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The project is often visualized using the Kanban method in agile software development. The Kanban method is divided into two implementations: a physical Kanban and a digital Kanban. Both have particular advantages and disadvantages. Operating Kanban methods having the advantages of both have been proposed, but they require a high synchronization cost or a high price instrument. The authors developed...
Encouraged by recent waves of successful applications of deep learning, some researchers have demonstrated the effectiveness of applying convolutional neural networks (CNN) to time series classification problems. However, CNN and other traditional methods require the input data to be of the same dimension which prevents its direct application on data of various lengths and multi-channel time series...
This paper discusses the basic design properties of the use of colors in Web documents, and also discusses the properties of combinations of colors in a Web document. The results can be a theoretical basis for a findable, readable and understandable design of documents. We usually use several different colors in documents with some intentions, but the quantitative effectiveness of using these colors...
This paper proposes a system which acquires feature patterns and makes classifiers for time series data without using background knowledge given by a user. Time series data are widely appeared in finance, medical research, industrial sensors, etc. The system acquires the feature patterns that characterize similar data in database. We focus on two aspects of the feature pattern: global and local frequency...
In this paper, we propose high-speed, accurate algorithms for detecting hazardous Web pages. Our algorithms automatically choose strings that appear especially in HTML elements of hazardous Web pages. We use these strings in combination as features of SVMs (support vector machines), and detect hazardous Web pages. Since our algorithms do not rely on the text parts of Web pages, they can detect Web...
Scalability is one of the main challenges for kernel-based methods and support vector machines (SVMs). The quadratic demand in memory for storing kernel matrices makes it impossible for training on million-size data. Sophisticated decomposition algorithms have been proposed to efficiently train SVMs using only important examples, which ideally are the final support vectors (SVs). However, the ability...
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