National Institute of Standards and Technology における超伝導研究及び生活

Anyone who watches the television news has seen images of firefighters rescuing people from burning buildings and paramedics treating bombing victims. How do these individuals make the split-second decisions that save lives? Most studies of decision making, based on artificial tasks assigned in laboratory settings, view people as biased and unskilled. Gary Klein is one of the developers of the naturalistic decision-making approach, which views people as inherently skilled and experienced. Since 1985, Klein has conducted fieldwork to find out how people tackle challenges in difficult, nonroutine situations. Sources of Power is based on observations of humans acting under such real-life constraints as time pressure, high stakes, personal responsibility, and shifting conditions. In addition to providing information that can be used by professionals in management, psychology, engineering, and other fields, the book presents an overview of the research approach of naturalistic decision making and expands our knowledge of the strengths people bring to difficult tasks.

Sources of Power: How People Make Decisions

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.

https://orbit.dtu.dk/files/117885360/PHOTO_D_15_00278R1B.pdf

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? In this work, we aim to provide a definite answer to this question. We first revisit two foremost proposals, i.e. Tsai et al.’s scheme and Li’s scheme, revealing some subtleties and challenges in designing such schemes. Then, we systematically explore the inherent conflicts and unavoidable trade-offs among the design criteria. Our results indicate that, under the current widely accepted adversarial model, certain goals are beyond attainment. This also suggests a negative answer to the open problem left by Huang et al. in 2014. To the best of knowledge, the present study makes the first step towards understanding the underlying evaluation metric for anonymous two-factor authentication, which we believe will facilitate better design of anonymous two-factor protocols that offer acceptable trade-offs among usability, security and privacy.

Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

/pdf/crowdsourcing-citizen-science-or-volunteered-geographic-4qhdzgct9c.pdf

Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information

Open card sorting is used by information architects to gather insights from users to incorporate feedback into an information architecture. In theory, it is one of the more inexpensive, user-centered design methods available to practitioners, but hidden costs make it less likely to be conducted properly and affect the quality of results produced. The following proposes a new card sorting method called the Modified-Delphi card sort to replace the Open card sort. The Modified-Delphi card sort is based on a well-known forecasting technique called the Delphi method. Instead of producing individual models that are then analyzed as a whole, participants work with a single model that is proposed and modified throughout the study. The Modified-Delphi card sorting method produces more useful results to aid in the design of an information architecture than the Open card sorting method.

A series of studies were conducted to directly compare the Modified-Delphi and Open cart sorting methods. First, two parallel studies using both methods were conducted with the same dataset and number of participants. Then, two studies were conducted on the results of the parallel studies: a heuristic review and ranking with information design experts and an Inverse card sort with additional users of the proposed architecture. The Modified-Delphi card sorting method produced results that were found to be at least as good as the Open card sorting method results and in some cases, better.

A modified Delphi approach to a new card sorting methodology

Most cyber security research is focused on detecting network intrusions or anomalies through the use of automated methods, exploratory visual analytics systems, or real-time monitoring using dynamic visual representations. However, there has been minimal investigation of effective decision support systems for cyber analysts. This paper describes the user-centered design and development of a decision support visualization for active network defense. Ocelot helps the cyber analyst assess threats to a network and quarantine affected computers from the healthy parts of a network. The described web-based, functional visualization prototype integrates and visualizes multiple data sources through the use of a hybrid space partitioning tree and node link diagram. We describe our design process for requirements gathering and design feedback which included expert interviews, iterative design, and a user study.

Ocelot: user-centered design of a decision support visualization for network quarantine

The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues. Challenge 2 (C2) involved identifying the issues of concern within a region of the Bank of Money network experiencing operational difficulties utilizing the provided network logs. Participants were asked to analyze the data and provide solutions and explanations for both challenges. The data sets were downloaded by nearly 1100 people by the close of submissions. The VAST Challenge received 40 submissions with participants from 12 different countries, and 14 awards were given.

/pdf/vast-challenge-2012-visual-analytics-for-big-data-k5cvlhiez3.pdf

VAST Challenge 2012: Visual analytics for big data

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use CyberPetri to support real-time situation awareness during the 2016 Cyber Defense Exercise.

CyberPetri at CDX 2016: Real-time network situation awareness

This paper describes a method for visualizing and analyzing co-occurrence in card-sorting data. Card sorting is a popular knowledge elicitation method used in information architecture and user experience design. However, analyzing card-sorting data can be a challenge. Detailed qualitative analysis is difficult and time consuming, especially for larger studies. Quantitative analysis can be automated and is scalable, but can be difficult to interpret.

A graph visualization offers a novel way to analyze and understand the relationships between cards and the mental models elicited in a card-sorting study. Graph visualizations are graphs that illustrate connections between concepts, such as cards in a card-sorting study. A visualization can quickly show relationships between cards and clusters of cards that represent topics that may not be obvious from traditional card-sort analysis methods. A case study describes how graph visualization can be used to analyze the data. The results of the analysis are compared and contrasted with a popular histogram-matrix analysis method. Strengths and weaknesses of the proposed graph-visualization analysis method are discussed.

Analyzing card-sorting data using graph visualization

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