Language use could not proceed without signals – the acts by which one person means something for another – but what exactly are they? The question is crucial because signals help define what is and what isn't language use – and language – and determine how communication is actually achieved. This chapter is addressed to what signals are and how they work.

The traditional assumption is that signals are “linguistic” objects – utterances of speech sounds, words, sentences – that work via their conventional meanings. That assumption is reflected in Austin's and Searle's terms locutionary, illocutionary, perlocutionary, and speech acts (Chapter 5). It is also reflected in the term pragmatics, the study of language use, which is treated as parallel to phonology, morphology, syntax, and semantics in the study of language. And it is reflected in the term language use, which I have felt obliged to use for this domain. More to the point, it is the working assumption of most students of language use.

That assumption, of course, isn't right. Many signals aren't “linguistic” at all (Chapters 3 and 5). The doctor waved his hand to signal Margaret that she had the measles. Sam waved a white flag to surrender. Elizabeth pointed at her mouth and an empty plate to ask for food. The sexton put one lamp in the belfry to signal Paul Revere that the Redcoats were coming by land. And as Grice (1957) noted, British bus conductors used to ring a bell twice to signal the bus driver to drive on.

As a statesman, Raja had to work with the imperfect foreign regimes he faced, not with counterparts he might have ideally wished for. This could sometimes make for unpalatable diplomacy with dubious and even repugnant characters. This was the part of his job that he confessed he disliked most and found most trying. “As a foreign minister, I meet people. I form my own impressions. Some of them I find completely distasteful,” he said. “But I cannot afford to let them know about this.” In politics as in diplomacy, at times one had to engage in some dissimulation, he said, “which I find distasteful but which is necessary”.

Few encounters tested his ability to deal with the darker side of diplomacy than negotiating with the notorious henchmen of the Pol Pot regime: Khieu Samphan, Ieng Sary, and the latter's wife, Ieng Thirith, who was also Pol Pot's sister-in-law and the highest-ranking female in the regime. It was an unavoidable part of the tortuous business of forging a new coalition out of three incompatible Kampuchean resistance factions.

Besides the communist Khmer Rouge leaders, there were the two non-communist nationalist leaders: former head of state Prince Norodom Sihanouk, who was based in Pyongyang and Beijing, and former prime minister Son Sann, who was based in Paris. Compared with the Khmer Rouge, these two men were paragons of virtue. But they shared one major weakness as resistance fighters: they lacked the brute military strength of the Khmer Rouge, which was putting up the only credible resistance with its 35,000 troops well armed with Chinese weapons. On the other hand, the non-communist nationalists had a valuable asset that eluded the repulsive and reviled Khmer Rouge: respectability.

If the non-communist groups could join forces with the Khmer Rouge in a united front, they would recast the image of Democratic Kampuchea and sustain international support for the struggle. Together, they could exert greater pressure on Vietnam to accept a political solution and withdraw its forces from Kampuchea. And as part of a coalition representing Democratic Kampuchea at the UN, the two noncommunist nationalist factions could legitimately receive aid, including weapons, from governments sympathetic to their cause – just the way the Vietnamese were being armed by the Soviets, and the Khmer Rouge by the Chinese. That was the strategic logic that underpinned the coalition scheme conceived by Raja, its principal architect.

Introduction

There are basic rules to which all probabilities must conform. Thus the manner in which probabilities of compound events are derived from those of simple events is quite invariant and independent of the methods by which the probabilities of the simple events have been initially obtained.

All probabilities are non-negative and expressed on a scale from 0 to 1. (Occasionally the scale is expressed in percentage terms from 0 to 100.) The greatest degree of probability which any future event can have is certainty, and the scale assigns this a probability of 1.00 (or 100%). At the other end of the scale, the lowest degree of probability that a future event can have is ‘impossibility’ to which is assigned a probability of zero. The next two sections introduce the basic rules for the addition and multiplication of probabilities when compound events are concerned.

Addition of probabilities

A stationery shop stocks three types of stapling machine. Examination of past records shows that 40% of customers purchase a machine of type A, 35% one of type B, and 25% of type C. Types A and B are made by manufacturer X, type C by manufacturer Y. A customer comes in to buy a stapling machine; what is the probability that he will buy one made by manufacturer X?

Denote by P(A) the probability that a machine of type A is purchased, etc. and by P(A + B) the probability that either type A or type B is purchased. Then Then

P(A) = 0.4, P(B) = 0.35 and P(A + B) = 0.75

In the last chapter, we discussed about single and multi dimensional array. We already learnt how to define arrays of different sizes and dimensions, how to initialize them, how to operate on arrays of different dimensions, etc. With this knowledge, we are ready to handle strings, which are, simply a special kind of array. String handling basically consists of:

Σ Input and output strings from/to keyboard/monitor or files.

Σ Copying and comparing strings with library functions.

Σ Finding substrings, determining length, extracting substring, concatenating strings with library functions.

Σ Manipulating case of characters in a string with library functions.

Σ Writing equivalent functions of string manipulating library functions.

We will discuss all important string manipulating library functions in this chapter; however a complete list of string manipulating library functions along with explanation is given in Appendix.

We will learn to write equivalent functions of string manipulating library functions in functions chapter.

STRING

A string is a sequence of characters terminated with a null character (‘\0’). It is usually stored as one-dimensional character array. A set of characters arranged in any sequence defined within double quotation is known as string constant. To manipulate text such as words and sentences, strings, i.e., character arrays are used. The way a group of integers can be stored in an integer array, is similar with a group of characters stored in a character array.

The Church of Scotland's primary role in the relief of the poor in the eighteenth century has meant that its practices for recording financial transactions have come into the view of historians concerned with how poverty was handled. In particular, two legal cases involving the church, those of Humbie (1751) and Cambuslang (1752), feature in the debate over the nature of Scottish poor relief in the period. The visibility of these cases is, in part, because of their inscription in books recording court decisions. Briefly, the Cambuslang decision turned on what expenses could be met from the funds collected for the benefit of the poor. Repairs to property occasioned by the enormous crowds who had attended the communion season in the parish as part of the ‘Cambuslang Revival’ were held to be improper, and doubt was cast on the payment of fees such as those for the session clerk. At Humbie, the decision was that heritors should be fully involved in the administration of funds raised for the poor. We will return to these cases in more detail later, but for now the important point is the way the cases have been used to draw conclusions about record keeping. For Mitchison the Humbie case was, in her original formulation, a turning point. Because of the decision that heritors should be fully involved, ‘[f]rom now on poor-law affairs were to be run more formally, kept in separate books from the general session register, and vetted in legalistic spirit by the heritors’. In a response, Cage argued that the Humbie case needed to be seen in the context of legal assessments (that is, the setting of a rate on landowners and others for poor relief) and that too much emphasis should not be placed on it. Although her initial response did not tackle the implications of this, in subsequent work, where she argues for two implications of Humbie, Mitchison was to modify her views. One implication was that ‘landowners were deliberately using their dominant position in the structure of justice to reduce the claim of the Church to autonomy’, something which we will see further in this chapter.

The last chapter described how a listener constructs a deeper meaning representation from the bare message conveyed by the speaker's words. It was suggested that the meaning representation then contributes to a discourse representation, the listener's recall of everything that has been said so far. It does indeed, but the model shown in Chapter 12 was something of a simplification. Before the new piece of information is added to the discourse representation, it undergoes a certain amount of editing by the listener. The way in which it is handled and the way in which the representation is updated form the topic of the present chapter.

In dealing with incoming information, a listener is generally assumed to make a series of quite simple decisions. She has to decide:

• How relevant is this piece of information?

• How does it relate to what went before?

• Is it consistent with what went before?

• How does it fit into a hierarchy of other pieces obtained so far – some important, some less so?

Figure 13.1 (see p. 242) offers an expanded version of the model from Chapter 12, with these additional stages added. We will examine each of them in turn and consider how they are handled by an expert listener.

Safe handling of microbes

The vast majority of microbial life on Earth is harmless to humans, and many microorganisms have beneficial effects. Since the dawn of civilisation, humans have harnessed microbial fermentations to make bread and alcoholic beverages, and to prolong the life of food. Today, technologists are exploiting microorganisms in the pharmaceutical industry, for food production, mineral extraction, the oil industry and in agriculture. There is hardly any aspect of modern life that is not touched by microbiology. However, a small minority of microbes do cause disease, and a minority of disease-causing microbes can cause fatal infections. Some, such as the human immunodeficiency virus, the cause of AIDS, may take several years to exert their lethal effect. Others such as Neisseria meningitidis, the cause of meningococcal meningitis, can kill within hours of the first symptoms of the disease.

When working with microbes, care must be taken to ensure that laboratory cultures do not escape to cause laboratory-acquired infections or to pollute the environment. Equally, it is important to ensure that laboratory cultures do not become contaminated with unwanted extraneous organisms from the environment. If care is not taken to avoid contamination of laboratory cultures, then the results of microbiological experiments are not reliably reproducible, and the data obtained would be unreliable. It is impossible to tell whether the observations made in such experiments are due to the properties of the desired organism, or arise from the activity of a contaminant.

Introduction

What is the issue? Programmers in any language endeavor to write bug-free programs, programs that never crash, programs that can handle any situation efficiently, and that can recover from unusual situations without causing the users any undue inconvenience. Good intentions aside, programs that cover all these points don’t exist. In real life, errors occur, either because the programmer didn’t anticipate every situation the code would get into (or did not have the time to test the programs enough), or because of situations outside the programmer’s control like bad data from users, corrupt files that do not have the right data in them, network connections that do not connect, hardware devices that do not respond, to name a few.

Requirement In Java, unusual events that may cause a program to fail are called exceptions. The dictionary meaning of “exception” is “an abnormal situation.” What will happen to the program if an abnormal situation occurs? For example, what result will your calculations return when divide x by y when y = 0? Or, if you want to store a value in the array in its 100th location while the size of the array is 50? In practice, in such a situation, your execution will be suspended immediately, for which you are not really prepared.

What do you do with your data once you have collected it? This chapter will elucidate the procedures for judicious handling of a large body of natural speech materials, such as audio files, interview reports, and consent forms.