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1. Marking Material Interactions with Computer Vision

   https://doi.org/10.1145/3544548.3580643  

   Gyory, Peter; Bae, S. Sandra; Yang, Ruhan; Do, Ellen Yi-Luen; Zheng, Clement (April 2023, ACM)

   The electronics-centered approach to physical computing presents challenges when designers build tangible interactive systems due to its inherent emphasis on circuitry and electronic components. To explore an alternative physical computing approach we have developed a computer vision (CV) based system that uses a webcam, computer, and printed fiducial markers to create functional tangible interfaces. Through a series of design studios, we probed how designers build tangible interfaces with this CV-driven approach. In this paper, we apply the annotated portfolio method to reflect on the fifteen outcomes from these studios. We observed that CV markers offer versatile materiality for tangible interactions, afford the use of democratic materials for interface construction, and engage designers in embodied debugging with their own vision as a proxy for CV. By sharing our insights, we inform other designers and educators who seek alternative ways to facilitate physical computing and tangible interaction design. 

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2. Lane Marking Verification for High Definition Map Maintenance Using Crowdsourced Images

   Binbin Li, Dezhen Song (October 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Oct. 25-29, 2020)

   null (Ed.)

   Autonomous vehicles often rely on high-definition (HD) maps to navigate around. However, lane markings (LMs) are not necessarily static objects due to wear \& tear from usage and road reconstruction \& maintenance. Therefore, the wrong matching between LMs in the HD map and sensor readings may lead to erroneous localization or even cause traffic accidents. It is imperative to keep LMs up-to-date. However, frequently recollecting data with dedicated hardware and specialists to update HD maps is not only cost-prohibitive but also unviable. Here we propose to utilize crowdsourced images from multiple vehicles at different times to help verify LMs for HD map maintenance. We obtain the LM distribution in the image space by considering the camera pose uncertainty in perspective projection. Both LMs in HD map and LMs in the image are treated as observations of LM distributions which allow us to construct posterior conditional distribution (a.k.a Bayesian belief functions) of LMs from either sources. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. We verify our approach using real data. Experimental results show that our method is capable of verifying and updating LMs in the HD map. 

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3. Conditions on pronominal marking in the Alor-Pantar languages

   https://doi.org/10.1515/ling-2013-0002  

   Fedden, Sebastian; Brown, Dunstan; Corbett, Greville; Holton, Gary; Klamer, Marian; Robinson, Laura C.; Schapper, Antoinette (January 2013, Linguistics)
4. Can Voters Detect Malicious Manipulation of Ballot Marking Devices?

   https://doi.org/10.1109/SP40000.2020.00118  

   Bernhard, Matthew; McDonald, Allison; Meng, Henry; Hwa, Jensen; Bajaj, Nakul; Chang, Kevin; Halderman, J. Alex (May 2020, 2020 IEEE Symposium on Security and Privacy (SP))

   null (Ed.)

   Ballot marking devices (BMDs) allow voters to select candidates on a computer kiosk, which prints a paper ballot that the voter can review before inserting it into a scanner to be tabulated. Unlike paperless voting machines, BMDs provide voters an opportunity to verify an auditable physical record of their choices, and a growing number of U.S. jurisdictions are adopting them for all voters. However, the security of BMDs depends on how reliably voters notice and correct any adversarially induced errors on their printed ballots. In order to measure voters' error detection abilities, we conducted a large study (N = 241) in a realistic polling place setting using real voting machines that we modified to introduce an error into each printout. Without intervention, only 40% of participants reviewed their printed ballots at all, and only 6.6% told a poll worker something was wrong. We also find that carefully designed interventions can improve verification performance. Verbally instructing voters to review the printouts and providing a written slate of candidates for whom to vote both significantly increased review and reporting rates-although the improvements may not be large enough to provide strong security in close elections, especially when BMDs are used by all voters. Based on these findings, we make several evidence-based recommendations to help better defend BMD-based elections. 

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5. Marking prominence: Towards cue-based annotation of prosodic prominence

   Brugos, A; Breen, M; Veilleux, N; Barnes, J; Shattuck-Hufnagel, S (August 2023, Guarant International)

   Skarnitzl, Radek; Volín; Jan (Ed.)