Zombie is an endurance management framework that enables a variety of error correction mechanisms to extend the lifetimes of memories that suffer from bit failures caused by wearout, such as phase-change memory (PCM). Zombie supports both single-level cell (SLC) and multi-level cell (MLC) variants. It extends the lifetime of blocks in working memory pages (primary blocks) by pairing them with spare blocks, i.e., working blocks in pages that have been disabled due to exhaustion of a single block's error correction resources, which would be 'dead' otherwise. Spare blocks adaptively provide error correction resources to primary blocks as failures accumulate over time. This reduces the waste caused by early block failures, making working blocks in discarded pages a useful resource. Even though we use PCM as the target technology, Zombie applies to any memory technology that suffers stuck-at cell failures.

In this paper, we continue our investigations of "web spam": the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatically detecting spam pages, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms. When combined, our heuristics correctly identify 2,037 (86.2%) of the 2,364 spam pages (13.8%) in our judged collection of 17,168 pages, while misidentifying 526 spam and non-spam pages (3.1%).

Two years ago, we conducted a study on the evolution of web pages over time. In the course of that study, we discovered a large number of machine-generated "spam" web pages emanating from a handful of web servers in Germany. These spam web pages were dynamically assembled by stitching together grammatically well-formed German sentences drawn from a large collection of sentences. This discovery motivated us to develop techniques for finding other instances of such "slice and dice" generation of web pages, where pages are automatically generated by stitching together phrases drawn from a limited corpus. We applied these techniques to two data sets, a set of 151 million web pages collected in December 2002 and a set of 96 million web pages collected in June 2004. We found a number of other instances of large-scale phrase-level replication within the two data sets. This paper describes the algorithms we used to discover this type of replication, and highlights the results of our data mining.

A resource may be abused if its users incur little or no cost. For example, e-mail abuse is rampant because sending an e-mail has negligible cost for the sender. It has been suggested that such abuse may be discouraged by introducing an artificial cost in the form of a moderately expensive computation. Thus, the sender of an e-mail might be required to pay by computing for a few seconds before the e-mail is accepted. Unfortunately, because of sharp disparities across computer systems, this approach may be ineffective against malicious users with high-end systems, prohibitively slow for legitimate users with low-end systems, or both. Starting from this observation, we research moderately hard functions that most recent systems will evaluate at about the same speed. For this purpose, we rely on memory-bound computations. We describe and analyze a family of moderately hard, memory-bound functions, and we explain how to use them for protecting against abuses.

The increasing importance of search engines to commercial web sites has given rise to a phenomenon we call "web spam", that is, web pages that exist only to mislead search engines into (mis)leading users to certain web sites. Web spam is a nuisance to users as well as search engines: users have a harder time finding the information they need, and search engines have to cope with an inflated corpus, which in turn causes their cost per query to increase. Therefore, search engines have a strong incentive to weed out spam web pages from their index.We propose that some spam web pages can be identified through statistical analysis: Certain classes of spam pages, in particular those that are machine-generated, diverge in some of their properties from the properties of web pages at large. We have examined a variety of such properties, including linkage structure, page content, and page evolution, and have found that outliers in the statistical distribution of these properties are highly likely to be caused by web spam.This paper describes the properties we have examined, gives the statistical distributions we have observed, and shows which kinds of outliers are highly correlated with web spam.

Abstract How fast does the Web change? Does most of the content remain unchanged once it has been authored, or are the documents continuously updated? Do pages change a little or a lot? Is the extent of change correlated to any other property of the page? All of these questions are of interest to those who mine the Web, including all the popular search engines, but few studies have been performed to date to answer them. One notable exception is a study by Cho and Garcia‐Molina, who crawled a set of 720 000 pages on a daily basis over 4 months, and counted pages as having changed if their MD5 checksum changed. They found that 40% of all Web pages in their set changed within a week, and 23% of those pages that fell into the .com domain changed daily. This paper expands on Cho and Garcia‐Molina's study, both in terms of coverage and in terms of sensitivity to change. We crawled a set of 150 836 209 HTML pages once every week, over a span of 11 weeks. For each page, we recorded a checksum of the page, and a feature vector of the words on the page, plus various other data such as the page length, the HTTP status code, etc. Moreover, we pseudo‐randomly selected 0.1% of all of our URLs, and saved the full text of each download of the corresponding pages. After completion of the crawl, we analyzed the degree of change of each page, and investigated which factors are correlated with change intensity. We found that the average degree of change varies widely across top‐level domains, and that larger pages change more often and more severely than smaller ones. This paper describes the crawl and the data transformations we performed on the logs, and presents some statistical observations on the degree of change of different classes of pages. Copyright © 2004 John Wiley & Sons, Ltd.

How fast does the web change? Does most of the content remain unchanged once it has been authored, or are the documents continuously updated? Do pages change a little or a lot? Is the extent of change correlated to any other property of the page? All of these questions are of interest to those who mine the web, including all the popular search engines, but few studies have been performed to date to answer them.One notable exception is a study by Cho and Garcia-Molina, who crawled a set of 720,000 pages on a daily basis over four months, and counted pages as having changed if their MD5 checksum changed. They found that 40% of all web pages in their set changed within a week, and 23% of those pages that fell into the .com domain changed daily.This paper expands on Cho and Garcia-Molina's study, both in terms of coverage and in terms of sensitivity to change. We crawled a set of 150,836,209 HTML pages once every week, over a span of 11 weeks. For each page, we recorded a checksum of the page, and a feature vector of the words on the page, plus various other data such as the page length, the HTTP status code, etc. Moreover, we pseudo-randomly selected 0.1% of all of our URLs, and saved the full text of each download of the corresponding pages.After completion of the crawl, we analyzed the degree of change of each page, and investigated which factors are correlated with change intensity. We found that the average degree of change varies widely across top-level domains, and that larger pages change more often and more severely than smaller ones.This paper describes the crawl and the data transformations we performed on the logs, and presents some statistical observations on the degree of change of different classes of pages.

No abstract available.

No abstract available.

The goal of the Argo system is to allow medium-sized groups of users to collaborate remotely from their desktops in a way that approaches as closely as possible the effectiveness of face-to-face meetings. In support of this goal, Argo combines high quality multi-party digital video and full-duplex audio with telepointers, shared applications, and whiteboards in a uniform and familiar environment. The shared applications can be unmodified X programs shared via a proxy server, unmodified groupware applications, and applications written using our toolkit. Workers can contact each other as easily as making a phone call, and can easily bring into a conference any material they are working on. They do so by interacting with an object-oriented, client/server conference control system. The same conference control system is used to support teleporting, i.e. moving the desktop environment from one workstation's display to another (for example, from office to home). This paper describes the system we have built to test the hypothesis that the effectiveness of remote collaboration can be substantially impacted by the responsiveness of the interaction media.

One important facet of common-sense reasoning is the ability to draw default conclusions about the state of the world, so that one can, for example, assume that a given bird flies in the absence of information to the contrary. A deficiency in the circumscriptive approach to common-sense reasoning has been its difficulties in producing default that Tweety ≠ Blutto using ordinary circumscription, or conclude by default that a particular bird flies, if some birds are known not to fly. In this paper, we introduce a new form of circumscription, based on homomorphisms between models, that remedies these two problems and still retains the major desirable properties of traditional forms of circumscription.

A common operation in multiprocessor programs is acquiring a lock to protect access to shared data. Typically, the requesting thread is blocked if the lock it needs is held by another thread. The cost of blocking one thread and activating another can be a substantial part of program execution time. Alternatively, the thread could spin until the lock is free, or spin for a while and then block. This may avoid context-switch overhead, but processor cycles may be wasted in unproductive spinning. This paper studies seven strategies for determining whether and how long to spin before blocking. Of particular interest are competitive strategies, for which the performance can be shown to be no worse than some constant factor times an optimal off-line strategy. The performance of five competitive strategies is compared with that of always blocking, always spinning, or using the optimal off-line algorithm. Measurements of lock-waiting time distributions for five parallel programs were used to compare the cost of synchronization under all the strategies. Additional measurements of elapsed time for some of the programs and strategies allowed assessment of the impact of synchronization strategy on overall program performance. Both types of measurements indicate that the standard blocking strategy performs poorly compared to mixed strategies. Among the mixed strategies studied, adaptive algorithms perform better than non-adaptive ones.

The number field sieve is an algorithm to factor integers of the form r, 4-s for small positive r and s.This note is intended as a 'report on work in progress' on this algorithm.We informally describe the algorithm, discuss several implementation related aspects, and present some of the factorizations obtained so far.We also mention some solutions to the problems encountered when generalizing the algorithm to general integers using an idea of Buhler and Pomerance.It is not unlikely that this leads to a general purpose factoring algorithm that is asymptotically substantially faster than the fastest factoring algorithms known so far, like the multiple polynomial quadratic sieve.

An on-line problem is one in which an algorithm must handle a sequence of requests, satisfying each request without knowledge of the future requests. Examples of on-line problems include scheduling the motion of elevators, finding routes in networks, allocating cache memory, and maintaining dynamic data structures. A competitive algorithm for an on-line problem has the property that its performance on any sequence of requests is within a constant factor of the performance of any other algorithm on the same sequence. This paper presents several general results concerning competitive algorithms, as well as results on specific on-line problems.

In a snoopy cache multiprocessor system, each processor has a cache in which it stores blocks of data. Each cache is connected to a bus used to communicate with the other caches and with main memory. For several of the proposed models of snoopy caching, we present new on-line algorithms which decide, for each cache, which blocks to retain and which to drop in order to minimize communication over the bus. We prove that, for any sequence of operations, our algorithms' communication costs are within a constant factor of the minimum required for that sequence; for some of our algorithms we prove that no on-line algorithm has this property with a smaller constant.