Mean P(best > random) for conferences that took place in the indicated years, for both the Scholar and Scopus datasets.

<p>Mean P(best > random) for conferences that took place in the indicated years, for both the Scholar and Scopus datasets.</p

Specifics of the Scopus data.

<p>The first figure in each cell is the number of non-best papers in the conference instance. The figure in parenthesis is the number of best papers.</p

P(best > random) for the two datasets analyzed herein.

<p>The entry “all” indicates the overall P(best > random). The error bar indicates the 95% confidence interval and the point at the center indicates the mean value of the probability that a best paper will receive more citations than a random non-best paper. The entries 2005 to 2011 indicate the mean and confidence interval of P(best > random) for conferences that took place in those years.</p

Multiple Parenting Phylogeny Dataset 1.0

<p>This material contains the description and the links where to find the datasets used in the evaluation of Multiple Parenting Phylogeny Evaluation</p

Specifics of the Google Scholar data.

<p>The first figure in each cell is the number of non-best papers in the conference instance. The figure in parenthesis is the number of best papers.</p

Using Visual Rhythms for Detecting Video-based Facial Spoof Attacks

<p>UVAD Dataset</p

End User License Agreement

<p>End user license agreement required to download the Unicamp Video-Attack Dataset (UVAD)</p

Composition of the cross-dataset training and testing.

<p>*The annotations SH and DH are added to form the training set in DR1, summing 180 images due to the overlap.</p

Laser Printer Attribution: Exploring New Features and Beyond

<p>Dataset of the paper Laser Printer Attribution:<br>Exploring New Features and Beyond</p> <p> </p

On the Reconstruction of Text Phylogeny Trees: Evaluation and Analysis of Textual Relationships

<div><p>Over the history of mankind, textual records change. Sometimes due to mistakes during transcription, sometimes on purpose, as a way to rewrite facts and reinterpret history. There are several classical cases, such as the logarithmic tables, and the transmission of antique and medieval scholarship. Today, text documents are largely edited and redistributed on the Web. Articles on news portals and collaborative platforms (such as <i>Wikipedia</i>), source code, posts on social networks, and even scientific publications or literary works are some examples in which textual content can be subject to changes in an evolutionary process. In this scenario, given a set of near-duplicate documents, it is worthwhile to find which one is the original and the history of changes that created the whole set. Such functionality would have immediate applications on news tracking services, detection of plagiarism, textual criticism, and copyright enforcement, for instance. However, this is not an easy task, as textual features pointing to the documents’ evolutionary direction may not be evident and are often dataset dependent. Moreover, side information, such as time stamps, are neither always available nor reliable. In this paper, we propose a framework for reliably reconstructing text phylogeny trees, and seamlessly exploring new approaches on a wide range of scenarios of text reusage. We employ and evaluate distinct combinations of dissimilarity measures and reconstruction strategies within the proposed framework, and evaluate each approach with extensive experiments, including a set of artificial near-duplicate documents with known phylogeny, and from documents collected from Wikipedia, whose modifications were made by Internet users. We also present results from qualitative experiments in two different applications: text plagiarism and reconstruction of evolutionary trees for manuscripts (stemmatology).</p></div