APRIL 7 @ 12:00 PM – 1:00 PM in N928
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.
Knarig Arabshian is an Assistant Professor in the Computer Science Department at Hofstra University, since Fall 2014. Prior to that she was a Member of Technical Staff at Bell Labs in Murray Hill, NJ. She received her Ph.D. in Computer Science from Columbia University in 2008.
Professor Arabshian’s interests lie in the field of semantic web, service discovery and composition, context-aware computing and distributed systems. The goal of her research is to drive forward the idea of a personalized web. Her work explores ways of describing data meaningfully and designing frameworks and systems for efficient data discovery. During her tenure at Bell Labs, she worked on different aspects of ontology creation, distribution and querying.
MARCH 24 @ 12:00 PM – 1:00 PM in N928
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.
Justin Hendrix connects companies seeking to advance digital media technologies with university capabilities in order to drive collaborative innovation. Before joining NYC Media Lab, Hendrix was Vice President, Business Development & Innovation for The Economist Group in the Americas, where he directed the Group’s innovation process, including prototyping, testing, and commercializing new digital media business concepts. Prior to this role, Hendrix directed brand marketing and communications and ran The Economist’s thought leadership events business in the Americas. He is a regular writer and speaker on media & innovation. Hendrix holds a Bachelor of Arts from the College of William & Mary and a Master of Science in Technology Commercialization from the McCombs School of Business, University of Texas at Austin. He lives in Brooklyn.
MARCH 10 @ 12:00 PM – 1:00 PM
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.
Aydin Saribudak received his Bachelor of Science degree, in 2005, from Electrical and Electronics Engineering Department of Middle East Technical University (METU), Turkey. After his graduation, he worked as software developer and researcher in telecommunication field for more than 5 years. Aydin is currently a Ph.D. candidate at the City College of the CUNY. His interests include biologically inspired computation algorithms, artificial intelligence, and their applications to personalized mathematical models for tumor growth and anti-cancer therapy.
MARCH 3 @ 12:00 PM – 1:00 PM in N928
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 is no longer needed; developers considering implementing an interface need only examine the interface itself.
In this talk, I will argue that both these benefits improve software modularity, and I will discuss our ongoing work in developing an automated refactoring tool that would assist developers in taking advantage of the enhanced interface feature for their legacy Java software.
Raffi Khatchadourian is an Assistant Professor in the Department of Computer Systems Technology (CST) at New York City College of Technology (NYCCT) of the City University of New York (CUNY) and an Open Educational Resources (OER) Fellow for the Spring 2016 semester. His research is centered on techniques for automated software evolution, particularly those related to automated refactoring and source code recommendation systems. His goal is to ease the burden associated with correctly and efficiently evolving large and complex software by providing automated tools that can be easily used by developers.
Raffi received his MS and PhD degrees in Computer Science from Ohio State University and his BS degree in Computer Science from Monmouth University in New Jersey. Prior to joining City Tech, he was a Software Engineer at Apple, Inc. in Cupertino, California, where he worked on Digital Rights Management (DRM) for iTunes, iBooks, and the App store. He also developed distributed software that tested various features of iPhones, iPads, and iPods.
Posted in Research
Tagged eclipse, interface, java, java 8, refactoring, research, software engineering, software evolution, software maintenance, software modularity, talk
FEBRUARY 25 @ 12:00 PM – 1:00 PM in N928
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 that make it sarcastic? To answer these questions I use a corpus of sarcastic and regular Amazon product reviews. I analyze the sentiment flow of these reviews and demonstrate that classification features based on sentiment flow can be used to reliably classify documents into sarcastic and non-sarcastic.
Multilingual Wikipedia: Wikipedia is currently used as THE source of information without doubting the quality of this information. However, the Wikipedia articles corresponding to the same entry (person, location, event, etc.) written in different languages have substantial differences regarding what information is included in these articles. I discuss the nature of information assymetries in Multilingual Wikipedia and outline my plan for using information assymetries for automatic extension of Wikipedia articles.
Bio: Dr. Filatova is an Assistant Professor in the Computer Systems Technology department at CUNY CityTech since Fall 2015. Prior to that she was a faculty member at the Forhdam CIS department. She received her Ph.D. in Computer Science from Columbia University in 2008