Visualization of diversity in large multivariate data sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.     

On the dispersion measure of OWA operators

We describe some basic features of the OWA operator. We turn to the problem of determining the weights associated with this operator and particularly the maximal dispersion (entropy) approach. We consider the possibility of using minimization of dispersion. After discussing concerns with both maximization and minimization of dispersion we investigate the possibility of finding an optimal solution intermediate to these extremes. We next consider alternative measures of dispersion. We introduce a fundamental requirement for a measure of dispersion called the Preference for Equal Division. A number of general classes of dispersion measures are provided notable among these are those based on t-norm and t-conorm operators.     

From Berman and Hafner’s teleological context to Baude and Sachs’ interpretive defaults: an ontological challenge for the next decades of AI and Law

This paper revisits the challenge of Berman and Hafner’s “missing link” paper on representing teleological structure in case-based legal reasoning. It is noted that this was mainly an ontological challenge to represent some of what made legal reasoning distinctive, which was given less attention than factual similarity in the dominant AI and Law paradigm, deriving from HYPO. The response to their paper is noted and briefly evaluated. A parallel is drawn to a new challenge to provide deep structure to the legal context of textual meaning, drawing on the forthcoming work of two Constitutional law scholars who appear to place some faith in the ways of thinking that AI and Law has developed.     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.     

Satellite image-based maps: Scientific inference or pretty pictures?

The scientific method has been characterized as having two distinct components, Discovery and Justification. Discovery emphasizes ideas and creativity, focuses on conceiving hypotheses and constructing models, and is generally regarded as lacking a formal logic. Justification begins with the hypotheses and models and ends with a valid scientific inference. Unlike Discovery, Justification has a formal logic whose rules must be rigorously followed to produce valid scientific inferences. In particular, when inferences are based on sample data, the rules of the logic of Justification require assessments of bias and precision. Thus, satellite image-based maps that lack such assessments for parameters of populations depicted by the maps may be of little utility for scientific inference; essentially, they may be just pretty pictures. Probability- and model-based approaches are explained, illustrated, and compared for producing inferences for population parameters using a map depicting three land cover classes: non-forest, coniferous forest, and deciduous forest. The maps were constructed using forest inventory data and Landsat imagery. Although a multinomial logistic regression model was used to classify the imagery, the methods for assessing bias and precision can be used with any classification method. For probability-based approaches, the difference estimator was used, and for model-based inference, a bootstrap approach was used.     

Indirect augmented reality

Developing augmented reality (AR) applications for mobile devices and outdoor environments has historically required a number of technical trade-offs related to tracking. One approach is to rely on computer vision which provides very accurate tracking, but can be brittle, and limits the generality of the application. Another approach is to rely on sensor-based tracking which enables widespread use, but at the cost of generally poor tracking performance. In this paper we present and evaluate a new approach, which we call Indirect AR, that enables perfect alignment of virtual content in a much greater number of application scenarios.To achieve this improved performance we replace the live camera view used in video see through AR with a previously captured panoramic image. By doing this we improve the perceived quality of the tracking while still maintaining a similar overall experience. There are some limitations of this technique, however, related to the use of panoramas. We evaluate these boundaries conditions on both a performance and experiential basis through two user studies. The result of these studies indicates that users preferred Indirect AR over traditional AR in most conditions, and when conditions do degrade to the point the experience changes, Indirect AR can still be a very useful tool in many outdoor application scenarios.     

The State of the Art in Topology‐Based Visualization of Unsteady Flow

Vector fields are a common concept for the representation of many different kinds of flow phenomena in science and engineering. Methods based on vector field topology are known for their convenience for visualizing and analysing steady flows, but a counterpart for unsteady flows is still missing. However, a lot of good and relevant work aiming at such a solution is available. We give an overview of previous research leading towards topology‐based and topology‐inspired visualization of unsteady flow, pointing out the different approaches and methodologies involved as well as their relation to each other, taking classical (i.e. steady) vector field topology as our starting point. Particularly, we focus on Lagrangian methods, space–time domain approaches, local methods and stochastic and multifield approaches. Furthermore, we illustrate our review with practical examples for the different approaches.     

Information technology innovation diffusion: an information requirements paradigm

Abstract.  Information technology (IT) innovation research examines the organizational and technological factors that determine IT adoption and diffusion, including firm size and scope, technological competency and expected benefits. We extend the literature by focusing on information requirements as a driver of IT innovation adoption and diffusion. Our framework of IT innovation diffusion incorporates three industry-level sources of information requirements: process complexity, clock speed and supply chain complexity. We apply the framework to US manufacturing industries using aggregate data of internet-based innovations and qualitative analysis of two industries: wood products and beverage manufacturing. Results show systematic patterns supporting the basic thesis of the information processing paradigm: higher IT innovation diffusion in industries with higher information processing requirements; the salience of downstream industry structure in the adoption of interorganizational systems; and the role of the location of information intensity in the supply chain in determining IT adoption and diffusion. Our study provides a new explanation for why certain industries were early and deep adopters of internet-based innovations while others were not: variation in information processing requirements.     

Building ensemble classifiers using belief functions and OWA operators

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.