ABI-ONE ghc program v14.indd - Grace Hopper Celebration of ...

achieves 88% accuracy, 2) deciding whether
the extracted contours are in a loop shape
in terms of geometric features of contours
and an Adaboost classifier based C4.5 which
achieves 85% precision and 83% recall.
Query Processing over Distributed
Wireless Sensor Networks in Future
Internet
Presenter: Shaila Pervin (Monash
University)
The terabyte torrent of data generated
by billions of sensors belonging to
distributed heterogeneous Wireless Sensor
Networks (WSNs) in Future Internet will only
be valuable if they are effectively used on
purpose and this necessitates an efficient
Internet scale query processing framework
for WSN. In this work, by generating hierarchical
characteristic model of each WSN,
we outline the overall query processing
methodology for Internet scale distributed
WSNs.
Raising the Level of Abstraction of
Parallel Programming
Presenter: Ritu Arora (University of
Alabama at Birmingham)
Writing efficient parallel applications is
a complex task, mainly due to extra
burden on the programmers to identify and
express concurrency. The main goal of this
research is to raise the level of abstraction
of parallel programming based upon MPI
and OpenMP standards such that there is a
significant reduction in the end-user effort
without compromising on performance.
Generative programming tools are used to
automate the process of generating parallel
code.
SRC POSTER SESSION
W e d n e s d ay | s e p T 3 0
SFS3: An Object-Oriented Simulation
Framework for Self-Stabilizing Systems
Presenter: Sally K. Wahba (Clemson
University)
protocol is self-stabilizing if its actions
A guarantee that a given legitimacy
predicate will eventually be satisfied regardless
of the initial state. The self-stabilizing
characteristic is particularly beneficial when
designing large-scale networked systems.
Despite the increasing use of self-stabilizing
protocols, current network simulation tools
do not support self-stabilizing systems. In
this poster, we present SFS3 -- an objectoriented
framework following a novel
architecture to simulate the behavior of
self-stabilizing systems.
Threshold Query Optimization for
Uncertain Data
Presenter: Yinian Qi (Purdue University)
The probabilistic threshold query (PTQ)
is one of the most common queries in
uncertain databases, where all results satisfying
the query with probabilities that meet
the threshold requirement are returned. We
investigate the general PTQ for arbitrary
SQL queries that involve selections, projections
and joins. We improve the efficiency
of query execution by enabling alternative
query plan enumeration for optimization
that leverages the threshold for pruning, and
experimentally verify our results.
Towards Fast Mapping of Jammed
Regions in WSNs
Presenter: Nabila Rahman (University of
Texas at Arlington)
Jamming attacks in Wireless sensor
networks (WSNs) is difficult to prevent
and in a large scale WSN, detection and
mapping out the jammed regions is critical
while maintaining the interactions among
the low-powered sensor nodes to minimal.
We propose a light-weight technique for
faster mapping of the jammed regions,
where the actual responsibility of mapping
is assigned to the central base station,
minimizing the load of the sensor nodes.
Using Reverse Feedback Hints for I/O
Load Balancing in MPI-IO and PVFS2
Presenter: Christina M. Patrick
(Pennsylvania State University)
In large scale machines, there is a possibility
of disproportionately distributing the
I/O-load among the I/O-servers. This reduces
the I/O-throughput causing bottlenecks. We
propose modifying the I/O-stack to introduce
hints. When these hints pass through the
I/O-stack, they activate the runtime layer
which collects statistics. This information is
passed back using reverse feedback hints.
Analysis of this information helps users
identify the bottleneck, and redistribute the
load to increase performance.
World Model Learning through First
Order Induction
Presenter: Kathryn L. Genter (University of
Texas at Austin)
Currently, simulators are often used to
predict the effect of an agent’s actions
in the world and in effect improve an agent’s
performance. My work explores the use of
the first order inductive learning algorithm
for learning rules which can be used to
implement the different parts of a simulator.
Specifically, I focused on building world
models for predicting opponents’ strategies
from collections of gameplay traces.
Anita Borg Institute for Women and Technology | Grace Hopper Celebration of Women in Computing 45