“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)
A selection of emoji
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, like 🙂 or 🙂 for a smiling face or 😦 for a sad one. These text-based pictures were known as ’emoticons’ (icons that added 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 peppered 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 ./
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
Further reading
Writing IRL (July 2019) Gretchen McCullock writing in Slate (IRL = In Real Life) – this is an excerpt about emoji from Gretchen’s fascinating book “Because internet” about internet culture, communication and linguistics (the study of language).
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.
*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.
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.
At the end you can find more links between magic and computer science, and human-computer interaction.
Negligent nurses? Or dodgy digital?
by Harold Thimbleby, Swansea University and Paul Curzon, Queen Mary University of London
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.
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.
There are a number of surprising parallels between magic and computer science and so we have a number of free magic booklets (The Magic of Computer Science 1, 2 and 3 among others) to tell you all about it. The booklets show you some magic and talk about the links with computing and computational thinking. From the way a magician presents a trick (and the way in which people interact with devices) to self-working tricks which behave just like an algorithm. For the keenest apprentices of magic we also have a new book ⬇️, Conjuring with Computation, which you can buy from bookshops or as an e-book. Here are a couple of free bonus chapters.
EPSRC supports this blog through research grant EP/W033615/1.
How do online word processing programs manage to allow two or more people to change the same document at the same time without getting in a complete muddle? One of the really key ideas that makes collaborative writing possible was developed by computer scientists, Clarence Ellis and Simon Gibbs. They called their idea ‘Operational transformation’.
Let’s look at a simple example to illustrate the problem. Suppose Alice and Bob share a document that starts:
"MEETING AT 10AM"
First of all one computer, called the ‘server’, holds the actual ‘master’ document. If the network goes down or computers crash then its that ‘master’ copy that is the real version everyone sees as the definitive version.
Both Alice and Bob’s computers can connect to that server and get copies to view on their own machines. They can both read the document without problem – they both see the same thing. But what happens if they both start to change it at once? That’s when things can get mixed up.
Let’s suppose Alice notices that the time in the document should be PM not AM. She puts her cursor at position 14 and replaces the letter there with P. As far as the copy she is looking at is concerned, that is where the faulty A is. Her computer sends a command to the server to change the master version accordingly, saying
CHANGE the character at POSITION 14 to P.
The new version at some point later will be sent to everyone viewing. However, suppose that at the same time as Alice was making her change, Bob notices that the meeting is at 1 not 10. He moves his cursor to position 13, so over the 0 in the version he is looking at, and deletes it. A command is sent to the server computer:
DELETE the character at POSITION 13.
Now if the server receives the instructions in that order then all is ok. The document ends up as both Bob and Alice intended. When they are sent the updated version it will have done both their changes correctly:
"MEETING AT 1PM"
However, as both Bob and Alice are editing at the same time, their commands could arrive at the server in either order. If the delete command arrives first then the document ends up in a muddle as first the 13th position is deleted giving.
"MEETING AT 1AM"
Then, when Alice’s command is processed the 14th character is changed to a P as it asks. Unfortunately, the 14th character is now the M because the deleted character has gone. We end up with
"MEETING AT 1AP"
Somehow the program has to avoid this happening. That is where the operational transformation algorithm comes in. It changes each instruction, as needed, to take other delete or insert instructions into account. Before the server follows them they are changed to ones so that they give the right result whatever order they came in.
So in the above example if the delete is done first, then any other instructions that arrive that apply to the same initial version of the document are changed to take account of the way the positions have changed due to the already applied deletion. We would get and so apply the new instructions:
STARTING FROM "MEETING AT 10AM" DELETE the character at POSITION 13. CHANGE the character at POSITION (14-1) to P.
Without Operational Transformation two people trying to write a document together would just be frustrating chaos. Online editing would have to be done the old way of taking it in turns, or one person making suggestions for the other to carry out. With the algorithm, thanks to Clarence Ellis and Simon Gibbs, people who are anywhere in the world can work on one document together. Group writing has changed forever.
This article was originally published on the CS4FN website.
(First appeared in Issue 23 of the CS4FN magazine “The women are (still) here”)
The stereotype of a computer scientist is someone who doesn’t understand people. For many, how people behave is exactly what they are experts in. Kavin Narasimhan is one. When a student at QMUL she studied how people move and form groups at parties, creating realistic computer models of what is going on.
We humans are very good at subtle behaviour, and do much of it without even realising it. One example is the way we stand when we form small groups to talk. We naturally adjust our positions and the way we face each other so we can see and hear clearly, while not making others feel uncomfortable by getting too close. The positions we take as we stand to talk are fairly universal. If we understand what is going on we can create computational models that behave the same way. Most previous models simulated the way we adjust positions as others arrive or leave by assuming everyone tries to both face, and keep the same distance from, the midpoint of the group. However, there is no evidence that that is what we actually do. There are several alternatives. Rather than pointing ourselves at some invisible centre point, we could be subconsciously maximising our view of the people around. We could be adjusting our positions and the direction we face based on the position only of the people next to us, or instead based on the positions of everyone in the group.
Kavin videoed real parties where lots of people formed small groups to find out more of the precise detail of how we position and reposition ourselves. This gave her a bird’s eye view of the positions people actually took. She also created simulations with virtual 2D characters that move around, forming groups then moving on to join other groups. This allowed her to try out different rules of how the characters behaved, and compare them to the real party situations.
She found that her alternate rules were more realistic than rules based on facing a central point. For example, the latter generates regular shapes like triangular and square formations, but the positions real humans take are less regular. They are better modelled by assuming people focus on getting the best view of others. The simulations showed that this was also a more accurate way to predict the sizes of groups that formed, how long they formed for, and how they were spread across the room. Kavin’s rules therefore appear to give a realistic way to describe how we form groups.
Being able to create models like this has all sorts of applications. It is useful for controlling the precise movement of avatars, whether in virtual worlds or teleconferencing. They can be used to control how computer-generated (CGI) characters in films behave, without needing to copy the movements from actors first. It can make the characters in computer games more realistic as they react to whatever movements the real people, and each other, make. In the future we are likely to interact more and more with robots in everyday life, and it will be important that they follow appropriate rules too, so as not to seem alien.
So you shouldn’t assume computer scientists don’t understand people. Many understand them far better than the average person. That is how they are able to create avatars, robots and CGI characters that behave exactly like real people. Virtual parties are set to be that little bit more realistic.
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