More Like this

1. Auto-Grading Jupyter Notebooks

   https://doi.org/10.1145/3328778.3366947  

   Manzoor, Hamza; Naik, Amit; Shaffer, Clifford A.; North, Chris; Edwards, Stephen H. (February 2020, SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education)
2. Grading-Based Test Suite Augmentation

   Osei-Owusu, Jonathan; Astorga, Angello; Butler, Liia; Xie, Tao; Challen, Geoffrey (November 2019, Proceedings of 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019), Short Paper)
3. Efficiency enhancement of ultrathin CIGS solar cells by optimal bandgap grading. Part III: piecewise-homogeneous grading

   https://doi.org/10.1364/AO.516850  

   Ahmad, Faiz; Monk, Peter_B; Lakhtakia, Akhlesh (April 2024, Applied Optics)

   In Parts I [Appl. Opt.58,6067(2019)APOPAI0003-693510.1364/AO.58.006067] and II [Appl. Opt.61,10049(2022)APOPAI0003-693510.1364/AO.474920], we used a coupled optoelectronic model to optimize a thin-film CIGS solar cell with a graded-bandgap photon-absorbing layer, periodically corrugated backreflector, and multilayered antireflection coatings. Bandgap grading of the CIGS photon-absorbing layer was continuous and either linear or nonlinear, in the thickness direction. Periodic corrugation and multilayered antireflection coatings were found to engender slight improvements in the efficiency. In contrast, bandgap grading of the CIGS photon-absorbing layer leads to significant enhancement of efficiency, especially when the grading is continuous and nonlinear. However, practical implementation of continuous nonlinear grading is challenging compared to piecewise-homogeneous grading. Hence, for this study, we investigated piecewise-homogeneous approximations of the optimal linear and nonlinear grading profiles, and found that an equivalent efficiency is achieved using piecewise-homogeneous grading. An efficiency of 30.15% is predicted with a three-layered piecewise-homogeneous CIGS photon-absorbing layer. The results will help experimentalists to implement optimal designs for highly efficient CIGS thin-film solar cells. 

   more » « less
4. Fair Grading Algorithms for Randomized Exams

   Jiale Chen; Jason Hartline; Onno Zoeter (April 2023, 4th Symposium on Foundations of Responsible Computing (FORC 2023))

   Kunal Talwar (Ed.)

   This paper studies grading algorithms for randomized exams. In a randomized exam, each student is asked a small number of random questions from a large question bank. The predominant grading rule is simple averaging, i.e., calculating grades by averaging scores on the questions each student is asked, which is fair ex-ante, over the randomized questions, but not fair ex-post, on the realized questions. The fair grading problem is to estimate the average grade of each student on the full question bank. The maximum-likelihood estimator for the Bradley-Terry-Luce model on the bipartite student-question graph is shown to be consistent with high probability when the number of questions asked to each student is at least the cubed-logarithm of the number of students. In an empirical study on exam data and in simulations, our algorithm based on the maximum-likelihood estimator significantly outperforms simple averaging in prediction accuracy and ex-post fairness even with a small class and exam size. 

   more » « less
5. IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

   https://doi.org/10.1016/j.media.2019.101561  

   Porwal, Prasanna; Pachade, Samiksha; Kokare, Manesh; Deshmukh, Girish; Son, Jaemin; Bae, Woong; Liu, Lihong; Wang, Jianzong; Liu, Xinhui; Gao, Liangxin; et al (January 2020, Medical Image Analysis)