1529609
Roogle: Supporting Efficient High-Dimensional Range Queries in P2P Systems
Multi-dimensional range query is an important query type and especially useful when the user doesn't know exactly what he is looking for. However, due to improper indexing method and high routing latency, existing schemes cannot perform well under high-dimensional situations. In this paper, we propose Roogle, a decentralized non-flooding P2P search engine that can efficiently support high-dimensional range queries in P2P systems. Roogle makes improvements on both indexing and routing. The high-dimensional data is indexed based on the maximum or minimum value among all dimensions. This simple indexing method performs rather well under high-dimensional situations and tolerates data points with missing values or different dimensionality. To speed query routing, Roogle is built on top of our proposed structured overlay – Aurelia, which has better routing performance by exploiting node heterogeneity. Aurelia also guarantees the data locality and efficiently support range queries. Experimental results from simulation validate the scalability and efficiency of Roogle.
16461681
Analytical Study on Improving DHT Lookup Performance under Churn
The phenomenon of churn degrades the lookup performance of DHT-based P2P systems greatly. To date, a number of approaches have been proposed to handle it from both the system side and the client side. However, there lacks theoretical analysis to direct how to make design choices under different churn levels and how to configure their parameters optimally. In this paper, we analytically study three important aspects on improving DHT lookup performance under churn, i.e., lookup strategy, lookup parallelism and lookup key replication. Our objective is to build a theoretical basis for DHT designers to make better design choices in the future. We first compare the performance of two representative lookup strategies - recursive routing and iterative routing, and explore the existence of better alternatives. Then we show the effectiveness of parallel lookup in systems with different churn levels and how to select the optimal degree of parallelism. Due to the importance of key replication on lookup performance, we also analyze the reliability of replicated keys under two different replication policies, and discuss how to make configuration in different environments. Besides analytical study, our results are also validated by simulation, and Kad is taken as a case to show the meaningfulness of our analysis
5578183
Learning restricted probabilistic link grammars
We describe a language model employing a new headed-disjuncts formulation of Lafferty et al.'s (1992) probabilistic link grammar, together with (1) an EM training method for estimating the probabilities, and (2) a procedure for learning some simple lexicalized grammar structures. The model in its simplest form is a generalization of n-gram models, but in its general form possesses context-free expressiveness. Unlike the original experiments on probabilistic link grammars, we assume that no hand-coded grammar is initially available (as with n-gram models). We employ untyped links to concentrate the learning on lexical dependencies, and our formulation uses the lexical identities of heads to influence the structure of the parse graph. After learning, the language model consists of grammatical rules in the form of a set of simple disjuncts for each word, plus several sets of probability parameters. The formulation extends cleanly toward learning more powerful context-free grammars. Several issues relating to generalization bias, linguistic constraints, and parameter smoothing are considered. Preliminary experimental results on small artificial corpora are supportive of our approach.
12598488
MLP Internal Representation as Discriminative Features for Improved Speaker Recognition
Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided by the pre-squashed outputs from a one hidden layer multi-layer perceptron (MLP) trained to recognise speech sub-units (phonemes) has previously been shown to significantly increase ASR performance. An analogous approach cannot be applied directly to speaker recognition because there is no recognised set of "speaker sub-units" to provide a finite set of MLP target classes, and for many applications it is not practical to train an MLP with one output for each target speaker. In this paper we show that the output from the second hidden layer (compression layer) of an MLP with three hidden layers trained to identify a subset of 100 speakers selected at random from a set of 300 training speakers in Timit, can provide a 77% relative error reduction for common Gaussian mixture model (GMM) based speaker identification.
61042466
The berkeley UNIX consultant project
UC (UNIX Consultant) is an intelligent, natural language interface that allows naive users to learn about the UNIX2 operating system. UC was undertaken because the task was thought to be both a fertile domain for artificial intelligence (AI) research and a useful application of AI work in planning, reasoning, natural language processing, and knowledge representation.The current implementation of UC comprises the following components: a language analyzer, called ALANA, produces a representation of the content contained in an utterance; an inference component, called a concretion mechanism, that further refines this content; a goal analyzer, PAGAN, that hypothesizes the plans and goals under which the user is operating; an agent, called UCEgo, that decides on UC's goals and proposes plans for them; a domain planner, called KIP, that computes a plan to address the user's request; an expression mechanism, UCExpress, that determines the content to be communicated to the user, and a language production mechanism, UCGen, that expresses UC's response in English.UC also contains a component, called KNOME, that builds a model of the user's knowledge state with respect to UNIX. Another mechanism, UCTeacher, allows a user to add knowledge of both English vocabulary and facts about UNIX to UC's knowledge base. This is done by interacting with the user in natural language.All these aspects of UC make use of knowledge represented in a knowledge representation system called KODIAK. KODIAK is a relation-oriented system that is intended to have wide representational range and a clear semantics, while maintaining a cognitive appeal. All of UC's knowledge, ranging from its most general concepts to the content of a particular utterance, is represented in KODIAK.
2144821
Aligning a Parallel English-Chinese Corpus Statistically with Lexical Criteria
We describe our experience with automatic alignment of sentences in parallel English-Chinese texts. Our report concerns three related topics: (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus; (2) experiments addressing the applicability of Gale & Church's (1991) length-based statistical method to the task of alignment involving a non-Indo-European language; and (3) an improved statistical method that also incorporates domain-specific lexical cues.
8337549
Exploring A Two-market Genetic Algorithm
The ordinary genetic algorithm may be thought of as conducting a single market in which solutions compete for success, as measured by the fitness funtion. We introduce a two-market genetic algorithm, consisting of two phases, each of which is an ordinary single-market genetic algorithm. The two-market genetic algorithm has a natural interpretation as a method of solving constrained optimization problems. Phase 1 is optimality improvement; it works on the problem without regard to constraints. Phase 2 is feasibility improvement; it works on the existing population of solutions and drives it towards feasibility. We tested this concept on 14 standard knapsack test problems for genetic algorithms, with excellent results. The paper concludes with discussions of why the two-market genetic algorithm is successful and of how this work can be extended.