Lists are often used as "glue" to connect separate parts of a program together.We propose an automatic teehnique for improving the efficiency of such programs, by removing many of these intermediate lists, based on a single, simple, local transformation.We have implemented the method in the Glasgow Haakell compiler.

This article defines a set of type inference rules for resolving overloading introduced by type classes, as used in the functional programming language Haskell. Programs including type classes are transformed into ones which may be typed by standard Hindley-Milner inference rules. In contrast to other work on type classes, the rules presented here relate directly to Haskell programs. An innovative aspect of this work is the use of second-order lambda calculus to record type information in the transformed program.

The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result. This paper introduces evaluation strategies: lazy higher-order functions that control the parallel evaluation of non-strict functional languages. Using evaluation strategies, it is possible to achieve a clean separation between algorithmic and behavioural code. The result is enhanced clarity and shorter parallel programs. Evaluation strategies are a very general concept: this paper shows how they can be used to model a wide range of commonly used programming paradigms, including divide-and-conquer parallelism, pipeline parallelism, producer/consumer parallelism, and data-oriented parallelism. Because they are based on unrestricted higher-order functions, they can also capture irregular parallel structures. Evaluation strategies are not just of theoretical interest: they have evolved out of our experience in parallelising several large-scale parallel applications, where they have proved invaluable in helping to manage the complexities of parallel behaviour. Some of these applications are described in detail here. The largest application we have studied to date, Lolita, is a 40,000 line natural language engineering system. Initial results show that for these programs we can achieve acceptable parallel performance, for relatively little programming effort.

Users of social networking sites often want to manage the sharing of information and content with different groups of people based on their differing relationships. However, grouping contacts places a significant configuration burden on the user. Automated approaches to grouping may have the potential to reduce this burden, however, their use remains largely untested.We investigate people's rationales when grouping their contacts for the purpose of controlling their privacy, finding six criteria that they commonly considered. We assess an automated approach to grouping, based on a network clustering algorithm, whose performance may be analogous to the human's use of some of these criteria. We find that the similarity between the groups created by people and those created by the algorithm is correlated with the modularity of their network. We also demonstrate that the particular clustering algorithm, SCAN, which detects hubs and outliers within a network can be beneficial for identifying contacts who are hard to group or for whom privacy preferences are inconsistent with the rest of their group.

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