6123650
Analyzing Multiple File Downloading in BitTorrent
Previous studies show that more than 85% of the peers have joined multiple torrents in BitTorrent, but theoretical work on multiple files BitTorrent downloading is rare. In this paper, we first consider the scenario of multi-torrent downloading. We present a fluid-model based analysis on the multi-torrent concurrent downloading scheme, which is implicitly adopted in practical applications, and quantitatively compare its performance with an alternative scheme of multi-torrent sequential downloading. We also consider the scenario of multi-file torrent downloading (e.g. multiple files shared within a single torrent), and find that the scheme of multi-file torrent concurrent downloading, which is explicitly engaged in practical applications, is inefficient. A new scheme named collaborative multi-file torrent sequential downloading is proposed, and we show via numerical analysis that the download performance could be improved by collaboration among the peers in different subtorrents. Finally, we propose a self-adaptive mechanism for practically deploying our multi-file torrent downloading scheme in a distributed fashion under situations when correlation among the files and majority peers' behaviors are unknown
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
16309430
Modeling, Analysis and Improvement for BitTorrent-Like File Sharing Networks
In this paper, a simple mathematical model is presented for studying the performance of the BitTorrent (1) file sharing system. We are especially interested in the distribution of the peers with different states of the download job completedness. With the model we find that in the stable state the distribution of the download peers follows a U-shaped curve, and the parameters such as the departure rate of the seeds and the abort rate of the download peers will influence the peer distribution in different ways notably. We also analyze the file availability and the dying process of the BitTorrent file sharing system. We find that the system's stability deteriorates with the clustering of the peers, and BitTorrent's built-in "tit-for-tat" unchoking strategy could not help to preserve the integrity of the file among the download peers when the size of the community is small. An innovative peer selection strategy which enables more peers to finish the download job and prolongs the system's lifetime is proposed, in which the peers cooperate to improve the stability of the system by making a tradeoff between the current download rate and the future service availability. Finally, experimental results are presented to validate our analysis and findings.
26320424
Aurelia: Building Locality-Preserving Overlay Network over Heterogeneous P2P Environments
Traditional DHT-based overlays destroy data locality and make it hard to support complex queries (e.g., range query, similarity query, etc) in P2P systems. Additionally, the node heterogeneity is widely ignored in most existing overlay designs. In this paper, we propose a locality-preserving overlay design, called Aurelia, which can adapt to the heterogeneous P2P environment and exploit node heterogeneity to realize efficient routing and robust indexing. Aurelia preserves the data locality by abandoning the use of hashing for data placement and each peer is responsible for a continuous range of value. In Aurelia, the routing table size and index range are proportional to the node capacity, and multicasting is adopted for scalable routing table maintenance.
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.
8762213
An Algorithm for Simultaneously Bracketing Parallel Texts by Aligning Words
We describe a grammarless method for simultaneously bracketing both halves of a parallel text and giving word alignments, assuming only a translation lexicon for the language pair. We introduce inversion-invariant transduction grammars which serve as generative models for parallel bilingual sentences with weak order constraints. Focusing on transduction grammars for bracketing, we formulate a normal form, and a stochastic version amenable to a maximum-likelihood bracketing algorithm. Several extensions and experiments are discussed.
9436642
A Kernel PCA Method for Superior Word Sense Disambiguation
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either naive Bayes or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. It is hoped that these highly encouraging first results on KPCA for natural language processing tasks will inspire further development of these directions.
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.
1713067
Improving Chinese Tokenization With Linguistic Filters On Statistical Lexical Acquisition
The first step in Chinese NLP is to tokenize or segment character sequences into words, since the text contains no word delimiters. Recent heavy activity in this area has shown the biggest stumbling block to be words that are absent from the lexicon, since successful tokenizers to date have been based on dictionary lookup (e.g., Chang & Chen 1993; Chiang et al. 1992; Lin et al. 1993; Wu & Tseng 1993; Sproat et al. 1994).We present empirical evidence for four points concerning tokenization of Chinese text: (1) More rigorous "blind" evaluation methodology is needed to avoid inflated accuracy measurements; we introduce the nk-blind method. (2) The extent of the unknown-word problem is far more serious than generally thought, when tokenizing unrestricted texts in realistic domains. (3) Statistical lexical acquisition is a practical means to greatly improve tokenization accuracy with unknown words, reducing error rates as much as 32.0%. (4) When augmenting the lexicon, linguistic constraints can provide simple inexpensive filters yielding significantly better precision, reducing error rates as much as 49.4%.