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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).

The Offshore Precipitation Capability(1.48 MB)

Date:
September 16, 2016
Published in:
Project Report ATC-430, MIT Lincoln Laboratory
Type:
Project Report
Topic:

Summary

The Offshore Precipitation Capability (OPC) uses machine learning and image processing methods to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar.

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.

Relating estimated cyclic spectral peak frequency to measured epilarynx length using Magnetic Resonance Imaging(272.05 KB)

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

Summary

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. Recent spectral processing techniques isolate a unique resonance with characteristics of the epilarynx previously shown via simulation, specifically cyclicity. Using Magnetic Resonance Imaging (MRI), the present work relates this estimated cyclic peak frequency to measured epilarynx length.