Pervasive Technology Institute

Permanent link for this communityhttps://hdl.handle.net/2022/357

The Pervasive Technology Labs will help Indiana University attain a position of international leadership in research accomplishment, and enhance the prosperity of the State of Indiana, enabling it to become a national leader in economic status and quality of life.

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    2011 annual report on training, education, and outreach activities of the Indiana University Pervasive Technology Institute and affiliated organizations
    (2012) Miller, Therese; Plale, Beth; Stewart, Craig A.
    This report summarizes training, education, and outreach activities for calendar 2011 of PTI and affiliated organizations, including the School of Informatics and Computing, Office of the Vice President for Information Technology, and Maurer School of Law. Reported activities include those led by PTI Research Centers (Center for Applied Cybersecurity Research, Center for Research in Extreme Scale Technologies, Data to Insight Center, Digital Science Center) and Service and Cyberinfrastructure Centers (Research Technologies Division of University Information Technology Services, National Center for Genome Assembly Support)
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    2012 annual report on training, education, and outreach activities of the Indiana University Pervasive Technology Institute and affiliated organizations
    (2014-05-08) Miller, Therese; Ping, Robert J.; Plale, Beth; Stewart, Craig A.
    This report summarizes training, education, and outreach activities for calendar 2012 of PTI and affiliated organizations, including the School of Informatics and Computing, Office of the Vice President for Information Technology, and Maurer School of Law. Reported activities include those led by PTI Research Centers (Center for Applied Cybersecurity Research, Center for Research in Extreme Scale Technologies, Data to Insight Center, Digital Science Center) and Service and Cyberinfrastructure Centers (Research Technologies Division of University Information Technology Services, National Center for Genome Assembly Support)
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    2013 annual report on training, education, and outreach activities of the Indiana University Pervasive Technology Institute and affiliated organizations
    (2014-05-08) Ping, Robert J.; Miller, Therese; Plale, Beth; Stewart, Craig A.
    This report summarizes training, education, and outreach activities for calendar 2013 of PTI and affiliated organizations, including the School of Informatics and Computing, Office of the Vice President for Information Technology, and Maurer School of Law. Reported activities include those led by PTI Research Centers (Center for Applied Cybersecurity Research, Center for Research in Extreme Scale Technologies, Data to Insight Center, Digital Science Center) and Service and Cyberinfrastructure Centers (Research Technologies Division of University Information Technology Services, National Center for Genome Assembly Support)
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    2013 annual report on training, education, and outreach activities of the Indiana University Pervasive Technology Institute and affiliated organizations
    (2014-05) Ping, Robert
    The 2013 report discusses the year's training, education, and outreach activities of the Indiana University Pervasive Technology Institute and affiliated organizations.
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    2015 Student Cluster Competition
    (2017-01) Michael, Scott A.
    The Student Cluster Competition is an HPC multi-disciplinary experience integrated within the HPC community’s biggest gathering, the Supercomputing Conference. The competition is a microcosm of a modern HPC center that teaches and inspires students to pursue careers in the field. It demonstrates the breadth of skills, technologies and science that it takes to build, maintain and utilize a supercomputer. In this real-time, non-stop, 48-hour challenge, teams of undergraduate and/or high school students assemble a small cluster on the exhibit floor and race to complete a real-world workload across a series of applications and impress HPC industry judges. For more information: http://studentclustercompetition.us/index.html.
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    2016 Annual Report
    (2017) Welch, Von
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    2016 NSF Community Cybersecurity Benchmarking Survey Report
    (2016) Cowles, Robert; Jackson, Craig
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    2016 Student Cluster Competition
    (2017-01) Michael, Scott A.
    The Student Cluster Competition is an HPC multi-disciplinary experience integrated within the HPC community’s biggest gathering, the Supercomputing Conference. The competition is a microcosm of a modern HPC center that teaches and inspires students to pursue careers in the field. It demonstrates the breadth of skills, technologies and science that it takes to build, maintain and utilize a supercomputer. In this real-time, non-stop, 48-hour challenge, teams of undergraduate and/or high school students assemble a small cluster on the exhibit floor and race to complete a real-world workload across a series of applications and impress HPC industry judges. For more information: http://studentclustercompetition.us/index.html.
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    2017 NSF Community Cybersecurity Benchmarking Survey Report
    (2018-06) Russell, Scott; Jackson, Craig; Cowles, Bob; Avila, Kay
    The NSF Community Cybersecurity Benchmarking Survey provides insight into the NSF science community's cybersecurity programs, practices, challenges, and concerns by collecting, analyzing, and publishing useful baseline benchmarking information."
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    2017 Student Cluster Competition
    (2018) Zhu, Xiao
    The Student Cluster Competition is an HPC multi-disciplinary experience integrated within the HPC community’s biggest gathering, the Supercomputing Conference. The competition is a microcosm of a modern HPC center that teaches and inspires students to pursue careers in the field. It demonstrates the breadth of skills, technologies and science that it takes to build, maintain and utilize a supercomputer. In this real-time, non-stop, 48-hour challenge, teams of undergraduate and/or high school students assemble a small cluster on the exhibit floor and race to complete a real-world workload across a series of applications and impress HPC industry judges.
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    2019 NSF Community Cybersecurity Benchmarking Survey Report
    (2019-12-20) Russell, Scott
    The purpose of Trusted CI’s Community Survey project is to collect, analyze, and publish useful baseline benchmarking information about the NSF science community’s cybersecurity programs, practices, challenges, and concerns.
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    2020 CACR AI/ML Lessons Learned Report
    (2020-07-31) Kiser, Ryan; Adams, Emily K.; Cushenberry, Austin; Abhinit, Ishan; Shute, Kelli
    Since Fall of 2019, the Indiana University Center for Applied Cybersecurity Research (CACR) has been exploring the application of machine learning to cybersecurity workflows with the intent of developing the applicable expertise necessary to maintain a commanding lead in the cybersecurity domain where machine learning solutions are expected to increasingly become the norm. In order to serve the objectives laid out in the project charter, CACR primarily worked in partnership with OmniSOC and researchers at Rochester Institute of Technology to explore the application of the ASSERT research prototype to SOC analyst workflows. The intent of this effort was to better understand both the utility of the ASSERT prototype and the challenges associated with the implementation of machine learning approaches to cybersecurity workflows more broadly.
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    2020 External Review of the Pervasive Technology Institute
    (2020-05-31) Stewart, Craig A.; Slavin, Shawn; Ping, Robert
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    2021 NSF Cybersecurity Summit Collection of Presentations
    (2021-10) Welch, Von
    2021 NSF Cybersecurity Summit Collection of Presentations of slides for Plenary, Workshops & Training
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    A Guide for Software Assurance for SWIP
    (2019-08) Heiland, Randy; Rynge, Mats; Vahi, Karan; Deelman, Ewa; Welch, Von
    The Scientific Workflow Integrity with Pegasus (SWIP) project adds data integrity checking to the Pegasus workflow management system (https://pegasus.isi.edu/). As part of SWIP, we perform software assurance (SwA) on the Pegasus software using the Software Assurance Marketplace (SWAMP, https://www.mir-swamp.org/). Initially, we planned to perform SwA only on the parts of the code base related to SWIP, i.e., only the code related to the data integrity checks. However, during the course of the SWIP project, a decision was made to perform SwA on the entire Pegasus code base. In addition, the project took on a research effort of trying to quantify differences in SwA results between Pegasus versions. We summarize our SwA process and results here. SwA results provide insight, but they are still subjective; developers of the software being assessed (Pegasus in this project) need to determine how those results need to be addressed.
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    A Hybrid Approach to Population Construction For Agricultural Agent-Based Simulation
    (2016) Chen, Peng; Evans, Tom; Frisby, Michael; Izquierdo, Eduardo; Plale, Beth
    An Agent Based Model (ABM) is a powerful tool for its ability to represent heterogeneous agents which through their interactions can reveal emergent phenomena. For this to occur though, the set of agents in an ABM has to accurately model a real world population to reflect its heterogeneity. But when studying human behavior in less well developed settings, the availability of the real population data can be limited, making it impossible to create agents directly from the real population. In this paper, we propose a hybrid method to deal with this data scarcity: we first use the available real population data as the baseline to preserve the true heterogeneity, and fill in the missing characteristics based on survey and remote sensing datasets; then for the remaining undetermined agent characteristics, we use the Microbial Genetic Algorithm to search for a set of values that can optimize the replicative validity of the model to match data observed from real world. We apply our method to the creation of a synthetic population of household agents for the simulation of agricultural decision making processes in rural Zambia. The result shows that the synthetic population created from the farmer register can correctly reflect the marginal distributions and the randomness of survey data; and can minimize the difference between the distribution of simulated yield and that of the observed yield in Post Harvest Survey (PHS).