@conference {14679,
	title = {Dynamic Enforcement of Knowledge-Based Security Policies},
	booktitle = {Computer Security Foundations Symposium (CSF), 2011 IEEE 24th},
	year = {2011},
	month = {2011/06/27/29},
	pages = {114 - 128},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper explores the idea of knowledge-based security policies, which are used to decide whether to answer queries over secret data based on an estimation of the querier{\textquoteright}s (possibly increased) knowledge given the results. Limiting knowledge is the goal of existing information release policies that employ mechanisms such as noising, anonymization, and redaction. Knowledge-based policies are more general: they increase flexibility by not fixing the means to restrict information flow. We enforce a knowledge-based policy by explicitly tracking a model of a querier{\textquoteright}s belief about secret data, represented as a probability distribution, and denying any query that could increase knowledge above a given threshold. We implement query analysis and belief tracking via abstract interpretation using a novel probabilistic polyhedral domain, whose design permits trading off precision with performance while ensuring estimates of a querier{\textquoteright}s knowledge are sound. Experiments with our implementation show that several useful queries can be handled efficiently, and performance scales far better than would more standard implementations of probabilistic computation based on sampling.},
	keywords = {abstract interpretation, belief networks, belief tracking, Data models, dynamic enforcement, Facebook, information flow, knowledge based systems, knowledge-based security, knowledge-based security policy, privacy, probabilistic computation, probabilistic logic, probabilistic polyhedral domain, probabilistic polyhedron, probability, query analysis, Security, security of data, semantics, Waste materials},
	isbn = {978-1-61284-644-6},
	doi = {10.1109/CSF.2011.15},
	author = {Mardziel,P. and Magill,S. and Hicks, Michael W. and Srivatsa,M.}
}
@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 {12479,
	title = {Moving vistas: Exploiting motion for describing scenes},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {1911 - 1918},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Scene recognition in an unconstrained setting is an open and challenging problem with wide applications. In this paper, we study the role of scene dynamics for improved representation of scenes. We subsequently propose dynamic attributes which can be augmented with spatial attributes of a scene for semantically meaningful categorization of dynamic scenes. We further explore accurate and generalizable computational models for characterizing the dynamics of unconstrained scenes. The large intra-class variation due to unconstrained settings and the complex underlying physics present challenging problems in modeling scene dynamics. Motivated by these factors, we propose using the theory of chaotic systems to capture dynamics. Due to the lack of a suitable dataset, we compiled a dataset of {\textquoteleft}in-the-wild{\textquoteright} dynamic scenes. Experimental results show that the proposed framework leads to the best classification rate among other well-known dynamic modeling techniques. We also show how these dynamic features provide a means to describe dynamic scenes with motion-attributes, which then leads to meaningful organization of the video data.},
	keywords = {Application software, Automation, Chaos, chaotic system, Computational modeling, Computer vision, dynamic scene categorization, Educational institutions, HUMANS, image recognition, in the wild dynamic scene, Layout, motion attribute, natural scenes, Physics, probability, scene recognition, Snow, video data},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539864},
	author = {Shroff, N. and Turaga,P. and Chellapa, Rama}
}
@conference {17633,
	title = {New Constructive Aspects of the Lovasz Local Lemma},
	booktitle = {2010 51st Annual IEEE Symposium on Foundations of Computer Science (FOCS)},
	year = {2010},
	month = {2010/10/23/26},
	pages = {397 - 406},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The Lov{\textquoteright}{a}sz Local Lemma (LLL) is a powerful tool that gives sufficient conditions for avoiding all of a given set of {\textquoteleft}{\textquoteleft}bad{\textquoteright}{\textquoteright} events, with positive probability. A series of results have provided algorithms to efficiently construct structures whose existence is non-constructively guaranteed by the LLL, culminating in the recent breakthrough of Moser \& Tardos. We show that the output distribution of the Moser-Tardos algorithm well-approximates the emph{conditional LLL-distribution} {\textendash} the distribution obtained by conditioning on all bad events being avoided. We show how a known bound on the probabilities of events in this distribution can be used for further probabilistic analysis and give new constructive and non-constructive results. We also show that when an LLL application provides a small amount of slack, the number of resamplings of the Moser-Tardos algorithm is nearly linear in the number of underlying independent variables (not events!), and can thus be used to give efficient constructions in cases where the underlying proof applies the LLL to super-polynomially many events. Even in cases where finding a bad event that holds is computationally hard, we show that applying the algorithm to avoid a polynomial-sized {\textquoteleft}{\textquoteleft}core{\textquoteright}{\textquoteright} subset of bad events leads to a desired outcome with high probability. We demonstrate this idea on several applications. We give the first constant-factor approximation algorithm for the Santa Claus problem by making an LLL-based proof of Feige constructive. We provide Monte Carlo algorithms for acyclic edge coloring, non-repetitive graph colorings, and Ramsey-type graphs. In all these applications the algorithm falls directly out of the non-constructive LLL-based proof. Our algorithms are very simple, often provide better bounds than previous algorithms, and are in several cases the first efficient algorithms known. As a second type of application we consider settings beyond the critical dependency threshold of the LLL: - - avoiding all bad events is impossible in these cases. As the first (even non-constructive) result of this kind, we show that by sampling from the LLL-distribution of a selected smaller core, we can avoid a fraction of bad events that is higher than the expectation. MAX $k$-SAT is an example of this.},
	keywords = {acyclic edge coloring, Algorithm design and analysis, Approximation algorithms, Approximation methods, computational complexity, Computer science, constant factor approximation algorithm, graph colouring, Linearity, Lovasz Local Lemma, MAX k-SAT, Monte Carlo Algorithm, Monte Carlo methods, Moser-Tardos algorithm, nonrepetitive graph coloring, output distribution, polynomial sized core subset, Polynomials, Probabilistc Method, probabilistic analysis, probabilistic logic, probability, Ramsey type graph, Sampling methods, Santa Claus Problem},
	isbn = {978-1-4244-8525-3},
	doi = {10.1109/FOCS.2010.45},
	author = {Haeupler,B. and Saha,B. and Srinivasan, Aravind}
}
@conference {18669,
	title = {Evaluating Files to Audit for Detecting Intrusions in FileSystem Data},
	year = {2009},
	month = {2009/07//},
	pages = {163 - 170},
	abstract = {Monitoring filesystem data is a common method used to detect intrusions. Once a computer is compromised, an attacker may alter files, add new files or delete existing files. The changes that attackers make may target any part of the filesystem, including metadata along with files (e.g., permissions, ownerships and inodes). The accuracy of detecting an intrusion depends on the data audited: if an intrusion does not manifest in the data, the intrusion will not be detected. Moreover, not all files, which contain filesystem activity, are suitable to detect intrusions, as some may fail to provide useful information. In this paper, we describe an empirical study that focused on filesystem attack activity after a SSH compromise. Three types of attacker action are considered: reconnaissance, password modification, and malware download. For each type of action, we evaluated the files to audit using metrics derived from the field of information theory and estimated with the empirical SSH compromise data.},
	keywords = {authorisation, Bayes methods, Bayesian metric, data auditing, empirical SSH compromise data, Entropy, entropy-based metric, file evaluation, file organisation, filesystem attack activity, filesystem data monitoring, honeypot, information theory, intrusion detection system, invasive software, malware download, meta data, optimisation, optimization problem, password modification, probability, reconnaissance action, unauthorized user},
	doi = {10.1109/NCA.2009.38},
	author = {Molina,J. and Michel Cukier}
}
@conference {17570,
	title = {Capacity of Asynchronous Random-Access Scheduling in Wireless Networks},
	booktitle = {IEEE INFOCOM 2008. The 27th Conference on Computer Communications},
	year = {2008},
	month = {2008/04/13/18},
	pages = {1148 - 1156},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.},
	keywords = {asynchronous random-access scheduling, channel access probability, Computer networks, Computer science, Educational institutions, Interference, Optimal scheduling, Peer to peer computing, probability, Processor scheduling, radio link, radio links, radio networks, Routing, scheduling, Throughput, throughput capacity, wireless channels, Wireless networks},
	isbn = {978-1-4244-2025-4},
	doi = {10.1109/INFOCOM.2008.170},
	author = {Chafekar,D. and Levin,D. and Kumar,V. S.A and Marathe,M. V and Parthasarathy,S. and Srinivasan, Aravind}
}
@article {12544,
	title = {Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/10//},
	pages = {1771 - 1785},
	abstract = {We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.},
	keywords = {3D voxel data segmentation, algorithms, Anatomic;Pattern Recognition, Artificial intelligence, Automated;Reproducibility of Results;Sensitivity and Specificity;Whole Body Imaging;, Computer simulation, Computer-Assisted;Imaging, curve fitting, Eigenvalues and eigenfunctions, graph theory, human articulation, human body graphical model, human motion analysis, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, image registration, Image segmentation, Laplace transforms, Laplacian eigenspace transform, model driven segmentation, probability, representative graph, skeleton registration, Smoothing methods, solid modelling, spline fitting error, splines (mathematics), Three-Dimensional;Joints;Models, top-down probabilistic approach, voxel neighborhood graph},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.70823},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {13393,
	title = {Online Filtering, Smoothing and Probabilistic Modeling of Streaming data},
	booktitle = {IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008},
	year = {2008},
	month = {2008/04/07/12},
	pages = {1160 - 1169},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.},
	keywords = {Data analysis, data streaming, declarative query, dynamic probabilistic model, Filtering, Global Positioning System, hidden Markov models, Monitoring, Monte Carlo methods, Noise generators, Noise measurement, online filtering, particle filter, particle filtering (numerical methods), probabilistic database view, probability, Real time systems, real-time application, relational database system, Relational databases, sequential Monte Carlo algorithm, Smoothing methods, SQL},
	isbn = {978-1-4244-1836-7},
	doi = {10.1109/ICDE.2008.4497525},
	author = {Kanagal,B. and Deshpande, Amol}
}
@article {12586,
	title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {888 - 892},
	abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements},
	keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893346},
	author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama}
}
@conference {19032,
	title = {Detection of denial-of-message attacks on sensor network broadcasts},
	year = {2005},
	month = {2005},
	pages = {64 - 78},
	abstract = {So far sensor network broadcast protocols assume a trustworthy environment. However in safety and mission-critical sensor networks this assumption may not be valid and some sensor nodes might be adversarial. In these environments, malicious sensor nodes can deprive other nodes from receiving a broadcast message. We call this attack a denial-of-message attack (DoM). In this paper we model and analyze this attack, and present countermeasures. We present SIS, a secure implicit sampling scheme that permits a broadcasting base station to probabilistically detect the failure of nodes to receive its broadcast, even if these failures result from an attacker motivated to induce these failures undetectably. SIS works by eliciting authenticated acknowledgments from a subset of nodes per broadcast, where the subset is unpredictable to the attacker and tunable so as to mitigate acknowledgment implosion on the base station. We use a game-theoretic approach to evaluate this scheme in the face of an optimal attacker that attempts to maximize the number of nodes it denies the broadcast while remaining undetected by the base station, and show that SIS significantly constrains such an attacker even in sensor networks exhibiting high intrinsic loss rates. We also discuss extensions that permit more targeted detection capabilities.},
	keywords = {authenticated acknowledgments, broadcast channels, broadcast protocols, broadcasting base station, countermeasures, denial-of-message attacks, DoM, game theory, game-theoretic approach, malicious sensor nodes, Mobile computing, optimal attacker, probabilistic detection, probability, Protocols, Sampling methods, secure implicit sampling, sensor network broadcasts, SIS, telecommunication security, Wireless sensor networks},
	author = {McCune, J.M. and Elaine Shi and Perrig, A. and Reiter, M.K.}
}
@conference {13367,
	title = {Exploiting Correlated Attributes in Acquisitional Query Processing},
	booktitle = {21st International Conference on Data Engineering, 2005. ICDE 2005. Proceedings},
	year = {2005},
	month = {2005/04/05/08},
	pages = {143 - 154},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate the selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques.},
	keywords = {acquisitional query processing, Computer networks, Costs, data acquisition, Delay, Distributed computing, distributed information system, Distributed information systems, distributed processing, exponential time algorithm, optimization techniques, polynomial-time heuristic, Polynomials, probability, Query processing, real-time systems, real-world sensor-network, Runtime, Sensor phenomena and characterization, Sensor systems},
	isbn = {0-7695-2285-8},
	doi = {10.1109/ICDE.2005.63},
	author = {Deshpande, Amol and Guestrin,C. and Wei Hong and Madden,S.}
}
@article {18908,
	title = {Inferencing in Support of Active Templates},
	year = {2005},
	month = {2005/02//},
	institution = {Department of Computer Science, University of Maryland, College Park},
	abstract = {The primary accomplishments of this project include the following: (1) HICAP is a general purpose planning architecture to assist military commanders with planning NEOs (Noncombatant Evacuation Operations). HICAP integrates a hierarchical task editor with a planning tool. Military planners select a task to decompose in the task editor and then use the planning tool to interactively refine it into an operational plan. (2) SHOP and SHOP2 are some simple, practical planning tools based on HTN planning. Their practical utility is shown by the mergence of an active set of users, which include government laboratories, industrial R\&D projects, and academic settings. SHOP2 received one of the top four awards in the 2002 International Planning Competition. (3) The Air Force Research Laboratory{\textquoteright}s Causal Analysis Tool (CAT) is a system for creating and analyzing causal models similar to Bayesian networks. We have enhanced CAT by developing an approach to quickly generate plans that have high probabilities of success.},
	keywords = {*ARTILLERY AMMUNITION, *HIGH CAPACITY PROJECTILES, AMMUNITION AND EXPLOSIVES, BAYES THEOREM, COALITION, Collaboration, Computer architecture, COMPUTER HARDWARE, Computer networks, HICAP(HIGH CAPACITY ARTILLARY PROJECTILES), MILITARY COMMANDERS, NEO(NONCOMBATANT EVACUATION OPERATIONS), NONCOMBATANT., PE63760E, planning, probability, PROJECTILE TRAJECTORIES, TASK SUPPORT, WUAFRLATEMPO02},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431023},
	author = {Nau, Dana S.}
}
@conference {13198,
	title = {Kernel-based Bayesian filtering for object tracking},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {227 - 234 vol. 1 - 227 - 234 vol. 1},
	abstract = {Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.},
	keywords = {approach;, approximation;, Bayes, Bayesian, Carlo, density, detection;, filtering;, functions;, Gaussian, interpolation;, kernel-based, methods;, mixtures;, Monte, nonGaussian, nonlinear, object, particle, probability, probability;, processes;, recognition;, sampling, sampling;, sequences;, system;, tracking;, video},
	doi = {10.1109/CVPR.2005.199},
	author = {Han,Bohyung and Zhu,Ying and Comaniciu, D. and Davis, Larry S.}
}
@conference {12100,
	title = {High-performance MAC for high-capacity wireless LANs},
	booktitle = {13th International Conference on Computer Communications and Networks, 2004. ICCCN 2004. Proceedings},
	year = {2004},
	month = {2004/10/11/13},
	pages = {167 - 172},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The next-generation wireless technologies, e.g., 802.11n and 802.15.3a, offer a physical-layer speed at least an-order-of-magnitude higher than the current standards. However, direct application of current MACs leads to high protocol overhead and significant throughput degradation. In this paper, we propose ADCA, a high-performance MAC that works with high-capacity physical layer. ADCA exploits two ideas of adaptive batch transmission and opportunistic selection of high-rate hosts to simultaneously reduce the overhead and improve the aggregate throughput. It opportunistically favors high-rate hosts by providing higher access probability and more access time, while ensuring each low-rate host certain minimum amount of channel access time. Simulations show that the ADCA design increases the throughput by 112\% and reduces the average delay by 55\% compared with the legacy DCF. It delivers more than 100 Mbps MAC-layer throughput as compared with 35 Mbps offered by the legacy MAC},
	keywords = {35 Mbit/s, access protocols, Aggregates, Bandwidth, batch transmission, Computer science, Educational institutions, high-capacity wireless LAN, high-performance MAC, Laboratories, Local area networks, Media Access Protocol, opportunistic selection, Physical layer, probability, Throughput, Wireless LAN},
	isbn = {0-7803-8814-3},
	doi = {10.1109/ICCCN.2004.1401615},
	author = {Yuan Yuan and Daqing Gu and Arbaugh, William A. and Jinyun Zhang}
}
@conference {13625,
	title = {Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {252 - 255 Vol.1 - 252 - 255 Vol.1},
	abstract = {In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.},
	keywords = {approximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper},
	doi = {10.1109/ICPR.2004.1334071},
	author = {Kang,Hee-Joong and David Doermann}
}
@conference {12686,
	title = {Robust Bayesian cameras motion estimation using random sampling},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1361 - 1364 Vol.2 - 1361 - 1364 Vol.2},
	abstract = {In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.},
	keywords = {3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide},
	doi = {10.1109/ICIP.2004.1419754},
	author = {Qian, G. and Chellapa, Rama and Qinfen Zheng}
}
@conference {12705,
	title = {Vehicle detection and tracking using acoustic and video sensors},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 793-6 vol.3 - iii - 793-6 vol.3},
	abstract = {Multimodal sensing has attracted much attention in solving a wide range of problems, including target detection, tracking, classification, activity understanding, speech recognition, etc. In surveillance applications, different types of sensors, such as video and acoustic sensors, provide distinct observations of ongoing activities. We present a fusion framework using both video and acoustic sensors for vehicle detection and tracking. In the detection phase, a rough estimate of target direction-of-arrival (DOA) is first obtained using acoustic data through beam-forming techniques. This initial DOA estimate designates the approximate target location in video. Given the initial target position, the DOA is refined by moving target detection using the video data. Markov chain Monte Carlo techniques are then used for joint audio-visual tracking. A novel fusion approach has been proposed for tracking, based on different characteristics of audio and visual trackers. Experimental results using both synthetic and real data are presented. Improved tracking performance has been observed by fusing the empirical posterior probability density functions obtained using both types of sensors.},
	keywords = {acoustic, applications;, audio, audio-visual, beam-forming, Carlo, chain, density, detection;, direction-of-arrival, DOA, empirical, estimation;, framework;, functions;, fusion, fusion;, joint, Markov, methods;, Monte, moving, multimodal, object, optical, posterior, probability, probability;, processes;, processing;, sensing;, sensor, sensors;, signal, Surveillance, surveillance;, systems;, target, techniques;, tracking;, vehicle, video},
	doi = {10.1109/ICASSP.2004.1326664},
	author = {Chellapa, Rama and Qian,Gang and Qinfen Zheng}
}
@article {12699,
	title = {Wide baseline image registration with application to 3-D face modeling},
	journal = {Multimedia, IEEE Transactions on},
	volume = {6},
	year = {2004},
	month = {2004/06//},
	pages = {423 - 434},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, baseline, biometrics;, Computer, configuration;, correspondence, doubly, error, extraction;, Face, feature, holistic, image, matching;, matrix;, minimization;, modeling;, models;, normalization, probability, probability;, procedure;, processes;, processing;, recognition;, registration;, representation;, sequences;, shapes;, Sinkhorn, spatial, statistics;, Stochastic, video, vision;, wide},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.827511},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama and Keaton, T.}
}
@conference {12729,
	title = {Activity recognition using the dynamics of the configuration of interacting objects},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II - 633-40 vol.2 - II - 633-40 vol.2},
	abstract = {Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal {\textquoteright}shape{\textquoteright} of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both {\textquoteright}drastic{\textquoteright} and {\textquoteright}slow{\textquoteright} abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.},
	keywords = {2D, abnormal, abnormality, abnormality;, acoustic, activity, analysis;, change;, Computer, configuration, configuration;, data;, DETECTION, detection;, distribution;, drastic, dynamics;, event;, filter;, hand-picked, image, infrared, interacting, learning;, location, low, mean, model;, monitoring;, MOTION, moving, noise;, noisy, object, object;, observation, observation;, particle, pattern, plane;, point, polygonal, probability, probability;, problem;, processing;, radar, recognition;, resolution, sensor;, sensors;, sequence;, SHAPE, shape;, signal, slow, statistic;, strategy;, Surveillance, surveillance;, target, test, tracking;, video, video;, visible, vision;},
	doi = {10.1109/CVPR.2003.1211526},
	author = {Vaswani, N. and RoyChowdhury, A. and Chellapa, Rama}
}
@conference {11905,
	title = {Non-parametric expectation maximization: a learning automata approach},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {3},
	year = {2003},
	month = {2003/10//},
	pages = {2996- 3001 vol.3 - 2996- 3001 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.},
	keywords = {Automatic speech recognition, bivariate simulation data, Computational modeling, Computer vision, Density functional theory, expectation maximization technique, learning automata, mixture parameters estimation, nonparametric expectation maximization, nonparametric statistics, optimisation, parameter estimation, Parzen Window, PDF, probability, probability density function, Speech processing, Speech recognition, stochastic learning automata, Stochastic processes, stochastic technique},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1244347},
	author = {Abd-Almageed, Wael and El-Osery,A. and Smith,C.E.}
}
@article {17809,
	title = {Temporal probabilistic object bases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {15},
	year = {2003},
	month = {2003/08//july},
	pages = {921 - 939},
	abstract = {There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.},
	keywords = {algebra;, algebraic, bases;, constraints;, data, database, database;, databases;, distribution, explicit, functions;, handling;, implicit, instances;, integrity;, intervals;, management;, model;, models;, object, object-oriented, operations;, probabilistic, probability, probability;, relational, temporal, theory;, Uncertainty, uncertainty;},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2003.1209009},
	author = {Biazzo,V. and Giugno,R. and Lukasiewicz,T. and V.S. Subrahmanian}
}
@article {12752,
	title = {A generic approach to simultaneous tracking and verification in video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/05//},
	pages = {530 - 544},
	abstract = {A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H<sub>1</sub> and H0, we first estimate posterior probabilities P(H<sub>0</sub>|Z) and P(H<sub>1</sub>|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.},
	keywords = {approach;, Carlo, configuration;, correspondence, data;, density, density;, estimated, estimation;, evaluation;, extraction;, Face, facial, feature, generic, human, hypothesis, image, measurement, methods;, Monte, object, performance, posterior, probability, probability;, problem;, processing;, propagation;, recognition;, road, sequence, sequences;, sequential, signal, space;, stabilization;, state, synthetic, temporal, testing;, tracking;, vector;, vehicle, vehicles;, verification;, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.1006400},
	author = {Li,Baoxin and Chellapa, Rama}
}
@conference {18684,
	title = {Performance evaluation of a probabilistic replica selection algorithm},
	year = {2002},
	month = {2002///},
	pages = {119 - 127},
	abstract = {When executing time-sensitive distributed applications, a middleware that provides dependability and timeliness is faced with the important problem of preventing timing failures both under normal conditions and when the quality of service is degraded due to replica failures and transient overload on the server. To address this problem, we have designed a probabilistic model-based replica selection algorithm that allows a middleware to choose a set of replicas to service a client based on their ability to meet a client{\textquoteright}s timeliness requirements. This selection is done based on the prediction made by a probabilistic model that uses the performance history of replicas as inputs. In this paper, we describe the experiments we have conducted to evaluate the ability of this dynamic selection algorithm to meet a client{\textquoteright}s timing requirements, and compare it with that of a static and round-robin selection scheme under different scenarios},
	keywords = {client-server systems, Dependability, distributed object management, dynamic selection algorithm, Middleware, probabilistic model, probabilistic model-based replica selection algorithm, probability, quality of service, real-time systems, replica failures, round-robin selection scheme, static scheme, time-sensitive distributed applications, timeliness, timing failures, transient overload},
	doi = {10.1109/WORDS.2002.1000044},
	author = {Krishnamurthy, S. and Sanders,W. H. and Michel Cukier}
}
@article {13827,
	title = {Mapping WorldNet Senses to a Lexical Database of Verbs},
	year = {2001},
	month = {2001/01//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper describes automatic techniques for mapping 9611 semantically classified English verbs to WordNet senses. The verbs were initially grouped into 491 semantic classes based on syntactic categories; they were then mapped into WordNet senses according to three pieces of information: (1) prior probability of WordNet senses; (2) semantic similarity of WordNet senses for verbs within the same category; and (3) probabilistic correlations between WordNet relationship and verb frame data. Our techniques make use of a training set of 1791 disambiguated entries representing 1442 verbs occurring in 167 of the categories. The best results achieved .58 recall and .72 precision, versus a lower bound of .38 recall and .62 precision for assigning the most frequently occurring WordNet sense, and an upper bound of .75 recall and .87 precision for human judgment.},
	keywords = {*DATA BASES, *ENGLISH LANGUAGE, *LEXICAL DATABASES, *LEXICOGRAPHY, *MAPPING, *VERBS, *WORD MAPPING, *WORDNET, AMBIGUITY, correlation, FRAMES, Frequency, INFORMATION SCIENCE, JUDGEMENT(PSYCHOLOGY), linguistics, PRECISION, probability, RECALL, semantics, syntax, WORDS(LANGUAGE), WSD(WORD SENSE DISAMBIGUATION)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458846},
	author = {Green,Rebecca and Pearl,Lisa and Dorr, Bonnie J}
}
@article {17667,
	title = {The one-inclusion graph algorithm is near-optimal for the prediction model of learning},
	journal = {IEEE Transactions on Information Theory},
	volume = {47},
	year = {2001},
	month = {2001/03//},
	pages = {1257 - 1261},
	abstract = {Haussler, Littlestone and Warmuth (1994) described a general-purpose algorithm for learning according to the prediction model, and proved an upper bound on the probability that their algorithm makes a mistake in terms of the number of examples seen and the Vapnik-Chervonenkis (VC) dimension of the concept class being learned. We show that their bound is within a factor of 1+o(1) of the best possible such bound for any algorithm},
	keywords = {Computer science, concept class, general-purpose algorithm, graph theory, learning prediction model, learning systems, near-optimal algorithm, Neural networks, one-inclusion graph algorithm, optimisation, Prediction algorithms, prediction theory, Predictive models, probability, Probability distribution, Upper bound, Vapnik-Chervonenkis dimension, Virtual colonoscopy},
	isbn = {0018-9448},
	doi = {10.1109/18.915700},
	author = {Li,Yi and Long,P. M and Srinivasan, Aravind}
}
@article {17820,
	title = {Probabilistic object bases},
	journal = {ACM Trans. Database Syst.},
	volume = {26},
	year = {2001},
	month = {2001/09//},
	pages = {264 - 312},
	abstract = {Although there are many applications where an object-oriented data model is a good way of representing and querying data, current object database systems are unable to handle objects whose attributes are uncertain. In this article, we extend previous work by Kornatzky and Shimony to develop an algebra to handle object bases with uncertainty. We propose concepts of consistency for such object bases, together with an NP-completeness result, and classes of probabilistic object bases for which consistency is polynomially checkable. In addition, as certain operations involve conjunctions and disjunctions of events, and as the probability of conjunctive and disjunctive events depends both on the probabilities of the primitive events involved as well as on what is known (if anything) about the relationship between the events, we show how all our algebraic operations may be performed under arbitrary probabilistic conjunction and disjunction strategies. We also develop a host of equivalence results in our algebra, which may be used as rewrite rules for query optimization. Last but not least, we have developed a prototype probabilistic object base server on top of ObjectStore. We describe experiments to assess the efficiency of different possible rewrite rules.},
	keywords = {consistency, object-oriented database, probabilistic object algebra, probabilistic object base, probability, query language, Query optimization},
	isbn = {0362-5915},
	doi = {10.1145/502030.502031},
	url = {http://doi.acm.org/10.1145/502030.502031},
	author = {Eiter,Thomas and Lu,James J. and Lukasiewicz,Thomas and V.S. Subrahmanian}
}
@conference {15589,
	title = {Nearly optimal expected-case planar point location},
	booktitle = {Foundations of Computer Science, 2000. Proceedings. 41st Annual Symposium on},
	year = {2000},
	month = {2000///},
	pages = {208 - 218},
	abstract = {We consider the planar point location problem from the perspective of expected search time. We are given a planar polygonal subdivision S and for each polygon of the subdivision the probability that a query point lies within this polygon. The goal is to compute a search structure to determine which cell of the subdivision contains a given query point, so as to minimize the expected search time. This is a generalization of the classical problem of computing an optimal binary search tree for one-dimensional keys. In the one-dimensional case it has long been known that the entropy H of the distribution is the dominant term in the lower bound on the expected-case search time, and further there exist search trees achieving expected search times of at most H+2. Prior to this work, there has been no known structure for planar point location with an expected search time better than 2H, and this result required strong assumptions on the nature of the query point distribution. Here we present a data structure whose expected search time is nearly equal to the entropy lower bound, namely H+o(H). The result holds for any polygonal subdivision in which the number of sides of each of the polygonal cells is bounded, and there are no assumptions on the query distribution within each cell. We extend these results to subdivisions with convex cells, assuming a uniform query distribution within each cell},
	keywords = {computational geometry, convex cells, data structure, expected search time, nearly optimal expected-case planar point location, optimal binary search tree, planar point location, planar polygonal subdivision, polygonal cells, polygonal subdivision, probability, search problems, search structure, subdivision, trees (mathematics)},
	doi = {10.1109/SFCS.2000.892108},
	author = {Arya,S. and Malamatos,T. and Mount, Dave}
}
@conference {17609,
	title = {Improved bounds and algorithms for hypergraph two-coloring},
	booktitle = {39th Annual Symposium on Foundations of Computer Science, 1998. Proceedings},
	year = {1998},
	month = {1998/11/08/11},
	pages = {684 - 693},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We show that for all large n, every n-uniform hypergraph with at most 0.7√(n/lnn){\texttimes}2n edges can be two-colored. We, in fact, present fast algorithms that output a proper two-coloring with high probability for such hypergraphs. We also derandomize and parallelize these algorithms, to derive NC1 versions of these results. This makes progress on a problem of Erdos (1963), improving the previous-best bound of n1/3-0(1){\texttimes}2n due to Beck (1978). We further generalize this to a {\textquotedblleft}local{\textquotedblright} version, improving on one of the first applications of the Lovasz Local Lemma},
	keywords = {algorithms, Application software, Approximation algorithms, bounds, computational geometry, Computer science, Contracts, Erbium, graph colouring, History, hypergraph two-coloring, Lab-on-a-chip, MATHEMATICS, n-uniform hypergraph, Parallel algorithms, Polynomials, probability},
	isbn = {0-8186-9172-7},
	doi = {10.1109/SFCS.1998.743519},
	author = {Radhakrishnan,J. and Srinivasan, Aravind}
}
@conference {15680,
	title = {Threshold-based admission control for multi-class video-on-demand systems},
	booktitle = {Performance, Computing and Communications, 1998. IPCCC {\textquoteright}98., IEEE International},
	year = {1998},
	month = {1998/02/16/18},
	pages = {154 - 160},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The next generation of distributed digital video-on-demand (VoD) systems will use admission control tests to ensure that users receive predictable and guaranteed service. If the system cannot support a new request then that request is blocked This paper presents an analytical model to evaluate unified admission control strategies for distributed VoD systems with multiple video classes. We prove that there exists a computationally efficient technique to determine the blocking probabilities for newly arriving requests. Through numerical analysis we show the effect of different admission control policies on overall system blocking rates},
	keywords = {Admission control, admission control tests, analytical model, Analytical models, blocking probabilities, buffer storage, Computer architecture, distributed digital VoD systems, File servers, guaranteed service, High-speed networks, interactive video, Motion pictures, multi-class video-on-demand systems, multimedia systems, Network servers, newly arriving requests, numerical analysis, Predictive models, probability, recursive algorithm, system blocking rates, telecommunication congestion control, threshold-based admission control, unified admission control},
	isbn = {0-7803-4468-5},
	doi = {10.1109/PCCC.1998.659937},
	author = {Mundur, Padma and Sood,A. and Simon,R.}
}
@conference {18687,
	title = {Probabilistic verification of a synchronous round-based consensus protocol},
	year = {1997},
	month = {1997/10//},
	pages = {165 - 174},
	abstract = {Consensus protocols are used in a variety of reliable distributed systems, including both safety-critical and business-critical applications. The correctness of a consensus protocol is usually shown, by making assumptions about the environment in which it executes, and then proving properties about the protocol. But proofs about a protocol{\textquoteright}s behavior are only as good as the assumptions which were made to obtain them, and violation of these assumptions can lead to unpredicted and serious consequences. We present a new approach for the probabilistic verification of synchronous round based consensus protocols. In doing so, we make stochastic assumptions about the environment in which a protocol operates, and derive probabilities of proper and non proper behavior. We thus can account for the violation of assumptions made in traditional proof techniques. To obtain the desired probabilities, the approach enumerates possible states that can be reached during an execution of the protocol, and computes the probability of achieving the desired properties for a given fault and network environment. We illustrate the use of this approach via the evaluation of a simple consensus protocol operating under a realistic environment which includes performance, omission, and crash failures},
	keywords = {business-critical applications, consensus protocol correctness, crash failures, formal verification, network environment, probabilistic verification, probabilities, probability, program verification, proper behavior, protocol behavior, Protocols, realistic environment, reliable distributed systems, safety-critical applications, simple consensus protocol, software reliability, stochastic assumptions, synchronous round based consensus protocol, synchronous round based consensus protocols, traditional proof techniques},
	doi = {10.1109/RELDIS.1997.632812},
	author = {Duggal,H.S. and Michel Cukier and Sanders,W. H.}
}
@conference {17660,
	title = {Splitters and near-optimal derandomization},
	booktitle = {, 36th Annual Symposium on Foundations of Computer Science, 1995. Proceedings},
	year = {1995},
	month = {1995/10/23/25},
	pages = {182 - 191},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a fairly general method for finding deterministic constructions obeying what we call k-restrictions; this yields structures of size not much larger than the probabilistic bound. The structures constructed by our method include (n,k)-universal sets (a collection of binary vectors of length n such that for any subset of size k of the indices, all 2k configurations appear) and families of perfect hash functions. The near-optimal constructions of these objects imply the very efficient derandomization of algorithms in learning, of fixed-subgraph finding algorithms, and of near optimal ΣIIΣ threshold formulae. In addition, they derandomize the reduction showing the hardness of approximation of set cover. They also yield deterministic constructions for a local-coloring protocol, and for exhaustive testing of circuits},
	keywords = {Boosting, Circuit testing, computational complexity, computational linguistics, Computer science, Contracts, derandomization, deterministic constructions, Educational institutions, Engineering profession, exhaustive testing, fairly general method, fixed-subgraph finding algorithms, hardness of approximation, Information systems, k-restrictions, learning, local-coloring protocol, MATHEMATICS, near-optimal constructions, near-optimal derandomization, Parallel algorithms, probabilistic bound, probability, Protocols, randomised algorithms, Set cover, splitters},
	isbn = {0-8186-7183-1},
	doi = {10.1109/SFCS.1995.492475},
	author = {Naor,M. and Schulman,L. J and Srinivasan, Aravind}
}
@conference {14855,
	title = {Stochastic completion fields: a neural model of illusory contour shape and salience},
	booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
	year = {1995},
	month = {1995/06//},
	pages = {408 - 415},
	abstract = {We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption-namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e. the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory contours. A computer model is demonstrated on numerous illusory contour stimuli from the literature},
	keywords = {boundary, completion, computational, Computer, contour, contours;, convolutions;, cortex;, curves, detection;, distribution;, edge, energy;, estimation;, fields;, fragments;, geometry;, illusory, image, lattice;, least, likelihood, mammalian, maximum, model;, nets;, neural, of, paths;, plane;, probability, probability;, random, recognition;, shape;, stimuli;, Stochastic, vector-field, visual, walk;},
	doi = {10.1109/ICCV.1995.466910},
	author = {Williams,L. R and Jacobs, David W.}
}
@conference {17577,
	title = {Computing with very weak random sources},
	booktitle = {, 35th Annual Symposium on Foundations of Computer Science, 1994 Proceedings},
	year = {1994},
	month = {1994/11/20/22},
	pages = {264 - 275},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {For any fixed ε>0, we show how to simulate RP algorithms in time nO(log n) using the output of a δ-source with min-entropy R(ε). Such a weak random source is asked once for R(ε) bits; it outputs an R-bit string such that any string has probability at most 2-R(ε). If ε>1-1/(k+1), our BPP simulations take time nO(log(k n)) (log(k) is the logarithm iterated k times). We also give a polynomial-time BPP simulation using Chor-Goldreich sources of min-entropy RΩ(1), which is optimal. We present applications to time-space tradeoffs, expander constructions, and the hardness of approximation. Also of interest is our randomness-efficient Leftover Hash Lemma, found independently by Goldreich and Wigderson},
	keywords = {Application software, BPP simulations, Chor-Goldreich sources, computational complexity, Computational modeling, Computer science, Computer simulation, cryptography, distributed algorithms, expander constructions, hardness, MATHEMATICS, min-entropy, Physics computing, Polynomials, probability, R-bit string, randomness-efficient Leftover Hash Lemma, RP algorithms simulation, Testing, time-space tradeoffs, very weak random sources},
	isbn = {0-8186-6580-7},
	doi = {10.1109/SFCS.1994.365688},
	author = {Srinivasan, Aravind and Zuckerman,D.}
}
