Smart yarns hold the potential to transform everyday textiles into functional platforms, yet current methods remain constrained. These include conductive yarns, made from silver or stainless steel, which retain the feel of conventional yarns but offer limited functions, and PCB-based solutions, which add capability at the cost of bulk and rigidity. We present Circuit2Yarn, a fabrication framework that transforms planar printed circuits into flexible yarns by rolling copper-traced TPU films with soldered surface-mount components, preserving the capabilities of rigid electronics while producing yarn-like forms suitable for textile integration. We demonstrate yarns as small as 0.8 mm that integrate LEDs and sensors, including temperature, humidity, light, IMU, and capacitive sensing modules, enabling applications ranging from smart garments and interactive musical instruments to responsive tea bags. Characterization confirms durability under bending/stretching. By rolling planar circuits into yarns, Circuit2Yarn paves the way toward comfortable, multifunctional, and interactive textiles in everyday life.

Life cycle assessment (LCA) is a methodology for holistically measuring the environmental impact of a product from initial manufacturing to end-of-life disposal.However, the extent to which LCA informs the design of computing devices remains unclear.To understand how this information is collected and applied, we interviewed 17 industry professionals with experience in LCA or electronics design, systematically coded the interviews, and investigated common themes.These themes highlight the challenge of LCA data collection and reveal distributed decision-making processes where responsibility for sustainable design choices-and their associated costs-is often ambiguous.Our analysis identifes opportunities for HCI technologies to support LCA computation and its integration into the design process to facilitate sustainability-oriented decision-making.While this work provides a nuanced discussion about sustainable design in the information and communication technologies (ICT) hardware industry, we hope our insights will also be valuable to other sectors.

Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness—the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies—remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for systematically evaluating data-aware reasoning on tabular data. We develop a framework to simulate data artifacts via programmatic perturbations to enable targeted evaluation of model behavior. RADAR comprises 2,980 table-query pairs, grounded in real-world data spanning 9 domains and 5 data artifact types. In addition to evaluating artifact handling, RADAR systematically varies table size to study how reasoning performance holds when increasing table size. Our evaluation reveals that, despite decent performance on tables without data artifacts, frontier models degrade significantly when data artifacts are introduced, exposing critical gaps in their capacity for robust, data-aware analysis. Designed to be flexible and extensible, RADAR supports diverse perturbation types and controllable table sizes, offering a valuable resource for advancing tabular reasoning.

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId’s success factors, limitations, and its generalizability to other crowdsourced science domains.

Public transportation provides vital connectivity to people with disabilities, facilitating access to work, education, and health services. While modern navigation applications provide a suite of information about transit options—including real-time updates about bus or train arrivals—they lack data about the accessibility of the transit stops themselves. Bus stop features such as seatings, shelters, and landing areas are critical, but few cities provide this information. In this demo paper, we introduce BusStopCV, a Human+AI web prototype for scalably collecting data on bus stop features using real-time computer vision and human labeling. We describe BusStopCV’s design, custom training with the YOLOv8 model, and an evaluation of 100 randomly selected bus stops in Seattle, WA. Our findings demonstrate the potential of BusStopCV and highlight opportunities for future work.

In this paper, we propose SwellSense, a fabrication technique to screen print stretchable circuits onto a special micro-capsule paper, creating localized swelling patterns with sensing capabilities. This simple technique will allow users to create a wide range of paper-based tactile interactive devices, which are mostly maintaining 2D planar form factor but can also be curved or folded into 3D interactive artifacts. We frst present the design guidelines to support various tactile interaction design including basic tactile graphic geometries, patterns with directional density, or fner interactive textures with embedded sensing such as touch sensor, pressure sensor, and mechanical switch. We then provide a design editor to enable users to design more creatively using the SwellSense technique. We provide a technical evaluation and user evaluation to validate the basic performance of SwellSense. Lastly, we demonstrate several application examples and conclude with a discussion on current limitations and future work.