Evaluation and Visualization Tools for Crowdsourcing Games
This project investigates the use of game analytics to evaluate games designed for citizen science and understand problem solving strategies in crowd sourced games.
The objective of this research is to develop a comprehensive theoretical and experimental cyber-physical framework to enable intelligent human-environment interaction capabilities by a synergistic combination of computer vision and robotics.
Specifically, the approach is applied to examine individualized remote rehabilitation with an intelligent, articulated, and adjustable lower limb orthotic brace to manage Knee Osteoarthritis, where a visual-sensing/dynamical-systems perspective is adopted to: (1) track and record patient/device interactions with internet-enabled commercial-off-the-shelf computer-vision-devices; (2) abstract the interactions into parametric and composable low-dimensional manifold representations; (3) link to quantitative biomechanical assessment of the individual patients; (4) facilitate development of individualized user models and exercise regimen; and (5) aid the progressive parametric refinement of exercises and adjustment of bracing devices. This research and its results will enable us to understand underlying human neuro-musculo-skeletal and locomotion principles by merging notions of quantitative data acquisition, and lower-order modeling coupled with individualized feedback. Beyond efficient representation, the quantitative visual models offer the potential to capture fundamental underlying physical, physiological, and behavioral mechanisms grounded on biomechanical assessments, and thereby afford insights into the generative hypotheses of human actions.
Knee osteoarthritis is an important public health issue, because of high costs associated with treatments. The ability to leverage a quantitative paradigm, both in terms of diagnosis and prescription, to improve mobility and reduce pain in patients would be a significant benefit. Moreover, the home-based rehabilitation setting offers not only immense flexibility, but also access to a significantly greater portion of the patient population. The project is also integrated with extensive educational and outreach activities to serve a variety of communities.
This project investigates the use of data analytics and visualization systems through games to understand strategy formation and diversity in problem solving tactics.
Many of the truly difficult problems limiting advances in contemporary science are rooted in our limited understanding of how complex systems are controlled. Indeed, in human cells millions of molecules are embedded in a complex genetic network that lacks an obvious controller; in society billions of individuals interact with each other through intricate trust-family-friendship-professional-association based networks apparently controlled by no one; economic change is driven by what economists call the “invisible hand of the market”, reflecting a lack of understanding of the control principles that govern the interactions between individuals, companies, banks and regulatory agencies.
These and many other examples raise several fundamental questions: What are the control principles of complex systems? How do complex systems organize themselves to achieve sufficient control to ensure functionality? This proposal is motivated by the hypothesis that the architecture of many complex systems is driven by the system’s need to achieve sufficient control to maintain its basic functions. Hence uncovering the control principles of complex self-organized systems can help us understand the fundamental laws that govern them.
Benjamin Alexander Raby
Frank D. Gilliland
Using the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), we will perform a series of systems-level genomic analyses that integrate clinical, environmental and various forms of “omic” data (genetics, genomics, and epigenetics) to better understand how molecular processes interact with critical environmental factors to impair asthma control.
The over-arching hypothesis of this proposal is that inter-individual differences in asthma control result from the complex interplay of both environmental, genomic, and socioeconomic factors organized in discrete, scale-free molecular networks. Though strict patient compliance with asthma controller therapy and avoidance of environmental triggers are important strategies for the prevention of asthma exacerbation, failure to maintain control is the most common health-related cause of lost school and workdays. Therefore, better understanding of the molecular underpinnings and the role of environmental factors that lead to poor asthma control is needed. Using the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), we will perform a series of systems-level genomic analyses that integrate clinical, environmental and various forms of “omic” data (genetics, genomics, and epigenetics) to better understand how molecular processes interact with critical environmental factors to impair asthma control. This proposal consists three Specific Aims, each consisting of three investigational phases: (i) an initial computational discovery phase to define specific molecular networks using the Asthma BRIDGE datasets, followed by two validation phases – (ii) a computational validation phase using an independent clinical cohort, and (iii) an experimental phase to validate critical molecular edges (gene-gene interactions) that emerge from the defined molecular network.
In Specific Aim 1, we will use the Asthma BRIDGE datasets to define interactome sub-module perturbed in poor asthma control;the regulatory variants that modulate this asthma-control module;and to develop a predictive model of asthma control.
In Specific Aim 2, we will study the effects exposure to air pollution and environmental tobacco smoke on modulating the asthma control networks, testing for environment-dependent alterations in network dynamics.
In Specific Aim 3, we will study the impact of inhaled corticosteroids (ICS – the most efficacious asthma-controller medication) on network dynamics of the asthma-control sub-module by comparing network topologies of acute asthma control between subjects taking ICS to those not on ICS. For our experimental validations, we will assess relevant gene-gene interactions by shRNA studies bronchial epithelial and Jurkat T- cell lines. Experimental validations of findings from Aim 2 will be performed by co-treating cells with either cigarette smoke extract (CSE) or ozone. Similar studies will be performed with co-treatment using dexamethasone to validate findings from Aim 2. From the totality of these studies, we will gain new insights into the pathobiology of poor asthma control, and define targets for biomarker development and therapeutic targeting.
Public Health Relevance
Failure to maintain tight asthma symptom control is a major health-related cause of lost school and workdays. This project aims to use novel statistical network-modeling approaches to model the molecular basis of poor asthma control in a well-characterized cohort of asthmatic patients with available genetic, gene expression, and DNA methylation data. Using this data, we will define an asthma-control gene network, and the genetic, epigenetic, and environmental factors that determine inter-individual differences in asthma control.
This project will develop new research methods to map and quantify the ways in which online search engines, social networks and e-commerce sites use sophisticated algorithms to tailor content to each individual user.
This project will develop new research methods to map and quantify the ways in which online search engines, social networks and e-commerce sites use sophisticated algorithms to tailor content to each individual user. This “personalization” may often be of value for the user, but it also has the potential to distort search results and manipulate the perceptions and behavior of the user. Given the popularity of personalization across a variety of Web-based services, this research has the potential for extremely broad impact. Being able to quantify the extent to which Web-based services are personalized will lead to greater transparency for users, and the development of tools to identify personalized content will allow users to access information that may be hard to access today.
Personalization is now a ubiquitous feature on many Web-based services. In many cases, personalization provides advantages for users, because personalization algorithms are likely to return results that are relevant to the user. At the same time, the increasing levels of personalization in Web search and other systems are leading to growing concerns over the Filter Bubble effect, where users are only given results that the personalization algorithm thinks they want, while other important information remains inaccessible. From a computer science perspective, personalization is simply a tool that is applied to information retrieval and ranking problems. However, sociologists, philosophers, and political scientists argue that personalization can result in inadvertent censorship and “echo chambers.” Similarly, economists warn that unscrupulous companies can leverage personalization to steer users towards higher-priced products, or even implement price discrimination, charging different users different prices for the same item. As the pervasiveness of personalization on the Web grows, it is clear that techniques must be developed to understand and quantify personalization across a variety of Web services.
This research has four primary thrusts: (1) To develop methodologies to measure personalization of mobile content. The increasing popularity of browsing the Web from mobile devices presents new challenges, as these devices have access to sensitive content like the user’s geolocation and contacts. (2) To develop systems and techniques for accurately measuring the prevalence of several personalization trends on a large number of e-commerce sites. Recent anecdotal evidence has shown instances of problematic sales tactics, including price steering and price discrimination. (3) To develop techniques to identify and quantify personalized political content. (4) To measure the extent to which financial and health information is personalized based on location and socio-economic status. All four of these thrusts will develop new research methodologies that may prove effective in other areas of research as well.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani. “The Parable of Google Flu: Traps in Big Data Analysis,” Science, v.343, 2014, p. 1203.
This is a study of the structure and dynamics of Internet-based collaboration. The project seeks groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities. The research also offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated communication and cooperation processes.
This is a study of the structure and dynamics of Internet-based collaboration. The project seeks groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities. The research also offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated communication and cooperation processes. The findings will inform decisions of leaders interested in optimizing all forms of collaboration in fields such as open-source software development, academic projects, and business. System designers will be able to identify interpersonal dynamics and develop new features for opinion aggregation and effective collaboration. In addition, the research will inform managers on how best to use crowdsourcing solutions to support innovation and marketing strategies including peer-to-peer marketing to translate activity within online communities into sales.
This research will analyze digital trace data that enable studies of population-level human interaction on an unprecedented scale. Understanding such interaction is crucial for anticipating impacts in our social, economic, and political lives as well as for system design. One site of such interaction is crowdsourcing systems – socio-technical systems through which online communities comprised of diverse and distributed individuals dynamically coordinate work and relationships. Many crowdsourcing systems not only generate creative content but also contain a rich community of collaboration and evaluation in which creators and adopters of creative content interact among themselves and with artifacts through overlapping relationships such as affiliation, communication, affinity, and purchasing. These relationships constitute multidimensional networks and create structures at multiple levels. Empirical studies have yet to examine how multidimensional networks in crowdsourcing enable effective large-scale collaboration. The data derive from two distinctly different sources, thus providing opportunities for comparison across a range of online creation-oriented communities. One is a crowdsourcing platform and ecommerce website for creative garment design, and the other is a platform for participants to create innovative designs based on scrap materials. This project will analyze both online community activity and offline purchasing behavior. The data provide a unique opportunity to understand overlapping structures of social interaction driving peer influence and opinion formation as well as the offline economic consequences of this online activity. This study contributes to the literature by (1) analyzing multidimensional network structures of interpersonal and socio-technical interactions within these socio-technical systems, (2) modeling how success feeds back into value-creation processes and facilitates learning, and (3) developing methods to predict the economic success of creative products generated in these contexts. The application and integration of various computational and statistical approaches will provide significant dividends to the broader scientific research community by contributing to the development of technical resources that can be extended to other forms of data-intensive inquiry. This includes documentation about best practices for integrating methods for classification and prediction; courses to train students to perform large-scale data analysis; and developing new theoretical approaches for understanding the multidimensional foundations of cyber-human systems.
Kristin Negulescu, Internet Archive
Matthew Weber, Rutgers University New Brunswick
This project has three goals: (1) to build a community of scholars focused on tackling next-generation questions of Internet research through the use of archival digital data; (2) to create sample databases and develop a prototype research tool, HistoryTracker, using data from the Internet Archive, a library of Web pages from the World Wide Web; and (3) to maintain an active community of scholars using the cutting-edge community platform HUBzero.
The BCC-SBE Collaborative Research Project, has three goals: (1) to build a community of scholars focused on tackling next-generation questions of Internet research through the use of archival digital data; (2) to create sample databases and develop a prototype research tool, HistoryTracker, using data from the Internet Archive, a library of Web pages from the World Wide Web; and (3) to maintain an active community of scholars using the cutting-edge community platform HUBzero. Co-PIs Weber, Lazer and Carpenter will lead a community building initiative to coalesce scholars around these three goals. In the long-run, this work will support ongoing interactions among community participants, including the collection of feedback, sharing research, and dissemination of databases collected through the course of this research and beyond. Convenient, efficient access to archival Internet data has the potential to open up countless new avenues of social science research. In addition, evidence from this research project will inform the creation and dissemination of general guidelines for conducting theoretically and methodologically rigorous longitudinal research using archival Web data.
Broader Significance/Nontechnical: The Internet Archive is the single largest repository of archive Web data in existence, yet there is a significant lack of research-ready databases and tools available to the scholarly community. Funding of the BCC-SBE Collaborative Research Project, “Using Archival Resources to Conduct Data-Intensive Internet Research,” will inform the creation and dissemination of general guidelines for conducting theoretically and methodologically rigorous longitudinal research using archival Web data. Co-PIs Weber, Lazer and Carpenter will work to bring together a community of scholars focused on research utilizing archival digital data. Subsequently, the principal investigators will lead the development of initial tools and sample databases for conducting ongoing research examining numerous issues pertaining to the development of the World Wide Web, and the behavior of individuals and organizations on the Internet.