Information Sciences Institute

About: Information Sciences Institute is a based out in . It is known for research contribution in the topics: Machine translation & The Internet. The organization has 918 authors who have published 2067 publications receiving 114614 citations. The organization is also known as: ISI & USC-ISI.

ROUGE: A Package for Automatic Evaluation of Summaries

Abstract: ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST.

A systematic comparison of various statistical alignment models

Abstract: We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer (1993), the hidden Markov alignment model, smoothing techniques, and refinements. These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignment models. As evaluation criterion, we use the quality of the resulting Viterbi alignment compared to a manually produced reference alignment. We evaluate the models on the German-English Verbmobil task and the French-English Hansards task. We perform a detailed analysis of various design decisions of our statistical alignment system and evaluate these on training corpora of various sizes. An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models. In the Appendix, we present an efficient training algorithm for the alignment models presented.

Globus: a Metacomputing Infrastructure Toolkit

Abstract: The Globus system is intended to achieve a vertically integrated treatment of application, middleware, and net work. A low-level toolkit provides basic mechanisms such as communication, authentication, network information, and data access. These mechanisms are used to con struct various higher level metacomputing services, such as parallel programming tools and schedulers. The long- term goal is to build an adaptive wide area resource environment AWARE, an integrated set of higher level services that enable applications to adapt to heteroge neous and dynamically changing metacomputing environ ments. Preliminary versions of Globus components were deployed successfully as part of the I-WAY networking experiment.

Next century challenges: scalable coordination in sensor networks

Abstract: Networked sensors-those that coordinate amongst themselves to achieve a larger sensing task-will revolutionize information gathering and processing both in urban environments and in inhospitable terrain. The sheer numbers of these sensors and the expected dynamics in these environments present unique challenges in the design of unattended autonomous sensor networks. These challenges lead us to hypothesize that sensor network coordination applications may need to be structured differently from traditional network applications. In particular, we believe that localized algorithms (in which simple local node behavior achieves a desired global objective) may be necessary for sensor network coordination. In this paper, we describe localized algorithms, and then discuss directed diffusion, a simple communication model for describing localized algorithms.

Geography-informed Energy Conservation for Ad Hoc Routing

Abstract: We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and turning off unnecessary nodes, keeping a constant level of routing fidelity. GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol. Moreover, simulations of GAF suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern). More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity.