Sorry Lewis, I read through it the other day and though I'd replied, clearly not.
+1 binding. On Thu, Jun 16, 2016 at 11:40 PM, Lewis John Mcgibbney < lewis.mcgibb...@gmail.com> wrote: > Hi Folks, > The SensSoft proposal needs a bit of TLC from general@ > If software-as-a-sensor, usability testing and user characterization are > your thing then please consider giving the proposal a read through. > At this stage in the game we are looking at obtaining one more binding PPMC > VOTE in order to push through with acceptance and incubation. > Thanks in advance to anyone who can facilitate this. > Lewis > > On Mon, Jun 13, 2016 at 3:07 PM, Lewis John Mcgibbney < > lewis.mcgibb...@gmail.com> wrote: > > > 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* > > > > > > -- > *Lewis* >
