183890
A Probabilistic Network of Predicates
Bayesian networks are directed acyclic graphs representing independence relationships among a set of random variables. A random variable can be regarded as a set of exhaustive and mutually exclusive propositions. We argue that there are several drawbacks resulting from the propositional nature and acyclic structure of Bayesian networks. 
 
To remedy these shortcomings, we propose a probabilistic network where nodes represent unary predicates and which may contain directed cycles. The proposed representation allows us to represent domain knowledge in a single static network even though we cannot determine the instantiations of the predicates before hand. The ability to deal with cycles also enables us to handle cyclic causal tendencies and to recognize recursive plans.
6491908
A Dependency-based Method for Evaluating Broad-Coverage Parsers
With the emergence of broad-coverage parsers, quantitative evaluation of parsers becomes increasingly more important. We propose a dependency-based method for evaluating broad-coverage parsers. The method offers several advantages over previous methods that are based on phrase boundaries The error count score. We propose here is not only more intuitively meaningful than other scores, but also more relevant to semantic interpretation. We will also present an algorithm for transforming constituency trees into dependency trees so that the evaluation method is applicable to both dependency and constituency grammars. Finally, we discuss a set of operations for modifying dependency trees that can be used lo eliminate inconsequential differences among different parse trees and allow us to selectively evaluate different aspects of a parser.
1394805
Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity
Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.
15698938
Automatic Retrieval and Clustering of Similar Words
Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.
15442120
Digital City Shanghai: Concepts, Foundations, and Current State
The fastest developing city in China is Shanghai; its annual growth rate (GDP) exceeded 11.9% average in every year from 1992 and GDP per person in 2003 reached 4909 USD. Economists, industries and the government have been considering how to keep Shanghai developing in a sustainable manner. In 1994, the Shanghai Municipal Government put forward the concept of information harbor and set the goal of establishing a series of information infrastructures in the following five years. The Shanghai city informatization project started in 1999, and by the end of 2001 it had spread across Shanghai. At the same time, some applications based on Internet technologies were designed and developed. In the beginning of 2002, based on the current information infrastructures and systems, Shanghai Municipal Government announced the Digital City Shanghai strategy. This chapter will introduce the background of the Digital City Shanghai project, the current state of the information infrastructure in Shanghai, and the concept of system design & user interface. The Digital City Shanghai project will be described in four parts: Spatial Data Infrastructure, City Informatization, Shanghai City Grid, and Shanghai Logistics Information Platform. We will also introduce some research projects. In addition to the strategy, we describe and discuss social perspective and problems.
32808871
Trajectory production with the adaptive time-delay neural network
Abstract The adaptive time-delay neural network (ATNN), a paradigm for training a nonlinear neural network with adaptive time delays, has a rich repertoire of capabilities that are characterized in this paper. This network, in which both time delays and weights are adapted, is used to generate circular and figure-eight trajectories, to perform signal production, and to learn repetitive spatial motions. Spatiotemporal signal production has the property that initial segments of signals that contain large amounts of noise can be “cleaned up” to result in trained trajectory motions. Spatiotemporal features are learned as opposed to a point-by-point memorization of the trajectory. Closed-loop, repetitive trajectories that are learned result from training the network to produce attractors consistent with those trajectories. Widely varying starting motions will result in the network being attracted to the repetitive attractor for which it is trained. The network also displays training position invariance, and noise removal when only part of the pattern is trained. Sampling rate effects versus training speed and performance were measured. A comparison between the ATNN and related networks (BP and the TDNN) is reviewed in an example with successful time series prediction of a chaotic series.
2261558
XACML Function Annotations
XACML is being increasingly adopted in large enterprise systems for specifying access control policies. However, the efficient analysis and integration of multiple policies in such large distributed systems still remains a difficult task. In this paper, we propose an annotation technique which is a simple extension to XACML, and may greatly benefit the policy analysis process. We also discuss an important consistency problem during XACML policy translation and point out a few possible research directions.
22202534
Subspace-based active user identification for a collision-free slotted ad hoc network
We propose a novel spreading code scheme, transmitter-receiver-based code, for wireless ad hoc networks. The design facilitates collision resolution using multiuser detection at each node, and is more bandwidth efficient than creating orthogonal channels in time or frequency. A subspace-based receiver structure is introduced, which identifies users of interest, or "active" users, with minimal prior information on the spreading code ensemble. A subspace-based blind multiuser detector can then be implemented to suppress multiaccess interference. The performance of the proposed active user identifier is studied by investigating its false alarm rate P/sub f/ and miss rate P/sub m/. Tradeoffs between P/sub f/ and P/sub m/ are discussed, and a graphical method to determine the threshold value d/sub th/ of the decision statistic used in discriminating between active and inactive channels is introduced.
1196662
A Nearest-Neighbor Method for Resolving PP-Attachment Ambiguity
We present a nearest-neighbor algorithm for resolving prepositional phrase attachment ambiguities. Its performance is significantly higher than previous corpus-based methods for PP-attachment that do not rely on manually constructed knowledge bases. We will also show that the PP-attachment task provides a way to evaluate methods for computing distributional word similarities. Our experiments indicate that the cosine of pointwise mutual information vector is a significantly better similarity measure than several other commonly used similarity measures.