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Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a(More)
Large-scale scientific and engineering computation is normally accomplished through the interaction of collaborating groups and diverse heterogeneous resources. Grid computing is emerging as an applicable paradigm, whilst, there is a critical challenge of authorization in the grid infrastructure. This paper proposes a Parallelized Subtask-level(More)
In this paper, we present a two-level distributed schedule model, and propose a scheduling approach with respect to overlap of computing and data transferring. On the basis of network status, node load, and the relation between task execution and task data access, data transferring and computing can occur concurrently in the following three cases: a) A task(More)
Widely adoption of GPS-enabled devices generates massive trajectory data every minute. The trajec-tory data can generate meaningful traffic patterns. In this demo, we present a system called PARec-ommender, which predicts traffic conditions and provides route recommendation based on generated traffic patterns. We first introduce the technical details of(More)
A pseudo-random generator is an algorithm to generate a sequence of objects determined by a truly random seed which is not truly random. It has been widely used in many applications, such as cryptography and simulations. In this article, we examine current popular machine learning algorithms with various on-line algorithms for pseudo-random generated data(More)