Shaping Opinion and Influencing Societies through Narrative Construction

Dance As Language: A Choreographic Exploration of the Interpretation of Idiomatic Phrases in the English Language

Time and location: 1:35-1:50pm in UC 312

Katelyn O’Connor, Dance
Mentor: Ann Sofie Clemmensen, Dance

This research addressed an interpretation of English idioms on a literal to figurative scale, with the presentation being dance performance. The motivation for this research stemmed from interacting with a variety of people with English as their second (or non-primary) language. There are an estimated 25,000 idioms in the English language, and while there are idioms in other languages as well, research has shown it is not nearly as extensive as in the English language. Through personal interpretations, surveys collected from over seventy native and non-native English-speaking adults, and in-depth choreographic exploration, this research showed that there is a large depth of interpretations when it comes to English idioms, ranging from extreme ends of the scale of literal to figurative. The final presentation included an exploration of body, object, and space, utilizing props and movement in tandem to convey the tension between the literal and figurative language.

This work was funded, in part, through an Undergraduate Research Award from the UMBC Division of Undergraduate Academic Affairs.

Leon Tang, Information Systems

Mentor: Zhiyuan Chen, Information Systems

Mobile Autonomous Systems refer to robots, ground vehicles, drones etc. that can move autonomously. Such a system relies on sensors such as cameras and GPS to learn the states of the system itself as well as its surroundings and often machine learning (such as computer vision) and AI for autonomous decision making (e.g., to navigate). Deception is a serious threat to securing such systems because both the input to sensors as well as the machine learning models used in decision making can be misled by injected deceptive information. This project conducts a comprehensive survey of existing deception-based attacks against mobile autonomous systems and identifies gaps in the existing research. Compared to existing surveys, our survey considers various degrees of sophistication of the attacking strategies and type of data being altered. Our work also identifies gaps of existing research such as relatively little work has been done on multi-stage attacks and attacks that influence long term decision making. Our work can be used to help better understand deception based attacks as well as developing better defense strategies.

Poster presentation, 10-11:30am

UC Ballroom