Classifying Numeric Information for Generalization

Learning programs that generalize from real-world examples will have to deal with many different kinds of data. Continuous numeric data can cause problems for algorithms that search for examples with identical property values. These problems can be surmounted by categorizing the numeric data. However, this process has problems of its own. In this paper, we look at the need for categorizing numeric data and several methods for doing so. We concentrate on the use of generalization-based memory, a memory organization where actual examples are stored along with generalizations, which leads to a generalization-based categorization algorithm. We also consider how to use a number heuristic, looking for gaps. These methods have been implemented in the UNIMEM computer system. Examples are presented of these algorithms categorizing data about the states of the United States

Effects of diagram format and user numeracy on understanding cash flow data

An issue of longstanding concern in accounting information systems is the effective presentation and communication of financial data to users with little accounting background. Cash flow statements in particular have been singled out as difficult to interpret. To increase user understanding of cash flow data, this study explores the potential merits of diagram formats, as well as possible effects of the user’s numeracy skills. The study covers an experiment (N = 100) in which users were queried on their understanding of the cash flows of a real-world company, and in which type of format was either a cash flow statement or a cash flow diagram. Understanding was measured by three different concepts: interpretation accuracy, company diagnosis, and clarity of presentation. The study confirms that, on those measures, diagrams do not necessarily outperform cash flow statements, and that format familiarity (irrespective of the type of format) is a key driver in understanding cash flows. In addition, the study finds that numeric preference, but not numeric ability, helps in understanding cash flow data. The study discusses the sobering implications for designers of accounting information systems

Modelling the permeability of polymers: a neural network approach

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to its permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities

On the optimality of ternary arithmetic for compactness and hardware design

In this paper, the optimality of ternary arithmetic is investigated under strict mathematical formulation. The arithmetic systems are presented in generic form, as the means to encode numeric values, and the choice of radix is asserted as the main parameter to assess the efficiency of the representation, in terms of information compactness and estimated implementation cost in hardware. Using proper formulations for the optimization task, the universal constant 'e' (base of natural logarithms) is proven as the most efficient radix and ternary is asserted as the closest integer choice.Comment: 10 pages, 3 figure

mSpace: What do Numbers and Totals Mean in a Flexible Semantic Browser

With the Semantic Web community’s growing interest in Human Computer Interaction, this paper addresses a challenge for user interface design and future shifts in search paradigms. Where browsers using current search paradigms often use numeric values to indicate volumes of sub-hierarchies, future semantic browsers will not be limited to fixed hierarchical datasets, but allow flexible exploration through multiple intersecting domains. With the future use of similar numeric indicators uncertain, research here suggests that the inclusion of such indicators should be based around focal data objects within each information domain. Further research is required, as a significant number of contradicting participant expectations were present. It is the concern of the Semantic Web community to make sure that future btic search paradigms can best support their users