Redirection Harvesting

Here in the Caribbean we are “surviving” hurricane Earl, so the IRW issue has been delayed. This and the upcoming two  issues will be a give away about different type of inverted index architectures and fast indexing techniques.  This is what I plan to cover:

Part One:    Inverted Index Types

Part Two:    Fast Indexing Techniques

Part Three:  Fast Posting Lists Intersecting & Sharding

The QA section will cover redirection harvesting. I’m going to do now something not done before: releasing the QA section in advanced, so those not subscribed to IRW will realize what they are missing.

Q: What is Redirection Harvesting?

A: Redirection harvesting is a phishing technique wherein a hacker or spammer identifies a trusted site that redirects users to specific pages by appending name-value commands to the redirection mechanism.

The mechanism is often a form or a URL without a security layer for filtering appended URLs. The idea is to replace the landing URL with the hacker or spammer’s URL which is often obfuscated.

Although it no longer works, the best known example of this was due to Ebay. For details, check http://www.google.com/search?q=ebay+redirections

The URL mechanism abused was

http://cgi4.ebay.com/ws/eBayISAPI.dll?MfcISAPICommand=RedirectToDomain&
DomainUrl=obfuscatedURLgoeshere

Note that a trusted site (Ebay) is the one doing the redirection.

Naïve or unaware users receiving an email with such doctored URLs might think these belong to Ebay and that it will redirect to a page within EBay when in fact it takes users to a malicious page. Once there, users are exposed to all kind of attacks.

Many large and popular sites, including educational and government sites, are still guilty of allowing this to happen. The lesson here is that redirection mechanisms without URL filtering layers can and will be abused.

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Matrix Algebra for Search Marketing

Today I feel like giving away a little quiz material on applied linear algebra. The topic is relevant these days wherein some misleading SEOs are playing the we-do-“science” game (quack “science”, after all).

The following is taken from the Search Engines Architecture grad course I lectured back in 2008. I’m providing only one exercise with multiple parts. The quiz with answers might be a great topic for an IRW issue.

1.1 A search engine has three types of revenue channels: pay-per-click (PPC), pay-per-placement (PPP), and pay-for-conversion (PFC). In quarter 1, the million-dollar revenues respectively were: 20, 4, and 9. In quarter 2, PPC revenues were 20% less, PPP revenues doubled, and PFC revenues remained constant.

1.1.1 Write a matrix M1 expressing the revenue and quarter vectors for the first two quarters.

1.1.2 If the goal in quarter 3 is to increase by 20% all revenues earned in quarter 2, update M1 so it reflects such a goal as a new matrix M2.

1.1.3 If the goal in quarter 4 is to meet the average revenues of each of the previous channels in quarter 4, update M2 such that it reflects that goal as a new matrix M3.

1.1.4 Express the above quarters as column unit vectors. Inspecting either rows or columns, construct a nearest neighbor similarity matrix Mnn and construct scalar clusters of quarters. Ignore cosine similarity deviations of 0.02 units or less. How similar the quarters are?

Have fun.

Understanding Fisher’s Z Transformations

12-20-2018 Update:

Not all researchers know that score-to-rank transformations can change the sampling distribution of a statistic (e.g. a correlation coefficient) and that Fisher transformations are sensitive to normality violations. Combining both types of transformations (scores to ranks and then applying Fisher Transformations) is a recipe for a statistical disaster.

See https://irthoughts.wordpress.com/2018/11/01/on-the-non-additivity-of-correlation-coefficients-part-3-the-bias-nature-of-correlation-coefficients/

 

9-3-2016 Update:

One of the best known technique for transforming correlation coefficient (r) values into weighted additive quantities is the r-to-Z transformation due to Fisher.

Fisher’s r-to-Z transformation is an elementary transcendental function called the inverse hyperbolic tangent function. The reverse, a Z-to-r transformation, is therefore a hyperbolic tangent function.

In Windows computers, these functions are built-in in their scientific calculator program which is accessible by navigating to Start > All Programs > Accessories > Calculator. Microsoft Excel also has these built-in as the ATANH and TANH functions.

Fisher’s r-to-Z transformation is applicable only to bivariate normal distributions; i.e. if the (x, y) paired variables both describe bell-shaped curves. Non trivial errors arise if one of the variables is not normally distributed.

5-22-2016 Update: We have developed a tool for easily computing these transformations and explaining the bivariate normality restriction.

The tool is available at

http://www.minerazzi.com/tools/fisher/transformations.php

Beware of Sloppy Calculations

Correlation coefficient arithmetic averages are not computable directly from individual values.

Indeed, it is not possible to add, subtract, average or take standard deviations out of raw r values.

Unfortunately some researchers with a limited knowledge on Statistics have published papers containing such gross errors. What is worse, reviewers of those papers are either not statisticians or have been lazy enough to overlook at the concept, leading graduate students and post docs into error.

Search marketers are also buying into the error. An example of this are the SEOs from SeoMOZ (now MOZ) promoting quack “science” and sloppy “statistical studies”. If you are an SEO and still want to believe their snakeoil marketing, that’s up to you.

On trusting search engine stemming and matching results

If you believe or care about it, then following resources might interest you.

On stemming and counting search results

http://jis.sagepub.com/content/early/2009/05/28/1363459309336801

Our results indicate that Google uses a document-based algorithm for stemming. It evaluates each document separately and makes a decision to index or not for the conflated forms of the words it has. It indexes documents only for word forms that are semantically strongly correlated. While it indexes documents for singulars and plurals frequently, it rarely indexes documents for word forms with the postfixes of -able or -tively.

http://jis.sagepub.com/content/35/4/469.short

This study investigates the accuracy of search engine hit counts for search queries. We investigate the accuracy of hit counts for Google, Yahoo and Microsoft Live Search, and the accuracy of single and multiple term queries. In addition, we investigate the consistency of hit count estimates for 15 days. The results show that all three provide estimates for the number of matching documents and the estimation patterns of their counting algorithms differ greatly. The accuracy of hit counts for multiple word queries has not been studied before. The results of our study show that the number of words in queries affects the accuracy of estimations significantly. The percentages of accurate hit count estimations are reduced almost by half when going from single word to two word query tests in all three search engines. With the increase in the number of query words, the error in estimation increases and the number of accurate estimations decreases.

http://webascorpus.org/Corpus_Analysis_of_the_World_Wide_Web.pdf

 

This article reviews the rewards and limitations of either acquiring Web content and processing it nto a static corpus or else accessing it directly as a dynamic corpus, a distinction captured in or / as corpus the process it surveys typical applications of such data to both academic analysis and real-world.

 

http://gplsi.dlsi.ua.es/congresos/qwe10/fitxers/QWE10_Funahashi.pdf

Abstract. In this paper, we investigate the trustworthiness of search engines’hit counts, numbers returned as search result counts. Since many studies adoptsearch engines’ hit counts to estimate the popularity of input queries, thereliability of hit counts is indispensable for archiving trustworthy studies.However, hit counts are unreliable because they change, when a user clicks the“Search” button more than once or clicks the “Next” button on the searchresults page, or when a user queries the same term on separate days. In thispaper, we analyze the characteristics of hit count transition by gathering varioustypes of hit counts over two months by using 10,000 queries. The results of ourstudy show that the hit counts with the largest search offset just before searchengines adjust their hit counts are the most reliable. Moreover, hit counts arethe most reliable when they are consistent over approximately a week.

For those that still believe in counting search results, despite the evidence from the above articles:

http://www2006.org/programme/item.php?id=3047

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!.
 

Great Resources on NonNegative Matrix Factorization (NMF)

Here are some great resources on NMF or NonNegative Matrix Factorization. I might put out my own tutorial on the subject, if I can find the time to do so.

Tutorial:

 http://spinner.cofc.edu/~langvillea/NISS-NMF.pdf?referrer=webcluster&

Research Papers:

http://research.microsoft.com/pubs/119077/DNMF.pdf

http://www.inma.ucl.ac.be/publi/441048.pdf

http://www.bsp.brain.riken.jp/publications/2008/MLSP-08-Cichocki-Phan-fin_corrected_CesarV1.pdf

http://lgm.fri.uni-lj.si/matic/clanki/smc2009.pdf

Will NMF be the next stop of the usual snakeoil marketers, SEO quack “science” sellers, and purveyors of falsehood? (Who said that last expression before? One crook that is now becoming one…ha, ha.

PS: I’ve added the following resources:

http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf

http://spinner.cofc.edu/~langvillea/SIAMSEAS-NMF.pdf

http://www.csie.ntu.edu.tw/~cjlin/nmf/index.html

http://www.stanford.edu/~vcs/papers/NMFCDP.pdf