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Approaches for Language Identification in Mismatched Environments

Date:
December 13, 2016
Published in:
Proceedings of SLT 2016, San Diego, Calif.
Type:
Conference Paper

Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system.

Bootstrapping and Maintaining Trust in the Cloud(469.63 KB)

Date:
December 5, 2016
Published in:
Proceedings of the 32nd Annual Computer Security Applications Conference, ACSAC 2016
Type:
Conference Paper

Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. In this paper we introduce keylime, a scalable trusted cloud key management system. Keylime provides an end-to-end solution for both bootstrapping hardware rooted cryptographic identities for IaaS nodes and for system integrity monitoring of those nodes via periodic attestation.

Leveraging Data Provenance to Enhance Cyber Resilience(273.48 KB)

Date:
November 3, 2016
Published in:
Proceedings of 1st IEEE Cybersecurity Development Conference (SecDev'16), Boston, Mass.
Type:
Conference Paper

Summary

Creating bigger and better walls to keep adversaries out of our systems has been a failing strategy. The recent attacks against Target and Sony Pictures, to name a few, further emphasize this. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers that manage to overcome the “hard-shell” defenses. In this paper, we provide background information on data provenance, details on provenance collection, analysis, and storage techniques and challenges.

POPE: Partial Order Preserving Encoding(589.23 KB)

Date:
October 16, 2016
Published in:
Proceedings of the ACM Conference on Computer and Communications Security (CCS)
Type:
Conference Paper
Topic:

Summary

Recently there has been much interest in performing search queries over encrypted data to enable functionality while protecting sensitive data. One particularly efficient mechanism for executing such queries is order-preserving encryption/encoding (OPE). In this paper, we propose an alternative approach to range queries over encrypted data that is optimized to support insert-heavy workloads as are common in “big data” applications while still maintaining search functionality and achieving stronger security.

Detecting Depression using Vocal, Facial and Semantic Communication Cues(308.97 KB)

Date:
October 15, 2016
Published in:
Proceedings of the Audio Visual Emotion Challenge and Workshop, Amsterdam, The Netherlands
Type:
Conference Paper
Topic:

Summary

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. These changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities.

Multi-Modal Audio, Video, and Physiological Sensor Learning for Continuous Emotion Prediction(451.61 KB)

Date:
October 15, 2016
Published in:
Proceedings of 2016 AVEC Workshop, ACM Multimedia
Type:
Conference Paper
Topic:

Summary

The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities.

How Deep Neural Networks Can Improve Emotion Recognition on Video Data(547.86 KB)

Date:
September 25, 2016
Published in:
Proceedings of 2016 IEEE International Conference on Image Processing (ICIP)
Type:
Conference Paper
Topic:

Summary

There have been many impressive results obtained using deep learning for emotion recognition tasks in the last few years. In this work, we present a system that performs emotion recognition on video data using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

High-throughput Ingest of Data Provenance Records into Accumulo(349.93 KB)

Author:
Date:
September 13, 2016
Published in:
Proceedings of IEEE High Performance Extreme Computing Conference (HPEC '16)
Type:
Conference Paper

Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data.

I-Vector Speaker and Language Recognition System on Android,

Date:
September 13, 2016
Published in:
Proceedings of IEEE High Performance Extreme Computing Conference (HPEC '16)
Type:
Conference Paper

Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android platform in the form of a fully functional application that allows speaker enrollment and language/speaker scoring within mobile contexts.

Relation of Automatically Extracted Formant Trajectories with Intelligibility Loss and Speaking Rate Decline in Amyotrophic Lateral Sclerosis(906.23 KB)

Date:
September 8, 2016
Published in:
Proceedings of Interspeech 2016, San Francisco, Calif.
Type:
Conference Paper
Topic:

Summary

Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS.