While there is a considerable amount of research on analyzing the change information stored in software repositories, only few researcher have looked at software changes contained in email archives in form of patches. In this paper we look at the email archives of two open source projects

for conservation

We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces

of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual

Analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCAbased local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image

The factorization method described in this series of reports requires an algorithm to track the motion of features in an image stream. Given the small inter-frame displacement made possible by the factorization approach, the best tracking method turns out to be the one proposed by Lucas and Kanade

frames/second, detects pedestrians at very small scales (as small as 20x15 pixels), and has a very low false positive rate

, to accommodate different types of constraints. Some constraints can be imposed without any modification of the algorithm, while others require the solution of a small associated linear system of equations. In particular, vertex location constraints, vertex normal constraints, and surface normal discontinuities

Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule- based methods. In this paper, we present a sim- ple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy coinparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a sinall set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, cor- pus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.

with distinct security requirements. We propose a solution to one, the managed-open scenario where no network infrastructure is pre-deployed, but a small amount of prior security coordination is expected. Our protocol, ARAN, is based on certificates and successfully defeats all identified attacks.