Projects

Description: Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students as they progress through the Computer Science (CS) curriculum. Our students need to be able to examine the social complexities in which technology development and use are situated. Also, aligning students’ personal goals and their ability to achieve them in their field of study is important for promoting motivation and a sense of belonging. Promoting communal goals while learning computing can help broaden participation, particularly among groups who have been historically marginalized in computing. Keeping these considerations in mind, we piloted an introductory Java programming course in which activities engaging students in ethical and socially responsible considerations were integrated across modules. Rather than adding social on top of the …

Description: The You Described, We Archived dataset (YuWA) is a collaboration between San Francisco State University and The Smith-Kettlewell Eye Research Institute. It includes audio description (AD) data collected worldwide 2013–2022 through YouDescribe, an accessibility tool for adding audio descriptions to YouTube videos. YouDescribe, a web-based audio description tool along with an iOS viewing app, has a community of 12,000+ average annual visitors, with approximately 3,000 volunteer describers, and has created over 5,500 audio described YouTube videos. Blind and visually impaired (BVI) viewers request videos, which then are saved to a wish list and volunteer audio describers select a video, write a script, record audio clips, and edit clip placement to create an audio description. The AD tracks are stored separately, posted for public view at https://youdescribe.org/ and played together with the YouTube video. The YuWA audio description data paired with the describer and viewer metadata, and collection timeline has a large number of research applications including artificial intelligence, machine learning, sociolinguistics, audio description, video understanding, video retrieval and video-language grounding tasks.

Description: Particle-based models represent cars as individual “particles” that interact and move according to predefined rules.