Employing industrial quality management systems for quality assurance in outcome-based engineering education: a review

With the world becoming flat with fluid boundaries, engineers have to be global in their outlook and their pedigree. Due to the need for international acceptance of engineering qualification, the incorporation of Outcome-Based Education (OBE) has become common and global accreditation treaties such as the Washington Accord have been ratified. Further, it becomes important, especially for an engineering university with a global outlook preparing its students for global markets, to ensure that its graduates attain the planned outcomes. Additionally, the higher education institutions need to make sure that all the stakeholders, including students, parents, employers, and community at large, are getting a quality educational service, where quality is categorized as (1) product-based ensuring that the graduate attained the planned outcomes and skills, and (2) process-based keeping an eye on whether the process is simple, integrated, and efficient. The development of quality movements, such as Total Quality Movement (TQM), Six Sigma, etc., along with quality standards such as ISO 9001 has been instrumental in improving the quality and efficiency in the fields of management and services. Critical to the successful deployment of a quality culture is the institutionalization of an integrated Quality Management System (QMS) in which formally documented processes work according to the Vision and Mission of an institute. At the same time, commitment to Continuous Quality Improvement (CQI) to close the loop through effective feedback, would ensure that the planned outcomes are attained to the satisfaction of all the stakeholders, and that the process overall is improving consistently and continuously. The successful adoption of quality culture requires buy-in from all the stakeholders (and in particular, the senior leadership) and a rigorous training program. In this paper, we provide a review of how a QMS may work for the provision of quality higher education in a 21st-century university

Cross-Region Building Counting in Satellite Imagery using Counting Consistency

Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every region is a costly, time-consuming, and difficult endeavor. In this paper, we propose an unsupervised domain adaptation method for counting buildings where we use a labeled source domain (developed regions) and adapt the trained model on an unlabeled target domain (developing regions). We initially align distribution maps across domains by aligning the output space distribution through adversarial loss. We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift. Within-image consistency enforces that building count in the whole image should be greater than or equal to count in any of its sub-image. Across-image consistency constraint enforces that if an image contains considerably more buildings than the other image, then their sub-images shall also have the same order. These two constraints encourage the behavior to be consistent across and within the images, regardless of the scale. To evaluate the performance of our proposed approach, we collected and annotated a large-scale dataset consisting of challenging South Asian regions having higher building densities and irregular structures as compared to existing datasets. We perform extensive experiments to verify the efficacy of our approach and report improvements of approximately 7% to 20% over the competitive baseline methods