683657
An adaptive social network for information access: Theoretical and experimental results
We consider a social network of software agents who assist each other in helping their users find information. Unlike in most previous approaches, our architecture is fully distributed and includes agents who preserve the privacy and autonomy of their users. These agents learn models of each other in terms of expertise (ability to produce correct domain answers) and sociability (ability to produce accurate referrals). We study our framework experimentally to study how the social network evolves. Specifically, we find that under our multi-agent learning heuristic, the quality of the network improves with interactions: the quality is maximized when both expertise and sociability are considered; pivot agents further improve the quality of the network and have a catalytic effect on its quality even if they are ultimately removed. Moreover, the quality of the network improves when clustering decreases, reflecting the intuition that you need to talk to people outside your close circle to get the best information.
8426940
A dynamic pricing mechanism for P2P referral systems
Most existing research on peer-to-peer systems focuses on protocol design. In this paper, we consider the issue of free riding in peer-to-peer referral systems. Free riders are agents that refuse either to answer a query or to give referrals. Free riding is detrimental to the system, since it may prevent requesters from finding high quality providers efficiently. To mitigate the issue of free riding, we propose a dynamic pricing mechanism to motivate the agents to behave rationally. Service providers learn appropriate prices of referrals and answers in order to maximize their payoffs through stochastic iterative learning algorithms.
10885412
Managing the pedigree and quality of information in dynamic information sharing environments
The quality of information is crucial for decision making in many mission-critical applications such as battlefield operations and intelligence analysis. However, as the system becomes larger and more diverse, it is becoming increasingly difficult to assess the quality of information from various operators or data sources. In this paper we propose an agent-based approach to managing the quality of information, e.g., its trustworthiness, in network centric information sharing environments, where software agents collaborate with each other to automatically represent and assess the trustworthiness of information from its pedigree within the framework of Dempster-Shafer theory.
5934185
Trust and reputation management in a small-world network
Successful commerce relies heavily upon the reputations that the different parties acquire through their dealings with each other. We view an e-commerce community as a social network, which supports reputations both for expertise (providing good service) and helpfulness (providing good referrals). We study the small-world phenomena such as the emergence of subcommunities, and pivot vertices (which link different subcommunities) in the social network, and discovered that the quality of the network improves with the presence of a pivot.
11424736
Extending the ONESAF Testbed into a C4ISR Testbed
This article describes how the modeling and simulation environment of the OneSAF Testbed Baseline (OTB) v1.0 has been extended to enable the testing of heterogeneous algorithms that are being designed for real-world C4ISR applications. This has been accomplished by building an architecture that extends functional and logical components of the OTB system in the following ways: the use of the OTB Compact Terrain Database for terrain analysis and preliminary threat assessment, the addition of the RETSINA-OTB Bridge for the real-time query and control of OTB entities, and the addition of new DIS-based sensor entities for interoperation with Command and Control algorithms, to name a few. This article illustrates how to make a few small but general extensions to a modeling and simulation system to create a larger testbed system with minimum impact on the native system and with great potential for the range of applications that can exploit it.
205431881
An integrated approach to high-level information fusion
In today's fast paced military operational environment, vast amounts of information must be sorted out and fused not only to allow commanders to make situation assessments, but also to support the generation of hypotheses about enemy force disposition and enemy intent. Current information fusion technology has the following two limitations. First, current approaches do not consider the battlefield context as a first class entity. In contrast, we consider situational context in terms of terrain analysis and inference. Second, there are no integrated and implemented models of the high-level fusion process. This paper describes the HiLIFE (High-Level Information Fusion Environment) computational framework for seamless integration of high levels of fusion (levels 2, 3 and 4). The crucial components of HiLIFE that we present in this paper are: (1) multi-sensor fusion algorithms and their performance results that operate in heterogeneous sensor networks to determine not only single targets but also force aggregates, (2) computational approaches for terrain-based analysis and inference that automatically combine low-level terrain features (such as forested areas, rivers, etc.) and additional information, such as weather, and transforms them into high-level militarily relevant abstractions, such as NO-GO, SLOW-GO areas, avenues of approach, and engagement areas, (3) a model for inferring adversary intent by mapping sensor readings of opponent forces to possible opponent goals and actions, and (4) sensor management for positioning intelligence collection assets for further data acquisition. The HiLIFE framework closes the loop on information fusion by specifying how the different components can computationally work together in a coherent system. Furthermore, the framework is inspired by a military process, the Intelligence Preparation of the Battlefield, that grounds the framework in practice. HiLIFE is integrated with a distributed military simulation system, OTBSAF, and the RETSINA multi-agent infrastructure to provide agile and sophisticated reasoning. In addition, the paper presents validation results of the automated terrain analysis that were obtained through experiments using military intelligence Subject Matter Experts (SMEs).
37838547
Automatic understanding of symbol-connected diagrams
A symbol-connected diagram is mainly composed of symbols and their connections. All symbols can be categorized into two groups: shape symbols and structure symbols. This paper proposes a system for automatic understanding of symbol-connected diagrams based on a graph, called the consistent attributed graph (CAG), which represents the diagram in a bottom-up fashion. The shape symbol processing is carried out by means of shape processing, followed by shape symbol identification with the use of two symbol windows in the CAG. Some experimental results for understanding logic circuit diagrams are given.
11758
A multi-stream adaptation framework for bandwidth management in 3D tele-immersion
Tele-immersive environments will improve the state of collaboration among distributed participants. However, along with the promise a new set of challenges have emerged including the real-time acquisition, streaming and rendering of 3D scenes to convey a realistic sense of immersive spaces. Unlike 2D video conferencing, a 3D tele-immersive environment employs multiple 3D cameras to cover a much wider field of view, thus generating a very large volume of data that need to be carefully coordinated, organized, and synchronized for Internet transmission, rendering and display. This is a challenging task and a dynamic bandwidth management must be in place. To achieve this goal, we propose a multi-stream adaptation framework for bandwidth management in 3D tele-immersion. The adaptation framework relies on the hierarchy of mechanisms and services that exploits the semantic link of multiple 3D video streams in the tele-immersive environment. We implement a prototype of the framework that integrates semantic stream selection, content adaptation, and 3D data compression services with user preference. The experimental results have demonstrated that the framework shows a good quality of the resulting composite 3D rendered video in case of sufficient bandwidth, while it adapts individual 3D video streams in a coordinated and user-friendly fashion, and yields graceful quality degradation in case of low bandwidth availability.
46138594
Document Representation and Its Application to Page Decomposition
Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.
2105049
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE
We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of thel1-regularized maximum likelihood estimator in the high-dimensional setting, where the number of nodes in the graph p, the number of edges in the graph s and the maximum node degree d, are allowed to grow as a function of the number of samples n. Our main result provides sufficient conditions on (n, p, d) for thel1-regularized MLE estimator to recover all the edges of the graph with high probability. Under some conditions on the model covariance, we show that model selection can be achieved for sample sizes n = Ω(d log(p)), with the error decaying as O(exp(−c log(p))) for some constant c. We illustrate our theoretical results via simulations and show good correspondences between the theoretical predictions and behavior in simulations.
7151675
Data spectroscopy: learning mixture models using eigenspaces of convolution operators
In this paper we develop a spectral framework for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often used for the identification of substances through their spectrum. Treating a kernel function K(x, y) as "light" and the sampled data as "substance", the spectrum of their interaction (eigenvalues and eigenvectors of the kernel matrix K) unveils certain aspects of the underlying parametric distribution p, such as the parameters of a Gaussian mixture. Our approach extends the intuitions and analyses underlying the existing spectral techniques, such as spectral clustering and Kernel Principal Components Analysis (KPCA).
 We construct algorithms to estimate parameters of Gaussian mixture models, including the number of mixture components, their means and covariance matrices, which are important in many practical applications. We provide a theoretical framework and show encouraging experimental results.