The rise of social networking services in recent years presents new research challenges for matching users with interesting content. While the content-rich nature of these social networks offers many cues on "interests" of a user such as text in user-generated content, the links in the network, and user demographic information, there is a lack of successful methods for combining such heterogeneous data to model interest and relevance. This paper proposes a new method for modeling user interest from heterogeneous data sources with distinct but unknown importance. The model leverages links in the social graph by integrating the conceptual representation of a user's linked objects. The proposed method seeks a scalable relevance model of user interest, that can be discriminatively optimized for various relevance-centric problems, such as Internet advertisement selection, recommendation, and web search personalization. We apply our algorithm to the task of selecting relevant ads for users on Facebook's social network. We demonstrate that our algorithm can be scaled to work with historical data for all users, and learns interesting associations between concept classes automatically. We also show that using the learnt user model to predict the relevance of an ad is the single most important signal in our ranking system for new ads (with no historical clickthrough data), and overall leads to an improvement in the accuracy of the clickthrough rate prediction, a key problem in online advertising.

This is the first empirical study of the use of the C macro preprocessor, Cpp. To determine how the preprocessor is used in practice, this paper analyzes 26 packages comprising 1.4 million lines of publicly available C code. We determine the incidence of C preprocessor usage-whether in macro definitions, macro uses, or dependences upon macros-that is complex, potentially problematic, or inexpressible in terms of other C or C++ language features. We taxonomize these various aspects of preprocessor use and particularly note data that are material to the development of tools for C or C++, including translating from C to C++ to reduce preprocessor usage. Our results show that, while most Cpp usage follows fairly simple patterns, an effective program analysis tool must address the preprocessor. The intimate connection between the C programming language and Cpp, and Cpp's unstructured transformations of token streams often hinder both programmer understanding of C programs and tools built to engineer C programs, such as compilers, debuggers, call graph extractors, and translators. Most tools make no attempt to analyze macro usage, but simply preprocess their input, which results in a number of negative consequences; an analysis that takes Cpp into account is preferable, but building such tools requires an understanding of actual usage. Differences between the semantics of Cpp and those of C can lead to subtle bugs stemming from the use of the preprocessor, but there are no previous reports of the prevalence of such errors. Use of C++ can reduce some preprocessor usage, but such usage has not been previously measured. Our data and analyses shed light on these issues and others related to practical understanding or manipulation of real C programs. The results are of interest to language designers, tool writers, programmers, and software engineers.

Abstract Scalable Vector Graphics (SVG) is a language that describestwo-dimensional vector graphics for storage and distribution

Linear equality and inequality constraints arise naturally in specifying many aspects of user interfaces, such as requiring that one window be to the left of another, requiring that a pane occupy the leftmost third of a window, or preferring that an object be contained within a rectangle if possible. Previous constraint solvers designed for user interface applications cannot handle simultaneous linear equations and inequalities efficiently. This is a major limitation, as such systems of constraints arise often in natural declarative specifications. We describe Cassowary---an incremental algorithm based on the dual simplex method, which can solve such systems of constraints efficiently. We have implemented the algorithm as part of a constraint-solving toolkit. We discuss the implementation of the toolkit, its application programming interface, and its performance.

Analyses of C source code usually ignore the C preprocessor because of its complexity. Instead, these analyses either define their own approximate parser or scanner, or else they require that their input already be preprocessed. Neither approach is entirely satisfactory: the first gives up accuracy (or incurs large implementation costs), while the second loses the preprocessor-based abstractions. We describe a framework that permits analyses to be expressed in terms of both preprocessing and parsing actions, allowing the implementer to focus on the analysis. We discuss an implementation of such a framework that embeds a C preprocessor, a parser, and a Perl interpreter for the action ‘hooks’. Many common software engineering analyses can be written surprisingly easily using our implementation, replacing numerous ad-hoc tools. The framework's integration of the preprocessor and the parser further enables some analyses that otherwise would be especially difficult to perform. Copyright © 2000 John Wiley & Sons, Ltd.

Cascading Style Sheets have been introduced by the W3C as a mechanism for controlling the appearance of HTML documents. In this paper, we demonstrate how constraints provide a powerful unifying formalism for declaratively understanding and specifying style sheets for web documents. With constraints we can naturally and declaratively specify complex behavior such as inheritance of properties and cascading of conflicting style rules. We give a detailed description of a constraint-based style sheet model, CCSS, which is compatible with virtually all of the CSS 2.0 specification. It allows more flexible specification of layout, and also allows the designer to provide multiple layouts that better meet the desires of the user and environmental restrictions. We also describe a prototype extension of the Amaya browser that demonstrates the feasibility of CCSS.

Presents advanced data compression techniques for predicting stock markets behavior under widely accepted market models in finance. The techniques are applicable to technical analysis, portfolio theory, and nonlinear market models. The authors find that lossy and lossless compression techniques are well suited for predicting stock prices as well as market modes such as strong trends and major adjustments. They also present novel applications of multispectral compression techniques to portfolio theory, correlation of similar stocks, effects of interest rates, transaction costs and taxes.>