HDTenEightyP/NetNews huggingface dataset:
From: hs@azu.informatik.uni-stuttgart.de (Heribert Schlebbe)
Newsgroups: comp.multimedia,alt.hypertext
Subject: Dexter, Trellis etc, Info & Bibl. wanted
Date: 12 Aug 92 08:38:59 GMT
Organization: Informatik, Uni Stuttgart, Germany
Originator: hs@azu

A Brazilian friend of mine, who is graduating at the Department of Computer 
Science of the University at Porto Alegre, Rio Grando do Sul, Brazil, is looking
for some informations about the following topics:

	- Hypertext Models: Dexter, Trellis,
	- Proposals and Extensions for Multimedia,
	- Data bases for Hypermedia,
	- A most recent Bibliography of Hypertext and Hypermedia.

He asked me to send this posting to the news group, because the news system is
not yet installed at their site. 

Please email your information to my address. I shall forward it to Brazil.

Thanks in advance and "muito obrigado"

	Heribert Schlebbe
==============================================================================
Heribert Schlebbe                     E-Mail:
Institut fuer Informatik              hs@informatik.uni-stuttgart.de
Universitaet Stuttgart
Breitwiesenstrasse 20/22              Fax:   (+49|0)711-780-1044
D-7000 Stuttgart 80 / Germany         Phone: (+49|0)711-7816-366
==============================================================================
From: gwe3409@bcsaic.boeing.com (Gerald Edgar)
Newsgroups: comp.multimedia
Subject: Re: Ascii to sound
Keywords: Speach, Text
Date: 13 Aug 92 14:13:43 GMT
Distribution: na
Organization: Boeing Computer Services AI Center, Seattle

In article <1992Aug12.154427.22158@public.sub.org> peterk@public.sub.org (Peter Kittel) writes:
>raum@isoa3.ba.ttu.edu (Raum Pattikonda) writes:
>
>>I would like to know if there is any software which could play the sound from an ascii file.
>>One of my friends is a blind person and this kind of software would be of great use to him.
>
I remember that there was software in the public domain that took english
text and from a set of rules output phonomes to have a speach synthesizer
talk. This was about 7 years ago when I had an S-100 (Pre IBM-PC). The
other aspect of this software that I remember is that I think was developed
by the U.S. Navy (but the last might be wrong). Does this ring any bells
for any one to help this guy (?) out.
Newsgroups: comp.multimedia
From: drapeau@oglala.Stanford.EDU (George Drapeau)
Subject: Re: request: NEC PC-VCR
Reply-To: drapeau@sioux.stanford.edu
Organization: Libraries and Information Resources, Stanford University
Date: 12 Aug 92 23:57:18

For the MAEstro Project, we wrote a number of drivers for videodisc
and videotape players, including the PC-VCR.  It is our most widely
used device, since we use it for student editing of video.  However,
if I were asked whether I would recommend it, I would say no.

The PC-VCR is incredibly flaky.  The command set is fine, and much
simpler to debug than any Sony command protocols we've worked with,
including VISCA (why the hell does VISCA encode digits as binary-coded
decimal?!).  But, it has a number of problems.  Sometimes the NEC will
simply refuse to listen to your commands.  Sometimes the NEC just
won't read the timecode on the tape, regardless of the condition of
the tape (it fails on brand-new, high quality S-VHS tapes).  The first
minute or so of your tape is pretty much useless as frame-addressable
tape.  The NEC doesn't reliably read the headers it's supposed to be
able to write to the tapes, rendering this feature pretty much
useless.

I could go on and on, but to summarize, our experience with the NEC
PC-VCR has been, shall we say, a learning experience.  We learned a
lot about using videotape for editing (and how much it's worth to pay
for decent hardware), but given the chance to purchase a NEC PC-VCR
today, I would not do so.

The one qualifying statement I can make about the NEC is that we
*have* used it for a number of projects on campus, successfully.  We
had to nursemaid the NEC through the projects sometimes, but for the
most part it did the job.

My recommendation?  Buy a Sony CVD-1000 (a.k.a., the "Vdeck").  It
costs about $100 less than the PC-VCR, it's much lighter and smaller,
the transport seems faster and is definitely more responsive, and most
importantly, the deck (so far) seems to be much more reliable.  Plus,
the Vdeck has a few special effects built-in (audio and video fade,
for example), and it can read the RC timecode burned onto Hi-8 tapes
by Sony camcorders that have that feature (saving you the time of an
extra pass through the tape).

By the way, the Vdeck is Hi-8 mm format.

It's a shame that NEC won't be selling the PC-VCR anymore, because it
probably means that they won't be re-engineering it to introduce a
successor product.  Too bad.

That's my opinion.  If you want more information about MAEstro, send
mail to "maestro@sioux.stanford.edu", or get the source for yourself
via anonymous ftp from the same host (sioux).  You can look at our NEC
driver code yourself if you like.

	George
 
--
______________________________________________________________________________
George D. Drapeau			Internet: drapeau@sioux.stanford.edu
Libraries and Information Resources
Stanford University
Newsgroups: comp.multimedia
From: ramz@beta.lanl.gov (Thomas Rick Ramsey)
Subject: Re: request: NEC PC-VCR
Organization: Los Alamos National Laboratory
Date: Thu, 13 Aug 1992 05:30:05 GMT

The NEC PC-VCR has been discontinued.

Rick Ramsey  ramz@beta.lanl.gov  Los Alamos, NM
From: ldo@waikato.ac.nz (Lawrence D'Oliveiro, Waikato University)
Newsgroups: comp.multimedia
Subject: Re: Mitsubishi E82 (was Re: request: NEC PC-VCR)
Date: 13 Aug 92 11:12:21 +1200
Organization: University of Waikato, Hamilton, New Zealand

In article <7IZ5HCU@zelator.in-berlin.de>, leo@zelator.in-berlin.de (Stefan Hartmann) writes:
> In <1992Aug12.002957.24887@wri.com> perelet@avignon.wri.com (Oleg Perelet) writes:
>
>>Does anybody uses [the NEC PC-VCR]?
>
> in Germany it is called Mitsubishi E82 and comes with the GSE RAPID
> timecode for the Control track on the SVHS (VHS) track.
>
> I just think about buying it to do single frame grabbing for MPEG
> encoding..

Is this a stock-standard Mitsubishi E82? I'm just intrigued because there
should also be an E82 model available in NZ. I just thought it was Mitsubishi's
consumer S-VHS player, but if it has time code and computer control
capability, that makes it somewhat more interesting...

Lawrence D'Oliveiro                       fone: +64-7-856-2889
Computer Services Dept                     fax: +64-7-838-4066
University of Waikato            electric mail: ldo@waikato.ac.nz
Hamilton, New Zealand    37^ 47' 26" S, 175^ 19' 7" E, GMT+12:00
Newsgroups: comp.multimedia
From: pesjogre@nada.kth.se (Peter Sj{gren)
Subject: SoundBlaster sampling frequency accuracy
Originator: pesjogre@byse.nada.kth.se
Reply-To: pesjogre@nada.kth.se (Peter Sj{gren)
Organization: Royal Institute of Technology, Stockholm, Sweden
Date: Thu, 20 Aug 1992 08:49:09 GMT

Hi,

I have an old version (1.5) of the SoundBlaster soundcard from Creative Labs
for PC and I would like to do accurate frequency measurements. As a first 
experiment I tried to record a 440 Hz tuning fork at 11025 Hz sampling 
frequency, but I got a frequency of about 436 Hz (give or take 0.7 Hz).

I therefore wonder about the accuracy of the sampling frequency and other
data on this model (1.5) and if they are better on later versions.


By the way: does anyone know the email-address to Creative Labs?

Thank you for your answers!

Peter Sjogren
Stockholm
Sweden


please answer to pesjogre@nada.kth.se or peter@jada.se
Newsgroups: comp.ai
From: arw4@cunixb.cc.columbia.edu (Aaron R Wininger)
Subject: Classes Suggestions
Reply-To: arw4@cunixb.cc.columbia.edu (Aaron R Wininger)
Organization: Columbia University
Date: Wed, 12 Aug 1992 18:44:00 GMT

I'm an undergrad Applied Physics major and have become extremely interested
in AI and expert systems (mainly as a result of reading too much sci-fi like
Hyperion and Nueromancer). Anyway, if somebody could suggest (generally) what
languages and courses (Applied Math, Comp Sci) (allready know Fortran,Basic)
would be helpful, I would appreciate it.
Thanks in advance.
Aaron

Aaron R. Wininger, Carnivore                 Columbia Plasma Physics Laboratory
arw4@cunixb.cc.columbia.edu                                 Columbia University
wininger@cuplvx.ap.columbia.edu                              New York, NY 10027
      'That which does not kill us, makes us stronger.'  -Nietzche
From: mpp@cs.brown.edu (Michael P. Perrone)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: Center for Neural Science, Brown University
Date: Thu, 13 Aug 1992 22:34:05 GMT

In article <1992Aug13.104511.29302@siesoft.co.uk>, tomc@siesoft.co.uk (Tom Crossland) writes:
|> 
|> If  you  used indirect addressing it could be overcome. ie, if
|> your  instruction  pointed to a block of memory, which in turn
|> had  a  flag  and the offset of another block of memory, which
|> also  had  a  flag  and the offset to another block of memory,
|> etc,  etc,  etc you could in theory address an infinite amount
|> of  memory  (OK  so  it  might  take  an  infinite  number  of
|> instruction executions, but that's a small price to pay ;-)).
|> 

any memory address in this scheme would only take a finite amount
of time to access.  (except for that last one... :-)
From: sut@dsbc.icl.co.uk (Secure Unix Team KID01)
Newsgroups: comp.ai.neural-nets
Subject: Re: Smell in Neural Network
Date: 18 Aug 92 15:33:25 GMT
Organization: ICL Bracknell, UK

In article <1992Aug18.115759.21078@csd.brispoly.ac.uk> c_tsai@csd.brispoly.ac.uk (C Tsai) writes:
>   If we can go to out space,like moon or any stars, how we can let computer to
>learn "this star can let human to live or not", this must depend on how much 
>oxygon or gas is in this star????? 
>

1. Living on stars may prove difficult. Sorry if I'm being pedantic.
2. If you mean planets, I'm sure there are better ways of measuring the
   composition of a planet's atmosphere than "sniffing" it! :-)

Sorry.

-- 
    ___
  /// \\\  The sole Ipswich Town fan in the frozen wastes of North Staffs is...
 |||O^O||| Martyn Amos -- Secure UNIX Team, ICL Kidsgrove, Staffs., ST7 1TL
 ||| ) ||| mra@zen.icl.co.uk (0782) 771000 Ext. 3152
 |||\_/||| "There's only one way of life, and that's your own" -- Levellers
Organisation: European Space Operation Centre (E.S.O.C)
Date: Friday, 14 Aug 1992 13:52:10 CET
From: <KHOLMLUN@ESOC.BITNET>
Newsgroups: comp.ai.neural-nets
Subject:    Adressing NN:s (CMAC)

I've been exposed to CMAC! The problem is that even though
I'm quite familiar with traditional image analysis techniques
and I have some knowlegde related to neural net approaches
to multispectral image analysis, I've never encountered
addressing neural nets. Therefore, any information (references,
thoughts etc) related to CMACs and CMAC pros et cons related to
this particular topic, would be welcomed.

kts
kth
From: overman@ssc-vax.uucp (Tim Overman)
Newsgroups: comp.ai.neural-nets
Subject: Where is S. Grossberg?
Date: 19 Aug 92 19:58:56 GMT
Organization: Boeing Aerospace & Electronics
Originator: overman@ssc-vax

I'm looking for a software simulation (pc, SUN, Silicon Graphics) of 
Steven Grossberg's Boundary Contour System (BCS) for evaluation.
If there is anyone out there that knows where I can ftp this or the E-mail
address/phone number of Steven Grossberg it would be appreciated.

---------
Tim Overman
Boeing Defense and Space Group
Seattle, WA
(206) 773-5534
overman@ssc-vax.UUCP

Newsgroups: comp.ai.neural-nets
From: mpp@cns.brown.edu (Michael P. Perrone)
Subject: Re: Reducing Training time vs Generalisation
Keywords: back propagation, training, generalisation
Organization: Center for Neural Science, Brown University
Date: Wed, 19 Aug 1992 17:22:22 GMT

If we define a training set Z = {(xi,f(xi))} for i=1,..,n
and we demand that our backprop network be able to fit f(.)
exactly on Z, then clearly, the global minimum is to fit
f(.) exactly.

If we further demand that for some range of x not in Z, the
network catastrophically misfits the true function, then
what we are really saying is that our choice of model was bad.

Clearly, this is a problem.  But it is not just a problem with 
backprop.  It is a fundamental problem whenever we are forced to
choose a model.  Unless we have some a priori knowledge about the
problem, our model choice is always open to pathological solutions.

The above problem is an example of the utility a priori smoothness 
assumptions.  For example instead of using backprop, we could have
chosen a kernel estimator using delta fuctions (i.e. no smoothing).
In this case, our error over Z would again be zero but our bias to
the training set can again lead us to "wild" solutions (which is
just another way of saying high variance).
Newsgroups: comp.ai.neural-nets
From: tjochem+@CS.CMU.EDU (Todd Jochem)
Subject: SUMMARY: Modular Neural Nets
Date: Wed, 12 Aug 92 16:57:34 GMT
Organization: Carnegie Mellon University


Below is the summary of references I have received. The first four are
ones that deal with modularity in the speech domain. I was somewhat surprised at the lack of responses -> lack of work?? being done in this area.


Waibel, Alex."Modular Contruction of Time Delay Neural Networks for Speech
Recognition", Neural Computation 1 pp 39-46. (1989)

Hampshire, John B. and Waibel, Alex. "The Meta-Pi Network: Building
Distributed Knowledge Representations for Robust Pattern
Recognition", Carnegie Mellon Tech. Report #CMU-CS-89-166-R (8/31/89)

Hampshire, John B. and Waibel, Alex. "Connectionist Architectures
for Multi-Speaker Phoneme Recognition Recognition", Carnegie Mellon
Tech. Report #CMU-CS-89-167 (8/31/89)

Waibel, Alex, Sawai, Hidefumi, and Shikano, Kiyohiro."Modularity and
Scaling in Large Phonetic Neural Networks" IEEE Transactions of
Acoustic, Speech, and Signal Processing. Vol 37 No.12 December 89.

Interpretation of Infrared Spectra with Modular Neural-Network Systems
J.R.M.~Smits, P.~Schoenmakers, A.~Stehmann, F.~Sijstermans, G.~Kateman
Accepted for publication in Chemometrics and Intelligent Laboratory Systems

"Stacked Generalization" by David Wolpert, LANL Technical Report LA-UR-90-3460

"A Large-Scale Neural Network which Recognizes Handwritten Kanji Characters"
by Yoshihiro Mori and Kazuki Joe. NIPS 2

Thanks to Anjo Smits and John Platt for their contributions.

--Todd
-------------------------------------------------------------------------------
Todd Jochem             The Robotics Institute              (412) 268-3653
                        Carnegie Mellon University
tjochem@cs.cmu.edu      Pittsburgh, PA 15213
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: Reducing Training time vs Generalisation
Date: 21 Aug 92 05:31:43 GMT
Article-I.D.: cs.BtBJCw.M2n.1
Organization: School of Computer Science, Carnegie Mellon


    From: arms@cs.UAlberta.CA (Bill Armstrong)
 
    Now let's see what it takes to get lazy evaluation: first of
    all, I think you would have to insist that the sign of all weights on
    an element be positive, and all signals in the net too.  Otherwise in
    forming a weighted sum of inputs, you can not be sure you are on one
    side of the sharp threshold or not until you have evaluated all inputs
    (not lazy!).  I think the signals would have to be bounded too.
    I think this would be OK.  ALNs are still faster, because they don't
    do arithmetic, but ALNs don't have as powerful nodes.
    
For lazy evalaution, the inputs and hidden unit values would have to be
bounded, but I think you could use bipolar weights.  Sort the weights for
each unit by magnitude.  Evaluate subtrees in order, biggest weight first.
Give up when no combination of remaining weights times the input limits
could pull the total back across threshold.

In fact, you wouldn't have to replace the sigmoids with thresholds.  Divide
the sgmoid into three regions: saturated-on, saturated-off, and in-between.
If you find yourself in one of the saturated regions and no combination of
other inputs can pull the net back into the in-between region, stop
evaluating and flush the remaining sub-tress.

    One argument for going whole hog into ALNs is that you don't have to
    train using sigmoids, then risk damaging the result of learning by
    going to sharp thresholds.  If there were a training procedure for
    networks of the above kind of node with a sharp threshold, that would
    be very promising.  I thought backprop required differentiability to
    work though.

It does.  The Perceptron learning rule (and variants such as Gallant's
pocket algorithm) can train sharp-threshold units, but not multiple layers.
    
    >Myself, I prefer to think in terms of parallel hardware, so lazy evaluation
    >isn't an issue. 
    
    Not true!  If you have a fixed amount of hardware, then to do large
    problems, you will have to iterate it...

Sorry, I should have said "sufficiently parallel hardware", meaning you
don't have to share.
    
-- Scott
===========================================================================

Scott E. Fahlman
School of Computer Science
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213

Internet: sef+@cs.cmu.edu
Newsgroups: comp.ai.neural-nets,comp.ai,comp.theory
From: dzik@access.digex.com (Joseph Dzikiewicz)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: Express Access Online Communications, Greenbelt, MD USA
Date: Fri, 14 Aug 1992 18:32:56 GMT

In article <BsxH2n.Ks4@math.uwaterloo.ca> jfbuss@math.uwaterloo.ca (Jonathan Buss) writes:
>In article <1992Aug12.150857.219@access.digex.com> dzik@access.digex.com (Joseph Dzikiewicz) writes:
>
>>This lack of infinite memory is why digital computers are of equal 
>>computing power to a finite state machine and considerably less powerful
>>than a Turing machine.
>>
>
>This observation begs the question, why are digital computers modelled
>as infinite-state machines?  Can one obtain more realistic results by
>using finite-state machines as the model?
>
I suspect it is because of the difficulties of dealing with finite-state
machines with really large numbers of states.  Analysis of algorithms
usually ignores the fact that any existing computer can only deal with
some finitely large input. 

Joe Dzikiewicz


>
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: Characterizing learnable functions
Organization: University of Alberta, Edmonton, Canada
Date: Wed, 12 Aug 1992 13:27:47 GMT

async@opal.cs.tu-berlin.de (Stefan M. Rueger) writes:

>arms@cs.UAlberta.CA (Bill Armstrong) writes:
>>Maybe consumers of NNs should ask for and insist on Lipschitz conditions:
>>   |output(a) - output(b)| <=  const * | a - b |
>>
>>for all a, b in the domain of the function. 
>>

>These Lipschitz-functions **are** continuous and thus covered  by the
>results of Cybernko, described in my original article.

>>                                             Better yet, replace the
>>const by some C(a) to take into account more a priori knowledge of
>>where you want a smoother function.
>>
>>Has anyone ever worked out this form of approximation?  At least
>>it would be more useful than continuity.

>If the C(a) have an upper bound, say K, (this is always true, if the
>function has a compact domain) then again those functions are of
>Lipschitz type and thus continuous. 

I agree with you (except that you don't need a bound K, just
finiteness of C(a)).  My point was that specifying continuity of
functions in a digital world is vacuous.  Any function whatsoever on
any set of floating-point numbers (always finite) can be extended to a
continuous one on the real line (for example).  What I'm saying is
that the theory of continuity (i.e. topology) is not applicable, and
is not what users of neural networks should be asking for.  They
should say in contracts: "The network resulting from training shall
satisfy a Lipschitz condition with constant ___".

The critical thing is this: with a Lipschitz condition, you can use a
finite test set together with the Lipschitz condition to conclude that
there are no points with values outside allowable limits.  If you just
test the net, then you have no knowledge of the values produced by the
net between test points.  The net can produce "wild" values at points you
haven't tested, and hence be unsafe to use.

In short: For purposes of producing useful nets, who cares whether any
continuous function is realizable or learnable?  It just means your
theory is satisfying provided you neglect questions of representations
of real numbers and computer arithmetic.  It doesn't mean a thing in
practice.

I like your idea of a bound K.  Can you compute it for a given net?
Can you force it to be small by appropriate training procedures?
--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
From: tjochem+@CS.CMU.EDU (Todd Jochem)
Newsgroups: comp.ai.neural-nets
Subject: Network Inversion
Date: 21 Aug 92 21:03:23 GMT
Article-I.D.: cs.BtCqHo.AtH.1
Organization: Carnegie Mellon University

I'm looking for references to network inversion papers. The basic idea
I'm interested in is presenting the network's output with a signal and
by back-propagating this signal though the layer(s), recreating an input
which could have created the applied output signal. I think that this could
be pretty tough to do with some network configs. because of underdetermined
linear equations, but would like pointers to any work that addresses this
topic anyway. I'll summarize to the net any responses I get. Thanks,

--Todd

-------------------------------------------------------------------------------
Todd Jochem             The Robotics Institute              (412) 268-3653
                        Carnegie Mellon University
tjochem@cs.cmu.edu      Pittsburgh, PA 15213
Newsgroups: comp.ai.neural-nets