13933896
Parametric type classes
We propose a generalization to Haskell's type classes where a class can have type parameters besides the placeholder variable. We show that this generalization is essential to represent container classes with overloaded data constructor and selector operations. We also show that the resulting type system has principal types and present unification and type reconstruction algorithms.
10895534
Aspect-Based Instrumentation for Locating Memory Leaks in Java Programs
Despite the built-in garbage collector, Java programs can still suffer the memory leak problem resulted from the unhealthy programming style of retaining unwanted references. This paper presents an aspect-based tool for assisting programmers in locating such references to fix the problem. This tool, FindLeaks, utilizes an aspect to collect memory consumption statistics and object references created during a program's execution and analyzes them for detecting memory leaks. The distinctive feature of FindLeaks is that it reports not only suspected classes of leaking objects but also where in the source the unwanted references were created. Besides, this paper also reports some preliminary experience with FindLeaks.
16803440
A Compilation Model for Aspect-Oriented Polymorphically Typed Functional Languages
Introducing aspect orientation to a polymorphically typed functional language strengthens the importance of type-scoped advices; i.e., advices with their effects harnessed by type constraints. As types are typically treated as compile time entities, it is highly desirable to be able to perform static weaving to determine at compile time the chaining of type-scoped advices to their associated join points. In this paper, we describe a compilation model, as well as its implementation, that supports static type inference and static weaving of programs in an aspect-oriented polymorphically typed lazy functional language, AspectFun. We present a type-directed weaving scheme that coherently weaves type-scoped advices into the base program at compile time. We state the correctness of the static weaving with respect to the operational semantics of AspectFun. We also demonstrate how control-flow based pointcuts (such as cflowbelow) are compiled away, and highlight several type-directed optimization strategies that can improve the efficiency of woven code.
15289343
A connectionist method for pattern classification with diverse features
A novel connectionist method is proposed to simultaneously use diverse features in an optimal way for pattern classification. Unlike methods of combining multiple classifiers, a modular neural network architecture is proposed through use of soft competition among diverse features. Parameter estimation in the proposed architecture is treated as a maximum likelihood problem, and an Expectation-Maximization (EM) learning algorithm is developed for adjusting the parameters of the architecture. Comparative simulation results are presented for the real world problem of speaker identification.
2853176
Loop Fusion via Retiming for DSP Applications
742966
Clustering with Noising Method
The minimum sum of squares clustering problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this article, a recent metaheuristic technique, the noising method, is introduced to explore the proper clustering of data sets under the criterion of minimum sum of squares clustering. Meanwhile, K-means algorithm as a local improvement operation is integrated into the noising method to improve the performance of the clustering algorithm. Extensive computer simulations show that the proposed approach is feasible and effective.
195349585
Identity Based Conference Key Distribution Scheme from Parings
A conference key distribution system can provide a session key of a conference with more than three participants via network. In this paper,we propose an ID-based conference key distribution scheme from pairings. The proposed scheme provides user anonymity. At the same time, we also show that the proposed scheme is secure against replaying attacks and conspiratorial impersonation attacks.
206764065
Adaptive smoothing via contextual and local discontinuities
A novel adaptive smoothing approach is proposed for noise removal and feature preservation where two distinct measures are simultaneously adopted to detect discontinuities in an image. Inhomogeneity underlying an image is employed as a multiscale measure to detect contextual discontinuities for feature preservation and control of the smoothing speed, while local spatial gradient is used for detection of variable local discontinuities during smoothing. Unlike previous adaptive smoothing approaches, two discontinuity measures are combined in our algorithm for synergy in preserving nontrivial features, which leads to a constrained anisotropic diffusion process that inhomogeneity offers intrinsic constraints for selective smoothing. Thanks to the use of intrinsic constraints, our smoothing scheme is insensitive to termination times and the resultant images in a wide range of iterations are applicable to achieve nearly identical results for various early vision tasks. Our algorithm is formally analyzed and related to anisotropic diffusion. Comparative results indicate that our algorithm yields favorable smoothing results, and its application in extraction of hydrographic objects demonstrates its usefulness as a tool for early vision.