62621279
Estimating Internet Users' Demand Characteristics
We investigate two parametric approaches and one non-parametric approach toestimating Internet users' value-of-time, an important characteristic ofdemand for Internet services. The advantages of these approaches are madeclear and their limitations discussed. The models are tested with datagenerated from our similation model of the Internet economy. Given thecharacteristics of the data, we investigate parametric count-data modelsfirst. While reasonably good results are obtained on all medium- andlarge-sized jobs, the method fails on small-sized ones. Second, we develop anonparametric quasi-Bayesian update algorithm for retrieving the underlyingdistribution function of Internet users' value-of-time purely fromobservations on their choices. Compared with the parametric count-data models,good results are obtained in every case.
9280619
Ethical, Legal and Economic Issues in the Digital Economy: Intellectual Property Rights, Piracy, Trust, Security and Privacy: Minitrack Introduction
5665849
Streamlining the Digital Economy: How to Avert a Tragedy of the Commons
To achieve its unique potential in a digital economy, the Internet must be able to guarantee a quality of service. Solving this problem requires the integration of economic principles and engineering design. The authors describe their work on a control mechanism that is decentralized and computationally feasible.
2140816
Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions
Many auctions involve selling several distinct items simultaneously, where bidders can bid on the whole or any part of the lot. Such auctions are referred to as combinatorial auctions. Examples of such auctions include truck delivery routes, industrial procurement, and FCC spectrum. Determining winners in such auctions is an NP-hard problem, and significant research is being conducted in this area. However, multiple-round (iterative) combinatorial auctions present significant challenges in bid formulations as well. Because the combinatorial dynamics in iterative auctions can make a given bid part of a winning and nonwinning set of bids without any changes in the bid, bidders are usually not able to evaluate whether they should revise their bid at a given point in time or not. Therefore, in this paper we address various computational problems that are relevant from the bidder's perspective. In particular, we introduce two bid evaluation metrics that can be used by bidders to determine whether any given bid can be a part of the winning allocation and explore their theoretical properties. Based on these metrics, we also develop efficient data structures and algorithms that provide comprehensive information about the current state of the auction atany time, which can help bidders in evaluating their bids and bidding strategies. Our approach uses exponential memory storage but provides fast incremental update for new bids, thereby facilitating bidder support for real-time iterative combinatorial auctions.
11409534
Risk profile and consumer shopping behavior in electronic and traditional channels
This paper develops an economic model that captures consumer shopping channel choices based on shopping channel characteristics and consumer risk profiles--risk-neutral or risk-averse. Analyses of results show that after making purchases through one channel, electronic or traditional, risk-averse consumers tend to be more loyal customers than risk-neutral consumers. Further, the two types of consumers may exhibit split channel behavior--risk-neutral consumers prefer one channel and risk-averse consumers prefer the other. However, risk-neutral consumers are not always more likely to prefer electronic channel than risk-averse consumers. Implications for retailer pricing strategies are discussed.
17889083
Managing computing resources in active intranets
The objective of this paper is to present an economic pricing-based resource management technique for Intranets that has the capability of managing Intranet resources from an organizational perspective. We discuss the adoption of this pricing scheme at three stages: application level, node level, and data-stream level implementations. These three levels demonstrate how a pricing based approach can be used with the current technology and also be migrated to evolving network architectures such as active networks.
17746198
Large—scale parallel numerical integration
We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subregion level, which enables handling local integration difficulties within individual problems. This results in a new, hierarchical algorithm which incorporates load balancing on the integral level and on the subregion level. We report test results of the software and show that the hierarchical approach leads to a scalable integration algorithm.
15213964
Performance Indicators in a 802.11 WLAN Deployment
Organizations and Institutions across the world are increasingly deploying 802.11 based WLAN (in short Wi-Fi) to have mobility benefits in their operations. However, due to the nature of 802.11 technology, there are more number of challenges for an administrator in ensuring the optimum performance levels for the deployed WLAN compared to a Wired Network. Degraded performance leads to connectivity problems and low data throughput for wireless clients using the Wi-Fi network. In the presented work, we have identified a set of indicators whose values at any point of time signals a possible degradation in WLAN performance. Each of the identified indicators is explained and the behavior of same is illustrated using an example with respect to presence or absence of performance anomalies in a WLAN deployment.
63054527
Volumetric segmentation of range images of 3D objects using superquadric models
Abstract The problem of part definition, description, and decomposition is central to the shape recognition systems. We present a geometric model-driven framework for segmenting dense range data of complex 3D objects into their constituent parts in terms of surface (biquadrics) and volumetric (superquadrics) primitives, without a priori domain knowledge or stored models. Surface segmentation uses a novel local-to-global iterative regression approach of searching for the best piecewise biquadric description of the data. The region adjacency information, surface discontinuities, and global shape properties are extracted from biquadrics to guide the volumetric segmentation. Superquadric models are recovered by a global-to-local residual-driven procedure, which recursively segments the scene to derive the part-structure. A set of acceptance criteria provide the objective evaluation of intermediate descriptions and decide whether to terminate the procedure, or selectively refine the segmentation. The control module generates hypotheses about superquadric models at clusters of underestimated data and performs controlled extrapolation of part-models by shrinking the global model. Results are presented for real range images of varying complexity, including objects with occluding parts, and scenes where surface segmentation is not sufficient to guide the volumetric segmentation.
5097328
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images. >
62172477
A unified framework for uncertainty propagation in automatic shape tracking
Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences [22]. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initialization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations.