearl Pu, Swiss Federal Institute
of Technology, Switzerland.
Monday, Feb. 6, 2006, 15:00, CSIM #209.
Preference-based search, defined as finding the most preferred item in a large
collection, is becoming an increasingly important subject in computer science
with many applications: multi-attribute product search, constraint-based plan
optimization, configuration design, and recommendation systems. Decision theory
formalizes what the most preferred item is and how it can be identified. In
recent years, decision theory has pointed out discrepancies between the normative
models of how people should reason and empirical studies of how they in fact
think and decide. However, many search tools are still based on the normative
model, thus ignoring some of the fundamental cognitive aspects of human decision
making. Consequently these search
tools do not find accurate results for users.
In this talk, I start by giving an overview of recent literature in decision
theory, and explaining the differences between descriptive, and normative
approaches. It then describes some of the principles derived from behavior
decision theory and how they can be turned into principles for developing
intelligent user interfaces to help users to make better choices while searching.
It develops in particular the issues of how to elicit user preferences, how
to support preference conflict resolution, how to actively recommend suggestions,
and how
to display results to users.
Dr. Pearl Pu is currently a research scientist and director of the HCI Group
in the School of Computer and Communication Sciences at the Swiss Federal
Institute of Technology in Lausanne (EPFL). She obtained her Master and Ph.D.
degrees from the University of Pennsylvania in artificial intelligence and
computer graphics. She was a visiting scholar at Stanford University in 2001,
both in the database and HCI groups. While there, she gave seminars at Xerox
PARC's weekly seminar series and Stanford's HCI Design Studio class
as guest lecturer.
She has worked and published widely in artificial intelligence and human computer
interaction: decision technologies for product search in e-commerce, information
visualization, scalable user experience, social navigation, advanced display
techniques, visual information retrieval interfaces, constraint satisfaction,
qualitative physics, case based reasoning, and multimedia management.
She was also co-founder of Iconomic Systems (1997-2001), and invented the
any-criteria search method for finding configurable and multi-attribute products
in heterogeneous electronic catalogs.
2006 Conferences
