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ISSN 1754-3657 (Print)ISSN 1754-3665 (Online)Computer Science for Fun Issue 6Special Issue onComputerScienceEverywhereYvonne Rogers: probing the woodsJoin the crowd… with swarm intelligenceDestroying the stereotypes… Females in films

Welcome to cs4fnSpecial Issue onComputerScienceEverywhereThe computers have goneinto hiding! Not long agothey were in your facesaying “Look at me I’m apowerful computer”. Nowthey are just as likely tobe hiding in the crevicesof your life. That’s onegoal of ComputerScientists – to help thecomputers hide!You are probably so used to using yourmobile phone you don’t think of it as acomputer, but that’s what it is. Yoursprobably has as much computing poweras the largest, most expensive computerdid a generation ago.Researchers want to create newtechnologies that are both mobile and fitseamlessly into our world. That is a goalof ubiquitous computing, or ubicomp,research: designing and creatingcomputer technologies thatare truly ubiquitous - presenteverywhere…but invisible.This issue of cs4fn is on “ComputerScience Everywhere” though not just inthe UbiComp sense. Computer Scienceis everywhere in other ways too. Itintertwines with other subjects insurprising ways. Even though computershave only existed for less than a century,the study of computation, which is whatComputer Science is about, has beenaround for a thousand years ormore…and has lead to cataclysmicchanges to the history of civilisation.Computer Science is also at thevanguard of the exploration of spaceand the search for life.Finally, Computer Science is foreverybody: making a difference forthe disadvantaged too, for example.It is not just for everyone in the senseof everyone being users, but also ofeveryone being able to contribute.Many people think it is only men whoare interested in computers, but womenare leading the way in many areas ofresearch. We focus on the work ofYvonne Rogers of the Open University,Kirsten Cater of Bristol and pioneer FranAllen of IBM. We also look at how SFfilms have reinforced the idea that it ismale dominated and hear what some ofthe real IT Pros think. As a result thisissue has been supported by Equalitecwho work to promote IT for all.As each new technology comes alongit opens up whole new avenues forcomputer scientists to explore. IT now.Life tomorrow. Thanks to ComputerScience.In this issue:Pg7Pg10Pg 16Pg18Swarm intelligenceYvonne Rogers: Ambient WoodsHelpless Women!?!The Dolphins phone home2Passionate about computer science?www.cs4fn.org

I Predict a RiotProfessor Nigel Smart Head of theDepartment of Computer Science at BristolUniversity tells us about the world ofmediascapes …It was Mark Weiser, chief scientist of XeroxPARC, and the visionary behind ubiquitouscomputing research, who set the ballrolling and the computers scurrying intothe background with his dream of a futurefree from battles with ‘IT’:“There is more information available at ourfingertips during a walk in the woods thanany computer system, yet people find awalk among the trees relaxing andcomputers frustrating. Machines that fitthe human environment, instead of forcinghumans to enter theirs, will make using acomputer as refreshing as taking a walk inthe woods.”Researchers at Bristol University and theOpen University together with collaboratorslike Hewlett Packard, the BBC andFutureLab have been taking small stepstowards Weiser’s dream using mobilephones and personal digital assistants(PDAs) to give people ‘located’experiences. These systems use naturalhuman movements, like walking, to controlapplications automatically: the ‘computer’changes behaviour depending on whereyou are.One way a computer can keep track oflocation is the Global Positioning System(GPS) - used in cars for SatNav systems.The GPS satellites tell the mobilecomputer where it is. It then displaysinformation appropriate to that location.Using GPS tracking to trigger the contentyou see means that you don’t have to beconsciously interacting with the technologyto control the experience. This kind ofinteraction reduces the need for complexuser interfaces and makes the experiencea lot more natural.SatNav is the obvious application but thereis a lot more potential than just SatNav forthose innovative enough to see thepossibilities. The Bristol team have calledthese types of experiences ‘mediascapes’:a collection of sounds and images placeddigitally in the landscape and accessedusing mobile devices. They are exploring awhole bunch of different kinds.The simplest mediascape application is toturn your mobile computer into a touristguide. Let’s say Jo hasn’t been to Bristolbefore so she downloads a tourist guide toher GPS-enabled mobile phone. She startswalking around the harbourside, and whenshe approaches the SS Great Britain themobile phone automatically starts to playaudio files about the ship and shows videoclips or images of the boat being launched.The effect is like audio guides in museumswhere you type in a number to get thespecific content for that location, but usingGPS means the system already knowswhere you are and where you have been.It is much closer to the way you’d interactwith a human guide. Jo only has to walkaround the ship naturally to trigger newcontent without having to interact with anycomplex interface or even speak.The research team has gone much furtherand created a series of different kinds ofapplications each exploring different kindsof novel mediascape experiences – liketaking part in a riot!As Kirsten Cater of Bristol University whowas one of the researchers behind thatproject, tells us: “The idea behind Riot!1831 was that when you walk into QueenSquare you actually enter a virtualhistorical re-enactment of the 19th centuryriot that took place there and as you walkaround the square you trigger differentscenes”.Cititag, led by Open University researchers,by contrast explores ubicomp as a game –combining the physical and virtual worldsinto a seamless gaming environment.Players roam their real home city searchingfor other players all of whom are in either‘red’ or ‘green’. The aim is to tag playersof the opposite colour before they tag you:“Urban space becomes a playground andeveryone is a suspect.”Most recently the Bristol team have beenexploring how they can help everyone joinin creating mediascapes. They have beentrying the ideas out by letting students atNailsea school design a mediascape oftheir yet to be built new school and soaffect the design (see the webzineMagazine+).In the future, location won’t be the onlysensor that will be used in mediascapes.Other sensors like heart rate monitors,temperature and sound sensors could beused too (see Fizees, page 12). Here is oneidea: mobile spy games might get you torun to a particular place but keeping yourheart rate and sound level below a certainamount.The real world has gone multimedia.Riots will never be the same again!Future authoring tools may allow you tocreate a whole range of new sensor-basedexperiences with your friends whereveryou like! You can already create your ownlocation-based mediascapes. See thewebzine, Magazine+ to find out more.Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use3

Someone towatch over meThey are everywhere! Camerason lampposts, street corners, inshops or train stations. We sadlylive in a world where crime isever present and videosurveillance plays a key partin helping the police keepus safe. How are computerscientists playing their part inchanging the way we policeour world today? Perhaps moreimportantly how far should we go?Human smart vsComputer SmartAt present most of the surveillancecameras around us send their imagesto central control rooms where teamsof observers keep an eye out for trouble.The problem is that as more and morepictures come in it becomes impossiblefor humans to keep track of all that’sgoing on. Enter the smart camera. Smartbecause it has artificial intelligence. Evensmarter because understanding what’sgoing on in a moving picture, which isreally, really easy for us, is really, reallytough for a computer.The human brain is estimated to useabout half of all its computing power tounderstand the visual world. Take a simpleexample. Recognising a friend’s face oneither a dark and rainy day or a sunnyday in a shadowy forest is easy for you.You don’t really need to think about it.You take vision for granted (but that’sbecause half your brain is working on it).But look at the information that acomputer has to work with and these twocases are very different. The intensityvalues (that is the amount of light in theimage) is very different in the twosituations. The numbers making up thepixels of a digital image are completelydifferent. In the forest the shadowschange the light bits of a face to dark.The tip of the nose has say value 10 onthe dark day and 100 on a sunny day.Somewhere in these two different sets ofdata is your friend’s familiar face, but howcan a computer find it? With currenttechniques it’s still tough, and that’swhere computer science researchers comein to help find the answers. These difficultcomputational problems of duplicatinghuman vision with a computer are juststarting to be solved. Some fascinatingadvances have been made but there is stilla long way to go.There are lots of applications waiting oncethe problems of making computers ‘see’are cracked. Of course even if a computercan recognise an expression, detectsomeone wandering around a car park inan unusual way or running from a stationadding these clues together doesn’t meanthat we can decide the person’s realintention, though we can alert someoneto the fact that something different ishappening. Computer Science researcherscan provide the tools but it is the widersociety that needs to decide whether thesetechnologies are appropriate to use, andhow they should be used. For better orworse we need to decide.4Passionate about computer science?www.cs4fn.org

In the mood toface the computerIf you’ve ever found yourself pulling facesat your computer, becoming evermorefrustrated as it fails to understand whatyou want it to do, then help from computerscience researchers may be on its way.We use facial expressions to signal toothers something about how we feel inside.It’s one of the mechanisms we have evolvedto let us live together in groups. Scientistsare working on systems that teach acomputer to recognise these facialexpressions, and so get an idea of themoods or emotions those expressions mightrepresent. A face is a very complex movingimage. It’s very bendy, but as we grow upwe learn how to interpret these movementsand turn them into useful measures of facialexpression. We can now also teach acomputer to recognise changes, as timeprogresses, of key parts of the face - knownas ‘spatio-temporal features’. By showing thecomputer a variety of expressive faces, andsome clever algorithms, we can train it totell the difference between, for example, asmile and a scowl.The results can be used to help develop‘affective computing’, that is computersthat are aware of, respond to, and changeaccording to our emotional state. Forexample, if you run into a problem whileworking with a particular piece ofsoftware, and start to look frustrated orangry, the computer will be able torecognise that emotion in your face, andautomatically produce the right set ofuseful notes to help you. Such sensitiveemotion-recognition software may appearon computers within the next few years.Our plastic pal whois fun to be with?But there are other applications foremotion-recognition software, for examplein the development of ‘syntheticcompanions’ for the elderly. Robots canplay a useful and valuable role in lookingafter the elderly or infirm – remindingthem to take their medication, or to lockthe front door or even perhaps acting asa type of pet you start to care for. But inorder to build a relationship with anindividual, to empathise with them,you have to be able to have a two-waycommunication. Expression recognitionsoftware would enable the robot torecognise a person’s emotions andmood and respond appropriately –including reflecting the appropriateexpression on their own face. You don’twant a ‘friend’ who when you areunhappy appears to just smirk! Gettingthe emotions right is important in makingthe relationship between individual andcarer much more socially realistic.It’s going to be important in the futurethat humans and robots ‘talk the samelanguage’, even if no words are spoken.Robots need emotions too!Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use 5

Something in theway she movesHow do you tell ifthe person walkingtowards you is aman or woman?Getting it wrongcan be incrediblyembarrassing!Hairstyle? Men have ponytails.Women have short hair.Body shape? What if they are wearingbaggy clothing?Is there actually any feature that isa reliable way to tell?Research by Kevin Shan at Queen Mary,University of London has shown thatcomputers can be taught to identifywhether a person is male or female,but in a surprising way. When we walk,our limbs act like pendulums, and thelength and swing of the pendulum largelydetermines our individual pattern ofmovement. This swing is different for boysand girls; it’s due to the difference in ourbody shapes and skeletons. By showing thecomputer examples of how lots of peoplewalk the computer can be taughtto recognise the difference.All the clues point to…To be even better at making the judgmentwe can combine this with computer facialanalysis techniques that are trained torecognise the sorts of shapes of male andfemale faces. This bringing together ofdifferent clues to solve the problem iscalled ‘data fusion’. It turns out it is a keyidea in saving the lives of babies too(see page 12).So if you have ever been in thatembarrassing situation when it dawns onyou have been chatting up a woman notthe man (or vice versa) you thought youwere talking to, at least you know theemotional robots of the future won’t havethe same problem.DATA FUSIONData fusion can go even further combiningthese and other visual clues such as bodytemperature, the direction people arelooking, or even how long a person hasbeen standing still. From this set of cluesa computer can, after it’s been trained andknows the patterns to look for, decide ifthere is suspicious behaviour going on andalert the appropriate authorities.6Passionate about computer science?www.cs4fn.org

Join the crowd…with swarm intelligenceNext time you are in a large crowd, have alook around you: all those people movingtogether, and mostly not bumping into eachother. How does it happen? Flocksof birds and schools of fish are also examplesof this “swarm intelligence”. Swarmingbehaviour requires the individuals (birds, fishor people) to have a set of rules about how tointeract with the individuals nearest to them.These so-called “local” rules are all that’sneeded to give rise to the overall or “global”behaviour of the swarm. We adjust ourindividual behaviour according to our currentstate but also according to the current state ofthose around us. If I want to turn left then I doit slowly so that others in the crowd can beaware of it and also start to turn. I know whatI am doing and I know what they are doing.This is how information makes its way fromthe edges of the crowd to the centre and viceversa.A swarm is bornThe way a crowd or swarm interacts can beexplained with simple maths. This maths isa way of approximating the complexpsychological behaviour of all theindividuals on the basis of local and globalrules. Back in 1995 James Kennedy, aresearch psychologist, and ComputerScientist Russ Eberhart, having beeninspired by the study of bird flockingbehaviour by biologist Frank Heppner,realised that this swarm intelligencecould be used to solve difficult computerproblems. The result was a techniquecalled PSO (particle swarm optimisation).Travel broadensthe mindAn optimisation problem is one where youhave a range of options to choose from andyou want to know the best solution. One ofthe classic problems is called ‘Thetravelling salesperson’ problem. You workfor a company and have to deliver packagesto say 12 towns. You look at the map.There are many different routes to take, butwhich is the one that will let you visit all 12towns using the least petrol? Here thechoices are the order in which you visit thetowns, and the constraint is that you wantto do the least driving. You could have aguess, select the towns in a random orderand work out how far you’d have to travel tovisit them in this order. You then try anotherroute through all 12 and see if it takes lessmileage, and so on. Phew! It could take along time to work out all the possible routesfor 12 towns to see which was best. Nowimagine your company grows and you haveto deliver to 120 towns or 1200 towns. Youwould spend all your time with the mapstrying to come up with the cheapestsolutions. There must be a better way? Wellactually simple as this problem seems it’san example of a set of computationalproblems known as NP-Complete (see Page14), and it’s not easy to solve! You needsome guidance to help you through andthat’s where swarm optimisation come in.It’s an example of a so-called metaheuristicalgorithm: a sort of ‘general rule of thumb’to help solve these types of problem. Itwon’t always work unless you have infinitetime but it’s better than just trying randomsolutions. So how does swarm optimisationwork here?State space:the final frontierFirst we need to turn the problem intosomething called a state space. Youprobably use state spaces all the time butjust don’t know it. Think about yourself.What are the characteristic you would useto tell people about your current state: age,height, weight and so on. You can identifyyourself by a list of say 3 numbers – one forage, one for height, one for weight. It’s noteverything about you of course but it doesdefine your state to some extent. Yournumbers will be different to other people’snumbers, if you take all the numbers foryour friends you would have a state space.It would be a 3 dimensional space withaxes: age, height and weight, and eachperson would be a point in that space atsome coordinate (X, Y, Z).So state spaces are just a way ofrepresenting the possible variations insome problem. With the 12 towns problemhowever you couldn’t draw this space: itwould be 12 dimensional! It would haveone axis for each town, with the position onthe axis an indication of where in the routeit was. Every point in that space would be adifferent route through the 12 towns, soeach point in the space would havecoordinates (x1, x2, x3, … x11, x12). Foreach point there would also be a mileageassociated with the route, and the task isto find the coordinate point (route) with thelowest mileage.Swarming all overthe mapEnter swarm optimisation. We create a setof ‘particles’ that will be like birds or fish,and will fly and swarm through our statespace. Each particle starts with a randomstarting location in the state space andcalculates the mileages involved for thecoordinates (route) they are at. Theparticles remember (store) this coordinateand also the value of the mileage at thatposition. Each particle therefore has it’sown known ‘local’ best value (where thelowest mileage was) but can compare thiswith other neighbouring particles to see ifthey have found any even better solutions.The particles then move onwards randomlyin a direction that tends to move themtowards their own local best and the bestvalue found by their neighbours. Theparticles ‘fly’ around the state space in aswarm homing in on even better solutionsuntil they all converge on the best they canfind: the answer.Where no particle hasswarmed beforeIt may be that somewhere in a part of thespace where no particle has been there isan even better solution, perhaps even thebest solution possible. Our swarm will havemissed it! That’s why this algorithm is aheuristic, a best guess to a tough problem.We can always add some more particles atthe start to fill more of the state space andreduce the chance of missing a goodsolution, but we can’t ever be 100% sure.Swarm optimisation has been appliedto a whole range of tough problems incomputing, electronic engineering,medicinal chemistry and economics. Allit needs is for you to create an appropriatestate space for your problem and let theparticles fly and swarm to a good solution.It’s yet another example of clevercomputing based on behaviours found inthe natural world. So next time you’re in acrowd, look around and appreciate all thatcollective interacting swarm intelligence…but make sure you remember to watchwhere you are stepping.Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use 7

The Dark History of AlgorithmsZin Derfoufi, a Computer Scienceundergraduate at Queen Mary,delves into some of the darksecrets of algorithms past.Algorithms are used throughout modernlife for the benefit of mankind whetheras instructions in special programs to helpdisabled people, computer instructions inthe cars we drive or the specific steps inany calculation. The technologies thatthey are employed in have helped savelives and also make our world morecomfortable to live it. However, beneathall this lies a deep, dark, secret historyof algorithms plagued with schemes, liesand deceit.Algorithms have played a critical role insome of History's worst and most brutalplots even causing the downfall and riseof nations and monarchs. Ever sincehumans have been sent on secretmissions, plotted to overthrow rulers ortried to keep the secrets of a civilisationunknown, nations and civilisations havebeen using encrypted messages and sohave used algorithms. Such messages aimto carry sensitive information recorded insuch a way that it can only make sense tothe sender and recipient whilst appearingto be gibberish to anyone else. There area whole variety of encryption methods thatcan be used and many people havecreated new ones for their own use: arisky business unless you are very goodat it.One example is the ‘Caesar Cipher’ whichis named after Julius Caesar who used itto send secret messages to his generals.The algorithm was one where each letterwas replaced by the third letter down inthe alphabet so A became D, B becameE, etc. Of course, it means that therecipient must know of the algorithm(sequence to use) to regenerate theoriginal letters of the text otherwise it isuseless. That is why a simple algorithmof “Move on 3 places in the alphabet”was used. It is an algorithm that is easyfor the general to remember. With a plainEnglish text there are around400,000,000,000,000,000,000,000,000different distinct arrangements of lettersthat could have been used! With thatmany possibilities it sounds secure. Asyou can imagine, this would cause anyambitious code-breaker many sleeplessnights and even make them go bonkers!It became so futile to try and break thecode that people began to think suchmessages were divine!But then something significant happened.In the 9th Century a Muslim, ArabicScholar changed the face of cryptographyforever. His name was Abu Yusuf Ya'qubibn Ishaq Al-Kindi – better known to theWest as Alkindous. Born in Kufa (Iraq)he went to study in the famous Dar al-Hikmah (house of wisdom) found inBaghdad. It was the centre for learningin its time which produced the likes of Al-Khwarzimi, the father of algebra - fromwhose name the word algorithmoriginates. It also produced many morescholars who have shaped the fields ofengineering, mathematics, physics,medicine, astronomy, philosophy andevery other major field of learning insome shape or form.Frequency Analysischanged the courseof historyAl-Kindi introduced the technique of codebreaking that was later to be known as'frequency analysis' in his book entitled:‘A Manuscript on DecipheringCryptographic Messages’.His idea was that to decrypt a messageall we have to do is find out how frequenteach letter is in both the sample and inthe encrypted message and match thetwo. If E is most common in English,it is likely to be in the message too.Obviously common sense and a degreeof judgement has to be used whereletters have a similar degree of frequency.Although it is a lengthy process itcertainly was the most efficient of itstime and, most importantly, effective.As decryption became possible, manyplots were foiled changing the course ofhistory. An example of this was how MaryQueen of Scots, a Catholic, plotted alongwith loyal Catholics to overthrow hercousin Queen Elizabeth I, a Protestant,and establish a Catholic England. Thedetails of the plots carried throughencrypted messages were intercepted anddecoded. On 15 October 1586 Mary wason trial for treason. Her life had dependedon whether one of her letters had beendecrypted or not. In the end, she wasfound guilty and publicly beheaded forhigh treason. Walsingham, Elizabeth'sspymaster, knew of Al-Kindi's approach.Today encryption is a major part of ourlives in the form of Internet security andbanking. Learn the art and science ofencryption and decryption and whoknows, maybe some day you mightsucceed in devising a new uncrackablecipher or crack an existing banking one!Either way would be a path to riches! Soif you thought that algorithms were a bore... it just got a whole lot more interesting.8Passionate about computer science?www.cs4fn.org

Smart TranslationComputers don’t speak English, or Urdu orCantonese for that matter. They have theirown special languages that humanprogrammers have to learn if they want tocreate new applications. Even thoseprogramming languages aren’t the languagecomputers really speak. They onlyunderstand 1s and 0s. The programmershave to employ translators to convert whatthey say into Computerese (binary): just asif I wanted to speak with someone fromPoland, I’d need a polish translator.Computer translators aren’t called translatorsthough. They are called ‘compilers’, and justas it might be a Pole who translated for meinto Polish, compilers are special programsthat can take text written in a programminglanguage and convert it into binary.The development of good compilershas been one of the most importantadvancements from the early years ofcomputing and Fran Allen, one of the starresearchers of computer giant, IBM wasawarded the ‘Turing Prize’ for hercontribution. That is the ComputerScience equivalent of a Nobel Prize.Not bad given she only joined IBM toclear her student debts from University.Fran was a pioneer with hergroundbreaking work on ‘optimizingcompilers’. Translating isn’t just abouttaking a word at a time and substitutingeach for the word in the new language.You get gibberish that way. The samegoes for computer languages.Things written in programming languagesare not just any old text. They areinstructions. You actually translate chunksof instructions together in one go. You alsoadd a lot of detail to the program in thetranslation, filling in every step.Suppose a Japanese tourist used aninterpreter to ask me for directions ofhow to get to Sheffield from Leeds. Imight explain it as “Follow the M1 Southfrom Junction 43 to Junction 33”. If theJapanese translator explained it as acompiler would they might actually say(in Japanese): Take the M1 South fromJunction 43 as far as Junction 42, thenfollow the M1 South from Junction 42 asfar as Junction 41, then follow … fromJunction 34 as far as Junction 33.Computers actually need all the minutedetail to follow the instructions.The most important thing about computerinstructions (i.e., programs) is usually howfast following them leads to the jobgetting done. Imagine I was on theInformation desk at Heathrow airport andthe tourist wanted to get to Sheffield. I’venever done that journey. I do know how toget from Heathrow to Leeds as I’ve done ita lot. I’ve also gone from Leeds toSheffield a lot, so I know that journey too.So the easiest way for me to giveinstructions for getting from London toSheffield, without much thought and besure it gets the tourist there might be tosay:Go from Heathrow to Leeds:1. Take the M4 West to Junction 4B2. Take the M25 clockwise to Junction 213. Take the M1 North to Leeds atJunction 43Then go from Leeds to Sheffield4. Take the M1 South to Sheffieldat Junction 33That is easy to write and made up ofinstructions I’ve written before perhaps.Programmers reuse instructions like this alot – it both saves their time and reducesthe chances of introducing mistakes intothe instructions. That isn’t the optimumway to do the journey of course. You passthe turn off for Sheffield on the way up.An optimizing compiler is an intelligentcompiler. It looks for inefficiency andactually converts it into a shorter andfaster set of instructions. The Japanesetranslator, if acting like an optimizingcompiler, would actually remove theredundant instructions and simplify it to:1. Take the M4 West to Junction 4B2. Take the M25 clockwise to Junction 213. Take the M1 North to SheffieldJunction 33Much faster! Much more intelligent!Happier tourists!Next time you take the speed of yourcomputer for granted, remember it is notjust that fast because the hardware isquick, but because, thanks to people likeFran Allen, the compilers don’t just dowhat the programmers tell them to do.They are far smarter than that.Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use 9

If you go down to thewoods todayWhat kind of emotion should computers evoke?Calm? Frustrating? Professor Yvonne Rogerstells us about her vision for the future.Mark Weiser’s dream of “CalmComputing” has led to lots of excitingresearch that has seen computersdisappearing into the background (seepage 3). His vision was driven by a desireto remove the frustration of usingcomputers but also the realization thatthe most profound technologies are theones that you just don’t notice. Hewanted technology to actively removefrustrations from everyday life, not justthe ones caused by computers.No one argues that computers shouldbe frustrating to use, but Yvonne Rogershas a different idea of what the newvision could be. Not calm. Anything butcalm in fact (apart from frustrating ofcourse). Not calm, but engaging andexciting!Not calm,but engagingand excitingHer vision of those tranquil woods is notrelaxing but provocative and playful. Toprove the point her team turned some realwoods in Sussex into an ‘Ambient Wood’.The ambient wood was an enhancedwood. When you entered it you tookprobes with you, that you could pointand poke with. They allowed you to takereadings of different kinds in easy ways.Time hopping ‘Periscopes’ placed aroundthe woods allowed you to see thosepatches of woodland at other times of theyear. There was also a special woodlandden where you could then see the biggerpicture of the woods as all your readingswere pulled together using computervisualisations.Not only is the Ambient Wood technologyvisible and in your face but it makes theinvisible side of the wood visible in a waythat provokes questions about the wildlife.You notice more. You see more. You thinkmore. A walk in the woods is no longer apassive experience but an active, playfulone. Woods are the exciting places ofchildhood stories again but now there areeven more things to explore.“The most importantthing theparticipants gainedwas a sense ofwonderment atfinding out all sortsof things and makingconnections throughdiscovering aspectsof the physicalwoodland (e.g.,squirrel's droppings,blackberries,thistles)”– Yvonne RogersThe idea behind the Ambient Wood,and similar ideas like Bristol’s Savannahproject where playing fields are turnedinto African Savannah (see the webzineMagazine+) is to revisit the original ideaof computers but in a new context.Computers started as tools, and toolsdon’t disappear, they extend our abilities.Tools originally extended our physicalabilities – a hammer allows us to hitthings harder, a pulley to lift heavierthings. They make us more effective andallow us to do things a mere humancouldn’t do alone. Computer technologycan do a similar thing but for the humanintellect…if we design them well.The Weiser dream is that technologyinvisibly watches the world and removesthe obstacles in the way before you evennotice them. It’s a little like the wayservants to the aristocracy were expectedto always have everything just right but atthe same time were not to be noticedby those they served. The way this isachieved is to have technology constantlymonitoring, understanding what is goingon and how it might affect us and thencalmly fixing things. The problem is itneeds really ‘smart’ technology – a highlevel of Artificial Intelligence to achieveand that so far has proved more difficultthan anyone imagined. Our behaviour anddesires are full of subtlety and muchharder to read than was imagined. Evena super-intellect would probably keepgetting it wrong.There are also ethical problems. If we doever achieve the dream of total calm wemight not like it. It is very easy to begung ho with technology and not realizethe consequences. Calm computing needsmonitors – the computer measuringeverything it can so it has as muchinformation as possible to make decisionsfrom (see Big Sister is Watching You,page 12).A classic example of how this can lead topeople rejecting technology intended tohelp is in a project to make a “smart”residential home for the elderly. The ideawas that by wiring up the house to trackthe residents and monitor them thenurses would be able to provide muchbetter care, and relatives be able to seehow things were going. The place wasfilled with monitors. For example, sensorsin the beds measured resident’s weightwhile they slept. Each night theoccupants weight could invisibly be takenand the nurses alerted of worrying weightloss over time. The smart beds could alsodetect tossing and turning so someonehaving bad nights could be helped. Asmart house could use similar technologyto help you or I have a good nights sleepand help us diet.10Passionate about computer science?www.cs4fn.org

The problem was the beds could tell otherthings too: things that the occupantspreferred to keep to themselves.Nocturnal visitors also showed up in therecords. That’s the problem if technologylooks after us every second of the day, therecords may give away to others far morethan we are happy with.Yvonne’s vision is different. It is notthat the computers try to second-guesseverything but instead extend ourabilities. It is quite easy for newtechnology to lead to our being poorerintellectually than we were. Calculatorsare a good example. Yes we can do morecomplex sums quickly now, but at thesame time without a calculator manypeople can’t do the sums at all. Ourabilities have both improved and beendamaged at the same time. That is whatthe probes do, allowing you to see thewoods in a new way, but to use theinformation however you wish. Cruciallythe probes encourage imagination too.The alternative to the smart house (orcalculator) that pampers allowing yourbrain to stay in neutral, or the residentialhome that monitors you for the sake ofthe nurses and your relatives is one wherethe sensors are working for you. Whereyou are the one the bed reports to helpingyou to then make decisions about yourhealth, or where the monitors you wearare part of a game that you play becauseits fun (see Fizees page 12).What next?“I'd like to see kidsdiscover new ways ofprobing their bodiesto find out whatmakes them tick.”– Yvonne RogersSo if Yvonne has her way, you won’t beheading for a soporific future while thecomputer deals with real life for you.Instead it will be a future where thecomputers are sparking your imagination,challenging you to think, filling you withdelight…and where the woods come aliveagain just as they do in the storybooks.For the full cs4fn interview withYvonne see the cs4fn webzineMagazine+Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use 11

Fizzees: the digital pet thatcares for YOUWho says computers can't be part of ahealthy lifestyle? It's often assumed thatspending too long playing games withcomputers has caused many of us to stopdoing the daily exercise that helps keepus fit and healthy. The Fizzees projectdeveloped by Futurelab aims to change that,by giving kids a clever bit of wearabletechnology: a watch containing their owncute digital Tamogotchi-style pet. You canwatch it grow and develop just like any pet,but the twist is that you need to do the workin the real world for your pet to prosper inits digital domain.Watch your watchThe way your digital pet grows anddevelops depends on the physical activityyou do. The watch device containing thepet is fitted with a monitor that measuresyour heart rate and your movement. A goodbit of exercise means that your pulseincreases and you move about a lot more,and this is converted into information thathelps your pet grow. Using these devicespeople start to understand what it takes fora healthy lifestyle. The pet does best whenits wearer undertakes the sorts of healthyactivities health experts think are mostsuitable for their age group.Putting the fun intohealthyThis interactive device is an exampleof how a clever idea combined withwearable computing can lead to devicesthat can improve our lives and also be a lotof fun as well. Perhaps in the future we willall have wearable digital devices that helpus have healthier lifestyles: digital pets thatactually look after us.Big Sister is watchingThe word “Surveillance” can have sinisterovertones. You’re being watched. Everythingyou do. All the time…There is at least one time in your lifewhen you might have wanted such 24-hour coverage: if you were a prematurebaby in an incubator, it would be aboutlife or death. Babies are wired up to lotsof probes that sound an alarm if anythingstrange is detected. The trouble is thealarms go off all the time – even becausea baby wriggles. For example, if a probesays the heart stopped instantly, the babyhasn’t died. It just means a probe hasbecome detached – babies’ hearts just donot stop suddenly like that. Over 90% arefalse alarms making a baby unit a verynoisy place, but more importantly it’spossible real alarms could be missed.Enter some Computer Science from theUniversity of Edinburgh. Professor ChrisWilliams and John Quinn of the School ofInformatics, and Professor of Child Lifeand Health, Neil McIntosh with fundingfrom the charity BLISS have developeda computer system that is capable ofmonitoring all the probes and only raisingthe alarm when the combined pattern ofdata suggests a problem.Computer Scientists have devised cleverways for computers to look for patternsin data, and these methods can then beused to solve lots of different problems,including the baby monitor. CombiningComputer Science methods with expertknowledge from Neil has resulted in asystem that has far fewer false alarms –but doesn’t miss the real problems.Night, Night, Sleep tight!Baby units could be much more peacefulin the future, thanks to the computer,always watching.12Passionate about computer science?www.cs4fn.org

Life on Mars?Lewis Dartnell, a PhD student atUCL and author of “Life in theUniverse: A Beginner's Guide”tells us about his search for life…I am searching for life on Mars. Now, beforeyou dismiss me as a crank, let me explain...I carry out research in the field ofastrobiology: the study of the 'origin,evolution, distribution and future of life inthe Universe'. Mars, our planetary next-doorneighbour, has a good chance of harbouringlife. The Martian environment billions ofyears ago was much like Earth's, with a thickwarm atmosphere, seas and lakes of wateron the surface, and probably lots ofinteresting organic chemistry going on.The problem is that Mars suffered acataclysmic environmental collapse.Most of Mars’ atmosphere has blown intospace and its surface is a dust-swept,freeze-dried desert. If life did evolve herelong ago then it's coping with some prettyunpleasant conditions on the Martiansurface right now. The ground is so cold thatany bacterial life remaining must be in deepfreeze, held dormant in the preserving ice.Another hazard on the Martian surface isradiation from outer space. Cosmic rays aremade up of particles accelerated to near thespeed of light by solar flares and explodingstars throughout the galaxy. These energeticparticles are extremely dangerous toorganisms as they shatter the delicatemolecules of life such as DNA and proteins.The Earth is shielded by a strong magneticfield and a nice thick atmosphere, but Marshas been laid bare to this onslaught ofradiation. So the big question is, how longcan frozen bacteria survive the radiation onMars' surface before they're all killed off?Mars suffered acataclysmicenvironmentalcollapseWe have a good idea what the radiationenvironment is in space, through measuringit with space probes. No particle detectorshave actually landed on Mars but calculatingthe radiation levels on, and beneath, theMartian surface is a perfect problem toattack with computer models. That is theessence of my PhD research so far:developing a computer program to handleall of the particle physics going on with thishigh-energy radiation, feeding in all theinformation about the properties of theMartian atmosphere and surface, decidingwhat data would be meaningful for themodel to output, and then letting a networkof computers churn through all thecomplicated maths to produce the desiredresults.The image shows a visualisation of the setupfor my computer model. I have designedit with a block of Mars rock beneath acolumn of atmosphere, realistically layeredso that the air gets denser nearer theground. An energetic cosmic ray (in blue)can be seen approaching Mars from outerspace, punching straight through the thinatmosphere and then triggering a greatshotgun blast of radiation underground (thered and green cascade). I need informationlike how many radiation particles arecreated, of which kind, and what radiationdose they deposit within bacterial cells livingat different depths underground. That allowsme to work out how long cells might survive.The particle physics going on inside theseradiation cascades is enormouslycomplicated, with millions of new particlesbeing created from each original cosmic ray,of tens of different kinds, and all interactingin different ways. The program uses acommon trick of computer models towork out what to do at each step in time itsimulates. During every time-step each ofthese fast-moving particles can decay, moveforward, or collide into something –interacting in many different ways. Physicistshave worked out the probability of eachhappening. All the computer model needs todo to simulate every time-step then is picka random number and check which of thepossibilities this relates to. This is like rollinga large multi-sided dice, and looking theresult up in a table of possible outcomes.It’s called a ‘Monte Carlo’ simulation, afterthe famous gambling casino resort.After I've modelled the complete cascadesgenerated by large numbers of originalcosmic ray particles and calculated theradiation dose at different depths beneathMars' surface I can use these toapproximate how long bacteria might surviveat different depths.The answer? Well the longest drill that spaceengineers have so far designed for a probebound for Mars is 2m. Looking at that deptheven the most radiation resistant bacteriaknown would be killed off in less than half amillion years.It looks like we're either going to have to drilldeeper to find surviving life on Mars, or landour probes at special locations where lifemay have been brought to the surface veryrecently. Not bad from a computer modelthat simulates space radiation by rolling afew dice … several billion times...Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use 13

Tantrix: P=NPYou can find computer science everywhere –even in popular Solitaire games andpuzzles. Most people play games likeTetris, Battleships, Mastermind, Tantrix andMinesweeper at some point. In fact all thesegames have a link to one of the deepest,fundamental problems remaining inComputer Science. They are all linked toa famous equation that is to do with theultimate limitations of computers.The sciences have many iconic equationsthat represent something fundamentalabout the world. The most famous is ofcourse Einstein's E=mc 2 , which even nonscientistshave heard of. The most famousequation in computer science is “P=NP”.The only trouble is no one has yet provedwhether it is true or not! There is even amillion dollar prize up for grabs for anyonewho does, not to mention great fame!P=NP boils down to the difference betweenchecking if someone's answer to a puzzle iscorrect, as against having to come up withthe answer in the first place.You are so NP!Computer Scientists call problems whereit is easy to check answers “NP problems”.Ones where it's also easy to come up withsolutions are “P problems”.Easy to check?A solution is in the cs4fn webzine if youwant to check it. In fact you can quicklycheck any claimed rotation puzzle‘solution’. All you do (the checking‘algorithm’) is look at each tile in turn andcheck each of its edges does match theedge of the tile it touches, if any. If you finda tile edge that doesn't match then the‘solution’ isn't a solution after all. How longwould that take to check? With say a 10piece puzzle there are 10 pieces to checkeach with 6 edges so in total that is 10x6 =60 things to check. That wouldn’t take toolong. Even with 100 pieces it would be only600 things to check – 10 minutes if youcould check an edge a second. So Tantrixrotation puzzles are NP puzzles – they canbe checked quickly.But can you solve it?The question is: “Are rotation puzzles Ppuzzles too?” Can they always be solvedquickly if you or a computer were cleverenough? You may have found that puzzleeasy to solve. It is much harder to come upwith a quick way that is guaranteed to solveany rotation puzzle I give you. One waywould be to methodically work throughevery combination of tile rotations to seeif it worked. That would take a long timethough.There are 6 positions for the first piece(ways to rotate it), but for each of those 6positions the second piece could be in 6positions too ... and so on for each otherpiece. Altogether for a 10-tile puzzle thereare 6x6x6x6x6x6x6x6x6x6 (i.e., over 60million) positions to check looking for asolution (and we might have to check themall). If you could check one position asecond, it would take you around 700days non-stop (no eating or sleeping). Thatis just for a 10-tile puzzle...now for a 100-tile puzzle – I'll leave you to work that out.It is not what computer scientists call“quick”.How clever do youhave to be?If P=NP is true it would mean there is aquick way of solving all Tantrix rotationpuzzles out there, if only someone wereclever enough to think of it. If P=NP is falsethen it might just not be possible howeverclever you are. Trouble is no-one knows if itis true or not…To find out more about Tantrix links tocomputer science and what are knownas NP-complete problems, go tothe cs4fn webzine Magazine+.So P=NP, if it were true, would just meanthat all problems that are easy to check arealso easy to solve.Let’s take Tantrix rotation puzzles to seewhat it is all about. Tantrix is a populardomino-type game using colouredhexagonal pieces like the ones here. Theidea of a Tantrix rotation puzzle is that youplace some tiles randomly on the table ina connected pattern. You are then notallowed to move the position of any piece.All you can do is rotate them on the spot.The problem is to rotate the pieces so thatall the coloured lines match where tilesmeet – red to red lines, blue to blue linesand so on.Have a go at the Tantrix rotation puzzlehere before you read on.Optimization isone way to getgood, if notperfect, solutionsto hard problems14Passionate about computer science?www.cs4fn.org

Carry onconjuringYou gather up the pile of cards from yourlast trick (perhaps the 21-card trick –see the cs4fn webzine Magazine+) aftertriumphantly revealing the card thevolunteer was thinking of. You now showthat you can not only read minds, but alsosee into the future.First, you write a prediction on a pieceof paper and seal it in an envelope so noone sees your prediction. You give it to amember of the audience to hold so thatthe sealed envelope remains in clear sightand cannot be tampered with.Next you ask the spectator to cut abouthalf the pack off the top.They decide how much, freechoice. They are going toselect a card from the tophalf of the pack that theyjust cut off but eventhey aren’t going toknow which one it will be.You recap for theaudience: a free cut of theoriginal pack, a fair deal toeliminate all but one fromtheir original free choice, a sealedprediction written at the start.Now you reveal your prediction from theenvelope…you predicted the card thatis now face up on the table!Magical mind reading…or is it?To find out how to make thetrick work and its link toComputer Science, go tothe Magazine+ sectionof the webzine.They deal the first card face down on tothe remnants of the pack, and the nextcard face up on the table, next card facedown on the remnants of the pack, nextface up on the table, and so on.Once they have finished with the cardsin their hands they start again, picking upthe face up pack turning over and dealingthe first face down on the pack remnantsand the next face up, until all cardsare dealt.Again they pick up the face-up cardsand deal in the same way. They continuedoing this until they have exhausted thecards in their hand and there is only oneleft face up on the table.Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use15

Killer Robot?Evil Scientist?!Helpless Woman?!?(You can be the one to tell Angelina Jolie)Lots of people think that ComputerScience and IT are strictly for men only.That’s really bizarre given that right fromthe start women like Grace Hopper andAda Lovelace played pivotal roles in thedevelopment of computers, and womenare still at the leading edge today. To be asuccessful modern IT Pro you have to be agood team player, not to mention great atdealing with clients, which are skillswomen are generally good at.“Geeky male computer scientist” is ofcourse just a stereotype, like “helplessfemale in need of rescue by male hunk”,“scientist as mad eccentric in white coat”,or “evil robot wanting to take over theworld”.Where do false stereotypes come from?Films play a part in the way their (usuallymale, non-scientist) directors decide torepresent characters.Students on a “Gender in ComputerScience” course at Siena College in theUS watched lots of films with ComputerScience plots from as far back as 1928 tosee how the way women, computers andcomputer scientists are portrayed haschanged over time. Here are their viewson some of those films.16Passionate about computer science?www.cs4fn.org

1928 MetropolisIn a city of the future the ruling elite live in luxury while the workers live poorly in theunderworld. An evil scientist substitutes a robot for a female worker activist. It purposelystarts a riot as an excuse so reprisals can be taken. All hell breaks loose until the malehero comes to the rescue…Computers X EvilWomen as IT Pros X HelplessComputer Scientists X Evil“Women are more or less portrayed as helpless … The computer scientist … as evil”1956 Forbidden PlanetAn all-male crew travel to Altair-4 to discover the fate of the colony there. They discover allthat is left is scientist Dr Morbius, his beautiful daughter Altaira and a servent robot calledRobby, programmed to be unable to harm humans. But what have Morbius’ machinesand experiments to do with the colony’s fate?Computers √ Helpful & HarmlessWomen as IT Pros X love interestComputer Scientists X Evil“Altaira plays a typical woman’s role…helpless…unintelligent …Barbie-like”1971 THX 1138In an Orwellian future, an android controlled police state where everyone is made to takedrugs that suppress emotion. LUH 3417 and THX 1138 stop taking their drugs, fall in loveand try to escape…Computers X Evil PoliceWomen as IT Pros X FewComputer Scientists X Heartless“The computer scientists are depicted as boring, heartless and easily confused”1982 Blade RunnerIn the industrial wastelands of a future Los Angeles, large companies have all the power.Robotic ‘Replicants’ are almost indistinguishable from humans but have incrediblestrength and no emotions. Deckard (Harrison Ford) must find and destroy a group ofReplicants that have developed emotions and so threaten humanity as they rebel againstbeing ‘slaves’.Computers X EvilWomen as IT Pros X NoneComputer Scientists X Caused the problem“A woman plays the minor role of a replicant…but is portrayed as a topless dancer”1995 HackersA group of genius teenage hackers become the target of the FBI after they stumble acrossa high-tech embezzling scheme that is likely to cause a horrific environmental disaster.Dade Murphy and Kate Libby (Angelina Jolie) square off in a battle of the sexes andcomputer skills.Computers X Used illegallyWomen as IT Pros √ Elite…but illegalComputer Scientists X Criminals“Angelina plays a hard hitting, elite hacker who is better than everyone in her groupexcept Dane who is her equal”TheRealProsSo much for fiction …what aboutreal Women IT Pros? Equalitec(www.equalitec.org.uk) asked afew about their jobs. Here is whatthe real professionals think are thebest things about what they do…“Creating the initial ideas,forming the game, making thestory… Being part of thecreative process and having ahands on approach”,– Nana Louise Nielsen, SeniorGame Designer, Sumo Digital“Working with customers tosolve their problems. The bestfeeling in the world is when youleave … knowing you’ve justmade a huge difference.”– Hannah Parker,IT Consultant, IBM“It changes so often…I am not always sure what theday will be like”– Madleina Scheidegger,Software Engineer, Google.“I enjoy being able to workfrom home”– Megan Beynon, SoftwareEngineer, IBM“I love to see our plans cometogether with another servicegoing live and the first positiveuser feedback coming in”– Kerstin Kleese van Dam, Headof Data Management, CCLRC“ …a good experienced teamaround me focused ondelivering results”– Anita King, Senior ProjectManager, MetropolitanPolice Service“I get to work with literallyevery single department in theorganisation.”– Jemima Rellie, Head ofDigital Programme, TateRemember stereotypes are fiction,careers are what you make of themand real robots are (usually) nice!Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use17

Even the dolphins usepocket switched networksEmail, texting, Instant Messaging, Instant response...one of the things about modern telecoms is thatthey fuel our desire to “talk” to people anytime, anywhere, instantly. The old kind of mail is dismissedas “snail mail”. A slow network is a frustrating network. So why would anyone be remotely interested indoing research into slow networks? Professor Jon Crowcroft of the University of Cambridge and his teamare, and his kind of network could be the network of the future. The idea is already used by the dolphins -not so surprising I suppose given they are, according to Douglas Adams' “The HitchHiker's Guide to theGalaxy”, the second most intelligent species on Earth...after the mice.From node to nodeTraditional networks rely on having lots offixed network ‘nodes’ with lots of fast linksbetween them. These network nodes arejust the computers that pass on themessages from one to the other until themessages reach their destinations. If onecomputer in the network fails, it doesn'tmatter too much because there are enoughconnections for the messages to be sent adifferent way.There are some situations where it’simpractical to set up a network like thisthough: in outer space for example. Thedistances are so far that messages will takea long time – even light can only go so fast!Places like the Arctic Circle are anotherproblem: vast areas with few people.Similarly, it's a problem under the sea.Signals don't carry very well through waterso messages, if they arrive at all, can bemuddled. After major disasters likeHurricane Katrina or a Tsunami thereare also likely to be problems.It is because of situations like these thatcomputer scientists started talking about‘DNTs’. The acronym can mean severalsimilar things: Delay Tolerant Networks (likein space the network needs to cope witheverything being slow), Disruption TolerantNetworks (e.g., in the deep sea where thelinks may come and go) or DisasterTolerant Networks (like a Tsunami wherelots of the network goes down at once).To design networks that work well in thesesituations you need to think in a differentway. When you also take into account thatcomputers have gone mobile – they nolonger just sit on desks but are in ourpockets or handbags, this leads to the ideaof a ‘ferrying network’ or as Jon Crowcroftcalls them: ‘Pocket Switched Networks’.The idea is to use the moving pocketcomputers to make up a completely newkind of network, where some of the timemessages move around because thecomputers carrying them are movingthemselves, not because the message itselfis moving. As they move around they passnear other computers and can exchangemessages, carrying a message on forsomeone else until it is near anothercomputer it can jump to.From Skidoo to youHow might such networks be useful inreality? Well one already exists for thereindeer farmers in the Arctic Circle. Theyroam vast icy wastelands on skidoos,following their reindeer. They are veryisolated. There are no cell phone masts orInternet nodes and for long periods they donot meet other people at all. The area isalso too large to set up a traditional networkcheaply. How can they communicate withothers?18Passionate about computer science?www.cs4fn.org

They have set up a form of pocket switchednetwork. Each carries a laptop on theirskidoo. There is also a series of computerssitting in tarns spread around the icylandscape. When the reindeer farmers wanta service, like delivering a message, thelaptop stores the request until they passwithin range of one of the other computersperhaps on someone else's skidoo. Thecomputer then automatically passes themessage on. The new laptop takes themessage with it and might later pass a tarn,where the message hops again then waitstill someone else passes by heading in theright direction. Eventually it makes a hop toa computer that passes within range of anetwork point connected to the Internet.It may take a while but the mail eventuallygets through – and much faster thanwaiting for the farmer to be back in netcontact directly.Chatting withDolphinsEven the dolphins are in on the act. USscientists wanted to monitor coastal waterquality. They hit on the idea of strappingsensors onto dolphins that measure thequality wherever they go. Only problem isdolphins spend a lot of time in deep oceanwhere the results can't easily be sent back.The solution? Give them a normal (welldolphin adapted) cell phone. Their phonestores the results until it is in range of theirservice provider off the coast. By putting areceiver in the bays the dolphins return tomost frequently, they can call home to passon the data whenever there. Theresearchers encountered an unexpectedproblem though. The dolphin's memorycards kept inexplicably filling up. Eventuallythey realised this was because the dolphinskept taking trips across the Atlantic wherethey came in range of the European cellnetworks. The European telecomcompanies, being a friendly bunch, sentlots of text messages welcoming thesenewly appeared phones to their network.The memory cards were being clogged upwith “Hellos”!The Dolphin’smemory cards werebeing clogged up with"Hellos"!Jon’s team are working on a pocketswitched network for urban use (by thehumans) called Haggle (See the webzinefor more about it). So the dolphins may bethe ‘early adopters’ of pocket switchednetworks but humans may be not farbehind. If so it could completely changethe way the telecom industry works...and ifwe (or the dolphins) ever do decide to headen-mass for the far reaches of the solarsystem, pocket switched networks likeHaggle will really come into their own.This article is based on a talk given atQueen Mary by Jon Crowcroft of CambridgeUniversity.Email us at cs4fndcs.qmul.ac.ukFeel free to photocopy pages from cs4fn for personal or class use19

Back (page) in BusinessDesigning for thedisabled – that must be aniche market mustn't it?Actually no: the disabledhave been the inspirationbehind some of thebiggest companies inthe world.Where do innovators get their ideas? Oftenthey come from people driven to supportthose disadvantaged in society. Theresulting technologies then not only helpthose with disabilities, but become theeveryday objects we all rely on.Alexander GrahamBell: Helping the deafBell, one of the greatest inventors of the19th century was inspired by his deafmother. His life's work, that ultimatelyled to the invention of the telephone, wasreally about helping the deaf to learn toread and write. Bell's company went onto become one of America’s greatesttelecommunications giants. The sad ironyof his work is that actually the telephonemade things worse for the deaf. Astelephones invaded the world of work,becoming a necessity to do business,many deaf employees were sackedbecause they could not use the phone.Thomas Edison:Helping the blindThomas Edison is one of the most prolificinnovators ever: so much so that hisinvention, the light bulb, is used to mean“innovation”. Another of his inventionswas the phonograph or record player, useduniversally before being overtaken by thedigital age of CDs then MP3 players as theway to listen to music. In his patent forthe phonograph, Edison put “listening tomusic” as only the fourth most importantuse. He was far more interested in reason2: “the spoken book” to allow the blind tolisten to books rather than have to readthem. For many, listening to spoken bookslike the Harry Potter series while drivingmake the commute to work bearable.Help the Disabled ...Help us allTemple Grandin andher squeeze-boxTemple Grandin is an animal scientist.As a result of her inventions and approachto animal welfare she has dramaticallyreduced animal suffering. She is heavily indemand from the meat packing industry tofix their problems. She is also autistic, andher career solving problems for animalsstarted out with her designing a “squeezebox”to help herself cope with autism.Herman Hollerith andlearning difficultiesPeople with disabilities have also beeninstrumental in the computer revolution.Early computers were controlled by punchcards – cards with holes in them thatrepresent the data. The system wasoriginally devised as a way of countingcensus data. Herman Hollerith foundeda company that made his fortune aroundthem. He had learning difficulties himself– a kind of cognitive disability. Hispersonal motivation for developing punchcard systems was so that people wouldnot have to do the counting themselves.His company later became IBM, acompany that helped propel us into thecomputer age.So if you care about society or just want asource of innovative ideas, you could doworse than thinking of how to help take thedisadvantage out of being disabled.You may just make life better for everyone.This article was inspired by a talk given atDundee University by IBM's Vicki Hanson,a leading expert on designing for thedisabled.cs4fn is written and edited by Paul Curzonand Peter McOwan of Queen Mary,University of London. This issue has beensupported by Equalitec(www.equalitec.org.uk) with additionalsupport from ARM, Intel and Microsoft.For more on the articles in thisissue go to the Magazine+section of the webzinewww.cs4fn.orgImages kindly supplied by Futurelab, Yvonne Rogers,Kirstin Cater and Lewis Dartnell for this issue.cs4fn is supported by EPSRC and the BCSMicrosoft, ARM and Intel provide support for printing.Passionate about computer science?www.cs4fn.org