Information & computer science publications

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.

This study applied the Pediatric RIFLE, AKI Network, and Kidney Disease Improving Global Outcomes criteria to a cohort of hospitalizations to compare AKI incidence and outcomes in ICU and non-ICU…

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.

Our work is motivated by the vision of automated asking and answering of questions related to a biology textbook—a capability which requires application of abstract reasoning patterns.

We propose a new memory-safe interpretation of the C abstract machine that provides stronger protection to benefit security and debugging.

In this paper, we focus on the behavior of individual switches, and demonstrate that even simple rule updates result in inconsistent packet switching in multi-table datapaths.

Methods of detection and removal of marine tar residues and their possible ecological effects are discussed, in addition to topics of marine tar research that warrant further investigation.

An investigation is carried out combining the data from multiple instruments that include the meteor wind radar over Thiruvananthapuram, India and the SABER instrument onboard the TIMED satellite.

We present a series of algorithms that draw from recent advances in Bayesian nonparametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction…