@conference {17162,
	title = {Finding comparable temporal categorical records: A similarity measure with an interactive visualization},
	booktitle = {IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009},
	year = {2009},
	month = {2009/10/12/13},
	pages = {27 - 34},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher{\textquoteright}s intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M\&M (Match \& Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M\&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M\&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M\&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.},
	keywords = {data visualisation, Educational institutions, Feedback, Information retrieval, interactive search tool, interactive systems, interactive visualization tool, large databases, M\&M Measure, Match \& Mismatch measure, Medical services, numerical time series, parameters customization, Particle measurements, Similan, similarity measure, Similarity Search, temporal categorical databases, Temporal Categorical Records, temporal databases, Testing, Time measurement, time series, transportation, usability, very large databases, visual databases, Visualization},
	isbn = {978-1-4244-5283-5},
	doi = {10.1109/VAST.2009.5332595},
	author = {Wongsuphasawat,K. and Shneiderman, Ben}
}
@conference {19649,
	title = {A Rao-Blackwellized particle filter for EigenTracking},
	booktitle = {Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004},
	volume = {2},
	year = {2004},
	month = {2004/06//},
	pages = {II - 980-II-986 Vol.2},
	abstract = {Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black{\textquoteright}s influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimization-based algorithms (including EigenTracking) often fail catastrophically after losing track. Particle filters have recently emerged as a robust method for tracking in the presence of multi-modal distributions. To use subspace representations in a particle filter, the number of samples increases exponentially as the state vector includes the subspace coefficients. We introduce an efficient method for using subspace representations in a particle filter by applying Rao-Blackwellization to integrate out the subspace coefficients in the state vector. Fewer samples are needed since part of the posterior over the state vector is analytically calculated. We use probabilistic principal component analysis to obtain analytically tractable integrals. We show experimental results in a scenario in which we track a target in clutter.},
	keywords = {analytically tractable integrals, Computer vision, EigenTracking, Filters, Gaussian processes, modal analysis, multi-modal distributions, NOISE, noisy targets, optimisation, optimization-based algorithms, Particle filters, Particle measurements, Particle tracking, Principal component analysis, probabilistic principal component analysis, Rao-Blackwellized particle filter, Robustness, SHAPE, State estimation, state vector, subspace coefficients, Subspace representations, target tracking, vectors},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {17642,
	title = {P5 : a protocol for scalable anonymous communication},
	booktitle = {2002 IEEE Symposium on Security and Privacy, 2002. Proceedings},
	year = {2002},
	month = {2002///},
	pages = {58 - 70},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a protocol for anonymous communication over the Internet. Our protocol, called P5 (peer-to-peer personal privacy protocol) provides sender-, receiver-, and sender-receiver anonymity. P5 is designed to be implemented over current Internet protocols, and does not require any special infrastructure support. A novel feature of P5 is that it allows individual participants to trade-off degree of anonymity for communication efficiency, and hence can be used to scalably implement large anonymous groups. We present a description of P5, an analysis of its anonymity and communication efficiency, and evaluate its performance using detailed packet-level simulations.},
	keywords = {Broadcasting, communication efficiency, Computer science, cryptography, data privacy, Educational institutions, Internet, large anonymous groups, P5 protocol, packet-level simulations, Particle measurements, Peer to peer computing, peer-to-peer personal privacy protocol, privacy, Protocols, receiver anonymity, scalable anonymous communication, security of data, sender anonymity, sender-receiver anonymity, Size measurement, telecommunication security},
	isbn = {0-7695-1543-6},
	doi = {10.1109/SECPRI.2002.1004362},
	author = {Sherwood,R. and Bhattacharjee, Bobby and Srinivasan, Aravind}
}
