Journal of the ACM

Semantic similarity measures play important roles in information retrieval and Natural Language Processing. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these applications, robustly measuring semantic similarity between two words (or entities) remains a challenging task. We propose a robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The proposed method exploits page counts and text snippets returned by a Web search engine. We deflne various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets. These difierent similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similarity measures by a wide margin, achieving a correlation coe‐cient of 0:834. Moreover, the proposed semantic similarity measure signiflcantly improves the accuracy (F-measure of 0:78) in a community mining task, and in an entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content.

/pdf/measuring-semantic-similarity-between-words-using-web-search-lew2szr9ob.pdf

Measuring Semantic Similarity between Words Using Web Search Engines

Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

We present eclipse attacks on bitcoin's peer-to-peer network. Our attack allows an adversary controlling a sufficient number of IP addresses to monopolize all connections to and from a victim bitcoin node. The attacker can then exploit the victim for attacks on bitcoin's mining and consensus system, including N-confirmation double spending, selfish mining, and adversarial forks in the blockchain. We take a detailed look at bitcoin's peer-to-peer network, and quantify the resources involved in our attack via probabilistic analysis, Monte Carlo simulations, measurements and experiments with live bitcoin nodes. Finally, we present countermeasures, inspired by botnet architectures, that are designed to raise the bar for eclipse attacks while preserving the openness and decentralization of bitcoin's current network architecture.

/pdf/eclipse-attacks-on-bitcoin-s-peer-to-peer-network-3g8l3awhuc.pdf

Eclipse attacks on Bitcoin's peer-to-peer network

This is a survey of the science and practice of web crawling. While at first glance web crawling may appear to be merely an application of breadth-first-search, the truth is that there are many challenges ranging from systems concerns such as managing very large data structures to theoretical questions such as how often to revisit evolving content sources. This survey outlines the fundamental challenges and describes the state-of-the-art models and solutions. It also highlights avenues for future work.

https://www.microsoft.com/en-us/research/wp-content/uploads/2010/01/1500000017.pdf

Web Crawling

We revisit a problem introduced by Bharat and Broder almost a decade ago: how to sample random pages from a search engine's index using only the search engine's public interface? Such a primitive is particularly useful in creating objective benchmarks for search engines.The technique of Bharat and Broder suffers from two well recorded biases: it favors long documents and highly ranked documents. In this paper we introduce two novel sampling techniques: a lexicon-based technique and a random walk technique. Our methods produce biased sample documents, but each sample is accompanied by a corresponding "weight", which represents the probability of this document to be selected in the sample. The samples, in conjunction with the weights, are then used to simulate near-uniform samples. To this end, we resort to three well known Monte Carlo simulation methods: rejection sampling, importance sampling and the Metropolis-Hastings algorithm.We analyze our methods rigorously and prove that under plausible assumptions, our techniques are guaranteed to produce near-uniform samples from the search engine's index. Experiments on a corpus of 2.4 million documents substantiate our analytical findings and show that our algorithms do not have significant bias towards long or highly ranked documents. We use our algorithms to collect fresh data about the relative sizes of Google, MSN Search, and Yahoo!.

/pdf/random-sampling-from-a-search-engine-s-index-2gny4ohyc6.pdf

Random sampling from a search engine's index

We revisit a problem introduced by Bharat and Broder almost a decade ago: How to sample random pages from the corpus of documents indexed by a search engine, using only the search engine's public interfaceq Such a primitive is particularly useful in creating objective benchmarks for search engines.The technique of Bharat and Broder suffers from a well-recorded bias: it favors long documents. In this article we introduce two novel sampling algorithms: a lexicon-based algorithm and a random walk algorithm. Our algorithms produce biased samples, but each sample is accompanied by a weight, which represents its bias. The samples, in conjunction with the weights, are then used to simulate near-uniform samples. To this end, we resort to four well-known Monte Carlo simulation methods: rejection sampling, importance sampling, the Metropolis--Hastings algorithm, and the Maximum Degree method.The limited access to search engines force our algorithms to use bias weights that are only “approximate”. We characterize analytically the effect of approximate bias weights on Monte Carlo methods and conclude that our algorithms are guaranteed to produce near-uniform samples from the search engine's corpus. Our study of approximate Monte Carlo methods could be of independent interest.Experiments on a corpus of 2.4 million documents substantiate our analytical findings and show that our algorithms do not have significant bias towards long documents. We use our algorithms to collect comparative statistics about the corpora of the Google, MSN Search, and Yahooe search engines.

/pdf/random-sampling-from-a-search-engine-s-index-3d57h5vsfv.pdf

Brahms: Byzantine resilient random membership sampling

We present Brahms, an algorithm for sampling random nodes in a large dynamic system prone to malicious behavior. Brahms stores small membership views at each node, and yet overcomes Byzantine attacks by a linear portion of the system. Brahms is composed of two components. The first one is a resilient gossip-based membership protocol. The second one uses a novel memory-efficient approach for uniform sampling from a possibly biased stream of ids that traverse the node. We evaluate Brahms using rigorous analysis, backed by simulations, which show that our theoretical model captures the protocol's essentials. We study two representative attacks, and show that with high probability, an attacker cannot create a partition between correct nodes. We further prove that each node's sample converges to a uniform one over time. To our knowledge, no such properties were proven for gossip protocols in the past.

/pdf/brahms-byzantine-resilient-random-membership-sampling-2g2ti75qca.pdf

Brahms: byzantine resilient random membership sampling

We address the problem of measuring global quality met-rics of search engines, like corpus size, index freshness, anddensity of duplicates in the corpus The recently proposedestimators for such metrics [2, 6] suffer from significant biasand/or poor performance, due to inaccurate approximationof the so called document degreesWe present two new estimators that are able to overcomethe bias introduced by approximate degrees Our estimatorsare based on a careful implementation of an approximateimportance sampling procedure Comprehensive theoreti-cal and empirical analysis of the estimators demonstratesthat they have essentially no bias even in situations wheredocument degrees are poorly approximatedBuilding on an idea from [6], we discuss Rao Blackwelliza-tion as a generic method for reducing variance in searchengine estimators We show that Rao-Blackwellizing ourestimators results in significant performance improvements,while not compromising accuracy

/pdf/efficient-search-engine-measurements-3v01qh211k.pdf

Maxim Gurevich

Papers

Random sampling from a search engine's index

Random sampling from a search engine's index

Brahms: Byzantine resilient random membership sampling

Brahms: byzantine resilient random membership sampling

Efficient search engine measurements