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1. Capacity Building Workshop for Two-Year Colleges Seeking U.S. National Science Foundation Funding

   https://doi.org/10.1080/10668926.2023.2236039  

   Mastronardi, Marialice; Brown, David R.; Borrego, Maura; Krupczak, John (July 2023, Community College Journal of Research and Practice)

   In this paper, we report the description and evaluation of an annual workshop titled “Capacity Building Workshops for Competitive S-STEM Proposals from Two-Year Colleges in the Western U.S.” which was offered in June of 2019, 2020, and 2021 with the goal of facilitating submissions to the NSF S-STEM program from 2-year colleges (2YCs). The two-day workshop was composed of separate sessions during which participants discussed several aspects of proposal preparation. Participants also received pre- and post-workshop support through webinars and office hours. To evaluate the program, post-workshop surveys were administered through Qualtrics™. The workshop and related activities received overall positive feedback with specific suggestions on how to better support participants. The paper discusses specific challenges faced by 2YC teams in preparing their proposals. Over three offerings, the program welcomed 103 participants on 51 teams from 2YCs. As of 2021, 11 teams total (from the 2019 cohort) submitted proposals. Among them, four were funded, which is approximately double the typical success rate. Six of the declined teams resubmitted and one of them is currently in negotiations. 

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2. ICPR 2020 - Competition on Harvesting Raw Tables from Infographics

   https://doi.org/10.1007/978-3-030-68793-9_27  

   Davila, Kenny; Tensmeyer, Chris; Shekhar, Sumit; Singh, Hrituraj; Setlur, Srirangaraj; Govindaraju, Venu. (February 2021, Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021)

   Del Bimbo, Alberto; Cucchiara, Rita; Sclaroff, Stan; Farinella, Giovanni M; Mei, Tao; Bertini, Marc; Escalante, Hugo J; Vezzani, Roberto. (Ed.)

   This work summarizes the results of the second Competition on Harvesting Raw Tables from Infographics (ICPR 2020 CHART-Infographics). Chart Recognition is difficult and multifaceted, so for this competition we divide the process into the following tasks: Chart Image Classification (Task 1), Text Detection and Recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and End-to-End Data Extraction (Task 7). We provided two sets of datasets for training and evaluation of the participant submissions. The first set is based on synthetic charts (Adobe Synth) generated from real data sources using matplotlib. The second one is based on manually annotated charts extracted from the Open Access section of the PubMed Central (UB PMC). More than 25 teams registered out of which 7 submitted results for different tasks of the competition. While results on synthetic data are near perfect at times, the same models still have room to improve when it comes to data extraction from real charts. The data, annotation tools, and evaluation scripts have been publicly released for academic use. 

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3. Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It

   Daniel Votipka, Kelsey R. (August 2020, USENIX Security Symposium)

   null (Ed.)

   Secure software development is a challenging task requiring consideration of many possible threats and mitigations.This paper investigates how and why programmers, despite a baseline of security experience, make security-relevant errors.To do this, we conducted an in-depth analysis of 94 submissions to a secure-programming contest designed to mimic real-world constraints: correctness, performance, and security.In addition to writing secure code, participants were asked to search for vulnerabilities in other teams’ programs; in total, teams submitted 866 exploits against the submissions we considered. Over an intensive six-month period, we used iterative open coding to manually, but systematically, characterize each submitted project and vulnerability (including vulnerabilities we identified ourselves). We labeled vulnerabilities by type, attacker control allowed, and ease of exploitation,and projects according to security implementation strategy.Several patterns emerged. For example, simple mistakes were least common: only 21% of projects introduced such an error.Conversely, vulnerabilities arising from a misunderstanding of security concepts were significantly more common, appearing in 78% of projects. Our results have implications for improving secure-programming APIs, API documentation,vulnerability-finding tools, and security education. 

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4. Reducing Faulty Jobs by Job Submission Verifier in Grid Engine

   https://doi.org/https://doi.org/10.1145/3332186.3338408  

   Ahmadian, M.; Rees, E.; Zhuang, Y.; Chen, Y. (January 2019, ACM Practice and Experience in Advanced Research Computing (PEARC'19))

   Grid Engine is a Distributed Resource Manager (DRM), that manages the resources of distributed systems (such as Grid, HPC, or Cloud systems) and executes designated jobs which have requested to occupy or consume those resources. Grid Engine applies scheduling policies to allocate resources for jobs while simultaneously attempting to maintain optimal utilization of all machines in the distributed system. However, due to the complexity of Grid Engine's job submission commands and complicated resource management policies, the number of faulty job submissions in data centers increases with the number of jobs being submitted. To combat the increase in faulty jobs, Grid Engine allows administrators to design and implement Job Submission Verifiers (JSV) to verify jobs before they enter into Grid Engine. In this paper, we will discuss a Job Submission Verifier that was designed and implemented for Univa Grid Engine, a commercial version of Grid Engine, and thoroughly evaluated at the High Performance Computing Center of Texas Tech University. Our newly developed JSV communicates with Univa Grid Engine (UGE) components to verify whether a submitted job should be accepted as is, or modified then accepted, or rejected due to improper requests for resources. It had a substantial positive impact on reducing the number of faulty jobs submitted to UGE by far. For instance, it corrected 28.6% of job submissions and rejected 0.3% of total jobs from September 2018 to February 2019, that may otherwise lead to long or infinite waiting time in the job queue. 

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5. Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It

   Daniel Votipka, Kelsey R. (January 2020, USENIX Security Symposium)

   Secure software development is a challenging task requiring consideration of many possible threats and mitigations. This paper investigates how and why programmers, despite a baseline of security experience, make security-relevant errors. To do this, we conducted an in-depth analysis of 94 submissions to a secure-programming contest designed to mimic real-world constraints: correctness, performance, and security. In addition to writing secure code, participants were asked to search for vulnerabilities in other teams’ programs; in total, teams submitted 866 exploits against the submissions we considered. Over an intensive six-month period, we used iterative open coding to manually, but systematically, characterize each submitted project and vulnerability (including vulnerabilities we identified ourselves). We labeled vulnerabilities by type, attacker control allowed, and ease of exploitation, and projects according to security implementation strategy. Several patterns emerged. For example, simple mistakes were least common: only 21% of projects introduced such an error. Conversely, vulnerabilities arising from a misunderstanding of security concepts were significantly more common, appearing in 78% of projects. Our results have implications for improving secure-programming APIs, API documentation, vulnerability-finding tools, and security education. 

   more » « less