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Cross-Domain Entity Resolution in Social Media(416.42 KB)

Date:
July 11, 2016
Published in:
Proceedings of Fourth International Workshop on Natural Language Processing for Social Media (SocialNLP 2016)
Type:
Conference Paper

Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques.

Charting a Security Landscape in the Clouds: Data Protection and Collaboration in Cloud Storage(1.6 MB)

Date:
July 7, 2016
Published in:
MIT Lincoln Laboratory Technical Report 1210
Type:
Technical Report
Topic:

Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers.

Collaborative Data Analysis and Discovery for Cyber Security

Date:
June 22, 2016
Published in:
Proceedings of the 12th Symposium on Usable Privacy and Security (SOUPS 2016)
Type:
Conference Paper
Topic:

Summary

In this paper, we present the Cyber Analyst Real-Time Integrated Notebook Application (CARINA). CARINA is a collaborative investigation system that aids in decision making by co-locating the analysis environment with centralized cyber data sources, and providing next generation analysts with increased visibility to the work of others.

The MITLL NIST LRE 2015 Language Recognition System

Date:
June 21, 2016
Published in:
Proceedings of Odyssey 2016, Bilbao, Spain
Type:
Conference Paper

Summary

In this paper we describe the most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a fusion of five core classifiers, with most systems developed in the context of an i-vector framework.

Channel Compensation for Speaker Recognition using MAP Adapted PLDA and Denoising DNNs(688.37 KB)

Date:
June 21, 2016
Published in:
Proceedings of Odyssey 2016, Bilbao, Spain
Type:
Conference Paper

Summary

Over several decades, speaker recognition performance has steadily improved for applications using telephone speech. For new data applications, we would like to effectively use existing telephone data to build systems with high accuracy while maintaining good performance on existing telephone tasks. In this paper we compare and combine approaches to compensate models parameters and features for this purpose.

A Vocal Modulation Model with Application to Predicting Depression Severity(2.33 MB)

Date:
June 14, 2016
Published in:
Proceedings of Body Sensor Networks 2016, San Francisco, Calif.
Type:
Conference Paper
Topic:

Summary

Speech provides a potential simple and noninvasive “on-body” means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area.

Broadband Optical Switch Based on Liquid Crystal Dynamic Scattering

Date:
June 13, 2016
Published in:
Optics Express, vol. 24, no. 13
Type:
Journal Article
Topic:

Summary

This work demonstrates a novel broadband optical switch, based on dynamic-scattering effect in liquid crystals (LCs). Dynamic-scattering-mode technology was developed for display applications over four decades ago, but was displaced in favor of the twisted-nematic LCs. However, with the recent development of more stable LCs, dynamic scattering provides advantages over other technologies for optical switching. We demonstrate broadband polarization-insensitive attenuation of light directly passing thought the cell by 4 to 5 orders of magnitude at 633 nm. The attenuation is accomplished by light scattering to higher angles. Switching times of 150 μs to 10% transmission have been demonstrated. No degradation of devices is found after hundreds of switching cycles. The light-rejection mechanism is due to scattering, induced by disruption of LC director orientation with dopant ion motion with an applied electric field. Angular dependence of scattering is characterized as a function of bias voltage.

BubbleNet: A Cyber Security Dashboard for Visualizing Patterns

Date:
June 6, 2016
Published in:
Proceeding of 2016 Eurographics Conference on Visualization (EuroVis)
Type:
Conference Paper
Topic:

Summary

The field of cyber security is faced with ever-expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data.

A Fun and Engaging Interface for Crowdsourcing Named Entities(275.07 KB)

Date:
May 23, 2016
Published in:
Proceedings of Tenth International Conference on Language Resources and Evaluation (LREC 2016)
Type:
Conference Paper

Summary

In this paper, we provide a case study in using crowd sourcing to curate an in-domain corpus for named entity recognition, a common problem in natural language processing. In particular, we present our use of fun, engaging user interfaces as a way to entice workers to partake in our crowd sourcing task while avoiding inflating our payments in a way that would attract more mercenary workers than conscientious ones.

Operational Assessment of Keyword Search on Oral History(313.43 KB)

Date:
May 23, 2016
Published in:
Proceedings of Tenth International Conference on Language Resources and Evaluation (LREC 2016)
Type:
Conference Paper

Summary

This project assesses the resources necessary to make oral history searchable by means of automatic speech recognition (ASR). We assess the impact of dataset size, word error rate and term-weighted value on human search capability through an information retrieval task on Mechanical Turk, we use English oral history data collected by Story Corps, and we show comparable search performance using a standard speech recognition system.