The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study.

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation.

The segmentation of blurred cell boundaries in cornea endothelium microscope images is challenging, which affects the clinical parameter estimation accuracy. Existing deep learning methods only consider pixel-wise classification accuracy and lack of utilization of cell structure knowledge. Therefore, the segmentation of the blurred cell boundary is discontinuous. This paper proposes a structural prior guided network (SPG-Net) for corneal endothelium cell segmentation. We first employ a hybrid transformer convolution backbone to capture more global context. Then, we use Feature Enhancement (FE) module to improve the representation ability of features and Local Affinity-based Feature Fusion (LAFF) module to propagate structural information among hierarchical features. Finally, we introduce the joint loss based on cross entropy and structure similarity index measure (SSIM) to supervise the training process under pixel and structure levels. We compare the SPG-Net with various state-of-the-art methods on four corneal endothelial datasets. The experiment results suggest that the SPG-Net can alleviate the problem of discontinuous cell boundary segmentation and balance the pixel-wise accuracy and structure preservation. We also evaluate the agreement of parameter estimation between ground truth and the prediction of SPG-Net. The statistical analysis results show a good agreement and correlation.

TAFFIES: Tailored Automated Feedback Framework for Developing Integrated and Extensible Feedback Systems.

Delivering high-quality, timely and formative feedback for students' code-based coursework submissions is a problem faced by Computer Science (CS) educators. Automated Feedback Systems (AFSs) can provide immediate feedback on students' work, without requiring students to be physically present in the classroom-an increasingly important consideration for education in the context of COVID-19 lockdowns. There are concerns, however, surrounding the quality of the feedback provided by existing AFSs, with many systems simply presenting a score, a binary classification (pass/fail), or a basic error identification ("The program could not run"). Such feedback, with little guidance for how to rectify the problem, raises doubts as to whether or not these systems can stimulate deep engagement with the related knowledge or learning activities. In this paper, we propose TAFFIES, a framework to scaffold the development of AFSs that promote high-quality, tailored feedback for student's solutions. We tested our framework by applying it to develop an AFS to mark and provide feedback to 160 CS students in an introductory databases class. In contrast to most introductory-level coursework feedback and marking, which typically generate significant student reaction and change requests, our AFS deployment resulted in zero grade challenges. There were also no identified marking errors, or suggested inconsistencies or unfairness. Student feedback on the AFS was universally positive, with comments indicating an AFS-related increase in student motivation. The experience of designing, deploying, and evolving the AFS using TAFFIES is examined through reflective practice, student evaluation, and focus group (involving peer teachers) analysis.

Towards Post-pandemic Transformative Teaching and Learning: Case Studies of Microlearning Implementations in two Post-secondary Educational Institutions.

The recent COVID-19 pandemic has presented challenges to post-secondary education, including that campuses have been closed, removing face-to-face instruction options. Meanwhile, this crisis has also presented unique opportunities to create a "tipping point" or conditions that foster innovative teaching practices. In light of such a "danger-opportunity," the feasibility of introducing microlearning (ML), a technology-mediated teaching and learning (T&L) strategy, has recently been revisited by some institutions. ML offers learning opportunities through small bursts of training materials that learners can comprehend in a short time, according to their preferred schedule and location. Initially considered as "add-on" complementary online learning resources to provide learners with an active and more engaging learning experience through flexible learning modes, the possibility of an institution-wide implementation of ML has been further explored during the COVID-19 lockdown. This paper presents an exploratory case study examining two post-secondary education institutions' ML introductions. Using the SAMR model as the lens, their approaches to adopting ML are examined through analysis of quantitative questionnaires and qualitative teacher reflections. Overall, ML appears to be a promising direction that may not only be able to help institutions survive, but possibly offer an enhanced teaching and learning experience, post-pandemic. However, its current implementations face many challenges, both practical and pedagogical, and their impacts have yet to achieve transformation. With the insights gained, some possible strategies for moving the adoption of ML to the next level are offered.

Metamorphic Testing for Cybersecurity.

Testing is a major approach for the detection of software defects, including vulnerabilities in security features. This article introduces metamorphic testing (MT), a relatively new testing method, and discusses how the new perspective of MT can help to conduct negative testing as well as to alleviate the oracle problem in the testing of security-related functionality and behavior. As demonstrated by the effectiveness of MT in detecting previously unknown bugs in real-world critical applications such as compilers and code obfuscators, we conclude that software testing of security-related features should be conducted from diverse perspectives in order to achieve greater cybersecurity.