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1. Work in Progress: Assessing Creativity of Alternative Uses Task Responses: A Detailed Procedure

   https://doi.org/10.18260/1-2--35612  

   Alhashim, Amin; Marshall, Megan; Hartog, Tess; Jonczyk, Rafal; Dickson, Danielle; van Hell, Janet; Okudan-Kremer, Gül; Siddique, Zahed (January 2020, Paper presented at 2020 ASEE Virtual Annual Conference)

   null (Ed.)

   Creativity is the driver of innovation in engineering. Hence, assessing the effectiveness of a curriculum, a method, or a technique in enhancing the creativity of engineering students is no doubt important. In this paper, the process involved in quantifying creativity when measured through the alternative uses task (AUT) is explained in detail. The AUT is a commonly used test for divergent thinking ability, which is a main aspect of creativity. Although it is commonly used, the processes used to score this task are far from standardized and tend to differ across studies. In this paper, we introduce these problems and move towards a standardized process by providing a detailed account of our quantification process. This quantification process takes into consideration four commonly used dimensions of creativity: originality, flexibility, fluency, and elaboration. AUT data from a preliminary case study were used to illustrate how the AUT and the quantification process can be used. The study was performed to understand the effect of the stereotype threat on the creativity of 25 female engineering students. The results indicate that after the stereotype threat intervention, participants generated more diverse and original ideas. 

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2. The impact of trait number and correlation on functional diversity metrics in real-world ecosystems

   https://doi.org/10.1371/journal.pone.0306342  

   Ohlert, Timothy; Kimmel, Kaitlin; Avolio, Meghan; Chang, Cynthia; Forrestel, Elisabeth; Gerstner, Benjamin P; Hobbie, Sarah E; Reich, Peter; Whitney, Kenneth D; Komatsu, Kimberly (September 2024, PLOS ONE)

   Boscutti, Francesco (Ed.)

   The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work with simulated communities and trait distributions shows sensitivity of functional diversity measures to the number and correlation of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. To address this gap, we used data from six grassland plant communities in Minnesota and New Mexico, USA to test how the number of traits and the correlation between traits used in the calculation of eight functional diversity indices impact the magnitude of functional diversity metrics in real plant communities. We found that most metrics were sensitive to the number of traits used to calculate them, but functional dispersion (FDis), kernel density estimation dispersion (KDE dispersion), and Rao’s quadratic entropy (Rao’s Q) maintained consistent rankings of communities across the range of trait numbers. Despite sensitivity of metrics to trait correlation, there was no consistent pattern between communities as to how metrics were affected by the correlation of traits used to calculate them. We recommend that future use of evenness metrics include sensitivity analyses to ensure results are robust to the number of traits used to calculate them. In addition, we recommend use of FDis, KDE dispersion, and Rao’s Q when ecologically applicable due to their ability to produce consistent rankings among communities across a range of the numbers of traits used to calculate them. 

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3. Assessing Paper Texture Similarity in Matisse Lithographs Using a Triplet Neural Network

   https://doi.org/10.1109/URTC54388.2021.9740159  

   Aguilar, Karen M.; Powers, Shelby; Lackey, Leah; Grootveld, Arick; Klein, Andrew G. (October 2021, 2021 IEEE MIT Undergraduate Research Technology Conference (URTC))

   This work explores the use of a triplet neural net-work for assessing the similarity of paper textures in a collection of Henri Matisse’s lithographs. The available dataset contains digital photomicrographs of papers in the lithograph collection, consisting of four views: two raking light orientations and both sides of the paper. A triplet neural network is first trained to extract features sensitive to anisotropy, and subsequently used to ensure that all papers in the dataset are in the same orientation and side. Another triplet neural network is then used to extract the texture features that are used to assess paper texture similarity. These results can then be used by art conservators and historians to answer questions of art historical significance, such as artist intent. 

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4. Uncertainty in humanities network visualization

   https://doi.org/10.3389/fcomm.2023.1305137  

   Conroy, Melanie; Gillmann, Christina; Harvey, Francis; Mchedlidze, Tamara; Fabrikant, Sara Irina; Windhager, Florian; Scheuermann, Gerik; Tangherlini, Timothy R.; Warren, Christopher N.; Weingart, Scott B.; et al (January 2024, Frontiers in Communication)

   Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations. 

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5. Geospatial Filter and Refine Computations on NVidia Bluefield Data Processing Units (DPU)

   Kaymak, Derda; Puri, Satish (November 2023, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'23))

   In this poster, we will show how to leverage NVidia’s Bluef ield Data Processing Unit (DPU) in geospatial systems. Existing work in literature has explored DPUs in the context of machine learning, compression and MPI acceleration. We show our designs on how to integrate DPUs into existing high performance geospatial systems like MPI-GIS. The workflow of a typical spatial computing workload consists of two phases- filter and refine. First we used DPU as a target to offload spatial computations from the host CPU. We show the performance improvements due to offload. Next we used DPU for network I/O processing. In network I/O case, the query data first comes to DPU for filtering and then the query goes to CPU for refinement. DPU-based filter and refine system can be useful in other domains like Physics where an FPGA is used to perform the filter to handle Big Data. We used Bluefield-2 and Bluefield-3 in our experiments. For scalability study, we have used up to 16 DPUs. 

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