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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)
With the development of advanced Internet technology, business applications across multiple enterprises based on Web Services Composition (WSC) paradigm are widely used. When system failure occurs, some tasks of the flow may be committed while others unscheduled, in this situation, it is important to accurately analyze execution log of composition business(More)
Wireless sensors and mobile devices have been widely deployed as data collecting devices for monitoring real world systems. A large amount of stream data is generated in real-time, which has to be processed in real-time as well. One of the common processing operations is clustering that automatically groups the elements of a stream into a number of clusters(More)
To provide personalized support in on-line course resources system, a semantic web-based personalized learning service is proposed to enhance the learner's learning efficiency. In this system four most important characteristics are semantically detailed to describe each learning object and learner. Then, a semantic mechanism is designed to compare the(More)
Applications running on the Internet of Things, such as the Wireless Sensor and Actuator Networks (WSANs) platform, generally have different quality of service (QoS) requirements. For urgent events, it is crucial that information be reported to the actuator quickly, and the communication cost is the second factor. However, for interesting events,(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)