In classic pattern recognition problems, classes are mutually exclusive by deÿnition. Classiÿcation errors occur when the classes overlap in the feature space. We examine a di erent situation, occurring when the classes are, by deÿnition, not mutually exclusive. Such problems arise in semantic scene and document classiÿcation and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classiÿcation, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a ÿeld scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a di erent treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classiÿcation; furthermore, our work appears to generalize to other classiÿcation problems of the same nature.

Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the "black arts" of data mining is setting the tunings that control the miner. Objective: We seek simple, automatic, and very effective method for finding those tunings. Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as an optimizer to explore the tuning space (as a first step) then tested the tunings using hold-out data. Results: Contrary to our prior expectations, we found these tunings were remarkably simple: it only required tens, not thousands, of attempts to obtain very good results. For example, when learning software defect predictors, this method can quickly find tunings that alter detection precision from 0% to 60%. Conclusion: Since (1) the improvements are so large, and (2) the tuning is so simple, we need to change standard methods in software analytics. At least for defect prediction, it is no longer enough to just run a data miner and present the result without conducting a tuning optimization study. The implication for other kinds of analytics is now an open and pressing issue.

No abstract

Many sequential applications are difficult to parallelize because of unpredictable control flow, indirect data access, and inputdependent parallelism. These difficulties led us to build a software system for behavior oriented parallelization (BOP), which allows a program to be parallelized based on partial information about program behavior, for example, a user reading just part of the source code, or a profiling tool examining merely one or few executions.The basis of BOP is programmable software speculation, where a user or an analysis tool marks possibly parallel regions in the code, and the run-time system executes these regions speculatively. It is imperative to protect the entire address space during speculation. The main goal of the paper is to demonstrate that the general protection can be made cost effective by three novel techniques: programmable speculation, critical-path minimization, and value-based correctness checking. On a recently acquired multicore, multi-processor PC, the BOP system reduced the end-to-end execution time by integer factors for a Lisp interpreter, a data compressor, a language parser, and a scientific library, with no change to the underlying hardware or operating system. Dedication This paper is dedicated to the memory of Ken Kennedy, who passed away on February 7, for his lasting leadership in parallel computing research.