Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. However, it requires pa-tient cooperation to fixate on multiple targets and stabilize their head utilizing both chin and forehead rests. Patient cooperation is particularly important for image montaging, where patients are asked to fixate on multiple targets to sequentially image different regions of interest on the retina. These individual volumes are then combined into a single large field of view volume. To automate the OCT image acquisition process, we previously developed a robot-mounted OCT scanner that auto-aligned with the retinal region of interest while compensating for subject motion. We utilized this system to self-align at multiple regions-of-interest and acquire stabilized volumes. We then montaged volume projections into a larger field of view image. The system tracked the 3D location of the subject's eye as well as their gaze orientation using a combination of face and pupil tracking cameras. We demonstrated automated OCT acquisition for live image montaging on free-standing subjects and evaluated the consistency of our live volumetric mapping on human subject data. Our results demonstrate that the system not only stabilized images, but also provided sufficient control of region-of-interest specific alignment to automatically acquire and montage OCT images, synthetically increasing the system's field of view by$20^{\circ}$.

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a “democratizing” effects in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.

Optical coherence tomography (OCT) has found great success in ophthalmology where it plays a key role in screening and diagnostics. Clinical ophthalmic OCT systems are typically deployed as tabletop instruments that require chinrest stabilization and trained ophthalmic photographers to operate. These requirements preclude OCT diagnostics in bedbound or unconscious patients who cannot use a chinrest, and restrict OCT screening to ophthalmology offices. We present a robotically-aligned OCT scanner capable of automatic eye imaging without chinrests. The scanner features eye tracking from fixed-base RGB-D cameras for coarse and stereo pupil cameras for fine alignment, as well as galvanometer aiming for fast lateral tracking, reference arm adjustment for fast axial tracking, and a commercial robot arm for slow lateral and axial tracking. We demonstrate the system's performance autonomously aligning with stationary eyes, pursuing moving eyes, and tracking eyes undergoing physiologic motion. The system demonstrates sub-millimeter eye tracking accuracy, 12 μm lateral pupil tracking accuracy, 83.2 ms stabilization time following step disturbance, and 9.7 Hz tracking bandwidth.

Contemporary online learning systems are increasingly common elements of post-secondary, workplace, and lifelong education. These systems typically employ the transmission model of education to teach students, an approach ill-suited for fostering deeper learning. This paper presents our latest findings related to ongoing research developing a generalizable framework for supporting deeper learning in online learning systems. In this work, we focus on the self-debriefing component of our framework and its impact on deeper learning in online learning systems. To pursue this line of inquiry, we conducted an exploratory study evaluating the Chimeria:Grayscale MOOC, an online learning system that implements our framework. Our results suggest that self-debriefing is crucial for effectively supporting students' reflections.

In deploying social media and other information technologies often not designed with MENA (the Middle East and North Africa) cultures in mind, users generate creative approaches to self-representation using virtual identities while preserving their cultural values. To understand and further empower such approaches, we present a mixed-method of computational and qualitative study, focusing on Qatar as a case of such communities in the MENA region. We analyzed a dataset of over 42,000 publicly available social media profiles using computational approaches (archetypal analysis) and qualitatively analyzed a separate set of 255 profiles. We augmented our descriptions with semi-structured interviews. As a result, we delineate a set of five needs/values exhibited by Qatari users supporting their creativity in effectively using virtual identities: Khaleeji features, self-expression, social connections, social monitoring, and physical and virtual identity contrasts. Finally, we propose an initial set of guidelines to support developers of virtual identity systems in better serving these users while preserving their cultural values and creative agency.