16700418
Identity Delegation in Policy Based Systems
Policy based systems have received considerable attention in the recent past from academia as well as the industry. Research on policy based systems encompasses a gamut of areas such as: models and languages for policy based systems, policy standards, domain specific implementations, policy tools etc. However an important issue, which did not receive much attention from researchers, is that of access control for policy execution. In this paper we present the concept of "identity delegation" which involves finding the 'correct' users/ identities, to whom task of policy execution can be delegated. Policies are generally defined by high level business executives (policy authors) and are implemented by policy enforcers who have sufficient access rights on the underlying systems. Given the increasing complexity of enterprise systems, we show in this paper that finding the right policy enforcers for a policy can be a fairly non-trivial task. We address this important problem by proposing a unique concept of 'implicit identity delegation', whereby an autonomic system automatically figures out the correct policy enforcers and implicitly delegates the task of policy execution. We present the Implicit Identity Delegation architecture which boasts of an efficient technique for performing implicit identity delegation and uses a plugin based architecture ensuring its applicability and use in diverse domains.
54110472
Fast and Accurate Text Classification via Multiple Linear Discriminant Projections
Publisher Summary 
Text classification is a standard problem in information retrieval (IR). A learner is presented with training documents, each labeled as containing or not containing material relevant to a given topic. Support vector machines (SVMs) have shown superb performance for text classification tasks. They are accurate, robust, and quick to apply to test instances. Their only potential drawback is their training time and memory requirement. SVMs have been trained on data sets with several thousand instances, but Web directories today contain millions of instances that are valuable for mapping billions of Web pages into Yahoo!-like directories. This chapter presents Simple Iterative Multiple Projection on Lines (SIMPL), a nearly linear-time classification algorithm that mimics the strengths of SVMs while avoiding the training bottleneck. It uses Fisher's linear discriminant, a classical tool from statistical pattern recognition, to project training instances to a carefully selected low-dimensional subspace before inducing a decision tree on the projected instances. SIMPL uses efficient sequential scans and sorts, and is comparable in speed and memory scalability to widely used naive Bayes (NB) classifiers, but it beats NB accuracy decisively.
14820946
A hierarchical monothetic document clustering algorithm for summarization and browsing search results
Organizing Web search results into a hierarchy of topics and sub-topics facilitates browsing the collection and locating results of interest. In this paper, we propose a new hierarchical monothetic clustering algorithm to build a topic hierarchy for a collection of search results retrieved in response to a query. At every level of the hierarchy, the new algorithm progressively identifies topics in a way that maximizes the coverage while maintaining distinctiveness of the topics. We refer the proposed algorithm to as DisCover. Evaluating the quality of a topic hierarchy is a non-trivial task, the ultimate test being user judgment. We use several objective measures such as coverage and reach time for an empirical comparison of the proposed algorithm with two other monothetic clustering algorithms to demonstrate its superiority. Even though our algorithm is slightly more computationally intensive than one of the algorithms, it generates better hierarchies. Our user studies also show that the proposed algorithm is superior to the other algorithms as a summarizing and browsing tool.
13205673
Automatic Generation of Domain Models for Call-Centers from Noisy Transcriptions
Call centers handle customer queries from various domains such as computer sales and support, mobile phones, car rental, etc. Each such domain generally has a domain model which is essential to handle customer complaints. These models contain common problem categories, typical customer issues and their solutions, greeting styles. Currently these models are manually created over time. Towards this, we propose an unsupervised technique to generate domain models automatically from call transcriptions. We use a state of the art Automatic Speech Recognition system to transcribe the calls between agents and customers, which still results in high word error rates (40%) and show that even from these noisy transcriptions of calls we can automatically build a domain model. The domain model is comprised of primarily a topic taxonomy where every node is characterized by topic(s), typical Questions-Answers (Q&As), typical actions and call statistics. We show how such a domain model can be used for topic identification of unseen calls. We also propose applications for aiding agents while handling calls and for agent monitoring based on the domain model.
27759262
Automatic categorization of web sites based on source types
An important issue with the Web is verification of the accuracy, currency and authenticity of the information associated with Web sites. One way to address this problem is to identify the "source" or "sponsor" of the Web site. However, source identification is non-trivial because the source of a Web site cannot always be determined by the URL or content of the site. In this paper, we propose a method for source identification that uses various types of inbound, outbound and internal interactions that arise due to hyperlinks between and within Web sites.
2324494
Cost-Performance Evaluation of SMP Clusters
Clusters of Personal Computers have been proposed as potential replacements for expensive compute servers. One limitation in the overall performance is the interconnection network. A possible solution is to use multiple processors on each node of the PC cluster. Parallel programs can then use the fast shared memory to exchange data within a node, and access the interconnection network to communicate across multiple nodes. The performance of such a system is likely to be influenced by the runtime support system, whether one uses a standard message passing library or a distributed shared memory system to simulate a large shared memory machine. This paper presents a cost-performance evaluation of a cluster of eight dual processor PCs and a ten processor SMP workstation using two benchmark suites. We evaluate the effect of using gigabit ethernet against fast ethernet, single processor PCs versus SMP nodes, and compare the overall performance of the PC cluster to the SMP workstation. It is found that the SMP machine provides the best performance in only 50 % of the programs examined. The PC cluster with a Gigabit network is better in most of the other cases, but the improvement over FastEthernet is marginal.
9104858
Moment-Linear Stochastic Systems
We introduce a class of quasi-linear models for stochastic dynamics, called moment-linear stochastic systems (MLSS). We formulate MLSS and analyze their dynamics, as well as discussing common stochastic models that can be represented as MLSS. Further studies, including development of optimal estimators and controllers, are summarized. We discuss the reformulation of a common stochastic hybrid system—the Markovian jump- linear system (MJLS)—as an MLSS, and show that the MLSS formulation can be used to develop some new analyses for MJLS. Finally, we briefly discuss the use of MLSS in modeling certain stochastic network dynamics. Our studies suggest that MLSS hold promise in providing a framework for modeling interesting stochastic dynamics in a tractable manner.
9572409
High-Throughput Multicast Routing Metrics in Wireless Mesh Networks
The stationary nature of nodes in a mesh network has shifted the main design goal of routing protocols from maintaining connectivity between source and destination nodes to finding high-throughput paths between them. In recent years, numerous link-quality-based routing metrics have been proposed for choosing high-throughput paths for unicast protocols. In this paper we study routing metrics for high-throughput tree or mesh construction in multicast protocols. We show that there is a fundamental difference between unicast and multicast routing in how data packets are transmitted at the link layer, and accordingly there is a difference in how the routing metrics for each of these primitives are designed. We adapt certain routing metrics for unicast for high-throughput multicast routing and propose news ones not previously used for high-throughput. We then study the performance improvement achieved by using different link-quality-based routing metrics via extensive simulation and experiments on a mesh network testbed, using ODMRP as a representative multicast protocol. Our testbed experiment results show that ODMRP enhanced with linkquality routing metrics can achieve up to 17.5% throughput improvement as compared to the original ODMRP.
9598402
Time-space tradeoffs in the counting hierarchy
Extends the lower-bound techniques of L. Fortnow (2000) to the unbounded-error probabilistic model. A key step in the argument is a generalization of V.A. Nepomnjas/spl caron/c/spl caron/ii/spl breve/'s (1970) theorem from the Boolean setting to the arithmetic setting. This generalization is made possible due to the recent discovery of logspace-uniform TC/sup 0/ circuits for iterated multiplication (A. Chiu et al., 2000). As an example of the sort of lower bounds that we obtain, we show that MAJ-MAJSAT is not contained in PrTiSp(n/sup 1+o(1)/, n/sup /spl epsiv//) for any /spl epsiv/<1. We also extend one of Fortnow's lower bounds, from showing that S~A~T~ does not have uniform NC/sup 1/ circuits of size n/sup 1+o(1)/, to a similar result for SAC/sup 1/ circuits.
1879700
Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker
We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set.