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1. Chemistry on the Cloud: From Wet Labs to Web Labs

   https://doi.org/10.1021/acs.jchemed.1c00585  

   Xie, Charles; Li, Chenglu; Ding, Xiaotong; Jiang, Rundong; Sung, Shannon (August 2021, Journal of Chemical Education)

   null (Ed.)

   Digital sensors allow people to collect a large quantity of data in chemistry experiments. Using infrared thermography as an example, we show that this kind of data, in conjunction with videos that stream the chemical phenomena under observation from a vantage point, can be used to construct digital twins of experiments to support science education on the cloud in a visual and interactive fashion. Through digital twins, a significant part of laboratory experiences such as observation, analysis, and discussion can be delivered on a large scale. Thus, the technology can potentially broaden participation in experimental chemistry, especially for students and teachers in underserved communities who may lack the expertise, equipment, and supplies needed to conduct certain experiments. With a cloud platform that enables anyone to store, process, and disseminate experimental data via digital twins, our work also serves as an example to illuminate how the movement of open science, which is largely driven by data sharing, may be powered by technology to amplify its impacts on chemistry education. 

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2. GraphWaGu: GPU Powered Large Scale Graph Layout Computation and Rendering for the Web.

   Landon Dyken, Pravin Poudel (June 2022, Eurographics Symposium on Parallel Graphics and Visualization)

   Bujack, Roxana and (Ed.)

   Large scale graphs are used to encode data from a variety of application domains such as social networks, the web, biological networks, road maps, and finance. Computing enriching layouts and interactive rendering play an important role in many of these applications. However, producing an efficient and interactive visualization of large graphs remains a major challenge, particularly in the web-browser. Existing state of the art web-based visualization systems such as D3.js, Stardust, and NetV.js struggle to achieve interactive layout and visualization for large scale graphs. In this work, we leverage the latest WebGPU technology to develop GraphWaGu, the first WebGPU-based graph visualization system. WebGPU is a new graphics API that brings the full capabilities of modern GPUs to the web browser. Leveraging the computational capabilities of the GPU using this technology enables GraphWaGu to scale to larger graphs than existing technologies. GraphWaGu embodies both fast parallel rendering and layout creation using modified Frutcherman-Reingold and Barnes-Hut algorithms implemented in WebGPU compute shaders. Experimental results demonstrate that our solution achieves the best performance, scalability, and layout quality when compared to current state of the art web-based graph visualization libraries. All of our source code for the project is available at https://github.com/harp-lab/GraphWaGu. 

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3. Toward the Analysis of Embedded Firmware through Automated Re-hosting

   Gustafson, E. (September 2019, 22nd International Symposium on Research in Attacks, Intrusions and Defenses)

   The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware. In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities. 

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4. User Profiling in Human-AI Design: An Empirical Case Study of Anchoring Bias, Individual Differences, and AI Attitudes

   https://doi.org/10.1609/hcomp.v12i1.31608  

   Nourani, Mahsan; Hashky, Amal; Ragan, Eric D (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   People form perceptions and interpretations of AI through external sources prior to their interaction with new technology. For example, shared anecdotes and media stories influence prior beliefs that may or may not accurately represent the true nature of AI systems. We hypothesize people's prior perceptions and beliefs will affect human-AI interactions and usage behaviors when using new applications. This paper presents a user experiment to explore the interplay between user's pre-existing beliefs about AI technology, individual differences, and previously established sources of cognitive bias from first impressions with an interactive AI application. We employed questionnaire measures as features to categorize users into profiles based on their prior beliefs and attitudes about technology. In addition, participants were assigned to one of two controlled conditions designed to evoke either positive or negative first impressions during an AI-assisted judgment task using an interactive application. The experiment and results provide empirical evidence that profiling users by surveying them on their prior beliefs and differences can be a beneficial approach for bias (and/or unanticipated usage) mitigation instead of seeking one-size-fits-all solutions. 

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5. From Abstract to Manipulatable: The Hybridization Explorer, A Digital Interactive for Studying Orbitals

   Wegwerth, Sarah E.; Overby, Jason S.; Douglas, Christopher J.; Winter, Julia E.; Manchester, Gianna M.; Engalan, Joseph (December 2020, Journal of chemical education)

   null (Ed.)

   As digital educational media use becomes more widespread, an opportunity exists to develop new methods to present abstract ideas to provide a more meaningful learning experience. Drawing from psychology and dynamic visualization research, new interactive tools can be thoughtfully designed but it is also necessary to establish how these media are used and to study the effects the new interactive tools have on concept understanding. In this technology report, we present the Hybridization Explorer, a web-based interactive learning tool for manipulating and experimenting with hybridization concepts. The explorer has three modes of use to explore both the combination of atomic orbitals, and the visual representation of both atomic and hybrid orbitals and corresponding bond formation. Case studies from an undergraduate- and graduate-level demonstration of the explorer are described. Finally, self-reported student confidence levels on solving hybridization questions both before and after use of the explorer are analyzed and discussed. 

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