+1 (binding) Good luck guys!
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* >
