Fuzzy extractors (Dodis et al., Eurocrypt 2004) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. To eliminate noise, they require an initial enrollment phase that takes the first noisy reading of the secret and produces a nonsecret helper string to be used in subsequent readings. This helper string reduces the entropy of the original secret—in th...

Information Extraction (IE) is the key technology enabling analytics over unstructured and semi-structured data. Not surprisingly, it is becoming a critical building block for a wide range of emerging applications. To satisfy the rising demands for information extraction in real-world applications, it is crucial to lower the barrier to entry for IE development and enable users with general comp...

We show a reduction from explicit two-source extractors with small error and arbitrarily small constant entropy rate to the existence of explicit t-non-malleable extractors with a small seed length. Previously, such a reduction was known either when one source had entropy rate above half [Raz05] or for general entropy rates but only for large error [CZ16]. As in previous reductions we start wit...

Emotion is considered to be an important factor in human decision making and consciousness. Due to its perceived importance, recently a number of works try to explore emotion in IR tasks. However, the complexity of emotion extractors and lack of understanding on their effectiveness hinder the progress of such works. In this paper, we conduct a comparative study on the effectiveness of three emo...

This paper presents and evaluates two linear feature extractors based on mutual information. These feature extractors consider general dependencies between features and class labels, as opposed to well known linear methods such as PCA which does not consider class labels and LDA, which uses only simple low order dependencies. As evidenced by several simulations on high dimensional data sets, th...