Publications

The performance of a spoken language system suffers when non-speech is incorrectly classified as speech. Singing is particularly difficult to discriminate from speech, since both are natural language. However, singing conveys a melody, whereas speech does not; in particular, a singer's fundamental frequency should not deviate significantly from an underlying...

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple...

The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on...

The Visualization for Cyber Security research community (VizSec) addresses longstanding challenges in cyber security by adapting and evaluating information visualization techniques with application to the cyber security domain. In this paper, we survey and categorize the evaluation metrics, components, and techniques that have been utilized in the past decade of...

Moving Target (MT) defenses have been proposed as a gamechanging approach to rebalance the security landscape in favor of the defender. MT techniques make systems less deterministic, less static, and less homogeneous in order to increase the level of effort required to achieve a successful compromise. However, a number of...

Code diversification has been proposed as a technique to mitigate code reuse attacks, which have recently become the predominant way for attackers to exploit memory corruption vulnerabilities. As code reuse attacks require detailed knowledge of where code is in memory, diversification techniques attempt to mitigate these attacks by randomizing what...

Anomaly detection in massive networks has numerous theoretical and computational challenges, especially as the behavior to be detected becomes small in comparison to the larger network. This presentation focuses on recent results in three key technical areas, specifically geared toward spectral methods for detection.

We demonstrate a ladar with 0.5 m class range resolution obtained by integrating a continuous-wave optical phased-array transmitter with a Geiger-mode avalanche photodiode receiver array. In contrast with conventional ladar systems, an array of continuous-wave sources is used to effectively pulse illuminate a target by electro-optically steering far-field fringes. From...

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we...

A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a...