AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

A memory insensitive technique for large model simplification

Abstract: The authors propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of P. Lindstrom (2000) which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems. The original OoCS algorithm has memory complexity that depends on the size of the output mesh, but no dependency on the size of the input mesh. That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm, the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses. Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.

User evaluation: Synthetic talking faces for interactive services

Abstract: Computer simulation of human faces has been an active research area for a long time. However, it is less clear what the applications of facial animation (FA) will be. We have undertaken experiments on 190 subjects in order to explore the benefits of FA. Part of the experiment was aimed at exploring the objective benefits, i.e., to see if FA can help users to perform certain tasks better. The other part of the experiment was aimed at subjective benefits. At the same time comparison of different FA techniques was undertaken. We present the experiment design and the results. The results show that FA aids users in understanding spoken text in noisy conditions; that it can effectively make waiting times more acceptable to the user; and that it makes services more attractive to the users, particularly when they compare directly the same service with or without the FA.

Mobile code security

Abstract: Sandboxes, code signing, firewalls, and proof carrying code are all techniques that address the inherent security risks of mobile code. The article summarizes the relative merits of each. It is concluded that each of these techniques offers something different, and the best approach is probably a combination of security mechanisms. The sandbox and code signing approaches are already being hybridized. Combining these with firewalling techniques such as the playground gives an extra layer of security. The PCC approach is not yet ready for prime time, but the ability to prove safety properties of code is an important element in the arsenal available for securing mobile code. None of the techniques can do much to protect users from social engineering attacks, where a user is somehow fooled into revealing something they shouldn't reveal. For example, JavaScript can be employed in a way that fools a user into revealing passwords to a remote server. Java applets could be used to do this as well, even under the strictest security policy. User education is the only way to combat mobile code attacks that are based on social engineering.

Approximating a data stream for querying and estimation: algorithms and performance evaluation

Abstract: Obtaining fast and good-quality approximations to data distributions is a problem of central interest to database management. A variety of popular database applications, including approximate querying, similarity searching and data mining in most application domains, rely on such good-quality approximations. Histogram-based approximation is a very popular method in database theory and practice to succinctly represent a data distribution in a space-efficient manner. In this paper, we place the problem of histogram construction into perspective and we generalize it by raising the requirement of a finite data set and/or known data set size. We consider the case of an infinite data set in which data arrive continuously, forming an infinite data stream. In this context, we present single-pass algorithms that are capable of constructing histograms of provable good quality. We present algorithms for the fixed-window variant of the basic histogram construction problem, supporting incremental maintenance of the histograms. The proposed algorithms trade accuracy for speed and allow for a graceful tradeoff between the two, based on application requirements. In the case of approximate queries on infinite data streams, we present a detailed experimental evaluation comparing our algorithms with other applicable techniques using real data sets, demonstrating the superiority of our proposal.

Deploying sensor networks with guaranteed fault tolerance

Abstract: We consider the problem of deploying or repairing a sensor network to guarantee a specified level of multipath connectivity (k-connectivity) between all nodes. Such a guarantee simultaneously provides fault tolerance against node failures and high overall network capacity (by the max-flow min-cut theorem). We design and analyze the first algorithms that place an almost-minimum number of additional sensors to augment an existing network into a k -connected network, for any desired parameter k . Our algorithms have provable guarantees on the quality of the solution. Specifically, we prove that the number of additional sensors is within a constant factor of the absolute minimum, for any fixed k . We have implemented greedy and distributed versions of this algorithm, and demonstrate in simulation that they produce high-quality placements for the additional sensors.