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2. Building Science Gateways for Humanities

   https://doi.org/10.1145/3311790.3396628  

   Zhou, Jun; Smith, Karen; Wilsbacher, Greg; Sagona, Paul; Reddy, David; Torkian, Ben (July 2020, PEARC '20: Practice and Experience in Advanced Research Computing)

   Building science gateways for humanities content poses new challenges to the science gateway community. Compared with science gateways devoted to scientific content, humanities-related projects usually require 1) processing data in various formats, such as text, image, video, etc., 2) constant public access from a broad audience, and therefore 3) reliable security, ideally with low maintenance. Many traditional science gateways are monolithic in design, which makes them easier to write, but they can be computationally inefficient when integrated with numerous scientific packages for data capture and pipeline processing. Since these packages tend to be single-threaded or nonmodular, they can create traffic bottlenecks when processing large numbers of requests. Moreover, these science gateways are usually challenging to resume development on due to long gaps between funding periods and the aging of the integrated scientific packages. In this paper, we study the problem of building science gateways for humanities projects by developing a service-based architecture, and present two such science gateways: the Moving Image Research Collections (MIRC) – a science gateway focusing on image analysis for digital surrogates of historical motion picture film, and SnowVision - a science gateway for studying pottery fragments in southeastern North America. For each science gateway, we present an overview of the background of the projects, and some unique challenges in their design and implementation. These two science gateways are deployed on XSEDE’s Jetstream academic clouding computing resource and are accessed through web interfaces. Apache Airavata middleware is used to manage the interactions between the web interface and the deep-learning-based (DL) backend service running on the Bridges graphics processing unit (GPU) cluster. 

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3. Urban building energy model: Database development, validation, and application for commercial building stock

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   null (Ed.)
4. Building Your Dream Team for Change

   https://doi.org/10.18260/1-2--32489  

   Margherio, Cara; Doten-Snitker, Kerice; Litzler, Elizabeth; Williams, Julia; Andrijcic, Eva; Mohan, Sriram (June 2020, 2019 ASEE Virtual Annual Conference Content Access)

   null (Ed.)

   his panel paper presents research on connecting theory to practice and the lessons learned in a change project, with a focus on team formation during the early stages of change making. An important yet often overlooked step in any change project is pulling together individuals to form a competent and efficient team. A functional change-making team requires a variety of complementary skill sets, which may come from different disciplinary backgrounds and/or different prior experiences. Kotter (1996) uses the term “guiding coalition” to refer to an effective change-making team. He identifies four key characteristics of guiding coalitions: position power, expertise, credibility, leadership. Kotter also goes on to examine the importance of trust and a common goal. In a review of the literature on guiding coalitions, Have, Have, Huijsmans, and Otto (2017) found that though the concept of a guiding coalition is widely advocated in the literature, only one study showed a moderate correlation between the existence of a guiding coalition and the success of a change process (Abraham, Griffin, & Crawford, 1999). Have et al. (2017) conclude that while the literature provides little evidence to the value of a guiding coalition, it does provide evidence that Kotter’s characteristics of a guiding coalition (position power, expertise, credibility, leadership skills, trust in leadership, and setting common goals) individually have positive effects on the outcomes of a change project. However, we don’t know how these characteristics interact. This analysis of team building and complementary skill sets emerges from our participatory action research with the NSF REvolutionizing engineering and computer science Departments (RED) teams to investigate the change process within STEM higher education. The research-to-practice cycle is integral to our project; data gathered through working with the RED teams provides insights that are then translated into applied, hands-on practices. We utilize an abductive analysis approach, a qualitative methodology that moves recursively between the data and theory-building to remain open to new or contradictory findings, keeping existing theory in mind while not developing formal hypotheses (Timmermans & Tavory, 2012). We find that many of the teams have learned lessons in the early stages of the change process around the guiding coalition characteristics, and our analysis builds on the literature by examining how these characteristics interact. For example, the expertise of the social scientists and education researchers help discern which change strategies have supporting evidence and fit the context, in addition to what is reasonable for planning, implementation, and evaluation. The results presented in this paper connect theory to practice, clarifying practices for building effective change-making teams within higher education. 

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5. Algorithmic Building Blocks for Asymmetric Memories

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   Gu, Yan Gu; Sun, Yihan; Blelloch, Guy E. (August 2018, 26th Annual European Symposium on Algorithms (ESA 2018))

   The future of main memory appears to lie in the direction of new non-volatile memory technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of energy, bandwidth, and latency. This asymmetry can have a significant effect on algorithm design, and in many cases it is possible to reduce writes at the cost of more reads. This paper studies which algorithmic techniques are useful in designing practical write-efficient algorithms. We focus on several fundamental algorithmic building blocks including unordered set/map implemented using hash tables, comparison sort, and graph traversal algorithms including breadth-first search and Dijkstra’s algorithm. We introduce new algorithms and implementations that can reduce writes, and analyze the performance experimentally using a software simulator. Finally, we summarize interesting lessons and directions in designing write-efficient algorithms that can be valuable to share. 

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