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From Lewis John Mcgibbney <>
Subject [RESULT] WAS Re: [VOTE] Accept SensSoft into the Apache Incubator WAS Re: [VOTE] Accept
Date Fri, 17 Jun 2016 01:20:28 GMT
Hi general@,
72 hours has come and gone I am therefore closing off this VOTE'ing thread.
Thank you to everyone that was able to VOTE.

[4] +1 Accept SensSoft into the Apache Incubator
Lewis John Mcgibbney*
Chris A Mattmann*
Tom Barber*
Henry Saptura*

[0] +/-0 Not overly bothered either way
[0] -1 DO NOT accept SensSoft into the Apache Incubator (please state why)

*IPMC binding

So great news, SensSoft is accepted into the Incubator :) :) :)
I'll progress with workflow.
Thanks folks.

On Mon, Jun 13, 2016 at 3:07 PM, Lewis John Mcgibbney <> wrote:

> Title here should be [VOTE] Accept SensSoft into the Apache Incubator
> On Mon, Jun 13, 2016 at 10:55 AM, Lewis John Mcgibbney <
>> 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
>> 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
>> 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
>> (
>> 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:
>> == 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
>> == 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:
>>  * (User ALE)
>>  * (Distill)
>>  * (STOUT and Extensions)
>>  *
>> == 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:
>>  * Logstash:
>>  * Kibana (optional):
>> === STOUT ===
>>  * Django:
>>    * django-axes
>>    * django-custom-user
>>    * django-extensions
>>  * Elasticsearch:
>>  * Gunicorn:
>>  * MySQL-python:
>>  * Numpy:
>>  * Pandas:
>>  * psycopg2:
>>  * pycrypto:
>>  * pymongo:
>>  * python-dateutil:
>>  * pytz:
>>  * requests:
>>  * six:
>>  * urllib3:
>>  * mongoDB:
>>  * R (optional):
>> === Distill ===
>>  * Flask:
>>  * Elasticsearch-dsl:
>>  * graph-tool:
>>  * OpenMp:
>>  * pandas:
>>  * numpy:
>>  * scipy:
>> === Portal ===
>>  * Django:
>>  * React:
>>  * D3.js:
>> === GNU GPL 2 ===
>> === LGPL 2.1 ===
>> === Apache 2.0 ===
>> === GNU GPL ===
>> == Required Resources ==
>>  * Mailing Lists
>>    *
>>    *
>>    *
>>  * Git Repos
>>    *
>>    *
>>    *
>>    *
>>  * Issue Tracking
>>    * JIRA SensSoft (SENSSOFT)
>>  * Continuous Integration
>>    * Jenkins builds on
>>  * Web
>>    *
>>    * wiki at
>> == 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 (
>>  * Laura Mariano (
>>  * Clayton Gimenez (
>>  * Alex Ford (
>>  * Steve York (
>>  * Fei Sun (
>>  * Michelle Beard (
>>  * Robert Foley (
>>  * Kyle Finley (
>>  * Lewis John McGibbney (
>> == Affiliations ==
>>  * Draper
>>    * Joshua Poore (
>>    * Laura Mariano (
>>    * Clayton Gimenez (
>>    * Alex Ford (
>>    * Steve York (
>>    * Fei Sun (
>>    * Michelle Beard (
>>    * Robert Foley (
>>    * Kyle Finley (
>>  * NASA JPL
>>    * Lewis John McGibbney (
>> == 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*


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