Yashika Jain

Yashika Jain, Ali Dabouei, Min Xu

Video Anomaly Detection (VAD) automates the identification of unusual events, such as security threats in surveillance videos. In real-world applications, VAD models must effectively operate in cross-domain settings, identifying rare anomalies and scenarios not well-represented in the training data. However, existing cross-domain VAD methods focus on unsupervised learning, resulting in performance that falls short of real-world expectations. Since acquiring weak supervision, i.e., video-level labels, for the source domain is cost-effective, we conjecture that combining it with external unlabeled data has notable potential to enhance cross-domain performance. To this end, we introduce a novel weakly-supervised framework for Cross-Domain Learning (CDL) in VAD that incorporates external data during training by estimating its prediction bias and adaptively minimizing that using the predicted uncertainty. We demonstrate the effectiveness of the proposed CDL framework through comprehensive experiments conducted in various configurations on two large-scale VAD datasets: UCF-Crime and XD-Violence. Our method significantly surpasses the state-of-the-art works in cross-domain evaluations, achieving an average absolute improvement of 19.6% on UCF-Crime and 12.87% on XD-Violence.

Naaz Sibia, Jessica Wen, Amber Richardson et al.

Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) offers cognitive design principles for coordinating views, but less is known about what shapes learners' engagement with available representations. We conducted a within-subjects study with 19 undergraduates who had completed CS1 and CS2. Students completed think-aloud tasks, reflective interviews, and webcam-based gaze tracking while using a multi-representational probe with synchronized code, memory, and metaphor views, and Python Tutor, across scope, while loops, and linked lists. Gaze analysis showed that students spent nearly half their time focused on code despite available visual scaffolds. Students without prior experience anchored even more heavily in code and engaged minimally with metaphor views. Interviews identified three factors shaping selective engagement: agency, as students sought control over cognitive effort rather than simply having it reduced; representational fit, as identical designs differed in whether they felt helpful or overwhelming; and legitimacy, as some students avoided metaphorical scaffolds they perceived as childish or insufficiently rigorous for university-level work. These findings suggest that multi-representational tools in computing education require attention to affective and social factors alongside cognitive design. Practical considerations include positioning visualizations as verification instruments, offering toggleable abstraction levels, and framing tools to signal disciplinary legitimacy. More broadly, the themes help explain why cognitively sound visualization tools may fail to engage the students they are designed to help.