Title here should be [VOTE] Accept SensSoft into the Apache Incubator On Mon, Jun 13, 2016 at 10:55 AM, Lewis John Mcgibbney < lewis.mcgibb...@gmail.com> wrote:
> Hi general@, > Since I am back from a bit of vacation and it seems the discussion has > died down, I am now calling a vote on accepting SensSoft into the Apache > Incubator. > > For those who are interested the DISCUSS thread can be found at > https://s.apache.org/senssoft_discuss > > This vote will run for the usual 72 hours. > > Please VOTE as follows > > [ ] +1 Accept SensSoft into the Apache Incubator > [ ] +/-0 Not overly bothered either way > [ ] -1 DO NOT accept SensSoft into the Apache Incubator (please state why) > > Thanks everyone who contributed to DISCUSS and is able to participate in > VOTE > > Best > Lewis > P.S. Here is my +1 > > ##################### > > = SensSoft Proposal = > > == Abstract == > The Software as a Sensor™ (SensSoft) Project offers an open-source (ALv2.0) > software tool usability testing platform. It includes a number of > components that work together to provide a platform for collecting data > about user interactions with software tools, as well as archiving, > analyzing and visualizing that data. Additional components allow for > conducting web-based experiments in order to capture this data within a > larger experimental framework for formal user testing. These components > currently support Java Script-based web applications, although the schema > for “logging” user interactions can support mobile and desktop > applications, as well. Collectively, the Software as a Sensor Project > provides an open source platform for assessing how users interacted with > technology, not just collecting what they interacted with. > > == Proposal == > The Software as a Sensor™ Project is a next-generation platform for > analyzing how individuals and groups of people make use of software tools > to perform tasks or interact with other systems. It is composed of a number > of integrated components: > * User Analytic Logging Engine (User ALE) refers to a simple Application > Program Interface (API) and backend infrastructure. User ALE provides > “instrumentation” for software tools, such that each user interaction > within the application can be logged, and sent as a JSON message to an > Elasticsearch/Logstash/Kibana (Elastic Stack) backend. > * The API provides a robust schema that makes user activities human > readable, and provides an interpretive context for understanding that > activity’s functional relevance within the application. The schema provides > highly granular information best suited for advanced analytics. This > hierarchical schema is as follows: > * Element Group: App features that share function (e.g., map group) > * Element Sub: Specific App feature (e.g., map tiles) > * Element Type: Category of feature (e.g., map) > * Element ID: [attribute] id > * Activity: Human imposed label (e.g., “search”) > * Action: Event class (e.g., zoom, hover, click) > * The API can either be manually embedded in the app source code, or > implemented automatically by inserting a script tag in the source code. > * Users can either setup up their own Elastic stack instance, or use > Vagrant, a virtualization environment, to deploy a fully configured Elastic > stack instance to ship and ingest user activity logs and visualize their > log data with Kibana. > * RESTful APIs allow other services to access logs directly from > Elasticsearch. > * User ALE allows adopters to own the data they collect from users > outright, and utilize it as they see fit. > * Distill is an analytics stack for processing user activity logs > collected through User ALE. Distill is fully implemented in Python, > dependent on graph-tool to support graph analytics and other external > python libraries to query Elasticsearch. The two principle functions of > Distill are segmentation and graph analytics: > * Segmentation allows for partitioning of the available data along > multiple axes. Subsets of log data can be selected via their attributes in > User ALE (e.g. Element Group or Activity), and by users/sessions. Distill > also has the capability to ingest and segment data by additional attributes > collected through other channels (e.g. survey data, demographics).This > allows adopters to focus their analysis of log data on precisely the > attributes of their app (or users) they care most about. > * Distill’s usage metrics are derived from a probabilistic > representation of the time series of users’ interactions with the elements > of the application. A directed network is constructed from the > representation, and metrics from graph theory (e.g. betweenness centrality, > in/out-degree of nodes) are derived from the structure. These metrics > provide adopters ways of understanding how different facets of the app are > used together, and they capture canonical usage patterns of their > application. This broad analytic framework provides adopters a way to > develop and utilize their own metrics > * The Test Application Portal (TAP) provides a single, user-friendly > interface to Software as a Sensor™ Project components, including > visualization functionality for Distill Outputs leveraging Django, React, > and D3.js. It has two key functions: > * It allows adopters to register apps, providing metadata regarding > location, app name, version, etc., as well as permissions regarding who can > access user data. This information is propagated to all other components of > the larger system. > * The portal also stages visualization libraries that make calls to > Distill. This allows adopters to analyze their data as they wish to; it’s > “dashboard” feel provides a way to customize their views with > adopter-generated widgets (e.g., D3 libraries) beyond what is included in > the initial open source offering. > * The Subject Tracking and Online User Testing (STOUT) application is an > optional component that turns Software as a Sensor™ Technology into a > research/experimentation enterprise. Designed for psychologists and HCI/UX > researchers, STOUT allows comprehensive human subjects data protection, > tracking, and tasking for formal research on software tools. STOUT is > primarily python, with Django back-end for authentication, permissions, and > tracking, MongoDB for databasing, and D3 for visualization. STOUT includes > a number of key features: > * Participants can register in studies of software tools using their own > preferred credentials. As part of registration, participants can be > directed through human subjects review board compliant consent forms before > study enrollment. > * STOUT stores URLs to web/network accessible software tools as well as > URLs to third party survey services (e.g., surveymonkey), this allows > adopters to pair software tools with tasks, and collect survey data and > comments from participants prior to, during, or following testing with > software tools. > * STOUT tracks participants’ progress internally, and by appending a > unique identifier, and task identifier to URLs. This information can be > passed to other processes (e.g., User ALE) allowing for disambiguation > between participants and tasks in experiments on the open web. > * STOUT supports between and within-subjects experimental designs, with > random assignment to experimental conditions. This allows for testing > across different versions of applications. > * STOUT can also use Django output (e.g., task complete) to automate > other processes, such as automated polling applications serving 3rd party > form data APIs (e.g.,SurveyMonkey), and python or R scripts to provide > automated post-processing on task or survey data. > * STOUT provides adopters a comprehensive dashboard view of data > collected and post-processed through its extensions; in addition to user > enrollment, task completion, and experiment progress metrics, STOUT allows > adopters to visualize distributions of scores collected from task and > survey data. > > Each component is available through its own repository to support organic > growth for each component, as well as growth of the whole platform’s > capabilities. > > == Background and Rationale == > Any tool that people use to accomplish a task can be instrumented; once > instrumented, those tools can be used to report how they were used to > perform that task. Software tools are ubiquitous interfaces for people to > interact with data and other technology that can be instrumented for such a > purpose. Tools are different than web pages or simple displays, however; > they are not simply archives for information. Rather, they are ways of > interfacing with and manipulating data and other technology. There are > numerous consumer solutions for understanding how people move through web > pages and displays (e.g., Google Analytics, Adobe Omniture). There are far > fewer options for understanding how software tools are used. This requires > understanding how users integrate a tool’s functionality into usage > strategies to perform tasks, how users sequence the functionality provided > them, and deeper knowledge of how users understand the features of software > as a cohesive tool. The Software as a Sensor™ Project is designed to > address this gap, providing the public an agile, cost-efficient solution > for improving software tool design, implementation, and usability. > > == Software as a Sensor™ Project Overview == > > {{attachment:userale_figure_1.png}} > > Figure 1. User ALE Elastic Back End Schema, with Transfer Protocols. > > Funded through the DARPA XDATA program and other sources, the Software as a > Sensor™ Project provides an open source (ALv2.0) solution for instrumenting > software tools developed for the web so that when users interact with it, > their behavior is captured. User behavior, or user activities, are captured > and time-stamped through a simple application program interface (API) > called User Analytic Logging Engine (User ALE). User ALE’s key > differentiator is the schema that it uses to collect information about user > activities; it provides sufficient context to understand activities within > the software tool’s overall functionality. User ALE captures each user > initiated action, or event (e.g., hover, click, etc.), as a nested action > within a specific element (e.g., map object, drop down item, etc.), which > are in turn nested within element groups (e.g., map, drop down list) (see > Figure 1). This information schema provides sufficient context to > understand and disambiguate user events from one another. In turn, this > enables myriad analysis possibilities at different levels of tool design > and more utility to end-user than commercial services currently offer. > Once instrumented with User ALE, software tools become human signal sensors > in their own right. Most importantly, the data that User ALE collects is > owned outright by adopters and can be made available to other processes > through scalable Elastic infrastructure and easy-to-manage Restful APIs. > Distill is the analytic framework of the Software as a Sensor™ Project, > providing (at release) segmentation and graph analysis metrics describing > users’ interactions with the application to adopters. The segmentation > features allow adopters to focus their analyses of user activity data based > on desired data attributes (e.g., certain interactions, elements, etc.), as > well as attributes describing the software tool users, if that data was > also collected. Distill’s usage and usability metrics are derived from a > representation of users’ sequential interactions with the application as a > directed graph. This provides an extensible framework for providing insight > as to how users integrate the functional components of the application to > accomplish tasks. > > {{attachment:userale_figure_2.png}} > > Figure 2. Software as a Sensor™ System Architecture with all components. > > The Test Application Portal (TAP) provides a single point of interface for > adopters of the Software as a Sensor™ project. Through the Portal, adopters > can register their applications, providing version data and permissions to > others for accessing data. The Portal ensures that all components of the > Software as a Sensor™ Project have the same information. The Portal also > hosts a number of python D3 visualization libraries, providing adopters > with a customizable “dashboard” with which to analyze and view user > activity data, calling analytic processes from Distill. > Finally, the Subject Tracking and Online User Testing (STOUT) application, > provides support for HCI/UX researchers that want to collect data from > users in systematic ways or within experimental designs. STOUT supports > user registration, anonymization, user tracking, tasking (see Figure 3), > and data integration from a variety of services. STOUT allows adopters to > perform human subject review board compliant research studies, and both > between- and within-subjects designs. Adopters can add tasks, surveys and > questionnaires through 3rd party services (e.g., SurveyMonkey). STOUT > tracks users’ progress by passing a unique user IDs to other services, > allowing researchers to trace progress by passing a unique user IDs to > other services, allowing researchers to trace form data and User ALE logs > to specific users and task sets (see Figure 4). > > {{attachment:userale_figure_3.png}} > > Figure 3. STOUT assigns participants subjects to experimental conditions > and ensures the correct task sequence. STOUT’s Django back end provides > data on task completion, this can be used to drive other automation, > including unlocking different task sequences and/or achievements. > > {{attachment:userale_figure_4.png}} > > Figure 4. STOUT User Tracking. Anonymized User IDs (hashes) are > concatenated with unique Task IDs. This “Session ID” is appended to URLs > (see Highlighted region), custom variable fields, and User ALE, to provide > and integrated user testing data collection service. > > STOUT also provides for data polling from third party services (e.g., > SurveyMonkey) and integration with python or R scripts for statistical > processing of data collected through STOUT. D3 visualization libraries > embedded in STOUT allow adopters to view distributions of quantitative data > collected from form data (see Figure 5). > > {{attachment:userale_figure_5.png}} > > Figure 5. STOUT Visualization. STOUT gives experimenters direct and > continuous access to automatically processed research data. > > == Insights from User Activity Logs == > > The Software as a Sensor™ Project provides data collection and analytic > services for user activities collected during interaction with software > tools. However, the Software as a Sensor™ Project emerged from years of > research focused on the development of novel, reliable methods for > measuring individuals’ cognitive state in a variety of contexts. > Traditional approaches to assessment in a laboratory setting include > surveys, questionnaires, and physiology (Poore et al., 2016). Research > performed as part of the Software as a Sensor™ project has shown that the > same kind of insights derived from these standard measurement approaches > can also be derived from users’ behavior. Additionally, we have explored > insights that can only be gained by analyzing raw behavior collected > through software interactions (Mariano et al., 2015). The signal processing > and algorithmic approaches resulting from this research have been > integrated into the Distill analytics stack. This means that adopters will > not be left to discern for themselves how to draw insights from the data > they gather about their software tools, although they will have the freedom > to explore their own methods as well. > Insights from user activities provided by Distill’s analytics framework > fall under two categories, broadly classified as functional workflow and > usage statistics: > Functional workflow insights tell adopters how user activities are > connected, providing them with representations of how users integrate the > application’s features together in time. These insights are informative for > understanding the step-by-step process by which users interact with certain > facets of a tool. For example, questions like “how are my users, > constructing plots?” are addressable through workflow analysis. Workflows > provide granular understanding of process level mechanics and can be > modeled probabilistically through a directed graph representation of the > data, and by identification of meaningful sub-sequences of user activities > actually observed in the population. Metrics derived provide insight about > the structure and temporal features of these mechanics, and can help > highlight efficiency problems within workflows. For example, workflow > analysis could help identify recursive, repetitive behaviors, and might be > used to define what “floundering” looks like for that particular tool. > Functional workflow analysis can also support analyses with more breadth. > Questions like, “how are my users integrating my tools’ features into a > cohesive whole? Are they relying on the tool as a whole or just using very > specific parts of it?” Adopters will be able to explore how users think > about software as cohesive tools and examine if users are relying on > certain features as central navigation or analytic features. This allows > for insights into whether tools are designed well enough for users to > understand that they need to rely on multiple features together. > Through segmentation, adopters can select the subset of the data -software > element, action, user demographics, geographic location, etc.- they want to > analyze. This will allow them to compare, for example, specific user > populations against one another in terms of how they integrate software > functionality. Importantly, the graph-based analytics approach provides a > flexible representation of the time series data that can capture and > quantify canonical usage patterns, enabling direct comparisons between > users based on attributes of interest. Other modeling approaches have been > utilized to explore similar insights and may be integrated at a later date > (Mariano, et al., 2015). > Usage statistics derive metrics from simple frequentist approaches to > understanding, coarsely, how much users are actually using applications. > This is different from simple “traffic” metrics, however, which assess how > many users are navigating to a page or tool. Rather usage data provides > insight on how much raw effort (e.g., number of activities) is being > expended while users are interacting with the application. This provides > deeper insight into discriminating “visitors” from “users” of software > tools. Moreover, given the information schema User ALE provides, adopters > will be able to delve into usage metrics related to specific facets of > their application. > Given these insights, different sets of adopters—software developers, > HCI/UX researchers, and project managers—may utilize The Software as a > Sensor™ Project for a variety different use cases, which may include: > * Testing to see if users are interacting with software tools in expected > or unexpected ways. > * Understanding how much users are using different facets of different > features in service of planning future developments. > * Gaining additional context for translating user/customer comments into > actionable software fixes. > * Understanding which features users have trouble integrating to guide > decisions on how to allocate resources to further documentation. > * Understanding the impact that new developments have on usability from > version to version. > * Market research on how users make use of competitors’ applications to > guide decisions on how to build discriminating software tools. > * General research on Human Computer Interaction in service of refining UX > and design principles. > * Psychological science research using software as data collection > platforms for cognitive tasks. > > == Differentiators == > > The Software as a Sensor™ Project is ultimately designed to address the > wide gaps between current best practices in software user testing and > trends toward agile software development practices. Like much of the > applied psychological sciences, user testing methods generally borrow > heavily from basic research methods. These methods are designed to make > data collection systematic and remove extraneous influences on test > conditions. However, this usually means removing what we test from dynamic, > noisy—real-life—environments. The Software as a Sensor™ Project is designed > to allow for the same kind of systematic data collection that we expect in > the laboratory, but in real-life software environments, by making software > environments data collection platforms. In doing so, we aim to not only > collect data from more realistic environments, and use-cases, but also to > integrate the test enterprise into agile software development process. > Our vision for The Software as a Sensor™ Project is that it provides > software developers, HCI/UX researchers, and project managers a mechanism > for continuous, iterative usability testing for software tools in a way > that supports the flow (and schedule) of modern software development > practices—Iterative, Waterfall, Spiral, and Agile. This is enabled by a few > discriminating facets: > > {{attachment:userale_figure_6.png}} > > Figure 6. Version to Version Testing for Agile, Iterative Software > Development Methods. The Software as a Sensor™ Project enables new methods > for collecting large amounts of data on software tools, deriving insights > rapidly to inject into subsequent iterations > > * Insights enabling software tool usability assessment and improvement can > be inferred directly from interactions with the tool in “real-world” > environments. This is a sea-change in thinking compared to canonical > laboratory approaches that seek to artificially isolate extraneous > influences on the user and the software. The Software as a Sensor™ Project > enables large scale, remote, opportunities for data collection with minimal > investment and no expensive lab equipment (or laboratory training). This > allows adopters to see how users will interact with their technology in > their places of work, at home, etc. > > * Insights are traceable to the software itself. Traditionally laboratory > measures—questionnaires, interviews, and physiology—collect data that is > convenient for making inferences about psychological states. However, it is > notoriously difficult to translate this data into actionable “get-well” > strategies in technology development. User ALE’s information schema is > specifically designed to dissect user interaction within the terminology of > application design, providing a familiar nomenclature for software > developers to interpret findings with. > > * Granular data collection enables advanced modeling and analytics. User > ALE’s information schema dissects user interaction by giving context to > activity within the functional architecture of software tools. Treating > each time-series of user activity as a set of events nested within > functional components provides sufficient information for a variety of > modeling approaches that can be used to understand user states (e.g., > engagement and cognitive load), user workflows (e.g., sub-sequences), and > users’ mental models of how software tool features can be integrated (in > time) to perform tasks. In contrast, commercial services such as Google > Analytics and Adobe Analytics (Omniture) provide very sparse options for > describing events. They generally advocate for using “boiler plate” event > sets that are more suited to capturing count data for interactions with > specific content (e.g., videos, music, banners) and workflows through > “marketplace” like pages. User ALE provides content agnostic approaches for > capturing user activities by letting adopters label them in domain specific > ways that give them context. This provides a means by which identical user > activities (e.g. click, select, etc.) can be disambiguated from each other > based on which functional sub-component of the tool they have been assigned > to. > > * Adopter-generated content, analytics and data ownership. The Software as > a Sensor™ Project is a set of open-source products built from other > open-source products. This project will allow adopters to generate their > own content easily, using open source analytics and visualization > capabilities. By design, we also allow adopters to collect and manage their > own data with support from widely used open source data architectures > (e.g., Elastic). This means that adopters will not have to pay for > additional content that they can develop themselves to make use of the > service, and do not have to expose their data to third party commercial > services. This is useful for highly proprietary software tools that are > designed to make use of sensitive data, or are themselves sensitive. > > == Current Status == > > All components of the Software as a Sensor™ Project were originally > designed and developed by Draper as part of DARPA’s XDATA project, although > User ALE is being used on other funded R&D projects, including DARPA > RSPACE, AFRL project, and Draper internally funded projects. > Currently, only User ALE is publically available, however, the Portal, > Distill, and STOUT will be publically available in the May/June 2016 > time-frame. The last major release of User ALE was May, 2015. All > components are currently maintained in separate repositories through GitHub > (github.com/draperlaboratory). > Currently, only software tools developed with Javascript are supported. > However, we are currently working on pythonQT implementations for User ALE > that will support many desktop applications. > > == Meritocracy == > The current developers are familiar with meritocratic open source > development at Apache. Apache was chosen specifically because we want to > encourage this style of development for the project. > > == Community == > The Software as a Sensor™ Project is new and our community is not yet > established. However, community building and publicity is a major thrust. > Our technology is generating interest within industry, particularly in the > HCI/UX community, both Aptima and Charles River Analytics, for example are > interested in being adopters. We have also begun publicizing the project to > software development companies and universities, recently hosting a public > focus group for Boston, MA area companies. > We are also developing communities of interested within the DoD and > Intelligence community. The NGA Xperience Lab has expressed interest in > becoming a transition partner as has the Navy’s HCIL group. We are also > aggressively pursuing adopters at AFRL’s Human Performance Wing, Analyst > Test Bed. > During incubation, we will explicitly seek to increase our adoption, > including academic research, industry, and other end users interested in > usability research. > > == Core Developers == > The current set of core developers is relatively small, but includes Draper > full-time staff. Community management will very likely be distributed > across a few full-time staff that have been with the project for at least 2 > years. Core personnel can be found on our website: > http://www.draper.com/softwareasasensor > > == Alignment == > The Software as a Sensor™ Project is currently Copyright (c) 2015, 2016 The > Charles Stark Draper Laboratory, Inc. All rights reserved and licensed > under Apache v2.0. > > == Known Risks == > > === Orphaned products === > There are currently no orphaned products. Each component of The Software as > a Sensor™ Project has roughly 1-2 dedicated staff, and there is substantial > collaboration between projects. > > === Inexperience with Open Source === > Draper has a number of open source software projects available through > www.github.com/draperlaboratory. > > == Relationships with Other Apache Products == > Software as a Sensor™ Project does not currently have any dependences on > Apache Products. We are also interested in coordinating with other projects > including Usergrid, and others involving data processing at large scales, > time-series analysis and ETL processes. > > == Developers == > The Software as a Sensor™ Project is primarily funded through contract > work. There are currently no “dedicated” developers, however, the same core > team does work will continue work on the project across different contracts > that support different features. We do intend to maintain a core set of key > personnel engaged in community development and maintenance—in the future > this may mean dedicated developers funded internally to support the > project, however, the project is tied to business development strategy to > maintain funding into various facets of the project. > > == Documentation == > Documentation is available through Github; each repository under the > Software as a Sensor™ Project has documentation available through wiki’s > attached to the repositories. > > == Initial Source == > Current source resides at Github: > * https://github.com/draperlaboratory/user-ale (User ALE) > * https://github.com/draperlaboratory/distill (Distill) > * https://github.com/draperlaboratory/stout (STOUT and Extensions) > * https://github.com/draperlaboratory/ > > == External Dependencies == > Each component of the Software as a Sensor™ Project has its own > dependencies. Documentation will be available for integrating them. > > === User ALE === > * Elasticsearch: https://www.elastic.co/ > * Logstash: https://www.elastic.co/products/logstash > * Kibana (optional): https://www.elastic.co/products/kibana > === STOUT === > * Django: https://www.djangoproject.com/ > * django-axes > * django-custom-user > * django-extensions > * Elasticsearch: https://www.elastic.co/ > * Gunicorn: http://gunicorn.org/ > * MySQL-python: https://pypi.python.org/pypi/MySQL-python > * Numpy: http://www.numpy.org/ > * Pandas: http://pandas.pydata.org/ > * psycopg2: http://initd.org/psycopg/ > * pycrypto: https://www.dlitz.net/software/pycrypto/ > * pymongo: https://api.mongodb.org/python/current/ > * python-dateutil: https://labix.org/python-dateutil > * pytz: https://pypi.python.org/pypi/pytz/ > * requests: http://docs.python-requests.org/en/master/ > * six: https://pypi.python.org/pypi/six > * urllib3: https://pypi.python.org/pypi/urllib3 > * mongoDB: https://www.mongodb.org/ > * R (optional): https://www.r-project.org/ > === Distill === > * Flask: http://flask.pocoo.org/ > * Elasticsearch-dsl: https://github.com/elastic/elasticsearch-dsl-py > * graph-tool: https://git.skewed.de/count0/graph-tool > * OpenMp: http://openmp.org/wp/ > * pandas: http://pandas.pydata.org/ > * numpy: http://www.numpy.org/ > * scipy: http://www.numpy.org/ > === Portal === > * Django: https://www.djangoproject.com/ > * React: https://facebook.github.io/react/ > * D3.js: https://d3js.org/ > > === GNU GPL 2 === > > > === LGPL 2.1 === > > > === Apache 2.0 === > > > === GNU GPL === > > > == Required Resources == > * Mailing Lists > * priv...@senssoft.incubator.apache.org > * d...@senssoft.incubator.apache.org > * comm...@senssoft.incubator.apache.org > > * Git Repos > * https://git-wip-us.apache.org/repos/asf/User-ALE.git > * https://git-wip-us.apache.org/repos/asf/STOUT.git > * https://git-wip-us.apache.org/repos/asf/DISTILL.git > * https://git-wip-us.apache.org/repos/asf/TAP.git > > * Issue Tracking > * JIRA SensSoft (SENSSOFT) > > * Continuous Integration > * Jenkins builds on https://builds.apache.org/ > > * Web > * http://SoftwareasaSensor.incubator.apache.org/ > * wiki at http://cwiki.apache.org > > == Initial Committers == > The following is a list of the planned initial Apache committers (the > active subset of the committers for the current repository on Github). > > * Joshua Poore (jpo...@draper.com) > * Laura Mariano (lmari...@draper.com) > * Clayton Gimenez (cgime...@draper.com) > * Alex Ford (af...@draper.com) > * Steve York (sy...@draper.com) > * Fei Sun (f...@draper.com) > * Michelle Beard (mbe...@draper.com) > * Robert Foley (rfo...@draper.com) > * Kyle Finley (kfin...@draper.com) > * Lewis John McGibbney (lewi...@apache.org) > > == Affiliations == > * Draper > * Joshua Poore (jpo...@draper.com) > * Laura Mariano (lmari...@draper.com) > * Clayton Gimenez (cgime...@draper.com) > * Alex Ford (af...@draper.com) > * Steve York (sy...@draper.com) > * Fei Sun (f...@draper.com) > * Michelle Beard (mbe...@draper.com) > * Robert Foley (rfo...@draper.com) > * Kyle Finley (kfin...@draper.com) > > * NASA JPL > * Lewis John McGibbney (lewi...@apache.org) > > == Sponsors == > > === Champion === > * Lewis McGibbney (NASA/JPL) > > === Nominated Mentors === > * Paul Ramirez (NASA/JPL) > * Lewis John McGibbney (NASA/JPL) > * Chris Mattmann (NASA/JPL) > > == Sponsoring Entity == > The Apache Incubator > > == References == > > Mariano, L. J., Poore, J. C., Krum, D. M., Schwartz, J. L., Coskren, W. D., > & Jones, E. M. (2015). Modeling Strategic Use of Human Computer Interfaces > with Novel Hidden Markov Models. [Methods]. Frontiers in Psychology, 6. > doi: 10.3389/fpsyg.2015.00919 > Poore, J., Webb, A., Cunha, M., Mariano, L., Chapell, D., Coskren, M., & > Schwartz, J. (2016). Operationalizing Engagement with Multimedia as User > Coherence with Context. IEEE Transactions on Affective Computing, PP(99), > 1-1. doi: 10.1109/taffc.2015.2512867 > > > > -- > Lewis > -- *Lewis*
