SIGCSE TS 2026, The 57th ACM Technical Symposium on Computer Science Education, February 18 - 21, 2026, St. Louis, MO,USA

The constant connectedness of smart home devices and their sensing capabilities pose a unique threat to individuals’ privacy. While users may expect devices to exhibit minimal activity while they are not performing their intended functions, this is not necessarily the case, and traditional idle mode designations are insufficient to address the current landscape of smart home devices. To address this we propose a passive mode designation based on a comprehensive categorization of smart home devices. We then measure the network traffic of thirty-two devices in their respective passive modes. We find that 97% of the devices exhibit near-constant network activity in these modes (exchanging over 3M messages in 24 hours), with many of the devices initiating and responding to LAN communications with other devices, which potentially exposes users to privacy leakages.

The overarching goal of this work is to enable the collection of language describing a wide variety of objects viewed in virtual reality. We aim to create full 3D models from a small number of ‘keyframe’ images of objects found in the publicly available Grounded Language Dataset (GoLD) using photogrammetry. We will then collect linguistic descriptions by placing our models in virtual reality and having volunteers describe them. To evaluate the impact of virtual reality immersion on linguistic descriptions of the objects, we intend to apply contrastive learning to perform grounded language learning, then compare the descriptions collected from images (in GoLD) versus our models.

Our work explores the use of extended reality (XR) to improve scientific discovery with numerical weather/climate models that inform Earth science digital twins, specifically the NASA Goddard Earth Observing System (GEOS) global atmospheric model. The overall project is named the Visualization And Lagrangian dynamics Immersive eXtended Reality Toolkit (VALIXR), which has two main areas of focus: (1) enhancing the understanding of and interaction with model output data through advanced visualizations in the XR environment, and (2) the integration of Lagrangian dynamics into the GEOS model, which allows a natural, feature-specific analysis of Earth science phenomena as opposed to traditional, fixed-point Eulerian dynamics. Here, we report initial work on these focus areas.

Earth’s ice sheets are the largest contributor to sea level rise. For this reason, understanding the flow and topology of ice sheets is crucial for the development of accurate models and predictions. In order to aid in the generation of such models, ice penetrating radar is used to collect images of the ice sheet through both airborne and ground-based platforms. Glaciologists then take these images and visualize them in 3D fence diagrams on a flat 2D screen. We aim to consider the benefits that an XR visualization of these diagrams may provide to enable better data comprehension, annotation, and collaborative work. In this paper, we discuss our initial development and evaluation of such an XR system.

Ice layers in glaciers, such as those covering Greenland and Antarctica, are deformed over time. The deformations of these layers provide a record of climate history and are useful in predicting future ice flow and ice loss. Cross sectional images of the ice can be captured by airborne radar and layers in the images then annotated by glaciologists. Recent advances in semi-automated and automated annotation allow for significantly more annotations, but the validity of these annotations is difficult to determine because ground-truth (GT) data is scarce. In this paper, we (1) propose GT-dependent and GT-independent metrics for layer annotations and (2) present results from our implementation and initial testing of GT-independent metrics, such as layer breakpoints, local layer density, spatial frequency, and layer orientation agreement.

Collecting data in robotics, especially human-robot interactions, traditionally requires a physical robot in a prepared environment, that presents substantial scalability challenges. First, robots provide many possible points of system failure, while the availability of human participants is limited. Second, for tasks such as language learning, it is important to create environments that provide interesting' varied use cases. Traditionally, this requires prepared physical spaces for each scenario being studied. Finally, the expense associated with acquiring robots and preparing spaces places serious limitations on the reproducible quality of experiments. We therefore propose a novel mechanism for using virtual reality to simulate robotic sensor data in a series of prepared scenarios. This allows for a reproducible dataset that other labs can recreate using commodity VR hardware. We demonstrate the effectiveness of this approach with an implementation that includes a simulated physical context, a reconstruction of a human actor, and a reconstruction of a robot. This evaluation shows that even a simple “sandbox” environment allows us to simulate robot sensor data, as well as the movement (e.g., view-port) and speech of humans interacting with the robot in a prescribed scenario.

It has been suggested that some forms of speech disfluencies, most notable interjections and parentheticals, tend to occur disproportionally at major clause boundaries [6] and thus might serve to aid parsers in establishing these boundaries. We have tested a current statistical parser [1] on Switchboard text with and without interjections and parentheticals and found that the parser performed better when not faced with these extra phenomena. This suggest that for current parsers, at least, interjection and parenthetical placement does not help in the parsing process.