Skip to content
A mature ebook/document reading program including text to speech and other useful features
Perl HTML Ragel Java Prolog Emacs Lisp Other
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Clear
doc
etc/clear
frdcsa.org/~andrewdo/WebWiki
frdcsa
lab
persistence
rss
scripts
systems
var/lib/clear/lists
004635177e249cddd5000000.txt
004635177e249cddd5000000.txt.voy.gz
02_CatalystBasics.html
02_CatalystBasics.html.voy.gz
Clear.pm
ExtractAbbrev.class
ExtractAbbrev.java
LICENSE
Makefile
README.md
a.mp3
build-stamp
cla
clear.d.pddl
clear.el
clear.p.pddl
configure-stamp
errors.txt
gpl.txt
lists
notes.pm
old-control
reading-list
reading-list-2
readinglist
readlist
semantic.cache
test
test.pl
to.do
todo
voyindex

README.md

clear

A mature ebook/document reading program including text to speech and other useful features

http://frdcsa.org/frdcsa/internal/clear

<title>CLEAR</title> Computer LEarning ARchitecture Are we CLEAR? Intelligent Tutoring System for distance learning. Maintains reading lists for content areas, automatically determines reading list dependencies. Uses TTS to read documents. Records user's attention during reading. Generates tests from documents for placement or assessment. Eventually, expect to integrate webcam based eye-tracking system, so that records of what has been read

CLEAR is a great way to have books, manuals, websites, etc, read to you, allowing you to pause, quit, resume, and filter out nonsense. Clear uses the Festival text-to-speech system and ccp to do this. It is very useful for studying. For instance, while browsing a researchers website in w3m-el, you can select a region over all of their publications and execute M-x clear-queue-all-links or "\C-c\C-mc" to queue all of their papers. A message containing a command to queue the links is then sent from Emacs-Unilang-Agent to UniLang, which sends it to CLEAR. The queued links are stored to the current readlist. Whenever you have a chance then, you tell CLEAR to resume reading and it reads you books. It sorts the reading list by topical dependency.

The objective is to model the user's reading flux at the sentence level. Conceptual understand may then be modelled through analysis of the sentences. A lot of data on the entire process is recorded. The purpose is so that the computer has another modality, in addition to things like expected background knowledge, apparent knowledge, to model which axioms the user is familiar with. (Eventually the Textbook Knowledge Formation tool chain we are assembling with the Chess Analysis Knowledge Base project will be used to model fields axiomatically as opposed to the document level granularity currently used by CLEAR.) By modelling the reading flux, a much better mental model can be created and the system can behave contingently. This information is critical to many tasks, such as:

  • Assessing the user's background knowledge.
  • Verifying familiarity with some tradecraft.

The motivation for CLEAR is inspired in part by the following information from the JAVELIN project: http://www.lti.cs.cmu.edu/Research/JAVELIN/

Utility-based Information Fusion. Any item of information I can be assigned a value representing its utility to analyst A with respect to task context T and question Q. The utility value can be used to rank the possible answers in a manner inspired by Maximal Marginal Relevance:

U = Argmaxk[F(Rel(I,Q,T),Nov(I,T,A),Ver(S,Sup(I,S)),Div(S),Cmp(I,A)),Cst(I,A)]

Essentially all information items (facts, links, inferred relations, etc.) in consideration for fusion into an answer may be ranked in utility to the analyst as a function of:

  • Rel: relevance to the requested information
  • Nov: novelty (likelihood that the analyst does not already know it)
  • Ver: veracity (of source S) and support for conclusion I within S
  • Div: source diversity (analyst may want contrasting or reinforcing views)
  • Cmp: comprehensibility of information by the analyst (one can assume a uniform distribution until the system learns otherwise from analyst feedback)
  • Cst: expected cost (e.g. time) for the analyst to assimilate the information.

The way CLEAR does this is by monitoring the users readers, and deriving belief models of the user's state (such as awareness, eye-position, whether they were listening (in the case of auditory media) etc), as these reader applications were running. Currently only reading is implemented, as I don't have a suitable eye-tracker.

In addition to reading, currently CLEAR can quiz the user, as in this example:

/var/lib/myfrdcsa/codebases/internal/clear $ clear -q lists/camo.rl 
Readlist is lists/camo.rl
Initializing TTS engine...
festival: no process killed
server    Tue Dec 28 20:10:06 2004 : Festival server started on port 1314
Reviewing library/us-army-field-manuals/extracted/20-3/US ARMY FM 20-3 Camoflage Concealment And Decoys/ch4.PDF...
client(1) Tue Dec 28 20:10:08 2004 : accepted from pc-sonicparts

Question 0 Make optimum use of concealed routes, hollows, gullies, and other terrain features that are dead-space areas to enemy observation and _____ positions. % fighting Incorrect! (firing)

Question 1 Although an enemy's use of radar and _____ aerial recon hinders operations at night, darkness remains a significant concealment tool. % visual Incorrect! (ir)

Question 2 When natural _____ and concealment are unavailable or impractical, the coordinated employment of smoke, suppressive fires, speed, and natural limited-visibility conditions minimize exposure and avoid enemy fire sacks. % cover Correct! (cover)

Question 3 Designate concealed _____ for movement into and out of an area. % routes Correct! (routes)

Question 4 A trade-off, however, usually exists in _____ of a slower rate of movement when using these types of routes. 4-18. % this Incorrect! (terms)

One can see how Question 4 is not as useful as the others, but overall, I find the system to be very effective in liu of a real system. If the shoe fits...

This system used to be called: Correctly Learning An Individual's Reasoning Visually Or Yielded Assuming Normal Cognitive Execution (CLAIRVOYANCE), but this is just sort of inaccurate. CLEAR is much, more, well, clear ;)

CLEAR is primarily useful for reading documentation, papers, books, websites, digital library content, and other sources that are of practical use to the project, so that we can get much more done, especially while resting or moving. A critical feature is the ability to read documentation for installed packages, because mastering newly installed software is critical to the FRDCSA mission goal. It also does very well with important documents, like the above survival and preparedness information (as the recent tsunami confirms). Lastly, and most important, it allows us to implement a system of instruction so that we can help to train project members in various capacities and verify their progress, without being burdensome at all - but rather entertaining. This can be combined with CRITIC for collaborative filtering based ratings to enhance the value of the information. CLEAR in conjunction with DigiLib is also used by job-search to ensure familiarity with position requirements. CLEAR uses functionality from CoAuthor to compose custom reading specific to users' tested proficiency and known reading history.

ccp, festival, libclass-methodmaker-perl, w3m, libchm-bin

You can’t perform that action at this time.