Model transformations are the key part of Model Driven engineering (MDE). However, their specification is not user-friendly, due to excessive use of metamodels and textual representation of transformation languages. T...
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Model transformations are the key part of Model Driven engineering (MDE). However, their specification is not user-friendly, due to excessive use of metamodels and textual representation of transformation languages. This paper briefly describes the approach and tool under development for user-centric transformation generation using concrete model visualisations.
The agile approaches are setting new paradigms to software development. The old ideas about testing at the end of the coding will no longer be applicable. Agile is about small teams incrementally delivering quality so...
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In the last decade, computers have become important equipment especially for classroom teaching and help students to learn better. This paper reports the design and development of a courseware to introduce computer li...
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In the last decade, computers have become important equipment especially for classroom teaching and help students to learn better. This paper reports the design and development of a courseware to introduce computer literacy for Sarawak rural children. We believe this is the first attempt to create a comprehensive courseware totally in Sarawak Malay dialect and Iban Language. The application is designed in Borneo's Sarawak Malay dialect and Bahasa Iban. The courseware contains eight learning modules with focus on the topics about basic computer knowledge. In the evaluation stage, we involved students and asked their comments about the courseware. The testing results revealed that using our courseware, the rural children are excited and satisfied to learn basic computer literacy.
Stock market is an important and active part of nowadays financial markets. Stock time series volatility analysis is regarded as one of the most challenging time series forecasting due to the hard-to-predict volatilit...
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ISBN:
(纸本)9780769547534
Stock market is an important and active part of nowadays financial markets. Stock time series volatility analysis is regarded as one of the most challenging time series forecasting due to the hard-to-predict volatility observed in worldwide stock markets. In this paper we argue that the stock market state is dynamic and invisible but it will be influenced by some visible stock market information. Existing research on financial time series analysis and stock market volatility prediction can be classified into two categories: in depth study of one market factor on the stock market volatility prediction or prediction by combining historical price fluctuations with either trading volume or news. In this paper we present a service-oriented multi-kernel based learning framework (MKL) for stock volatility analysis. Our MKL service framework promotes a two-tier learning architecture. In the top tier, we develop a suite of data preparation and data transformation techniques to provide a source-specific modeling, which transforms and normalizes a source specific input dataset into the MKL ready data representation. Then we apply data alignment techniques to prepare the datasets from multiple information sources based on the classification model we choose for cross-source correlation analysis. In the next tier, we develop model integration methods to perform three analytic tasks: (i) building one sub-kernel per source, (ii) learning and tuning the weights for sub-kernels through weight adjustment methods and (iii) performing multi-kernel based cross-correlation analysis of market volatility. To validate the effectiveness of our service oriented MKL approach, we performed experiments on HKEx 2001 stock market datasets with three important market information sources: historical prices, trading volumes and stock related news articles. Our experiments show that 1) multi-kernel learning method has a higher degree of accuracy and a lower degree of false prediction, compared to exist
Topic detection is an hot research in the area of information retrieval. However, the new environment of Internet, the content of which are usually user-generated, asks for new requirements and brings new challenges. ...
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Topic detection is an hot research in the area of information retrieval. However, the new environment of Internet, the content of which are usually user-generated, asks for new requirements and brings new challenges. Topic detection has to resolve the problem of its lower quality and large amount of noisy. This paper not only provides a solution for detecting hot topics, but also giving its semantic descriptions as result. Our method integrates two kinds of term features (local features and global features), and use single pass clustering to perform topic detection in a web forum. It's efficient to filter non-topic documents and get readable descriptions of topic in our system. By comparison with baseline and topic model LDA, our method gets better performance and readable result.
Current QoS-aware automatic service composition queries over a network of Web services are often one-time innature. After a network of Web services is built, such queries are issued once, and answers are found from th...
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3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large am...
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In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put o...
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In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put on fast action selection motivating the use of Monte-Carlo techniques in MOCART-CGA. Exploration of the space is guided by using corridors which direct simulations in the neighbourhood of the best found moves. MOCART-CGA limits how many times a particular state-action pair is explored to balance exploration of the neighbourhood of the state and exploitation of promising actions. MOCART-CGA is evaluated using four standard pathfinding benchmark maps, and over 1000 instances. The empirical results show that MOCART-CGA outperforms existing techniques, in terms of search time, in dynamic and partially observable environments. Experiments have also been performed in static (and partially observable) environments where MOCART-CGA still requires less time to search than its competitors, but typically finds lower quality plans.
To obtain a better understanding of WIL rationale and practices in Australian ICT degrees, a survey of managers and educational leaders of ICT was undertaken. These survey results were analysed and informed by discuss...
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The specification of Modeling and Analysis of Real-time and Embedded Systems (MARTE) is an extension of UML in the domain of real-time and embedded Systems. However, unified modeling of continuous and discrete variabl...
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