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1. Let’s Do Ranking & Selection

   Nelson, Barry L. (December 2022, Proceedings of the 2022 Winter Simulation Conference)

   Feng, B.; Pedrielli, G; Peng, Y.; Shashaani, S.; Song, E.; Corlu, C.; Lee, L.; Chew, E.; Roeder, T.; Lendermann, P. (Ed.)

   Many tutorials and survey papers have been written on ranking & selection because it is such a useful tool for simulation optimization when the number of feasible solutions or “systems” is small enough that all of them can be simulated. Cheap, ubiquitous, parallel computing has greatly increased the “all of them can be simulated” limit. Naturally these tutorials and surveys have focused on the underlying theory of R&S and have provided pseudocode procedures. This tutorial, by contrast, emphasizes applications, programming and interpretation of R&S, using the R programming language for illustration. Readers (and the audience) can download the code and follow along with the examples, but no experience with R is needed. 

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2. Ecological Forecasting and Dynamics: A graduate courseon the fundamentals of time series and forecasting in ecology

   https://doi.org/10.21105/jose.00198  

   Ernest, S. K.; Ye, Hao; White, Ethan P. (August 2023, Journal of Open Source Education)

   Ecological Dynamics and Forecasting' is a semester-long course to introduce students to the fundamentals of ecological dynamics and forecasting. This course implements paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course focuses on papers with an open-access or free-to-read version where possible, though some materials still rely on access to closed-access papers. The course is structured around two sessions per week, with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. Because the material is organized around content themes, instructors can modify and remix materials based on their course goals and student levels of background knowledge. These course materials have been taught for several years at the authors’ university and have also generated significant online engagement with course videos tens of thousands of times. 

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3. DS.js: Turn Any Webpage into an Example-Centric Live Programming Environment for Learning Data Science

   https://doi.org/10.1145/3126594.3126663  

   Zhang, Xiong; Guo, Philip J. (January 2017, ACM User Interface Software and Technology Symposium)

   Data science courses and tutorials have grown popular in recent years, yet they are still taught using production-grade programming tools (e.g., R, MATLAB, and Python IDEs) within desktop computing environments. Although powerful, these tools present high barriers to entry for novices, forcing them to grapple with the extrinsic complexities of software installation and configuration, data file management, data parsing, and Unix-like command-line interfaces. To lower the barrier for novices to get started with learning data science, we created DS.js, a bookmarklet that embeds a data science programming environment directly into any existing webpage. By transforming any webpage into an examplecentric IDE, DS.js eliminates the aforementioned complexities of desktop-based environments and turns the entire web into a rich substrate for learning data science. DS.js automatically parses HTML tables and CSV/TSV data sets on the target webpage, attaches code editors to each data set, provides a data table manipulation and visualization API designed for novices, and gives instructional scaffolding in the form of bidirectional previews of how the user’s code and data relate. 

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4. Comparison of Learning Programming Between Interactive Computer Tutors and Human Teachers

   https://doi.org/10.1145/3300115.3309506  

   Shen, Ruiqi; Wohn, Donghee Yvette; Lee, Michael J. (January 2019, ACM Global Computing Education Conference (CompEd))

   People typically learn programming from teachers in in-person courses or online tutorials. Interactive computer tutors---systems that deliver learning content interactively---have become more prevalent in online settings for teaching skills such as computer programming. Research has shown the efficiency and effectiveness of learning programming from teachers, interactive computer tutors, and a combination of both. However, there is limited understanding of learners' comparative perspectives about their experience learning from these different resources. We conducted an exploratory study using semi-structured interviews, recruiting 20 participants that had experience learning programming from both teachers and interactive computer tutors. We identified factors that learners like and dislike from both learning methods and discussed the strengths and weaknesses of them. Based on our findings, we propose suggestions for designers of interactive computer tutors, and for programming educators. 

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5. Checkpointing OpenSHMEM Programs Using Compiler Analysis

   https://doi.org/10.1109/FTXS51974.2020.00011  

   Shahneous Bari, Md Abdullah; Basu, Debasmita; Lu, Wenbin; Curtis, Tony; Chapman, Barbara (November 2020, 2020 IEEE/ACM 10th Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS))

   null (Ed.)

   The importance of fault tolerance continues to increase for HPC applications. The continued growth in size and complexity of HPC systems, and of the applications them- selves, is leading to an increased likelihood of failures during execution. However, most HPC programming models do not have a built-in fault tolerance mechanism. Instead, application developers usually rely on external support such as application- level checkpoint-restart (C/R) libraries to make their codes fault tolerant. However, this increases the burden on the application developer, who must use the libraries carefully to ensure correct behavior and to minimize the overheads. The C/R routines will be employed to save the values of all needed program variables at the places in the code where they are invoked. It is important for correctness that the program data is in a consistent state at these places. It is non-trivial to determine such points in OpenSHMEM, which relies upon single-sided communications to provide high performance. The amount of data to be collected, and the frequency with which this is performed, must also be carefully tuned, as the overheads introduced by C/R calls can be extremely high. There is very little prior work on checkpoint-restart support in the context of the OpenSHMEM programming interface. In this paper, we introduce OpenSHMEM and describe the challenges it poses for checkpointing. We identify the safest places for inserting C/R calls in an OpenSHMEM program and describe a straightforward approach for identifying the data that needs to be checkpointed at these positions in the code. We provide these two functionalities in a tool that exploits compiler analyses to propose checkpoints and the sets of data for saving at them, to the application developer. 

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