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In the next step, IDLP calls the CORBA IDL compiler (supplied by the ORB vendor) to generate the header le, client stub, and the server skeleton The C++ parser then analyzes the structure of the header le, client stub, and server skeleton and performs the necessary modi cations required for the incorporation of the.

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not reported in many of the studies where it was examined In journal articles where a distinction was made between visual and sound details a pattern emerged Sound details in particular are more likely to be present in truthful accounts than in deceptive accounts The ndings about contextual embeddings are mixed, but, again, many of the studies where it was measured do not clarify how the criterion was de ned and coded Studies (mostly journal articles) that look at spatial and temporal details separately show a pattern Those details are more likely to occur in truthful accounts than in deceptive accounts There is one inconsistent nding, because Bond and Lee (2005) reported that truth tellers included fewer spatial details in their stories than liars This study differs in one important aspect from all the other studies They used an automatic computerised coding system rather than a manual coding system.

I return to this issue later on in this chapter Many studies did not nd a difference between truth tellers and liars in terms of reporting how they actually felt during the event (affect), and in the three studies where a difference was found, they contradicted each other This criterion is related to CBCA Criterion 12 In CBCA research the criterion did yield more support than in RM research, although it is not amongst the criteria with the strongest support in CBCA in research (Appendix 81) Several studies have revealed that truthful stories are easier to reconstruct than deceptive stories This criterion is related to the three general CBCA criteria (Criteria 1 3) which also yielded considerable support in CBCA research Moreover, several researchers have found that truthful stories sound more realistic than deceptive studies This criterion is related to the verbal cue plausibility, discussed in 4.

in lie detection tasks and show a truth-bias when assessing strangers The following explanation sounds reasonable Women are better than men in decoding the information someone wants to convey During deception, however, liars try to hide their true feelings and thoughts When detecting lies, observers should not examine what people want to convey but should look at what they try to conceal Perhaps, when women try to detect lies in strangers, they are too affected by what that person tries to convey, which will result in errors in lie detection and in a truth-bias Women, however, do no appear to lose their advantage over men in reading nonverbal behaviour when they attempt to detect truths and lies in people they know, such as their romantic partners or friends McCornack and Parks (1990) found that women were superior to men in detecting lies in their romantic partners.

pricing model: a built-in self-correcting mechanism that very easily lets users correct for any theoretical nonsense and deliver reliable outputs. By simply tweaking the volatility number, traders can obtain the price that they (and not some theoretical construct) deem practically relevant. No other model lets pros be in charge and impose their own law in such a way. Conveniently allowing traders to adapt the model to real-world realities (even those as earth-shatteringly unexpected as the 1987 crash), Black-Scholes proved its practical worth and earned its well-deserved preeminence. In sum, the same event that highlighted the untrustworthiness of the model (which internal mechanics, arguably, contributed to the event taking place in the rst place) helped underscore the reasons for its wild popularity. Rather than con ne the model to the dustbin of history, a Black-Scholes-inspired disaster unveiled its unquestionable real-life usefulness. By producing the volatility smile following Black Monday, traders effectively rescued Black-Scholes from its self-dug graveyard.

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