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The next question is how to deal with the side effects of suppressing certain statistics. Suppose, for example, that a university wants to release average grades for various combinations of courses, so that people can check that the grading is fair across courses. Suppose now that the table in Figure 8.4, contains the number of students studying two science subjects, one as their major subject and one as their minor subject. Next suppose that our minimum query set size is 3 (if we set it at 2, then either of the two students who studied geology with chemistry could trivially work out the other s grade); then we cannot release the average for geology with chemistry. But if the average for chemistry is known, then this can easily be reconstructed from the averages for biology with chemistry and physics with chemistry. Therefore, we have to suppress at least one other average in the chemistry row; and for similar reasons we need to suppress one in the geology column. But if we suppress geology with biology and physics with chemistry, then we d also better suppress physics with biology to prevent these values being worked out in turn. The remaining table is shown in Figure 8.5.

It is worth noting that the speci cation of a design pattern may contain errors. The conditions to satisfy the pattern may be in con ict with the semantics of the modeling language, or they may be in con ict with each other. Such logic errors can be detected by using the LAMBDES tool and SPASS theorem prover. In particular, let Spec(P) be a speci cation of a pattern P. If AxmD Spec(P) false, we can conclude that Spec(P) contains such errors. In the development of the pattern speci cation repository, using LAMBDES and SPASS we have proved that for all speci cations of design patterns P in the repository, AxmD Spec(P) false. So all the speci cations in our repository are consistent with the axioms of descriptive semantics. Another application of LAMBDES and SPASS in the logic analysis of design patterns is to prove relations between patterns: for example, to prove that one pattern is a specialization of another. Bayley and Zhu [4] argued that the relationship that a design pattern P is a specialization of pattern Q can be written as Spec(P) Spec(Q). Such a relationship can be proved formally by using LAMBDES and SPASS to infer that AxmD Spec(P) Spec(Q). In the context of descriptive semantics, we can now prove the following property of the pattern specialization relation. Theorem 5.4.3 Let Dom be a subject domain that is consistent with FOPL. If AxmD Spec(P) Spec(Q), then for all systems x Dom, if x is an instance of P, x is also an instance of pattern Q [i.e., x (x |= Spec(P) x |= Spec(Q))]. 5.5 CONCLUSIONS

as comfortablydeveloped and accessible as in the world of standard input devices. But psychological research as well as common sense tell us that users are much more efficient, successful, and satisfied when they are offem3 input devices that resemble physical objects of the application domain. For example, Norman [ 1988, p. 23 E ]talks in detail about the advantages of natural map. pings from input device to system function, and gives a good example of a device perfectly designed for its dedicated purpose: a seat adjustment control in a car which is shaped like a miniature seat itself. To adjust his own smt, the user simply pushes the corresponding part of the miniature seat into the deId have been far more cumbersome to seat controls if they had been designed as a set of industry-standardbuttons on the dashboard (and, if we imagine using those controls ourselves, it would probably also Rave been Iess fun).

Though the SEC does not have a specific definition for material information, the term is generally interpreted to mean anything that would affect the understanding and decision making of an investor i.e. anything that would cause a rational investor to act.

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