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1. Phrase-Verified Voting: Verifiable low-tech remote boardroom voting

   Blanchard, Enka ; Robucci, Ryan ; Selker, Ted ; Sherman, Alan T. ( January 2021 , Cryptologia)

   null (Ed.)

   We present Phrase-Verified Voting, a voter-verifiable remote voting system easily assembled from commercial off-the-shelf software for small private elections. The system is transparent and enables each voter to verify that the tally includes their ballot selection without requiring any understanding of cryptography. This system is an example of making voter verification usable. The paper describes the system and an experience with it in fall 2020, to vote remotely in promotion committees in a university. Each voter fills out a form in the cloud with their selection $V$ for each race and a two-word passphrase $P$. The system generates a verification prompt of the $(V,P)$ pairs and a tally of the votes, organized to help visualize how the votes add up. After the polls close, each voter verifies that this table lists their $(V,P)$ pair and that the tally is computed correctly. The system is especially appropriate for any small group making sensitive decisions. Because the system would not prevent a coercer from demanding that their victim use a specified passphrase, it is not designed for applications where such malfeasance would be likely or go undetected. Results from 43 voters show that the system performed effectively for its intended purpose, and introduced users to the concept of voter-verified elections. Compared to the commonly-used alternatives of paper ballots or voting by email, voters found the system easier to use, and that it provided greater privacy and outcome integrity. 

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2. Boardroom Voting: Verifiable Voting with Ballot Privacy Using Low-Tech Cryptography in a Single Room

   Blanchard, Nikola K. ; Selker, Ted ; Sherman, Alan T. ( October 2020 , IEEE Transactions on Systems, Man and Cybernetics: Systems, submitted)

   A boardroom election is an election that takes place in a single room — the boardroom — in which all voters can see and hear each other. We present an initial exploration of boardroom elections with ballot privacy and voter verifiability that use only “low-tech cryptography” without using computers to mark or collect ballots. Specifically, we define the problem, introduce several building blocks, and propose a new protocol that combines these blocks in novel ways. Our new building blocks include “foldable ballots” that can be rotated to hide the alignment of ballot choices with voting marks, and “visual secrets” that are easy to remember and use but hard to describe. Although closely seated participants in a boardroom election have limited privacy, the protocol ensures that no one can determine how others voted. Moreover, each voter can verify that their ballot was correctly cast, collected, and counted, without being able to prove how they voted, providing assurance against undue influence. Low-tech cryptography is useful in situations where constituents do not trust computer technology, and it avoids the complex auditing requirements of end-to-end cryptographic voting systems such as Prêt-à-Voter. This paper’s building blocks and protocol are meant to be a proof of concept that might be tested for usability and improved. 

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3. Stochastic replica voting machine prediction of stable cubic and double perovskite materials and binary alloys

   https://doi.org/10.1103/PhysRevMaterials.3.063802  

   Mazaheri, T. ; Sun, Bo ; Scher-Zagier, J. ; Thind, A. S. ; Magee, D. ; Ronhovde, P. ; Lookman, T. ; Mishra, R. ; Nussinov, Z. ( June 2019 , Physical Review Materials)
4. Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events

   https://doi.org/10.1016/j.jag.2023.103561  

   Pasquarella, Valerie J. ; Morreale, Luca L. ; Brown, Christopher F. ; Kilbride, John B. ; Thompson, Jonathan R. ( December 2023 , International Journal of Applied Earth Observation and Geoinformation)

   Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances. 

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5. Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS 2 Based Synapses

   https://doi.org/10.1021/acsnano.1c05042  

   Dodda, Akhil ; Das, Saptarshi ( October 2021 , ACS Nano)