Publications

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution...

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.

Today's analysts manually examine social media networks to find discussions concerning planned cyber attacks, attacker techniques and tools, and potential victims. Applying modern machine learning approaches, Lincoln Laboratory has demonstrated the ability to automatically discover such discussions from Stack Exchange, Reddit, and Twitter posts written in English.

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature...

Deep belief networks (DBNs) have become a successful approach for acoustic modeling in speech recognition. DBNs exhibit strong approximation properties, improved performance, and are parameter efficient. In this work, we propose methods for applying DBNs to speaker recognition. In contrast to prior work, our approach to DBNs for speaker recognition...

Detecting accurately when a person whose face is visible in an audio-visual medium is the audible speaker is an enabling technology with a number of useful applications. The likelihood-ratio test formulation and feature signal processing employed here allow the use of high-dimensional feature sets in the audio and visual domain...

Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using...

In this demo paper we introduce VizLinc; an open-source software suite that integrates automatic information extraction, search, graph analysis, and geo-location for interactive visualization and exploration of large data sets. VizLinc helps users in: 1) understanding the type of information the data set under study might contain, 2) finding patterns...

Twitter is a massive platform for open communication between diverse groups of people. While traditional media segregates the world's population on lines of language, age, physical location, social status, and many other characteristics, Twitter cuts through these divides. The result is an extremely diverse social network. In this work, we...