lace punk, satin punk, petticoat punk, oh my!

This article was first published on the fashion blog Mad Hot and Artsy on 20 November 2018. Special thanks to them for featuring my content!

To satirize my proud (and adopted) feminine modality, I developed a personal clothing style I call “lace punk”, “satin punk”, and/or “petticoat punk”. It carries steampunk, burlesque, and pin-up influences; and emphasizes poise, class, and elegance. In other words, I give the appearance of “high class”. This combination kills when combined with sound posture and a confident stride—my satire has since evolved into a robust display of raw power.

Key elements involve millinery, corsetry, lace or satin gloves, heels, and frequent appearance in gowns.

The “punk” in all this is me: Accomplished hacker. Cyberpunk. Skilled guitar shredder. Free-thinking anarchist. These outfits tweak guys’ expectations when I talk engineering circles around them—“geek chic” never celebrated femininity quite like this.

I perform under the stage name “Napalm Fatale”. Have released two albums freely available at https://napalmfatale.bandcamp.com. Wrote an important article called “This is Transgender Music” describing this work and my musical goals.

I founded the company Whole-Systems Enterprises, Inc. to pay the bills. Am extremely interested in applying artificial intelligence to the fashion industry.

how to sit with ladylike poise, class, and elegance

Everybody knows I live an absolutely prim and proper life. For that reason I insist on spreading my copious knowledge to quality ladies everywhere! Here I demonstrate the “Duchess Slant” and the “Cambridge Cross”:

The Duchess Slant and the Cambridge Cross, attributed to Kate Middleton and Diana Spencer respectively, prevent your knickers from showing while seated, particularly in front of a camera [1]. We wouldn’t want the press to snap a photograph of the royal panties now would we?

Duchess Slant

English royals prove well practiced [2]:

Legs join at the knees and ankles (not crossing), and then tilt to one side. Heels touch the floor. Hands gently rest in the lap, ideally with one folded over the other [3]. Good posture—with back not touching the chair [4]—finalizes the position.

On my side of the Atlantic, I tend add a pronounced head tilt because I think it’s cute; compare to Ms. Middleton’s very slight tilt in the picture above.

Cambridge Cross

Again with the English royals [4]:

As the name implies, ankles gently cross when performing the Cambridge Cross, but otherwise one follows the Duchess Slant technique.

Why Bother?

Besides protecting modesty, these positions also slim and lengthen leg appearance [3].

I learned to sit properly from a modeling school’s video. Unfortunately I do not know which one so I cannot cite it. The video argued that given two equally qualified candidates for a photo shoot, the one that sits better will most likely get the job.

We can expand this idea to cover any job interview, and to cover career growth in general. Carrying oneself with class and poise, including how you sit, takes you places.

What About Crossing at the Knee?

The talking heads of etiquette have not sorted out amongst themselves whether crossing at the knee, while keeping angles together, proves appropriate for a lady of class [3]. But Ms. Spencer performs this position very elegantly:

Word to the wise: Prevent your petticoat from getting between your angles as demonstrated here:

The medical community continually debates the potential health hazards of sitting with legs crosses at the knee, but the jury is out [5].

How Not to Sit

Above I emphasized that one can only properly cross legs at the knee whilst keeping angles together, as demonstrated in the last two pictures. For contrast, this image [6] shows a less elegant form which ladies must avoid:

References

  1. https://www.popsugar.com/celebrity/What-Duchess-Slant-44944499
  2. https://people.com/royals/meghan-markle-duchess-slant-kate-middleton/
  3. https://www.vogue.com.au/culture/features/what-is-the-duchess-slant-and-does-it-really-matter-in-2018/news-story/e205f9b71548f9e9a004e15c6c573ba8
  4. https://brightside.me/wonder-people/12-exclusive-rules-from-the-duchess-of-cambridge-372910/
  5. http://www.bbc.com/future/story/20151013-is-crossing-your-legs-bad-for-you
  6. http://faze.ca/is-sitting-with-your-legs-crossed-bad-for-you/

 

thrift store mysticism

I treat thrift store shopping as a mystical experience, as a spiritual discipline.

Being somewhat of a mystic, and a massive optimist (see my post “curvilinear optimism“), I tend to believe that the Universe (or the Divine if you prefer) provides what we need to accomplish our missions in life as we need it (or immediately before).

Today I went to Goodwill and found four perfectly-fitting ladies’ business suits. All match my design ethic of “obvious femininity”—i.e., they are not simply men’s styles adapted for women. All have skirts, because, as readers of this blog know, I refuse to wear pants. All were well-made and extremely inexpensive.

The occasion is timely: I’m preparing to meet regularly with potential investors in the startup I contract with, representing the technical side of the company (I currently serve indirectly as that company’s R&D leader). Therefore I need managerial-level business attire, and a lot of it.

My optimistic, mystical self interpreted this Goodwill shopping haul as a “sign” that I’m “ready” for the business responsibility coming my way.

Asserting the Feminine

I stressed above the “obvious femininity” of the outfits. Feminism in the 1970’s and 1980’s urged women in corporate America to “act more like men”. That ethic led to women’s suit designs that really just mimicked masculine designs. (Shoulder pads, anyone?).

But diminishing the feminine to advance in the business world only marginalizes femininity in general—and makes many women simply unhappy. The truth is, while gender definitely moves on a spectrum at individual resolution, as a whole we can argue that women differ from men. We can argue further that that difference can (and should) add just as much value to the corporate world as masculine traits do.

So I for one will only wear business attire that screams “feminine”. I will not mimic a man. And I’ve taken a hit in corporate America for doing so… but I don’t give a shit because I know women are the future of business (but that’s a whole different topic).

Part of this practice goes back to my early days of living as a woman, where I learned quickly that to be called “she” I had to wear extremely feminine attire. In other words, I had to donate all my t-shirts to Goodwill and stop wearing pants. Now that my face has been surgically modified, my voice is higher in pitch, and my hair is longer I no longer experience this issue. But my memory proves long…

Strange Effects

The corporation I hold majority shares in gives 10% of its income to secular charities. Goodwill Industries of San Diego receives most of it, and the cash donations are made through local stores. As a result, the staff of the North County Goodwill stores have come to know me, resulting in two unexpected effects:

First, recognizing that my personal style is almost entirely constructed from thrift store finds, they now seek my opinion on displays, which I am thrilled to give. It’s nice to be seen as a style authority!

Second, the women working in these stores have become familiar with the kinds of items I typically look for, so when I enter a store I can now find these women first-thing and ask for recommendations based on their knowledge of what has recently been placed on the racks. But they don’t just try to accommodate my style, they suggest their own ideas. This proves fun for everyone involved.

The money the business gives created this situation, but the fact that I’m simply nice to everybody nurtures it along.

See Also

curvilinear optimism

how to be a rock star

Lesson One

Stop caring about what other people think of you.

Lesson Two

While nervousness proves a real, honest challenge, never let it show!

Lesson Three

Make it look easy.

Lesson Four

Be prepared! Strings break, equipment fails, batteries die, and sound systems and room acoustics often suck. Be prepared!

Lesson Five

The show must go on!  No matter how depressed or anxious you are, perform your absolute best!

See Also

this is transgender music

axis evil

the “system” and all the bars on the cage

If you only see one bar at a time, you will not know that you reside in a cage. Here I attempt a systemic view, a whole picture. This work requires further refinement, but it’s a good start:

Update 5 August 2018

I received the following (welcome) response to this graphic:

I responded as follows:

If you only see one bar at a time, you will not know that you reside in a cage. The hope in this is that a picture explains quickly what takes 1,000 CNN and Huffington Post editorials to say.

I don’t know how to teach lateral, non-linear thinking, but hope lies in teaching young people that actions often have compounding and unexpected consequences. I know from being an engineer (engineers are the worst culprits of linear thinking) how easy it is to miss the big picture.

I summarized this concept in “Emily’s 1st Law of System Complexity“:

“For every reduction, there is a greater and opposite clusterfuck.”

My hope flies high, or I wouldn’t be writing my book, wouldn’t be mapping systems, wouldn’t be operating a business. And I usually operate “one soul at a time”–mentoring individuals, providing friendship, giving my best to relationships in the moment they take place.

It’s a labor of love, and love is built on hope:

“And now these three remain: faith, hope and love. But the greatest of these is love.” – 1 Corinthians 13:13 (NIV)

 

women’s style recommendation with artificial intelligence (part #2)

In “women’s style recommendation with artificial intelligence (part #1)”, I introduced my work toward developing artificial intelligence (AI) for fashion and style recommendation. Essentially, its an expert system built on a Bayesian belief network. Now I discuss model validation and next steps in the design iteration process.

I first wanted to see if the trained network correctly returned known recommendations (“wear” or “don’t wear”) based on known clothing selections. This procedure successfully validated the code I wrote. Then I wanted to see if the model can derive new style rules. Experienced partial success on this account; I will outline a possible strategy for improving it.

The rest of this article details the processes summarized in the previous paragraph:

Consider the following trained Bayesian belief network structure:

While calculating the structure, the learning algorithm also calculated the node value probability distributions from the training set:

We first evaluate the model on three fashion rules, asking whether the selected node combination’s values are okay to wear:

  • IF body shape = “apple” AND skirt zipper = “on front” THEN wear = “No!” [1]
  • IF body shape = “apple” AND skirt zipper = “on side” THEN wear = “Yes” [1]
  • IF shoes = “flip-flops” THEN wear = “No!” [2]

(I trained the model upon 126 such rules simultaneously).

Running the inference code:

All looks good. As a control, I added “shoe = pumps” (instead of flip-flops) to the above calculation, and see that these are okay to wear as expected. (However, see the discussion below where I ran into trouble).

So now I start to derive novel new style rules from the model. Suppose we want to simply find out if it is okay to “wear” an “apple” body shape. We expect the model to report “yes”, as it does, assigning a probability to the conclusion:

However, the model cannot handle the addition of a shoe type to the “apple” body shape query above:

The problem is “fixed” when I add a style rule specifically allowing apple-shaped folks to wear pumps, but I am not happy with this. Ideal outcome would be for the inference to conclude this. I’m first going to check the dependencies encoding… which, if that solves the problem, stresses the importance of specifying dependencies well in additional to lateral relationships. For example, I might establish a “human” node, and indicate that each clothing article and feature proves appropriate for humans to wear. Then I’ll declare that each body shape associates with “human = true”.

Nonetheless, the progress reported here is significant!

I’ll keep you posted.

– Emily

References

women’s style recommendation with artificial intelligence (part #1)

Introduction

We know several basic style “rules” (ha!) based on body shape:

  • Skirts:
    • “Apple” Body Shape:
      • IF body shape is apple AND skirt has front zipper THEN don’t wear
      • IF body shape is apple AND skirt has side zipper THEN wear
      • IF body shape is apple AND skirt has no zipper THEN wear
    • “Rectangular” Body Shape:
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt is A-line THEN wear
  • Pants:
    • “Apple” Body Shape:
      • IF body shape is apple AND jeans have flare THEN wear
      • IF body shape is apple AND jeans have pleats THEN don’t wear
      • IF body shape is apple AND jeans have stretch THEN wear
      • IF body shape is apple AND trousers have flare THEN wear
      • IF body shape is apple AND trousers have pleats THEN don’t wear
      • IF body shape is apple AND trousers have stretch THEN wear
    • “Rectangle” Body Shape:
      • IF body shape is rectangle AND jeans have flare THEN wear
      • IF body shape is rectangle AND jeans have pleats THEN wear
      • IF body shape is rectangle AND jeans have stretch THEN wear
      • IF body shape is rectangle AND trousers have flare THEN wear
      • IF body shape is rectangle AND trousers have pleats THEN wear
      • IF body shape is rectangle AND trousers have stretch THEN wear

We want to create an artificially intelligent system to probabilistically decide, given a query such as “I have an ‘apple’ body shape and am thinking of wearing a skirt with a zipper in front. Should I?”. To accomplish this we use these rules to train a Bayesian network, and then use the network to make inferences upon queries such as the one given above.

Training the Network

From these we derive the 13 nodes of our Bayesian network:

Node
apple
jeans.with.flare
jeans.with.pleats
jeans.with.stretch
rectangle
skirt.with.a.line
skirt.with.front.zipper
skirt.with.no.zipper
skirt.with.side.zipper
trousers.with.flare
trousers.with.pleats
trousers.with.stretch
wear

We use the rules and the nodes to produce an automatically generated graph. Put to help it along, we will apply some expert knowledge and specify some

We seed the model structure identification algorithm with some basic expert knowledge by manually specifying the following 12 causal relationships:

From To
rectangle wear
apple wear
skirt.with.front.zipper wear
skirt.with.side.zipper wear
skirt.with.no.zipper wear
skirt.with.a.line wear
jeans.with.flare wear
jeans.with.stretch wear
jeans.with.pleats wear
trousers.with.flare wear
trousers.with.stretch wear
trousers.with.pleats wear

(We will see later that the automated graph structure learning procedure adds one more edge).

We save these relationships in “output/style_edges.csv” for later import using R.

We then encode the rules in dictionaries/hashes for items co-joint in a rule. For example, we express the skirt-related rules pertaining to apple-shaped bodies in JSON as:

    {
        "wear": "Yes",
        "apple": "1",
        "skirt.with.no.zipper": "1"
    },
    {
        "wear": "Yes",
        "apple": "1",
        "skirt.with.side.zipper": "1",
    },
    {
        "wear": "No",
        "apple": "1",
        "skirt.with.front.zipper": "1",
    }

For each entry, we zero out all other nodes (expect for “wear”, which is set to “No”), and express all 19 rules as a data frame, where the index order corresponds to the node order displayed above:

0,0,0,0,1,0,0,1,0,0,0,0,Yes
0,0,0,0,1,0,1,0,0,0,0,0,Yes
0,0,0,0,1,0,0,0,1,0,0,0,Yes
0,0,0,0,1,1,0,0,0,0,0,0,Yes
0,1,0,0,1,0,0,0,0,0,0,0,Yes
0,0,0,1,1,0,0,0,0,0,0,0,Yes
0,0,1,0,1,0,0,0,0,0,0,0,Yes
0,0,0,0,1,0,0,0,0,1,0,0,Yes
0,0,0,0,1,0,0,0,0,0,0,1,Yes
0,0,0,0,1,0,0,0,0,0,1,0,Yes
1,0,0,0,0,0,0,1,0,0,0,0,Yes
1,0,0,0,0,0,0,0,1,0,0,0,Yes
1,0,0,0,0,0,1,0,0,0,0,0,No
1,1,0,0,0,0,0,0,0,0,0,0,Yes
1,0,0,1,0,0,0,0,0,0,0,0,Yes
1,0,1,0,0,0,0,0,0,0,0,0,No
1,0,0,0,0,0,0,0,0,1,0,0,Yes
1,0,0,0,0,0,0,0,0,0,0,1,Yes
1,0,0,0,0,0,0,0,0,0,1,0,No

We save this data frame as “output/style_rules.csv” for later import by R.

In R, we load the necessary libraries and the CSV files. We also ensure everything is a factor in the rules data frame:

We look at the expert-specified edges, noting the existence of 12 relationships. After running the hill climbing algorithm to derive the network structure from the prior-specified edges and the rules, we notice that now 13 edges are present:

Here is the added edge:

From To
apple rectangle

We derive the model’s parameters from the training data, and then compile it for use in inference.

Results

Suppose we have an “apple” body shape, and want to choose a skirt using this model. We try the following skirt types against the apple body shape to infer whether or not to wear a particular skirt:

The first result in the image above resoundingly rejects wearing a skirt having a front zipper when one carries and apple-shaped body. By contrast, the second result approves of skirts having side zippers for apple-shaped folks. Both results concord with the IF-THEN-ELSE rules initially specified. The third result proves interesting—we did not provide a rule for apple-shaped bodies and A-line skirts, so the model provides no conclusion.

We observe similar results for trousers: The first two outcomes match the rules, but the third provides no decision because we provided no information about whether flare and stretch may be used together in a pair of trousers for apple-shaped bodies, or for any body shape for that matter!

Issues to Resolve

As indicated in the last paragraph, in practice a pair of trousers may have both flare and the ability to stretch. Each of these traits alone proves great for apple-shaped individuals. So together I manually infer that the two together are at least okay and may be even preferable. However, the model does not derive such a conclusion. In other words, we need to add rules saying these two traits may coexist.

Also, this effort took a lot of manual “expert” specification of the initial “seed” graph structure. Ideally one would learn the final structure purely from rules. My thinking is that the rule data frame is rather sparse, making it hard to learn the structure in an automated fashion. On the other hand, I may not have chosen the best learning algorithm.

Stay tuned…

– Emily

Update 16 April 2018

I’m onto the next iteration of the model design. A visual of results so far:

genderpunk360.com hacked (cracked) for the first time

I don’t like to use the word “hacked” to refer to unwanted system intrusion and unwanted system change, but that is the word everyone understands. See my post “on my frequent use of the word ‘hacker’” for an explanation of my objection. I prefer the term “cracked” instead.

Anyway someone recently cracked my Transgender Resources Page earlier today. Fortunately I discovered the breach promptly and the fix proved easy.

However it reminded me to diligently update my security software, my Linux kernel, and my blog/wiki software immediately following update releases.

The cracker did not apparently object to my content, they merely replaced my wiki’s main page with link spam. However I’m likely to attract those hostile to my material in the future and need to stand vigilant.

If you are curious, here is what my main page looked like immediately following the attack. Note all the link spam:

After the fix, the main page redirects to the “Transgender Resources” page:

After my repair, going to the main wiki page delivers:

Unfortunately, I’m using a current copy of MediaWiki, so a possible security update would not help. However, I discovered that you can “protect” pages:

We’ll see how well this works.

constant self-reinvention: my profound habit for creating success

Video of speech I gave at a Toastmasters meeting about my primary method for obtaining success:

a non-linear and holistic work ethic

I started working out a holistic map of my work ethic and work values, and quickly found that linearity failed to cut it. Essentially, I need to capture the interdependencies between spiritual, social, and financial wealth. More importantly, I need to illustrate the crucial balance between these factors. Enter non-linear system dynamics:

Simulation, based on ad-hoc parameterization (because there is no way to actually measure most of these variables), demonstrates that I’m at least moving in the correct direction:

Method

Used Vensim PLE to create and simulate the model.

why we still need cyberpunk

William Gibson penned Neuromancer over thirty years ago, and the 1990’s ended viciously on 9/11. With the exception of cyberfeminism, I wrote off “cyberpunk” as an ethic once we as a society stopped saying “cyber” and replaced the word with “online”.

Yesterday I traced partial assets of an individual I distrusted—and needed the straight dope from—from my laptop. Dating while transgender proves dangerous and a girl must protect herself!

In between I persistently beleaguered Microsoft as a career-long Linux hacker.

Once declared squatter’s rights on a piece of land I identified though data mining.

I walk with the Big Data devils to broadcast my signal, a means to an end. Twitter, Google, Amazon, and Facebook receive my data, and in exchange they amplify my cultural imperative.

And they know where the real value in data lies: Not in the records themselves but in the interconnections between them.

Emergent properties steered by unholy gods.

“Cyber”: Greek for “to steer”.

Steering a boat requires connecting the data: Position, velocity, acceleration, time. State variables alone won’t suffice.

When we get burned by Cambridge Analytica or the Russian Federation, we realize our individual technological vulnerability.

Propaganda is hacking: Implant bias, implant ideas, grow emergent outcomes. Seduction is a system intrusion.

Technological warfare and psychological warfare forever link.

Class war must proceed asymmetrically.

I only trust the Prophets, not the Church, not the State, not the Oligarchs.

And we can be prophets in cyberspace. We can create technology that liberates the world.

We can steer toward our own emergent outcomes.

We can end material scarcity.

Love forward. Program. Network. Build enterprise. Produce art. Write. Love forward.

Jam the system, and prepare to be jammed by the punks that follow you.

The 1990’s are dust, but the “system” still remains cybernetic control. Therefore resistance remains cyberpunk.

encoding fashion rules into mathematical data structures (part one)

As we build our fashion recommendation engine, we seek rules to populate it with. With few exceptions (e.g. [1]), we find these rules encoded in prose or infographic form, rather than a semantic web form suitable for computation. For example, [2] provides written advice on dressing fabulously for a “rectangular” women’s body type. The writers meant this document for a human reader, not a computer program.

However, we can’t scale a process consisting of manual extraction of rules to the level we would like to achieve in this project, so we turn to natural language processing to extract rules from texts in an automated fashion. We begin by identifying parts of speech and the syntax relationships between words in sentences. For example, consider the following two fashion rules from [2]:

  • If you are a heavy or tall rectangle, choose a big bag.
  • If you are a petite rectangle, choose a petite bag.

We then create a directed graph with words as nodes, each with an attribute indicating its part of speech, and edges indicating the syntactic relationships between the nodes (e.g., “heavy” is a modifier of “rectangle”). We also add edges to specify the direction of sentence flow. Visualizing the above two sentences in this form using Neo4j [3] yields:

Next Steps

In the next phase, we plan to automatically derive computationally useful IFTHENELSE rules from such mappings. For example, the above two sentences express in IFTHENELSE form as:

  • IF rectangle AND (heavy OR tall) THEN choose a big bag
  • IF rectangle AND petite THEN choose a petite bag

Once we form a comprehensive set of such rules, we will load them into an expert system or related system to enable fuzzy reasoning on the rules, enabling custom fashion recommendations!

After this, we will come up with a way to reconcile similar recommendations. For example, suppose we find the following two IFTHENELSE rules from two different sources:

  • IF rectangle AND (heavy OR tall) THEN choose a big bag
  • IF rectangle AND (heavy set OR tall) THEN select a big bag

These say the same thing. We will devise a way to combine them into one recommendation such that the weight (value) of the recommendation doubles due to its backing by two distinct sources.



References

  1. Vogiatzis, D. Pierrakos, G. Paliouras, S. Jenkyn-Jones, B.J.H.H.A. Possen, Expert and community based style advice, Expert Systems with Applications, Volume 39, Issue 12, 2012, Pages 10647-10655, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2012.02.178. (http://www.sciencedirect.com/science/article/pii/S0957417412004411) Keywords: Style advice; Recommender system; Fashion ontology; User modeling
  2. http://www.styled247.com/rectangle-body-shape
  3. https://neo4j.com