is of the order of a few hundred bytes per document. However, for efficient large scale web indexing it is not necessary to determine the actual resemblance value: it suffices to determine whether newly encountered documents are duplicates or near-duplicates of documents already indexed. In other words

In this paper, we present a novel near-duplicate document detection method that can easily be tuned for a particular domain. Our method represents each document as a real-valued sparse k-gram vector, where the weights are learned to optimize for a specified similarity function, such as the cosine

For the task of near-duplicated document detection, both traditional fingerprinting techniques used in database community and bag-of-word comparison approaches used in information retrieval community are not sufficiently accurate. This is due to the fact that the characteristics of near-duplicated

This paper describes how a program called dup can be used to locate instances of duplication or near-duplication in a software system. Dup reports both textually identical sections of code and sections that are the same textually except for systematic substitution of one set of variable names

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

In this paper we evaluate the effectiveness of Random Early Detection (RED) over traffic types categorized as nonadaptive, fragile and robust, according to their responses to congestion. We point out that RED allows unfair bandwidth sharing when a mixture of the three traffic types shares a link

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techniques constitute Directed Automated Random Testing,or DART for short. The main strength of DART is thus that testing can be performed completely automatically on any program that compiles – there is no need to write any test driver or harness code. During testing, DART detects standard errors

A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns

Detection of a visual signal requires information to reach a system capable of eliciting arbitrary responses required by the experimenter. Detection latencies are reduced when subjects receive a cue that indicates where in the visual field the signal will occur. This shift in efficiency appears