Abstract-The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the distance prediction problem as matrix completion where unknown entries of an incomplete matrix of pairwise distances are to be predicted. The problem is solvable because strong correlations among network distances exist and cause the constructed distance matrix to be low rank. The new formulation circumvents the wellknown drawbacks of existing approaches based on Euclidean embedding.A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed to solve the network distance prediction problem. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach but also its usability in real Internet applications.

No abstract

The importance of team brand associations in sport management research is well documented, but the formation and stability of these associations has not been investigated. The current research tested the development, change, and predictive ability of brand associations over time. Longitudinal quantitative data were collected from consumers of a new Australian Football League (AFL) team (N = 169) at 3 points in time. One-sample t-tests revealed that brand associations had developed through marketing communications and the launch of the team before the team had played its first AFL game. Repeated-measures multivariate analysis of variance and latent growth modeling showed that brand associations changed over time, reflecting consumers’ experiences with the team. A cross-lagged panel model highlighted that brand associations influenced consumer loyalty in the future. Consequently, sport managers are provided with insights on the development of and change in brand associations that new consumers link with sport teams.

Abstract-Socially aware services often have a large user base and data of users have to be partitioned and replicated over multiple geographically distributed clouds. Choosing in which cloud to place data, however, is difficult. Effective data placements entail meeting multiple system objectives, including reducing the usage of cloud resources, providing good service quality to users, and even minimizing the carbon footprint, while facing critical challenges such as the interconnection of social data, the conflicting requirements of different objectives, and the customized multi-cloud data access policies.In this paper, we study multi-objective optimization for placing users' data over multiple clouds for socially aware services. We build a model framework that can accommodate a range of different objectives, and based on this model we formulate the optimization problem. Leveraging graph cuts, we propose an optimization approach that decomposes our original problem into two simpler subproblems and solves them alternately in multiple rounds. We carry out evaluations using a large group of realworld geographically distributed users with realistic interactions, and place users' data over 10 clouds all across the US. We demonstrate results that are significantly superior to standard and de facto methods in all objectives, and also show that our approach is capable of exploring trade-offs among objectives, converges fast and scales to a huge user base.

This paper presents a novel approach to tracking people in multiple cameras. A target is tracked not only in each camera but also in the ground plane by individual particle filters. These particle filters collaborate in two different ways. First, the particle filters in each camera pass messages to those in the ground plane where the multi-camera information is integrated by intersecting the targets' principal axes. This largely relaxes the dependence on precise foot positions when mapping targets from images to the ground plane using homographies. Secondly, the fusion results in the ground plane are then incorporated by each camera as boosted proposal functions. A mixture proposal function is composed for each tracker in a camera by combining an independent transition kernel and the boosted proposal function. Experiments show that our approach achieves more reliable results using less computational resources than conventional methods.

No abstract

No abstract

In large-scale networks, full-mesh active probing of end-toend performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers.In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection.