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Colloquium
Schedule for 2007/2008
| date | speaker | title | host |
|---|---|---|---|
| Sep 25 | Panos Papadimitratos, EPFL | Securing Vehicular Communications | Kotz |
| Oct 1 | |||
| Oct 8 | Evdokia Nikolova, MIT | A Strategic Model for Information Markets | Fleischer |
| Oct 15 | |||
| Oct 22 | |||
| Oct 29 | |||
| Nov 5 | |||
| Nov 7 | Ben Shneiderman, UMD | Information Visualization for Knowledge Discovery | Pellacini |
| Nov 12 | Chris Rose | Kotz | |
| Nov 19 | Mark Moir | Jayanti | |
| Nov 26 | |||
| Dec 3 | |||
| Dec 10 |
Abstracts
Panos Papadimitratos, Securing Vehicular Communications Moore B03 at 4:00 pm
Vehicular communications (VCs) and vehicular ad hoc networks (VANETs) lie
at the core of several on-going industry and academic research initiatives.
Vehicles and roadside infrastructure units equipped with sensors, computers,
and wireless transceivers enable a range of applications that enhance
transportation safety and efficiency. VCs offer a rich set of tools but also
make possible a formidable set of abuses. For example, an adversary could
'contaminate' large portions of the VANET with false information; or, intercept
vehicle-originating messages, track the vehicles locations and transactions,
and infer sensitive information about their passengers.
Without security mechanisms, VCs can make antisocial and criminal behavior
easy, essentially jeopardizing the benefits of deploying VCs systems.
In this talk, we discuss this new and uniquely constrained problem: how to
secure vehicular communications. First, we discuss design principles and
requirements as well as elements of a secure VCs architecture.
Then, we present mechanisms to enhance privacy yet provide strong security;
evict misbehaving or faulty nodes; and, extend the traditional notion of
trust to data-centric trust, that is, attribute trustworthiness to
node-reported data per se. The presented results reflect recent work,
jointly with researchers of the Univ. of Maryland, the Pol. of Torino,
and the SeVeCom and Car-2- Car communication consortia.
Evdokia Nikolova, A Strategic Model for Information Markets Carson L01 at 4:30 pm
This talk is about information (aka prediction) markets--these are markets
designed specifically to aggregate traders' information, and are becoming
increasingly popular for predicting future events.
Recent research in information markets has resulted in two new designs,
market scoring rules and dynamic parimutuel markets. We develop an analytic
method to guide the design and strategic analysis of information markets.
Our central contribution is a new abstract betting game, the projection game,
that serves as a useful model for information markets. We demonstrate that
this game can serve as a strategic model of dynamic parimutuel markets, and
also captures the essence of the strategies in market scoring rules.
The projection game is tractable to analyze, and has an attractive geometric
visualization that makes the strategic moves and interactions more transparent.
We use it to prove several strategic properties about the dynamic
parimutuel market. We also prove that a special form of the projection game
is strategically equivalent to the spherical scoring rule, and it is
strategically similar to other scoring rules.
Finally, we illustrate two applications of the model to analysis of complex
strategic scenarios: we analyze the precision of a market in which traders
have inertia, and a market in which a trader can profit by manipulating
another trader's beliefs.
Joint work with Rahul Sami.
Ben Shneiderman, Information Visualization for Knowledge Discovery
Interactive information visualization tools provide researchers with remarkable capabilities to support discovery. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional data. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100msec updates even for million-record databases. This talk will start by reviewing the growing commercial success stories such as www.spotfire.com, www.smartmoney.com/marketmap and www.hivegroup.com. Then it will cover recent research progress for visual exploration of large time series data applied to financial, medical, and genomic data (www.cs.umd.edu/hcil/timesearcher ). Our next step was to combine these key ideas to produce the Hierarchical Clustering Explorer 3.0 that now includes the rank-by-feature framework (www.cs.umd.edu/hcil/hce). By judiciously choosing from appropriate ranking criteria for low-dimensional axis-parallel projections, users can locate desired features of higher dimensional spaces. Finally, these strategies of unifying statistics with visualization are applied to network data. Demonstrations will be shown.