Fingerspelling is a critical part of American Sign Language (ASL) recognition and has become an accessible optional text entry method for Deaf and Hard of Hearing (DHH) individuals. In this paper, we introduce SpellRing, a single smart ring worn on the thumb that recognizes words continuously fingerspelled in ASL. SpellRing uses active acoustic sensing (via a microphone and speaker) and an inertial measurement unit (IMU) to track handshape and movement, which are processed through a deep learning algorithm using Connectionist Temporal Classification (CTC) loss. We evaluated the system with 20 ASL signers (13 fluent and 7 learners), using the MacKenzie-Soukoref Phrase Set of 1,164 words and 100 phrases. Offline evaluation yielded top-1 and top-5 word recognition accuracies of 82.45% (9.67%) and 92.42% (5.70%), respectively. In real-time, the system achieved a word error rate (WER) of 0.099 (0.039) on the phrases. Based on these results, we discuss key lessons and design implications for future minimally obtrusive ASL recognition wearables.

As the current COBOL workforce retires, entry-level developers are left to keep complex legacy systems maintained and operational. This creates a massive gap in knowledge and ability as companies are having their veteran developers replaced with a new, inexperienced workforce. Additionally, the lack of COBOL and mainframe technology in the current academic curriculum further increases the learning curve for this new generation of developers. These issues are becoming even more pressing due to the business-critical nature of these systems, which makes migrating or replacing the mainframe and COBOL unlikely anytime soon. As a result, there is now a huge need for tools and resources to increase new developers’ code comprehension and ability to perform routine tasks such as debugging and defect location. Extensive work has been done in the software engineering field on the creation of such resources. However, the proprietary nature of COBOL and mainframe systems has restricted the amount of work and the number of open-source tools available for this domain. To address this issue, our work leverages the publicly available technical forum data to build an open-source collection of COBOL programs embodying issues/defects faced by COBOL developers. These programs were reconstructed and organized in a benchmark suite to facilitate the testing of developer tools. Our goal is to provide an open-source COBOL benchmark and testing suite that encourage community contribution and serve as a resource for researchers and tool-smiths in this domain.

In many domains, large image collections are key ways in which information about relevant phenomena is retained and analyzed, yet it remains challenging to use such data in research and practice. Our aim is to investigate this problem in the context of a forensic unlabeled dataset of over 1M human decomposition photos. To make this collection usable by experts, various body parts first need to be identified and traced through their evolution despite their distinct appearances at different stages of decay from "fresh" to "skeletonized". We developed an unsupervised technique for clustering images that builds sequences of similar images representing the evolution of each body part through stages of decomposition. Evaluation of our method on 34,476 human decomposition images shows that our method significantly outperforms the state of the art clustering method in this application.

Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards curating such datasets. In this work, we present a workflow for unsupervisedly clustering a large collection of forensic images. The workflow utilizes classic clustering on deep feature representation of the images in addition to domain-related data to group them together. Our manual evaluation shows a purity of 89% for the resulted clusters.

Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. To employ contemporary supervised image analysis methods on such image data, they must first be cleaned and organized, and then manually labeled for the nomenclature employed in the specific domain, which is a time consuming and expensive endeavor. To address this issue, we designed and implemented the Plud system. Plud provides an iterative semi-supervised workflow to minimize the effort spent by an expert and, due to the fact that it does not make any assumption about its input, can handle realistic large collections of images regardless of their size and type. Plud is an iterative sequence of unsupervised clustering, human assistance, and supervised classification. With each iteration 1) the labeled dataset grows, 2) the generality of the classification method and its accuracy increases, and 3) manual effort is reduced. We evaluated the effectiveness of our system, by applying it on over a million images documenting human decomposition. In our experiment comparing manual labeling with labeling conducted with the support of Plud, we found that it reduces the time needed to label data and produces highly accurate models for this new domain.