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Ravuri, S. (2011).  On the Use of Spectro-Temporal Features in Noise-Additive Speech.
Feldman, J., Pingle K.. K., Binford T.. O., Falk G., Hay A.., Pau R.., et al. (1971).  The Use of Vision and Manipulation to Solve the 'Instant Insanity' puzzle. 359-364.
Fillmore, C. J. (1986).  U-Semantics, Second Round. Quaderni di Semantica. 49-58.
Lei, H., Choi J., Janin A., & Friedland G. (2011).  User Verification: Matching the Uploaders of Videos Across Accounts. 2404-2407.
Ruiz, P. M., Botia J., & Gómez-Skarmeta A. (2004).  User-Aware Adaptive Applications for Enhanced Multimedia Quality in Heterogeneous Networking Environments. Lecture Notes in Computer Science.
Botia, J., Ruiz P. M., & Gómez-Skarmeta A. (2004).  User-Aware Videoconference Session Control Using Software Agents. Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2004).
Helbig, H. (1997).  User-Friendly Information Retrieval in Data Bases and in the World Wide Web.
Friedland, G. (2012).  Using a GPU, Online Diarization = Offline Diarization.
Morgan, N. (1994).  Using A Million Connections for Continuous Speech Recognition. 1439-1444.
Jurafsky, D., Wooters C., Segal J., Stolcke A., Fosler-Lussier E., Tajchman G., et al. (1995).  Using A Stochastic Context-Free Grammar as a Language Model for Speech Recognition. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 95).
López, J. Ferreiros, & Ellis D. P. W. (2000).  Using Acoustic Condition Clustering to Improve Acoustic Change Detection on Broadcast News. Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000). 4, 568-571.
Knox, M. Tai, Friedland G., & R. Smith P. (2012).  Using Acoustic Diarization for Duplicate Detection.
Friedland, G., Gottlieb L., & Janin A. (2009).  Using Artistic Markers and Speaker Identification for Narrative-Theme Navigation of Seinfeld Episodes. 511-516.
Hung, H., Jayagopi D., Yeo C., Friedland G., Ba S., Odobez J-M., et al. (2007).  Using Audio and Video Features to Classify the Most Dominant Person in Meetings. Proceedings of ACM Multimedia 2007. 835-838.
Schwenk, H.. (1999).  Using Boosting to Improve a Hybrid HMM/Neural Network Speech Recognizer. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1999).
Liu, Y., Stolcke A., Shriberg E., & Harper M. P. (2005).  Using Conditional Random Fields For Sentence Boundary Detection in Speech. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005). 451-458.
Maron-Katz, A., Ben Simon E., Jacob Y., Rosenberg K., Karp R. M., Hendler T., et al. (2011).  Using Contiguous Bi-Clustering for Data Driven Temporal Analysis of fMRI Based Functional Connectivity. Proceedings of the 4th Annual INCF Neuroinformatics Congress.
Xie, S., & Liu Y. (2008).  Using Corpus and Knowledge-Based Similarity Measure in Maximum Marginal Relevance for Meeting Summarization. 4985-4988.
Beckman, K.. B., Abel K.. A., Braun A.., & Halperin E. (2006).  Using DNA Pools for Genotyping Trios. Nucleic Acids Research. 34(19), 
Chang, S-Y., Morgan N., Raju A., Alwan A., & Kreiman J. (2015).  Using Fast and Slow Modulations to Model Human Hearing of Fast and Slow Speech.
Bian, F.., Li X.., Govindan R., & Shenker S. (2006).  Using Hierarchical Location Names for Scalable Routing and Rendevous in Wireless Sensor Networks. International Journal of Ad Hoc and Ubiquitous Computing. 1(4), 179-193.
Yegneswaran, V., Barford P., & Paxson V. (2005).  Using Honeynets for Internet Situational Awareness. Proceedings of the Fourth Workshop on Hot Topics in Networks (Hotnets-IV).
Ellis, D. P. W. (1999).  Using Knowledge to Organize Sound: The Prediction-driven Approach to Computational Auditory Scene Analysis and Its Application to Speech/Nonspeech Mixtures. Speech Communication. 27(3-4), 281-298.
Gleich, D., & Mahoney M. W. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
Liu, Y., Shriberg E., Stolcke A., & Harper M. P. (2004).  Using Machine Learning to Cope with Imbalanced Classes in Natural Speech: Evidence from Sentence Boundary and Disfluency Detection. Proceedings of International Conference on Spoken Language Processing.