Congratulations to CS junior Sarah Almeda, who won first prize in the Undergraduate category at the ACM Student Research Competition at the ACM SIGCSE Technical Symposium last week!

Sarah presented her research on developing a translation and teaching tool for American Sign Language (ASL) using the LeapMotion Controller.  Her project was titled “Accessible Sign Language Recognition with the Leap Motion Controller” and she was mentored by Dr. Andrea Salgian during the Fall 2018 semester.  Sarah also received the Phi Kappa Phi Student-Faculty Research Award for her project last fall.

Sarah’s submission to the ACM Student Research Competition was one of 19 graduate and undergraduate submissions selected to be presented at the conference. After the poster presentation, the top three presenters qualified for the oral presentation, where she placed first. 

Congratulations again to Sarah!

CS majors Michael Altschuler (Class of 2019), Garrett Beatty (2019), and Ethan Kochis (2020), presented two research papers at the IEEE ICSC 2019 conference in Newport Beach, California, held January 29 – February 1. Both papers were written in collaboration with CS faculty member Dr. Michael Bloodgood as part of the students’ mentored research conducted in fall 2018. Both papers were supported by TCNJ’s Support of Scholarly Activities (SOSA) program and by use of the ELSA high performance computing cluster at TCNJ, supported by the National Science Foundation under grant number OAC-1828163. Dr. Bloodgood also attended the conference and served as a session chair during the conference.

Michael Altschuler and Dr. Bloodgood co-authored a paper titled: “Stopping Active Learning based on Predicted Change of F Measure for Text Classification.” In this paper, a new stopping method called Predicted Change of F Measure is introduced that provides users an estimate of how much performance of machine learning models can be expected to change at each iteration of learning.

Garrett Beatty, Ethan Kochis, and Dr. Bloodgood co-authored a paper titled: “The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification.” This paper compares and contrasts the advantages and disadvantages of methods for stopping machine learning of text classification systems using three different information sources that have not been compared and contrasted before, with the perhaps surprising result that methods that use unlabeled data are more effective than methods that use labeled data. This paper was also supported by TCNJ’s Mentored Undergraduate Summer Experience (MUSE) program.

More information about IEEE ICSC 2019 can be found at: https://semanticcomputing.wixsite.com/icsc-2019

On Friday, February 1 the Computer Science Department will host its first colloquium of the Spring 2019 semester. Junmin Liu from Bloomberg Princeton will give a technical talk on UI testing entitled “UI Test: To Automate or Not To, Is That Even a Question?“.  An abstract of his talk can be found below.

Abstract:
This lecture will discuss software testing and its critical role in software development life cycle, and introduce different testing types and levels. Topics include the importance of UI test
automation, how to design and develop automated UI testing with emphasis on web application, various web testing tools including headless Chrome browser. It concludes with a demo of an inhouse
automated test tool named “Professor Charles”.

Bio:
Junmin Liu, works in Bloomberg as Senior Software Engineer, member of Web Services team in Data Technologies department, primarily responsible for developing external client-facing web sites; has many years’ experiences in full-stack web development including large scale global e-commerce platforms. Interests include distributed system, web technologies, everything related to data e.g. data standards, data modeling, ETL pipeline, data visualization and data mining.