Small-scale classification schemes which are used extensively in the coordination of cooperative work are discussed. The study investigates the creation and use of a classification scheme for handling the system requi...
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Small-scale classification schemes which are used extensively in the coordination of cooperative work are discussed. The study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. The requirements classification provide the software engineers with an important means of discretely balancing the contractual aspect of requirements engineering. The requirements classification is analyzed in terms of complementary concepts of boundary objects.
As we have moved to a more technology-based classroom, much attention has been paid to using computers to present material. computer-based presentations can be a powerful tool for faculty, but as more and more student...
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As we have moved to a more technology-based classroom, much attention has been paid to using computers to present material. computer-based presentations can be a powerful tool for faculty, but as more and more students are coming to class with laptop and tablet computers, attention is being paid to questioning the opportunities this could present to these students in the form of enhanced note-taking opportunities. This paper explores the idea of faculty (rather than the students) using a computer note-taking system during student presentations to support interaction, evaluation, and feedback. Good annotations and records of a presentation can assist in more organized and effective feedback both during class as well as after class. It can also be beneficial in the evaluation of grades during the assignment stage of a faculty member's work. The techniques discussed can be extended to a wide variety of student presentation assignments.
Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to e...
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Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features
We explore and extend the metaphor of "piles" to include computationally-enriched piles, portable regions of automation in Dynapad, a multiscale workspace. We describe these collection-management tools and h...
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We explore and extend the metaphor of "piles" to include computationally-enriched piles, portable regions of automation in Dynapad, a multiscale workspace. We describe these collection-management tools and how their design was informed by observing people organizing collections of personal digital photographs
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation,...
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In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation, we introduce two kinds of information, that is, unit and modular information. Unit information represents information content obtained by individual elements in all modules. On the other hand, modular information is information content obtained by each module. We try to increase both types of information simultaneously. We applied our method to two classification problems: random data classification and Web data classification. In both cases, we observed that modular structures were automatically generated.
In this paper, we propose a new computational method for a network-growing method called greedy network-growing [R. Kamimura, et al., (2002)]. We have so far introduced a network-growing algorithm called greedy networ...
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In this paper, we propose a new computational method for a network-growing method called greedy network-growing [R. Kamimura, et al., (2002)]. We have so far introduced a network-growing algorithm called greedy network-growing based upon information theoretic competitive learning. For competitive unit outputs, we have used the inverse of the squares of Euclidean distance between input patterns and connections. The algorithm has extracted very faithful representations of input patterns. However, one problem is that learning is very slow, and sometimes ambiguous final representations are obtained. To remedy these shortcomings, we introduce a new activation function, that is, Gaussian activation functions for competitive units. By changing a parameter for the Gaussian activation functions, we can build a network that does not focus on faithful representations of input patterns, but try to extract the main characteristics of input patterns. Because this method are not concerned with detailed parts of input patterns, learning is significantly accelerated and salient features should be extracted. We applied the method to a road classification problem. Experimental results confirmed that learning was significantly accelerated and salient features could be extracted.
In this paper, low-complexity block-constrained trellis coded quantization (BC-TCQ) structures are introduced, and a predictive BC-TCQ encoding method is developed for quantization of line spectrum frequencies (LSF) p...
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In this paper, low-complexity block-constrained trellis coded quantization (BC-TCQ) structures are introduced, and a predictive BC-TCQ encoding method is developed for quantization of line spectrum frequencies (LSF) parameters for wideband speech coding applications. The performance is compared to the linear predictive coding (LPC) vector quantizers used in the AMR-WB (ITU-G.722.2) speech coding standard, demonstrating reduction in spectral distortion and significant reduction in encoding complexity.
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation,...
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ISBN:
(纸本)0780386442
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation, we introduce two kinds of information, that is, unit and modular information. Unit information represents information content obtained by individual elements in all modules. On the other hand, modular information is information content obtained by each module. We try to increase both types of information simultaneously. We applied our method to two classification problems: random data classification and web data classification. In both cases, we observed that modular structures were automatically generated.
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