A Framework for Exploring and Evaluating Mechanics in Human Computation Games

Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG design knowledge that accommodates the needs of both players and tasks. We propose a formal representation of the mechanics in HCGs, providing a structural breakdown to visualize, compare, and explore the space of HCG mechanics. We present a methodology based on small-scale design experiments using fixed tasks while varying game elements to observe effects on both the player experience and the human computation task completion. Finally we discuss applications of our framework using comparisons of prior HCGs and recent design experiments. Ultimately, we wish to enable easier exploration and development of HCGs, helping these games provide meaningful player experiences while solving difficult problems.Comment: 11 pages, 5 figure

Evaluating Singleplayer and Multiplayer in Human Computation Games

Human computation games (HCGs) can provide novel solutions to intractable computational problems, help enable scientific breakthroughs, and provide datasets for artificial intelligence. However, our knowledge about how to design and deploy HCGs that appeal to players and solve problems effectively is incomplete. We present an investigatory HCG based on Super Mario Bros. We used this game in a human subjects study to investigate how different social conditions---singleplayer and multiplayer---and scoring mechanics---collaborative and competitive---affect players' subjective experiences, accuracy at the task, and the completion rate. In doing so, we demonstrate a novel design approach for HCGs, and discuss the benefits and tradeoffs of these mechanics in HCG design.Comment: 10 pages, 4 figures, 2 table

Intrinsic Elicitation : A Model and Design Approach for Games Collecting Human Subject Data

Applied games are increasingly used to collect human subject data such as people’s performance or attitudes. Games a ord a motive for data provision that poses a validity threat at the same time: as players enjoy winning the game, they are motivated to provide dishonest data if this holds a strategic in-game advantage. Current work on data collection game design doesn’t address this issue. We therefore propose a theoretical model of why people provide certain data in games, the Rational Game User Model. We derive a design approach for human subject data collection games that we call Intrinsic Elicitation: data collection should be integrated into the game’s mechanics such that honest responding is the necessary, strategically optimal, and least e ortful way to pursue the game’s goal. We illustrate the value of our approach with a sample analysis of the data collection game Urbanology

ZikaPLAN: addressing the knowledge gaps and working towards a research preparedness network in the Americas.

Zika Preparedness Latin American Network (ZikaPLAN) is a research consortium funded by the European Commission to address the research gaps in combating Zika and to establish a sustainable network with research capacity building in the Americas. Here we present a report on ZikaPLAN`s mid-term achievements since its initiation in October 2016 to June 2019, illustrating the research objectives of the 15 work packages ranging from virology, diagnostics, entomology and vector control, modelling to clinical cohort studies in pregnant women and neonates, as well as studies on the neurological complications of Zika infections in adolescents and adults. For example, the Neuroviruses Emerging in the Americas Study (NEAS) has set up more than 10 clinical sites in Colombia. Through the Butantan Phase 3 dengue vaccine trial, we have access to samples of 17,000 subjects in 14 different geographic locations in Brazil. To address the lack of access to clinical samples for diagnostic evaluation, ZikaPLAN set up a network of quality sites with access to well-characterized clinical specimens and capacity for independent evaluations. The International Committee for Congenital Anomaly Surveillance Tools was formed with global representation from regional networks conducting birth defects surveillance. We have collated a comprehensive inventory of resources and tools for birth defects surveillance, and developed an App for low resource regions facilitating the coding and description of all major externally visible congenital anomalies including congenital Zika syndrome. Research Capacity Network (REDe) is a shared and open resource centre where researchers and health workers can access tools, resources and support, enabling better and more research in the region. Addressing the gap in research capacity in LMICs is pivotal in ensuring broad-based systems to be prepared for the next outbreak. Our shared and open research space through REDe will be used to maximize the transfer of research into practice by summarizing the research output and by hosting the tools, resources, guidance and recommendations generated by these studies. Leveraging on the research from this consortium, we are working towards a research preparedness network

Nitrous Acid Chemistry in Two Polluted Environments: Urban Boundary Layers and Biomass Burning Plumes

Nitrous acid (HONO) is an important radical precursor that can influence secondary pollutant levels, especially in areas impacted by urban emissions and wildfire smoke. Due to uncertainties in its emissions and heterogeneous formation mechanisms, models often under predict HONO concentrations. A number of heterogeneous sources at the ground and on aerosols have been proposed but there is no consensus about which play a significant role in these polluted environments. In this thesis, a new one-dimensional chemistry and transport model (PACT-1D) is used to interpret field measurements and analyze the HONO budget. PACT-1D performs surface chemistry based on molecular collisions and chemical conversion, allowing the addition of detailed HONO formation chemistry at the ground and on particles.Model runs were conducted for the 2010 CalNex campaign, finding good agreement with observations of key species such as O3, NOx, and HOx. With the ground sources implemented, the model captures the diurnal and vertical profile of the HONO observations. Primary HOx production from HONO photolysis is 2-3 times more important than O3 or HCHO photolysis at mid-day, below 10 m. The HONO concentration, and its contribution to HOx decreases quickly with altitude. Heterogeneous chemistry at the ground provided a HONO source of 2.5 x 10^11 molecules cm^-2 s^-1 during the day and 5 x 10^10 molecules cm^-2 s^-1 at night. The night time source was dominated by NO2 hydrolysis. During the day, photolysis of surface HNO3/nitrate contributed 45-60% and photo-enhanced conversion of NO2 contributed 20-45%. Sensitivity studies addressing the uncertainties in both photolytic mechanisms show that, while the relative contribution of either source can vary, HNO3/nitrate is required to produce a surface HONO source that is strong enough to explain observations.Two wildfire smoke plumes from the 2019 FIREX-AQ experiment were also successfully simulated. Both plumes show high levels of HONO, with concentrations exceeding 20 ppb in young smoke. PACT-1D captures the rapid decay of HONO with smoke age, due to a mix of chemical loss and dilution. Vertical profiles show highest levels in the plume center that quickly decrease towards the plume top and bottom. The decrease occurs more rapidly than less-reactive species, creating a narrower vertical distribution of HONO. The large quantities of HONO emitted from fires provide the dominant source and result in HONO providing 80% of the total HOx formation in young smoke. Sensitivity studies show that emissions also drive O3 production, with O3 levels reduced up to 10 ppb when HONO emissions are omitted. Low photolysis rates and minimal vertical mixing in the plumes allow these emissions to be transported hours downwind, continuing to provide a source of OH. HONO's contribution to the HOx budget decreases as emissions are lost and by 6 hours downwind, HCHO photolysis becomes the dominant HOx source. Secondary sources of HONO are minor compared to emissions but pNO3 photolysis becomes significant in older smoke and helps HONO continue to contribute at least 5% to HOx production

Crowd-Driven Computer Vision

Thesis (Ph.D.)--University of Washington, 2019Artificial intelligence and machine learning are rapidly advancing the ability of computers to see, listen, understand, and interact in the real world. Data is crucial to training these systems, and the quality of the data, the source of the data, and how it is collected and labeled are as important as the data itself for building systems that are effective, robust, and ethical. Computer vision, as a subset of machine learning dealing visual processing, is at a point where there are powerful algorithms and ample computing power, but there is a bottleneck of high quality labeled data available to train these algorithms. At the same time, there is an untapped source of data available; humans possess innate visual processing abilities, cameras are ubiquitous, and the humans using these systems on their camera-enabled mobile devices can be invited to participate as active teachers of these systems. When computer vision moves from the research lab to the real world, there is often a mismatch between data used by researchers and the practical requirements of real-world applications. Issues of representation, reliability, and how the data was sourced bias datasets in ways that are often at odds with what is seen in the real world. Sometimes the conditions match well enough to build a useful application; other times, edge cases fail to work, or algorithms cause real harm (e.g., by perpetuating problematic human biases). There is a need to develop new ways of gathering data for computer vision that better match what is encountered in the real world and enable successful application of computer vision technology. This thesis presents Crowd-Driven Computer Vision, a framework for crowdsourcing new datasets by building interactive computer vision systems that let members of the crowd test and improve these systems, and accumulate data in areas they care about. This framework has been evaluated through three projects, all deployed as games, in three different areas of computer vision, ranging from photogrammetry, to geometric reconstruction, to facial expression recognition. In each project, novice users interacted with deployed systems and collected data that was highly relevant to the system and would be unrealistic to collect in other ways. In addition to computer vision, this work draws on research in human-computer interaction for crowdsourcing complex work, and on game design as a lens for building systems that are interactive, engaging, and teach players to understand complex systems. Crowd-driven computer vision is an effort to center humans in the development of this technology

Emergent Remix Culture in an Anonymous Collaborative Art System

Many crowdsourcing systems have a contribution model that is shallow but massively parallel, with contributors rarely processing or iterating upon the work of others. Few systems, even those crowdsourcing creativity or artistic talent, are designed to allow deep chains where the ideas of one individual feed into and directly inspire another individual. To explore the ways in which creative ideas arise and evolve under the influence of specific artifacts created by others, we examine patterns from over 50,000 sketches created and uploaded with Sketch-a-bit, a collaborative mobile drawing application in which each sketch is directly prompted by a previous sketch. In this paper, we report results from two analyses of content created in the system's first two years of deployment. First, we apply qualitative coding to survey the range of effort and creativity in user actions (including actions ranging from unintentioned scribbles to subtly inspired reimaginations of source material through the unexpected preparation of blank canvases for others). Second, we perform an exploratory analysis of large-scale behaviors manifest in chains or trees of sketches (such as open-ended conversations and structured gameplay). The intent of this work is to describe an iterative model of collaborative creativity and to demonstrate a range of remixing behaviors that can be expected to arise in unrestricted, anonymous collaborative creativity applications

Travel Choices in Alcohol-Related Situations in Virginia

Using survey data from 3004 respondents aged 21 and older in Northern Virginia, Richmond, and the Tidewater area, this paper identifies factors associated with respondents’ travel choices in alcohol-related situations: (1) the last time the respondent consumed alcohol, (2) when avoiding driving after drinking, and (3) when avoiding riding with a driver who had been drinking. Travel options included using various transportation modes and no travel (spending the night). Multinomial logit models (with and without random parameters) were developed to identify factors associated with each of the three alcohol-related cases. Heterogeneous effects were present in the first two models but not the third. For (1), significant factors included age, income, level of education, occupation, household characteristics, gender, comfort with credit cards tied to applications, location where alcohol was last consumed outside the home (e.g., bar, house of friend, restaurant), and place of residence. For (2), significant factors included age, gender, income, full time employment, living alone, taking multiple modes of transportation in a single trip during a typical week, region of residence, consumption of alcohol at a bar/tavern/club, consumption of alcohol at the home of friends/acquaintances, comfort with credit cards tied to applications, and use of an app for hotel reservations and/or air transportation arrangements. Significant factors for (3) were similar to those for (2). Based on the data (rather than a model), for the subset of those last consuming alcohol in a bar, more people reported using TNCs than driving. It is possible that TNCs draw from other sober driver alternatives by offering greater independence for the traveler and less burden on designated drivers or friends/family