Abstract not found

Information visualization is an effective way to easily comprehend large amounts of data. For such systems to be truly effective, the information visualization designer must be aware of the ways in which their system may be manipulated and protect their users from attack. In addition, users should be aware of potential attacks in order to minimize or negate their effect. These attacks target the information visualization system as well as the perceptual, cognitive and motor capabilities of human end users. To identify and help counter these attacks we present a framework for information visualization system security analysis, a taxonomy of visualization attacks and technology independent principles for countering malicious visualizations. These themes are illustrated with case studies and working examples from the network security visualization domain, but are widely applicable to virtually any information visualization system.

In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets. The design

correlation-regression approach to model testing, contending that if a priori measurements are assumed to be proper psychological values and if the correct model is known, correlations can be higher for the better model. But since psychologists cannot know in advance the correct scales and models, popular correlational techniques are inappropriate for investigating psychological processes. It is necessary to separate measurement from the evaluation of a model. A further attempt is made here to clarify the relationships between different methods of analysis. Birnbaum (1973) criticized a currently popular use of correlation that confounds measurement with model testing, demonstrating that a poorer model can achieve higher correlations with the data when a priori measurements are used. Recent replies by Alf and Abrahams (1974) and by Rorer (1974) correctly contend that once the data have been properly diagnosed by other techniques, it may be possible to use regression so that the correlation coefficient is higher for the correct model. But the fundamental question should be: What are the advantages or disadvantages of correlational techniques for exploring psychological theories under conditions where the correct models and psychological values of the stimuli are unknown? Under these conditions, correlations of fit can be misleading since they depend on such factors as (a) unreliability of response, (b) experimental design (which includes variation and covariation of independent variables), (c) stimulus metric, (d) response metric, and (e) number of estimated parameters, as well as (/) the "goodness " of the model. When correlational analyses are reported, the journal reader has no way of knowing what the original data (and the pattern of devia-1

: Statistical literacy is the ability to read and interpret data: the ability to use statistics as evidence in arguments. Statistical literacy is a competency: the ability to think critically about statistics. This introduction defines statistical literacy as a science of method, compares statistical literacy with traditional statistics and reviews some of the elements in reading and interpreting statistics. It gives more emphasis to observational studies than to experiments and thus to using associations to support claims about causation. Keywords: Teaching, Epistemology, Critical Thinking, Strength of Belief, Observational Studies Statistical literacy is a basic skill: the ability to think critically about arguments using statistics as evidence. Consider the story of two hunters being chased by a bear. [Adapted from David Friedman, (1996)] The first says, "It's hopeless! This bear can run twice as fast as we can." The second, realizing the flaw in the argument says, "No, it's not...

Graphical presentations can be very effective in communicating large datasets and the patterns, trends and relationships in them. Charts and graphs used in reporting data usually tend to highlight relevant points and suppress unnecessary ones. This can be problematic, since such charts can be used to selectively emphasize desired aspects: in fact, a recent study of several hundred annual reports found that more than 30% of charts regarding a company's financial performance used design parameters that facilitated favorable inferences by users and hindered others that were not. Furthermore, since studies show that average users are not graphically sophisticated, many of the techniques used to achieve these biased effects may not be obvious to them. One solution to this problem is to make these design choices explicit to the user. This paper presents a data analysis interface that allows users to explore the visualization space by modifying these design decisions. This int...

Practitioners and teachers should be able to justify their chosen techniques by taking into account research results: This is evidence-based practice (EBP). We argue that, specifically, statistical practice and statistics education should be guided by evidence, and we propose statistical cognition (SC) as an integration of theory, research, and application to support EBP. SC is an interdisciplinary research field, and a way of thinking. We identify three facets of SC—normative, descriptive, and prescriptive— and discuss their mutual influences. Unfortunately, the three components are studied by somewhat separate groups of scholars, who publish in different journals. These separations impede the implementation of EBP. SC, however, integrates the facets and provides a basis for EBP in statistical practice and education.

Abstract. In this paper, we discuss the scientific method and how it can be applied to computer security experiments. We reiterate a number of general scientific principles, such as falsifiable hypotheses, scientific controls, reproducible results, and data quality. 1

Much has been written in recent years on statistical literacy, but what do we mean by “statistical literacy”? I will take a pragmatic approach and provide resources to help you to define this term for yourself and implement your idea of statistical literacy in the classroom. This paper includes a bibliography of relevant resources. The text provides notes on the bibliography and ruminates on the issues involved. Even if my conclusions differ from yours, the bibliography and discussion may still prove useful in defining, defending and implementing a statistical literacy program. Planning a statistical literacy program I will discuss the improvement of statistical literacy in the context of school, particularly in colleges or universities, the context I know best. However, much that I say will be relevant to a potential high school course, or to efforts to enhance statistical literacy by integrating it into the K-12 curriculum. There are three main steps to improving statistical literacy. 1. Define “statistical literacy”. 2. Gather the resources you need to achieve it. 3. Implement a specific plan. Defining statistical literacy We can provisionally define “statistical literacy ” as the skills a person needs in order to deal with issues of probability and statistics that arise in everyday life. That is still pretty general (e.g., “Which issues are ‘statistical ’?”). Recent years have seen many recommendations to improve quantitative and statistical literacy — usually as part of an effort to reform the teaching of mathematics and statistics

This memory would be as fast as cache memory, but it would be under the control of the compiler rather than the hardware. We use the results from the memory allocation study to show that this memory space could be quite small, and we present an algorithm that the compiler could employ to utilize this space. We also present experimental results that suggest that this method would have a significant impact on a program's runtime