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stereax · 10 months ago

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15 People 15 Questions with Stereax - thanks @simmyfrobby for the direct tag and @jonassiegenthighler for the GC tag <3

1. are you named after anyone?

Nope! As far as I'm aware, I was named from one of those baby name books but my parents fucked up the spelling on purpose to be "special".

2. when was the last time you cried?

Probably just before Christmas when I was realizing just how fucked up my childhood and family dynamics are in a chat with a friend. Before that, in class while I was bombing a presentation. Before that, in class when the teacher told me to... raise my hand before I spoke... I don't really ever cry unless in public and it's always for a stupid reason.

3. do you have kids?

I have a lot of stuffed animals! And a bunch of kids I tutor. But none of my own, that's not in the plans.

4. what sports do you play/have you played?

My parents tried me in everything, but mostly tennis stuck. I'm not that good at it, I'm not really athletic in general and my forehand was busted to the point where I would only do lobs with it, but I was fairly okay, especially when it came to my backhand, so I got by. Haven't played in a few years though.

5. do you use sarcasm?

A lot more in real life than online.

6. what’s the first thing you notice about people?

One of the things I try to pay attention to is the hands. I also end up looking at teeth a lot. Both of those can kind of give you an idea of who a person is. I don't know if I'm good at eye contact or not, but I don't really think so?

7. what’s your eye color?

Hahahaha. It's kind of... everything? Not in a Mary Sue way but like, the outer ring is a greyish blue-green and the inner ring is like, a golden hazel-y green? And depending on the lighting or glasses, that changes? So... my ID says green.

8. scary movies or happy endings?

Scary movies mostly annoy me, so happy endings. I don't like sad endings that much; I tend to dwell on them too long.

9. any talents?

Um, I used to be a mathlete when I was a kid? So I can do a lot of math in my head, generally exceeding the capabilities of most people I know, and usually with speed too. But that's not really a "talent". I guess I speak several languages, but again, not really a "talent". I don't really know. I don't really consider myself good at anything, you know? I write? Sometimes? But not well either. So.

10. where were you born?

Ridgewood, New Jersey. Apparently because my parents thought it would be a "good city to be born in" and that it would "always be on my birth certificate". I don't know the logic either.

11. what are your hobbies?

I really like mobile games! I'm currently obsessed with Path to Nowhere, used to play a lot of King's Raid before they destroyed that game with awful updates, and also play Pokemon Go and Pikmin Bloom regularly to motivate myself to leave the house. Also puzzles, both solving and sometimes creating my own! And I'm also slowly getting into the NFL as well as the NHL (obviously). My thing is that when I get into something, I need to know everything about it and be the "best" at it, which screws me up a lot, but. Knowledge!

12. do you have any pets?

I used to have fish when I was a kid. I'm not in the right conditions to own a pet, both logistically and mentally.

13. how tall are you?

I don't know exactly but I think around 5'5" to 5'6", or 165 to 167 cm. I always wanted to end up around 5'9", so 175 cm, but I didn't get enough tall genes, I guess :( still have markings on the wall about ideal height :')

14. favorite subject in school?

When I was growing up, math; more currently, probably law classes.

15. dream job?

I've always envisioned myself ending up in law or business or especially politics, but like, not as a scumbag? If that makes sense. Like, a senator or even higher than that, but not a corrupt piece of shit. Which I know is impossible and an oxymoron. So I guess the answer is, most generally, a leader of some sort.

I'd tag the GC but Eliot did that already sooooo um, if you're following me and see this consider yourself tagged!

#stereax plays a tag game

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can-i-make-image-descriptions · 2 months ago

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[Image IDs: A series of tweets from from verified user Christopher Ingraham (@/ _cIngraham) on 12/28/18 reading: So, a shipment of crickets for the lizard arrived via FedEx today. It was my first time ordering bulk crickets off the internet, and I naively assumed that they would be in like, a bag or some other contraption to facilitate easy transfer to another container. They were not.

The were in a cardboard box. And I cut the tape and opened the box and Surprise! Crickets everywhere. It was the middle of the workday and I didn't have time to deal with cricket logistics, so I put the tape back on the box.

And then I put the box in the upstairs bathroom, the only semi-contained place in the house where I knew the kids and the cats and the dogs wouldn't be able to get at the box and tear it open and unleash 250 hungry crickets into our warm, semi-humid environment.

About 20 minutes later I'm back at work on my computer, and I hear my wife in the kitchen: "where are these goddamn crickets coming from." I freely admit I had not kept her fully up-to-date on my cricket purchasing plans.

And at first I was like "okay, maybe one or two go out when I initially opened the box. No biggie." I kept working.

With the benefit of hindsight, this was a mistake.

I'm trying to wrap up a story but I keep hearing cricket-related exclamations coming from the kitchen. Eventually I get up to investigate. I say, "So uh the crickets got here today--" "I Realize That," she says. "Why Are They All Over The Kitchen"

I say "That's a good question. Let me check something." I walk over to the bathroom. I open the door. There are crickets. Everywhere.

Crickets on the floor. Crickets on the walls. Crickets in the sink. Crickets in the toilet.

For some reason my first instinct is to flush the toilet, as if that will do anything to solve the problem of crickets in all the other places that were not the toilet. I shut the door. "Uh, don't come in here!" I try to sound cheerful.

Apparently I had not sealed the box shut as well as I should have. I ended up rushing out to the shed, in the 18" of snow and below zero temperatures, to pick up a spare aquarium we had. I spent about 45 minutes collecting crickets from the bathroom.

Of course by this point many had migrated elsewhere. They were in the close. In the shoes. Making their way downstairs to the playroom. The cats were having what I can only imagine was the greatest day of their lives.

I tried to collect all of them. It was like the world's shittiest game of Pokemon. But here we are, roughly 10 hours after the initial catastrophe, and stray crickets are still turning up in odd places.

I make this information public because if I do not send any tweets tomorrow, it is because my wife murdered me after finding a cricket in our bed in the middle of the night.

And that's the news from Red Lake Falls. /End IDs]

#i made an image description

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perexcri · 1 year ago

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hi hi

i just wanted to come on here and show my appreciation for your writing because you truly never fail to befuddle, gobsmack, and ofc flabbergast

like,, literally obsessed with you,, anyway

i dont comment stuff on ao3 because i am Terrified but um i do have a notion board where i log all of my reading stuff and i make comments on there so i thought id share some of my favs that i made on your works

this ripped my soul in half you cannot comprehend

this eternally altered my brain chemistry

this is what love is fr fr

fuck dude

as always this is very well written however i did predict like the entire plot but that is ok i love being right and it was very good some may even say slay idk if id go so far to say that, it may be too high concept for me but it was very good, i did however keep imagining that they both had stinky breath, cuz like will just ate mystery chili and mike presumably hasn’t brushed his teeth in months,, do vampires brush their teeth?

(and then promptly followed by)

one month later i am officially marking this as slay because i haven’t been able to stop thinking about it,, like that last scene where they make out against a cross so mike is being burned while hes turning will,, they’re both in pain together,, just sacrificing your whole life to be with the person you love, oposite of dorothea type thing tbh yah i just think i should grant it its slayage

yah this was so good like insane writing of tension and like idk all of the internal stuff pays of and OMG THE PAYOFF HOLY SHIT THE ENDING SO WORTH IT SO EARNED LIKE INSANE HOLY FUCKKK

i couldn’t stop reading i genuinely felt like i was going to puke from the tension/anticipation/suspense,, the dialogue was really good too and so was the like character study idk if i completely agree with this take on wills character but i really enjoyed in none the less and all in all it did deliver on the rom com of it all and had a very satisfying rom com esc ending that made me cry until it was actually solved because i really cant deal with lack of communication and unresolved tension anyway thats it this was really really good

this might be a bit above my reading level but i read emily dickinson for fun so im powering through just fine

ahhh hello!! thank you for sending such a nice ask and for the appreciation 🥺 i'm glad you decided to share these thoughts you have written down with me!! and i understand commenting can feel a little strange and terrifying, but just know it is appreciated regardless :D (as long as you aren't like,,,actively shitting on the fic ofc lol)

umm i wasn't sure exactly how to tackle this because some of them are clear to me which fics they go to and some of them aren't, but i'm gonna try my hardest and we'll see how it goes 😌✨

1.) so the first one with the "this ripped my soul in half" - i'm gonna guess that's beneath these boughs? maybe? that's the one where i've gotten responses most similar to what you have written down

2.) those next two paragraphs i 100% know are from come to me again!! you can tell i definitely wasn't as focused on the logistics of their later kiss for that fic because i didn't think about the whole brushing teeth/chili thing AT ALL. i picked chili because i thought that would be an easy thing to scrounge up in an enclosed environment and to make easy substitutions for, and i just,,,,,did not think about whether people would brush their teeth in a coldtown or not alfjlas. ah well, it was mainly about them making out on the cross while Mike's flesh is burning and he's turning Will into a vampire, right? that was the main goal and the biggest scene i wanted to write, and i did!! so i consider it a success 😌

3.) okay this one i think might be what a match?? it's either what a match or cheer up baby, but i'm leaning more towards what a match because of the mention of tension

4.) this one is definitely to hell and back again - so i'm glad you liked it despite the tension heheheh. and honestly i would maybe have to agree with you on the thing about Will's character? i guess the big thing with that fic is 1. it turned into an entirely different beast while i was writing it from what i originally intended for it to be, and on top of that it ended up being the first book-length thing i ever wrote, so i was a little in over my head and not entirely sure how to handle it, and 2. i was so focused on whatever the hell had happened to Mike in vol 2 that i did really focus on him more than Will in that fic so i do think Will comes off as a little ahhhh flat? not entirely himself? idk, i'd like to think i've learned a bit more since that fic, and that i made up for Will's lack of character in it with my fantasy au a flower that resembles you, which was very much centered on Will and his complexities

5.) ok i genuinely can't figure the last one out lol. i would maybe say beneath these boughs? but that's what i guessed for the first one, so maybe the first one is a flower that resembles you and this one is beneath these boughs? not sure haha

anyway!! umm idk if my guesses were right, but thanks again for sharing these thoughts!! it does help me to know that you are enjoying what i write and that you have these kinds of thoughts about it :D thanks for stopping by, and i hope you enjoy whatever comes next from this half-functioning brain of mine heheheh :] 💜💜💜

#ask

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justthehiddleswrites · 4 years ago

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Accidently Married | Tom Hiddleston x OFC | Chapter 2 | Be Careful with Clive, I Have Grown Attached to Him

A/N: Tom makes certain comments about an ex (who is unnamed). It is a fictional girlfriend, take from it what you will. Keep your hate to yourself.

SERIES MASTERLIST HERE

Pairing: Tom Hiddleston x OFC (Molly Bishop)

Summary: Tom is stuck in a news cycle from hell; Molly is stuck in the dead end job of bartending with a pile of student and credit debt. Tom has an idea to solve all their problems. Get married, get the paparazzi off his back, divorce after a year and Tom pays off Molly’s debts. Tom has everything figured out, that is until he sees Molly as more than a just a friend and so does someone else. In this vying for affections who will win, the handsome Brit or the boy from Boston?

This Chapter: Tom and Molly are now married. Surprise! These two talk about the logistics of Tom’s half-baked plan. And Molly moves to London to face the firing squad, aka the paparazzi.

Warnings: fake marriage, smut (vaginal sex), mentions of: child abuse/neglect, foster care, substance abuse, cheating.

TAGLIST IS OPEN! PLEASE LET ME KNOW IF YOU WANT TO BE TAGGED! THANK YOU FOR READING!

–

After they signed the license along with the apostille, there had been dancing. That much Molly remembered. And drinking. Specifically drinking champagne. Tom danced with abandon, pulling Molly into the whirlwind of activity he created around him.

But now it was morning, and Molly woke up in a bed that wasn’t her own. She groaned as her head pounded, having forgotten that champagne and her have a love-hate relationship. Molly saw the faint outline of Tom asleep on the couch, his long body stretched out, still wearing his suit from last night. After glancing at the alarm clock, Molly fell back asleep.

Several hours, Molly woke up again and headed to the bathroom, not noticing the now opened curtains.

“Hey good lookin, Whatcha got cookin,” Tom’s voice twanged as he stepped out of the shower. His head pounded a bit, but not the worst hangover he had.

“AHHH!!!” Molly screamed as she stepped into the bathroom.

They both froze, which was more embarrassing for Tom, as at least Molly was still wearing her dress from last night.

“You’re naked.” Molly blinked, her head darting around the room until she focused on an interesting corner of the room.

Tom chuckled, grabbing a towel and wrapping it loosely around his waist. “I don’t normally shower in my clothes. You can look back now.”

She slowly turned back around. “Sorry.” She shuffled her feet. “I should have knocked.”

“It’s quite alright.” He moved towards the door. “Shower is yours and we should talk things over.”

Molly nodded. “We should.”

While Molly showered, Tom dressed in the other room. After finding a clean t-shirt for Molly to wear over her dress until she could change, he called the airlines and changed his single ticket for that morning to a later flight for two, fishing Molly’s ID out of her wallet.

“Thanks for the shirt.” she stepped out.

“It looks good on you.” Tom gestured to the sofa. “Sit. Would you like some breakfast?” Her stomach growled. They both laughed. “That would be a yes.” Tom shoved the room service menu. “Order what you like.”

She selected an egg white frittata while Tom got the pancakes. Tom put in the order and returned his attention to Molly.

“So let’s talk about how this will work.” Tom shifted in his seat.

“An excellent idea. You mentioned living together in London. When do we leave?”

“This afternoon.”

Molly coughed. “That quick?”

“I’m afraid so.” Tom’s hands fidgeted in his lap. She noticed he was still wearing the spider ring. “I have work obligations back home and in order for it to be believable you would need to live with me.”

“Naturally.” Molly slapped her thighs. “So after breakfast, I can head back to my apartment, pack up what little I have, say goodbye to my roommate, and change into appropriate clothing. And you need to get us some proper rings.” She waved her hot pink ring in the air. “Unless of course you intend for your bride to wear a ring from the top of a cupcake.”

“Only if I get to keep my ring. I’ve grown quite attached to Clive.”

She raised an eyebrow. “You named the spider?”

“Yes.” There was a knock on the door. “That will be the food. Allow me.” He disappeared and returned shortly with a rolling table, ladened with food. Tom poured a cup of coffee and offered one to Molly.

“I don’t drink coffee.”

“I can have them bring up a teapot.”

“I’m pretty sure there are some complimentary ones in the room. Now,” She cut into her food and took a bite. “how will everything else work? Living with you, your life, the paparazzi? That is the whole point of this charade.”

“You do get down to business. So yes, I would expect you to live in my home. In a separate bedroom, I can set up another room as an office for you. We would need to attend events together and generally appear as a loving couple on the outside.”

“And my debts? That is part of the deal, right?”

“Right,” Tom gazed over at her while eating his pancakes. “I would assume the payments while we are together, and after the divorce is final, I would pay off any balance. I would also take care of your daily expenses while we are married. You are welcome to work if you want, but I will give you spending money.”

“So I would be a trophy wife?” Her brown eyes glinted.

Tom waved his hands in front of him. “Not that is not what I meant… I…”

“I am kidding, Tom. If you prefer, I can not work. I don’t mind. Give me some time to figure things out.” A thought came to her. “What about…” Molly searched for the words. “… other needs? Or if you wish to engage in a romantic relationship?” Her cheeks blushed as the words fell out of her mouth.

Tom blushed as well. “I have great self-control and I think if either of us get to that point, we can discuss it. I don’t want you to feel trapped.”

“And I don’t want you to be trapped either. I guess that is as good of an answer I could expect. Anything you want to ask me?”

Tom stared at Molly. The air hung heavy. “Do you regret saying yes?”

“No. Do you regret asking?”

“No.”

Molly downed the rest of her juice. “Well then, it is all settled. I am going to take off to pack. And you have some shopping to do. My ring size is a 7.”

Tom finished up the last bite of pancakes. “Right. We need to leave here by 3 to make it to the airport.”

“I shouldn’t be more than a few hours. Do you have a key to the room I could borrow?”

Tom fished one out of his discarded jacket’s pocket. “Here I will have the front desk make me another one.”

She tapped the key against her nails. “Thanks, Tom. For the help and for being a decent guy.”

“I should be thanking you.”

“You already have.” She grabbed her purse and headed out the door.

Tom headed downstairs, asked the front desk for a new key to the room, and also inquired where the nearest jewelry store might be. The front clerk handed him a key and directed him to a small collection of luxury stores in the hotel. He found Tiffanys and purchased a classic platinum solitaire engagement ring and plain platinum band for Molly and a yellow gold band for himself.

Molly wasn’t back when he returned, so he set about packing up for the flight. His phone buzzed. Luke.

It appears you had a good time in Vegas. The papers say you are drowning your sorrows. Looks like the story is here to stay. Call me when you wake up from your nap at home.

Tom typed back.

I did have a good time. I have a feeling the papers will soon find another story soon. Still in Vegas, taking a later flight. Talk to you soon.

His phone rang. He clicked it off, seeing it was Luke. Rather to get all the yelling done in person. The door opened and Molly came in, dragging a suitcase behind.

“Sorry! My roommate had questions.”

“So does my publicist.”

Tom took in Molly for the first time, really. Outside of the light of a casino floor. And not in a wedding dress purchased for fifty dollars on the way to the chapel. She wore faded jeans, a pair of beat up black Converse and a boxy white tee tucked in. A large black cardigan tucked under her arm. Dark hair in a bun. Quite lovely, if Tom told the truth.

“Are you in some sort of trouble?” Her brows knitted together.

“Not yet.” Tom tucked his phone into his jean pocket. “Here.” He pulled out the little blue bag.

Molly gasped. “I thought you would go buy some costume jewelry. This is too much.”

“Nonsense. This marriage may be fake, but the jewelry will be real.” Tom opened up the boxes. “May I do the honors?”

Molly held out her hand, and Tom slipped off the plastic ring before replacing it with the wedding set. “Much better. And yours?”

Tom slapped the box into her hand. “Be careful with Clive.” Molly pursed her lips as she pulled off the spider ring and replaced it with the gold band, putting the plastic ring in the Tiffanys box. “Here you go. Clive’s new home.”

Tom tucked the box into his luggage. “Ready to go?”

Molly rocked back on her heels. “Yep.”

Tom held out his arm. “Let’s go home, Mrs. Hiddleston.”

The flight back was uneventful, Molly and Tom dozed off, leaning against each other for support. Molly woke up first. She stared down at her rings. This was not how she expected this weekend going. Molly thought she would scrap together enough tips to make an extra payment on her credit card. Not flying to London with a Tiffany diamond ring on her finger and a famous actor as her husband.

“Life does throw you curveballs from time to time.”

“What was that, darling?” Tom muttered, stretching in his seat.

“Just commenting on the craziness of all of this to myself.” She held out her hand again. Tom laced his fingers with hers.

“I have done the same thing myself. Now when we land, there will probably be paparazzi around. Are you up for getting this whole thing off and running?”

Molly perked up. “What do I need to do?”

Tom tightly gripped Molly’s hand throughout the concourse and baggage claim. They eyed the doors.

“Ready?” she asked, squeezing his hand.

“I promise to be gentle.” Tom squeezed back, smiling.

As they stepped through the doors, Tom flashed a killer smile and Molly did as well, giggling as his arm wrapped around her waist. He leaned over and pressed his lips to hers. Molly melted against him, making sure her rings were visible as she cupped his cheek. She was right, Tom was an excellent kisser. After making sure any photographers had plenty of time to snap a pic, they parted.

“Think they got my good side?” Molly giggled.

“Do you have a bad side?” Tom asked.

“Just wait and see. Now take me home, darling!” She threw her arm over her eyes dramatically.

“Drama queen.” Tom pinched her side.

Tom’s home was cozy and clean. Definitely a bachelor’s home, as evidenced by the empty fridge except for a few bottles of beer and some questionable brown sauce.

“I can go shopping later.” Tom dragged a toe along the kitchen floor.

“I can go shopping later.” She reached up and smacked his face playfully. “What kind of wife would I be if I didn’t feed my husband?”

“Fair point. I will call the bank tomorrow and get a card in your name. Just run any big purchases past me first. And we will need to get your name changed, passport, etc. I can have someone help you.” Tom prattled on.

“Why don’t you show me the rest of the place first?”

Tom held out his arm. “This way.”

Tom’s book collection was impressive along with his collection of movies.

“I clear some space if you need it.”

“I only packed clothes. My roommate is selling the rest, including my car and wiring me the money.”

“Oh.” Tom’s face fell. “Let me show you the bedrooms.”

He showed you a small guest room. “This could be an office for you and next door is a bigger bedroom for you.” Tom hustled along the hallway to open the next door. “Here.”

It was a bigger room with a queen bed and a wardrobe. Spare and clearly used for company.

“It will do just fine. And the bathroom is across the hall which is nice. Where’s your room?”

Tom made his way to the end of the hall and opened the door to his room, decorated in tones of grey and navy. A large king sized bed taking up most of the room along with a dresser. A bathroom en suite and a small closet completed the space.

“Very nice. Do you mind if I steal the color palette to decorate my room?”

“Please do. I never got around to decorate it. My sisters and mother are the only ones who stay in there.”

Molly paled a bit. She hadn’t thought about Tom’s family. “I supposed I will meet them soon.”

“I supposed so. It would be odd for my wife not to meet them. I hadn’t thought about it.”

Molly rocked back and forth. “Now why don’t I go shopping and you unpack and relax?”

“I would feel better if I came with you. You are in a different country, a strange city. And what if you have problems with the card?”

“Then let’s go and you can point out some of your favorite foods.”

“It’s a deal.”

“When I said pick out your favorite foods, I didn’t expect it to be only sweets. Did I marry a seven-year-old?”

“I’m 35, thank you. and I enjoy those sweets.”

“You eat like a college frat boy.”

“Guilty.”

“That is definitely changing now that I am around. You can’t continue to eat like that. There are things called vegetables.”

Tom snapped his fingers. “I’ve heard of those.”

“Get out of here!” Molly swatted at him. “I am certain you have things to attend to, and I need to familiarize myself with the kitchen.”

“Are you kicking me out of my kitchen?”

“Our kitchen. And yes.” Molly smirked.

“I yield! I yield. I’ll be in my study if you need me.” Tom walked out of the kitchen and towards his study.

He spied his phone sitting on the desk, still off from the flight. By now, any pictures should have been posted somewhere. Tom collapsed into his desk chair and clicked the phone on. While he waited for it to start up, he could overhear Molly puttering about in the kitchen, muttering to herself as she put away the groceries.

Buzz. Ten messages and eleven missed calls. He didn’t bother to listen to them and instead dialed Luke.

“Luke, I’m back in town. Thought I wou—” Tom started in as soon as Luke picked up.

“I WASN’T FUCKING SERIOUS WHEN I SAID TO GET MARRIED??! HAVE YOU LOST YOUR FUCKING MIND?!”

Tom pulled the phone away from his ear. “No, I haven’t. But I am married. To a wonderful girl. Her name is Molly. Molly Bishop. You should meet her, Luke.”

“YOU ARE FUCKING RIGHT I’LL MEET HER. AS SOON AS POSSIBLE! SHE CAN HELP IDENTIFY YOUR BODY, THOMAS!” Luke continued to scream on the phone.

“Can you dial back the volume, Luke? I would like to preserve my hearing. Is there something wrong with marrying the woman I love?”

Luke cleared his throat. Tom understood Luke was doing his best to collect himself. “Apologies. There is nothing wrong with marrying the woman you love, Tom. Nothing at all. Except I don’t think you love this woman, since until a few weeks ago you were in love with—”

“Don’t say her name, it will ruin my marital bliss. I’m a hopeless romantic, Luke.”

“Hopeless, yes. Romantic, the jury is still out. And your fans don’t count, they are blinded by you. But I see the truth.”

“Which is?”

“You are not as smart as you think you are.”

“Did any of the articles mention her?” Tom inquired, spinning his wedding band on his finger.

“No.”

“Then I am exactly as smart as I think I am.”

There was a clatter from the kitchen.

“Tom!” Molly called out. “I need your help.”

“Got to go, Luke. My wife needs my help.” Tom emphasized the word “wife.”

“This isn’t over, Tom.”

“It never is. Bye.”

More clattering and another cry. “Tom!”

Tom rushed into the kitchen to find Molly perched on top of the kitchen counter, reaching high into a cabinet.

“Why is everything so high in here?”

Tom chuckled and reached around her, pressing his torso against her back. Molly jumped for a moment at the touch.

“I’m not used to sharing my space. I’m six two, I put things where I can reach them. What are you grabbing?”

“The roasting pan.”

Tom pulled it down and placed it on the counter. His phone buzzed in his pocket. He ignored it.

“Thank you. Well, I am five six, so unless you want me climbing counters for the next year, we need to rearrange some things.”

“But you’re so cute climbing around like a little monkey.”

Molly frowned. “Is that supposed to be a compliment? If so, then try again.”

Tom opened his mouth and closed it. “I’ll pull things down after dinner.”

“Thank you.” She rubbed his arm. “Now to try my hand at a roast dinner. Did you get stuff done?”

His phone buzzed again.

“I called my publicist. The pictures posted.” Tom pulled out his phone to shut it off.

“Oh good. So I take it, I had the desired effect.” Molly crunched on a carrot and offered one to Tom, who wrinkled his nose.

The two of you. My office 8 a.m. tomorrow. No excuses. I want to meet the blushing bride.

Tom frowned at the screen.

“It would appear so. I suggest you go to bed early because you are meeting Luke, my publicist tomorrow.”

Molly’s mouth fell open. “Should I be worried?”

Tom smiled at her. “No, I should be.”

#tom hiddleston #tom hiddleston fanfiction #tom hiddleston fanfic #tom hiddleston imagine #tom hiddleston fluff #tom hiddleston x ofc #tom hiddleston angst #tom hiddleston smut #accidently married

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faraway-in-headspace · 4 years ago

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I?? I searched Chicken Choice Judy on google out of curiosity because it sounds oddly familiar like there’s a similar-sounding name and I found 4 websites selling the shirt design. But the descriptions on these pages are BUCK WILD??

Written version of the descriptions under the cut (very long).

[Begin ID

First image states: Long ago, when I had hair, I was an undergrad living in a house with nine other men. Near as I can tell, three of them (not sure which three) never bought food, just lived off what they stole from the Chicken Choice Judy shirt But I will love this other seven. We had several house meetings about it, but nothing changed. One day, I came in from grocery shopping. By coincidence, all 10 of us were in the kitchen. I started putting my stuff away. 1st thing I pulled out of the bag was my half-gallon of milk. I opened the carton, took a couple of drinks from the carton, then gargled some of it, and spit it back in. I opened my tub of margarine and licked the whole surface. By now, the room chatter had stopped because the other nine jaws had dropped open.) To your original question, those specific topics would take several years to build, as they depend on several layers of pre-requisites, which would require either that more advanced topics such as algebraic topology to be taught in elementary school, or that the buildup process happened blazingly fast during high school – both of which probably stretch the biological limits of what pre-teens and teenagers can reasonably be expected to accomplish. I spit on all my veggies, took the bread out of the package, and licked and spit on it, then carefully put it all back in the plastic bag. Remind teenage daughters to look through them before going on date with the boyfriend, in case they want to use one. I labeled it all and put it away. None of it was stolen. I never said a word, but I made it a point to repeat the performance anytime anyone was around to see it. Others began to emulate my approach and food theft stopped. Even I found it revolting, but it solved the problem. Works even better if you are sick or can at least make your thieving roommates think you are. While some cities are starting to reopen in the wake of the COVID-19 pandemic, people around the country are continuing to wear masks in public and practice social distancing. Vogue is committed to staying safe, and offering hopeful, optimistic content that highlights moments of camaraderie and exceptional acts of heroism from around the world. We are all looking for a little comfort too—be it a soothing Instagram account or a stylish creator on TikTok. It reminds us of the power of little things.

Second image states: A couple of guests informed me my office was too minimalist and that they expected more things to be hanging on my wall the Chicken Choice Judy shirt besides I will buy this next time they visited my wife’s and my home. I kinda hope they held their breath while they were waiting for our next invitation. They both went on to backstab me and my wife pretty bad a few years later. Another set of guests tried to squat. I had driven them all the way from Florida to Massachusetts under the impression that they had jobs and a place to live lined up. They offered no money for gas, hotels on the three-day trip, or compensation for the inconvenience and effort. He even tried to weasel out of the dinner he offered as a thank you by forgetting his wallet. The dude got me off the streets years ago and I wanted to pay him back in some way, but my wife and I were in no position to have extra residents in our home. We just don’t have the room or money. I made all of this VERY clear and told my old buddy that we could only house them for a couple of days max. There are MANY other details, but the disrespectful thing my former friend said was wordless. As I was kicking them out and they were angrily loading stuff into my car to bring them anywhere but here, my buddy left his gigantic knife right in the center of my wife’s desk. Like that was supposed to make us change our minds and let them stay? In the days of dial-up, I had a family call and not be able to get through because we were online. They decided to show up unannounced. They literally caught me in my underwear as they were let into the apartment before I could even react to being rudely surprised. Some of my family members have a history of abuse, violence, and stalking, something at least one of the visitors, my mother, was quite aware of since she lived through it with me. Her tagalong friend decided to put in her two cents and tell me I should get a call waiting or a second line because they were trying to call me. That did it! I suddenly forgot I was just wearing underwear and angrily asked my mother’s friend if she was paying my phone bill. My mother-in-law, stepfather and mom’s friend beat a hasty retreat and NEVER did the pop-in ever again.

Third image states: That was why when we did get to reality shows, Etro and then Dolce & Gabbana plus Jacquemus later in France, it was wonderful. Clothes are all about contact: As a wearer, you feel them on your skin, and as a watcher, you process them with your eye. The watching part can be done secondhand, but the Chicken Choice Judy shirt in contrast I will get this impact will always be second to the real thing. I read some commentators in the U.S. saying, “Too soon” or “Wear a damn mask!” which I always did, but these opinions while valid enough lack perspective. Milan and its surrounding region Lombardy went through what New York did but earlier. Through sagacious governmental management much more effective than that of the U.S., Italy has managed dramatically to flatten the curve across the rest of its territory. These shows just like the reopening of flights, stores, factories, and restaurants were symptomatic of recovery that, far from being taken for granted, is being tended to with vigilance and cherished with gratitude. The digital Fashion Weeks were better than no Fashion Weeks at all, but as an upgrade on the real thing? Nah. Like everyone, I missed the shows in the experiential sense this season. But for the first time since I began covering the collections several years ago, I didn’t miss a single brand or designer’s contribution to Paris Fashion Week. Which is to say, thanks to the Fédération’s online platform, I was able to watch every name on the haute couture and men’s calendars. This brand on-demand convenience not to mention being spared the logistical headaches of zigzagging across the city was pretty great. Also, everything was on time, from the films to the manner in which we filed our reviews. While efficiency can be satisfying, it’s not necessarily exciting. Ultimately, we had to accept that the focus this season wasn’t going to be the clothes but rather the brands conveying some combination of identity, process, and values. And in the absence of standardized criteria as in, showing a minimum number of looks, specifying a time range, it was interesting to observe how heterogeneous these experiments proved to be quasi–ad campaigns versus short films, conceptual or fantastical visions versus raw and documentary style. A proper kimono takes nearly an hour to put on – I’m sure most Japanese girls would much rather spend a few seconds and slip on a dress. Get survey responses from targeted consumers today.

Fourth image states: Around a decade ago, not long after he started his own label, Massimo Alba made a great mistake. A batch of shirts and T-shirts he was working on that had already been garment-dyed one color were mistakenly exposed to another. Speaking at his showroom presentation this weekend, Alba said: “It’s very interesting to me that so many good things start out as mistakes like this.” That accident was to Alba what the Chicken Choice Judy shirt moreover I will buy this mold-infected petri dish was to Alexander Fleming: a stumbled-upon eureka that led to a career-defining course of the investigation. This collection featured a series of softly tailored jackets, corduroy pants, and shorts, plus light cashmere sweaters that were hand-overdyed two, and sometimes three colors. It’s a process that led to variations in tone that included acid-trip floods of purple on purple to subtle bleeding of magenta across mustard yellow. Like most of Alba’s garments, these dyed pieces appeared at first glance conventionally prosaic. The more attention you gave them, however, the more their exceptional qualities became evident. Take a pale blue jacket, for instance, which at that first glance seemed passingly related to a surgeon’s scrubs. To the hand it was light and almost textureless in its softness: The fabric was a cotton mousseline developed for Alba by Albini. Long-sleeved, in a delicately mottled finish of washed-out sky blue, it made for an ideal mid-summer shake in pink, sleeveless, it was an impactful shirting second skin. Other interesting developments this season included a cotton pant named the Myles with acutely kinking stitched gather at knee-level on both legs and another handsome pant, baggy in white poplin, with patch pockets. A blue tropical weight jacket named the Lenny, after Bernstein, was Alba’s interpretation of a bohemian creative’s ideal piece of workwear. Collarless shirts in ripstop linen and button-up short-sleeves in terry were further finely effective coups de théâtre. Alba is a self-deprecating yet dangerous designer: Try just one carefully chosen piece and that’s it, you’re spoiled for good because nobody else quite compares. The museum in Prague where this portrait is held describes the ring on her first finger as the ring given to her at her wedding. It’s not comfortable. Maybe a lot of girls think that a see-through blouse can attract the attention of boys or they think that it will make her look much smarter. Meghan has no dress sense: no knowledge of fabrics, fit, styles that flatter, proper tailoring, Her father raised her in L.A. Enough said. Her idea of dressing for an event is “dress up” like a little girl dressing up as a princess. Shiny! Tight! Celebrity “fashion” not elegant, just flashy.

/end ID]

#infinity train #chicken choice judy #mine

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roaringup · 4 years ago

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I feel like I should record something about my first “real” day of work. I went to the office to get my equipment and had a moderately fun time going through a supply closet and picking out pens to bring home. I got an ID badge with a terrible photo on it. I typed a lot of unfamiliar passwords. I misunderstood how to use a docking station, then talked to some people who helped me solve my problem. I had a lot of conversations in which I absorbed way less of the information than people usually do in conversations. That last part made me feel a bit bad, but I feel like it has to be completely normal at the start of a job, that you’re just like: okay, I have no idea how to do anything or what anyone expects. I remember it from before.

I definitely did not “do” any “work,” which was sort of interesting because I was still doing “work things” all day—like, it was all the logistical bits of an office job (accepting invitations, thanking people for things, adjusting elements of software you’ll be using) without any of the actual function-performing.

Everyone was genuinely nice to me, actually. And my cat yelled during a meeting with my boss but her cat was on her shoulder at the time. So!

#mine

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srasamua · 6 years ago

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Using Python to recover SEO site traffic (Part three)

When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.

This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.

As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.

Let’s get started.

URL matching vs content matching

When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.

Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.

How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.

For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.

We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.

A scientist’s bottom-up approach

In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.

BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.

When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”

One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.

Hamlet’s observation and a simpler solution

For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.

I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.

I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.

Now let’s get to the fun part and get to code some machine learning code in Python!

Collecting training data

We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.

In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.

What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).

Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.

Here we load the raw data already collected.

Feature engineering

Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.

Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.

It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.

We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.

Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.

Here is what our processed data set looks like.

Adding ground truth labels

As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.

We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:

Model training and grid search

Finally, the good stuff!

All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.

We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!

Evaluating performance

After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.

In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.

When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.

Here is the code to put together the confusion matrix:

Finally, here is the code to plot the model evaluation:

Resources to learn more

You might be thinking that this is a lot of work to just tell page groups, and you are right!

Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!

I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.

If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:

Attend a Pydata event I got motivated to learn data science after attending the event they host in New York.

Hands-On Introduction To Scikit-learn (sklearn)

Scikit Learn Cheat Sheet

Efficiently Searching Optimal Tuning Parameters

If you are starting from scratch and want to learn fast, I’ve heard good things about Data Camp.

Got any tips or queries? Share it in the comments.

Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter @hamletbatista.

The post Using Python to recover SEO site traffic (Part three) appeared first on Search Engine Watch.

from Digtal Marketing News https://searchenginewatch.com/2019/04/17/using-python-to-recover-seo-site-traffic-part-three/

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afriendlyirin · 6 years ago

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Sympathetic villains and redemption arcs have become something of a hot-button issue in fandom in recent years. In light of this, I think it's useful to examine the way Last Scenario handles Castor, and how, in my opinion, it does this well.

I think there are three major components that make Castor's redemption work:

1. There is a clear path to redemption. All of Castor's evil can be traced back to a single chain of reasoning: he wants personal security, he believes the only way to obtain this is through power, and in turn believes that the only way for him to gain the power he desires is by sowing war and death. That is the only reason he does evil. He is not simply sadistic; he does not hurt people for no reason. Nor is his evil tied to multiple murky, interconnected motivations that are difficult to disentangle. It's very clear-cut: if you can convince him his (fundamentally irrational) plan is unnecessary, he will stop hurting people. He has no other reason to do evil.

This setup makes it very easy to imagine what a redemption can look like for him. It allows the heroes to have realistic expectations about saving him and a clear plan of action through which they can do so, making it reasonable for them to attempt it when just shooting him would solve the problem more expediently.

Without this component, a redemption arc is hard to pull off logistically. Even if you can picture a concrete and easy way to redeem the villain, if you can't convey that to the audience, they stand a good chance of weighing the options and deciding the villain isn't worth the effort. Saving the villain necessarily means placing their needs over their victims', even if only temporarily. The easier and clearer that option is, the more justifiable it becomes.

2. There is something worth saving. We see repeatedly that Castor is a genuinely good person. The very first time he meets Hilbert face-to-face is when he is personally protecting civilians from the fallout of the war, cleaning up after his own mess to minimize the harm to innocent bystanders. He is overwhelmed with guilt at the knowledge Helio died because of him, and his soldiers repeatedly demonstrate outstanding loyalty to him. By the end, he claims his motivation is to make a world where no one will ever have to suffer like he did again, and I think he genuinely believes it. He genuinely believes he is doing the right thing and that the ends justify the means.

This makes Castor's story a tragedy. He is a good person who was made to do evil by the cruelties of the world. We are shown evidence that good person is still in there, and that makes us believe he is worth saving.

Without this component, a redemption arc is hard to pull of emotionally. As mentioned in the first point, redemption takes a lot of work, and the heroes' resources are not infinite. If the heroes are taking this path, there is an implicit question of why. That question must be adequately answered not just by the characters, but by the narrative. If the audience isn't emotionally on the same page as your heroes, their attempt to save the villain is going to fall flat. "Why don't ya just shoot him?" is a question to ask the heroes, not just the villains.

(I think it's worth noting here, because this is a pitfall I've seen a lot of authors fall into, that just making a villain sympathetic isn't enough. Suffering doesn't automatically make you a good person. You can be as sad as you want, if you're hurting people without remorse you're still a bad person and have to be stopped for your victims' safety. Remember that "I suffered; why shouldn't they?" is a response to suffering as well, but it's not a good one. Castor, despite getting it twisted, still responded to his trauma with "No one should suffer like I suffered," and this is clear in his actions.)

Which leads us nicely into…

3. We can see ourselves in the villain. Castor is, to one degree or another, relatable. Even if you have not suffered comparable trauma, his motivation is understandable. We want to feel safe, we want to feel important, we want to be self-sufficient, we want to be independent. And there's one part of him that will definitely resonate with gamers in particular: we want power. Castor takes these normal urges and takes them to a horrifying extreme, holding up a dark mirror to our own desires. Through Castor, we see how easy it is to slip. We could be him.

This makes his redemption personal for the audience. He is an id demon; we see our darkest flaws in him. By redeeming him, we therefore redeem ourselves. That, more than anything, is going to make the audience root for the heroes to save him.

I'd say this element is the most optional, but also the most significant. This is what will make your narrative stick with people, and what will make them empathize with the villain even if you can't pull off a logical or emotional component.

And a related but orthogonal topic, I think, is how privilege and power dynamics factor into your villain. If the villain is just evil because they don't see lower classes/other races as people, that's… going to put up some pretty big barriers to all three components. Unlearning prejudice is extremely difficult and takes a lifetime of work; doing it with a single dramatic speech is going to feel tacky and unrealistic. The place they're coming from isn't going to be universally relatable, and is almost certainly going to beg the question of what about them is even worth saving. Notice how Augustus is much more sympathetic and liked than Helga, despite both of them causing pretty much equal amounts of harm and having similarly selfish motivations. Us peasants can see ourselves in Augustus and the way he had to work for every bit of power he had, even if he ultimately abused it for his own ends; not so much with Helga. It's not impossible to redeem a villain of this type, but it will be an uphill battle.

Altogether, the most important takeaway is that you should make the audience want to save the villain. That's what all these components do. There is nothing more hated than artificially imposing a moral stance on a narrative where it doesn't fit. If you think a villain is worth saving, show us why. When the villain says, "Help me," you should want the audience to say Yes.

Feel free to mention additional case studies in the comments.

#redemption arcs #writing #fandom #steven universe #last scenario #my posts #meta #last scenario meta

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goodnewsjamaica · 6 years ago

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19-Yr-Old Applies Facial Recognition AI In Improving Fraud Detection

New Post has been published on https://goodnewsjamaica.com/news/19-yr-old-applies-facial-recognition-ai-in-improving-fraud-detection/

19-Yr-Old Applies Facial Recognition AI In Improving Fraud Detection

Artificial Intelligence and the economy feature machine-learning computer models in Jamaica. These models are computer algorithms, or smart apps that seek to give computers the ability to learn, like children, to do a variety of tasks. Here, we highlight how an author’s work may solve a particular set of real-world tasks or problems. By doing this, we aim to encourage more local research and development in artificial intelligence.

Today, we will highlight machine learning applied to automatic facial recognition and improving fraud detection. This is work being done by Leon Wright, a 19-year-old Jamaican artificial intelligence researcher and programmer from Ctrl-IT Inc. Intriguingly, Wright is one of the brightest, most resourceful and reliable employees at Ctrl-ITInc, but he is yet to complete his university degree.

Bennett: What is the most significant thing you’ve used machine learning to do at your company?

Wright: Among a few projects, I worked to build an account-opening application for a local financial institution. It’s an application where a person would open an account at the institution and the staff would take out a tablet and ask for the person’s identification, then scan the person’s ID. Our algorithm would then grab the TRN from the ID, locating the face in the ID. If the picture on the identification card is good enough in quality, it is stored as a part of a database of face-images as an image we can later compare to the face on the person’s identification card.

So, we then compare the image on the ID with a selfie the person takes. The selfie would be used as a way to ask our learning algorithm, if the selfie matched the face on the ID. The algorithm would have gained the ability to detect the person the next time the person came in to the institution, through the camera there. In this way, when the person next comes in with his or her identification card, our algorithm would try to take an image of the person from the institution’s camera or a selfie, and try to match it with data belonging to users that had already signed up. Thus, we’re able to quickly verify if the identification card the user brings in, indeed belongs to the correct user, instead of perhaps some impersonator. In this way, we’ve sensibly applied machine learning to build towards a type of fraud prevention when it comes to quickly verifying peoples’ identities.

We’re still working to roll out more products that concern more ways to prevent fraud. For example, we’ve already composed a video-based application in relation to call centres. This application is equipped with facial-detection algorithms like the one I discussed above, that would enable a similar level of security against fraud, where the person that calls in would likely not be able to fake [his/her] identity, given that we would have had the correct person’s face on file, and given that our algorithm would be able to quickly return whether the person calling was actually a person in our database, or really, it would detect if that person was who he/she claimed to be. This is an added layer of security or verification, where we would facilitate video calls so that callers could be seen and verified with our learning algorithms.

What type of learning algorithm did you use, for example, did you use a convolutional neural network, or something else? Also, remind us why we don’t need to ‘re-create the wheel’ when it comes to applying these machine learning models.

We essentially used a class of learning algorithms called convolutional neural networks (CNNs). Convolutional neural networks are loosely inspired by actual brains. We used a library called TensorFlow, that already has the CNNs packaged as models. These models are flexible, and we adjust the TensorFlow CNN representations to our particular needs. With these models, we don’t need to start from scratch, as the models that comprise thousands of lines of computer code, are already composed by PHDs in the field through Google, and released in the form of TensorFlow libraries we can utilise with few lines of computer code.

Tell us a little more about the convolutional neural network, such as what it is, what goes on in your application of the CNN, how many layers you used, etc.

CNNs are basically a type of mathematical sequence of operations called convolutions that form artificial layers of calculations. CNNs enable us to compose learning algorithms that do well on machine-learning tasks involving processing images.

The model is moderately large, with 132 layers of computation. CNNs can be trained in a way that the model will learn how to do things like detect faces. We trained the CNN by feeding to it labelled images of faces that belong to persons from the financial institution. We employed something called a triplet loss that enables us to match faces to persons. We ‘query’ the CNN, asking it if it thinks it’s seeing a particular person’s face. (Like, say, when somebody walks in and we capture their face on camera, and we want to see if he/she is in the database.) When the query happens, the CNN outputs an array or collection of values that represent each face.

Each collection or values that represent an object, such as a face, is called an embedding in machine learning. Embeddings of persons’ faces are generated by the CNN, and we store those somewhere for later use. When a person comes into the financial institution, we take the input picture or selfie and ask the CNN if the person exists in the database. The query happens when the camera image of the person is taken and passed through the CNN’s structure of artificial neurons and synapses. A new embedding is made that represents the face of the person that just walked in. We then compare the new embedding to prior embeddings generated at sign-up time. When we do this, we calculate the distance between a database record belonging to a person, and a person’s camera image taken when he/she walks in. Close distances signify that the camera and database image pairs likely belong to the same person, where a decision is made based on a predefined threshold that represents whether the faces match or not.

PROBLEMS FACED

Any problems with the machine-learning, face-detection model you guys would like to improve?

There are problems with facial recognition. For example, there is an employee here named Varij, who currently wears a big beard. In most of his pictures, he’s not wearing any beard and his face appears skinnier. So, his face almost looks completely different than it does in his pictures. So there was a quite high error rate when it came to trying to match his current face to the face pictures of him we had on file. In this type of problem, the two things we’re trying to match, although pertaining to some singular object, may be so different that it causes errors. In our problem scenario above, the distance for Varij was quite high, and that’s difficult to solve without more representative images of his face in the database. This type of distance algorithm is good enough most of the times for scenarios when data is lacking.

What methods could be used to improve the learning algorithms in paper?

We could work to improve how much data we can feed the algorithm. The more data we have, the more opportunities that the algorithm may get to train on.

Tell us briefly about some societal impacts of your application?

Our algorithms can help to reduce a lot of fraud, and crimes.

What types of smart apps or machine learning models do you plan to work on soon?

I plan on continuing my work on facial recognition while improving the accuracy of my current algorithms. I also plan on using natural language processing and sentiment analysis to aid me in building my very own stocks and cryptocurrency platform. Also, I plan on using machine learning in route planning in a logistics application I am conceptualising at this time.

I’m looking forward to collaborating with you on machine-learning projects.

Next week, we will highlight more Jamaican persons applying machine learning.

– Jordan Micah Bennett is inventor of the Supersymmetric Artificial Neural Network and author of ‘Artificial Neural Networks for Kids’. Send feedback to [email protected], or [email protected].

By: Jordan Micah Bennett

Original Article Found Here

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milovanovic · 7 years ago

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What is Cognitive Computing?

Most probably anyone who is even remotely aware of the nature of contemporary Data Science landscape will recognize the truth of the following two statements: (a) Data Wrangling is necessary with almost every new project, and (b) Data Wrangling is difficult and tedious. Following all the investment and enthusiasm that you have put in your education until now (that long, challenging but painful road to becoming a Data Scientist…), could it be possible that data cleaning, formatting, fusing, and restructuring is taking as much as 80% of your working hours? Wasn’t it all meant to be about statistical modeling, graphs, and applied artificial intelligence? Of all those beautiful R and Python packages that you have studied, is it true that {dplyr}, {tidyr}, and pandas are your best friends at the end of the day? No. You did not sign up for this job.

This blog, from a Data Scientist, to a Data Scientist; it might help rediscover the initial beauty that you thought will be always be inherent to the landscape. I want to ask the following: why is Data Wrangling so difficult and time consuming? With all the automation that we write to let our computers do the job by themselves, what is specifically difficult in Data Wrangling operations that prevents us from automating them too? Here’s a working example: say, we have a number of tables in some RDBS, a lot of data distributed across them, and we want to build a discrete choice model (say, a multinomial logistic regression) on these data. However, not all data in our database are useful. Moreover, not all data make sense to build such a model from. Imagine that we have some transactional, timestamped data there: well, not a millisecond resolution will be exactly what we’re looking for, but most probably something like day, week, or month of transaction could play a role of a predictor (strsplit()… enjoy). We need the data from the last two years, so select … where 2015, 2016, 2017. Assume we are mixing categorical and continuous predictors: for example, a primary key (ID int NOT NULL AUTO_INCREMENT) would do well as a categorical predictor? Not really. This is easy: tell R not to select the keys. Good. Consumer age and gender would do if and when available, right. Wait, what’s this: “… the column contains any of the following: 0, 2, 6, 7, referring to type of most frequent item type purchase made before automatic user re-registration in 2012; not in use since…” - no, no, we don’t want this... You know the drill.

Why is automatic data wrangling so difficult?

Can’t we expect from our super-smart algorithms to infer automatically this type of common knowledge and expectations among data analysts upon being given a command of a type: build me a multinomial regression model from Y as criterion and select all meaningful data as predictors; iterate model selection until best model is selected? It turns out that what is solved by mere project specification and some bare intuition in your mind – before it starts taking long hours of coding - presents a rather difficult riddle when posed as computational, algorithmic problem. Why is that so?

Let’s assume that we want to solve the problem by imposing a set of formal constraints upon the eligible data types that can enter the model. In R, continuous predictors would fall under the double type, however, sometimes integer needs to be treated as continuous in regression; character, factor, and integer would do as categorical predictors; in a discrete model, the dependent is always categorical. This is extremely easy to automate, but it would help only for the datasets where the variable semantics are all set; in other words, having the problem of letting the algorithm decide what variables do and what do not make sense as predictors is what is really makes the automation of Data Wrangling difficult. Obviously, we would need to build a semantic model, a structured knowledge repository that would be addressed by our automation of Data Wrangling in order to inspect all variable names and descriptions and see which of them match some predefined schema: the schema that defines what is allowable and what not in building a particular statistical model. Our task would then be to define the binding of all columns from our SQL tables to a set of abstract variables from our semantic model to perform the appropriate selection, and then easily build a statistical model in the desired programming language. We can probably solve this kind of Data Wrangling automation for a more or less wide class of Data Science projects; but can we solve the general case that would do for any given relational database and a wide class of statistical models? We are now well aware of the scope of the problem: its solution would almost be a true artificial intelligence. That fact is what takes up to 80% of your daily work routine.

The meaning of cognitive computing

I was motivated to write-up this short summary of the Data Wrangling automation problem a long time ago, maybe because my background as a cognitive psychologist makes me think about similar problems in the cognitive ergonomics of computer programming more often then it sparks the imagination of my colleagues with a background in software engineering and similar. But the motivation for this very blog post came as a consequence of reading some recent discussions on how to define cognitive computing properly. What is cognitive computing? On one hand, we are being told that it is essentially programming computers to perform cognitive operations in a way similar to what the natural minds do. But, at least half of the typical Data Scientist’s toolkit comes from there: people with background in cognitive sciences would be able to list a dozen of fundamental research areas that have spawned mathematical models used by Data Scientist nowadays, but that were initially developed in order to understand the workings of the human mind. On the other hand, sometimes the explanation of what cognitive computing is seems to be tightly related to the aspects of UX/UI design: cognitive computing means computers being able to react adaptively to our natural language or motor inputs and manage their outputs so to much our original intentions. I guess the automation of Data Wrangling as I have discussed it falls close to this second connotation. I have sometimes encountered that cognitive computing is not the same as AI because the former is of a probabilistic nature while the later is not, which is really true only if you put an equality sign between AI and the old classic AI research program based on the idea of rule-guided behavior (cognitive psychologists have started writing about the “probabilistic turn” in the study of human cognition more than ten years ago, not to mention the study of probabilistic causal networks that has it roots back in the 80s). The whole contemporary discourse of cognitive computing is obviously motivated by some recent developments that have created the need to redefine the meaning of the term, but the redefinition in itself seems to be taking too much time and struggle with fine-grained distinctions from similar terms; it seems to be so edgy that even rumors on cognitive computing being just another marketing hype started appearing.

A view from the perspective of cognitive ergonomics

Well, one take home message is that cognitive computing is certainly not a marketing hype in itself; as I have tried to illustrate above on the example of Data Wrangling automation problem, the problems it may address are real and many would benefit from their solution. A realistic research and development program (semantic modeling + you-name-it-probabilistic-learning-approach) is available to address a more or less wide classes of typical problems of the similar type, and the application of such programs is well under way. The most likely source of too much uncertainty in the discussion on what computing is and what it is not is probably the natural relation of this term to the possibility of obtaining general solutions for wide classes of problems similar to my illustration. Then, we should maybe start making a distinction between cognitive computing in a general and a narrow sense: the former addressing the typical fundamental questions of AI research (irrespective of whether the specific approach under discussion is deterministic or probabilistic), and the later reserved for cognitive applications that solve a constrained class of problems that prevent the user to interact with the computer in cognitively ergonomic way.

Another, final remark, to keep us in line with the nature of problem as exemplified in the beginning of this post. In a similar way that we have tried to discover why Data Wrangling is difficult, we could ask and try to understand why coding in general is hard. Every cognitive psychologist can testify that the human mind does not exhibit too much of a preference for abstraction. In our everyday lives, we rarely organize our thinking around general categories and abstract concepts. In our natural, intuitive mental operations, we look for a glass of water, we desire a conversation with a close friend, we like to have a bite of an apple; we are not chaining large numbers of formal inferences leading from the abstract goal of “being fine”, across many categories that define all that is encompassed by that general state, in order to reach to particulars like “a glass of water”, “a friend nearby”, or “an apple”. Quite contrary, these – so called “basic level categories” - seem to be readily accessible to our minds, and immediately active when a particular goal needs to be met and a particular action performed. But digital computers work exactly the other way around: on the very deep level of their operation, they derive everything by accessing the more general properties and then inferring the particular results. You can thus think of indexing in SQL as a rudimentary step in cognitive engineering: it organizes a data structure in such a way to be able meet the most probable intentions of the user who need to fetch the data from it. You can also think of developing a general semantic model for Data Wrangling in the same way: it needs to adaptively engineer a data structure in a way that matches the intended statistical modeling. From this perspective, cognitive computing can be understood as really spanning across its general and narrow senses: connecting the (a) need to develop adaptive computing that reflects the semantic understanding of (narrowed down) human needs with (b) the (general) algorithmic means of accomplishing that goal (by mimicking the operations of the human mind that it needs to adapt to).

Goran S. Milovanović, PhD Data Science Consultant, SmartCat

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noisyunknownturtle · 5 years ago

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The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress

The Blockchain Africa conference produced a swathe of optimism for Africa to become a driving force behind the development and use of new blockchain-powered technologies.

Over the past few years, blockchain has replaced cryptocurrency as the “it” word in the fintech space. It is a fact that was mirrored by the Blockchain Africa conference itself, with speakers focusing more on the possibilities of blockchain answering a number of industry inefficiencies, and far less on cryptocurrency trading and tokenized solutions.

Africa has its own unique challenges in the global space given that many of its countries are trailing behind the rest of the world in infrastructure development. While the asymmetric digital subscriber lines and fiber internet connectivity is still being rolled out in many countries, mobile tower services have driven the proliferation of mobile payments systems.

To say that Africans have taken to these services would be an understatement. The M-Pesa mobile payment service is a prime example that shows Africans can quickly adopt technologies that improve their day-to-day lives. Mobile network provider Vodafone estimates that over 37 million people across seven African countries currently use M-Pesa, which was launched in 2007.

This is just one example of how people in Africa have benefitted from a future-forward solution to build a bridge to the people that are unbanked on the continent. In general, fintech solutions are being readily adopted and driven by African countries and companies. As Cointelegraph reported in an event recap of the Blockchain Africa conference, blockchain technology is already being explored by trade finance, supply chain and self-sovereign identity sectors. Here are the main use cases that can be observed right now:

A solution for Africa’s ID problems

The issue of Self-Sovereign Identity is a particularly interesting one in an African context, given the difficulty many people on the continent face when trying to obtain ID documentation. By way of definition, SSI refers to a situation where individuals hold and control their own identification credentials.

Victor Mapunda, CEO and founder of startup FlexFinTx, made a compelling case for a move to digital-based identities at the Blockchain Africa conference. In his presentation, data quoted by Mapunda estimates that nearly 400 million Africans do not have proper identification credentials. This then leads to a multitude of difficulties, as these people are unable to open bank accounts, apply for insurance or other financial products.

Related: Blockchain Digital ID — Putting People in Control of Their Data

Being banked and having insurance is a luxury when considering the deeper problems that are plaguing the continent. Referring to information supplied by the Mo Ibrahim Foundation, only eight African countries have birth registration systems that cover 90% of the population.

Countries like Chad and Tanzania are only able to cover 12% of births in the country. Conversely, Egypt, Mauritius and Seychelles are the only three African countries that register deaths covering more than 90% of their population.

The key takeaway is that there is a sizable gap in providing Africans with vital identification documentation, which is primarily due to institutional inefficiencies. Data capturing and information sharing is therefore impacted, leaving various institutions lacking in information, unable to serve the public needs efficiently.

Mapunda hails from Zimbabwe and began exploring the issue of SSI when he faced his own difficulties in trying to register a bank account after studying abroad. FlexFinTx seeks to provide people with a digital ID through WhatsApp, which facilitates the issuance of a FlexID that is cryptographically secured by the Algorand blockchain. Users then have self-sovereign control over how their data is shared. Speaking to Cointelegraph after his presentation, Mapunda said that African people can quickly take to solutions that solve wide-ranging problems:

“I think Africans, when it comes to adoption of technology, are some of the most dynamic people in the world, this is because, for the most part, we don’t have a lot of legacy infrastructure and institutions. Most of the things we’ve grown up with didn’t work.”

Mapunda pointed to innovations such as mobile money and internet-based communication applications drastically improving Africans’ quality of life, saying, “Mobile money is a great example. We jumped on it,” and adding that no one even had to market it to the population. He went on to expand further:

“WhatsApp is a very good example of an application that didn’t have a single billboard, yet it managed to spread like wildfire across Africa. It solved a major problem — the cost of communication was too expensive and it’s a natural solution that people gravitate to.”

An answer to supply chain challenges

Blockchain technology has long been touted as a key tool in improving current supply chain systems across the world. In the past three years, major strides have been conducted in this regard, providing real use cases to back up the theory. The subject was covered extensively at the Blockchain Africa conference and was particularly important considering the implementation of the African Continental Free Trade Area in May last year.

The move created a free-trade area that now includes 28 African countries, which requires member states to remove tariffs to provide the free trade of goods and services. While it improves the ease of trade, there are still some hurdles to clear in the trade finance and supply chain.

Thavash Govender, a data and AI specialist at Microsoft South Africa, spoke to Cointelegraph during the summit and said that blockchain technology could hold a number of benefits for trade across the continent:

“The one challenge that we have at the moment is trust between different countries. If I’m going to drop my trade barriers and say you can bring all your products into my country, I need to know that we aren’t allowing counterfeit goods in.”

Perhaps more importantly, Govender suggested that systems that are improved through the use of blockchain technology could drastically reduce the amount of time it takes for trade to take place due to inefficiencies in various processes, elaborating:

“If I’m an SME, I’m going to open up to a whole bunch of institutions that I just don’t know. If we’re all part of the same blockchain consortium, then I know I can trust what is written on the chain. Because I can trust the information, I can move a lot quicker. It’s not going to take me weeks of investigation, so I can grant loans quicker or get the trade finance process going a lot faster.”

Public procurement and corruption

Another interesting implementation of blockchain technology is in the space of public procurement by government organizations. Corruption is not a uniquely African problem, but it is one that affects many countries on the continent. Sope Williams-Elegbe, a professor and deputy director of the African Procurement Law Unit at Stellenbosch University, gave a presentation on the possibilities of blockchain addressing corruption in public procurement.

Related: Zimbabwe U-Turns on Crypto, Looking to Stabilize Local Economy

Williams-Elegbe said that 15%–22% of South Africa’s gross domestic product goes to public procurement. The problem is that the country loses 50% of this to corruption and fraud.

The professor believes that blockchain could be used to address procurement corruption but admitted that there are few to no use cases as of now. There is a lack of technical applications for public procurement, and it presents an opportunity for new solutions.

Forget the hype, build on working tech

Michelle Nsanzumuco, who acts as a senior advisor to the government of Bermuda and the Africa lead for Fintech4Good, spoke about a number of the sectors described above as being potential drivers of blockchain technology.

In an interview with Cointelegraph, Nsanzumuco highlighted supply chain and logistics as the key industry that can leverage blockchain due to the complexities of trade created by the sheer number of players in a value chain. Nsanzumuco said that a number of entrepreneurs and SMEs that she has interacted with often complained about the difficulties they face when conducting trade inside their own country:

“They’re finding barriers just within their own countries because they’re dealing with so many different players, fill in so much documentation before they can even get their products from A to B. Now we haven’t even talked about cross-border transactions and trade. I can see it being a very strong use case for Africa specifically around supply chain and health care.”

Nsanzumuco added that blockchain solutions could improve the way health care systems track vaccinations and medications. Another factor is improving government services by digitizing a variety of manual data-capturing processes. Additionally, while strongly agreeing that the continent could be a leader in the blockchain space, Nsanzumuco cautioned against touting “blockchain” tech because of its marketability:

“A big warning for me having traveled around the world is not getting caught up in the hype. Let’s leverage real solutions in particular sectors where it can have an impact in Africa.”

The post The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress appeared first on For Crypto.

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angryconnoisseurface · 5 years ago

Text

The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress

The Blockchain Africa conference produced a swathe of optimism for Africa to become a driving force behind the development and use of new blockchain-powered technologies.

A solution for Africa’s ID problems

Related: Blockchain Digital ID — Putting People in Control of Their Data

An answer to supply chain challenges

Public procurement and corruption

Related: Zimbabwe U-Turns on Crypto, Looking to Stabilize Local Economy

Williams-Elegbe said that 15%–22% of South Africa’s gross domestic product goes to public procurement. The problem is that the country loses 50% of this to corruption and fraud.

Forget the hype, build on working tech

“A big warning for me having traveled around the world is not getting caught up in the hype. Let’s leverage real solutions in particular sectors where it can have an impact in Africa.”

The post The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress appeared first on Tip Crypto.

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coinfirst · 5 years ago

Text

The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress

The Blockchain Africa conference produced a swathe of optimism for Africa to become a driving force behind the development and use of new blockchain-powered technologies.

A solution for Africa’s ID problems

Related: Blockchain Digital ID — Putting People in Control of Their Data

An answer to supply chain challenges

Public procurement and corruption

Related: Zimbabwe U-Turns on Crypto, Looking to Stabilize Local Economy

Williams-Elegbe said that 15%–22% of South Africa’s gross domestic product goes to public procurement. The problem is that the country loses 50% of this to corruption and fraud.

Forget the hype, build on working tech

“A big warning for me having traveled around the world is not getting caught up in the hype. Let’s leverage real solutions in particular sectors where it can have an impact in Africa.”

The post The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress appeared first on Coin First.

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cryptowavesxyz · 5 years ago

Text

The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress

The Blockchain Africa conference produced a swathe of optimism for Africa to become a driving force behind the development and use of new blockchain-powered technologies.

A solution for Africa’s ID problems

Related: Blockchain Digital ID — Putting People in Control of Their Data

An answer to supply chain challenges

Public procurement and corruption

Related: Zimbabwe U-Turns on Crypto, Looking to Stabilize Local Economy

Williams-Elegbe said that 15%–22% of South Africa’s gross domestic product goes to public procurement. The problem is that the country loses 50% of this to corruption and fraud.

Forget the hype, build on working tech

“A big warning for me having traveled around the world is not getting caught up in the hype. Let’s leverage real solutions in particular sectors where it can have an impact in Africa.”

The post The Blockchain Africa Participants Optimistic About Continent Becoming Center of Progress appeared first on Crypto Waves.

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