In this work, we take a step towards understanding and defending against spying browser extensions. These are extensions repurposed to capture online activities of a user and communicate the collected sensitive information to a third-party domain. We conduct an empirical study of such extensions on the Chrome Web Store. First, we present an in-depth analysis of the spying behavior of these extensions. We observe that these extensions steal a variety of sensitive user information, such as the complete browsing history (e.g., the sequence of web traversals), online social network (OSN) access tokens, IP address, and geolocation. Second, we investigate the potential for automatically detecting spying extensions by applying machine learning schemes. We show that using a Recurrent Neural Network (RNN), the sequence of browser API calls made by an extension can be a robust feature, outperforming hand-crafted features (used in prior work on malicious extensions) to detect spying extensions. Our RNN based detection scheme achieves a high precision (90.02%) and recall (93.31%) in detecting spying extensions.

Automated topic identification is an essential component invarious information retrieval and knowledge representationtasks such as automated summary generation, categorization search and document indexing. In this paper, we present the Wikitop system to automatically generate topic trees from the input text by performing hierarchical classification using the Wikipedia Category Network (WCN). Our preliminary results over a collection of 125 articles are encouraging and show potential of a robust methodology for automated topic tree generation.

A novel similarity measure for edge-detection that is robust to varying luminance and contrast is presented. It incorporates a regularization term and employs directional FIR edge filters with hyperbolic tangent profiles to ensure improved noise performance and edge localization compared to classical methods.

This paper presents the use of a PCA based approach to segment cells from RGB light microscope images. The proposed segmentation is accurate and robust under uneven illumination, lighting variation, and noise. Principal component analysis (PCA) is first applied to the RGB color bands of the image. The image corresponding to the principal component has significantly better contrast over the original image. A set of eigenfilters is then obtained by applying PCA to local neighborhoods of this image. A pair of filters from this set, corresponding to the second and third largest eigenvalues, resembles ramp edge filters with orientations that adapt to the image. These edge filters are used to obtain the edgemap of the image. We define a criterion that enables accurate detection of valid edges of cells while suppressing noise.

Some engineering applications require structures to expand and contract in size, while retaining their exterior shape. The applications range from mundane daily life objects to more fancy art structures. In contrast to a multiple degree-of-freedom structure, a single degree-of-freedom structure can be driven by a single actuator, reducing the cost and simplifying the control. In this paper, we study single degree-of-freedom structures that can be formed by a lattice of single degree-of-freedom polyhedral expanding units. Due to built-in symmetries, the entire structure can expand and contract as one of the units in the structure is actuated.