Author: Jo Brodie

https://twitter.com/JoBrodie same on BlueSky and other pages. Old photo!

3D models in motion

by Paul Curzon, Queen Mary University of London
based on a 2016 talk by Lourdes Agapito

Wireframe mesh image by Gerd Altmann from Pixabay

The cave paintings in Lascaux, France are early examples of human culture from 15,000 BC. There are images of running animals and even primitive stop motion sequences – a single animal painted over and over as it moves. Even then, humans were intrigued with the idea of capturing the world in motion! Computer scientist Lourdes Agapito is also captivated by moving images. She is investigating whether it’s possible to create algorithms that allow machines to make sense of the moving world around them just like we do. Over the last 10 years her team have shown, rather spectacularly, that the answer is yes.

People have been working on this problem for years, not least because the techniques are behind the amazing realism of CGI characters in blockbuster movies. When we see the world, somehow our brain turns all that information about colour and intensity of light hitting our eyes into a scene we make sense of – we can pick out different objects and tell which are in front and which behind, for example. In the 1950s psychophysics* researcher Gunnar Johansson showed how our brain does this. He dressed people in black with lightbulbs fastened around their bodies. He then filmed them walking, cycling, doing press-ups, climbing a ladder, all in the dark … with only the lightbulbs visible. He found that people watching the films could still tell exactly what they were seeing, despite the limited information. They could even tell apart two people dancing together, including who was in front and who behind. This showed that we can reconstruct 3D objects from even the most limited of 2D information when it involves motion. We can keep track of a knee, and see it as the same point as it moves around. It also shows that we use lots of ‘prior’ information – knowledge of how the world works – to fill in the gaps.

Shortcuts

Film-makers already create 3D versions of actors, but they use shortcuts. The first shortcut makes it easier to track specific points on an actor over time. You fix highly visible stickers (equivalent to Johansson’s light bulbs) all over the actor. These give the algorithms clear points to track. This is a bit of a pain for the actors, though. It also could never be used to make sense of random YouTube or CCTV footage, or whatever a robot is looking at.

The second shortcut is to surround the action with cameras so it’s seen from lots of angles. That makes it easier to track motion in 3D space, by linking up the points. Again this is fine for a movie set, but in other situations it’s impractical.

A third shortcut is to create a computer model of an object in advance. If you are going to be filming an elephant, then hand-create a 3D model of a generic elephant first, giving the algorithms something to match. Need to track a banana? Then create a model of a banana instead. This is fine when you have time to create models for anything you might want your computer to spot.

It is all possible for big budget film studios, if a bit inconvenient, but it’s totally impractical anywhere else.

No Shortcuts

Lourdes took on a bigger challenge than the film industry. She decided to do it without the shortcuts: to create moving 3D models from single cameras, applied to any traditional 2D footage, with no pre-placed stickers or fixed models created in advance.

When she started, a dozen or so years ago, making any progress looked incredibly difficult. Now she has largely solved the problem. Her team’s algorithms are even close to doing it all in real time, so making sense of the world as it happens, just like us. They are able to make really accurate models down to details like the subtle movements of their face as a person talks and changes expression.

There are several secrets to their success, but Johansson’s revelation that we rely on prior knowledge is key. One of the first breakthroughs was to come up with ways that individual points in the scene like the tip of a person’s nose could be tracked from one frame of video to the next. Doing this well relies on making good use of prior information about the world. For example, points on a surface are usually well-behaved in that they move together. That can be used to guess where a point might be in the next frame, given where others are.

The next challenge was to reconstruct all the pixels rather than just a few easy to identify points like the tip of a nose. This takes more processing power but can be done by lots of processors working on different parts of the problem. Key to this was to take account of the smoothness of objects. Essentially a virtual fine 3D mesh is stuck over the object – like a mask over a face – and the mesh is tracked. You can then even stick new stuff on top of the mesh so they move together – adding a moustache, or painting the face with a flag, for example, in a way that changes naturally in the video as the face moves.

Once this could all be done, if slowly, the challenge was to increase the speed and accuracy. Using the right prior information was again what mattered. For example, rather than assuming points have constant brightness, taking account of the fact that brightness changes, especially on flexible things like mouths, mattered. Other innovations were to split off the effect of colour from light and shade.

There is lots more to do, but already the moving 3D models created from YouTube videos are very realistic, and being processed almost as they happen. This opens up amazing opportunities for robots; augmented reality that mixes reality with the virtual world; games, telemedicine; security applications, and lots more. It’s all been done a little at a time, taking an impossible-seeming problem and instead of tackling it all at once, solving simpler versions. All the small improvements, combined with using the right information about how the world works, have built over the years into something really special.

*psychophysics is the “subfield of psychology devoted to the study of physical stimuli and their interaction with sensory systems.”

This article was first published on the original CS4FN website and a copy appears on pages 14 and 15 in “The women are (still) here”, the 23rd issue of the CS4FN magazine. You can download a free PDF copy by clicking on the magazine’s cover below, along with all of our free material.

Another article on 3D research is Making sense of squishiness – 3D modelling the natural world (21 November 2022).

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EPSRC supports this blog through research grant EP/W033615/1.

Frequency Analysis for Fun

Scrabble letters
Scrabble letters image by christopher Walkey from Pixabay

Frequency Analysis, a technique beloved by spies for centuries, and that led to the execution of at least one Queen, also played a part in the development of the game Scrabble, over a hundred million copies of which have been sold worldwide.

Frequency Analysis was invented by Al-Kindi, a 9th Century Muslim, Arabic Scholar, as a way of cracking codes. He originally described it in his “A Manuscript on Deciphering Cryptographic Messages“. Frequency analysis just involves taking a large amount of normal text written in the language of interest and counting how often each letter appears. For example in English, the letter E is the most common. With simple kinds of ciphers that is enough information to be able to crack them, just by counting the frequency of the letters in the code you want to crack. Now large numbers of everyday people do frequency analysis just for fun, solving Cross Reference puzzles.

The link between frequency analysis and puzzles goes back earlier. When the British were looking for potential code breakers to staff their secret code breaking establishment at Bletchley Park in World War II, they needed people with frequency analysis like skills and problem solving skills. They did this by setting up Crossword competitions and offering those who were fastest jobs at Bletchley: possibly the earliest talent competition with career changing prizes!

Earlier still, in the 1930s, Architect Alfred Mosher Butts, hit on the idea of a new game that combined crosswords and anagrams, which were both popular at the time. The result was Scrabble. However, when designing the game he had a problem in that he needed to decide how many of each letter the game should have and also how to assign the scores. He turned to frequency analysis of the front page of the New York Times to give the answers. He also did it an easy way – looking at how many of each letter the printers had – the more they had meant the more often the same letters were needed at once. He broke the pattern of his frequency analysis though, including fewer letter Ss (the second most common word in English) than there should be so the game wasn’t made too easy because of plurals.

Sherlock Holmes, of course, was a master of frequency analysis as described in the 1903 story “The Adventure of the Dancing Men”. Sir Arthur Conan Doyle wasn’t the first author to use it as a plot device though. Edgar Alan Poe had based a short story called “The Gold Bug” around frequency analysis in 1843. It was Poe who originally popularised frequency analysis with the general public rather than just with spymasters. Poe had discovered how popular the topic was as a result of having set a challenge in a magazine for people to send in ciphers – that he would then crack, giving the impression at the time that he had near supernatural powers. The way it was done was then described in detail in “The Gold Bug”.

Paul Curzon, Queen Mary University of London

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For younger kids we have some fun free kriss-kross puzzles – they’re like crosswords but you’re given the words and you have to fit them into the crossword shape. You need to think like a computer scientist and use logical thinking, pattern matching and computational thinking to complete them. (For even younger kids these can also be used as a way of practising spelling, phonics and writing out words).

EPSRC supports this blog through research grant EP/W033615/1.

Keeping secrets on the Internet – encryption keeps your data safe

Padlock over encrypted text
Computer encryption padlock image by OpenClipart-Vectors from Pixabay

How do modern codes keep your data safe online? Ben Stephenson of the University of Calgary explains

When Alan Turing was breaking codes, the world was a pretty dangerous place. Turing’s work helped uncover secrets about air raids, submarine locations and desert attacks. Daily life might be safer now, but there are still threats out there. You’ve probably heard about the dangers that lurk online – scams, identity theft, viruses and malware, among many others. Shady characters want to know your secrets, and we need ways of keeping them safe and secure to make the Internet work. How is it possible that a network with so many threats can also be used to securely communicate a credit card number, allowing you to buy everything from songs to holidays online?

The relay race on the Internet

When data travels over the Internet it is passed from computer to computer, much like a baton is passed from runner to runner in a relay race. In a relay race, you know who the other runners will be. The runners train together as a team, and they trust each other. On the Internet, you really don’t know much about the computers that will be handling your data. Some may be owned by companies that you trust, but others may be owned by companies you have never heard of. Would you trust your credit card number to a company that you didn’t even know existed?

The way we solve this problem is by using encryption to disguise the data with a code. Encrypting data makes it meaningless to others, so it is safe to transfer the data over the Internet. You can think of it as though each message is locked in a chest with a combination lock. If you don’t have the combination you can’t read the message. While any computer between us and the merchant can still view or copy what we send, they won’t be able to gain access to our credit card number because it is hidden by the encryption. But the company receiving the data still needs to decrypt it – open the lock. How can we give them a way to do it without risking the whole secret? If we have to send them the code a spy might intercept it and take a copy.

Keys that work one way only

The solution to our problem is to use a relatively new encryption technique known as public key cryptography. (It’s actually about 40 years old, but as the history of encryption goes back thousands of years, a technique that’s only as old as Victoria Beckham counts as new!) With this technique the code used to encrypt the message (lock the chest) is not able to decrypt it (unlock it). Similarly, the key used to decrypt the message is not able to encrypt it. This may sound a little bit odd. Most of the time when we think about locking a physical object like a door, we use the same key to lock it that we will use to unlock it later. Encryption techniques have also followed this pattern for centuries, with the same key used to encrypt and decrypt the data. However, we don’t always use the same key for encrypting (locking) and decrypting (unlocking) doors. Some doors can be locked by simply closing them, and then they are later unlocked with a key, access card, or numeric code. Trying to shut the door a second time won’t open it, and similarly, using the key or access code a second time won’t shut it. With our chest, the person we want to communicate with can send us a lock only they know the code for. We can encrypt by snapping the lock shut, but we don’t know the code to open it. Only the person who sent it can do that.

We can use a similar concept to secure electronic communications. Anyone that wants to communicate something securely creates two keys. The keys will be selected so that one can only be used for encryption (the lock), and the other can only be used for decryption (the code that opens it). The encryption key will be made publicly available – anyone that asks for it can have one of our locks. However, the decryption key will remain private, which means we don’t tell anyone the code to our lock. We will have our own public encryption key and private decryption key, and the merchant will have their own set of keys too. We use one of their locks, not ours, to send a message to them.

Turning a code into real stuff

So how do we use this technique to buy stuff? Let’s say you want to buy a book. You begin by requesting the merchant’s encryption key. The merchant is happy to give it to you since the encryption key isn’t a secret. Once you have it, you use it to encrypt your credit card number. Then you send the encrypted version of your credit card number to the merchant. Other computers listening in might know the merchant’s public encryption key, but this key won’t help them decrypt your credit card number. To do that they would need the private decryption key, which is only known to the merchant. Once your encrypted credit card number arrives at the merchant, they use the private key to decrypt it, and then charge you for the goods that you are purchasing. The merchant can then securely send a confirmation back to you by encrypting it with your public encryption key. A few days later your book turns up in the post.

This encryption technique is used many millions of times every day. You have probably used it yourself without knowing it – it is built into web browsers. You may not imagine that there are huts full of codebreakers out there, like Alan Turing seventy years ago, trying to crack the codes in your browser. But hackers do try to break in. Keeping your browsing secure is a constant battle, and vulnerabilities have to be patched up quickly once they’re discovered. You might not have to worry about air raids, but codes still play a big role behind the scenes in your daily life.

Ben Stephenson, University of Calgary

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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Composing from Compression

Recoloured Cranium head abstract image by Gordon Johnson from Pixabay

Computers compress files to save space. But it also allows them to create music!

Music is special. It’s one of the things, like language, that makes us human, separating us from animals. It’s also special as art, because it doesn’t exist as an object in the world – it depends on human memory. “But what about CDs? They’re objects in the world”, you might say and you’d be right, but the CD is not the music. The CD contains data files of numbers. Those numbers are translated by electronics into the movements in a loudspeaker, to create sound waves. Even the sound waves aren’t music! They only become music when a human hears them, because understanding music is about noticing repetition, variation and development in its structure. That’s why songs have verses and choruses: so we can find a starting point to understand its structure. In fact, we’re so good at understanding musical structure, we don’t even notice we’re doing it. What’s more, music affects us emotionally: we get excited (using the same chemicals that get us excited when we’re in love or ready to flee danger) when we hear the anthem section of a trance track, or recognise the big theme returning at the end of a symphony.

Surprisingly, brains seem to understand musical structure in a way that’s like the algorithms computer scientists use to compress data. It’s better to store data compressed than uncompressed, because it takes less storage space. We think that’s why brains do it too.

Even more surprisingly, brains also seem to be able to learn the best way to store compressed music data. Computers use bits as their basic storage unit, but we can make groups of bits work like other things (numbers, words, pictures, angry birds…); brains seem to do something similar. For example, pitch (high vs. low notes) in sequence is an important part of music: we build melodies by lining up notes of different pitch one after the other. As we learn to hear music (starting before birth, and continuing throughout life), we learn to remember pitch in ever more efficient ways, giving our compression algorithms better and better chances to compress well. And so we remember music better.

Our team use compression algorithms to understand how music works in the human mind. We have discovered that, when our programs compress music, they can sometimes predict musical structures, even if neither they nor a human have “heard” them before. To compress something, you find large sections of repeated data and replace each with a label saying “this is one of those”. It’s like labelling a book with its title: if you’ve read Lord of the Rings, when I say the title you know what I mean without me telling the story. If we do this to the internal structure of music, there are little repetitions everywhere, and the order that they appear is what makes up the music’s structure.

If we compress music, but then decompress it in a different way, we can get a new piece of music in a similar style or genre. We have evidence that human composers do that too!

What our programs are doing is learning to create new music. There’s a long way to go before they produce music you’ll want to dance to – but we’re getting there!

Geraint Wiggins, Queen Mary University of London

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Balls, beams and quantum computers – performing calculations with patterns of light

Photo credit: Galton Box by Klaus-Dieter Keller, Public Domain, via Wikimedia Commons, via the Wikipedia page for the Galton board

Have you played the seaside arcade game where shiny metal balls drops down to ping, ping off little metal pegs and settle in one of a series of channels? After you have fired lots of balls, did you notice a pattern as the silver spheres collect in the channels? A smooth glistening curve of tiny balls forming a dome, a bell curve forms. High scores are harder to get than lower ones. Francis Galton pops up again*, but this time as a fellow Victorian trend setter for future computer design.

Francis Galton invented this special combination of row after row of offset pins and narrow receiving channels to demonstrate a statistical theory called normal distribution: the bell curve. Balls are more likely to bounce their way to the centre, distributing themselves in an elegant sweep down to the left and right edges of the board. But instead of ball bearings, Galton used beans, it was called the bean machine. The point here though is that the machine does a computation – it computes the bell curve.

Skip forward 100 years and ‘Boson Samplers’, based on Galton’s bean machine, are being used to drive forward the next big thing in computer design, quantum computers.

“Stripes beams lights” image by David from Pixabay

Instead of beans or silver balls computer scientists fire photons, particles of light through minuscule channels on optical chips. These tiny bundles of energy bounce and collide to create a unique pattern, a distribution though one that a normal digital computer would find hard to calculate. By setting it up in different ways, the patterns that result can correspond to different computations. It is computing answers to different calculations set for it.

Through developing these specialised quantum circuits scientists are bouncing beams of light forwards on the path that will hopefully lead to conventional digital technology being replaced with the next generation of supercomputers.

Jane Waite, Queen Mary University of London

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*Francis Galton appears earlier in Issue 20, you can read more about him on page 15 of the PDF. Although a brilliant mathematician he held views about people that are unacceptable today. In 2020 University College London (UCL) changed the name of its Galton Lecture Theatre, which had been named previously in his honour, to Lecture Theatre 115.

EPSRC supports this blog through research grant EP/W033615/1.

Getting off the beach, fast

by Paul Curzon, Queen Mary University of London

Paul goes on holiday and sees how a car park can work like a computer.

Fibre optic cables image by Chaitawat Pawapoowadon from Pixabay

Computers get faster and faster every year. How come? Because computer scientists and electronic engineers keep thinking up new tricks, completely new ways to make them go faster. One way has been to shrink the components so signals don’t have as far to go. Another is to use the same trick they were using in a beach car park I came across on holiday.

Woolacombe Sands in Devon is one of the most popular beaches around. There is a great expanse of beautiful sand as well as rocks for kids to climb on and good surfing too. The weather is even good there – well most of the time. The car park, right on the edge of the beach fills in the morning. Since most people arrive early and stay all day it’s a standard price of £5.50 for the day. Entry and exit barriers control the numbers. The entry barrier only allows a car to go in if there is a space and another allows people out when they have paid.

That’s where there is a problem though. The vast majority of people leave around 5pm as the ice cream vans pack up and it’s time to look for dinner. The machine only takes coins, and you insert the money from your car at the barrier. Each driver has to fumble with 5 one-pound coins and a 50p and that takes time. Once the current car moves on out there is then another delay as the driver behind pulls forward to get into a position to put their money in. Without some thought it would lead to long queues behind. Not only that it wouldn’t be very green. Cars are at there worst pumping out pollution when in a jam.

The last thing you want to do to a family who’ve had a great day on your beach is then irritate them by clogging them up in a traffic jam when they try to leave. So what do you do? How can you speed things up (and make sure you aren’t just moving the queue to the morning or to some other ticket machine somewhere else)?

The problem is similar to one in designing a computer chip. Think of the cars as data waiting to be processed (perhaps as part of a calculation) and the barrier as a processing unit where some manipulation of that data is needed. Data waiting to be processed has to be fetched before it can be used, just as the cars have to move up to the barrier before the driver can pay. The fact that the problems are so similar suggests that a solution to one may also be a a solution to the other.

Speed it up

There are lots of ways you could change the system to improve the speed of cars being processed in the car park. This speed that data passes through a system is called the ‘throughput’ of the system. Woolacombe have thought of a simple way to improve their throughput. They put a person with a bit of change next to the barrier to help the drivers. This allows them to keep the relatively simple barrier system they have. It also has advantages in keeping the money in one place and being a foolproof way of ensuring there is a space for everyone who enters. It still maintains all the safeguards of the ticket barrier though. How can that one person speed things up?

What would you do?

So what would YOU do if you were that person? Would you speed things up? Or would you just stand there powerless watching the misery of all those families?

The first thing you could do is to stand by the machine and take the change off the driver and insert it yourself. That will speed things up a little bit because it takes longer for drivers to put the money in as they have to stretch out the window of a car. Also if the driver only has a five pound note you can take it and just insert coins from your change bag rather than wasting time passing it back to the driver to then insert. Similarly if the driver only has 50 pence pieces say, rather than wasting time inserting 10 of them you can take them and insert 5 one-pound coins.

You’ve done some good, and removed problems of the slow people inserting coins but you haven’t really solved the bad problems. Cars aren’t moving at all while you are inserting the 6 coins, and after each car moves through the barrier you are doing nothing but waiting for the next car to pull forward. In an ideal system, with the best throughput, the cars barely stop at all and you are constantly busy.

A Pipeline of Cars

It turns out you can do something about that. It’s called pipelining. There is a way you can be busy dealing with the next car even before it’s got to you. You just have to get ahead of yourself!

How? Before the first car arrives, insert 5 pound coins into the machine and wait. As the driver gets to you and gives you the money, insert his or her 50p, keeping the rest. The barrier opens immediately for the driver who barely has to stop. Better still you are now holding 5 pound coins that you can insert as the next car arrives, leaving you back in an identical situation. That means the next car can drive straight through too, and you are constantly busy as long as there are cars arriving.

Speedy data

So you’ve helped the families leaving the beach, but how might a similar trick speed up a computer? Well you can do a similar thing in the way you get a computer processor to execute the instructions from a program. Suppose your program requires the processor to get some numbers from storage, process them (perhaps multiplying the numbers together) and then store the result somewhere else for later use. Typically a program might do that over and over again, varying where the data comes from and how it is processed.

Early computers would do each instruction in turn – doing the fetching, processing and storing of one instruction before starting the next. But that is just like a car in our car park coming to the barrier, being processed and leaving before the next one moves. Can we pull off the same trick to speed things up? Well, yes of course.

All you need to do is overlap the separate parts. Just as at any time in the car park a car will be driving out, a second will be handing over money and a third pulling forward, the same can happen in the computer. As the first instruction’s result is being stored, the next instruction can already be being processed and the data from the one after that can be fetched from memory. Just by reorganising the way the work is done, we have roughly tripled the speed of our computer as now three things are happening at once.

What we have done is set up a ‘pipeline’ – with a series of instructions all flowing through it, being executed, at the same time. Woolacombe has a pipeline of cars, but in a computer we pipeline data. Either way things get done faster and people are happier.

Computer science happens in some unexpected places – even at the beach – but then perhaps that isn’t so surprising given computers are made of sand!

This article was originally published on the CS4FN website.

Other beach-themed articles on this blog include how the origins of how Paul learned to program while on holiday (“The beach, the missionary and my origin myth”) and messages hidden (steganography) within the stripes of deckchairs (“Encrypted deckchairs”).

The beach, the missionary and my origin myth
Encrypted Deckchairs

EPSRC supports this blog through research grant EP/W033615/1.

“A mob for the Earth”

Online communities and flashmobs supporting the environment and businesses too

Lots of fresh carrots image by Prasanna Devadas from Pixabay

One Saturday afternoon one spring in San Francisco, a queue of people stretched down the pavement from a neighbourhood market. There was no shortage of other food shops nearby, so why were hundreds of people waiting to buy everything from crisps to cat litter at this one place? Because that shop had pledged to donate more than a fifth of that day’s profits to improving its environmental footprint.

Pillow fights and parties

The organisation behind the busy shopping day is called Carrotmob. The tactics they used to summon so many people to the tiny market in San Francisco had already been working all over the world for less serious stuff. From a huge pillow fight in New York’s Times Square to a mass disco at Victoria Station in London where people danced along to their MP3 players, the concept of the flashmob can seem to create a party out of thin air. From a simple idea, word can spread over social networking sites, email and word of mouth until a few people have turned into a huge crowd.

Start the bidding

Carrotmob’s founder, Brent Schulkin, wanted to try and entice businesses into going green using a language he thought they’d understand: cash. In return for getting loads of new customers to buy things, the owners had to give back some of their windfall profit to the Earth. To test his idea he went round to food shops in his neighbourhood. He said he could bring lots of extra customers to the shop on a particular day, and asked each of them how much of that day’s profit they’d be willing to spend on making their businesses more environmentally friendly. K&D Market won the bidding war by promising to spend 22% of the profits putting in greener lighting and making their fridges more energy-efficient. Now that K&D had agreed to the deal, Brent had to bring in the punters. He needed a flashmob.

Flashmobs work because it’s now so easy to stay in touch with large numbers of people. If we find out about a cool event we can share it with all our friends just by making one post on sites like Facebook or Twitter. We can make plans to do something as a group just by sending a few texts. When lots of people spread word around like this, suddenly a small idea like Carrotmob, armed with only a website and a few videos, can drop an hour-long queue on the doorstep of a market in San Francisco.

Success!

It’s not easy to enjoy yourself when you’re waiting for an hour to buy a packet of instant noodles, but that’s another advantage of the flashmob: the party atmosphere, the feeling that you’re part of something big. The results were big: the impromptu shoppers brought in more than $9000 – four times what the shop ordinarily rings up on a Saturday afternoon. Lots of the purchases went to a food bank, so even more people shared in the benefits. In the end the shop did well, the Earth did well, and the Carrotmobbers got a party. Plus the good feeling you get from helping the environment probably stays with you longer than the good feeling from getting hit in the face with a pillow.

Paul Curzon, Queen Mary University of London

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EPSRC supports this blog through research grant EP/W033615/1.

Happy World Emoji Day – 📅 17 July 2023 – how people use emoji to communicate and what it tells us about them 😀

“Emoji didn’t become so essential because they stand in for words – but because they finally made writing a lot more like talking.”

Gretchen McCulloch (see Further reading below)
Emoji samples © Emojipedia 2025.

The emoji for ‘calendar‘ shows the 17th July 📅 (click the ‘calendar’ link to find out why) and, since 2014, Emojipedia (an excellent resource for all things emoji, including their history) has celebrated World Emoji Day on that date. 

Before we had emoji (the word emoji can be both singular as well as plural, but 'emojis' is fine too) people added text-based 'pictures' to their texts and emails to add flavour to their online conversations, such as 
:-) or :)  - for a smiling face 
:-( or :( - for a sad one.

These text-based pictures are known as ’emoticons’ (icons that add emotion) because it isn’t always possible to know just from the words alone what the writer means. They weren’t just used to clarify meaning though, people started to pepper their prose with other playful pictures, such as :p where the ‘p’ is someone blowing a raspberry / sticking their tongue out* and created other icons such as this rose to send to someone on Valentine’s Day @-‘-,->—-, or this polevaulting amoeba ./

Here are the newly released emoji for 2023.

People use emoji in very different ways depending on their age, gender, ethnicity, personal writing style. In our “The Emoji Crystal Ball” article we look at how people can tell a lot about us from the types of emoji we use and the way we use them.

The Emoji Crystal Ball

Fairground fortune tellers claim to be able to tell a lot about you by staring into a crystal ball. They could tell far more about you (that wasn’t made up) by staring at your public social media profile. Even your use of emojis alone gives away something of who you are. Walid Magdy’s research team … Continue reading

Unicode Poo

The Egyptians had a hieroglyph for it, so unicode has a number for it. There’s even more unicode poo in the emoji character set but the Egyptians got there 1000s of years earlier. Here is how the Ancient Egyptians wrote or carved poo … Continue reading

Further reading

*For an even better raspberry-blowing emoticon try one of the letters (called ‘thorn’) from the Runic alphabet. If you have a Windows computer with a numeric keypad on the right hand side press the Num Lock key at the top to lock the number keypad (so that the keys are now numbers and not up and down arrows etc). Hold down the Alt key (there’s usually one on either side of the spacebar) and while holding it down type 0254 on the numeric keypad and let go. This should now appear wherever your cursor is: þ. Or for the lower case letter it’s Alt+0222 = Þ – for when you just want to blow a small raspberry :Þ

For Mac users press control+command+spacebar to bring up the Character Viewer and just type thorn in the search bar and lots will appear. Double-click to select the one you want, it will automatically paste into wherever your cursor is.

EPSRC supports this blog through research grant EP/W033615/1.

Negligent nurses? Or dodgy digital? – device design can unintentionally mask errors

Magicians often fool their audience into ‘looking over there’ (literally or metaphorically), getting them to pay attention to the wrong thing so that they’re not focusing on what the magician is doing and can enjoy the trick without seeing how it was done. Computers, phones and medical devices let you interact with them using a human-friendly interface (such as a ‘graphical user interface’) which make them easier to use, but which can also hide the underlying computing processes from view. Normally that’s exactly what you want but if there’s a problem, and one that you’d really need to know about, how well does the device make that clear? Sometimes the design of the device itself can mask important information, sometimes the way in which devices are used can mask it too. Here is a case where nurses were blamed but it was later found that the medical devices involved, blood glucose meters, had (unintentionally) tripped everyone up. A useful workaround seemed to be working well, but caused problems later on.

Negligent nurses? Or dodgy digital?

by Harold Thimbleby, Swansea University and Paul Curzon, Queen Mary University of London

Blood glucose meter image by Katherine Ab from Pixabay

It’s easy to get excited about new technology and assume it must make things better. It’s rarely that easy. Medical technology is a case in point, as one group of nurses found out. It was all about one simple device and wearable ID bracelets. Nurses were taken to court, blamed for what went wrong.

The nurses taken to court worked in a stroke unit and were charged with wilfully neglecting their patients. Around 70 others were also disciplined though not sent to court.

There were problems with many nurses’ record-keeping. A few were selected to be charged by the police on the rather arbitrary basis that they had more odd records than the others.

Critical Tests

The case came about because of a single complaint. As the hospital, and then police, investigated, they found more and more oddities, with lots of nurses suddenly implicated. They all seemed to have fabricated their records. Repeatedly, their paper records did not tally with the computer logs. Therefore, the nurses must have been making up the patient records.

The gadget at the centre of the story was a portable glucometer. Glucometers allow the blood-glucose (aka blood sugar) levels of patients to be tested. This matters. If blood-sugar problems are not caught quickly, seriously ill patients could die.

Whenever they did a test, the nurses recorded it in the patient’s paper record. The glucometer system also had a better, supposedly infallible, way to do this. The nurse scanned their ID badge using the glucometer, telling it who they were. They then scanned the patient’s barcode bracelet, and took the patient’s blood-sugar reading. They finally wrote down what the glucometer said in the paper records, and the glucometer automatically added the reading to that patient’s electronic record.

Over and over again, the nurses were claiming in the notes of patients that they had taken readings, when the computer logs showed no reading had been taken. As machines don’t lie, the nurses must all be liars. They had just pretended to take these vital tests. It was a clear case of lazy nurses colluding to have an easy life!

What really happened?

In court, witnesses gave evidence. A new story unfolded. The glucometers were not as simple as they seemed. No-one involved actually understood them, how the system really worked, or what had actually happened.

In reality the nurses were looking after their patients … despite the devices.

The real story starts with those barcode bracelets that the patients wore. Sometimes the reader couldn’t read the barcode. You’ve probably seen this happen in supermarkets. Every so often the reader can’t tell what is being scanned. The nurses needed to sort it out as they had lots of ill patients to look after. Luckily, there was a quick and easy solution. They could just scan their own ID twice. The system accepted this ‘double tapping’. The first scan was their correct staff ID. The second scan was of their staff card ID instead of the patient ID. That made the glucometer happy so they could use it, but of course they weren’t using a valid patient ID.

As they wrote the test result in the patient’s paper record no harm was done. When checked, over 200 nurses sometimes used double tapping to take readings. It was a well-known (at least by nurses), and commonly used, work-around for a problem with the barcode system.

The system was also much more complicated than that anyway. It involved a complex computing network, and a lot of complex software, not just a glucometer. Records often didn’t make it to the computer database for a variety of reasons. The network went down, manually entered details contained mistakes, the database sometimes crashed, and the way the glucometers had been programmed meant they had no way to check that the data they sent to the database actually got there. Results didn’t go straight to the patient record anyway. It happened when the glucometer was docked (for recharging), but they were constantly in use so might not be docked for days. Indeed, a fifth of the entries in the database had an error flag indicating something had gone wrong. In reality, you just couldn’t rely on the electronic record. It was the nurses’ old fashioned paper records that really were the ones you could trust.

The police had got it the wrong way round! They thought the computers were reliable and the nurses untrustworthy, but the nurses were doing a good job and the computers were somehow failing to record the patient information. Worse, they were failing to record that they were failing to record things correctly! … So nobody realised.

Disappearing readings

What happened to all the readings with invalid patient IDs? There was no place to file them so the system silently dropped them into a separate electronic bin of unknowns. They could then be manually assigned, but no way had been set up to do that.

During the trial the defence luckily noticed an odd discrepancy in the computer logs. It was really spiky in an unexplained way. On some days hardly any readings seemed to be taken, for example. One odd trough corresponded to a day the manufacturer said they had visited the hospital. They were asked to explain what they had done…

The hospital had asked them to get the data ready to give to the police. The manufacturer’s engineer who visited therefore ‘tidied up’ the database, deleting all the incomplete records…including all the ones the nurses had supposedly fabricated! The police had no idea this had been done.

Suddenly, no evidence

When this was revealed in court, the judge ruled that all the prosecution’s evidence was unusable. The prosecution said, therefore, they had no evidence at all to present. In this situation, the trial ‘collapses’: the nurses were completely innocent, and the trial immediately stopped.

The trial had already blighted the careers of lots of good nurses though. In fact, some of the other nurses pleaded guilty as they had no memory of what had actually happened but had been confronted with the ‘fact’ that they must have been negligent as “the computers could not lie”. Some were jailed. In the UK, you can be given a much shorter jail sentence, or maybe none at all, if you plead guilty. It can make sense to plead guilty even if you know you aren’t — you only need to think the court will find you guilty. Which isn’t the same thing.

Silver bullets?

Governments see digitalisation as a silver bullet to save money and improve care. It can do that if you get it right. But digital is much harder to get right than most people realise. In the story here, not getting the digital right — and not understanding it — caused serious problems for lots of nurses.

It takes skill and deep understanding to design digital things to work in a way that really makes things better. It’s hard for hospitals to understand the complexities in what they are buying. Ultimately, it’s nurses and doctors who make it work. They have to.

They shouldn’t be automatically blamed when things go wrong because digital technology is hard to design well.

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Digital lollipop: no calories, just electronics!

A mug of lollipops
Mug of lollipops image by Vinzenz Lorenz M from Pixabay

Can a computer create a taste in your mouth? Imagine scrolling down a list of flavours and then savouring your sweet choice from a digital lollipop. Not keen on that flavour, just click and choose a different one, and another and another. No calories, just the taste.

Nimesha Ranasinghe, a researcher at the National University of Singapore is developing a Tongue Mounted Digital Taste Interface, or digital lollipop. It sends tiny electrical signals to the very tip of your tongue to stimulate your taste buds and create a virtual taste!

One of UNESCO’s 2014 ’10 best innovations in the world’, the prototype doesn’t quite look like a lollipop (yet). There are two parts to this sweet sensation, the wearable tongue interface and the control system. The bit you put in your mouth, the tongue interface, has two small silver electrodes. You touch them to the tip of your tongue to get the taste hit. The control system creates a tiny electrical current and a minuscule temperature change, creating a taste as it activates your taste buds.

The prototype lollipop can create sour, salty, bitter, sweet, minty, and spicy sensations but it’s not just a bit of food fun. What if you had to avoid sweet foods or had a limited sense of taste? Perhaps the lollipop can help people with food addictions, just like the e-cigarette has helped those trying to give up smoking?
Perhaps the lollipop can help people with food addictions

But eating is more than just a flavour on your tongue, it is a multi-modal experience, you see the red of a ripe strawberry, hear the crunch of a carrot, feel sticky salt on chippy fingers, smell the Sunday roast, anticipate that satisfied snooze afterwards. How might computers simulate all that? Does it start with a digital lollipop? We will have to wait and see, hear, taste, smell, touch and feel!

Taste over the Internet

The Singapore team are exploring how to send tastes over the Internet. They have suggested rules to send ‘taste’ messages between computers, called the Taste Over Internet Protocol, including a messaging format called TasteXML They’ve also outlined the design for a mobile phone with electrodes to deliver the flavour! Sweet or salt anyone?

Jane Waite, Queen Mary University of London

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EPSRC supports this blog through research grant EP/W033615/1.