@conference {17643,
	title = {Predicting Trust and Distrust in Social Networks},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {418 - 424},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.},
	keywords = {distrust prediction, Electronic publishing, Encyclopedias, graph theory, inference algorithm, Inference algorithms, inference mechanisms, Internet, negative trust, online social networks, positive trust, Prediction algorithms, probability, random graphs, security of data, social media, social networking (online), spring-embedding algorithm, Training, trust inference, trust probabilistic interpretation, user behavior, user satisfaction, user-generated content, user-generated interactions},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.56},
	author = {DuBois,T. and Golbeck,J. and Srinivasan, Aravind}
}
@conference {17653,
	title = {Rigorous Probabilistic Trust-Inference with Applications to Clustering},
	booktitle = {IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT {\textquoteright}09},
	volume = {1},
	year = {2009},
	month = {2009/09/15/18},
	pages = {655 - 658},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.},
	keywords = {Clustering algorithms, Conferences, Educational institutions, Extraterrestrial measurements, Inference algorithms, Intelligent agent, random graphs, Social network services, trust inferrence, Visualization, Voting, Web sites},
	isbn = {978-0-7695-3801-3},
	doi = {10.1109/WI-IAT.2009.109},
	author = {DuBois,Thomas and Golbeck,Jennifer and Srinivasan, Aravind}
}
@article {19662,
	title = {MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/12//},
	pages = {1960 - 1972},
	abstract = {In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC},
	keywords = {algorithms, approximate inference, Artificial intelligence, auxiliary variable particle filter, Computational efficiency, continuous state space, downdating, Image Enhancement, Image Interpretation, Computer-Assisted, Inference algorithms, Information Storage and Retrieval, laser range scanner, laser range scanner., Least squares approximation, least squares approximations, Least squares methods, linear least squares, Markov chain Monte Carlo, Markov processes, MCMC data association, merged measurements, Monte Carlo methods, Movement, multiple merged measurements, multitarget tracking, particle filter, particle filtering (numerical methods), Particle filters, Pattern Recognition, Automated, probabilistic model, QR factorization, Radar tracking, Rao-Blackwellized, real time multitarget tracking, Reproducibility of results, Sampling methods, Sensitivity and Specificity, sensor fusion, sparse factorization updating, sparse least squares, State-space methods, Subtraction Technique, target tracking, updating},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {17854,
	title = {Model-based OpenMP implementation of a 3D facial pose tracking system},
	booktitle = {2006 International Conference on Parallel Processing Workshops, 2006. ICPP 2006 Workshops},
	year = {2006},
	month = {2006///},
	pages = {8 pp.-73 - 8 pp.-73},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Most image processing applications are characterized by computation-intensive operations, and high memory and performance requirements. Parallelized implementation on shared-memory systems offer an attractive solution to this class of applications. However, we cannot thoroughly exploit the advantages of such architectures without proper modeling and analysis of the application. In this paper, we describe our implementation of a 3D facial pose tracking system using the OpenMP platform. Our implementation is based on a design methodology that uses coarse-grain dataflow graphs to model and schedule the application. We present our modeling approach, details of the implementation that we derived based on this modeling approach, and associated performance results. The parallelized implementation achieves significant speedup, and meets or exceeds the target frame rate under various configurations},
	keywords = {3D facial pose tracking system, application modeling, application program interfaces, application scheduling, coarse-grain dataflow graphs, Concurrent computing, data flow graphs, Educational institutions, face recognition, IMAGE PROCESSING, image processing applications, Inference algorithms, Message passing, OpenMP platform, parallel implementation, PARALLEL PROCESSING, parallel programming, Particle tracking, Processor scheduling, SHAPE, shared memory systems, shared-memory systems, Solid modeling, tracking},
	isbn = {0-7695-2637-3},
	doi = {10.1109/ICPPW.2006.55},
	author = {Saha,S. and Chung-Ching Shen and Chia-Jui Hsu and Aggarwal,G. and Veeraraghavan,A. and Sussman, Alan and Bhattacharyya, Shuvra S.}
}
