14083998
Towards CST-enhanced summarization
In this paper, we propose to enhance the process of automatic extractive multi-document text summarization by taking into account cross-document structural relationships as posited in Cross-document Structure Theory (CST). An arbitrary multidocument extract can be CST-enhanced by replacing low-salience sentences with other sentences that increase the total number of CST relationships included in the summary. We show that CST-enhanced summaries outperform their unmodified counterparts using the relative utility evaluation metric. We also show that the effect of a CST relationship on an extract depends on its type.
8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
1690812
Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that classifies CST relationships between sentence pairs extracted from topically related documents, exploiting both labeled and unlabeled data. We investigate a binary classifier for determining existence of structural relationships and a full classifier using the full taxonomy of relationships. We show that in both cases the exploitation of unlabeled data helps improve the performance of learned classifiers.
13300239
Mining Inter-Entity Semantic Relations Using Improved Transductive Learning
This paper studies the problem of mining relational data hidden in natural language text. In particular, it approaches the relation classification problem with the strategy of transductive learning. Different algorithms are presented and empirically evaluated on the ACE corpus. We show that transductive learners exploiting various lexical and syntactic features can achieve promising classification performance. More importantly, transductive learning performance can be significantly improved by using an induced similarity function.
11664234
Secure Name Service: A Framework for Protecting Critical Internet Resources
We propose a novel Secure Name Service (SNS) framework for protecting critical Internet resources from unauthorized accesses, denial of service (DoS) and other attacks. The key idea is to enforce packet-origin authentication through resource virtualization and utilize dynamic name binding for protecting servers under attacks and improving service availability. Different from static network-level security schemes such as IPsec and VPN, SNS is able to dynamically bind the names of critical resources at the service level, which allows us to actively protect the service resources through a distributed filtering mechanism built on authenticated packet forwarding paths. Our prototype implementation of authenticated packet forwarding components on Pentium 4 Linux machines demonstrates that regular Linux platforms are sufficient to support SNS authenticated packet forwarding on 100Mbps or lGbps LANs.
36195188
On the Three Threshold Policy in the Multi-Server Queueing System with Vacations
In this paper, we study an M/M/c queue with a three threshold vacation policy denoted by (e, d, N). With such a policy, the servers keep serving the customers until the number of idle servers reaches d and then e of d servers start taking a vacation together. These e servers keep taking vacations until the number of customers in the system is at least N at a vacation completion instant, then the e servers return to serve the queue again. Using the matrix analytic method, we obtain the stationary performance measures and prove the conditional stochastic decomposition properties for the waiting time and queue length. This model is a generalization of previous multi-server vacation models and offers a useful performance evaluation and system design tool in multi-task server queueing systems.
7151005
On-Line Optimal Wavelength Assignment in WDM Networks With Shared Wavelength Converter Pool
In this paper, we study on-line wavelength assignment in wavelength-routed WDM networks under both unicast and multicast traffic where nodes in the networks have wavelength conversion ability. Since wavelength converters are still expensive and difficult to implement, we consider the case where nodes in networks have only a limited number of converters that are shared by all input channels. We study the problem of setting up connections in such networks using minimum number of wavelength converters. For unicast traffic, we first study the problem of setting up a lightpath on a given link-path with minimum number of conversions. We give a simple algorithm that solves it in O(tk) time where t is the number of links on the path and k is the number of wavelengths per fiber, as compared to the best known existing method that needs to construct an auxiliary graph and apply the Dijkstra's algorithm. We also consider the problem of setting up a lightpath while using wavelength converters at nodes with fewer available converters only when necessary, and give an O(tk) time algorithm. We then generalize this technique to WDM networks with arbitrary topologies and give an algorithm that sets up an optimal lightpath network-wide in O(Nk+Lk) time, where N and L are the number of nodes and links in the network, respectively. We also consider multicast traffic in this paper. Finding an optimal multicast light-tree is known to be NP-hard and is usually solved by first finding a link-tree then finding a light-tree on the link-tree. Finding an optimal link-tree is also NP-hard and has been extensively studied. Thus, we focus on the second problem which is to set up a light-tree on a given link-tree with minimum number of conversions. We propose a new multicast conversion model with which the output of the wavelength converter is split-table to save the usage of converters. We show that under this model the problem of setting up an optimal light-tree is NP-hard and then give efficient heuristics to solve it approximately
12558561
Statistical Analysis of Generalized Processor Sharing Scheduling Discipline
In this paper, we consider the problem of providing statistical guarantees (for example, on the tail distribution of delay) under the Generalized Processor Sharing (GPS) scheduling discipline. This work is motivated by, and is an extension of, Parekh and Gallager's deterministic study of GPS scheduling discipline with leaky-bucket token controlled sessions [PG93a,b, Parekh92]. Using the exponentially bounded burstiness (E.B.B.) process model introduced in [YaSi93a] as a source traffic characterization, we establish results that extend the deterministic study of GPS: for a single GPS server in isolation, we present statistical bounds on the tail distributions of backlog and delay for each session. In the network setting, we show that networks belonging to a broad class of GPS assignments, the so-called Consistent Relative Session Treatment (CRST) GPS assignments, are stable in a stochastic sense. In particular, we establish simple bounds on the tail distribution of backlog and delay for each session in a Rate Proportional Processor Sharing (RPPS) GPS network with arbitrary topology.
28540558
A Serving Agent for Integrating Soft Computing and Software Agents
This paper discusses the design and implementation of a serving agent for integrating soft computing and software agents.