In addition to assuming that social networks can be applied to nearly any business endeavor, there was also the sense that a greater utilization of these networks would somehow improve some sort of social awareness, and by corollary, provide the participating users with greater ‘access’ to individuals in otherwise inaccessible or distant locations.

To a certain degree, this is true.  People can keep tabs on distant friends, families, and colleagues in very informal fashion using facebook and twitter, but there has to be limits to how many meaningful social connections an individual can maintain, at a minimum level of awareness…

The general sense seems to be that “more friends=better”, and entire careers seem to have been built on exploiting social networks for self promotion.  However, turning your personal social network into a competitive  ‘numbers game’ seems… well… wrong.  It turns out there are pretty well defined ‘averages’ for how many meaningful relationships an individual can maintain.  Basically, there are three thresholds for social connections.

1. 5 individuals = A number influenced by our working short term memory.  This seems to be the maximum level of individuals that you can become ‘intimately familiar’ with.  You might be able to predict how this individual would respond to given situations, or finish their sentences for them.
2. 15 individuals = The maximum number of individuals you can form a “deep trust” with.  These are individuals that you feel completely relaxed and unguarded around.
3. 150 individuals = The maximum number of individuals you can “enumerate” and identify with in a meaningful fashion.

The 150 number is a far cry from the thousands of friends on facebook/myspace that many people seem driven to acquire.  In fact, the very act of ‘friending’ someone on facebook may have undermined the  meaning of the word ‘friend’.  Perhaps all that facebook/myspace needs is clearer semantics behind the connections its users form, but it does feel to me at times that the notion of and online ‘friend’ has become garbled… and it seems that limiting the amount of connections you can make might be a start towards making them more useful.

Designing for social networks using these real-world social/cognitive limits might lead to some different interaction arrangements.  I had thought of the following:

• For my “select 5″, I would give them the opportunity to directly connect/interrupt me at almost any time.  Richer communication channels (video/voice) would be enabled by default.
• For my “trusted 15″, I would grant the opportunity to directly respond on any blog postings/etc without explicit editorial permission.  I would publish personal status messages and inside jokes through a limited public interface.
• For my “group of 150″, I would grant permission to see pictures of me and my other friends (I think face recognition technolgy would be great to automatically tag and filter photos so that I wouldn’t have to).  I would publish less personal/professional messages that they would be able to see.
• For any individuals above that number, they would be able to see non-personally-identifiable pictures, blog posts, and professional messages.

In general, I think as social networks mature…’less’ will be/mean more.

Anyways, I’ve started doing some things that I think help greatly, and since many of my friends/colleagues are also going through this same process, and mentioned the same problems, I thought I could share some of what I’ve started doing that seems to help.  I’ll give them here in order of importance:

1. Mood:  It’s really easy to get into weird mood swings, particularly if you’re like me, and are no longer on campus, and don’t have a bunch of others around to keep you in check.  If you have serious emotional problems, then this advice is not for you.  However, I think most people will go through some sort of strange mood while writing, it’s just part of the process.  What has helped me a lot in this area has been a simple amino acid called L-Methionine.  This is a biochemical precursor to Sam-e, a more expensive natural hormone related to joint strength and mood control.  I take L-Methionine, and have noticed a marked improvement in mood with no adverse side effects.  I realize of course that all of this is unproven according to the FDA, and some people consider it useless.  However, it is perfectly safe.  YMMV.
2. Sleep: I’ve always had problems sleeping, and it gets worse when I travel.  Some of my collegaues suggested Melatonin, which they use to get over jet lag.  I have to give it my endorsement as well.  I’ve tried prescription strength sleep aids, but this is far better.  No side-effects, non-habit forming, and it doesn’t even make me dangerously drowsy.  I just feel ready to go to sleep, and sleep better than I do without it.
3. Exercise:  It’s remarkable to me how exercise can change my mental state.  It only takes about 30 min. of jogging, etc. before I have a new perspective on things.  It’s also my “canary in the coal mine” for my weekly routine.  If I’m not having a good workout, it usually means I’m not getting enough sleep, or not eating right, etc.  Then it’s time for a change.
4. Diet:  Some people are able to control all of these factors with diet and exercise.  More power to them.  I like more of  a variety, so I tend to eat whatever I want (within reason).  I’ve got nothing really profound here:  the wrong kinds of food can throw everything off kilter, so it’s good to be aware of how they affect you.  Generally, I eat first, and ask questions later.  It doesn’t get me into too much trouble.

Anyways, these are just tips that seem to work for me, and aren’t supposed to be a guideline.  I also don’t consider myself one of the “all-natural” crowd, nor do I endorse any sort of chemical stimulant/barbituate (everything I’ve referenced here has weaker side effects than caffeine/tylenol pm).  Writing the dissertation is shaping up to be a long, drawn out affair, and I’m very aware of the percentage of people who end up being A.B.D, which may be as high as 50% in some areas.  I just am going to use every tool I have available, and wanted to share them as well.

This is way off topic for me, but I’ve been really bothered by all of the anti-wealth sentiment that seems to be growing in the US… that somehow people with a lot of money are the cause of the economic collapse, or that they are hurting the economy by continuing to take their pre-collapse salaries.  This sentiment is routinely expressed (of course) towards the CEO’s of big firms, but also now for college basketball coaches.

Apparently, limiting the salaries of corporate/organization heads is not enough, and so right now there is growing sentiment that dividends should be taxed higher, since many heads of companies invest in these stocks to generate income, and by doing so avoid higher tax rates on salaries.

The article goes on to assert that the investment in dividend producing stocks led to greater speculation, which in turn led to the current market state.

One example of “speculative dividend investment gone awry” are the Madoff funds, where individuals and companies bought into his Ponzi scheme, and ended up paying taxes for dividends they never received. However, this had nothing to do with the real culprit of the crash, the mortgage-backed security.  The folks that invested with Madoff were duped and robbed.  They have nobody to blame but themselves for getting hoodwinked by one of the oldest investment cons in the book.   It doesn’t mean the underlying dividend system is flawed, and it doesn’t mean the dividend system contributed to the current market situation.

The reason why dividend income is taxed lower is that it has already been taxed.  The companies that offer dividends have already paid taxes on their earnings.  Companies (not funds) that offer dividends typically are older, more stable companies that have limited opportunity for growth.  By offering dividends, they offer an incentive for investment over another faster growing, but less established company.

The Christian Science Monitor article is wrong on several points.  Investing in dividend producing companies is almost always less speculative than non-dividend producing companies.  You are typically investing in companies that are producing positive cash flow, and in companies that have been around for a while.  This is far safer than investing in other types of companies/funds.  This method of investment is also not the sole domain of the super-rich.  Anyone can do it, and it remains a powerful tool for people looking to make safer investments in the market.

The government has meddled with investment strategies at its own peril.  They encouraged us to buy homes we couldn’t afford, and directly brought about arrangements for loan agencies to buy and sell bad debt. The last thing they should be doing now is discouraging us from investing in strong, stable companies.

It is my belief that we are going to get out of this bad market through “back to basics” economic policies and investment strategies, not by eating the rich.

I already talked about why I liked the approach, and now I’d like to distinguish it a bit semantically.  The ISMIR paper builds on a 2005 Nature paper, which talks about how the low level auditory system is “tuned” to pick up speech related sounds with a very high efficiency using gammatone like wavelet filters.  However, labeling the technique  “sparse” in general is a bit misleading.  This is because the relative “sparsity” of the signal representation depends on the gammatone basis functions, the encoding routine, as well as the signal characteristics themselves. So, respectively, these techniques use biologically observed gammatone-like wavelets, using a matching pursuit algorithm to minimally (sparsely) encode signals, on “natural” occurring sounds.

To characterize their technique more plainly, it’s appropriate to call it a form of neural audio analysis.  While the standard matching pursuit encoding method doesn’t completely emulate the mechanism for a neural response  (partly because it is usually performed as an offline process), it does come far closer to mimicking what our brain is doing over other techniques like short time Fourier transforms.

Furthermore, neural audio analysis differs from other more general wavelet analyses (using general Gabor atoms, etc.) in that it is using a predefined, limited, neurologically-based dictionary of wavelet functions.  For better or for worse, these chirps and blips are the building blocks of how we perceive sound.  They may perform very efficiently for speech, but they are probably lacking (non-sparse) for many other types of signals.

How did we get these wavelet filters?  The hard way!  De Boer et. al literally had to wire a cat brain into a signal processing loop in order to extract the individual neural responses to audio stimulus.  Cats were chosen because they have auditory faculties that are very similar to humans.  Luckily, the Boston Ear Lab provides a database of these filters so that other researchers don’t have to repeat this same experiment on the poor cats any more.

The point is, using these neurological wavelet filters is important, even if they’re non-efficient ways of representing certain signals.  They come closest to representing how our brains are responding to an audio stimuli, and therefore provide better building blocks for forming higher level representations of sound.  I think that even though the underlying techniques are not new, the potential for “neural audio analysis” is still fairly untapped.

As a side note, I’m glad that Manzagol et. al wrote that paper, it’s finally putting my “Mind” category to use.

All plots taken from either De Boer & De Jongh 1978 or Smith & Lewicki 2004,2005.

In fact there’s so many that it’s hard to sort them out since they aren’t easy to parse in his index.  Here’s an overview:

1. Tutorials
   1. Chuck Tutorial
   2. Tutorial Overview
2. Day One
   1. Day One Overview
   2. Geometry of Musical Chords
   3. Plenary Session 1
   4. Plenary Session 2
   5. Day One Posters
3. Day Two
   1. Music Recommendation Industry Panel
   2. Plenary Session 3
   3. Plenary Session 4 (Couldn’t find this one)
   4. Day Two Posters
4. Day Three
   1. Keynote
   2. Plenary Session 5
   3. Mirex Panel
   4. Mining the MySpace Social Graph
   5. Plenary Session 6
   6. Day Three Posters (Mirex)
5. Day Four
   1. Plenary Session 7,8
   2. Plenary Session 9 (he had to prepare his Explaura demo, and didn’t cover them)

Thanks again Paul!

Icy road video
Jamiroquai video

For maximum comedic effect: Play the jamiroquai video as the soundtrack to the first video… and thank God you’re not an insurance agent.

I also think one of those cars looks like a Pontiac Aztec, and I’m pretty sure the guy was trying to total it intentionally… not that I blame him.

This conflict becomes even more fascinating as it spills onto the internet.  Iran, which is light years behind in the information technology sector, is nevertheless trying to use technology as a weapon for their cause… and is launching attacks from a government sponsored facility (Ahmadenijad’s webserver).  Israel replies with coordinated guerilla tactics, by launching several hundred thousand attacks in a non-centralized fashion (with no way of tracing the source back to the government).  The latter method is far more devastating.  It appears that “guerilla” style attacks will be the best tactic for the internet.

Edit: It gets even better. Iran ran a contest for “holocaust cartoons” in response to the cartoons lampooning the prophet Muhammed. Israel is using SEO tactics to move pages detailing these cartoons down on the web search results.

This is pretty interesting behavior, and is evidence of the first couple of rounds of “internet warfare” that can exist between countries or organizations. This type of warfare includes limiting or preventing information sources from disseminating ideas, or by attacking certain recipients of information. However, only methods in the “virtual world” are being used. Nobody’s knocking out communication centers with bombs, or jailing paper editors, etc. It’ll be interesting to see how larger countries leverage these same tactics if and when they are forced to use them.

The expectations of the user or gamer are taken, in part, from how our senses are “tuned”.  Our stereoscopic vision is geared to detect and interpret movement in three dimensions, and to determine shape, color, and texture through the effects of light as it is reflected off of various objects.

Because vision is our most “sensitive” and “trained” sense, it is the most viable sense to use as an “interaction pathway” between humans and complex data.  This is of course true with nearly any form of computer interaction task, but it is especially true in this case.  There is a large amount of interesting work in this field, and I won’t be able to describe everyone of interest here.  (However, two people stick out in my mind: Tufte‘s work on data graphics, and MacEachren’s work on How Maps Work.)

Using vision as a main sensory tool is sort of a no-brainer.  However, it carries a pretty stiff limitation.  You can’t “see” anything in higher than three dimensions.  It’s hard for us even to conceive of something as existing in more than three dimensions, but it’s rather trivial for computers to deal with.  Furthermore, for all practical purposes, most representations will be limited to two dimensions, such as that offered by a computer monitor.

Finally, a lot of the information that is useful for describing complex systems does not fit neatly into an “a priori” dimension.  Rather, the most important dimensions of a complex system usually emerge from the relationships between data points.  An example of this is the PageRank system that Google uses to score webpages.  The nodes cannot be placed onto this dimension without first considering the connections of (pretty much) every other node in their network.

There is a profound difference between the basic three dimensional euclidean scheme that constrains our natural world, and the hyper-dimensional schemes that pervade much of complexity and network theory.  However, there are methods that one can use to “summarize” the structure of a network in a lower dimension.  The convenient metaphor for this is a map.  Maps of the world are useful two dimensional representations of a three dimensional space.  Nobody lives, works, or moves in the center of the earth, and we only move off the surface in marginal cases (such as in airplanes). Therefore, we can effectively use polar coordinates to pinpoint a person’s location in three dimensional space with only two pieces of information (lattitude and longitude).  We all effectively live on a manifold of three (or four if you really want to be picky) dimensional space.  We can “squash” this manifold into two dimensions to serve as a practical and effective summary of our surroundings. 

In this same manner, we can squash the higher dimensionalities of networks into a more digestable visual format.  However, squashing dimensions almost always involves distortion.  Any map of the world that you look at will invariably be “wrong” in the proportioning or distances between locations.  Certain techniques involve splitting the sphere into sections like an orange peel, or by distorting the northern and southern polar regions.  Luckily, we’ve got a lot of oceans and uninteresting/ uninhabitable terrain (Antarctica) which serve as good neutral areas for skewing, stretching, or slicing.

There are two main approaches towards summarizing the structure of networks in two or so dimensions.  The first approach is known as a “force directed” embedding.  These plots involve treating connections between nodes as attractive or repulsive forces, and then simulating the network dataset as a physical system.  The system updates its layout continuously until it reaches a point of equilibrium, where the general structure of the network is readily apparent.  These plots are also wonderful interactive interfaces.  Many such systems treat user interaction as just another force acting upon the network.  So, a user can drag, stretch, or isolate parts of the network that he or she finds interesting through simple point and click interactions.  However, this method is not without its drawbacks.  First, the representation produced by the force directed algorithm is not correct, reproducible, or even tractable in many cases.  The force directed algorithm may end up placing parts of the network structure in different, less optimal orientations depending on how the layout algorithm pans out.  Also, the nodes must constantly update their position based on their attraction/repulsion to other nodes.  This calculation can quickly become intractable for large datasets on modern computing hardware, especially in cases where the plots are to be used as an interactive interface as described previously.

The other option is to use eigendecomposition (also called spectral decomposition), a strange sounding method that is used in countless science and engineering applications. To do this, the network is described as a matrix, where each row/column pair describes the connection between two nodes. Eigendecomposition itself is a special case of singular value decomposition (SVD).  SVD’s are a very simple factorization of matrices:

(From Wikipedia)
Suppose M is an m-by-n matrix whose entries come from the field K, which is either the field of real numbers or the field of complex numbers. Then there exists a factorization of the form

where U is an m-by-m unitary matrix over K, the matrix Σ is m-by-n with nonnegative numbers on the diagonal and zeros off the diagonal, and V^* denotes the ]]> http://scwn.net/2006/08/03/staring-into-the-abyss-ii/feed/ 1 jjdonald M = U\Sigma V^*, \,\! http://scwn.net/2006/01/25/interaction-design-challenges/ http://scwn.net/2006/01/25/interaction-design-challenges/#comments Thu, 26 Jan 2006 03:17:54 +0000 Justin Donaldson http://ethos.informatics.indiana.edu/~jjdonald/wordpress/?p=65 ]]>
BostonInformation3.JPG,
originally uploaded by arekd.

Interaction design challenges pervade every aspect of HCI design, and usually involve trade-offs that are fundamental to the very notion of what design (in general) is. Lately, I’ve been concerning myself with handling “information overload”.

The notion of information overload should be pretty well understood by most. It’s the state of helplessness and frustration caused by large amounts of data that one must sort through in order to find something useful.
(I won’t clog up Planet Informatics with my rantings, click below to continue reading)

Initially, computers organized information according to norms that people were familiar with. For instance, disk storage could be thought of as a place for storing digital text files, which in turn can be thought of as paper. So… why not make an organizational hierarchy similar to a filing cabinet? Heck, why don’t we even put a miniature “trashcan” on a “desktop”. I’m not knocking these ideas, I think they’re still great for most situations. However, anyone who uses computers as much as I do realizes that information is not limited to what we write, it also includes what we listen to, watch, interact with, revise, purchase, promote, protect, collaborate on, and ignore… periodically. All of these behaviors and manifestations of information will thwart most attempts at classical navigational hierarchies, especially in contexts where there are “mixed media” pieces of digital information, such as a diagram that was chatted about on a Thursday with a distant colleague using an instant messenger client.

Network systems often provide a degree of flexibility in organizing and storing information. I like the idea of defining things in terms of how they relate to each other. The brain works in a similar fashion, as the neurons and synapses that make up our representations of objects are really defining relationships between certain percepts. In other words, nearly everything we think about is connected somehow in our brain. This is why certain sights, sounds, and smells can trigger a whole barrage of related emotional and visual (as well as other sensory) memories.

I believe that the main challenge of human computer interaction design is going to be for the handling/navigation of large stores of information, and that this challenge will be met with network based relational information. This isn’t to say that other dimensions of interaction design won’t be met with resounding success to the benefit of mankind and so forth. I just think that right now, the opportunity for our age is to meet the challenge of “too much information”, and that the first couple of companies that I feel are doing this (Google, Flickr, Yahoo), are really delivering on the original promises of the world wide web and the information age.

However, the notion of networks and relational information is sort of obtuse, and difficult to resolve with many of our cultural understandings of design and aesthetics. In other words, there are very few people who could look “under the hood” of Google and say, “Gosh, that’s beautiful”.

In fact, there are only a few people I can seem to think of that are moving towards an aesthetic appreciation of this form of data. One such person is W. Bradford Paley, whose TextArc program won several design awards and accolades for its innovative structuring, organization, and interaction methods for network based textual data. Another individual focused in this field is our own Katy Börner, who is a world renowned network visualization expert. Check out the “infostethics” blog for more network visualzation projects.

Paley made several claims in his talk that he was merely borrowing techniques used by master painters to highlight, differentiate, or “texturize” information in his work, but I think that network visualizations also allow for dynamics in the context of movement and interaction that master painters could never dream of. Also… who says that network information must be visualized to be understood? Perhaps other senses can make these hidden relationships apparent.

]]> http://scwn.net/2006/01/25/interaction-design-challenges/feed/ 2 jjdonald BostonInformation3.JPG