Oscar Meruvia-Pastor

Aiur Nanzatov, Lourdes Peña-Castillo, Oscar Meruvia-Pastor

Two-factor authentication (2FA) has become widely adopted as an efficient and secure way to validate someone's identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their real-world surroundings. We present NRXR-ID, a technique to implement two-factor authentication while using extended reality systems and smartphones. The proposed method allows users to complete an authentication challenge using their smartphones without removing their HMD. We performed a user study where we explored four types of challenges for users, including a novel checkers-style challenge. Users responded to these challenges under three different configurations, including a technique that uses the smartphone to support gaze-based selection without the use of VR controllers. A 4X3 within-subjects design allowed us to study all the variations proposed. We collected performance metrics and performed user experience questionnaires to collect subjective impressions from 30 participants. Results suggest that the checkers-style visual matching challenge was the most appropriate option, followed by entering a digital PIN challenge submitted via the smartphone and answered within the VR environment.

Edward Kendall, Paraham Hajishafiezahramini, Matthew Hamilton et al.

Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the reading process unnecessarily leads to costly follow-up and patient anxiety. Automated methods promise to provide more efficient, consistent and effective reading. To facilitate their development, a number of datasets have been created. With the aim of specifically targeting population screening programs, we introduce NL-Breast-Screening, a dataset from a Canadian provincial screening program. The dataset consists of 5997 mammography exams, each of which has four standard views and is biopsy-confirmed. Cases where radiologist reading was a false-positive are identified. NL-Breast is made publicly available as a new resource to promote advances in automation for population screening programs.