Výsledky vyhledávání - "Iftekhar Naim"

Akademický článek

Feature-Based Decipherment for Machine Translation

Autor: Iftekhar Naim, Parker Riley, Daniel Gildea

Publikováno v: Computational Linguistics, Vol 44, Iss 3, Pp 525-546 (2018)

Orthographic similarities across languages provide a strong signal for unsupervised probabilistic transduction (decipherment) for closely related language pairs. The existing decipherment models, however, are not well suited for exploiting these orth

Externí odkaz: https://doaj.org/article/36d2bf5e9ece46c2be3554857c91789d

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Autor: Jeffrey P. Bigham, Walter S. Lasecki, Raja S. Kushalnagar, Iftekhar Naim, Adam Sadilek, Daniel Gildea, Christopher D. Miller

Publikováno v: Communications of the ACM. 60:93-100

Quickly converting speech to text allows deaf and hard of hearing people to interactively follow along with live speech. Doing so reliably requires a combination of perception, understanding, and speed that neither humans nor machines possess alone.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::58cf262b67909523991fa79353b340c8
https://doi.org/10.1145/3068663

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SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 1: Algorithm design

Autor: James S. Cavenaugh, Jonathan Rebhahn, Tim R. Mosmann, Suprakash Datta, Gaurav Sharma, Iftekhar Naim

Publikováno v: Cytometry

We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70abe4c137b8adf3a6602c16344739b4
https://doi.org/10.1002/cyto.a.22446

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Aligning movies with scripts by exploiting temporal ordering constraints

Autor: Iftekhar Naim, Daniel Gildea, Henry Kautz, Abdullah Al Mamun, Young Chol Song, Jiebo Luo

Publikováno v: ICPR

Scripts provide rich textual annotation of movies, including dialogs, character names, and other situational descriptions. Exploiting such rich annotations requires aligning the sentences in the scripts with the corresponding video frames. Previous w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7ab7acfe9086d40070e88a5ba50415c0
https://doi.org/10.1109/icpr.2016.7899895

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ROC comment

Autor: Ru Zhao, Facundo Ciancio, Mohammad Rafayet Ali, Iftekhar Naim, Mohammed Ehsan Hoque

Publikováno v: UbiComp

We present an automated interface, ROC Comment, for generating natural language comments on behavioral videos. We focus on the domain of public speaking, which many people consider their greatest fear. We collect a dataset of 196 public speaking vide

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3897cc4e6fba0d1b840096c80ab15740
https://doi.org/10.1145/2971648.2971743

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Automated prediction and analysis of job interview performance: The role of what you say and how you say it

Autor: Iftekhar Naim, M. Iftekhar Tanveer, Daniel Gildea, Mohammed Ehsan Hoque

Publikováno v: FG

Ever wondered why you have been rejected from a job despite being a qualified candidate? What went wrong? In this paper, we provide a computational framework to quantify human behavior in the context of job interviews. We build a model by analyzing 1

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::581a249883b7df75ff87a17eb50548b0
https://doi.org/10.1109/fg.2015.7163127

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Sliding Alignment Windows for Real-Time Crowd Captioning

Autor: Daniel Gildea, Iftekhar Naim, Rahman Lavaee, Mohammad Kazemi

Publikováno v: ACL (2)

The primary way of providing real-time speech to text captioning for hard of hearing people is to employ expensive professional stenographers who can type as fast as natural speaking rates. Recent work has shown that a feasible alternative is to comb

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::12e935489064f263c482de1047e433b0
https://doi.org/10.3115/v1/p14-2039

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SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation

Autor: Gaurav Sharma, Iftekhar Naim, James S. Cavenaugh, Tim R. Mosmann, Jonathan Rebhahn, Suprakash Datta, Jason M. Weaver

Publikováno v: Cytometry

A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the da

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f11a46e6deada705138f972ac0646458
https://pubmed.ncbi.nlm.nih.gov/24532172

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Swift: Scalable weighted iterative sampling for flow cytometry clustering

Autor: Tim R. Mosmann, Gaurav Sharma, James S. Cavenaugh, Iftekhar Naim, Suprakash Datta

Publikováno v: ICASSP

Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets which pose a significant challenge for traditional manual bivariate an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9d4610fc9b4745b2a5cc4c12430f9d6e
https://doi.org/10.1109/icassp.2010.5495653

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