Information & computer science publications

We formally define the bootstrap problem, design a polynomial-time L-approximation algorithm using a novel method of rounding of a linear program, and show a matching hardness result: (L — epsilon) - inapproximability for any epsilon…

This paper presents an approach to training in VEs that directly addresses these challenges and summarizes its application to a weapons maintenance task.

We present SGDPLL(T ), an algorithm that solves probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter.

In order to help generate new research hypotheses, this study explored spatial and temporal patterns of preterm birth in a large, total-population dataset.

This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope.

We present snap-n-eat, a mobile food recognition system. The system can recognize food and estimate the calorific and nutrition content of foods automatically without any user intervention.

We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data. The first system combines various lexical and prosodic features in a Conditional Random Field model for…

In this paper, we open the black box and devise a new algorithm for this problem domain that we call conflict-directed graph coverage.

We construct the first implementable encryption system supporting greater-than comparisons on encrypted data that provides the “best-possible” semantic security.

This work presents a deep neural network (DNN)—hidden Markov model (HMM) based acoustic model where articulatory features are used in addition to mel-frequency cepstral coefficients (MFCC) for the Aurora-4 speech recognition task.