The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa. We train a convolutional neural network to predict ground truth labels from the Afrobarometer Round 6 survey using Landsat 8 and Sentinel 1 satellite imagery.Our best models predict infrastructure quality with AUROC scores of 0.881 on Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using Landsat 8. These performances are significantly better than models that leverage OpenStreetMap or nighttime light intensity on the same tasks. We also demonstrate that our trained model can accurately make predictions in an unseen country after fine-tuning on a small sample of images. Furthermore, the model can be deployed in regions with limited samples to predict infrastructure outcomes with higher performance than nearest neighbor spatial interpolation.

Educators have faced new challenges in effective course assessment during the recent, unprecedented shift to remote online learning during the COVID-19 pandemic. In place of typical proctored, timed exams, instructors must now rethink their methodology for assessing course-level learning goals. Are exams appropriate-or even feasible-in this new online, open-internet learning environment? In this experience paper, we discuss the unique exams framework: our framework for upholding exam integrity and student privacy. In our Probability for Computer Scientists Course at an R1 University, we developed autogenerated, unique exams where each student had the same four problem skeletons with unique numeric variations per problem. Without changing the process of the traditional exam, unique exams provide a layer of security for both students and instructors about exam reliability for any classroom environment-in-person or online. In addition to sharing our experience designing unique exams, we also present a simple end-to-end tool and example question templates for different CS subjects that other instructors can adapt to their own courses.

Scalable shape encoding is one of the important steps to achieving highly scalable object-based video coding. In this paper, a new scalable vertex-based shape intra-coding scheme has been described. To improve the encoding performance, we propose a new vertex selection scheme, which can reduce the number of approximation vertices. We also propose a new vertex encoding method, in which the information on the coarser layers and statistical entropy coding are exploited for high encoding efficiency. Experimental results show that the proposed scheme can provide 25-60% gain over the scalable encoding method in Buhan Jordan et al. (1998). For some sequences, it can achieve 5-10% gain over the conventional non-scalable vertex-based coding method (O’Connell (1997)) in bit rate, at the price of additional complexity

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