Installation

Using Yarn to install

There are three steps:

git clone [HTTP-project-url]
Run yarn. It will install necessary packages
Run yarn dev. will compile the project and when it succeed, pop up the app

Usage

On the canvas layout, the shortcut pattern is different for different platforms.

For Mac users, fn+ctrl+[key] is the shortcut pattern. For example, to connect a source node (claim/pdf, etc.) with one or some arbitrary target nodes, please select the source nodes, and fn+ctrl+[right click] on the target nodes.
For Windows users, fn+ctrl+[key] is the shortcut pattern.

Views

The right panel of the app has 3 views. Each view has different roles.

Canvas layout allows you to write and organize grounded claims
Autograb list view shows a list of relevant sentences throughout the paper that fit your interest. The name "autograb" means these sentences are automatically grabbed by the text classification model.
DocEditor view is an wizard-of-oz interface to synthesis claims. As you writing, the interface will pops up a similar grounded claim that you have wrote before.

For mac users, ctrl+alt+[view_ID] is the shortcut to switch between different views.

Dictionary

Auto-grab: the feature that automatically grabs detailed information from the text content of the pdf.

Build the application

To build the project into a standalone deskop application, run yarn dist:mac.

By default, it will take 3-5 mins and once finished, will save a .zip file in the path dist/compressor-0.0.1-mac.zip.

Roadmap

Study 1: Do grounded claims improve sensemaking?

Subjects: ~40. undergrads? class credit?
Task Constraints: under 1.5 hours. topics subjects can handle.
Task: Given our design and the default

ctrl-f
transitions
- text: anything
- table: object attributes
- network: relationships encoded with proximity, angle, containment, lines
- map: has named sequential axis
clustering
- initialize canvas coords with tsne
chrome extension + firebase
- select text, right click send to firebase list of {url, time, query}
- firebase onlist add, knn search, list view
Fine-tuned, conditional semantic vectors for KNN search
- Fine-tuned by machines
  - fastai + gorbid our pdfs?
- conditional
  - paper/website title, authors, venue, section header
  - knowledge graph links
document editor
- insert one doc in another
- transcullison?
- paraphrasing something imported
- create node from selected text
- create nodes/links from outline heirarchy
- insert viewbox into doc
- change font size of any character
- change font color
- drag lines to create links between items in the editor
queries -> search for anything, traverse the graph, large UI
- query builder
  - a modal that take up entire window
  - autocomplete for search parameters
    - consider neo4j queries
autocomplete -> small UI
- text matching algorithm
- could be ghost text instead of popup
multi-instance customizable layouts
- takes a query as input
- canvas
- list view
  - drag order
  - drag increase height
  - show links in margin?
  - add/remove/edit any node
  - drag links between
- grid view
  - drag increase col/row size
  - show links in gutters?
pdf viewer
- manually fix autograb
- good ole' fashion text highlighting
- text highlighting from auto-grab
- autocomplete for phrases in selected area
- perf
  - zoom
  - prevent rerender
graph viewer
- select all left/right/up/down
- move many with arrows
- force directed
- collision
- alignment tools
- html-to-image?
- perf: react spring + transform + opacity
Bug reports
- text editor id can be undefined. maybe fixed?

Slate-TroubleShooting

** Can't focus multiple instances of slate editors, e.g. in a list: ** Cause: conflicting keys used to find editor in the dom Solution: editorUtils.tsx/initKeySafeSlate()

Dev-Reference

Sweet path from Matt's use

This would be either field deployment inspiration or walk path for a demo video.

process: random youtube rec -> watch 30 sec vid -> skim paper -> find interesting bit -> download into pdf dir -> open up electron -> grab bits to share -> screenshot -> paste into slack -> add context message in slack (edited)

Design

Predictive Interactions
- easily dismissible, correctness, alternate possibilities, refinements
- Read Eric Horvitz Principles of Mixed-Initiative User Interfaces

CMD

ngrok http 3000 -host-header="localhost:3000"

tours

https://reactour.js.org

npm graphs

https://github.com/levelgraph/levelgraph 1200 # easy save to disk 3x slower than graphology for neighbors https://github.com/graphology/graphology#readme 200 # 3x faster than levelgraph for neighbors http://js.cytoscape.org/

graph layouts

https://ialab.it.monash.edu/webcola/index.html https://github.com/d3/d3-force

electron perf notes

https://www.infoq.com/presentations/electron-pitfalls

state management

https://github.com/CharlesStover/reactn https://github.com/diegohaz/constate

async validation

https://indicative.adonisjs.com/docs/syntax-guide

hooks

https://github.com/neo/react-use-scroll-position https://github.com/LeetCode-OpenSource/rxjs-hooks#apis

node perf testing

https://github.com/bestiejs/platform.js#readme https://benchmarkjs.com/

async handling

https://github.com/staltz/callbag-basics # push and pull? https://mostcore.readthedocs.io/en/latest/api.html # fastest https://github.com/ReactiveX/rxjs

formalism

level of detail/precision/specificity + attrs (esp. causality + utility) broadcasting, static docs, cognitive tutors

queries

what's the expected outcome of some scenario (what's y given x) what scenario is optimal for creating some outcome (what predicts y) what does the author mean by x (use expands to & same as ) how confident should I be in statement X

fast static kdtree: not sorted by distance?

https://github.com/mourner/kdbush#readme

DIY graph db

http://nodejsconfit.levelgraph.io/#17

matt-erhart/lab