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February 2016
Two Projects in Text Data Mining and Natural Language Processing
Two Projects in Text Data Mining and Natural Language Processing ELENA FILATOVA Department of Computer Systems Technology, New York City College of Technology, City University of New York In this presentation I will describe two projects I am working on: Automatic Sarcasm Detection and Information Assymetries in Multilingual Wikipedia. Sarcasm detection: Humans are good at identifying sarcasm in text and speech. Can we teach a computer to identify sarcasm? Is it possible to point out the parts of the review…
Find out more »March 2016
Towards Improving Interface Modularity in Legacy Java Software Through Automated Refactoring
The skeletal implementation pattern is a software design pattern consisting of defining an abstract class that provides a partial interface implementation. However, since Java allows only single class inheritance, if implementers decide to extend a skeletal implementation, they will not be allowed to extend any other class. Also, discovering the skeletal implementation may require a global analysis. Java 8 enhanced interfaces alleviate these problems by allowing interfaces to contain (default) method implementations, which implementers inherit. Java classes are then free to extend a different class, and a separate abstract class…
Find out more »Bio-inspired Computation Approach for Tumor Growth with Spatial Randomness Analysis of Kidney Cancer Xenograft Pathology Slides
In our research, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13:58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.
Find out more »NYC, Media and Technology: What’s Hot
Founded in 2010, NYC Media Lab is dedicated to driving innovation and ultimately job growth in media and technology by facilitating collaboration between the City’s universities and its companies. Comprised of a consortium including New York City Economic Development Corporation, New York University, Columbia University, The New School, CUNY, and Pratt Institute, NYC Media Lab's goals include generating research and development, knowledge transfer, and talent development across all of the City’s campuses. Justin will describe NYC Media Lab, its projects, and the curiosities of its member companies.
Find out more »April 2016
Ontology-based Classification and Faceted Search Interface for APIs
This work introduces faceted service discovery. It uses the Programmable Web directory as its corpus of APIs and enhances the search to enable faceted search, given an OWL ontology. The ontology describes semantic features of the APIs. We have designed the API classification ontology using LexOnt, a software we have built for semi-automatic ontology creation tool. LexOnt is geared toward non-experts within a service domain who want to create a high-level ontology that describes the domain. Using well- known NLP algorithms, LexOnt generates a list of top terms and phrases from the Programmable Web corpus to enable users to find high-level features that distinguish one Programmable Web service category from another. To also aid non-experts, LexOnt relies on outside sources such as Wikipedia and Wordnet to help the user identify the important terms within a service category. Using the ontology created from LexOnt, we have created APIBrowse, a faceted search interface for APIs. The ontology, in combination with the use of the Apache Solr search platform, is used to generate a faceted search interface for APIs based on their distinguishing features. With this ontology, an API is classified and displayed underneath multiple categories and displayed within the APIBrowse interface. APIBrowse gives programmers the ability to search for APIs based on their semantic features and keywords and presents them with a filtered and more accurate set of search results.
Find out more »December 2017
Energy Management as a Service (EMaaS)
Yu-Wen Chen, PhD Assistant Professor, CST Dept. December 7, 12pm-1pm, Room N907 Dr. Chen presents an introduction to smart grid and cloud computing as the foundation for the design of customer-oriented energy-efficient systems (EmaaS). These systems provide financial incentives to customers thus enhancing the renewable energy sources(solar, wind, electrical) integration with the smart grid community. Smart grid enables the two-way communication between the electricity utility and its customers and adopts new technologies such as electric vehicle and distributed energy resources…
Find out more »March 2018
Applied AI Techniques
Will Ross Business Development, M& A IBM Watson Group In a world of open data and consumer platforms it is easy to forget the significant quantities of high-value data still held by entities who view or require those assets to be proprietary. A myriad of parties from corporations to government entities are keen to explore new advances in AI but do not recognize the challenges that will befall them as they try to protect the data assets that…
Find out more »April 2018
Eager Execution in TensorFlow
Alexandre Passos Software Engineer, Google In this talk we'll go over TensorFlow, an open- source cross-platform machine learning library developed by Google, and explore its new feature: eager execution. We'll go over how to use it to write dynamic models, to debug and profile models, and to learn deep learning.
Find out more »November 2018
Building Noise Robust Machine Listeners with Data and Inspiration from Humans
Michael Mandel Assoc. Professor Brooklyn College, Graduate Center November 8, 12pm-1pm Room N919 Matching human performance is one of the most difficult problems for a variety of speech communication technologies, including automatic speech recognition, voice processing in hearing aids, and mobile telephony. One theory of human noise robustness is that listeners pick out reliable "glimpses" of a target sound and utilize contextual clues to fill in missing information using top-down knowledge. This talk presents work that brings both of…
Find out more »November 2019
Sports Data Science
Claudio T. Silva Professor, Computer Science & Engineering. New York University November 14, 2019 12pm-1pm Room N918 New technology is starting to enable the capture of game play at unprecedented levels of detail, including the tracking of positions of all players and game events at all times. This data deluge requires the development of novel visualization and machine learning tools and is leading to major new developments in sports data science. In this talk, we will review recent developments…
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