Category: Women in Computing

Joyce Wheeler: The Life of a Star

Exploding star
Star image by Dieter from Pixabay

The first computers transformed the way research is done. One of the very first computers, EDSAC (Electronic Delay Storage Automatic Calculator), contributed to the work of three Nobel prize winners: in Physics, Chemistry and Medicine. Astronomer, Joyce Wheeler was an early researcher to make use of the potential of computers to aid the study of other subjects in this way. She was a Cambridge PhD student in 1954 investigating the nuclear reactions that keep stars burning. This involved doing lots of calculations to work out the changing behaviour and composition of the star.

Joyce had seen EDSAC on a visit to the university before starting her PhD, and learnt to program it from its basic programming manual so that she could get it to do the calculations she needed. She would program by day and let EDSAC number crunch using her programs every Friday night, leaving her to work on the results in the morning, and then start the programming for the following week’s run. EDSAC not only allowed her to do calculations accurately that would otherwise have been impossible, it also meant she could run calculations over and over, tweaking what was done, refining the accuracy of the results, and checking the equations quickly with sample numbers. As a result EDSAC helped her to estimate the age of stars.

– Paul Curzon, Queen Mary University of London

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The Devil is in the Detail: Lessons from Animal Welfare? (Temple Grandin)

Several cows poking their heads through railings to look at the camera.
Cows image by -Rita-👩‍🍳 und 📷 mit ❤ from Pixabay

What can Computer Scientists learn from a remarkable woman and the improvements she made to animal welfare and the meat processing industry?

Temple Grandin is an animal scientist – an animal welfare specialist and a remarkable innovator on top. She has extraordinary abilities that allow her to understand animals in ways others can’t. As a result her work has reduced the suffering of countless farm animals. She has designed equipment, for example, to restrain animals. It makes it easier to give them shots because, in contrast to the equipment it replaces, it does not discomfort the animals as they enter. By being able to see the detail that an animal perceives she is able to design to overcome the problems. Paradoxically perhaps for someone who cares so much about animals, she works with slaughter houses – Meat Processing factories like those of McDonalds.

Her aim, given people do eat meat, is to ensure the animals are treated humanely throughout the process of rearing an animal until its death. Her work has been close to miraculous in the changes she has brought about to ensure that farm animals do not suffer. She is good for business too. If cattle are spooked by something as they enter the processing factory (also known as a ‘plant’), whether by the glint of metal or a deep shadow, the plant’s efficiency drops. Fewer animals are processed per hour and that is a big problem for managers.

As a result of her work she has turned round plants, both in welfare terms and in terms of rescuing plants that might otherwise have been shut down. Suddenly plants she audits are treating their livestock humanely.

See the Bigger Picture

Where do Temple’s extraordinary abilities come from? In fact she was originally labelled as being mentally disabled. She is actually autistic. As a result her brain doesn’t quite work the way most people’s do. Autistic people as a result of these brain differences often have difficulties socialising with others. They can find it very hard to understand the nuances of human-human communication that the rest of us take for granted. This is in part because autistic people perceive the world differently. A non-autistic person misses vast amounts of the detail in front of their eyes. Instead just a bigger picture of what they are seeing is passed to their conscious selves. An autistic person doesn’t have that sub-conscious ability to filter out detail, but instead perceives every small thing all at once. That is why autistics can sometimes be overcome by their surroundings, finding the world too much to cope with. They think in terms of a series of pictures full of detail, not abstractly in words.

Temple Grandin argues that that is what makes her special when it comes to understanding farm animals. In some ways they see the world very much like she does. Just as a cow does, she notices the shadows and the glint of metal, the bright patch on the floor from the overhead lights or the jacket laid over the fence that is spooking it. The plant managers and animal handlers don’t even register them never mind see them as a problem.

Who ya gonna call?

Because of this ability to quickly spot the problems everyone else has missed, Temple gained a reputation for being the person to call when a problem seemed intractable. She has also turned it into a career as an animal welfare auditor, checking processing plants to ensure their standards are sufficiently high. This is where she has helped force through the biggest improvements, and it all boils down to checklists.


Tick that box

Checking that lists of guidelines are being adhered to is a common way to audit quality in many areas of life. Checklists are used in a computer science context as checks for usability (for example that a new version of some application is easy to use) and accessibility (could a blind person, or for that matter someone who was autistic, successfully use a website say). Checklists tend to be very long. After all it must be the case that the more you are checking, the higher the quality of the result, mustn’t it? Surprisingly that turns out not always to be true! That is why Temple Grandin has been so successful. Rather than have a checklist with hundreds of things to check she boiled her own set of questions to ask down to just 10.

Traditional animal welfare audits have checklist questions such as “Is the flooring slippery?” and “Is the electric prod used as little as possible?”. Even apart from the number to work through this kind of checklist can be very hard to follow, not least due to the vagueness.

Ouch!

Temple’s checklist includes questions like: “Do all animals remain unconscious after being stunned?”, “Do no more than 3% of animals vocalise during handling or stunning?” (a “Moo” in this situation means “Ouch”) They are precise, with little room for dispute – it isn’t left to the inspectors judgement. That also means everyone knows the target they are working towards. The fact that there are only 10 also means it is easy for everyone involved to know them all well. Perhaps most importantly they do not focus on the state of the factory, or the way things are done. Instead, they focus on the end results – that animals are humanely treated. The point is that one item covers a multitude of sins that could be causing it. If too many animals are crying out in pain then you have to fix ALL the causes, even if it is something new that no-one thought of putting on a checklist before.

Temple’s 10 point approach to checklists can apply to more than just animal welfare of course. The principles behind it could just as well apply to other areas like usability and accessibility of websites.

Some usability evaluation techniques do follow similar principles. Cognitive Walkthrough, a method of auditing that systems are easy to use on first encounter, has some of the features of this kind of approach. The original version involved a longish set of questions that an expert was to ask him/herself about a system under evaluation. After early trials the developers of the method Cathleen Wharton, John Rieman, Clayton Lewis and Peter Polson quickly realised this wasn’t very practical and replaced it by a 4 question version. It has since then even been replaced by a 3-question walkthrough. One of the questions, to be asked of each step in achieving a task, is: “Will a user know what to try and do at this point?” This has some of the flavour of the Grandin approach – it is about the end result not about some specific thing going wrong.

Let’s look at accessibility. Currently, where web designers think about it at all (UK law requires them to) the long checklist approach tends to be followed. Typical items to check are things like “Ensure that all information conveyed with colour is also available without colour”. Automatic systems are often used to do audits. That is good in one sense as the criteria have then to be very precise for a mere computer to make the decision. On the other hand it encourages items in the checklist to just be things a computer can check. It also encourages the long list of fine detail approach that Temple rejected. Worse, it also can lead to people conforming to the checklist without deeply understanding what the point actually is. A classic example is a web designer adding as the last item on a web page “If you are partially sighted click here”. As far as an automatic checker is concerned they may have done everything right – even providing alternative facilities that are clearly available (if you can see them). A partially sighted person however would only get to that instruction on the screen after they have struggled through the rest of the page. The designer got the right idea but missed the point.

Temple Grandin’s approach would suggest instead having checklists that ask about the outcomes of using the page: “Do 97% of partially-sighted people successfully complete their objective in using the site?” for example. That is why “user testing” is so important, at least as one of the evaluation approaches you follow. User testing involves people from a wide variety of backgrounds actually trying using your prototype software or web pages before they are released. It allows you to focus on the big picture. Of course if you are trying to ensure a web page is accessible your users must include people with different kinds of disabilities.


The Big Picture

One of Temple Grandin’s main messages is that the big advantage that arises as a result of her autism is that she thinks in concrete pictures not in abstract words. Whilst thinking verbally is good in some situations it seems to make us treat small things as though they were just as important as the big issues.

So whatever you are doing, whether looking after animals or designing accessible websites, don’t get lost in the detail. Focus on the point of it all.

Paul Curzon, Queen Mary University of London

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

Ingrid Daubechies: Wiggly lines help catching crime

A pulse signal on a spherical monitor surface
Image by Gerd Altmann from Pixabay 

Computer scientists rely on maths a lot. As mathematicians devise new mathematical theories and tools, computer scientists turn them into useful programs. Mathematicians who are interested in computing and how to make practical use of their maths are incredibly valuable. Ingrid Daubechies is like that. Her work has transformed the way we store images and much besides. She works on the maths behind digital signal processing – how best to manipulate things like music and images in computers. It boils down to wiggly lines.

Pixel pictures

The digital age is founded on the idea that you can represent signals: whether sound or images, radio waves, or electrical signals, as sequences of numbers. We digitise things by breaking them into lots of small pieces, then represent each piece with a number. As I look out my window, I see a bare winter tree, with a robin singing. If I take a picture with a digital camera, the camera divides the scene into small squares (or pixels) and records the colour for each square as a number. The real world I’m looking at isn’t broken into squares, of course. Reality is continuous and the switch to numbers means some of the detail of the real thing is lost. The more pieces you break it into the more detail you record, but when you blow up a digital image too much, eventually it goes blurry. Reality isn’t fuzzy like that. Zoom in on the real thing and you see ever more detail. The advantage of going digital is that, as numbers, the images can be much more quickly and easily stored, transmitted and manipulated by Photoshop-like programs. Digital signal processing is all about how you store and manipulate real-world things, those signals, with numbers.

Curvy components

There are different ways to split signals up when digitising them. One of the bedrocks of digital signal processing is called Fourier Analysis. It’s based on the idea that any signal can be built out of a set of basic building blocks added together. It’s a bit like the way you can mix any colour of paint from the three primary colours: red, blue and yellow. By mixing them in the right proportions you can get any colour. That means you can record colours by just remembering the amounts of each component. For signals, the building blocks are the pure frequencies in the signal. The line showing a heartbeat as seen on a hospital monitor, say, or a piece of music in a sound editing program, can be broken down into a set of smooth curves that go up and down with a given frequency, and which when added together give you the original line – the original signal. The negative parts of one wave can cancel out positive parts of another just as two ripples meeting on a pond combine to give a different pattern to the originals.

This means you can store signals by recording the collection and strength of frequencies needed to build them. For images the frequencies might be about how rapidly the colours change across the image. An image of say a hazy sunset, where the colours are all similar and change gradually, will then be made of low frequencies with rolling wave components. An image with lots of abrupt changes will need lots of high frequency, more spiky, waves to represent all those sudden changes.

Blurry bits

Now suppose you have taken a picture and it is all a bit blurry. In the set of frequencies that blurriness will be represented by the long rolling waves across the image: the low frequencies. By filtering out those low frequencies, making them less important and making the high frequency building blocks stronger, we can sharpen the image up.

more like keyhole surgery on a signal
than butchering the whole thing.

By filtering in different ways we can have different effects on the image. Some of the most important help compress images. If a digital camera divides the image into fewer pixels it saves memory by storing less data, but you end up with blocky looking pictures. If you instead throw away information by losing some of the frequencies of a Fourier version, the change may be barely noticeable. In fact, drawing on our understanding of how our brains process the world to choose what frequencies to drop we might not see a change in the image at all.

The power of Fourier Analysis is that it allows you to manipulate the whole image in a consistent way, editing a signal by editing its frequency building blocks. However, that power is also a disadvantage. Sometimes you want to have effects that are more local – doing something that’s more like keyhole surgery on a signal than butchering the whole thing.

Wiggly wavelets

That is where wavelets come in. They give a way of focussing on small areas of the signal. The building blocks used with wavelets are not the smooth, forever undulating curves of Fourier analysis, but specially designed functions, ie wiggly lines, that undulate just in a small area – a bit like a single heart beat signal. A ‘mother’ wavelet is combined with variations of it (child wavelets) to make the full set of building blocks: a wavelet family.

Wavelets were perhaps more a curiosity than of practical use to computer scientists, until Ingrid Daubechies came up with compact wavelets that needed only a fixed time to process. The result was a versatile and very practical tool that others have been able to use in all sorts of ways. For example, they give a way to compress images without losing information that matters. This has made a big difference with the FBI’s fingerprint archive, for example. A family of wavelets allows each fingerprint to be represented by just a few wavelets, so a few numbers, rather than the many numbers needed if pixels were stored. The size of the collection takes up 20 times less storage space as wavelets without corrupting the images. That also means it can be sent to others who need it more easily. It matters when each fingerprint would otherwise involve storing or sending 10 Megabytes of data.

People have come up with many more practical uses of Wavelets, from cleaning up old music to classifying stars and detecting earthquakes. Not bad for a wiggly line.

Paul Curzon, Queen Mary University of London

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

Edie Schlain Windsor and same sex marriage

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US Supreme court building Image by Mark Thomas from Pixabay
US Supreme court building Image by Mark Thomas from Pixabay

Edie Schlain Windsor was a senior systems programmer at IBM. There is more to life than computing though. Just like anyone else, Computer Scientists can do massively important things aside from being very good at computing. Civil rights and over-turning unjust laws are as important as anything. She led the landmark Supreme Court Case (United States versus Windsor) that was a milestone for the rights of same-sex couples in the US.

Born to a Jewish immigrant family, Edie worked her way up from an early data entry job at New York University to ultimately become a senior programmer at IBM and then President of her own software consultancy where she helped LGBTQ+ organisations become computerised.

Having already worked as a programmer at an energy company called Combustion Engineering, she joined IBM on completing her degree in 1958 so was one of the early generation of female programmers, before the later idea of the male programmer stereotype took hold. Within ten years she had been promoted to the highest technical position in IBM, that of a Senior Systems Programmer: so one of their top programmers lauded as a wizard debugger. She had started out programming mainframe computers, the room size computers that were IBM ‘s core business at the time. They both designed and built the computers as well as the operating system and other software that ran on them. Edie became an operating systems expert, and a pioneer computer scientist also working on natural language processing programs, aiming to improve the interactivity of computes. Natural Language Processing was then a nascent area but that by 2011 IBM led spectacularly with its program Watson winning the quiz show Jeopardy! answering general knowledge questions playing against human champions.

Before her Supreme Court case overturned it, a law introduced in 1996 banned US federal recognition of same-sex marriages. It made it federal law that marriage could only exist between a man and a woman. Individual states in the US had introduced same-sex marriage but this new law meant that such marriages were not recognised in general in the US. Importantly, for those involved it meant a whole raft of benefits including tax, immigration and healthcare benefits that came with marriage were denied to same-sex couples.

Edie had fallen in love with psychologist Thea Spyer in 1965, and two years later they became engaged, but actually getting married was still illegal. They had to wait almost 30 years before they were even allowed to make their partnership legal, though still at that point not marry. They were the 80th couple to register on the day such partnerships were finally allowed. By this time Thea had been diagnosed with multiple sclerosis, a disease that gradually leads to the central nervous system breaking down, with movement becoming ever harder. Edie was looking after her as a full time carer, having given up her career to do so. They both loved dancing and did so throughout their life together even once Thea was struggling to walk, using sticks to get on to the dance floor and later dancing in a wheelchair. As Thea’s condition grew worse it became clear she had little time to live. Marriage was still illegal in New York, however, so before it was too late, they travelled to Canada and married there instead.

When Thea died she left everything to Edie in her will. Had Edie been a man married to Thea, she would not have been required to pay tax on this inheritance, but as a woman and because same-sex marriages were deemed illegal she was handed a tax bill of hundreds of thousands of dollars. She sued the government claiming the way different couples were treated was unfair. The case went all the way to the highest court, the Supreme Court, who ruled that the 1996 law was itself unlawful. Laws in the US have as foundation a written constitution that dates back to 1789. The creation of the constitution was a key part of the founding of the United States of America itself. Without it, the union could easily have fallen apart, and as such is the ultimate law of the land that new laws cannot overturn. The problem with the law banning same sex marriage was that it broke the 5th amendment of the constitution added in 1791, one of several amendments made to ensure people’s rights and justice was protected by the constitution.

The Supreme Court decision was far more seismic than just refunding a tax bill, however. It overturned the law that actively banned same-sex marriage, as it fell foul of the constitution, and this paved the way for such marriages to be made actively legal. In 2014 federal employees were finally told they should perform same-sex marriages across the US, and those marriages gave the couple all the same rights as mixed-sex marriages. Because Edie took on the government, the US constitution, and so justice for many, many couples prevailed.

– Paul Curzon, Queen Mary University of London

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This blog is funded through EPSRC grant EP/W033615/1.

Understanding matters of the heart – creating accurate computer models of human organs

Colourful depiction of a human heart
Heart image by Gordon Johnson from Pixabay

Ada Lovelace, the ‘first programmer’ thought the possibilities of computer science might cover a far wider breadth than anyone else of her time. For example, she mused that one day we might be able to create mathematical models of the human nervous system, essentially describing how electrical signals move around the body. University of Oxford’s Blanca Rodriguez is interested in matters of the heart. She’s a bioengineer creating accurate computer models of human organs.

How do you model a heart? Well you first have to create a 3D model of its structure. You start with MRI scans. They give you a series of pictures of slices through the heart. To turn that into a 3D model takes some serious computer science: image processing that works out, from the pictures, what is tissue and what isn’t. Next you do something called mesh generation. That involves breaking up the model into smaller parts. What you get is more than just a picture of the surface of the organ but an accurate model of its internal structure.

So far so good, but it’s still just the structure. The heart is a working, beating thing not just a sculpture. To understand it you need to see how it works. Blanca and her team are interested in simulating the electrical activity in the heart – how electrical pulses move through it. To do this they create models of the way individual cells propagate an electrical system. Once you have this you can combine it with the model of the heart’s structure to give one of how it works. You essentially have a lot of equations. Solving the equations gives a simulation of how electrical signals propagate from cell to cell.

The models Blanca’s team have created are based on a healthy rabbit heart. Now they have it they can simulate it working and see if it corresponds to the results from lab experiments. If it does then that suggests their understanding of how cells work together is correct. When the results don’t match, then that is still good as it gives new questions to research. It would mean something about their initial understanding was wrong, so would drive new work to fix the problem and so the models.

Once the models have been validated in this way – shown it is an accurate description of the way a rabbit’s heart works – they can use them to explore things you just can’t do with experiments – exploring what happens when changes are made to the structure of the virtual heart or how drugs change the way it works, for example. That can lead to new drugs.

They can also use it to explore how the human heart works. For example, early work has looked at the heart’s response to an electric shock. Essentially the heart reboots! That’s why when someone’s heart stops in hospital, the emergency team give it a big electric shock to get it going again. The model predicts in detail what actually happens to the heart when that is done. One of the surprising things is it suggests that how well an electric shock works depends on the particular structure of the person’s heart! That might mean treatment could be more effective if tailored for the person.

Computer modelling is changing the way science is done. It doesn’t replace experiments. Instead clinical work, modelling and experiments combine to give us a much deeper understanding of the way the world, and that includes our own hearts, work.

Paul Curzon, Queen Mary University of London

The charity Cardiac Risk in the Young raises awareness of cardiac electrical rhythm abnormalities and supports testing (electrocardiograms and echocardiograms) for all young people aged 14-35.

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

The Dark History of Algorithms

An Arabic pattern on a crescent moon
Image by Mohammad Shahriyar from Pixabay

Zin Derfoufi, a Computer Science student at Queen Mary, delves into some of the dark secrets of algorithms past.

Algorithms are used throughout modern life for the benefit of mankind whether as instructions in special programs to help disabled people, computer instructions in the cars we drive or the specific steps in any calculation. The technologies that they are employed in have helped save lives and also make our world more comfortable to live it. However, beneath all this lies a deep, dark, secret history of algorithms plagued with schemes, lies and deceit.

Algorithms have played a critical role in some of History’s worst and most brutal plots even causing the downfall and rise of nations and monarchs. Ever since humans have been sent on secret missions, plotted to overthrow rulers or tried to keep the secrets of a civilisation unknown, nations and civilisations have been using encrypted messages and so have used algorithms. Such messages aim to carry sensitive information recorded in such a way that it can only make sense to the sender and recipient whilst appearing to be gibberish to anyone else. There are a whole variety of encryption methods that can be used and many people have created new ones for their own use: a risky business unless you are very good at it.

One example is the ‘Caesar Cipher’ which is named after Julius Caesar who used it to send secret messages to his generals. The algorithm was one where each letter was replaced by the third letter down in the alphabet so A became D, B became E, etc. Of course, it means that the recipient must know of the algorithm (sequence to use) to regenerate the original letters of the text otherwise it would be useless. That is why a simple algorithm of “Move on 3 places in the alphabet” was used. It is an algorithm that is easy for the general to remember. With a plain English text there are around 400,000,000,000,000,000,000,000,000 different distinct arrangements of letters that could have been used! With that many possibilities it sounds secure. As you can imagine, this would cause any ambitious codebreaker many sleepless nights and even make them go bonkers!!! It became so futile to try and break the code that people began to think such messages were divine!

But then something significant happened. In the 9th Century a Muslim, Arabic Scholar changed the face of cryptography forever. His name was Abu Yusuf Ya’qub ibn Ishaq Al-Kindi -better known to the West as Alkindous. Born in Kufa (Iraq) he went to study in the famous Dar al-Hikmah (house of wisdom) found in Baghdad- the centre for learning in its time which produced the likes of Al-Khwarzimi, the father of algebra – from whose name the word algorithm originates; the three Bana Musa Brothers; and many more scholars who have shaped the fields of engineering, mathematics, physics, medicine, astrology, philosophy and every other major field of learning in some shape or form.

Al-Kindi introduced the technique of code breaking that was later to be known as ‘frequency analysis’ in his book entitled: ‘A Manuscript on Deciphering Cryptographic Messages’. He said in his book:

“One way to solve an encrypted message, if we know its language, is to find a different plaintext of the same language long enough to fill one sheet or so, and then we count the occurrences of each letter. We call the most frequently occurring letter the ‘first’, the next most occurring one the ‘second’, the following most occurring the ‘third’, and so on, until we account for all the different letters in the plaintext sample.

“Then we look at the cipher text we want to solve and we also classify its symbols. We find the most occurring symbol and change it to the form of the ‘first’ letter of the plaintext sample, the next most common symbol is changed to the form of the ‘second’ letter, and so on, until we account for all symbols of the cryptogram we want to solve”.

So basically to decrypt a message all we have to do is find out how frequent each letter is in each (both in the sample and in the encrypted message – the original language) and match the two. Obviously common sense and a degree of judgement has to be used where letters have a similar degree of frequency. Although it was a lengthy process it certainly was the most efficient of its time and, most importantly, the most effective.

Since decryption became possible, many plots were foiled changing the course of history. An example of this was how Mary Queen of Scots, a Catholic, plotted along with loyal Catholics to overthrow her cousin Queen Elizabeth I, a Protestant, and establish a Catholic country. The details of the plots carried through encrypted messages were intercepted and decoded and on Saturday 15 October 1586 Mary was on trial for treason. Her life had depended on whether one of her letters could be decrypted or not. In the end, she was found guilty and publicly beheaded for high treason. Walsingham, Elizabeth’s spymaster, knew of Al-Kindi’s approach.

A more recent example of cryptography, cryptanalysis and espionage was its use throughout World War I to decipher messages intercepted from enemies. The British managed to decipher a message sent by Arthur Zimmermann, the then German Foreign Minister, to the Mexicans calling for an alliance between them and the Japanese to make sure America stayed out of the war, attacking them if they did interfere. Once the British showed this to the Americans, President Woodrow Wilson took his nation to war. Just imagine what the world may have been like if America hadn’t joined.

Today encryption is a major part of our lives in the form of Internet security and banking. Learn the art and science of encryption and decryption and who knows, maybe some day you might succeed in devising a new uncrackable cipher or crack an existing banking one! Either way would be a path to riches! So if you thought that algorithms were a bore … it just got a whole lot more interesting.

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

Jacquie Lawson: the multi-million pound greeting

by Paul Curzon, Queen Mary University of London (from the cs4fn women are here special issue)

Happy Birthday in a circle of hearts
Image adapted from Pixabay

There is real money to be made out there in the virtual world – if you are willing to put in the effort to develop appropriate skills.

You don’t have to be young or a geek either. At the age of 62, grandmother Jacquie Lawson turned a hobby into a multi-million pound business. She is a trained illustrator having originally studied art at St Martins School of Art in London. She bought her first computer in 1998. Despite struggling at the start she taught herself to draw computer animations using Macromedia Flash.

Just for fun she made an animated Christmas e-card and sent it to friends. Her skill as an illustrator combined with her artistic flair meant that suddenly she was inundated with people wanting them from around the world – a wonderful example of viral marketing.

“The Internet is such a fantastic medium.
It ought to be better.”

She set up a business, launched the http://www.jacquielawson.com e-card website and is now the market leader – with double the visitors of its nearest rival. As Jacquie says about the Internet: “It’s such a fantastic medium. It ought to be better”.

She believes there is a lot of rubbish on the Internet – which means there is scope for skilled, creative people to make a difference by focusing on detail in what they do. Quality can stand out.

So develop the basic skills, have a great idea, throw in some business savvy…but most of all do it for fun, if you want to end up with a successful business.

Activity

Be inspired by Jacquie Lawson. Make your own computer greeting card for some special occasion (whether Valentine’s day, a birthday, Mothers day or Fathers Day…). It might be a still drawing or an animation. Perhaps it could even be a program. Use a technology you are familiar with, or learn one you haven’t used before for the occasion – Scratch perhaps or a drawing program. Learning new skills can be very rewarding and sometimes can lead to new opportunities.

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

Cognitive crash dummies

Cartoon showing a crash test dummy in a sitting position.
Car crash dummy image by OpenClipart-Vectors from Pixabay

by Paul Curzon, Queen Mary University of London

The world is heading for catastrophe. We’re hooked on power hungry devices: our mobile phones and iPods, our Playstations and laptops. Wherever you turn people are using gadgets, and those gadgets are guzzling energy – energy that we desperately need to save. We are all doomed, doomed…unless of course a hero rides in on a white charger to save us from ourselves.

Don’t worry, the cognitive crash dummies are coming!

Actually the saviours may be people like professor of human-computer interaction, Bonnie John, and her then grad student, Annie Lu Luo: people who design cognitive crash dummies. When working at Carnegie Mellon University it was their job to figure out ways for deciding how well gadgets are designed.

If you’re designing a bridge you don’t want to have to build it before finding out if it stays up in an earthquake. If you’re designing a car, you don’t want to find out it isn’t safe by having people die in crashes. Engineers use models – sometimes physical ones, sometimes mathematical ones – that show in advance what will happen. How big an earthquake can the bridge cope with? The mathematical model tells you. How slow must the car go to avoid killing the baby in the back? A crash test dummy will show you.

Even when safety isn’t the issue, engineers want models that can predict how well their designs perform. So what about designers of computer gadgets? Do they have any models to do predictions with? As it happens, they do. Their models are called ‘human behavioural models’, but think of them as ‘cognitive crash dummies’. They are mathematical models of the way people behave, and the idea is you can use them to predict how easy computer interfaces are to use.

There are lots of different kind of human behavioural model. One such ‘cognitive crash dummies’ is called ‘GOMS’. When designers want to predict which of a few suggested interfaces will be the quickest to use, they can use GOMS to do it.

Send in the GOMS

Image showing a modern touch-screen mobile phone with colourful circular icons distributed randomly on the screen.
Smartphone gadget image by Vladislav Klimenkov from Pixabay

Suppose you are designing a new phone interface. There are loads of little decisions you’ll have to make that affect how easy the phone is to use. You can fit a certain number of buttons on the phone or touch screen, but what should you make the buttons do? How big should they be? Should you use gestures? You can use menus, but how many levels of menus should a user have to navigate before they actually get to the thing they are trying to do? More to the point, with the different variations you have thought up, how quickly will the person be able to do things like send a text message or reply to a missed call? These are questions GOMS answers.

To do a GOMS prediction you first think up a task you want to know about – sending a text message perhaps. You then write a list of all the steps that are needed to do it. Not just the button presses, but hand movements from one button to another, thinking time, time for the machine to react, and so on. In GOMS, your imaginary user already knows how to do the task, so you don’t have to worry about spending time fiddling around or making mistakes. That means that once you’ve listed all your separate actions GOMS can work out how long the task will take just by adding up the times for all the separate actions. Those basic times have been worked out from lots and lots of experiments on a wide range of devices. The have shown, on average, how long it takes to press a button and how long users are likely to think about it first.

GOMS in 60 seconds?

GOMS has been around since the 1980s, but wasn’t being used much by industrial designers. The problem is that it is very frustrating and time-consuming to work out all those steps for all the different tasks for a new gadget. Bonnie John’s team developed a tool called CogTool to help. You make a mock-up of your phone design in it, and tell it which buttons to press to do each task. CogTool then worked out where the other actions, like hand movements and thinking time, are needed and makes predictions.

Bonnie John came up with an easier way to figure out how much human time and effort a new design uses, but what about the device itself? How about predicting which interface design uses less energy? That is where Annie Lu Luo, came in. She had the great idea that you could take a GOMS list of actions and instead of linking actions to times you could work out how much energy the device uses for each action instead. By using GOMS together with a tool like CogTools, a designer can find out whether their design is the most energy efficient too.

So it turns out you don’t need a white knight to help your battery usage, just Annie Lu Luo and her version of GOMS. Mobile phone makers saw the benefit of course. That’s why Annie walked straight into a great job on finishing university.

This article was originally published on the CS4FN website and appears on pages 12 and 13 of issue 9 (‘Programmed to save the world‘) of the CS4FN magazine, which you can download (free) here along with all of our other free material.

See also the concept of ‘digital twins’ in this article from our Christmas Advent Calendar: Pairs: mittens, gloves, pair programming, magic tricks.

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Daphne Oram: the dawn of music humans can’t play

Music notes over paint brush patterns
Image by Gerd Altmann from Pixabay

What links James Bond, a classic 1950s radio comedy series and a machine for creating music by drawing? … Electronic music pioneer: Daphne Oram.

Oram was one of the earliest musicians to experiment with electronic music, and was the first woman to create an electronic instrument. She realised that the advent of electronic music meant composers no longer had to worry about whether anyone could actual physically perform the music they composed. If you could write it down in a machine readable way then machines could play it electronically. That idea opened up whole new sounds and forms of music and is an idea that pop stars and music producers still make use of today.

She learnt to play music as a child and was good enough to be offered a place at the Royal College of Music, though turned it down. She also played with radio electronics with her brothers, creating radio gadgets and broadcasting music from one room to another. Combining music with electronics became her passion and she joined the BBC as a sound engineer. This was during World War 2 and her job included being the person ready during a live music broadcast to swap in a recording at just the right point if, for example, there was an air raid that meant the performance had to be abandoned. The show, after all, had to go on.

Composing electronic music

She went on to take this idea of combining an electronic recording with live performance further and composed a novel piece of music called Still Point that fully combined orchestral with electronic music in a completely novel way. The BBC turned down the idea of broadcasting it, however, so it was not played for 70 years until it was rediscovered after her death, ultimately being played at a BBC Prom.

Composers no longer had to worry
about whether anyone could actually
physically perform the music they composed

She started instead to compose electronic music and sounds for radio shows for the BBC which is where the comedy series link came in. She created sound effects for a sketch for the Goon Show (the show which made the names of comics including Spike Milligan and Peter Sellers). She constantly played with new techniques. Years later it became standard for pop musicians to mess with tapes of music to get interesting effects, speeding them up and down, rerecording fragments, creating loops, running tapes backwards, and so on. These kinds of effects were part of amazing sounds of the Beatles, for example. Oram was one of the first to experiment with these kinds of effects and use them in her compositions – long before pop star producers.

One of the most influential things she did was set up the BBC Radiophonic Workshop which went on to revolutionise the way sound effects and scores for films and shows were created. Oram though left the BBC shortly after it was founded, leaving the way open for other BBC pioneers like Delia Derbyshire. Oram felt she wasn’t getting credit for her work, and couldn’t push forward with some of her ideas. Instead Oram set herself up as an independent composer, creating effects for films and theatre. One of her contracts involved creating electronic music that was used on the soundtracks of the early Bond films starring Sean Connery – so Shirley Bassey is not the only woman to contribute to the Bond sound!

The Music Machine

While her film work brought in the money, she continued with her real passion which was to create a completely new and highly versatile way to create music…by drawing. She built a machine – the Oramics Machine – that read a composition drawn onto film reels. It fulfilled her idea of having a machine that could play anything she could compose (and fulfilled a thought she had as a child when she wondered how you could play the notes that fell between the keys on a piano!).

The 35mm film that was the basis of her system that dates all the way back to the 19th century when George Eastman, Thomas Edison and Kennedy Dixon pioneered the invention film based photography and then movies. It involved a light sensitive layer being painted on strips of film with holes down the side that allowed the film to be advanced. This gave Oram a recording media. She could etch or paint subtle shapes and patterns on to the film. In a movie light was shone through the film, projecting the pictures on the film on to the screen. Oram instead used light sensors to detect the patterns on the film and convert it to electronic signals. Electronic circuitry she designed (and was awarded patents for) controlled cathode ray tubes that showed the original drawn patterns but now as electrical signals. Ultimately these electrical signals drove speakers. Key to the flexibility of the system was that different aspects of the music were controlled by patterns on different films. One for example controlled the frequency of the sound, others the timbre or tone quality and others the volume. These different control signals for the music were then combined by Oram’s circuitry. The result of combining the fine control of the drawings with the multiple tapes meant she had created a music machine far more flexible in the sound it could produce than any traditional instrument or orchestra. Modern music production facilities use very similar approaches today though based on software systems rather than the 1960s technology available to Oram.

Ultimately, Daphne Oram was ahead of her time as a result of combining her two childhood fascinations of music and electronics in a way that had not been done before. She may not be as famous as the great record producers who followed her, but they owe a lot to her ideas and innovation.

Paul Curzon, Queen Mary University of London

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Kimberly Bryant: Black Girls Code

Kimberly Bryant in 2016, Ståle Grut / nrkbeta, CC BY-SA 2.0, via Wikimedia Commons

Kimberly Bryant was born on 14 January 1967 in Memphis, Tennessee and was enthusiastic about maths and science in school, describing herself as a ‘nerdy girl’. She was awarded a scholarship to study Engineering at university but while there she switched to Electrical Engineering with Computer Science and Maths. During her career she has worked in several industries including pharmaceutical, biotechnology and energy.

She is most known though for founding Black Girls Code. In 2011 her daughter wanted to learn computer programming but nearly all the students on the nearest courses were boys and there were hardly any African American students enrolled. Kimberly didn’t want her daughter to feel isolated (as she herself had felt) so she created Black Girls Code (BGC) to provide after-school and summer school coding lessons for African American girls. BGC has a goal of teaching one million Black girls to code by 2040 and every year thousands of girls learn coding with their peers.

She has received recognition for her work and was given the Jefferson Award for Community Service for the support she offered to girls in her local community, and in 2013 Business Insider included her on its list of The 25 Most Influential African-Americans in Technology. When Barack Obama was the US President the White House website honoured her as one of its eleven Champions of Change in Tech Inclusion – Americans who are “doing extraordinary things to expand technology opportunities for young learners – especially minorities, women and girls, and others from communities historically underserved or underrepresented in tech fields.”

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