@conference {19505,
	title = {The Provenance of WINE},
	booktitle = {Dependable Computing Conference (EDCC), 2012 Ninth European},
	year = {2012},
	month = {2012///},
	pages = {126 - 131},
	abstract = {The results of cyber security experiments are often impossible to reproduce, owing to the lack of adequate descriptions of the data collection and experimental processes. Such provenance information is difficult to record consistently when collecting data from distributed sensors and when sharing raw data among research groups with variable standards for documenting the steps that produce the final experimental result. In the WINE benchmark, which provides field data for cyber security experiments, we aim to make the experimental process self-documenting. The data collected includes provenance information {\textendash} such as when, where and how an attack was first observed or detected {\textendash} and allows researchers to gauge information quality. Experiments are conducted on a common test bed, which provides tools for recording each procedural step. The ability to understand the provenance of research results enables rigorous cyber security experiments, conducted at scale.},
	keywords = {Benchmark testing, CYBER SECURITY, cyber security experiments, data attacks, data collection, dependability benchmarking, distributed databases, distributed sensors, experimental research, field data, information quality, MALWARE, Pipelines, provenance, provenance information, raw data sharing, research groups, security of data, self-documenting experimental process, sensor fusion, software, variable standards, WINE, WINE benchmark},
	author = {Tudor Dumitras and Efstathopoulos, P.}
}
@conference {15544,
	title = {Efficient Kriging via Fast Matrix-Vector Products},
	booktitle = {Aerospace Conference, 2008 IEEE},
	year = {2008},
	month = {2008/03//},
	pages = {1 - 7},
	abstract = {Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrix-vector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.},
	keywords = {cokriging technique, fast matrix-vector products, fast multipole methods, geophysical techniques, image fusion, Interpolation, iterative methods, nearest neighbor searching, optimal scattered data estimator, Remote sensing, remotely sensed data, scattered data points, sensor fusion, time efficiency},
	doi = {10.1109/AERO.2008.4526433},
	author = {Memarsadeghi,N. and Raykar,V.C. and Duraiswami, Ramani and Mount, Dave}
}
@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.}
}
@article {18485,
	title = {Joint audio-visual tracking using particle filters},
	journal = {EURASIP J. Appl. Signal Process.},
	volume = {2002},
	year = {2002},
	month = {2002/01//},
	pages = {1154 - 1164},
	abstract = {It is often advantageous to track objects in a scene using multimodal information when such information is available. We use audio as a complementary modality to video data, which, in comparison to vision, can provide faster localization over a wider field of view. We present a particle-filter based tracking framework for performing multimodal sensor fusion for tracking people in a videoconferencing environment using multiple cameras and multiple microphone arrays. One advantage of our proposed tracker is its ability to seamlessly handle temporary absence of some measurements (e.g., camera occlusion or silence). Another advantage is the possibility of self-calibration of the joint system to compensate for imprecision in the knowledge of array or camera parameters by treating them as containing an unknown statistical component that can be determined using the particle filter framework during tracking. We implement the algorithm in the context of a videoconferencing and meeting recording system. The system also performs high-level semantic analysis of the scene by keeping participant tracks, recognizing turn-taking events and recording an annotated transcript of the meeting. Experimental results are presented. Our system operates in real time and is shown to be robust and reliable.},
	keywords = {audio-visual tracking, Monte-Carlo algorithms, sensor fusion},
	isbn = {1110-8657},
	doi = {10.1155/S1110865702206058},
	url = {http://dx.doi.org/10.1155/S1110865702206058},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {18463,
	title = {An audio-video front-end for multimedia applications},
	booktitle = {2000 IEEE International Conference on Systems, Man, and Cybernetics},
	volume = {2},
	year = {2000},
	month = {2000///},
	pages = {786-791 vol.2 - 786-791 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications such as video gaming, virtual reality, multimodal user interfaces and videoconferencing, require systems that can locate and track persons in a room through a combination of visual and audio cues, enhance the sound that they produce, and perform identification. We describe the development of a particular multimodal sensor fusion system that is portable, runs in real time and achieves these objectives. The system employs novel algorithms for acoustical source location, video-based person tracking and overall system control, which are also described},
	keywords = {Acoustic noise, acoustical source location, Application software, audio cues, audio-video front-end, CAMERAS, Computer vision, Microphones, multimedia applications, multimedia systems, multimodal sensor fusion system, multimodal user interfaces, Position measurement, REAL TIME, Real time systems, real-time systems, sensor fusion, sound, Speech recognition, User interfaces, video cameras, video gaming, video-based person tracking, Videoconference, videoconferencing, Virtual reality, visual cues, Working environment noise},
	isbn = {0-7803-6583-6},
	doi = {10.1109/ICSMC.2000.885945},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S. and Haritaoglu,I.}
}
@conference {18464,
	title = {Smart videoconferencing},
	booktitle = {2000 IEEE International Conference on Multimedia and Expo, 2000. ICME 2000},
	volume = {3},
	year = {2000},
	month = {2000///},
	pages = {1597-1600 vol.3 - 1597-1600 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The combination of acoustical and video processing to achieve a smart audio and video feed from a set of N microphones and M cameras is a task that might conventionally be accomplished by camera persons and control room staff. However, in the context of videoconferencing, this process needs to be performed by control software. We discuss the use of a multi-camera multi-microphone set-up for unattended videoconferencing, and present details of a prototype implementation being developed},
	keywords = {acoustical processing, Automatic control, CAMERAS, computerised control, control software, Control systems, Intelligent sensors, Layout, Microphones, multi-camera multi-microphone set-up, multimedia systems, Protocols, prototype implementation, Prototypes, sensor fusion, smart videoconferencing, Switches, Teleconferencing, unattended videoconferencing, video processing},
	isbn = {0-7803-6536-4},
	doi = {10.1109/ICME.2000.871075},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Philomin,V. and Davis, Larry S.}
}
