Facets of the Fully Mixed Nash Equilibrium Conjecture

In this work, we continue the study of the many facets of the Fully Mixed Nash Equilibrium Conjecture, henceforth abbreviated as the FMNE\mathsf{FMNE} Conjecture, in selfish routing for the special case of n identical users over two (identical) parallel links. We introduce a new measure of Social Cost, defined as the expectation of the square of the maximum congestion on a link; we call it Quadratic Maximum Social Cost. A Nash equilibrium is a stable state where no user can improve her (expected) latency by switching her mixed strategy; a worst-case Nash equilibrium is one that maximizes Quadratic Maximum Social Cost. In the fully mixed Nash equilibrium, all mixed strategies achieve full support.     

An empirical examination of the sustainability of social bookmarking websites

Given the continuous growth of social bookmarking sites, we investigated the reasons behind their sustainability as online social structures. Bookmarking sites offer users the option to post and tag web resources privately or to make them publicly available to other users of the sites. A prevalence of private tagging would threaten the sustainability of the social aspects at these sites. Using activity data from a sample of 1,000 users of Simpy, a popular social bookmarking site, we found that the majority of users contribute most of their tagged resources to the public repository, despite the option to keep them private. We also found that in social bookmarking sites there is a minority of extremely prolific contributors who are responsible for a large portion of public tagged resources. Finally, we found that new users make larger proportions of their contributions public than existing users and that there is a larger number of regular contributors to the site than irregular ones. All of these findings are consistent with the mechanisms predicted by a theoretical model of sustainable online social structures.     

Modeling Content for Semantic-Level Querying of Multimedia

Many semantic content-based models have been developed for modeling video and audio in order to enable information retrieval based on semantic content. The level of querying of the media depends upon the semantic aspects modeled. This paper proposes a semantic content-based model for semantic-level querying that makes full use of the explicit media structure, objects, spatial relationships between objects, events and actions involving objects, temporal relationships between events and actions, and integration between syntactic and semantic information.     

DCAF: An MPEG-21 Dynamic Content Adaptation Framework

Universal Multimedia Access aims at providing a gratifying end user-experience by either adapting the content, be it static or dynamic, to suit the usage environment or adapting the usage environment, be it client- or server-centric, to suit content. This paper presents our MPEG-21 Dynamic Content Adaptation Framework, acronym DCAF, which uses a fusion of Genetic Algorithms and Strength Pareto Optimality to adapt content in order to suit the usage environment.

A bayesian approach to deformed pattern matching of iris images

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: it normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications     

Statistical performance evaluation of biometric authentication systems using random effects models

As biometric authentication systems become more prevalent, it is becoming increasingly important to evaluate their performance. This paper introduces a novel statistical method of performance evaluation for these systems. Given a database of authentication results from an existing system, the method uses a hierarchical random effects model, along with Bayesian inference techniques yielding posterior predictive distributions, to predict performance in terms of error rates using various explanatory variables. By incorporating explanatory variables as well as random effects, the method allows for prediction of error rates when the authentication system is applied to potentially larger and/or different groups of subjects than those originally documented in the database. We also extend the model to allow for prediction of the probability of a false alarm on a "watch-list" as a function of the list size. We consider application of our methodology to three different face authentication systems: a filter-based system, a Gaussian mixture model (GMM)-based system, and a system based on frequency domain representation of facial asymmetry     

Approximate Equilibria and Ball Fusion

We consider selfish routing over a network consisting of m parallel links through which $n$ selfish users route their traffic trying to minimize their own expected latency. We study the class of mixed strategies in which the expected latency through each link is at most a constant multiple of the optimum maximum latency had global regulation been available. For the case of uniform links it is known that all Nash equilibria belong to this class of strategies. We are interested in bounding the coordination ratio (or price of anarchy) of these strategies defined as the worst-case ratio of the maximum (over all links) expected latency over the optimum maximum latency. The load balancing aspect of the problem immediately implies a lower bound Ω(ln m ln ln m) of the coordination ratio. We give a tight (up to a multiplicative constant) upper bound. To show the upper bound, we analyze a variant of the classical balls and bins problem, in which balls with arbitrary weights are placed into bins according to arbitrary probability distributions. At the heart of our approach is a new probabilistic tool that we call ball fusion; this tool is used to reduce the variant of the problem where balls bear weights to the classical version (with no weights). Ball fusion applies to more general settings such as links with arbitrary capacities and other latency functions.     

Performance updating of concrete bridges using proactive health monitoring methods

Uncertainties associated with modelling of deteriorating bridges strongly affect management decisions, such as inspection, maintenance and repair actions. These uncertainties can be reduced by the effective use of health monitoring systems, through which information regarding in situ performance can be incorporated in the management of bridges.The objectives of this paper are twofold; first, an improved chloride induced deterioration model for concrete bridges is proposed that can quantify degradation in performance soon after chlorides are deposited on the bridge, rather than when initiation of corrosion at the reinforcement level takes place. As a result, the implications of introducing proactive health monitoring can be assessed using probabilistic durability criteria. Thus, the second objective of the paper is to present a methodology for performance updating of deteriorating concrete bridges fitted with a proactive health monitoring system.This methodology is illustrated via a simple example of a typical bridge element, such as a beam or a part of a slab. The results highlight the benefits from introducing ‘smart’ technology in managing bridges subject to deterioration, and quantify the reduction in uncertainties and their subsequent effect on predictions of future bridge performance.     

Towards concept anchoring for cognitive robots

We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.