This experience report introduces a novel, adaptable course-based undergraduate research (CURE) model designed to nurture research interest in undergraduate CS majors. Traditional CURE models often target specific disciplines. Our model allows students to explore research questions within their chosen CS subfields. A pilot study assessed the model's effectiveness. Undergraduate CS majors participated, completing pre- and post-course surveys to gauge research motivation. The course culminated in a final research project requiring students to delve into a specific CS area, propose a novel approach, and outline a research plan. Findings from this pilot will inform the model's refinement for wider implementation. Further, the developed materials are shared openly at https://github.com/amandanko/UTSA-CS-CURE.

Undergraduate research experiences have a demonstrated impact on computer science students, however scaling these experiences with growing enrollments can be difficult. Creating experiences tailored to the diverse research fields within computer science can require the support of a large number of faculty and mentors dedicated to these students. Instead, initiatives for group work with focused research topics have been proposed, implemented through the CURE model where students enroll in a course and collaborate to complete research tasks. This work instead proposes a reconfigured CURE for CS, which will enable students to individually pursue a topic in their subfield of interest through a course focused on fundamental research methods and with the support of peers in special interest groups (SIG). The proposed CS-CURE retains the original elements of scientific approach, discovery, iteration, collaboration, and contribution, identified as critical components of a CURE. This course is scheduled to be offered at the University of Texas at San Antonio (UTSA) in Spring 2024.

As AI-generated code promises to become an increasingly relied upon tool for software developers, there is a temptation to call for significant changes to early computer science curricula. A move from syntax-focused topics in CS1 toward abstraction and high-level application design seems motivated by the new large language models (LLMs) recently made available. In this position paper however, we advocate for an approach more informed by the AI itself - teaching early CS learners not only how to use the tools but also how to better understand them. Novice programmers leveraging AI-code-generation without proper understanding of syntax or logic can create "black box" code with significant security vulnerabilities. We outline methods for integrating basic AI knowledge and traditional software verification steps into CS1 along with LLMs, which will better prepare students for software development in professional settings.

This paper describes our team's experience in designing and delivering the online teacher professional development (PD) program, Computer Science for San Antonio (CS4SA), aimed at empowering educators with computer science (CS) knowledge to increase Latinx participation in CS and STEM education within a large, urban predominantly Latinx school district in South Texas. This paper highlights the successes, challenges, and lessons learned while facilitating two cohorts of the CS PD through online platforms during the COVID-19 pandemic. As a result of this program, participants recognized the importance of integrating CS into their classroom and becoming advocates for the discipline at the high school level. Additionally, teachers, investigators, and other personnel learned important lessons for enhancing the program's impact through collaboration with district administrators and refinement of the online learning experience.

CS4SA is an in-service teacher professional development (PD) program designed by our team for secondary teachers from a predominately Latinx school district to create more computer science (CS) opportunities for their diverse classrooms. This poster presents our findings on participants' self-reflections as emerging advocates for CS following the online PD program.

Virtual reality (VR) technologies are used in a diverse range of applications. Many of these involve an embodied conversational agent (ECA), a virtual human who exchanges information with the user. Unfortunately, VR technologies remain inaccessible to many users due to the phenomenon of cybersickness: a collection of negative symptoms such as nausea and headache that can appear when immersed in a simulation. Many factors are believed to affect a user's level of cybersickness, but little is known regarding how these factors may influence a user's opinion of an ECA. In this study, we examined the effects of virtual stairs, a factor associated with increased levels of cybersickness. We recruited 39 participants to complete a simulated airport experience. This involved a simple navigation task followed by a brief conversation with a virtual airport customs agent in Spanish. Participants completed the experience twice, once walking across flat hallways, and once traversing a series of staircases. We collected self-reported ratings of cybersickness, presence, and perception of the ECA. We additionally collected physiological data on heart rate and galvanic skin response. Results indicate that the virtual staircases increased user level's of cybersickness and reduced their perceived realism of the ECA, but increased levels of presence.

Identifying smooth and meaningful object boundaries of noisy 3D point-clouds presents a challenge. Rather than rely on the points of the cloud itself, we identify a smooth implicit surface to represent the boundary of the cloud. By constructing a scalar field using a semantically-informative pseudopotential function, we take an arbitrary-resolution iso-surface and apply standard computer vision morphological transformations and edge detection on 2D slices of the pseudopotential field. When recombined, these slices comprise a new point-cloud representing the 3D boundary of the object as determined by the chosen isosurface. Our method leverages the strength and accessibility of 2D vision tools to identify smooth and semantically significant boundaries of ill-defined 3D objects, and additionally provides a continuous scalar field containing insight regarding the internal structure of the object. Our method enables a powerful and easily implementable pipeline for 3D boundary identification, particularly in domains where natural candidates for pseudopotential functions are already present.

Accurate performance prediction for cloud applications is an essential component to support many cloud resource management and auto-scaling policies. However, most existing studies on performance prediction for cloud applications in multitenant clouds are at the system level and may require access to performance counters in hypervisors. In this work, we propose uPredict, a user-level profiler-based performance predictive framework for single-VM (virtual machine) applications in multitenant clouds. We designed three micro-benchmarks to assess the contention of CPUs, memory and disks in a VM, respectively. Based on the measured performance of an application and micro-benchmarks, the application and VM-specific predictive models are derived by exploiting various regression and neural network based techniques. These models can then be used to predict the application's performance using the in-situ profiled resource contention with the micro-benchmarks. We evaluated uPredict extensively with representative benchmarks from PARSEC, NAS Parallel Benchmarks and CloudSuite, on a private cloud and two public clouds. The results show that the average prediction errors are between 10.4% to 17% for various predictive models on the private cloud with high resource contention, while the errors are within 4% on public clouds. A smart load-balancing scheme powered by uPredict is presented and can effectively reduce the execution and turnaround times of the considered application by 19% and 10%, respectively.