Jerome McClendon

Universal access in e-voting for the blind

Since the inception of elections and election technologies, all segments of the voting population have never been granted equal access, privacy and security to voting. Modern electronic voting systems have made attempts to include disabled voters but have fallen short. Using recent developments in technology a secure, user centered, multimodal electronic voting system has been developed to study a multimodal approach for providing equity in access, privacy and security in electronic voting. This article will report findings from a study at the Alabama Institute for the Deaf and Blind where more than thirty-five blind or visually impaired participants used the multimodal voting system. The findings suggest that the proposed multimodal approach to voting is easy to use and trustworthy.

Everyone Counts: Voting Accessibility

There are approximately 37.5 million disabled Americans of voting age. Current voting technologies have failed to provide Americans with disabilities a voting system that allows them to vote without assistance. Through the use of natural interaction a voting system called Prime III provides a secure and usable voting system for all voters regardless of ability. Prime III was recently tested at the Alabama Institute for the Deaf and Blind in which a number of Americans with various disabilities had the opportunity to vote. Participants were tasked with casting their vote using Prime III. The results of this study showed that Prime III allowed voters who where blind, and/or hearing impaired the ability to cast their vote without any additional assistance. The participants noted that Prime III was easy to use and trusted the system to successfully cast their vote.

Prime III: Defense-in-Depth Approach to Electronic Voting

Abstract Usability and security are critical issues in electronic voting system development. With these as the main concern, the Prime III electronic voting system implements usability with security such that all eligible voters regardless of their ability or disability to privately and securely vote using the same model of election machines. The Prime III electronic voting system has openly addressed many of the associated problems of usability, by using a multimodal user interface that enables voters to cast their vote by touch and/or voice. The purpose of this article is to examine the security components within Prime III because very little attention has been given to potential solutions to issues in electronic voting.

Serious games for training, rehabilitation and workforce development

We present a set of demonstrations of current work in the Clemson University Virtual Environments Group involving the development and testing of interactive virtual environments for applications in training, rehabilitation and workforce development. All of these applications are designed to leverage commodity hardware such as HDTVs, game controllers, and the Kinect sensor.

Interface Design and Display Modalities to Improve the Vehicle Inspection Process

There is a direct need in industry to improve the in-production vehicle inspection process and to support mobility for inspection stations. In this paper we present a novel interface design implemented on three multi-modal prototype systems, in which design was based on results from an initial field study we conducted. The design of these systems incorporate two main objectives: 1) enforce a systematic check on each of the items on the list to reduce missed items and 2) facilitate mobility in that the tools used to assist in inspection can be installed at one area and then later easily moved to another area. Our novel graphical software interface aims to enforce systematic checks through incorporation of a system-directed delivery of the checklist items with options for error correction and support of dynamic inspection, where items identified for inspection may differ among checkpoints. We have designed three hardware configurations that support our interface, with aims to achieve mobility from one inspection area to another, leave both hands free for inspection, and incorporate a more convenient way to refer to the list while conducting an inspection. This paper additionally presents preliminary feedback and suggestions for improvement from a pilot study conducted on our interface implemented on three hardware configurations. In the future we plan to incorporate the suggestions from the pilot study and to conduct a more formal evaluation on our multi-modal systems.Copyright

An Empirical Evaluation of the Effects of IoT Messaging Protocols

In this paper, we evaluate the performance of feedback control with different publish and subscribe architectures. Specifically we look to evaluate the difference between ROS, MQTT, and Kafka under the three network conditions. Our results show that latency alone can not be used to assess performance of a real time control system.

Evaluation of System-Directed Multimodal Systems for Vehicle Inspection

ed the inspection task. Three concentric geometric shapes comprised one symbol that simulated a checkpoint, called an “inspection item marker” (Fig. 5(b)). The inspection item markers were used as indicators for the location and status of the item and how to perform inspection. The outermost shape, called the “location indicator,” matched a shape description on the checklist. Location indicators had three parameters: size (large, inscribed inside an 8.5 11 in. piece of paper, or small, inscribed inside a quarter-sheet of paper), color (red, yellow, green, or blue), and shape (triangle, circle, or square). For example, through text-tospeech and/or screen output, a participant is informed that the current item to check is a “Large Red Triangle.” The participant would then find that shape on the car. No two location indicators were the same on any checklist so that the location of the current checkpoint was unambiguous. Once the participant found the shape, the participant would then look at the shapes within the location indicator. The next concentric shape, called the “task indicator,” indicated how the participant should inspect the item. Depending on whether the shape is a square, pentagon, or hexagon, the participant should either look at but not touch the marker, touch the marker with only one hand, or touch the marker with two hands, respectively. Finally, within the task indicator, there was a set of four dots in a row called the “defect indicator.” The shading of these four dots indicated to the participant whether an item had a defect. An odd number or zero shaded dots indicated an item should pass inspection while an even number of shaded dots indicated a defect, which should fail inspection. In designing the abstracted task, our goal was to simulate the cognitive load and time requirements for actual vehicle inspection. We chose three-word item identifiers to roughly match the phrase length of items that occur on a typical inspection checklist and the time it would take text-to-speech to read them. Requiring the user to look at shapes to determine what action to take, and whether the item passed inspection, simulated the time and cognitive load it would take an inspector to determine the status of an actual inspection item, since in real vehicle inspection an associate must look at a part, recall how to inspect it, and then determine whether the part passes or fails inspection. Inspection item markers were placed throughout the vehicle frame to simulate the various checkpoint locations in a vehicle. The percentage of defective items per checklist approximated the defect rate reported through actual vehicle inspection at BMW. Since inspectors are provided with reference material at their stations should they forget how to inspect a particular checkpoint, we provided our participants with a lanyard holding a reference sheet for the meanings of the task and defect indicators. For each trial, there were 20 inspection item markers placed on the vehicle, providing the 15 items on the checklist plus five items as distracters, since expert inspectors would not inspect each item present on a vehicle. The participant was presented with one checklist of 15 items, ordered by their location moving counterclockwise around the vehicle. This simulated the actual inspection process, since an inspector is directed to check only certain vehicle features. Participants were given 106 s to complete the checklist, based on the time frame standard for factory inspections. If the participant finished before time was up, he or she said stop to complete the trial. If the participant did not finish in time, the system automatically stopped accepting input. We created unique checklists for ten trials. The first checklist was used for a practice trial before using any devices for input. In this first trial, the participant inspected all 20 checkpoints with the help of an experimenter to help familiarize them with the inspection item markers and the locations of the checkpoints on the vehicle. The user then completed nine trials, with three trials per device. 4.3 Measures. For each participant, we gathered data through preand postquestionnaires, a debriefing interview, Fig. 5 (a) Vehicle body used for experimental evaluation. (b) Example of shapes used for abstracted inspection task. This item would be called “Small Blue Square,” would require a one-handed touch for the inspection action since the shape is a pentagon and would pass inspection since an odd number of dots are shaded. RealVNC: http://www.realvnc.com/. Journal of Computing and Information Science in Engineering MARCH 2013, Vol. 13 / 011002-5 Downloaded From: http://computingengineering.asmedigitalcollection.asme.org/ on 10/04/2016 Terms of Use: http://www.asme.org/about-asme/terms-of-use experimenter logs, and software logs. The prequestionnaire gathered information about demographics, level of use of various hardware configurations, vehicle knowledge, and learning styles. We recorded the number of items inspected and not inspected, the number of items inspected correctly and incorrectly, the time it took for inspection completion, and each voice command the user spoke. While the participant conducted an inspection, an experimenter marked each checkpoint on a clipboard to indicate if the user had inspected the correct item with the correct action (look, one-hand touch, or two-hand touch). Finally, in the postquestionnaire and debriefing interview, we asked questions related to usability, preferences, and effectiveness of the interfaces and hardware configurations. 4.4 Results. Of 25 participants from Clemson University, there were 10 females and 15 males, aged 18–53 (mean1⁄4 23). Participants rated themselves as having low (N1⁄4 12), average (N1⁄4 8), and high (N1⁄4 5) levels of vehicle knowledge. Twentyfour participants were college students and one participant was a postdoctoral fellow. Since the time to complete each trial was limited to 106 s, many participants did not complete all trials for an input device. Overall, 13 participants completed all three trials using the handheld configuration (H), 14 participants completed all three trials with the large screen configuration (L), and 18 participants completed all three trials with the monocular display configuration (M). The task performance data were treated with a repeated measures 3 3 analysis of variance (ANOVA) to test for the within subject effects of hardware configurations and the within subject effects by trial. Data reported from the postquestionnaires were analyzed using the Chi-square test. The F and v tests that are reported for analysis used an alpha level of 0.05 to indicate significance. 4.4.1 Accuracy. The accuracy percentage of correctly checked items was determined by dividing the number of items that were correctly checked by the total number of items checked in each trial. Correctly checked items are those that the participant both performed the correct inspection action and reported the correct inspection result. There was no significant main effect of hardware configuration type for mean accuracy percentage of correctly checked items, F(2,38)1⁄4 1.58, p1⁄4 0.22, n 1⁄4 0:08. All configurations allowed for high accuracy with monocular display (M) as the highest and handheld (H) being the lowest. There was no significant main effect found among the sets of the trials or interaction effect of device by trial. The defect detection percentage was determined by dividing the number of defects that were correctly detected by the total number of defects for each trial. Since the number of defects varied over each trial, the total accuracy of defect detection for each trial was averaged across trials and participants, and then analyzed using a one-way ANOVA. There was no significant main effect found for the defect detection accuracy nor any significant interaction effect of device by trial. However, all configurations allowed for high accuracy as listed from highest to lowest: monocular display (M), large screen (L), and handheld (H). No significant differences were found for accuracy grouped by vehicle knowledge, device usage, or learning style. Unfortunately, no accuracy data are recorded for BMW inspectors, so we could not compare our accuracy results to the baseline accuracy achieved in the manufacturing environment. 4.4.2 Task Completion Times. Analysis revealed a significant main effect for hardware configuration type for overall task completion time. The handheld configuration (H) allowed for significantly faster overall completion time than the monocular display configuration (M) and the large screen configuration (L) (Table 1). In addition, participants’ overall performance became significantly faster by the third and last trial, F(2,38)1⁄4 4.38, p1⁄4 0.019, n 1⁄4 0:19. There was no significant interaction effect of hardware configuration by trial. A few participants discontinued the inspection task accidentally, indicating that participants were having difficulty or accidentally executed a command. A participant possibly discontinued the task accidentally if the overall task completion time was less than 105.5 s (due to rounding error) and did not check all 15 items on the list. There were eight accidental discontinuations for the handheld configuration (H) possibly due to difficulties with the touch screen interface, while there were no accidental discontinuations of the task for the monocular (M) or large screen (L) configurations, likely due to the Wizard-of-Oz setup. As a result of several participants not completing the full trial, it may be more informative to analyze completion time per individual item. This was calculated as a result of each participant’s overall time divided by the number of items each participant actually inspected. We did not find a significant main effect for hardware configuration type for task completion time per item, F(2,38)1⁄4 1.83, p1⁄4 0.18, n 1⁄4 0:09. Ho

DeskTop: a design guideline to creating a multi-touch desk prototype

In many multi-touch tables, a projector is used to project an image onto the surface and a camera is used to detect user touches. The optical paths for both the camera and projector limits the physical design of multitouch tables. Our research focuses on the creation of a multi-touch desk that improves on the physical design of past multi-touch tables by using a combination of multiple cameras and a liquid crystal display to create a physical design that is ergonomic, mobile, collaborative/scalable and simplistic in design.