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Metrics, Metrics, Metrics: Negative Hedonicity
IEEE Intelligent Systems, 2008Intelligent technologies such as performance support systems and decision aids represent a key aspect of modern sociotechnical systems. When new tools are introduced into the workplace, they represent hypotheses about how cognitive work is expected to change.
Hoffman, Robert R. +2 more
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Metrics, Metrics, Metrics, Part 2: Universal Metrics?
IEEE Intelligent Systems, 2010A previous article in this department from 2008 introduced the topic of measures and metrics. The focus of that essay was on measurement of the "negative hedonics" of work-the frustrations, uncertainties, mistrust, and automation surprises caused by poorly designed technology that is not human-centered.
Robert R. Hoffman +2 more
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THE METRIC DIMENSION OF METRIC MANIFOLDS
Bulletin of the Australian Mathematical Society, 2015In this paper we determine the metric dimension of $n$-dimensional metric $(X,G)$-manifolds. This category includes all Euclidean, hyperbolic and spherical manifolds as special cases.
Heydarpour, Majid, Maghsoudi, Saeid
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The Metric Dimension of Metric Spaces
Computational Methods and Function Theory, 2013Let \((X,d)\) be a metric space. A non-empty subset \(A\) of \(X\) resolves \((X,d)\) if \(d(x,a)=d(y,a)\) for all \(a\) in \(A\) implies \(x=y\), and if that is so we may regard the distances \(d(x,a)\), where \(a\in A\), as the coordinates of \(x\) with respect to \(A\).
Bau, Sheng, Beardon, Alan F.
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Metrics, quasi-metrics, hemi-metrics
2013Metrics, hemi-metrics, and open balls A natural instance of topological space is given by metric spaces , as already studied in Chapter 3. Note that metrics are symmetric: the distance between x and y is the same as the distance between y and x .
Dutour, Mathieu, Deza, Michel
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Communications of the ACM, 2018
Your biggest mistake might be collecting the wrong data.
Nicole Forsgren, Mik Kersten
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Your biggest mistake might be collecting the wrong data.
Nicole Forsgren, Mik Kersten
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2017
Abstract In this chapter I present the key ideas and develop the essential quantitative metrics needed for modeling and inference with limited information. I provide the necessary tools to study the traditional maximum-entropy principle, which is the cornerstone for info-metrics.
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Abstract In this chapter I present the key ideas and develop the essential quantitative metrics needed for modeling and inference with limited information. I provide the necessary tools to study the traditional maximum-entropy principle, which is the cornerstone for info-metrics.
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A Survey of Evaluation Metrics Used for NLG Systems
ACM Computing Surveys, 2023Ananya B Sai, Mitesh M Khapra
exaly