Properly understanding interactive numerals is essential for all computer applications that involve numerical data entered by users, including finance, medicine, aviation and science.Although Arabic numerals (like ‘2016’ and ‘3.14’) are ubiquitous, we show that in interactive computer applications they are often misleading and surprisingly unreliable. We introduce  Interactive numerals highlight a wide range of hitherto unnoticed issues with entering and using numbers reliably with interactive computer systems. The reach of the concept is very broad, covering calculators, spreadsheets, medical devices, computerized forms, web sites, airplane cockpits—in fact, all forms of numerical data entry system.This paper introduces the new concept of Interactive numerals highlight common avoidable defects in many interactive systems where people use numbers. Incorrectly implemented interactive numerals cause subtle and sometimes critical problems. A proper understanding of interactive numerals should help researchers identify related problems and seek suitable solutions, as well as help practitioners recognize design problems and to decide on effective responses to problems: whether re-implementing systems, training users, upgrading or replacing or even banning the systems.Understanding interactive numerals makes error management more reliable: deciding what to do after an error has occurred requires understanding the root causes of the error. In particular, until interactive numerals are properly implemented, using data logs in investigations of alleged user error is unreliable .This paper shows that poorly implemented interactive numerals are surprisingly widespread; even systems developed by the world’s leading programmers are not immune. In hospitals—to take just one example of a critical application of interactive numerals—clinicians routinely enter drug doses and radiation doses into interactive systems, where numerical errors can be fatal and are often treated as criminal. It is, therefore, important to properly understand interactive numerals and any limitations they have in current systems. Indeed, this paper shows that many numerical errors are caused, not by users, but by the poor design of the systems. We will present some worrying examples taken from a wide range of systems in the body of the paper.The implication is that interactive numerals are not well understood; this paper aims to correct that. This paper provides an analysis that should help everyone appreciate the causes of the problems and the limitations of current interactive systems. The aim is to begin to move towards more robust implementations of numbers in all digital devices, systems and services.2.numerals, ways of printing or writing number values. When we see 2016, we may think of a particular number, but what we see is not the number but strictly speaking it is just various ink patterns that we interpret to represent a numerical value. More specifically, the four ink patterns ‘2016’ make up an Arabic numeral, just like ‘two thousand and sixteen’ makes up an English numeral, and MMXVI is a Roman numeral. Note that the same number can be represented by many different numerals, and even by many different Arabic numerals .We take it for granted that 2016 and 3.14 are numbers, but strictly they are We now think of Roman numerals as being rather awkward. For example, the Romans had no sensible way of writing 3.14, and Roman numerals seriously limited how people could use and think about numbers. Nevertheless, after Fibonacci introduced Arabic numerals to Europe around 1200 [interactive numerals.Today we are facing a new transition, we argue as dramatic as the move away from Roman numerals to Arabic numerals. We call the new way of thinking about numbers As well as popularizing decimal positional notation, Arabic numerals transformed arithmetic by introducing the cipher, an explicit symbol for zero. Arabic numerals, however, cannot distinguish between no number and zero, as ‘no number’ has no valid Arabic representation. It turns out that this and many other subtle problems are rife in interactive computer systems—and often cause problems that are hard for users to understand or avoid. Interactive numerals help us think clearly about such issues, and—properly implemented—directly address such issues, as we will show in this paper.then we read them as numbers. Interactive numerals look much the same but, unlike Arabic numerals which are only read and used after being written, interactive numerals are read and processed by a computer that has to represent them (at least on the screen if nowhere else) as they are being written. Thus interactive numerals consider the process of writing down a numeral, corrections and all, not just its final form as a full-fledged numeral. As a special case, interactive numerals handle the case of no numeral yet written. By contrast, nothing yet written is impossible to represent correctly as an Arabic numeral as it has no digits.Arabic numerals, which we are now very familiar with, are written down usually on paper or displayed on screens and , then  then  and so on. This raises many subtle problems. For example, – itself is not a number at all, and −0 is strictly equal to 0. Yet the computer has to decide how to represent the interactive numeral at every step, even before the user has finished entering it. In many cases, as we will show, the computer makes premature decisions about the representation that also change the meaning of what is being entered.Imagine typing the number −0.5 into a computer; unlike the traditional piece of paper, the computer has to make sense of each and all of the intermediate steps as we type Interactive numerals occur everywhere users enter numeric data into computers. Put very briefly, the problem is that not all interactive numerals are Arabic numerals, but assuming they are—which is a common conceptual error—leads to problems and use errors that are very hard to recognize, let alone correct. Indeed, in our early work , where wUnfortunately, as we will show, problems with interactive numerals are widespread. When mathematicians, programmers and scientists notice and can reason about these problems and hence seek solutions, every user (and their work) will benefit—even if they do not notice any changes.3.3.1.Numerals are sequences of symbols that follow agreed conventions for interpreting them as number values. In this paper, we consider digit-based systems of numerals for representing numbers rather than, say, counters or linguistic word-based numerals like English phrases.S, generally called digits, a numeral is a non-empty string of symbols S+ together with a surjective function π∈S, may or may not be considered numerals depending on the context.Given a set of symbols S can include signs , radix points1 and digit block separators (e.g. the space or comma), and of course generalizing to other bases than 2 is trivial. Further notation may be used to represent precision, choice of base and so on [There are trivial generalizations so nd so on . Since x,y we have x equals y or even that x is y. It is therefore common and very easy, and frequently harmless, to confuse numerals and numbers.10 , at least if sensibly refined from our explicit specification above. This is obviously something of a toy example, but it highlights that any implementation of evaluation on a computer has to make explicit things so the computer can do them that are not necessarily explicit in the specification.Now, depending on how cannot implement the natural numbers, One fundamental implementation problem that is unavoidable is that computers, being finite, 3.2.interactive numeralincludes the process intended to create a numeral. In other words, an interactive numeral additionally specifies the procedural implementation of a numeral, in contrast with the conventional purely declarative specification of an ordinary numeral, which abstracts away from the process of construction.The definition of numeral, above, takes a numeral to be a fixed entity that denotes a numerical value. By contrast, an interactive numeral may therefore be defined by a vector of string buffers bi∈B, an initial contents b0=d , a set of actions a on buffer bi gives bi+1=a, together with a surjective function V ∪E including exception and other conditions E. Buffers generally have associated invariants, for instance on their length, e.g. ∀i:0≤|bi|≤8.An An implementation will usually optimize buffers into a single object, such as a string, and update it on each action rather than creating a new buffer each time. In many incorrect implementations of interactive numerals, buffers are optimized directly to a numeric value, which of course cannot correctly represent exceptions .1 is an action that will create a buffer bi+1=bi1 by appending 1 to the previous buffer bi. However, if bi is too long, then 1 cannot be appended, and some exception condition will be flagged (or be ignored or be handled inappropriately) and typically we will have bi+1=bi (for some i). Interactive numerals may additionally having timed actions, so for instance if there is a timeout (e.g. when ‘no other action occurs for 1 minute’), then typically bi+1=d.For example, pressing the digit A realizable value is an implementable representation of a numeric value; for example, for a cash machine (ATM) it may be ∈A to ‘submit’ a buffer (and perhaps leave it unchanged), an action to clear a buffer =d, and so on. When actions can include mouse movement,  and  the buffer is generally refined to include pointers or stacks. In any case, note that A must include every possible action, not just a small set of conventional digits—the set A has to cover the possibility that the user can do anything. If the user presses a non-numeral like the space bar, what should happen? In particular, we cannot rely on type checking , it is very easy to turn a blind eye to the daunting complexity of 3.3.quasi-numerals.Strings of symbols that look like numerals are frequently used in contexts that might be considered more strictly to be names or identifiers. We call these Bank account numbers and book numbers (ISBNs) are examples of quasi-numerals. Despite commonly being called numbers, arithmetical operations (such as doubling or adding one) make little sense with such numbers. In other respects, however, they behave very like interactive numerals. They are entered as digits and have a structure, and the value denoted must fall in a particular range to be valid. In fact, the range of correct values may be highly constrained by hidden arithmetic operations that specify a check digit as part of the numeral. (Incorrect check digits should cause exceptions.)But bank account ‘numbers’ do not denote members of a set of conventional numeric values like quasi-numerals  to be created are very similar to the user interfaces that permit interactive numerals to be created. Sometimes, and confusingly, the user interfaces are identical. Therefore, there is considerable and interesting overlap in design issues across the various types of numeral and quasi-numeral. In this paper, however, we are particularly concerned where, somehow, the user interface must preserve relevant arithmetical properties of the numbers the interactive numerals are taken to represent. There is a large area of concern where the natural conception—the user’s natural conception—of The user interfaces that permit 3.4.Interactive numerals arise in many areas and applications, from web pages, spreadsheets, to handheld calculators, as well as in special purpose instrumentation, burglar alarms, medical devices and more. However, it is important not to confuse interactive numerals for the applications that use them. In many areas the applications blur the distinctions, which plays into our natural tendency to equate numerals and numbers.SUM it has value 0, but subject to PRODUCT it has value 1.2 Furthermore, if the numeral entered is ‘1.’ it will typically be displayed as exactly 1, without a decimal point. These interactions are complex to explain in adequate detail, and can be confusing traps for both spreadsheet implementors and users.Spreadsheets make the problems particularly acute. A cell in a spreadsheet can certainly allow the user to enter an interactive numeral. However, the number value it finally denotes depends on the formats and formulae in its and other cells. For example, if the numeral ‘5.5 kg’ is entered in Microsoft Excel, if it is subject to the operation Another area of confusion is with calculators, which come as basic calculators (left-to-right operator precedence), so-called ‘algebraic’ calculators that respect conventional operator precedence, and reverse Polish notation (RPN) calculators. In all forms of calculator the issues of interactive numeral are very similar; however, the main differences between types of calculator are in arithmetic expression parsing—which is independent of interactive numeral treatment. In particular, while RPN calculators are popular among certain communities, they have serious problems that interfere with their otherwise consistent arithmetic parsing: the RPN stack is generally limited to a small size, and stack overflow has undefined effects . Such co3.5.There are many generalized types of numeral, ‘things that denote number values’—such as dice, dials, increase/decrease chevron buttons, speech, clocks, counters, etc. , as wellsensors may be considered a broad generalization of numeral for when physical or other processes rather than humans define numeric values. Clearly, to measure a temperature, either a thermal sensor can be directly connected to a computer, or a human can use a conventional thermometer and enter a numeral denoting the temperature that has been ‘read off’ numerals shown on the thermometer. Sensors, however, often employ numeric transforms such as low pass filters to improve measurement dependability.We note that 4.4.1., with two decimal points. You will want to correct this error. But try this on almost any calculator, and you will find that the calculator has already ‘corrected’ your error: it has decided you entered 0.5 before you have even started correcting it. Ironically, that means if you do correct the error, you will make things worse, perhaps entering 5 instead. The computer decided what you were entering before you had finished entering it; it ignored the second decimal point, which then made correcting it counter-productive.One of the best inventions of computers is the delete key, which allows us to correct mistakes. Suppose you want to enter 0.5 but you mistakenly enter The computer  wants to ensure syntactically correct numerals so they can always be represented as simple number values along with allowing you to correct them with delete. Specifically, the computer is programmed to display a valid number because that is how the value is represented internally (e.g. as a floating point number). But numbers cannot represent all interactive numerals: for example, nothing with two decimal points has any number value to represent it, but it can be corrected to be a valid numeral. It follows that trying to do both leads to unreliable and inconsistent behaviour. This is a problem, one of ‘premature semantics’: computers generally try to treat the user’s errors as valid numbers too soon when no sensible numerical meaning can or should be assigned to them at that point. Premature semantics is explained more fully below, in §5.1. It may be argued that a user might have entered  intentionally, for instance because they know how the user interface works, and pressing decimal point twice increases the chance it has been correctly pressed at least once. If so, what is the problem? The problem is that the programmer has prematurely committed the user’s probable error to be a valid number, and the error and its consequences are now unknown to the user. The programmer does not know what a user intends, and an error is usually indicative of a failure to carry out an action as intended. For example, the user may have intended to enter 0.05, but because the  and  keys are close, the  key was pressed accidentally. The programmer has no idea. Throwing away information on an assumption is dangerous, and avoidable. leads to the result of 3 , which means Not a Number. NaN is an internal value representation (e.g. defined in IEEE floating point standards) that means nothing to non-technical users—clearly, displaying it to general users is a fault in the calculator itself: it has ignored an error, and its number representation code has failed to display it as either a number or as an error message.And if you correct some mistakes, say typing  crashes Apple’s iOS v. 9.3.3 calculator (the effect of  on iOS is achieved by swiping the finger left/right across the numeric display). But the same sequence displays 0 on both Apple’s MacOS calculators .Typing  gives −8  or –89 . The key  is handled differently too: on iOS,  gives −89 but on MacOS it gives 89 on both calculators, but  gives either −89 or 9.Keying So three modern calculators from a leading manufacturer that are clearly designed to look the same, which all have the same ‘look and feel’, and therefore would be expected to be identical in behaviour, all implement the same actions differently—and all behave incorrectly by any standard interpretation. Arguably they should be consistent, even if we disagree about what should be correct behaviour.Evidently, handling interactive numerals properly is problematic. It is surprising that calculators, which have had their current form for over 40 years, are still implemented inconsistently and incorrectly. Defects of this sort are rife in all data entry devices, not just calculators. then , all calculators will display 5 correctly. If instead you paste 2+3=, the calculators respond differently: iOS and MacOS pane give 2, but the MacOS app calculator gives 23 . None report an error or give any indication that keystrokes are being adjusted or discarded. But cut and paste can be done correctly: in the Windows 7 calculator v.6.1, for instance, pasting ‘2+3=’ produces a 5 in the display.Cut and paste, like delete, is a basic feature of modern computers: in particular, typing text and pasting text typed elsewhere should be interchangeable. The Apple calculators, however, respond to pasting differently. If you type  9999999999999…, a number almost as large as you like, will display as exactly 999,999,999  and then doing  will display 1e9 (i.e. meaning 109) which is wrong as the correct answer is much larger. However, on the Apple MacOS app calculator, as larger numbers are entered they get displayed in progressively smaller and smaller fonts, and finally become so small they are completely unreadable. Then pressing , as before, the large number is presented in a clear full-size 1E43 (or whatever). In other words, the calculators can display large numbers, but not when the user enters them directly!To give another example from Apple’s calculators: typing neither are the standard mathematical notation. In standard notation, 1E43 or 1e43 should be written 1043 or more generally, a number like 2.3E43 (or 2.3e43) would be written 2.3×1043, which all of the Apple calculators, with their high-resolution displays, are technically able to display.Note that E and e are different notations, and in fact can then enter large numbers directly—except using a key labelled , not , one speculates because having two very similarly named keys, namely   and  (raising to a power of ten), would have been thought to be too confusing. We would argue, then, if the key names would be confusing, this is another reason not to use the non-standard notation E (or e) at all in numerals!4Curiously, when the iOS calculator is turned sideways (to make it landscape) it becomes a scientific calculator and the user seem to get working. But interactive numerals are not obvious and there has been a failure to learn and employ rigorous computational thinking throughout design—apparently nobody considered it necessary! The result is that there is no standard approach. Swapping one calculator for another will give different results for exactly the same calculation, and (as we have shown elsewhere) there are also many other bugs in calculators [Although we have used Apple for the concrete examples above, other leading manufacturers, such as Casio and Microsoft, are not immune to these problems. It seems that before clarifying the concept of interactive numerals (as we do in the present paper) or equivalent, nobody thought about these details, probably as numeric data entry seems trivial and does not require much thought to culators . It is dWe note that people use calculators primarily because they do not know or are not certain of the answers, so bugs in calculators are very hard for them to spot and work around. Though these details may sound like nit-picking, they have real effects. For example, two students narrowly avoided death after an incorrect calculation performed on a mobile phone [le phone .in a different way remains good advice, not just because it helps protect against user error but it also helps protect against design error.Doing a calculation more than once, and 4.2.We have elsewhere given examples of medical devices with interactive numeral problems –11 too. Screen shots from an award-winning app are shown in It is interesting that despite the obvious programming problems, as illustrated in the figure, the app not only won prizes but is well known for being the first British ‘CE marked’ medical app. CE marking is a European legal mark, here meaning that an app is approved for clinical use in Europe, so evidently the CE approval process failed in this case to adequately check interactive numeral processing. We know many other problematic medical devices that have CE marks –12, so t4.3.In 2007, Greta Fossbakk lost 500 000 Norwegian Krone  due to an interactive quasi-numeral bug, a problem that still persists in many bank systems . FossbakApart from not silently truncating numbers, the problem would also be reduced if numerals are displayed with separators , breaking the digits up into groups thus making them easier to read and, indeed, easier to type correctly. Thus, because the computer system failed her, Fossbakk alone had to notice the difference between 71581555022 and 71581555502; this would have been much easier if the numerals had been displayed as 715-815-555-502 and 715-815-555-022, which are more obviously different. Indeed, it is concerning that credit card numbers are usually displayed with account numbers split into chunks, but user interfaces typically delete the gaps thus deliberately making the numerals much harder to read, type and check.Problems with interactive numerals are widespread, and users like Fossbakk are often penalized for design failings. We suspect that manufacturers are in denial for a combination of reasons: they do not explore the issues because of an unfortunate interpretation of product liability (they do not want to discover any problems that may be their fault) and by assuming their ‘competent’ programmers would never make mistakes over something so ‘trivial’. And of course, logging exactly what users do and exactly how systems respond to them would expose manufacturers to the truth, which may work against them in court.4.4.IP numbers (both IPv4 and IPv6) are made up from blocks of digits. IP version 6 numbers are blocks of four hexadecimal digits separated by colons. Each block can have leading zeros suppressed, so 0012 and 12, for instance, would be treated as identical.In 2011, Nigel Lang was arrested for downloading illegal material onto a PC owned by his wife. The police had added an extra digit onto somebody else’s IP number, obtained his wife’s physical address, then assumed he had committed an offence . His arrest and subsequent stigma ruined Lang’s family life. Fortunately, his efforts to defend his innocence eventually resulted in a settlement of £60 000 from the police for making the errors , that haIt is alarming that a single digit error in a user interface can have such unchecked consequences—as well as taking over 6 years to resolve. If nothing else, the design of IP numbers  and IP user interfaces used by the police (and others) shows little maturity about human factors, such as using check digits .4.5.Forms often include interactive numerals. In addition to problems with individual interactive numerals , forms often impose constraints on the relations between some or all numerical values they allow, as well as how they can be entered.always valid; if so, this is a case of premature semantics, which we discuss more generally in §5.1 below.For example, dates may be entered on a form as a triple of numerals: the day number, the month number and the year number. If the user edits the date 12/30/17 (representing 30 December 2017), say, to 2/28/17 (perhaps starting by deleting the first 1 in 12/30/17) there are possible intermediate values that are not valid dates (such as 30 February)—yet the user interface must permit editing to allow the date to be corrected. Many (bad) forms force the user to make a circuitous edit so the date is We note that, as with many interactive numerals, it is rarely possible for a user to save a form before it is valid. For a single interactive numeral, this may be a useful precaution as ‘saving’ and ‘acting on’ a single value are closely related and perhaps too easily confused, but with a form the user may be inhibited from saving many values (such as names and addresses); in this case, the imposition of premature semantics seems to us to be a serious step backwards from paper forms, which of course save the user’s work continually regardless of its validity.4.6.Although the examples come from diverse contexts, the problems can fall into one of two types: problems of construction and problems of representation. Interactive numerals are constructed by the user but as they are being constructed they are also being interpreted by the system, at the very least so they can be represented in the display. People, though, make mistakes and so in the process of constructing interactive numerals they can be expected both to make mistakes and to try to correct those mistakes. Mistakes are also made by programmers—typically in unconsciously assuming that the input of numbers is trivial.In the case of Fossbakk, the mistake was not detected by the computer even though it could have been. In the calculator examples, unlike bank accounts, it is not possible to know what numbers or sorts of numbers are intended by users, but it should be expected that users will attempt to correct mistakes they notice. However, as we showed, the use of standard features like using the delete key, which is supposed to help can, ironically, lead to confusion.These are serious problems. In our experiments ,16, we hnoticing error, and hence rarely note the important role that computers can have in helping users notice (and hence correct or manage) numerical and other errors, particularly because computers could in principle help notice errors and, in particular, help notice many sorts of errors humans are poor at noticing (such as errors in check digits). Insidious cases like those discussed in Moore [We note that standard texts on human factors and error (e.g. Reason’s classic error taxonomy ) rarely in Moore  about raOther common methods of construction, such as cut and paste, fail to alert the user to interpretations of what was constructed that deviate substantially from what was pasted.The problems of representation are unavoidable for interactive numerals because the numbers must be represented and are unbounded, but displays are finite. Either a display truncates the interactive numerals leading to incorrect values or attempts to display them leading to the problems of unreadable displays as seen in both calculators and medical apps. The representations also fail to match with existing mathematical notations and moreover do not allow users to use such notations in a consistent way.The conclusion from these various examples must be that this is not simply manufacturer sloppiness so much as interactive numerals are deceptively different to standard Arabic numerals; they are a newly identified and surprisingly complex phenomenon.Indeed, realizing that interacting with numbers is complex and a serious design challenge   makes sense. By contrast, for safety, there are insiders who are expected and often required to be professional; blaming the user is then an easy option as the user has apparently failed to do a good job—they are the proverbial bad apple. For safety, then, it often seems the user has failed rather than the system. Hence little investment or research is demanded for user interface safety.The science of interactive numerals (and other classes of user interface design issues) has not been extensively studied, particularly in the HCI (human–computer interaction) field, which one might feel would be its natural home. By contrast, say, to the widespread activity in security , this laSince poor interactive numeral design induces use error, we hope accident and other investigators will consider it as a possible cause of incidents, and thus we hope this paper will stimulate new avenues of critical investigation and research that will reduce error and its consequences.5.a+(b+c)=(a+b)+c, but on a computer this rule fails because of rounding errors. For example, 0.1 as a decimal number is a recurring binary fraction, so it cannot be represented as a simple binary value precisely.5It has long been recognized that computers do not do normal arithmetic. Thus, normal addition satisfies the associative rule, The field of numerical analysis concerns itself with such error analysis, so-called ill-conditioning and related issues . Good prThe corresponding problems of interactive numerals have only just been recognized , but they are no less serious, as our examples above show. Unfortunately there has been nothing like ‘a numerical analysis’ for interactive numerals, so we aim to address that.The problems of interactive numerals fall into the following categories:5.1.Premature semantics is an idea we identify in this paper for the first time.bi at each step i as the user enters it; furthermore, any user action that makes  (and many others) cannot be processed correctly because the program has prematurely implemented the user’s input as a number and there is no floating point value that represents anything with two decimal points!For a computer program to interpret the user’s data entry, it must impose some semantics. For example, it may interpret numerals as floating point numbers; this semantics is very easy to implement and means that as a user enters a number it must be a floating point number at all times. Using the definition of interactive numerals from §3.2, the program will use eventually wants a number, in this case it has prematurely imposed the semantics of number instead of, for instance, string semantics (which can represent arbitrary keyed input from a user).In other words, although the program The example NaN given above is a symptom of premature semantics. Here, the computer has been programmed to prematurely use a number to represent the user’s input, but the user has attempted some interactive numeral action implemented on a number that the programmer has not correctly anticipated. This results in the program performing a non-numeric operation on what was prematurely (and incorrectly) programmed as a number value. The computer itself has responded with the number turning into an invalid number, namely NaN. Then the bad programming has failed to recognize this, and NaN  is displayed directly to the user. In other words, displaying NaN proves the program has prematurely implemented something as a simple number (probably an IEEE float).Shneiderman describes a common data quality problem : a hospiIt is understandable that any such system needs some way of handling missing data but forcing a semantically correct number value here led to (perhaps well-intentioned) violations, a specific type of error  that canNow we recognize premature semantics as such, it explains a wide range of familiar problems. Web forms, for example, often require a user to fill in details they do not yet know: this is because the form implements the fields as simple values , which premature semantics prevents from being left blank or partly filled in—a routine procedure that is very convenient and indeed trivial on paper forms.premature representation—the representation is a consequence of an internal, premature, semantic choice. Many systems display a numeral even before anything has been entered, because the display is assumed to always display a number:— Many interactive systems display 0 if the user has entered nothing: they then cannot distinguish a user who has entered nothing from one who has explicitly entered zero. More generally, interactive systems displaying default values (such as 0) when the user has entered nothing makes it very difficult to distinguish a user entering a default value and actually doing nothing—not even recognizing ‘they’ have entered a value, which may therefore not be what they intended. rapidly alternating with  so they are obviously on and working.— Some systems display 0 to show that they are switched on, but this creates the ambiguity whether the user has started to interact or not. Instead, they could display patterns like  or : they cannot know from the display whether pressing  will change the number to either 0.5 or 5. This lack of predictability is a high price to pay for the ‘simplicity’ of always displaying a decimal point even when one has not been entered.— Many systems permanently display a decimal point , so if aA special case of premature semantics might be called 5.2.Feature interaction is the name given to unanticipated interactions between system features [interferes [features . Featureterferes : we do nOn calculators one feature is that we want users to be able to enter large numbers, and another feature is that the display screen can only show up to a fixed number of digits, typically 8 or so. The feature interaction here happens when a user keys in more than the allowed 8 digits—what should happen? Each feature separately makes sense, and in fact each feature is so straightforward and obvious it hardly needs thinking about at all. Yet, put together the two features conflict. Most calculators ignore the resulting feature interaction, with the result that the users will perform incorrect calculations without warning—and generally without noticing.We want to allow users to make and correct errors, but that obviously desirable feature interacts with the equally obvious feature that only correct numbers are displayed. What should happen when a user keys in a syntactically incorrect number? How can it be corrected? This is another feature interaction. ) key changes the sign of a number and on most number entry systems it can be pressed at any time as a numeral is entered, in part because it is always represented as a prefixed minus sign whenever it is pressed. Thus, on the iOS calculator and many other devices with a change sign key, all of the following : ; ; and  all result in −12 being displayed.Another feature interaction is the way negate works combined with the way delete works. The negate , or it should be expected to be.Delete seems simple, but is an interesting feature. In general, one would expect that a sequence of keystrokes … is pressed is unpredictable—it may or may not delete the sign prefix. The problem, of course, is that change sign key is implemented to put the negative sign at the left of the numeral, yet the delete key works on the right-most end of the numeral. After a change sign, the delete key cannot work both at the end and at the start of a numeral, and that causes an unfortunate feature interaction. A simple solution would be for the delete key to work on the most recent key pressed, not on the right-most key shown—but this is a little harder to implement.However, a feature interaction between change sign and delete arises. What happens when  feature interaction is caused by premature semantics: treating an interactive numeral as a simple signed number, and then not having any representation of what the user has done so that  can be disambiguated.The 5.3.If a numeral is ambiguous or unreadable  it is unreliable.c) it is easy to overlook. Other number entry systems routinely truncate numerals to fit displays, causing potential problems that are very hard to see. Elsewhere we have discussed legibility in more detail [Legibility seems a self-evident criterion for a good interactive numeral, but as the Apple MacOS app example shows c it is ee detail , and in Legibility ought to be an obvious requirement, and achieving it  is completely unproblematic. Yet poor legibility continues to be a factor in accidents and other problems —note tha5.4.Sooner or later, users make errors, and they may or may not be aware of their errors . We have: it deletes the decimal part of a number, so  enters 3.5, whereas many other systems (like the Apple iOS and MacOS pane but not the MacOS app) will just ignore additional decimal points, even though they key-click, misleadingly confirming the keys are being processed normally.In practice, interactive number entry systems rarely detect user error. We gave examples above of a user entering numbers that are too large: the results are wrong—yet detecting this error is trivial programming. In some systems, user error is ironically treated as a ‘feature’. For example, on the Graseby 3400 infusion pump (a drug delivery device) the decimal point is treated as a special case of b).Displaying NaN is a simple example of ignoring error. NaN is displayed when the program attempts to perform an invalid operation on a number: the hardware  has detected the error that the programmer has ignored b., is pressed the interactive numeral processing calculates d′=10d+9, updating the displayed number d to d′, but eventually 10d or 10d+9 is too large for the underlying floating point representation to handle. A correct program would check d was in range before attempting an invalid calculation—even better, it would also check d′ would be displayed correctly . Even displaying the resulting NaN to the user is another ignored error.Unlike the other Apple calculators, the MacOS app allows the user to enter arbitrarily large numbers, which as we explained above get displayed in a smaller and smaller font until they are unreadable. Continuing using the app eventually makes an erroneous calculation and displays NaN. Presumably, as each digit, say All the three Apple calculators allow pasting in a number. For example, 1,2,3.4.5 becomes 123.4 on the iOS and pane calculators, but 123.45 on the app. None of the calculators detect the errors. Ignoring the commas ‘makes sense’ only because it is an easy way to handle correct numbers like 10,000. But the same reasoning also ignores problems with erroneous numerals like 10,00. Ignoring errors, including discarding digits and decimal points, is very unhelpful—users expect correct answers, not any old answers.Many systems, however, do detect use error; for example, one of many studies using ‘smart pumps’ (interactive drug infusion devices with number range error detection) along with other interventions reduced error rates by 73% with paediatric patients . Given tIgnoring error is a special case of poor programming, which we discuss next.5.5.Poor programming explains many problems.continual mapping of that abstract data type to computer number representations (e.g. floating point numbers) as the user enters and edits numbers becomes a mess if not programmed correctly.We can describe the programming problem succinctly: user operations on numeral syntax define an abstract date type for data entry. The within a program and helps improve program quality generally, but it does not specifically help improve interaction. To make the point, Type checking helps detect inconsistencies When interacting with programs, users can do anything, and a dependable interactive program has to respond coherently to all possible user interactions. Interestingly, neither the philosophies of static or dynamic type checking align well with this requirement: incorrect types cause incorrect programs to be rejected, and the programmer has to start again after fixing the errors. While that makes sense and is undeniably a very useful approach for dependable programming, by contrast, in user interface interaction rejecting the user’s input is rarely a good option—an interactive program has to keep running despite error, and it has to manage error as the user gradually repairs the input to something acceptable. Even if it may be a good idea for the user to ‘start again’  the program cannot terminate.Very often there are unhelpful compromises: the user can make an error, but their input will not be saved (or otherwise processed) until all the data are ‘correct’. This is essentially the type correct part of the program refusing to allow the user to continue interaction until all detected errors are fixed. If the user needs to pause while they decide how to proceed, many programs will further aggravate the situation by timing out and discarding the user’s partial input. Put in other words, the problem is the ‘correct’ program has required numbers prematurely, but the user interface is supporting interactive numerals—and the program is expecting the user only to save when all interactive numerals are well-formed numerals . As explained elsewhere in this paper (§5.1), this is a premature semantics.Users learn how to enter numbers on systems and will acquire habits (such as shortcuts and specific ways to use the correction features). Thus users will internalize the ‘mess’ and if the mess is different on different systems, this will induce unnecessary errors . The unnecessary variation, despite their very similar look and feel, between the three Apple calculators is a case in point.Examples earlier showed large numbers may be accurately displayed when they are the result of a calculation, but not when the user enters them. Thus, although numbers as such are handled well, interactive numerals are not. Worse, there is usually no warning to the user that displayed values are incorrect when numbers entered by the user have been truncated.The problems with large numbers probably happen because a standard, general-purpose, number output routine is used to display calculated values, but user-input numbers are displayed differently: a basic programming inconsistency. Considering the additional inconsistencies over cut and paste discussed above, it is likely that those calculators do not use standard text entry routines, but handle button presses in ad hoc ways.must be used to mitigate these predictable problems, and evaluation techniques must be used to evaluate whether designs meet their requirements when they are used.We conclude that correct interactive numeral programming is harder than most people think—poor programming is deceptively easy. The cognitive effort to program well creates tunnel vision: that is, programmers make mistakes they are unaware of . Modern The evidence presented in this paper suggests even major manufacturers are not using such techniques to help avoid problems. We would argue that any computer system programmed by people who do not use such techniques  should n Some readers of this paper have pointed out that some programming languages, such as Haskell and Swift , have built-in mechanisms to help avoid premature semantics. However, the problems this paper has pointed out can be avoided in any programming language. The issue is not the programming language used, or the programming language that might have been used, or even whether the programmer uses data validation. The issue is the lack of knowledge about interactive numerals.6.Interactive numerals are more complex than most programmers think, so they implement them simplistically. Even the world’s very best programmers are not immune. If programmers are unaware of premature semantics and feature interaction, they will make mistakes, which will then catch out users later. Understandably, users are unaware of these issues and they risk being misled, particularly in situations of error—precisely, then, at the very times when being misled is especially counter-productive.This paper makes several important points:(i) Our definition of interactive numerals is essentially equivalent to a high-level specification that programmers could implement. Variations from this specification would allow programmers to identify with clarity any design compromises, and so reason about the consequences of their compromises in their approach to interactive numerals.(ii) The everyday practice of programming has not yet advanced to the point where interactive numerals are reliably implemented, nor to a point where users can reliably use them. We have developed some evaluation tools  that can(iii) Premature semantics is a concept describing a common type of defect in programming, and is particularly prevalent in poor interactive numeral implementations. Now named, it can be actively managed by programmers.after premature semantics has over-simplified it. Logs do not give any reliable insight into what users did  Problems with interactive numeral implementations mean that logs of interactive system usage may record what systems do but do not reliably record what was done by users. This hinders accurate understanding of any use problems, and may have legal repercussions: logs must not be naïvely used as evidence of what users have or have not done —they onllone why ), unless do? Indeed, the synchronization of the user model and the program’s semantic model at the keystroke level is a topic that has been excluded from classic research [(v) Interactive numerals can be discussed rigorously because numeral syntax and number semantics are familiar and are well-defined, precise concepts. It is, therefore, easy (if one asks the right questions) to show whether they are implemented correctly. There are, however, many other forms of interactive features that beg further study. For example, typing letters with accents  requires several keystrokes; what then should key sequences like research .are as dependable as they seem, remains problematic. For the time being, in safety and mission critical areas users must compensate for poor design by adopting strategies  to help detect error. It was beyond the scope of the present paper to discuss human factor mitigations, such as range validation and the user interface redisplaying numbers in contrasting numeral formats (e.g. spoken words) to help the user confirm that the number they intended to enter was indeed correctly entered.Being able to buy and use safe interactive numeral systems, let alone assure yourself they With this paper, we hope we have clarified the fundamental role of interactive numerals in numerical interactive systems—that is, almost all interactive systems. From articulating the design and use errors around interactive numerals, our examples show that users, designer and procurers need to pay very close attention to their correct implementation. And implementations more consistent with our definitions are demonstrably possible. For instance, we can indicate contingent semantic correctness ,33 or sy


Selection, Optimization, and Compensation (SOC) may contribute to successful aging by helping older people maximize well-being in the context of physical decline. To explore this hypothesis, and to investigate the potential for narrative analysis to improve understanding of SOC, we analyze interviews conducted with 15 members of the 6-Day Sample, a cohort of Scots born in 1936.Interviewees were chosen based on their physical function and well-being scores. Interviews were analyzed to investigate “SOC talk,” that is, older people’s talk about SOC behaviors in everyday life. Types and amounts of SOC talk were quantified, and talk was narratively analyzed. We hypothesized that older people who engaged in more SOC talk would have higher well-being.Older people who engaged in high levels of SOC talk had high well-being despite low physical function. Those who engaged in little SOC talk had low well-being despite higher physical function.The concept of successful aging is valuable in part because of its narrative quality: One must strive to keep one’s life story developing despite physical decline and other losses. We provide evidence, from the perspectives of older people themselves, of the ways in which SOC may play a role in that process. The concept of successful aging has prompted debates over how to define success and who gets to define it . Most moactivities that individuals engage in as they pursue their goals. Such activities represent “goal-directed instrumental behaviors” . Group 2 scored in the bottom quartile in both measures. The other two groups were off-diagonal, in that their well-being scores were at least 1 Following the selection of our analytic subsample, we reanalyzed their interviews, counting and coding each instance of SOC talk related to physical function, including talk about physical activities. Our coding builds on earlier conceptualizations of activity-focused SOC in everyday life . In suchIn our coding, we adopt a moderately broad measure of physical activity. In addition to more strenuous physical activities, we include those such as gardening, household repairs, and housekeeping; we do not include more sedentary activities such as knitting and driving. We include SOC talk about the individual him/herself, not talk about others. When narratives contained multiple examples of SOC, for example LBS and Compensation in the same narrative, or two examples of Compensation, each example of SOC was coded individually. Different levels of physical function were associated with different types of SOC talk. Group 1, which had the highest physical function, spoke more than other groups about ES and Optimization. Group 2 had the lowest physical function and used Compensation the most. Group 3 had slightly higher physical function and used a broad mix of SOC strategies. Group 4 engaged in little SOC.In the remainder of this section, we first provide an overview of interviewees’ SOC talk. We then draw on content and narrative analyses to compare the four groups, discussing how their differing patterns of SOC talk might be interpreted in terms of successful aging.Members of Group 1 engaged in relatively high amounts of ES, a process that appeared to be related to their high level of physical functioning. This high level of ES included undertaking new, physically demanding activities. Brenda, for example, had recently adopted a puppy and had begun volunteering in the local hospital garden. Colin had taken up hillwalking see , and theLBS was an important strategy for all groups, and interviewees spoke of two primary approaches to this strategy: using LBS to continue engaging in cherished activities but in less resource intensive forms and using LBS to choose from among a range of goal-relevant activities.LBS also appeared to play an important role in identity maintenance. A number of men in the qualitative subsample were lifelong handymen and spoke of using LBS to continue doing routine domestic repairs and improvements, despite physical decline. As Neil (Group 1) said:I don’t do ladder work now. I think there was a year when I felt my wrists weren’t strong enough to deal with the ladder, you know, and I just said, ‘Right, that’s it.’ I stopped.However, Neil went on to emphasize that he continued to do all the “normal maintenance” in his home and did not have to seek help for anything other than ladder work. Owen (Group 3) suffered from worse physical function than Neil but provided a similar narrative, saying that whereas he no longer climbed ladders, he could still “do the woodwork” and perform other domestic improvements. In their late 70s, these and other men were now forced to forego the most physically demanding aspects of an activity that had long been important to their identity. However, they were able to use LBS to continue engaging in that activity and to continue seeing themselves as men who were handy around the house. LBS thus offered these men a means for maintaining identity in the face of physical decline. Women’s narratives of household-related LBS typically had a slightly different structure. Whereas men were more likely to provide narratives in which they continued engaging in important activities but in less resource intensive forms, women were more likely to describe cutting back on one resource intensive activity, particularly housework, in order to devote their reduced physical resources to other, more favored activities, especially gardening.Optimization was the least commonly cited strategy. This finding conflicts with that of Compensation was particularly important for individuals with low physical function. Compensation devices typically took two forms: tools such as walking sticks, and adaptations to one’s home. Several interviewees said they had made such adaptations in order to avoid or at least delay being forced out of the family home by physical decline. For example, Owen (Group 3), who had low physical function, had installed a set of handrails on the stairs and said they hadstood us in good stead.… It’s nice, when you happen to meet your wife coming down and she’s holding one of them [and you’re holding] another one on the other side.Attitudes to Compensation strategies were nuanced and reflected a desire to remain as autonomous as possible. Mary (Group 3) saidAfter the first [hip] operation I wasn’t allowed to bend, so I’d sweep the kitchen floor and then I’d have to get [HUSBAND] to come in and put it into a dustpan. That really irritated me no end. He wasn’t irritated but, you know, I was. And I got one of the long-handled dustpans and brushes…and it’s been fabulous.Though help from another is a form of Compensation, accepting such help had negative impacts on Mary’s well-being, perhaps because it highlighted her diminished autonomy . MaintenGroup 1 had high well-being and physical function and engaged in medial amounts of SOC talk. As might be expected given their high physical function, this group engaged in little Compensation but relatively high levels of ES and Optimization. From a narrative perspective, this group used SOC to add new chapters to their life stories by adopting new activities and pursuing new goals. Group 1 also had high expectations regarding future chapters. For example, James said that he had recently told his young granddaughter that he planned “to be able to dance at your wedding”—an event that was unlikely to occur before he was 100 years. A high user of SOC, James felt confident that he could sustain a healthy and dynamic Third Age  for decaGroup 2 had low well-being and physical function. This group engaged in almost as much SOC talk as Group 1, and approximately twice as much as the other low well-being group (Group 4). Group 2 showed greater reliance on Compensation than did groups with higher physical function. Despite poor physical health, including severe chronic pain, members of Group 2 used SOC, especially Compensation and LBS, to continue pursuing meaningful goals, albeit in reduced form. Eleanor, for example, was a lifelong dog lover and gardener but could no longer engage in these activities, so she had “downsized” to a cat and had shifted from being a “proper gardener” to a “potted gardener” (LBS). From a narrative perspective, Group 2 used SOC to forestall narrative foreclosure  and fighGroup 3 was “off-diagonal,” with high well-being despite low physical function. This group engaged in more SOC talk than the other three groups and, like Group 1, featured a varied mix of different types of SOC talk. However, whereas Group 1’s SOC talk was weighted toward ES, Group 3 featured high levels of LBS and Compensation.Like Group 2, who also suffered from low physical function, Group 3 spoke of pursuing fewer goals than before. Unlike the former group, however, Group 3 still strived to pursue key goals in a non-diminished form. For example, Mary appeared to see gardening as central to her identity, and thus prioritized its role in her life. She used Compensation and LBS to continue maintaining a large garden, and refused to downsize to what she referred to as “a geriatric garden,” despite severe mobility problems and several garden-induced falls. Another member of Group 3, Catherine, said that even though she suffered worse health than many of her peers, she remained more active, thanks to skilful use of Compensation and LBS. Thus, despite poor physical function, members of Group 2 continued to pursue valued goals and strived to keep key chapters of their life stories developing.Group 4 was off-diagonal in that it had low well-being despite relatively high physical function. Quantitatively, this group engaged in much less SOC talk than their peers: roughly half as much as Groups 1 and 2, and one-third as much as Group 3. Narratively, a key difference between this and the other groups was the lack of adaptive resolutions in Group 4’s discussions of physical decline. All groups provided narratives of physical decline and its impact on activities. These narratives typically took the form of:(1) Because of X ,(2) I had to give up Y (a cherished activity).Groups 1–3 typically transformed these stories into narratives of adaptive resolution, that is, narratives in which LBS or Compensation was used to adapt to decline and continue pursuing valued goals. These adaptive resolutions typically took the following forms:(3) So I started doing a less demanding version of Y  OR(4) But that left more time and energy to focus on Z .For example, Mary (Group 3) provides an example in which the loss of one physical activity is made up for by the ability to engage in a more preferred activity:I can’t see me being able to play golf again…I’ve got a very stiff neck and just trying to look to see where my ball’s going [is] uncomfortable. But…I’ve got plenty to keep me going anyway just here in the garden.For Group 4, however, narratives of activity loss typically ended without adaptive resolutions—for example, Ian said, “I used to do a lot of shooting…and I don’t shoot now.” The narrative ends there, without moving onto an activity he has replaced shooting with. Rather than pursue a smaller number of goals, or less demanding ones, Group 4 seemed more willing to allow chapters of their life story to close. At least in the physical domain, they appeared to engage in relatively little SOC to adapt to the losses associated with aging.In summary, Group 1 took advantage of their good physical function to engage in high levels of ES, pursue new goals alongside old ones, and add new chapters to their life story. Group 2 primarily used Compensation and LBS to adapt to low physical function, maintain the pursuit of valued goals , and to slow the closing of valued chapters in their life story, that is, to avoid narrative foreclosure. Group 3 maintained high well-being despite poor physical function and engaged in extensive SOC to continue pursuing important goals and to keep key life chapters developing. Group 4 had low well-being despite relatively good physical function and made very little use of SOC.We found support for our hypothesis that older people who maintained high well-being despite low physical function would engage in more SOC talk than their peers, and those who reported lower well-being despite relatively high physical function would engage in less. Group 3, who had high well-being despite low physical function, engaged in almost three times as much SOC talk as Group 4, who had higher physical function but low well-being. The two “on-diagonal” groups both engaged in medial levels of SOC talk. This finding suggests that SOC may play a role in mediating the relationship between physical function and well-being. Our analysis has methodological and theoretical implications for the study of SOC specifically and successful aging more generally. Methodologically, we add to the limited body of qualitative research on SOC. Our qualitative approach to everyday SOC behaviors complements and adds to quantitative research on general SOC propensities by providing insights into how, when, why, and in what ways older people incorporate SOC into their daily lives to maximize well-being. Even when older people were not explicitly asked about SOC, they frequently talked about it in relation to everyday activities, indicating that it is possible to study these processes in a general qualitative data source see also .Theoretically, our analysis brings together outcome- and process-focused approaches to successful aging. Group 1 appeared to be aging successfully both in terms of outcomes and processes: This group had high well-being and physical function and used SOC to pursue new goals and add new chapters to their life story. We would argue that Group 3 should also be seen as aging successfully: Despite low physical function, this group had high well-being and appeared to make extensive use of SOC to remain active, continue pursuing valued goals, and keep key chapters of their life story developing. This successful use of SOC brings to mind In this light, it may be instructive to compare Group 2, which had poor scores on both outcome measures, to Group 4, which had low well-being despite relatively high physical function. Outcome-only definitions of successful aging would suggest that Group 4 was aging more successfully than Group 2 because of the former group’s higher physical function. However, Group 4 showed very limited use of SOC and thus appeared more prone to narrative foreclosure, at least in the physical domain. If definitions of successful aging are to include outcome and process focuses, as perhaps they should , Group 4 may be aging less successfully than Group 2, despite better physical function.Our study has a number of limitations, including the size of the qualitative sample and the cross-sectional nature of the data drawn on for this article. Future research should draw on larger samples, particularly for the off-diagonal groups. On this project, coding of SOC talk was done by researchers who were aware of the interviewees’ physical function and well-being scores, introducing potential bias into the coding process. However, the SOC talk quantification process may also help to improve the rigor of the results, by ensuring that qualitative claims about amounts and types of SOC talk were clearly grounded in numerical tallies, thus reducing the likelihood of confirmation bias.Whereas some scholars e.g.,  have sugThe authors were supported by a Research Council UK Life Long Health and Wellbeing Programme grant (MRC G1001401/1). I. D. Deary and C. E. Brett are supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Life Long Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged, as is the valuable contribution of Roona Simpson.The authors declare no conflicts of interest.


The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly.Unlike the majority of the work done in this field  which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based.The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style. Identifying and attributing authorship of different text passages can be beneficial for various tasks and areas including bibliometrics, information retrieval and plagiarism detection. In previous work, we illustrated how to incorporate ideas of authorship attribution into the mentioned areas. We introduced the concept of extending the retrieval process by including authorship information to allow for identifying text passages written by a particular author. In Rexha et al. , we applThis follow-up paper [an extension of Rexha et al. ] investiStylometry is the application of the study of linguistic style, usually to written language.Stylometry is the application of the study of linguistic style. It is usually applied to written language.The information of these two sentences is the same, yet they slightly differ in the way, how this information is conveyed. In Example 1, the author appears to favor longer sentences; in Example 2 shorter sentences. In case of these constructed examples content features will not be sufficient to distinguish between authors; to do that we need to focus on content-agnostic, i.e. pure stylometric features. Consider the case of a journal hiring a new employee and asking her to adhere to a  specific style in her articles. Providing a tool that compares her writing style to the journal style can shorten the settling in process. The same logic applies to intelligence agencies to support the analysis of aggressive and threatening messages, i.e. identify the responsible author. Furthermore, commercial products (i.e. Grammarly)As a first step towards building such a system, we conducted two different pilot studies to understand whether humans are capable of distinguishing between writing styles without considering the content as a discriminating feature. As already mentioned, this makes our study different from most of plagiarism detection tasks. The first is a quantitative study with annotators from the crowdsourcing platform CrowdFlower. This study has the disadvantage of the unknown quality of the judgments. Even though we use mechanisms to avoid random choices from the crowd, we cannot fully prevent it. To overcome such a problem, we performed a qualitative study, which represents the main contribution of this article. In this study, the current authors evaluate the list of experiments and give a detailed description of the thinking process while annotating.The outcome of the two studies was quite similar. Even though it appears more likely not to have random evaluations (72% confidence), our findings revealed that this task is challenging, even for humans. We also conducted an exploratory data analysis where we statistically compared the decisions against content features and content-agnostic features. We didn’t find any correlation that explains the different judgments between annotators. In addition, we make our dataset publicly available.bibliometrics as well as scientometrics which both aim to measure and quantify the scientific output. This ongoing growth of the volume of scholarly publications poses significant challenges leading to the incorporation of ideas from other fields such as computer linguistics to improve and enhance the measuring and analysis processes. In recent work, we proposed to add the concept of author attribution into the pre-processing of the analysis of scientific publications. In Rexha et al. . At the end of this phase, we chose 66 experiments (22 per each category of experiments previously described).From the previously selected documents, we chosen a subset and decided to make the annotators rank text snippets which are drawn from the beginning of the introduction section (we select the first sentences until the one ending after the 400-th character). The rationale behind this choice is twofold: 1) it gets more and more difficult for the user to remain focused on the task while reading a long text; (2) we hypothesize that the introduction contains less topological information than other parts of the scientific papers. Having selected the text snippets, we designed three experiments  for each annotator. For each of the experiments we presented the annotators with a source and four target snippets, subject to different problem settings , performed using the crowd-sourcing platform CrowdFlower.Numeric Features, which contain numeric information about the writing style. We used the features listed in the Table Common selected phrases, representing a list of selected phrases present in both, source and target snippet, like “although”, “most common”, etc.Descriptive features, informing whether the first sentence of the source and the target starts with a definition (for example: “Breast cancer is...”) or whether hyphens are used in both snippets.Outliers, representing a list of Numeric Features that have a larger value than the 95th percentile and smaller than the 5th percentile of the distribution in all the snippets of the experiments.Then, we presented them to the users . Table The job in CrowdFlower was performed by 56 different annotators from 29 different countries. Since our goal is to rank based on the writing style, the level of understanding English isn’t of a big concern for the task. To avoid random selection, we configure the system to disallow annotation in less than 20 s. At first glance, the annotators have a small agreement in the ranking of the similarity between source and target snippets. Without considering the rank itself, a full agreement is achieved in 26 targets, 160 have an agreement of two annotators, and 78 of the targets have no agreement at all. For a more detailed analysis, we use Krippendorff’s alpha .Specifically in scientific literature, there are only a few authors that make use of words that are considered too emotional or imprecise, for example “tremendous” or “dramatic”. The level of preciseness also varies between authors, some may state “approximately 500,000 cases”, while others try to be more exact, for example “528,000 cases”. Another potential indicator for specific authors is the use of “slang abbreviations”, for example “can’t” instead of “cannot”. Given that authors are consistent and use the same spelling for all their written text, different authors could be distinguished. This also applies to synonyms (“high blood pressure” vs. “hypertension”) or alternative spellings (“etiology” vs. “aetiology”).The initial words of a sentence often appear to convey information about the specific writing style. Some authors use phrases to link two sentences. Examples are: “Indeed”, “Although”, “Hence”, “Thus”, “While”, “Moreover” and many more. The usage of such words or just specific words might be indicative of certain authors. Furthermore, the presence (or absence) of a comma following such words, might be a useful feature to distinguish two writing styles. Some authors also appear to have the initial words to indicate a temporal aspect, for example “Since then”.Trigger phrases may also occur within the sentence, which are characteristic for specific writing style, for example: “not very long ago”. A special case of such phrase is “comprise”, where some authors add the word “of”. This might also be indicative for the distinction between native and non-native speakers. Native speakers may also tend to use words, which are less frequent. Another obvious difference is whether the author uses British or American spelling, which might also be due to the intended target audience. The way numbers are written might also be specific to the locale of the text in addition to the preference of the author, for example “55,000”, “55,000” or “55,000”. Similar to this feature is the usage of the “Oxford comma”, where a comma is also used for the last item in an enumeration ; its presence might help to separate two authors. In general, the usage of semicolons, dashes and brackets might also be specific to some authors.Another feature, which requires a certain level of consistency is the usage of multi-word terms, which might be written as single word, separated by a hyphen or written as separate words. Examples for this observation are: “US-born” versus “US born”, “over weight” versus “overweight” and “crossing-over” versus “crossing over”.After getting an overview of the first example snippets, it appeared that the informative value of typical features like the comma usage, sentence length or usage of brackets was not sufficient to arrive at a conclusion. In particular when the style is, in some cases, dependent upon the publisher, which is typically the case in scientific literature (e.g. citation style). In general, it seemed that stylistic choices and preferences of the author had a bigger impact. And sometimes the distinct absence of particular styles was more informative. Some authors appear to favor including precise figures within their text while others tend to use descriptive language.  In general, the usage, or their absence, of precise figures within the text seems to be a highly informative characteristic of the authors. Furthermore, some authors seem to have the tendency to list particular information, separated by commas, within their text, while others do not use this style at all. Another distinctive feature was the usage of abbreviations. First, some authors used a lot of abbreviations while others did not use them at all. Second, some defined these abbreviations and did not reuse them later in the text, although it would have been appropriate.The identification of the similarity in the writing style starts by analyzing the structure of the sentences for each snippet. The formulation of the first sentence is the main observed characteristic judged in a snippet. Authors expressing the same concepts once as a subject and once as an object leads to considering the texts as written by different authors. For example, “Both obesity and metabolic syndrome (MetS) are well known.” and “A major obstacle in the treatment of overweight and obesity is hunger.” express “obesity” as either the subject or the object.In the cases where the first sentence starts similarly in various target snippets, the way it is expressed also provide a differentiating feature. Some authors favor details compared to the short sentence. Furthermore, the distinction extends to the structure of the snippet. Some authors tend to favor long sentences to short ones. The structure of these sentences is seen as a characteristic that helps to decide the similarity between the presented texts.The way numeric values are represented .Whether there was a different representation of statistics The use of enumeration .Other aspects that were used to judge were:Also, the specific phrases used, gave hints about the authorship attribution. For example, phrases like, “which” or “although” were considered very informative.For the sake of comparison between rankings based on writing style versus content, we add the ranking approach of Annotator #4:After the first ranking task, it became apparent that an understanding of the source’s content was essential to conduct the ranking. Similar to a summarization task, factual information was identified. This identification procedure can be best described as identifying answers to the 5W question types, i.e. who, what, when, where and how. It could be observed that the temporal dimension, for instance, the year the source’s facts were referring to, was helpful when ranking the targets. Comparing the facts from the source with the ones to be ranked turned out as the common procedure for the ranking; for this part the support of domain experts familiar with synonymous medical expressions would have sometimes been beneficial.As a first step, targets which were off-topic or out-of-domain were placed at the bottom of the ranking. Then, a closer look was taken at the targets sharing most of the facts with the source. These targets were ranked according to the coverage of content, i.e. the number of shared facts. In some experiments for example, the targets were missing facts about a certain region a disease was found. In several cases, two targets shared an equal number of facts with the source; yet one of them offered more information, i.e. more facts than the other one. These cases were considered as less similar to the source as well. In cases where the amount of facts were more or less equal, the following two indicators influenced the ranking. First, the order in which the information is presented—same order as in the source equalled a higher similarity than a different order. Second, if the order of information was the same, readability tipped the scales in favor of the more readable than the less readable one.In this paper, we have conducted two extensive studies to gain a better understanding (1) how humans judge an author’s writing style (being faced with text passages exhibiting a high-content similarity) and (2) which content-agnostic, stylometric features they preferably use to identify an author. These experiences and observations contribute to automate (mimick) this process by identifying as well as by distinguishing specific features used by humans in their decision making process. We provide detailed descriptions and observations of this author identification process which will prove valuable in an effort to develop algorithms, for instance, in areas like plagiarism detection or forensic analyses. Furthermore, our findings indicate that the task turns out to be very challenging, especially with the current experiment settings. The results also indicate that the annotation process from the crowd is more likely not to have random evaluations (72% confidence). In addition, we have made our data set publicly available to the research community to enable further investigations and algorithm development.In future work, we plan to take the author’s institution into account to serve as a dimension in the paper selection process—similarly with papers published in the same journal. We also plan to take into account text passages written within a distance of years by the same author. We plan to extend our study by increasing and diversifying the set of experiments aiming to capture, from human annotators, properties of the thinking process while performing this task. We also intend to automatically learn and suggest personalized features for each of the annotators, helping them rank, according to their metrics.


In a recent commentary in this journal Seifert et al.  propose One could argue that scientific controversies should not be decided by majority rule alone, but there are sound reasons why biological systematists overwhelmingly favor a phylogenetic classification. Such a scheme is simply more informative, accurate, and predictive. Birds really are a kind of modified reptile; to place them in a different group, separate from reptiles, obscures this important fact. Similarly, inclusion of termites in the order Blattodea emphasizes that they are indeed “social cockroaches” and this leads to a more insightful understanding of their biology and evolution , precluding a simple splitting into several monophyletic subgroups.Consider the examples to which Seifert et al.  objectedTemnothorax ants have undergone an impressive radiation in the Caribbean, producing species that are, at least superficially, far more divergent morphologically from typical Holarctic species of Temnothorax than the social parasites ”, until usage of the species-group name takes over. There are numerous examples among ants of other satellite genera that were previously synonymized under their containing clades: Doronomyrmex under Leptothorax; Sifolinia under Myrmica; Anergatides, Bruchomyrma, Sympheidole and others under Pheidole; various former genera under Strumigenys, etc. No-one is any longer decrying the loss of these genus names; the species names  are still available and permit ready communication about the taxa concerned.The argument that communication is hindered by adoption of a phylogenetic classification also does not stand up to scrutiny. The former parasite genera can be referred to using informal species-group names. For a period of time one could append the old genus name, e.g., “It is ironic that Seifert et al.  exhort tOf course there can be challenges to the establishment of a ranked phylogenetic classification—in principle, for example, when dealing with putatively ancestral taxa (Schmidt-Lebuhn Pogonomyrmex and Vollenhovia; Smith et al. We are at an exciting time in the study of ants and other social insects where molecular data—increasingly at the genome scale (e.g., Blaimer et al.


We lost a beloved scholar, mentor, and environmental justice warrior when Steve Wing passed away on 9 November 2016. He was at home with his family in Pittsboro, North Carolina.Steve was an epidemiologist who studied topics that other scientists wouldn’t touch, and he did so in collaboration with the people most affected. He documented the health impacts of landfills, industrial-scale livestock production, and sewage sludge applied to rural fields—issues that often disproportionately affect low-income communities of color.His dedication to highlighting injustices, with unwavering humility, kindness, and grace, won broad respect across North Carolina and around the world. Through community-based participatory research, or CBPR, Steve helped communities educate themselves and gather information they could use to organize and engage with elected officials regarding their concerns.“Steve helped communities confidently speak up and speak out about issues and what the impact could possibly be on their health and the environment,” said longtime collaborator Naeema Muhammad, co-director and community organizer for the North Carolina Environmental Justice Network (NCEJN). “He was truly ‘the people’s professor.’”Steve’s diagnosis with advanced cancer in February 2015 stunned his collaborators at the NCEJN and his colleagues at the University of North Carolina at Chapel Hill (UNC-CH), where he was a faculty member in the Department of Epidemiology. Ever committed to fighting injustices, he remained engaged in his work, as his health allowed, even while undergoing chemotherapy. In his last weeks, he was still contributing to several environmental justice–related lawsuits.“His concern for the way things are kept him working,” said Muhammad. “He wanted to get as much done as he could, for as long as he could.”Steve was born in New Orleans, Louisiana, on 3 October 1952. As a child, he was moved by the funeral parades he witnessed at the African-American church near his family’s home. Both the music and the racial inequities he observed made a deep impression on his young mind, contributing to a lifelong love of jazz and an equally strong commitment to social justice.When Steve was 12, his family moved to Durham, North Carolina. He attended Vassar College in New York, where he met his wife, Betsy. They returned to North Carolina and built a home in rural Chatham County, where they raised two daughters, Ann and Marion. Steve earned a master’s degree in sociology from Duke University and a PhD in epidemiology from UNC-CH. He joined the UNC-CH faculty soon after, initially studying trends in cardiovascular and occupational health.Steve’s academic career took a turn when he began studying workers’ health at the nuclear weapons plant at Oak Ridge National Laboratory in Tennessee. He unexpectedly found that the workers’ radiation badge readings were associated with later cancer mortality, but only if the workers were followed long enough for this to become evident . Other sHe learned something critical from the experience—that he could have a public health impact by working on focused topics where community organizations could use the findings.“No one had ever complained when I wrote a paper about social inequality, but if it was a paper about ionizing radiation, it caused a huge uproar, and it was of interest to labor groups, and even to this day is of interest to workers,” said Steve in a 2015 interview .In the 1990s Steve learned of community concerns about large-scale hog farms that were proliferating in eastern North Carolina. He began attending meetings organized by the Concerned Citizens of Tillery (CCT) to understand more about the concentrated animal feeding operations, or CAFOs, as they were called, eventually joining with Gary Grant of CCT and the late Nan Freeland to found the NCEJN in 1997.Steve and NCEJN began conducting CBPR to document the disproportionate health impacts of CAFOs on communities of color and low-income communities in North Carolina . He was CBPR seemed to come naturally for Steve. He saw the approach as a democratic way to distribute university resources to systematically disadvantaged areas and balance the typical power differential between researchers and research participants. His thoughtful personality, deep respect for others, and comfort with sharing leadership roles made this possible.These same qualities made Steve a beloved mentor. He was known for asking difficult questions and challenging students to think differently, be it about an epidemiologic study design or how privilege may have influenced their perspectives. When NCEJN organized a celebration for Steve in the fall of 2015 , tribute“When I think about the most important lessons I learned from Steve Wing, they are not lessons that you learn in a textbook,” wrote UNC alumna Jennifer Norton, now in New York City. “I learned the importance of maintaining the integrity of the science so the findings can be used to support and advocate for policy changes to improve people’s lives. I also learned to maintain my integrity as a person and keep doing the right thing, even, and especially, when others may try to dissuade you.”At this same celebration a year ago, Steve spoke to the packed room about why to fight social and environmental injustices. “We don’t do it because we’re going to win tomorrow or the next day. We do it because this is what we love, and we love each other,” he said. “We’re not here very long, so while we’re here, let’s get together and challenge the injustices. Because what else is more fun than that?”


While the influence of nature (genes) and nurture (environment) on elite sporting performance remains difficult to precisely determine, the dismissal of either as a contributing factor to performance is unwarranted. It is accepted that a complex interaction of a combination of innumerable factors may mold a talented athlete into a champion. The prevailing view today is that understanding elite human performance will require the deciphering of two major sources of individual differences, genes and the environment. It is widely accepted that superior performers are endowed with a high genetic potential actualised through hard and prodigious effort. Heritability studies using the twin model have provided the basis to disentangle genetic and environmental factors that contribute to complex human traits and have paved the way to the detection of specific genes for elite sport performance. Yet, the heritability for most phenotypes essential to elite human performance is above 50% but below 100%, meaning that the environment is also important. Furthermore, individual differences can potentially also be explained not only by the impact of DNA sequence variation on biology and behaviour, but also by the effects of epigenetic changes which affect phenotype by modifying gene expression. Despite this complexity, the overwhelming and accumulating evidence, amounted through experimental research spanning almost two centuries, tips the balance in favour of nature in the “nature” and “nurture” debate. In other words, truly elite-level athletes are built – but only from those born with innate ability. Περί Διαίτης (Dietetics) he noted:Eating alone will not keep a man well; he must also take exercise. For food and exercise, while possessing opposite properties, yet contribute mutually to maintain health. For it is the nature of exercise to use up material, while of food and drink to restore them. And it is necessary, as it appears, to determine exactly the powers of various exercises, both natural exercises and artificial, and which of them contribute to the development of muscle and which to wear and tear; and not only this, but also to proportion exercise to the quantity of food, to the predisposition of the person, to his age, to the season of the year, to the changes of the winds, to the geographical place in which the person resides, and to the climatological conditions of the specific year”.“The making of champions and achieving elite performance in sport have long been the subject of intense debate – from both a theoretical and a practical perspective. The “nature” versus “nurture” debate dates as far back as the fifth century BC, with one of the first known accounts for the relative nature versus nurture contribution to health and “regimen” having been presented by Hippocrates (460–370 BC), universally honored as the father of medicine, In his Book 1 positive health, but also to an individual’s “genetic predisposition” – in other words, heritability.Hippocrates made reference not only to nurture  as a requisite for Many a person has amused himself by throwing bits of stick into a tiny brook and watching their progress; how they are arrested, first by one chance obstacle, then by another and again, how their onward course is facilitated by a combination of circumstances. He might ascribe much importance to each of these events, and think how largely the destiny of the stick had been governed by a series of trifling accidents. Nevertheless, all the sticks succeed in passing down the current, and in the long run, they travel at nearly the same rate. The one element that varies in different individuals, but is constant in each of them, is the natural tendency; it corresponds to the current in the stream, and inevitably asserts itself...There is no escape from the conclusion that Nature prevails enormously over Nurture”.“Centuries later, Galton  seemed to be the first academic to give an opinion as to which is more important, with the Galtonian model advocating a hereditary ceiling to physical and mental capacities , 2 and owww.sub2hrs.com); the first dedicated international research initiative made up of specialist multidisciplinary scientists from academia, elite athletes and strategic industry partners with the aim to promote high performance marathon running without doping. While there are no guarantees the SUB2 marathon project will succeed in delivering a 1:59:59 marathon within 5 years, the SUB2 team boast a 100% marathon success record in 2016 and the second fastest marathon in history at the 2016 Berlin Marathon with a time of 2:03:03.If one accepts that differences between elite and less accomplished performers reflect inherent abilities , then it is reasonable to assume that any improvement in performance beyond a fixed maximal level is unmodifiable by extrinsic environmental factors. Empirical evidence has repeatedly refuted this assumption across a wide and diverse range of attributes, including physical performance and motor skills. In particular, improved performance in sport – evidenced by faster times on the track and greater distances in the field events as measured and recorded under strict standardised conditions at national and world level – has clearly been demonstrated despite some leveling off of these increases in some events. However, these improvements in sporting performance over the years can adequately be explained by increases in the duration and intensity of training, new training methods, and even changes to equipment and rules. A recent example of the contribution of extrinsic factors other than nature to performance improvement is the SUB2 marathon project , ranging from weak amateurs to grandmasters. Their findings reaffirmed the importance of practice for the attainment of high levels of performance but also revealed large variability. Notably, some players needed 8 times as much practice to reach master level than others. The authors concluded that practice was necessary, but not a sufficient condition for the acquisition of expertise and that some additional factors seemed to differentiate chessplayers and non-chessplayers. In terms of studies involving sporting domains, Hodge and Deakin’s [The theoretical framework for “n et al.  argued tn et al. . Despiten et al. , The genn et al. , Bounce n et al. , the evin et al. . The then et al. , 11. Then et al.  also sign et al. , 13. ForDeakin’s  investigDeakin’s , which fThe large variability in all essential attributes and/or responses is precisely what would be predicted and underpins the present day concept of precision/personalised medicine, where major international consortia are attempting to correlate genomic and other high-throughput “omics” data in order to identify individual differences in the response to treatment of major medical conditions such as cancer, and type 2 diabetes. The development of biomarkers for personalised oncology is a striking example of how this large inter-individual variability in response can potentially be harnessed to improve efficacy of treatment. In the last decade there have been significant advances in the development of biomarkers for novel drug targets and new treatment strategies for patients with advanced-stage cancers are moving away from traditional treatment strategies to biomarker driven treatment algorithms based on the molecular profile of each tumor. As such, predictive biomarkers are increasingly being used to match targeted therapies with patients, and prevent toxicity of standard therapies .n = 15) and dizygotic (n = 10) twins. The model developed by Klissouras assumed comparable environmental influences between the two sets of twins and the absence of gene-environment interactions. Klissouras also found that maximal anaerobic capacity and maximal heart rate are heavily dependent on genes, which accounted for 81% and 86% of the variation of traits, respectively. Subsequent studies applying path analysis of twin and nuclear family data also reported a high genetic component for Heritability studies on physical performance and functional adaptability provided strong evidence of a significant genetic component to various parameters that ultimately determine elite performance. Over the past two decades there has been a clear shift in terms of how sports and exercise genetics research has been conducted and the ever-increasing focus on determining specific genes linked to performance. Early family studies/twin models  and the α-actinin-3 (ACTN3) R577X polymorphisms have been the most extensively studied (see Table www.athlomeconsortium.org). This international collaborative initiative brings together a large databank, expertise and state-of-the-art “omics” technologies from around the world, aiming to understand genetic variation underlying athletic performance, adaptation to exercise training, and injury predisposition. The review by Wang et al. [status quo to the era of sport and exercise genomics. In particular, an unbiased exploration of the human genome is needed utilising the full power of genomics, epigenomics, and transcriptonomics, in combination with large-scale, replicable study designs [Despite some early progress, the question remains as to which genetic variants are those that irrefutably define elite athletic performance and trainability, as numerous attempts to discover candidate genes have largely proved inconclusive, even when genetic superiority was widely assumed as in the African runners phenomenon , 46 or cg et al.  presents designs . Notable designs  and the  designs . Specifiwww.encodeproject.org) and the 1000 Genomes Project   Project  and nurture (environment) on elite sporting performance remains difficult to precisely determine, the dismissal of either as a contributing factor to performance is unjustified. It is accepted that a complex interaction of a combination of innumerable factors may mold a talented athlete into a champion. In their most basic form, these factors amount to genetics, training and preparation. The contribution of each is absolutely necessary in the making of a world-class athlete. The essential role of practice and training is widely and indisputably recognised. Individual variation, however, in terms of starting performance levels and subsequent response to training , clearly illustrate the prominent role of genetic factors and their interaction with training and the environment. The overwhelming and accumulating evidence, amounted through empirical and experimental research spanning over almost two centuries, tips the balance in favour of nature in the “nature” and “nurture” debate. In other words, truly elite-level athletes are built – but only from those born with innate ability. This conclusion is in line with the prophetic text by Galton, some 150 years ago who wrote “there is nothing in what I am about say that shall underrate the sterling value of nurture, including all kinds of sanitary improvements; may, I wish to claim them as powerful auxiliaries to my cause; nevertheless, I look upon race as far more important than nurture.” .