Michael Feldstein, Assistant Director, SUNY Learning Network, says that there is no such thing as a learning object.

We learn by doing. We consider. We compare. We measure, discuss, debate, critique, test, and explore. We try, fail, and try again. Learning is an activity. It’s a process. Given this undeniable fact, the term “learning object” can only be an oxymoron. An object is a thing. We don’t learn from things. We learn from doing things.
(…)
I believe the term “learning object” has become harmful. It hides the same old, bad lecture model behind a sexy buzz phrase.

David Wiley said that learning objects are a bad idea. Albert Ip points out that standards in eLearning technology bring no visible benefit. Now, Harold Jarche adds his two cents to this “debate” (if “debate” there is):

In a recent project where I reviewed the business case for SCORM implementation, I found no evidence of a market for digital learning objects. There were several vendors offering SCORM conversion or SCORM implementation assistance, but no one was actually buying and selling objects. The bet seems to be that standards will create the market, as shipping containers enabled the free flow of goods over various forms of transportation. Here I disagree, because learning cannot be “containerised”.
In theory, reusable digital learning objects make sense, but in practice they don’t work. The problem is that learning objects cannot be separated from their context.

In the drive to make money in the learning business, too many people are trying to find a way to codify pieces of the messy, personal process known as learning. The learning content market is based on the premise that these pieces can be quantified and therefore owned by someone.

JabRef reference manager is a Java-based tool to manage your BibTeX references. “JabRef runs on the Java VM (version 1.4.2 or greater), and should work equally well on Windows, Linux and Mac OS X.”

(Thanks to Harold Boley for pointing it out.)

A pretty exciting article in the New York Times about what is “talent”. The short story is that you should practice hard while ensuring you have immediate feedback and have clearly defined goals.

when it comes to choosing a life path, you should do what you love — because if you don’t love it, you are unlikely to work hard enough to get very good. (…) what they really lack is the desire to be good and to undertake the deliberate practice that would make them better.

(..) there is surprisingly little hard evidence that anyone could attain any kind of exceptional performance without spending a lot of time perfecting it.

(…) Students should be taught to follow their interests earlier in their schooling, the better to build up their skills and acquire meaningful feedback. (…)

(…) two key elements of deliberate practice: immediate feedback and specific goal-setting.

(Got it from Unreasonable.)

Jedox will release a free open source Linux MOLAP server by the end of the year. A pre-release of the software is expected by mid of 2005.

All data is stored entirely in memory. Data can not only be read from but also written back to the cubes. Like in a spreadsheet, all calculations and consolidations are carried out within milliseconds in the server memory while they are written back to the cube.

I sure hope that by “memory” they include “external” memory because otherwise, their cubes are going to have to be quite small. Normally, you’d at least memory map large files as Lemur OLAP does.

Slope One in Automated Collaborative Filtering
By
Bo Xu

Examining Committee:
Supervisor(s):            Dr. Huajie Zhang
                          Dr. Bruce Spencer
Chairperson:              Dr. Mike Fleming
Internal Reader           Dr. Yuhong Yan
External Reader           Dr. Donglei Du                                                                       

Thursday, May 18, 2006
10:00 a.m.
IT-C317 (Information Technology Center)

ABSTRACT

We focus in the Collaborative Filtering field on how
to improve the quality of the prediction and
recommendation and how to improve  the
performance on large datasets. Only rating-based,
non-sequential problem space is discussed. A
detailed survey of current technologies which
might be used in recommender systems is
presented to the readers, followed by a thorough
analysis of a newly proposed algorithm by
Daniel Lemire and Anna MacLachlan: Slope One.
To test the family of the Slope One schemes'
prediction quality as well as their performance,
experiments are conducted by means of
comparing them with other representative,
up-to-date collaborative filtering and machine
learning algorithms. The results show that
Slope One, in spite of its simplicity, is an
efficient, accurate within reason, and scalable
Collaborative Filtering algorithm and therefore
especially applicable for online E-Commerce
recommender systems.

ALL GRADUATE STUDENTS ARE ENCOURAGED TO ATTEND

*********************
Linda Sales
Administrative Assistant
Faculty of Computer Science
University of New Brunswick
540 Windsor Street
Fredericton, NB
E3B 5A3

Phone: 506-458-7285
Fax: 506-453-3566

The SIAM Conference on Data Mining 2007 (SDM07) will be held in Minneapolis, May 3-5, 2007.

Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.

In Information Retrieval, you can’t have both great recall and great precision, so you have to balance the two. What are the possible criteria to pick the best recall/precision?

What I found so far, on wikipedia of all places, is the so-called F-measure or balanced F-score, and it is merely the harmonic mean of the recall and the precision. This seems to have almost no theoretical foundation?

Anyone out there has proposed an exciting way to pick your recall and precision?

I don’t want to be too critical of the field of Information Retrieval, but sometimes, when I read papers from the 1950′s, it feels like they knew everything there was to know. I sure hope that people came up with more exciting ideas than just “use the harmonic mean” to pick the best recall?

Update: I found this related paper:
Cyril Goutte and Eric Gaussier, A Probabilistic Interpretation of Precision,
Recall and F-score, with Implication for
Evaluation, but it is only a partial answer to my question.

Using Serialization for long term storage, is a common mistake. In fact, Microsoft made with with Microsoft Word and it is a well known source of trouble (ever had a corrupted file you could not recover from?). Serialization in Java was never advertized as a viable storage long term mechanism. We serialize in order to send objects over a wire (RMI), or for lightweight persistance (especially for non-critical data). I’m not making this up, this is how Sun documents it.

Also, Sun makes no promise that you’ll be able to deserialize, if your code changes. Ever heard of the java.io.InvalidClassException class? That’s what you’ll get on your face if you ever change the class you used to serialize (even if you change it just a little bit).

Think about the following scenario:

1. You serialize some objects you care about.
2. Weeks pass by.
3. For some reason or other, you change the class. Suppose, for example, that you delete a field or you move the class up or down in the hierarchy. You don’t keep the old class around anymore.
4. That’s it, you can’t deserialize your objects ever unless you do reverse engineering. It won’t stop and ask you how you want it fixed, it will just throw an exception with no direct way for you to fix this. You’ll need to “hand recover you data”. Have fun. If the data was not your own, and it was hand crafted by a client, you are probably going to lose your job.

And let’s not even get into what happens if you must exchange your data with other software not written in Java.

If you really care about your data, dump it in a custom XML format. It isn’t that hard.

Can anyone do better than this ugly hack?

def flatten(x):
flat = True
ans = []
for i in x:
if ( i.__class__ is list):
ans = flatten(i)
else:
ans.append(i)
return ans

Update. I like this solution proposed by one of the commenters (sweavo):

def flatten(l):
if isinstance(l,list):
return sum(map(flatten,l))
else:
return l

Can anyone do better?

See also my posts YouTube scalability, Yield returns are not esoteric anymore, Efficient FIFO/Queue data structure in Python and Autocompletion in the Python console.

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